intro.Rmd
The quantarcticR
package provides access to Quantarctica data sets for R users, without needing QGIS to be installed.
This vignette:
Quantarctica is a collection of Antarctic geographical datasets which works with the free, cross-platform, open-source software QGIS. It includes community-contributed, peer-reviewed data from ten different scientific themes and a professionally-designed basemap.
Quantarctica is is published and made available under under a Creative Commons Attribution 4.0 International License.
If you use it, please cite it:
Matsuoka K, Skoglund A, Roth G (2018) Quantarctica dataset
. Norwegian Polar Institute. https://doi.org/10.21334/npolar.2018.8516e961
In addition, published works produced using Quantarctica are asked to cite each dataset that was used in the work. Please consult the abstract of each data set for the relevant citation.
The quantarcticR R package provides you with flexibility to either temporarily or persistently store the data that is downloaded from Quantarctica. By default a temporary directory is used, which will only persist for the current R session. This means that data will not be re-used from session to session, and you may end up re-downloading the same data if you run the same script in different sessions.
You can instead choose to save the data to a persistent directory, by issuing the command qa_cache_dir("persistent")
after loading the quantarcticR
package. This will use a standard user data directory (e.g. under the user’s AppData
directory on Windows operating systems). You could also specify a particular directory to use, if you prefer:
qa_cache_dir("c:/my/data/directory/")
You can switch cache directories at any time, and you can find out the current cache directory by calling qa_cache_dir()
with no arguments.
Start by loading the package:
library(quantarcticR)
In order to return a list of the datasets available, use the qa_datasets
function.
datasets <- qa_datasets()
head(datasets)
## # A tibble: 6 x 5
## layername main_file type cached download_size
## <chr> <chr> <chr> <lgl> <fs::bytes>
## 1 Overview place na… /tmp/RtmpQE0343/quantarcticR-c… shape… FALSE 19.74K
## 2 COMNAP listed fac… /tmp/RtmpQE0343/quantarcticR-c… shape… FALSE 691.92K
## 3 Subantarctic stat… /tmp/RtmpQE0343/quantarcticR-c… shape… FALSE 691.92K
## 4 SCAR Composite ga… /tmp/RtmpQE0343/quantarcticR-c… shape… FALSE 329.05M
## 5 IBO-IOC GEBCO Fea… /tmp/RtmpQE0343/quantarcticR-c… shape… FALSE 1.25M
## 6 IBO-IOC GEBCO Fea… /tmp/RtmpQE0343/quantarcticR-c… shape… FALSE 1.25M
In the datasets
object we can see the following:
layername
which is the name of the datasetmain_file
is the primary data file associated with each datasettype
which is the object type (currently “shapefile” or “raster”)cached
whether it has been downloaded to the local cache or notdownload_size
which is the size of the dataset.In order to view the details of a dataset use the qa_dataset
function. This function gives more information about the dataset (but does not download or return the actual data). For example, with the simple basemap called “ADD Simple basemap”:
basemap <- qa_dataset("ADD Simple basemap")
basemap
## Rows: 1
## Columns: 12
## $ layername <chr> "ADD Simple basemap"
## $ datasource <chr> "Miscellaneous/SimpleBasemap/ADD_DerivedLowresBasemap…
## $ layer_attributes <list> <NULL>
## $ srs_attributes <list> [<tbl_df[1 x 4]>]
## $ provider <chr> "ogr"
## $ abstract <chr> NA
## $ extent <list> <NULL>
## $ palette <list> <NULL>
## $ type <chr> "shapefile"
## $ download_size <fs::bytes> 2M
## $ main_file <chr> "/tmp/RtmpQE0343/quantarcticR-cache/Miscellaneous/Sim…
## $ bb_source <tibble[,16]> <tbl_df[1 x 16]>
To actually fetch the data, use the qa_get
function. You can provide it with either the name of the dataset (i.e. layername
as returned by qa_datasets()
) or the dataset object (as returned by qa_dataset()
).
Here we’ll fetch a dataset called “AntGG Free-air gravity anomaly (10km)”.
ga_info <- qa_dataset("AntGG Free-air gravity anomaly (10km)") ## the dataset info
ga_data <- qa_get(ga_info, verbose = TRUE) ## fetch the actual data
##
## Thu Jul 8 11:57:03 2021
## Synchronizing dataset: AntGG Free-air gravity anomaly (10km)
## Source URL https://ads.nipr.ac.jp/gis/quantarctica/Quantarctica3/Geophysics/ANTGG/
## --------------------------------------------------------------------------------------------
##
## this dataset path is: /tmp/RtmpQE0343/quantarcticR-cache/Geophysics//ANTGG
## visiting https://ads.nipr.ac.jp/gis/quantarctica/Quantarctica3/Geophysics/ANTGG/ ... done.
## downloading file 1 of 2: https://ads.nipr.ac.jp/gis/quantarctica/Quantarctica3/Geophysics/ANTGG/ANTGG_FreeAirGravityAnomaly_10km.tif ... done.
## downloading file 2 of 2: https://ads.nipr.ac.jp/gis/quantarctica/Quantarctica3/Geophysics/ANTGG/ANTGG_FreeAirGravityAnomaly_10km.tif.aux.xml ... done.
##
## Thu Jul 8 11:57:05 2021 dataset synchronization complete: AntGG Free-air gravity anomaly (10km)
class(ga_data)
## [1] "RasterLayer"
## attr(,"package")
## [1] "raster"
The gravity anomaly data set is a raster, so we can use the raster package to plot it.
The Quantarctica project maintainers have gone to quite a lot of effort to create nice visual representations of the data layers, by defining colour palettes and similar. The full range of visual information is not yet available through quantarcticR, but it is a work in progress. In this case, there is a colour palette for the gravity anomaly layer:
cmap <- ga_info$palette[[1]]
cmap
## label alpha color value
## item -75 mGal 255 #9a0079 -75.00
## item1 -67.5 mGal 255 #0041ff -67.50
## item2 -60 mGal 255 #398fff -60.00
## item3 -52.5 mGal 255 #66ccff -52.50
## item4 -45 mGal 255 #4ab4ab -45.10
## item5 -37.5 mGal 255 #35a16b -37.50
## item6 -30 mGal 255 #5ab157 -29.90
## item7 -22.5 mGal 255 #89c53d -22.50
## item8 -15 mGal 255 #add529 -15.00
## item9 -7.5 mGal 255 #d5e614 -7.57
## item10 0 mGal 255 #faf500 0.00
## item11 7.5 mGal 255 #fbd900 7.57
## item12 15 mGal 255 #fcb500 15.00
## item13 22.5 mGal 255 #fd9400 22.50
## item14 30 mGal 255 #fd7a00 29.90
## item15 37.5 mGal 255 #ff4d00 37.50
## item16 45 mGal 255 #ff2800 45.00
## item17 52.5 mGal 255 #d52b00 52.50
## item18 60 mGal 255 #9d2f00 60.00
## item19 67.5 mGal 255 #663300 67.50
## item20 75 mGal 255 #663300 75.00
We need to make some tweaks to the palette to cope with the differences in how QGIS defines them compared to how raster
expects them (we will endeavour to automatically deal with such differences in future versions of quantarcticR):
Re-plot using that colour map, and with a land layer underneath:
basemap <- qa_get("ADD Simple basemap")
plot(basemap)
plot(ga_data, breaks = breaks, col = cmap$color, add = TRUE, legend = FALSE)
which is a little closer to the Quantarctica-rendered version of the same data layer:
Read in the simple basemap “ADD Simple basemap” data as an sf object and use the ggplot2 and sf packages to create a plot.
library(sf)
library(ggplot2)
surface_sf <- qa_get("ADD Simple basemap", shapefile_reader = sf::st_read)
## Reading layer `ADD_DerivedLowresBasemap' from data source
## `/tmp/RtmpQE0343/quantarcticR-cache/Miscellaneous/SimpleBasemap/ADD_DerivedLowresBasemap.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 1338 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -5791904 ymin: -5791904 xmax: 5791904 ymax: 5791904
## Projected CRS: WGS 84 / Antarctic Polar Stereographic
class(surface_sf)
## [1] "sf" "data.frame"


You can create a title and add other actors to that title without using the Magic Lantern Studio authoring tools. To do this, you will need to follow some basic steps. You have to
It is important to understand the Magic Lantern directory structure because you will need to locate the different runtime libraries and modules used by your title, in addition to your own source code and media content files.The Magic Lantern directory structure is as follows:
<to be supplied>
<to be supplied>
<to be supplied>
In order build a title outside the Magic Lantern Studio IDE, certain conventions should be followed. This section discusses how to set up the build environment targeting the C++ language.
<to be supplied>
You can integrate the Rehearsal Player with the dbx debugger by using the environmental variable MLEDBX in your title makefile. This variable allows you to attach the debugger to the Rehearsal Player window and then run the program in the debugger.
All titles begin with a main routine. Understanding this routine helps you to understand the structure of a Magic Lantern title. In addition, you need to understand the default main routine to write a custom main.
Magic Lantern provides a default main routine, which resides in the player library. Note that there are multiple player libraries: for example, there is one for the Rehearsal Player and one for the Windows BRender Player. Each player has its own default main routine.
For example, in the Rehearsal Player, the main routine is called mlplay. mlplay consists of two pieces of source code: main.cxx and title.cxx. In the Inventor Player, the title name itself is the main, and it is found in the library libplayer.a. On the Windows BRender platform, the main is found in the title's directory, in the main.cxx source code file. The associated title.cxx source file is in the Magic Lantern library directory for the Windows Player.
Note: On the Windows platform, there is a win32 set of directories and a game developers kit set of directories.
You can find the main program in the main.cxx source file. The title.cxx source file contains the implementations of the functions that are called by the main program.
This section discusses the main entry points, or application lifecycle, of a Magic Lantern title.
The standard main program for a title is fairly simple. It allocates memory for the required global variables and calls three functions, InitEnv(), MainLoop(), and CleanupEnv().
main() calls mlMalloc() to allocate memory for the title data structure. mlMalloc() by default is merely a Magic Lantern function that calls system malloc(). This allows programmers to replace the standard memory allocation routine with a custom implementation.
The file main.c++ looks like this:
Example 1-1: main program in C++
// Include Magic Lantern header files. #include "mle/types.h" #include "mle/malloc.h" #include "mle/titleenv.h" #include "mle/assert.h" // Declare external references. extern MlBoolean InitEnv(int, char **); extern int MainLoop(void); extern MlBoolean CleanupEnv(void); // // main() // int main(int argc, char *argv[]) { // Create a new title environment. theTitle = (MleTitleEnv*)mlMalloc(sizeof *theTitle); MLE_ASSERT(theTitle); // Initialize the title environment structure. InitEnv(argc, argv); // Start the main title loop. MainLoop(); // Clean up the title environment. CleanupEnv(); return(0); }
main calls three principal functions:
These functions initialize the title, execute the main title loop, and clean up when the title completes. You can find this code in title.cxx. They are explained in this section.
Before examining the main functions, you should understand the titleenv.h header file because it defines some important title data structures.
The struct MleTitleEnv contains global data for the title, including pointers to the scheduler and the event dispatch manager. It also defines the quit flag, the Digital Playprint reader, a pointer to data specific to a target platform, and a pointer to global data specific to the title. See Example 1-2.
Example 1-2: MleTitleEnv Structure
typedef struct MleTitleEnv { MleScheduler *scheduler; MleEventMgr *eventMgr; MlBoolean quit; // quit flag MlePpInput *pp; // playprint reader void* platformData; // target platform data void* titleData; // title global data } MleTitleEnv;
titleenv.h also defines the enum MleTitlePhases for the scheduler phases. This contains the names of the built-in, or default, phases. If you create your own custom phase, you must add its name to MleTitlePhases.
Example 1-3: Scheduler Phase enum
typedef enum { PHASE_ACTOR, PHASE_ROLE, PHASE_SET, PHASE_STAGE, TOTAL_NUM_PHASES } MleTitlePhases;
The InitEnv() function performs a number of important tasks. In the Rehearsal Player, it locates, opens, and loads the DWP file into memory. On the mastering side (the player platforms), it locates, open, and loads the Digital Playprint file into memory. It allocates memory and then creates a new Playprint object, as follows:
MlBoolean InitEnv(int argc, char **argv) { char *ppName; ... ppName = (char *) mlMalloc(strlen(argv[0]) + 4); ... theTitle->pp = new FwPpInput (ppName, MLE_PPFINDPPBE);
Note: The MLE_PPFINDPPBE constant is specific for Big Endian boxes, where the order of significant bits is big endian. On the Windows platform, this would be MLE_PPFINDPPLE, because the Intel hardware uses little endian bit order.
Next, this function reads in the title components into the Playprint object:
... theTitle->pp->readHeader(NULL, NULL); theTitle->pp->readTOC(MlePpInput::storeTOC,theTitle->pp); theTitle->pp->beginGroupList();
InitEnv() then creates the scheduler and the stage. It initializes the scheduler with the number of phases, or different classes of objects, it needs to schedule each cycle. ("Using the Magic Lantern Scheduler" for more information on the scheduler.)
There are different types of stages. On the Windows platform, you can create an Inventor or BRender stage, both written in C++. You can optionally create other managers, such as mouse and keyboard input managers. (See "Handling Input and Output" for more information on input managers.) InitEnv() also initializes the quit variable that is within the Title global structure to false so that the user can exit the title main loop. Finally, it loads the boot scene from the Playprint. This is the first scene of the title, and it appears when Magic Lantern launches the title.
The InitEnv() code segment that follows creates a BRender stage. It also creates a poll-driven keyboard input manager.
// Initialize the scheduler. theTitle->scheduler = new MleScheduler(NUM_PHASES, NUM_FUNCS); // Create the stage. MleBRenderStage *stage = new MleBRenderStage; // Initialize the keyboard input device. new MleKeyboardPolled; // Initialize the quit variable to false. theTitle->quit = FALSE; // Load the boot groups. (void) mlLoadBootScene(); return TRUE; }
The MainLoop() function, which takes no arguments, is the title-specific main loop function. Its role is to cycle through the scheduler phases until the quit flag is set to true.
MainLoop() looks for event occurrences and calls the scheduler function go for each of the scheduler phases. The four phases shown in Example 1-4 are the required default phases.
Example 1-4: MainLoop
int MainLoop(void); { // Loop forever. while (1) { // Execute Actor phase. theTitle->scheduler->go(PHASE_ACTOR); // Execute Role phase. theTitle->scheduler->go(PHASE_ROLE); // Execute Set phase. theTitle->scheduler->go(PHASE_SET); // Execute Stage phase. theTitle->scheduler->go(PHASE_STAGE); // Check for the exit key. if (MleKeyboardPolled::keyboardManager->keyDown(MLE_KEY_Q)) theTitle->quit = TRUE; // Check for request to quit title. if (theTitle->quit) break; } return 0; }
If you create a custom phase, such as a global clock update phase or a collision detection phase, then you must add your phase to the MainLoop. For example, if you add a collision detection phase, and you want collision detection to take place immediately after the actor phase, you might add a line such as the following between the actor and role phases:
... theTitle->scheduler->go(PHASE_ACTOR); theTitle->scheduler->go(PHASE_COLLISION_DETECTION); theTitle->scheduler->go(PHASE_ROLE); ...
Use the CleanupEnv() function to reverse what InitEnv() set up. CleanupEnv() deletes the scheduler, any managers that were created, and deallocates memory.
MlBoolean CleanupEnv(void); { return TRUE; }
The Magic Lantern scheduler organizes the rendering sequence of the title within a single frame of real time. It is strictly a sequential process. To use the scheduler, you need to understand the scheduler phases and its functions.
There are four distinct steps or phases that occur on each frame when a title plays. The scheduler starts each of these steps, in the following order:
The scheduler uses these Magic Lanetern constants to refer to the phases, as follows:
To use the Magic Lantern scheduler, a title sets up the set to schedule itself. Most actor and role programmers do not have to include code to schedule a set, because this is done for you by the stage. When you create a set, it is added to the stage and the stage then schedules the set for execution.
The order that sets appear in the DWP determines the order of rendering. You can intermix 2D and 3D sets within the DWP. For example, you can have a 2D set first to display background, then a 3D set for animation, and lastly another 2D set for a control panel. In this way, you draw the background first, then the animation on top of the background, and then the control panel on top of the animation.
The stage phase renders the sets. The stage phase draws first the set that appears first in the DWP. When the stage draws the set, the set begins drawing its images based on its linked list. The stage draws last the last set listed in the DWP. The last set is displayed on top of the other sets.
The class MleScheduler defines the API for the Magic Lantern scheduler. Example 1-5 lists these functions.
Example 1-5: MleScheduler Functions
MleScheduler(unsigned int phases, unsigned int items); ~MleScheduler(); void go(unsigned int phase); MleSchedulerItem* insertFunc(unsigned int phase, void (*func)(void*), void* data, void* tag, unsigned int interval =1, unsigned int firstInterval = 1); void remove(MleSchedulerItem* item); void remove(void* tag);
When you use the constructor function MleScheduler() to create a new scheduler for your title, you specify two things:
Each actor, role, set, or stage (that is, each object) in a MagicLantern title must have a call to the scheduler's insertFunc() routine. insertFunc() creates a hook for every actor, role, set, or stage that calls it. The scheduler uses this hook to call the Magic Lantern object during the appropriate phase. The Magic Lantern object uses insertFunc() to tell the scheduler what function the scheduler should call on this object and the phase it should be called in. The scheduler adds the object's function to its list of functions that it calls for the specified phase.
Note: The maximum number of functions that you can insert into the scheduler's list is determined by the items parameter in the scheduler constructor.
The order in which you insert functions for a phase determines the order that the scheduler calls these functions for that phase. You can control this insertion order by the ordering of functions in the DWP. However, you cannot change the scheduler's order of functions to call during a phase once they have been inserted.
insertFunc() requires, as parameters, the scheduler phase in which to call your object's function and a pointer to the function. You also designate a reference to data to pass to your function. The tag parameter lets you specify that, when Magic Lantern deletes the object, it should delete all functions associated to the object. Use the interval parameter to specify how often this function should be called, every frame, every other frame, and so on. interval specifies the number of frames between calls to this function. When you set interval to one, the function is called every frame; when you set it to two, it is called every other frame. firstInterval specifies the first frame to call the function.
The go function executes each designated phase. go takes as a parameter the name of the phase that it is to execute. The name of the phase is a defined constant. (See "Scheduler Phases") You call the scheduler's go function for each phase to be executed in a frame. This is illustrated in Example 1-4.
The API provides two ways to remove functions from the scheduler. You can use the remove() function and specify the MleSchedulerItem you created with the constructor. This removes just the specified function. Or, you can use remove() with a tag argument and remove all functions associated with tag.
You can also add your own custom phase to the scheduler. Adding your own phase gives you the ability to do certain functions at particular points, such as collision detection. Add your phase to the scheduler at the point within the scheduler's phase loop so that it will execute at the moment you want it to execute. See "Executing the Main Loop".
Be sure and correctly specify the number of phases when you call MleScheduler to create an instance of the scheduler. In addition, you must update the enum in titleenv.h that lists the scheduler phases to include your custom phase. You must also change the scheduler code itself (that is, MainLoop in title.cxx) to add your phase to the scheduler's phase loop.
Note: While you can remove phases from the scheduler, do this with extreme care.


| DOSE: |
25 mg |
oral |
2C-I |
(powder / crystals) |
T+00- ingest 25mg of 2ci dissolved in water
T+30min-taking off, very speedy, slight stomach upset, some anxiety
T+1hr-well into it, room much brighter, still disoriented from the quick flight, walking quite a bit.
T+1hr30min-getting my bearings, anxiety lightening up, room very bright, closed eye visuals
T+2hr-feel the plateau, feel very smart but I'm enjoying the physical so much I don't indulge in too much thought. Grinning and rubbing things.
T+2hr20min-just as Phormer mentioned, there seems to be quite a seratonin release.
I'm so happy. I sit on my deck and look out at the highway and the small hills.  Birds leave their tracks across the sky. The cars herk and jerk, pushing each other (I realize they are teenagers cruising and feel great sympathy for their plight), then I feel condescending and start walking and rubbing again. I have never felt this good before on 2ct7 or 2 or mushrooms, and only a few times on acid. And I think, 'how easy it was to get here.' Very little price to pay, so what's the catch? I'm horny as heck but put off calling my girlfriend, besides, how could she possibly understand where I
am and it would probably bring me down to explain myself. Yes, this is great alone. In fact there are quite a few things worse than being alone.
Oneness creeps over me as I again look outside from my deck, but I feel too good for it to be the sort of spirituality found with lsd or mushrooms. I just let it all BE for a few minutes.
T+3:30min-a life changing thought about my past occurs and I have the means to act on it right now. I do fearlessly.  It occurs to me I might be changed forever because of this, or is it just the drug?-we'll see. Confronting this particular problem this fearlessly is change enough for me.
T+4hr 30min-OK that was the experience I've been looking for from these research drugs, now what?  The night is beautiful, quite a few fireworks. The colors are much better than I anticipated.
T+5hr30min-that's it, coasting down now. Breathless, what an experience. Hope I hang on to the changes, but either way, I'm coming back here in a few weeks.
T+12hr-finally some sleep, about 2hrs worth. The coming down always makes me doubt the experience, except this time there's no guilt about just having fun. I was wide open to the search; I found bliss and a huge answer; a hangover is a small price to pay.
T+36hrs.-the change is clear in my mind. Strange, it's almost as if it had never been a problem even though it had plagued me for 20 years. The past problem and the confrontation during the 2CI both seem like dreams, and now I'm here typing this. I have so many unhealthy behavioral responses hooked up to the problem that now seems gone-wonder what will happen to them? Of course I'll still que on them, but will I eventually discard them or simply hook them to something else?
What is it about this drug that makes one write on and on to the point of being tiresome and pedantic. Look at the other reports on Erowid and here, and I swore I wouldn't take off on a tangent. If we could learn how to be brief and concise on 2CI, we'd have something to say. Sensual while taking it, smart after it, sometimes lifechanging, beats the hell out of the rest of the research drugs.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Resume
I'm is an experienced ed martial artist, highly focused on my training, aiming to become the strongest I can. I'm silent, meek, humble, submissive, self-deprecating, and respectful individual. I wanders the world with the desire for complete mastery of my martial art, and takes both my travels and training seriously.
Experience in fights
-
Street Fighter (1987)
In this game, the player plays as martial artist Ryu, who competes in a worldwide martial arts tournament spanning five countries and 10 opponents.
-
Street Fighter II (1991)
Street Fighter II was the first one-on-one fighting game to give players a choice from a variety of player characters with different moves, allowing for more varied matches.
-
Street Fighter Alpha (1995)
Street Fighter Alpha 2 features all-new stages, music, and endings for some characters, some of which overlap with those from the original Alpha. It also discards the Chain Combo system in favor of Custom Combos, which requires a portion of the Super Combo meter to be used.
-
Street Fighter EX (1996)
A sequel was released in 1998, Street Fighter EX2, developed for the ZN-2 hardware. Custom combos were reintroduced and the character roster was expanded upon even further. In 1999, EX2 also received an upgraded version, Street Fighter EX2 Plus. A port of EX2 Plus was released for the PlayStation in 1999.
-
Street Fighter III (1997)
Street Fighter III: New Generation made its debut in the arcades on the CPS3 hardware in 1997
-
Street Fighter IV (2008)
The original Street Fighter IV game concept, Street Fighter IV Flashback, never made it past the proposal stage. In 2007, more than eight years since the release of Street Fighter III 3rd Strike for the arcades, Capcom unveiled Street Fighter IV at a Capcom Gamers Day event in London. Conceived as a direct sequel to the early Street Fighter II games
-
Street Fighter V (2016)
Street Fighter V was released exclusive to the PlayStation 4 and PC, enabling cross platform gameplay, in 2016. In 2018, the game received a major update, Street Fighter V: Arcade Edition and in 2019, Street Fighter V: Champion Edition was released as downloadable content.
-
Versus (1996)
Capcom also produced a series of similar crossover fighting games with rival fighting game developer SNK Playmore. The games, produced by Capcom, include Capcom vs. SNK in 2000, which features characters primarily from the Street Fighter and King of Fighters series.
-
- You must defeat Sheng Long to stand a chance."
- "Is that all of your power? If you have any regrets, come at me!!"
- "If you want to be a true fighter... go home and train!"
- "You did quite well, but you need more training to defeat me!"
- "To live is to fight, to fight is to live!"
- "I really broke a sweat! How about another round?"
- "I know you can do better! Get up and try again!"
-
24th January 2020
FCBD 2020 STREET FIGHTER #100 RYU VS CHUN LI at (W) Ken Siu-Chong (A/CA) Panzer
click In Shops on Free Comic Book Day 2020!-
JAN200044 at Street Fighter universe.
- Samurai Among these Street Fighter League newcomers stands Miky “Samurai” Chea, returning to the team-based tournament after competing in both seasons one and two with the most wins in the series. With a wealth of experience under his belt, this Akuma main promises to bring his invaluable strategies — and famous meditative practices — to season three alongside his teammates.
- Angrybird Amjad “Angrybird” Alshalabi (not to be confused with teammate Big Bird) is a shining jewel in the UAE’s talent pool. In fact, captain Big Bird claimed that he and Angrybird together single-handedly put their region on the esports map — a lofty claim that can be backed up by both of their impressive tournament results.
-
Other appearance
-
Street Fighter EX
The eternal challenger, Ryu. Will his battle ever end? Will his challenge last through all eternity? What is he searching for? Will he... "I just need to meet a man who is stronger than I am."
-
Street Fighter EX Plus α
"I shall never rest as long as the fight vibrates within my mind." Ryu's journey continues with no end in sight... The Path of the True Warrior. Will he find an "answer" at the end of the road...? He continues walking, believing in his own fist. The road he seeks is still a long way to go."
-
Street Fighter EX2"
"Ryu realized that he needed to overcome himself and forge his mind with true strength. Returning to the old mountain home where he trained together with Ken and their mutual master, Ryu began his training all over again from a fundamental principle, forbidding himself from using the special moves he has learned. Ryu strengthens himself with such a humble method, believing that it will someday guide him to the end of the fight and reveal the answer he has been seeking. Tomorrow's Ryu could be you."
-
Street Fighter EX2 Plus
"Ryu was there, under the moonlight. He raises his own fist to the heavens. "What should I grasp?" The wind flows. Until the moment the answer he is looking for is found."
-
Street Fighter EX3
"Ryu has always chosen the road less traveled on his quest for the ultimate challenge. And as he walks away he can only think of the most recent events of his life, 'What is the purpose? What is the point? How many more challenges must I overcome before I can feel like I have accomplished my goal? Maybe there is no answer, and that is the point..."
Biography
Concept
In the early days of most Street Fighter media, Ryu often had a more "rough and tumble" personality, as well as a bit of a goofy and lighthearted side to him. He was also shown to be quite irritable when hungry. In his short story in the manga Street Fighter Gaiden, he was shown to be a rather happy individual, a trait common among shounen main characters, such as Son Goku from the Dragon Ball series and Luffy from the One Piece series. Some of these traits are preserved in modern portrayals.
Appearance
Ryu's most recognizable attire consists of a white karate gi with the sleeves torn off, medium length hair, a long red headband, a black martial arts belt and bare feet (a choice he makes of his own, believing it to be more comfortable. In many of his appearances, Ryu carries around a large, white duffel bag containing items important to his travels, such as clothing, plane tickets, passports, and local currency.
Personality
Ryu is a silent, meek, humble, submissive, self-deprecating, and respectful individual; which is often juxtaposed against the light-hearted, fiery persona of his childhood friend, Ken. He wanders the world with the desire for complete mastery of his martial art, and takes both his travels and training seriously.Though he does not actively seek the role of a hero, he possesses a strong sense of justice as well; protecting the innocent against the predations of bullies and tyrants is the one of the few things that Ryu will prioritize over a satisfying fight.
Languages
Which do you prefer?
Powers & Abilities
- Messatsu Gou Hadou
- Tatsumaki Zankukyaku
- Ryusokyaku: An overhead axe kick.
- Ashura Senku: After glowing red, Evil Ryu glides across the battlefield while being temporarily invulnerable, which in practice makes this move akin to teleporting.
- Messatsu-Goshoryu: A series of three consecutive Shoryukens. If the final hit connects, Evil Ryu harnesses more of the Satsui no Hado’s power to slam the opponent’s head into the ground.
- Shun Goku Satsu: Also known as the Raging Demon, this is the Satsui no Hado’s ultimate and most dangerous technique. After warping towards his opponent.
Additional abilities
- Shakunetsu Hadoken: A Hadoken imbued with the power of fire. Acts as a separate move in SFII and Alpha, and as Ryu’s EX Hadoken from the III series onward.
- Denjin Hadoken: A chargeable, electrified version of the Shinku Hadoken. Those hit with a fully-charged blast are stunned, giving Ryu another opening to attack.
- Shinku Hadoken: A powerful, multi-hitting Hadoken which can beat out smaller or weaker projectiles
- Metsu Hadoken: A variant of the Shinku Hadouken done with the Satsui no Hado. Ryu can perform this without transforming, but still retains this as Evil Ryu.
- Ren Hadoken
- Baku Hadoken
- Hado no Kamae
- Hashogeki
- Collarbone Breaker: An overhead punch primarily meant to break the guard of crouching opponents.
- Solar Plexus Punch: A crushing blow to the enemy’s midsection.
Feats
Strength 😎
- Lifted a boulder with Oro on top with it, who weighs 71 kg, twice.
- His Hadoken can shatter solid stone.
- Punched Ken Masters through a wooden door.
- At his peak, comparable to Akuma who sunk an island with a punch (6 megatons) and shattered Uluru’s Rock.
- Threw off a group of thugs with a Tatsumaki Senpukyaku.
- Created a crater with a single punch.
- Matched Akuma’s Raging Demon as Evil Ryu./li>
- Hashogeki
- Destroyed a chunk of a skyscraper.
Speed 😏
- Dodged point-blank bullets with little effort while trying to save someone.
- Dodged Sagat’s strikes.
- Reacted to an ambush and quickly takes out three guys.
- Dodged Ken’s kick.
- Faster than Eagle who moved in tandem with Bison’s satellite laser. (Mach 1669)
- Blocked all of Rufus’s strikes at once.
Skills 😈
- Strives to be the best he can be.
- Suppressed the Satsui No Hado.
- Defeated the likes of Rashid, Charlie Nash, and Sagat.
- Defeated Necalli, a soul-eating Aztec warrior.
- Defeated Twelve, who could copy the appearances and fighting styles of other fighters.
- Fought Gen to a draw.
- Defeated Sagat's ex-student, Adon.
- Defeated a Satsui no Hado empowered Sakura.
- Defeated Seth, the genetically-enhanced S.I.N. super soldier.
- Defeated the martial arts movie star, Fei-Long.
- Defeated the martial arts movie star, Fei-Long.
- Defeated a bio-engineered warrior that not even Chun-Li, Ken and Birdie could defeat.
Education
-
Every day
Live and fights for justice
-
December 1976 – April 1980Street Fighter: The Battle for Shadaloo at DC Comics
-
1st September 1998 – 30th June 2001Street Fighter Zero 3 in Street Fighter comics at Marcelo Cassaro and Erica Awano
-
21st October 2001 – 23rd May 2005Certificate of PROFESSIONAL striker at CryptoLogic
-
15 September 2006 – 21st June 2009Kill as fast as can at Universal Fighting System
-
3rd November 2012 – 21st July 2018Great fighter at Jasco Games


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- If a letter is given (between quotation marks), then the function returns 1000 nucleotides of that letter
- If multiple letters are given (between quotation markes), then the function returns at least 1000 nucleotides of that letter combination
- If a list of letters is given, then the function returns 1000 nucleotides, randomly selecting from the list
(MAKE-RANDOM-RNA "U")
:: "UUUUUUUUUUUUUUUUUUUUUU..."(MAKE-RANDOM-RNA "UC")
:: "UCUCUCUCUCUCUCUCUCUCUC..."(MAKE-RANDOM-RNA {"U" "C" "C" "C"} ) ; in BioBIKE mode
(MAKE-RANDOM-RNA (LIST "U" "C" "C" "C") ) ; in BioLISP mode
:: "CCCAACCCCACCCCCACCCCCC..."


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Overview
These notes are a result of my preparation for a midterm exam in Kate Larson’s introductory course on Artificial Intelligence at University of Waterloo in the winter 2016 term. The contents are therefore based on the corresponding presentation slides available online.
Introduction
Definition dimensions:
| Reasoning | Behavior | |
|---|---|---|
| Human-like | Thinking like a human | Acting like a human |
| Rational | Thinking rationally | Acting rationally (course focuses on this field) |
Rational agents:
- Agent: entity that perceives and acts
- Rationality: acting optimally towards a specific goal in a given environment
- Task environment: performance measure, environment, sensors and actuators
Properties of the Task Environment:
- Fully observable vs Partially observable
- Deterministic vs Stochastic
- Episodic vs Dynamic
- Discrete vs Continuous
- Single agent vs Multi agent
Search
Task environment:
- Fully observable
- Deterministic
- Episodic
- Discrete
- Single Agent
Uninformed Search
Techniques:
| Depth First | Breadh First | Iterative Deepening | Uniform Cost | |
|---|---|---|---|---|
| acronym | DFS | BFS | IDS | UCS |
| dequeuing method | LIFO | FIFO | alternating LIFO and FIFO | minimal backward cost of path |
| complete? | no | yes | yes | if all $\epsilon > 0$ and $C^*<\infty$ |
| optimal? | no | for ident. cost | for ident. cost | yes |
| time complexity | $O(b^m)$ | $O(b^d)$ | $O(b^d)$ | $O(b^{C^*/\epsilon})$ |
| space complexity | $O(bm)$ | $O(b^d)$ | $O(bd)$ | $O(b^{C^*/\epsilon})$ |
Variables:
- $b$: branching factor of search tree
- $m$: maximum depth of search tree
- $d$: depth of shallowest goal node
- $C^*$: cheapest solution cost
- $\epsilon$: minimum edge cost
Helper technique:
If the state space graph is cyclic the search tree will be infinite. In this case, a “closed list” may be used to keep track of nodes which have already been expanded in order to avoid infinite traversals of cyclic structures
Informed Search
Heuristic function: function $h(n)$ that estimates the cost of reaching a goal from a given state (requirement: $h(n_{goal})=0$)
Admissibility: a heuristic is admissible if $0 \leq h(n) \leq h’(n)$ where $h’(n)$ is the true shortest path from node $n$ to one of the goal states
Consistency: a heuristic is consistent if $h(n) \leq cost(n,n’)+h(n’)$
Backward cost: function $g(n)$ that tells how expensive it was to reach node $n$ from the start node
Estimate of cost of entire path: function $f(n)=g(n)+h(n)$
Greedy Best First Search: expand the most promising node according to the heuristic only; only complete when used with a closed-list
Techniques:
| Greedy Best First | A* | Iterative Deepening A* | Simplified Memory-bounded A* | |
|---|---|---|---|---|
| acronym | GBFS | A* | IDA* | SMA* |
| dequeuing method | minimal $h(n)$ | minimal $f(n)$ | minimal $f(n)$ with f-limit | see A* |
| complete? | no | yes | see A* | if memory needed for path to shallowest goal node $\leq$ memory size |
| optimal? | no | only for graph-search and a consistent $h(n)$ | see A* | see above |
| time complexity | $O(b^m)$ | $O(b^m)$ | see A* | see A* |
| space complexity | $O(b^m)$ | $O(b^m)$ | less than A* | will drop nodes from memory if it runs out of memory |
Constraint Satisfaction Problems
A special subset of search problems where:
- States are defined by (continuous or discrete) variables $X_i$ with values from (finite or infinite) domains $D_i$
- Goal test is a set of (unary, binary, higher-order or soft) constraints specifying allowable combinations of values for subsets of variables
- Commutativity is in place, i.e., the order of actions taken does not effect the outcome; variables can be assigned in any order
Formal abstraction as a search problem:
- States: partial assignments of values to variables
- Initial State: empty assignment ${}$
- Successor Function: assign a value to an unassigned variable
- Goal Test: the current assignment is complete and satisfies all constraints
Backtracking:
- Select unassigned variable $X$ and try out first valid assignment $x_i$
- If a valid assignment is found move to next variable
- If no valid assignment is found back up and try a different assignment for $X$
Improvements to backtracking using:
- Ordering:
- Most Constrained Variable: choose the variable which has the fewest legal moves
- Most Constraining Variable: choose variable with most constraints on unassigned variables (tie-breaker for most constrained variable)
- Least Constraining Value: given a variable, choose the value that rules out the fewest values in unassigned variables
- Filtering:
- Forward Checking: keep track of remaining legal values for unassigned variables and terminate search if any variable has no legal values
- Arc Consistency: given two domains $D_1$ and $D_2$, an arc from $D_1$ to $D_2$ is consistent if, for all $x$ in $D_1$, there is a $y$ in $D_2$ such that $x$ and $y$ are consistent
- Structure:
- Independent Subproblems: break down constraint graph into connected components and solve them separately; can reduce time complexity from $O(d^n)$ to $O(d^c n/c)$ where $d$ is the domain size, $n$ is the total number of variables and $c$ is the average number of variables per component
- Tree Structures: perform topological sort; back to front: make mutually consistent between children and parents; front to back: assign values consistent with parent; time complexity is $O(nd^2)$
- Cutsets: choose a subset $S$ of variables such that the constraint graph becomes a tree when $S$ is removed ($S$ is the cycle subset); for each possible valid assignment to the variables of $S$: remove from the domains of remaining variables all values that are inconsistent with $S$; if the remaining CSP has a solution, return it; time complexity is $O(d^c(n-c)d^2)$ where $c$ is the size of the cutset
- Tree Decomposition: decompose graph into subproblems that constitute a tree structure; solve each subproblem independently; solve constraints connecting the subproblems using the tree-based algorithm; time complexity is $O(nd^w)$ where $w$ is the size of the largest subproblem
Local Search
For many problems, the search path is unimportant. Instead, oftentimes it is simply important to find a viable/good comnbinatorial solution without knowing the path to get there.
Iterative Improvement
- Approach:
- Start at some random point
- Generate all possible points to move to (i.e., the moveset)
- If the set is empty, restart
- If the set is not empty, choose point from it and move to it
- Methods:
- Hill Climbing / Gradient Descent
- Idea: always take a step in the direction that improves the current solution value the most
- Pros:
- straightforward implementation
- low memory consumption
- Cons:
- not complete
- not optimal (can get stuck in local optima/plateaus)
- Modifications:
- allow sideway moves to escape plateaus
- random restarts to escape local optima
- random selection of next move, but only take the step if it improves the solution
- allow bad moves to escape local optima (see simulated annealing)
- Simulated Annealing
- Idea:
- choose random move from moveset
- if it improves the solution make the move
- if not (bad move) take it anyways with probability $p$
- $p=e^\frac{V(S_i)-V(S)}{T}$ (Boltzmann distribution)
- $T$ is a temperature parameter which will decrease over time:
- exploration phase when $T$ is high (random walk)
- exploitation phase when $T$ is low (randomized hill climbing)
- Properties:
- optimal if $T$ decreases slowly enough
- Idea:
- Hill Climbing / Gradient Descent
Genetic Algorithms
- Idea: simluation of natural evolutionary processes to approach a global optimum
- Requirements:
- Encoding representation of individuals (normally a bitstring)
- Fitness function to evaluate the quality of an individual
- Operations:
- Selection: selection of candidates for reproduction may be…
- fitness-proportionate (can lead to overcrowding)
- tournament-based (select two individuals at random and, with constant probability, choose the fitter one)
- rank-based
- softmax-based
- Crossover
- Mutation (normally done with a low probability)
- Selection: selection of candidates for reproduction may be…
- Algorithm:
- Initialize population randomly
- Compute fitness for each individual
- $N$ times do:
- Select two parents
- Crossover the parents to create new child
- With low probability, mutate child
- Add child to population
- Return “fittest” individual in population
Planning
Purpose: construct a sequence of actions for performing some task / reaching some goal
Stanford Research Institute Problem Solver (STRIPS) language:
- Domain: set of typed, concrete objects (no variables allowed)
- States: conjunctions of first-order predicates over objects (no variables allowed)
- Goals: conjunctions of positive ground literals (no negative ground literals allowed)
- Closed-World Assumption: any conditions not mentioned in a state are assumed to be false (see Frame Problem)
- Actions: tuples of preconditions (conjunction of function-free positive literals) and effects (description of how the state changes when the action is executed, sometimes defined as delete- and add-lists)
Planning as Search: planning is a specific type of search in which the search space is reduced significantly by the use of a highly structured and restriced planning language (e.g., Planning Domain Definition Language PDDL, a generalization of STRIPS):
- Progression Planning (Forward Planning): classical search which can strongly benefit from good heuristics
- Regression Planning (Backward Planning): start from goal state and find a sequence of consistent (i.e., must not undo any desired state), relevant (i.e., must achieve one of the conjuncts of the goal) actions
Frame Problem: when the consequences of an action are described the frame problem poses the question what has happened to components of the world that were not mentioned in this description
Sussman’s Anomaly: stack-based regression planning might not work if a problem is decomposed into sub-problems that are interdependent
Planning Graphs: a form of representation of a planning problem
- Levels:
- $S_0$ has a node for each literal that holds in the initial state
- $A_0$ has a node for each action that could be taken in $S_0$
- $S_i$ contains all literals that could hold given the actions taken in level $A_{i-1}$
- $A_i$ contains all actions whose preconditions could hold in $S_i$
- Persistence Actions (no-op): literal will persist until an action negates it
- Mutual Exclusion (Mutex) links: record conflicts between actions or states that cannot occur together for one of the following reasons:
- Inconsistent Effects (actions)
- Interference (actions)
- Competing Needs (actions)
- Inconsistent Support (states)
- Heuristics:
- Level-cost: for a single goal literal, the level in which it appears first
- Max-level: $argmax_i levelcost(g_i)$
- Sum-level: $\sum_i levelcost(g_i)$ (may be inadmissible!)
- Set-level: for multiple goal literals, the first level where all appear and are not mutex (dominates max-level)
- GraphPlan:
- Forward construction of the planning graph (in polynomial time)
- Solution extraction (backward search through the graph, may be intractable because PSPACE-complete)
Adversarial Search
Task environment: multi-agent
Types of Games:
| Perfect Information | Imperfect Information | |
|---|---|---|
| Deterministic | Chess | Other Card Games |
| Stochastic | Rolling the Dice | Poker |
Zero-sum Perfect Information Games:
- Agents:
- MAX: aims to maximize the utility of the terminal node (i.e., win the game)
- MIN: aims to minimize the utility of the terminal node (i.e., make MAX lose the game)
- Goal: finding an optimal strategy for MAX (i.e., a strategy that leads to outcomes at least as good for MAX as any other strategy, given that MIN is playing optimally)
- Minimax: a search algorithm to extract the optimal strategy
- Complete if tree is finite
- Time complexity: $O(b^m)$
- Space complexity: $O(bm)$ (DFS)
- Alpha-Beta Pruning: elimination of large parts of the minimax search tree
- $\alpha$: value of best choice (highest value) we have found so far on path for MAX
- $\beta$: value of best choice (lowest value) we have found so far on path for MIN
- Prune branches that are worse than $\alpha$ or $\beta$ for MAX and MIN respectively
- Evaluation Functions: compute expected utility for non-terminal states (and actual utility for terminal states) to allow for real-time decisions instead of going down the search tree for part of the search space
Stochastic Games:
- Agents:
- MIN and MAX like above
- CHANCE
- Expectiminimax:
- CHANCE will compute the expected value
- MIN will compute the minimum
- MAX will compute the maximum
Decision Making
A decision problem under uncertainty is $<D,S,U,P>$ where:
- $D$ is a set of decisions
- $S$ is a set of states
- $U$ is a function that maps a real utility value to every state $\in S$ (unique up to a positive affine transformation)
- $P$ is a probability distribution which will tell how likely it is that decision $d$ will lead to state $s$
Expected Utility: $EU(d)=\sum_{s \in S}P_d(s)U(s)$
Solution: any $d’ \in D$ such that $EU(d’) \geq EU(d)$ for all $d \in D$
Policies: for a sequence of actions, a policy assigns an action decision to each state; policies may be obtained by bottom-up analysis of decision trees, incorporating a NATURE agent, representing probability distributions of outcomes for actions, taken in states
Markov Decision Processes
Markov Chain:
- A set of probability distributions of the next state given the current state (may be represented as a transition probability matrix)
- History Independence (Markov Property): the probability of reaching state $s_{t+1}$ from state $s_t$ does not depend on how the agent got to the current state $s_t$
- Discounted sum of future rewards $U’(s)$ of state $s$: is the sum of the reward for state $s$ and of all future rewards that can be reached from state $s$ where the utility of each future state $x$ which is $n$ steps away will be discounted by a factor of $\gamma^n$, $\gamma$ being a constant discount factor with $0 < \gamma < 1$:
- $U’(s_i)=r_i+\gamma\sum_{j=1}^nP_{ij}U’(s_j)$
- $U=(I-\gamma P)^{-1}R$ ($P$ being the transition probability matrix and $R$ being the rewards vector)
- This system may be solved directly by matrix inversion or, if this is too costly, approximated by Value Iteration:
- Compute $U^n(s)$ values for each state $s$ and step length $n$ (starting with $n=1$)
- Use dynamic programming by computing $U^n(s)$ using the previously computed and stored values of $U^{n-1}(s)$
Markov Decision Process (MDP): similar to a Markov Chain, but incorporating the notion of actions. In every state $s_i$, the agent may decide to take an action $a_k$ which may lead to state $s_j$ with probability $P(s_j \mid s_i,a_k)$
- Expected discounted sum of future rewards assuming the optimal policy and a step length of $t$, starting from state $s_i$, $V^t(s_i)$:
- $V^{t+1}(s_i)=max_k r_i+\gamma\sum_{j=1}^nP_{ij}^kV^t(s_j)$
- $V^*(s_i)$ is $V^t(s_i)$ with $t=\infty$
- Policy Optimization: for every MDP, there is an optimal policy (i.e., a mapping from state to action) such that for every possible start state, there is no better option than to follow the policy; it can be found in polynomial time (in the number of states) by:
- Value Iteration: iteratively compute $V^*(s_i)$ for all $s_i$ and select the best action $k$ according to $argmax_k r_i+\gamma\sum_{j=1}^nP_{ij}^kV^t(s_j)$
- Policy Iteration:
- Policy Evaluation: given policy $\pi$, compute $V_i^\pi$ for all states $s_i$
- Policy Improvement: calculate a new policy $\pi_{i+1}$ using 1-step lookahead
- Repeat both steps until $V^\pi(s_i)$ converges
Partially Observable MDP (POMDP): in a POMDP, the agent does not know for sure what state it is in; therefore, it also stores a set of observations $O={o_1,…,o_k}$, an observation model $P(o_t \mid s_t)$ and a belief state $b$ which is a probability distribution over all possible states; $b(s)$ is the probability assigned to state $s$; here, a policy is a mapping from a belief state to an action; generally, finding an approximately optimal policy is PSPACE-hard
Reinforcement Learning
Task Environment:
- Fully observable
- Stochastic
- Dynamic
- Discrete
- Single Agent
Characteristics:
- the agent learns a policy to act with the aim to maximize the resulting reinforcement signals (numerical reward)
- the reinforcement signals may be delayed (credit assignment problem)
- the goal is to find the optimal policy, but we start without knowing the underlying Markov Decision Process (MDP), i.e., the rewards and transition probabilities are not known
- formally, we can describe this as the following problem: learn policy $\pi:S \mapsto A$ that maximizes $E[r_t+\gamma r_{t+1}+\gamma^2r_{t+2}+…]$ from any starting state $\in S$
Forms of Reinforcement Learning:
| Passive (evaluate a given policy) | Active (learning to act optimally) | |
|---|---|---|
| Model-based | Adaptive Dynamic Programming (ADP): evaluate a given policy, based on observations after running the policy a number of times | ${}$ |
| Model-free | Temporal Difference: use observed transitions to adjust values of observed states so that they satisfy Bellman equations according to the following update rule: $V^\pi(s_i) \rightarrow V^\pi(s_i) + \alpha \sum_{m=i}^\infty \lambda^{m-i} [r(s_m) + \gamma V^\pi(s_{m+1} - V^\pi(s_{m})]$; empirically, $\lambda=0.7$ works well | Q-Learning: learn a function $Q: SxA \rightarrow R$, then, if the Q values are learned correctly, the optimal policy is defined as $\pi’(s)=\arg!\max_aQ(s,a)$ and the expected utility is defined as $V’(s)=\max_aQ(s,a)$; algorithm: loop over the following steps: 1. select action $a$ with probability $p(a)=\frac{e^{Q(s,a)/T}}{\sum_ae^{Q(s,a)/T}}$ (Boltzmann exploration); 2. receive immediate reward $r$; 3. observe new state $s’$; 4. update according to the following update rule: $Q(s,a) = Q(s,a) + \alpha(r + \gamma \max_{a’}Q(s’,a’) - Q(s,a))$; 4. set $s = s’$ |
Reward Shaping: consider delays in rewards and add additional rewards for “making progress” using domain knowledge about important steps for reaching the final reward; this bears the risk of the agent optimizing for the pseudo rewards
Bayes Nets
A Bayes Net (BN) is a graphical representation of direct dependencies over a set of variables + a set of conditional probability distributions (CPTs) quantifying the strength of the influences.
The structure of the BN means: every $X_i$ is conditionally independent of all of its nondescendents given its parents.
Determining conditional independence in a Bayes net:
-
Non-descendents: a node is conditionally independent of its non-descendents, given its parents
-
Markov blanket: a node is conditionally independent of all other nodes in the network, given its parents, its children and its children’s parents
-
D-separation: two nodes are conditionally independent from each other, given evidence E if E blocks all undirected paths P between the two nodes (E blocks p in P if there is a node z in p which is also in E and where at least one arc in p goes out of z, or if there is a node z in p two arcs in p go into with neither z nor any of its descendents being in E)
Inference in BNs:
- Forward with upstream evidence
- Backward with downstream evidence
Variable elimination: algorithm, representing conditional probability tables (CPTs) in BNs as factors and defining ways to answer arbitrary queries involving the variables of a given BN in an algorithmic manner. Solving queries is linear in the number of variables (i.e., nodes) in the BN and exponential in the size of the largest CPT.
Decision Networks
Decision Networks provide a representation for decision making, consisting of:
- random variables like in Bayes Nets
- decision variables controlled by the agent, parent nodes reflect information available at time of decision; decisions are made in sequence; information available to previous decisions remains available for current decision;
- utility variables stating how good certain states are, value only depends on state of parents, generally only one utility node per network
Policies: a policy $\delta$ is a set of mappings $\delta_i$, one for each decision node $D_i$, associating a decision for each parent assignment of $D_i$. The value of a policy $\delta$ is the expected utility given that decision nodes are executed according to $\delta$.
Optimal Policy: an optimal policy $\delta’$ is such that $EU(\delta’) \geq EU(\delta) \forall \delta$; optimal policies can be constructed working from the last decision backwards
Value of Information: information has value to the extent that it is likely to cause a change of plan and that the new plan will be better than the old plan; for any single-agent decision-theoretic scenario, the value of information is non-negative!
Multiagent Systems
Game Theory: describes how self-interested agents should behave; per definition, it is a formal way to analyze interactions among a group of rational agents that behave strategically:
- Normal form game: also known as matrix or strategic game, assumes that agents are playing simultaneously
- Ingredients:
- $I$: set of agents ${1, …, N}$
- $A_i$: set of actions for each agent ${a_i^1, a_i^2, …, a_i^m}$
- Outcome of a game, defined by a profile: $o = (a_1, …, a_n)$
- Utility functions: $u_i: O \rightarrow \mathbb{R}$
- Zero-sum games: $\sum_{i=1}^N u_i(o) = 0 \forall o$
- Dominant strategies: strategy $a_i’$ strictly dominates strategy $a_i$ if $u_i(a_i’,a_{-i}) > u_i(a_i,a_{-i}) \ \forall \ a_{-i}$; dominated strategies will never be played!
- Nash Equlibrium (NE):
- Pure NE: an action profile $a^e$ is a Nash equilibrium if $\forall \ i \ u_i(a^e_i,a^e_{-i}) \geq u_i(a_i,a^e_{-i}) \ \forall \ a_i$, i.e., if no agent has incentive to change given that others do not change.
- Mixed NE: for probabilistic decision making, a notion of mixed NE was defined as follows:
- mixed strategy: $s_i$ is a probability distribution over $A_i$
- strategy profile: $s = (s_1, …, s_N)$
- expected utility: $u_i(s) = \sum_a p(a) u_i(a)$ where $p(a) = \prod_j s(a_j)$
- Nash equilibrium: $s^e$ is a mixed Nash equilibrium if $\forall \ i \ u_i(s^e_i,s^e_{-i}) \geq u_i(s_i,s^e_{-i}) \ \forall \ s_i$
- Theorem (Nash 1950): every game in which the action sets are finite has a mixed Nash equilibrium, and if there is an even number of actions then there will be an odd number of equilibria.
- Ingredients:
-
Extensive form game: assumes that agents take turns, they can be represented as decision trees; every extensive form game can be transformed into a normal form game for which equilibria can be computed as explained before
- Sub-game perfect equilibrium (SPE): equilibrium in an extensive form game that is a Nash equilibrium in all sub-games (i.e., in all sub-trees of the game’s decision tree)
- Theorem (Kuhn): every finite extensive form game has a sub-game perfect equilibrium
Mechanism Design: describes how we should design systems to encourage certain behaviours from self-interested agents
- Ingredients:
- $O$: set of possible outcomes
- $N$: set of $n$ agents
- Each agent has a type $\theta_i$ from a set of possible types $\Theta_i$ capturing all private information relevant to the agent’s decision making
- Utility functions $u_i(o, \theta_i)$
- Social choice function: $f: \Theta_1 \times … \times \Theta_n \rightarrow O$
- Mechanisms:
- A mechanism $M = (S_1, …, S_n, g(\cdot))$ is a tuple of strategy spaces (one $S_i$ for each agent $i$) and an outcome function $g: S_1 \times … \times S_n \rightarrow O$, mapping specific strategies to an outcome.
- A mechanism implements a social choice function $f(\theta)$ if there is an equilibrium $s’ = (s’_1(\theta_1), …, s’_n(\theta_n))$ such that the mechanism’s outcome function, given the strategies of equilibrium $s’$, will lead to the same outcome as the social choice function, given the true preferences of the agents, irrespective of what these true types are: $g(s’_1(\theta_1), …, s’_n(\theta_n)) = f(\theta_1, …, \theta_n) \forall (\theta_1, …, \theta_n) \in \Theta_1 \times … \times \Theta_n$.
- A mechanism is called a direct mechanism for social choice function $f(\theta)$ if its strategies do simply output a type (not necessarily the agent’s true type) and that the outcome for the same type profile $\theta$ is the same for social choice function $f(\theta)$ and the outcome function of the mechanism $(g(\cdot)$: $S_i = \Theta_i \forall i$ and $g(\theta) = f(\theta) \forall (\theta_1, …, \theta_n) \in \Theta_1 \times … \times \Theta_n$.
- A direct mechanism is incentive-compatible if it has a strategy equilibrium $s’$ such that each strategy of the equilibrium outputs the agent’s true type, independent of the specific type: $s’_i(\theta_i) = \theta_i \forall i$ and $\theta_i \in \Theta_i$.
- A direct, incentive-compatible mechanism is strategy-proof if this equilibrium is the dominant strategy such that, resulting in the agents always revealing their true type simply by acting rationally.
- Revelation Principle Theorem: If there is a mechanism $M$ that implements social choice function $f$ in dominant strategies, then there is a direct, strategy-proof mechanism $M’$ that implements $f$.
- Gibbard-Satterthwaite Theorem: For any finite outcome space with more than two outcomes where each outcome can be achieved by social choice function $f$ and where the type space $\Theta$ includes all possible orderings over $O$, then $f$ is implementable in dominant strategies if and only if $f$ is dictatorial. Relaxations of these requirements may lead to non-dictatorial social choice functions that are implementable in dominant strategies such that strategy-proof mechanisms may be constructed; in particular, these restrictions can be relaxed by:
- using a weaker equilibrium concept
- designing mechanisms where computing manipulations is computationally hard
- restrict the structure of agents’ preferences:
- single-peaked preferences (e.g., median voter rule)
- quasi-linear preferences: introduces the concept of transfers (e.g., money) in the outcome representation ($o = (x, t_1, …, t_2)$) where the agents’ utility functions are linear in the agents’ corresponding transfer value: $u_i(o, \theta_i) = v_i(x, \theta_i) - t_i$; the following mechanisms operate with quasi-linear preferences:
- Groves Mechanisms: with choice rule $x’=\arg \max_x \sum_i v_i(x, \theta_i)$ (efficient $=$ maximizing social welfare) and transfer rules $t_i(\theta) = h_i(\theta_{-i}) - \sum_{j \neq i} v_j(x’, \theta),\theta_j)$ where $\theta_{-i}$ means all types except the one for agent $i$. The Vickrey-Clarke-Groves (VCG) mechanism is a Groves mechanism with $h_i(\theta_{-i}) = \sum_{j \neq i} v_j(x^{-i},\theta_j)$ where $x^{-i}$ is the outcome that would have arisen if agent $i$ had not existed. This results in zero transfers for all agents for which, if they had not participated in the game, the outcome would still be the same as with them participating. An example implementation of a VCG mechanism is the Vickrey auction where the highest bidder gets the object and has to pay the second-highest bid, all the other ones do not have to pay anything.
- Sponsored Search: bids are placed for ad placements and every ad has a quality score; ads are ranked by the product of the bid and the ad’s quality score; if an ad is clicked, the bidder has to pay the minimum amount it would have had to bid in order to have ended up in the same position in the current ranking.
Statistical Learning
Parameter learning: with complete data and Laplace smoothing using Maximum Likelihood (ML); given observations $x = (x_1, x_2, …, x_d)$ from $N$ trials, estimate parameters $\theta = (\theta_1, \theta_2, …, \theta_d)$ using $\theta_i = \frac{x_i + \alpha}{N + \alpha d}$ with $\alpha > 0$
Naive Bayes model: with observed attribute values $x_1, x_2, …, x_n$, assuming that all attributes are independent given the class, we can compute the posterior class probability by $P(C \mid x_1, x_2, …, x_n) = \alpha P(C) \prod_i P(x_i \mid C)$
Expectation Maximization
A technique to estimate model parameters despite the fact that there may be hidden variables or missing values in the observed data points. The general algorithm goes as follows:
- Guess the parameters of the maximum likelihood hypothesis $h_{ML}$
- Loop over the two following steps until the parameters of $h_{ML}$ converge:
- Expectation: based on $h_{ML}$, compute expectated (missing) values
- Maximization: based on expected (missing) values, compute new $h_{ML}$


Toy Storage
问题描述 :
Mom and dad have a problem: their child, Reza, never puts his toys away when he is finished playing with them. They gave Reza a rectangular box to put his toys in. Unfortunately, Reza is rebellious and obeys his parents by simply throwing his toys into the box. All the toys get mixed up, and it is impossible for Reza to find his favorite toys anymore.
Reza’s parents came up with the following idea. They put cardboard partitions into the box. Even if Reza keeps throwing his toys into the box, at least toys that get thrown into different partitions stay separate. The box looks like this from the top:
We want for each positive integer t, such that there exists a partition with t toys, determine how many partitions have t, toys.
Reza’s parents came up with the following idea. They put cardboard partitions into the box. Even if Reza keeps throwing his toys into the box, at least toys that get thrown into different partitions stay separate. The box looks like this from the top:
We want for each positive integer t, such that there exists a partition with t toys, determine how many partitions have t, toys.
输入:
The input consists of a number of cases. The first line consists of six integers n, m, x1, y1, x2, y2. The number of cardboards to form the partitions is n (0 < n <= 1000) and the number of toys is given in m (0 < m <= 1000). The coordinates of the upper-left corner and the lower-right corner of the box are (x1, y1) and (x2, y2), respectively. The following n lines each consists of two integers Ui Li, indicating that the ends of the ith cardboard is at the coordinates (Ui, y1) and (Li, y2). You may assume that the cardboards do not intersect with each other. The next m lines each consists of two integers Xi Yi specifying where the ith toy has landed in the box. You may assume that no toy will land on a cardboard.
A line consisting of a single 0 terminates the input.
输出:
For each box, first provide a header stating “Box” on a line of its own. After that, there will be one line of output per count (t > 0) of toys in a partition. The value t will be followed by a colon and a space, followed the number of partitions containing t toys. Output will be sorted in ascending order of t for each box.
样例输入:
4 10 0 10 100 0 20 20 80 80 60 60 40 40 5 10 15 10 95 10 25 10 65 10 75 10 35 10 45 10 55 10 85 10 5 6 0 10 60 0 4 3 15 30 3 1 6 8 10 10 2 1 2 8 1 5 5 5 40 10 7 9 0
样例输出:
Box 2: 5 Box 1: 4 2: 1
解题代码:
/*<EMAIL_ADDRESS>*/ import java.io.*; import java.util.Map; import java.util.TreeMap; public class Main { public static void main(String[] args) throws Exception{ BufferedReader br = new BufferedReader(new InputStreamReader(System.in)); int n, m, y1, y2, i, t; String s; String[] ss; Point p,p2; while((s=br.readLine())!=null&&!s.equals("0")){ ss = s.split(" ",6); n=parseInt(ss[0]); m=parseInt(ss[1]); y1=parseInt(ss[3]); y2=parseInt(ss[5]); LineSet ls = new LineSet(n); Statistics st = new Statistics(); for(i=0;i< n;i++){ s=br.readLine(); ss=s.split(" ",2); t=parseInt(ss[0]); p=new Point(t,y1); p2=new Point(parseInt(ss[1]),y2); ls.add(t,new Line(p,p2)); } for(i=0;i< m;i++){ s=br.readLine(); ss=s.split(" ",2); t=ls.binFind(new Point(parseInt(ss[0]),parseInt(ss[1]))); st.register(t); } st.printFre(); } br.close(); } static int parseInt(String s){ if(s.startsWith("-")) return -parseInt(s.substring(1)); int t = 0; for(char ch: s.toCharArray()){ t *= 10; t += ch-'0'; } return t; } } class Statistics{ Map< Integer,Integer> map=new TreeMap< Integer,Integer>(); Map< Integer,Integer> fre=new TreeMap< Integer,Integer>(); void register(int x){ Integer t = map.get(x); if(t==null){ map.put(x, 1); Integer t2 = fre.get(1); if(t2==null){ fre.put(1, 1); } else{ fre.put(1, t2+1); } } else{ map.put(x, t+1); Integer t2 = fre.get(t); fre.put(t, t2-1); t2 = fre.get(t+1); if(t2==null){ fre.put(t+1, 1); } else{ fre.put(t+1, t2+1); } } } void printFre(){ System.out.println("Box"); int t; for(Map.Entry< Integer, Integer> me: fre.entrySet()){ if((t=me.getValue())>0) System.out.println(me.getKey()+": "+t); } } } class LineSet{ Map< Integer,Line> mil; Object[] lines; LineSet(int n){ mil = new TreeMap< Integer,Line>(); } void add(int key, Line line){ mil.put(key, line); } //Binary search method int binFind(Point p){ lines=mil.values().toArray(); int i=binFind0(p,0,lines.length-1); return i; } private int binFind0(Point p, int left, int right){ if(p.compareTo((Line)lines[left])< 0) return left; if(p.compareTo((Line)lines[right])>0) return right+1; int mid = (left+right)/2; if(p.compareTo((Line)lines[mid])< 0) return binFind0(p,left,mid-1); else return binFind0(p,mid+1,right); } } class Point implements Comparable< Line>{ int x; int y; Point(int x,int y){ this.x=x; this.y=y; } @Override public int compareTo(Line line) { return x*line.x0+y*line.y0+line.c; } } class Line{ //x0 must be positive int x0; int y0; int c; //p must be up, p2 must be down Line(Point p, Point p2){ x0=p.y-p2.y; y0=p2.x-p.x; c=-(p.x*x0+p.y*y0); } @Override public String toString(){ return "Line: "+x0+"x+"+y0+"y+"+c+"=0."; } }
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隐藏最深的bug是(start+end)/2有可能上溢
题目需要求解的是最小值，而且没有考虑可能存在环
这个应该是比较简单的解法了，如果有更好的办法可以告诉我
楼主你能把scanf( "%d%*" ,&T );中%*的用法详细说明吗
第一句可以忽略不计了吧。从第二句开始分析，说明这个花色下的所有牌都会在其它里面出现，那么还剩下♠和♦。第三句，可以排除2和7，因为在两种花色里有。现在是第四句，因为♠还剩下多个，只有是♦B才能知道答案。
你好，这题给出的代码好像弄错了，这个是pow那道题目的解法
算法还停留在大学的知识中
正是我要找的，适合新手
补充：此算法求出的不一定是最优解，之前理解错了
网站不错


How many tournaments have you played?
What division did you play?
What would you like to see improved?
Please let us know any other thoughts/comments you have about your tournament experience:


We're setting up some demos for a few sites… if you could go so some searches on the test pages, that would be great.
Hint: remember to use the 'back' key to search only
from our form.. searching a second time
from
the native result list form will not work.
Feel free to look at reports, but don't add any results just yet. If you *do* find a good suggestion page for one of your searches and that good result is not on the results you get, make a note and we'll use it when we define results.
Site: General Services Administration
Search for: government related things: taxes, IRS,
faa, child care, health insurance, etc.
search at http://sv2.ideaeng.net/gsa
admin at http://sv2.ideaeng.net:9110/?password=gsaadmin&command=report&nie_context=admin&days=1
Site: Gruuve
Search for music artists… when searching for one name ('beatles')
doesn’t do as well as full names ('the beatles') let me know - they want some
automatic redirection as well as suggestions.
Search at http://sv2.ideaeng.net/gruuve
admin at http://sv2.ideaeng.net:9105/?password=gruuveadmin&command=report&nie_context=admin&days=1
Site: Unocal
Search for oil/public company things - price, stock, environment, jobs,
etc
Search at http://sv2.ideaeng.net/unocal
admin at http://sv2.ideaeng.net:9085/?password=unocaladmin&command=report&nie_context=admin&days=1


Chennai engagements for Tuesday, November 26
RELIGION
Gita: Rukmini Ramamurthy, Kalyana Nagar Association, West Circular Rd., Mandavelipakkam, 4.30 p.m.; Satyavratananda, Kesari HSS., Pondy Bazaar Extn., T. Nagar, 6 p.m.
Mukunda Mala: Moksha Vidhyaananda Sarasvati, 77, Thirupugazh St., Kamatchi Nagar, Valasaravakkam, 11 a.m.
Soundarya Lahari: B. Sundarkumar, Sri Kamakshi Sathsang, 2nd St., Balaji Nagar, Royapettah, 7 p.m.
Thiruarutpa: A. Mahadevan, Sakthi Vinayagar Temple, P.T. Rajan Salai, K.K. Nagar, 6 p.m.
Nama Sangeerthanam: J. Sundarraman Bhagavathar, 4.30 p.m.; and Kovai Jayarama Bhagavathar, Sri Krishna Gana Sabha, T. Nagar, 6.30 p.m.
CULTURE
Loyola College: Play on ‘The Tale of an Anklet’ - French based on the Tamil epic ‘Silappadikaram’, Nungambakkam, 5.30 p.m.
GENERAL
S.A. Engineering College: 70th Indian Constitution Day celebration, Thiruverkadu, 11 a.m.
SC/ST People Confederation: Indian Constitution Day celebrations, B.R. Ambedkar Manimandapam, R.A. Puram, 3.30 p.m.
YMCA Pattimanram: Talk on ‘Tholkappiam Comentator Kalladar B. Thamaraikkannan, S Guruswamy’, YMCA Esplanade, NSC Bose Rd., 6 p.m.
Narcotics Anonymous: Meeting, Self Help Group, Community Service Center, Balfour Rd., Kilpauk, 7 p.m
Al-Anon: Meetings, Blessings AFG, Divine School, S.V. Nagar, Oragadam, Ambattur; CSI James Church, Ullagaram; Conscious AFG, Ezhil Model School, Kodungaiyur; Living Sober AFG, CSI Church, Kalaignar Nagar, Othavadai, Thiruvottiyur; and St. Anne Church, Pallavan Salai, Nesapakkam, 7 p.m.
Alcoholics Anonymous: Meetings, Santhome HSS., Mylapore; CBF Church, Banu Nagar, Pudur, Ambattur; C.S.I. James Church, Medavakkam Main Rd., Ullagaram; St. Josephs Anglo Indian HSS., Vepery; Dr. M.G.R. MHSS., Arcot Rd., Saligramam; Bedhasda Swimming Pool, Elaya Mudali St., Tondiarpet; Christ the King Church, G.K.M. Colony, Peravallur; St. Teresa Church, Nungambakkam High Rd.; ECI Church, Dr. Ambedkar St., Arumbakkam; Bharath Dass School, Perungudi; Karnataka HSS., Habibullah Rd., T. Nagar S.B.M. School, Perumal Kovil St., Thiruverkadu; and English Language Meeting, Church of Christ, W-76, Anna Nagar, 7 p.m.


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Default
Fake, Links
Fake, Links & Buttons
Fake, Links, Selected
Single-Select, Unselected
Single-Select, Selected
Multi-Select, Selected
Single-Select, Selected, Inherit Colour, Red
Single-Select, Selected, Inherit Font-Size (200%) (broken, spacing should not scale)


Navigate to this page on mobile phone (and "initialize" it by clicking the dumb button).
Place mobile phone inside TNT block, close the block.
Spawn some friends, everyone standing in a circle (how very un'craft)
funtime. Bump/tap the TNT block to "ignite". Don't be the one holding the TNT when it blows! Toss it to someone else!
ProTip®TM #1: Make sure the print out is big enough; perhaps upwards of 40cm along the longest 4-face part, assuming a longish, modern mobile phone that will sit in the box diagonally.
ProTip®TM #2: It helps to turn screen time out off or to a very long time for the duration of the game. If the screen goes dark and locks, the javascript will no longer receive sensor events.
ProTip®TM #3: Actually, probably better print out on paper, but also trace it out on some cardboard and glue or tape it on to that. Which in turn means some skills like scoring and creasing. A plain sheet of paper will probably not hold up very well.
Credit to super0noor for the TNT block cutout, found at PixelPapercraft.


[% extends 'pages/templates/boxPage.html' %] [% region contents %] The page you were looking for could not be found! But don't worry, there's lots of other cool stuff to see! [% end region %]


Field Operation Diagnostics
Turn On/Off By Schedule Step
Turn On/Off By Controller Pin
Trendlines
Past Alerts
Past Actions
Pending Actions
Recent Flow History
Recent Coil History
Automatic Operations
Control Operation
Set Rain Day
Set Eto Management
Manage Eto Values
Manage Irrigation Queue
Schedule Statistics
Composite Flow Statistics
Composite Current Statistics
Detail Statistics
Schedule Related Configurations
Add Schedule
Copy Schedule
Delete Schedule
Edit Schedule
Flow Limits
Current Limits
Misc Config
Configure System Actions
ETO Configuration
Configure Filter Cleaning Interval
Configure Max Flow Rate Cutoff
Operational Diagnostics
Coil Resistance
Coil Resistance Limits
ETO Raw Data
Soil Moisture Data
System Diagnostics
Past System Alerts
Past System Actions
Equip Environ Params
Modbus Statistics
Modbus Ping
View PLC Current History
View Running Python Processes
View Python Reboot Time Stamps
Reboot The System Over The Web
Site Map
Valve Group Site Map
Controller Site Map
Avocado Block Map
Controller Pin Map


The Icebergs Terrace Has Been Transformed Into a Beachside Oasis for Summer
Soak in some of the best views of the city while sipping botanical cocktails and eating freshly shucked oysters and burgers.
by Karina AroraWhile Icebergs swimming pool and the elegant dining room and bar are super famous, its terrace is almost unknown. That may change this summer, with the launch of the Ketel One Botanical bar.
The intimate outdoor space is at the south end of the Icebergs complex, discretely tucked off Notts Avenue, which leads you to the Bondi to Bronte walk. You enter the space through a timber-lined tunnel, and as you step onto the deck, it’s sea breeze and views of Bondi and the ocean for days.
The deck used to be lined with astroturf but now it’s decked out in blond timber – from the floors and the walls to the tabletops (Sydney landscape architects Secret Gardens are behind the design). There’s native Australian flora dotted around the space, umbrellas shading punters from the sun and well-dressed waiters taking table orders of cocktails and snacks.
The dishes are made in the fine diner’s kitchen and include a beef burger (grilled beef, cheese, tomato, lettuce, sauce), fish burger (crumbled hoki with cheese, tartare and lettuce), caprese salad, fries, shucked oysters and Ortiz anchovies served with toast and finger lime. There’s a small selection of vino – one prosecco, a white, a rosé and a red – as well as a Byron Bay Brewery lager.
As the name suggests, there’s also a lot of Ketel One vodka mixes. You can pick from two cocktails or mix and match the Ketel One Botanical range (cucumber and mint, grapefruit and rose, peach and orange blossom) with sodas made at the Icebergs bar.
Inspired by nature, they include fig leaf and mango; sea rosemary, coconut and jasmine; and macadamia, lemon myrtle and sandalwood. The new Ketal One vodka range feeds into the trend we’re seeing of lower alcohol by volume (it’s 30 per cent). These also have no artificial flavours and are only 50 calories per serve.
The Icebergs Terrace x Ketel One Botanical is open daily from 12.30 to 8pm till the end of March, and 12.30 to 6pm every Saturday and Sunday from April 2020 onwards.


vignettes/singleCellTK.Rmd
singleCellTK.Rmd
The Single Cell Toolkit (SCTK) is an interactive scRNA-Seq analysis package that allows a user to upload raw scRNA-Seq count matrices and perform downstream scRNA-Seq analysis interactively through a web interface, or through a set of R functions through the command line. The package is written in R with a graphical user interface (GUI) written in Shiny. Users can perform analysis with modules for conducting quality control, clustering, batch correction, differential expression, pathway enrichment, and sample size calculator, all in a simple to use point and click interface. The toolkit also supports command line data processing, and results can be loaded into the GUI for additional exploration and downstream analysis.
Due to the range of features that are available in SCTK, we have prepared several vignettes to help you get started, which are all available at https://www.camplab.net/sctk/
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.6.3 cli_3.6.1 knitr_1.43
## [4] rlang_1.1.1 xfun_0.39 stringi_1.7.12
## [7] purrr_1.0.1 textshaping_0.3.6 jsonlite_1.8.7
## [10] glue_1.6.2 rprojroot_2.0.3 htmltools_0.5.5
## [13] ragg_1.2.5 sass_0.4.7 rmarkdown_2.23
## [16] evaluate_0.21 jquerylib_0.1.4 fastmap_1.1.1
## [19] lifecycle_1.0.3 yaml_2.3.7 memoise_2.0.1
## [22] bookdown_0.34 BiocManager_<IP_ADDRESS> stringr_1.5.0
## [25] compiler_4.3.1 fs_1.6.3 systemfonts_1.0.4
## [28] digest_0.6.33 R6_2.5.1 magrittr_2.0.3
## [31] bslib_0.5.0 tools_4.3.1 pkgdown_2.0.7
## [34] cachem_1.0.8 desc_1.4.2


Cross platform buildsystem.
Creates Makefiles (e.g. GNU, Borland, MS)
supports C++,C,Java,Fortran
simple script language
Used by e.g. KDE 4, Chicken Schema, libgphoto


作者: 康凯森
日期: 2021-12-19
分类: OLAP
在 StarRocks 将近两年的时间里面，我们查询团队从零实现了向量化执行器，CBO 查询优化器，Pipeline 并行查询引擎，刷新国产 OLAP 数据库性能的里程碑，本文整理了我平时参考和学习的一些数据库资料，希望对大家有所帮助，也欢迎大家参与 StarRocks 开源社区。
本文章会努力持续更新，也欢迎大家一起贡献和修改。
TODO:
How does a relational database work
AnalyticDB: Real-time OLAP Database System at Alibaba Cloud
Apache Arrow: In Theory, In Practice
Introducing AresDB: Uber’s GPU-Powered Open Source, Real-time Analytics Engine
Ceph: A Scalable, High-Performance Distributed File System
CFS: A Distributed File System for Large Scale Container Platforms
The Secrets of ClickHouse Performance Optimizations
CockroachDB: The Resilient Geo-Distributed SQL Database.pdf)
DB2 with BLU Acceleration: So Much More than Just a Column Store
Using Apache Arrow, Calcite and Parquet to build a Relational Cache
Druid: A Real-time Analytical Data Store
F1 Query: Declarative Querying at Scale
Apache Flink™: Stream and Batch Processing in a Single Engine
FoundationDB: A Distributed Unbundled Transactional Key Value Store
Dremel: Interactive Analysis of Web-Scale Datasets
Procella: Unifying serving and analytical data at YouTube
Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google
Why Greenplum is the best compared with others
HAWQ: A Massively Parallel Processing SQL Engine in Hadoop
Apache Hudi Design And Architecture
Impala: A Modern, Open-Source SQL Engine for Hadoop
Kudu: Storage for Fast Analytics on Fast Data
Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing
Pinot: Realtime OLAP for 530 Million Users
Amazon Redshift and the Case for Simpler Data Warehouses
Data Warehousing in the Cloud: Amazon Redshift vs Microsoft Azure SQL
SAP HANA: A Data Platform for Enterprise Applications Purpose Built for Modern Hardware ★★★★★
SAP HANA: A Data Platform for Enterprise Applications Purpose Built for Modern Hardware 视频
Spanner: Google’s Globally-Distributed Database
SnappyData: A Unified Cluster for Streaming,Transactions, and Interactive Analytics
The Snowflake Elastic Data Warehouse
Building An Elastic Query Engine on Disaggregated Storage
Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing
Spark SQL: Relational Data Processing in Spark
Splice Machine – An HTAP DB at Scale
Tidb a raft based HTAP database
The Vertica Analytic Database: C-Store 7 Years Later
X-Engine: An Optimized Storage Engine for Large-scale E-commerce Transaction Processing
Alibaba Hologres: A Cloud-Native Service for Hybrid Serving/Analytical Processing
编程语言是工具，工具重在使用，所以学习编程语言和学习英语一样，提升最快的方式就是多使用，多实践，不需要等到对语言完全熟悉或者精通后才开始使用，实践中遇到了某个点不清楚，就一个点一个点逐一突破。
待完善
如果学习了某部分理论，却不知如何实践，欢迎参与 StarRocks 开源社区，上面所有的理论在 StarRocks 中都有可以实践的地方，欢迎大家的 Star, Issue 和 PR。


Syntax: %c('dialog caption', 'value') Displays a forum list from which the user can select a forum and/or topic. The value field determines whether the list shows only forums or both forums and topics.
Value Meaning
1 Display both forums and topics.
2 Display forums only
If the user presses the Esc key, the entire script is aborted and is not added to the Out Basket. If the user presses the Enter key, the highlighted topic or forum replaces the string prompt.
For example:
put 'mod %c('Add Participant','2')'
This displays a forums list showing only forums and with the caption 'Add Participant'. If the user selected the ameol_reg forum from the pick-list. then the entire string is updated to,
put 'mod ameol_reg'
This string is then output to the output script file which will subsequently be interpreted by Ameol when it comes to execute the entire script.


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Version 2.0.7
- Fix missing continue button in crop image screen.
Version 2.0.6
Version 2.0.5
- The text fairy will give a warning if the image is too blurry.
- Feedback can be sent directly after an unsuccessful scan.
- Minor bug fixes.
Version 2.0.3
- Bugfix: Pdf's contain text again.
- Minor bug fixes.
Version 2.0.1
Version 1.1.22
- Fix for Pdf creation.
- Pdf files get opened with a Pdf Viewer, if it exists on the phone.
- Boundary markers for cropping the image are bigger.
- Increased stability.
- Text to Speech volume can now be adjusted by the hardware keys.
Version 1.1.21
- Increased performance of loading documents.
- Option to show original images now works.
- Becoming a beta tester is easier now.
- Changed text gets saved before orientation change or when swiping to the next page.
Version 1.1.20
- Resolved bug that prevented the scan result from being saved on some other phones.
- Share text now takes the complete text of all pages.
- Added border around images is now always white.
Version 1.1.19
- Resolved bug that prevented the scan result from being saved on some phones.
- Fix for Thai language.
Version 1.1.17
- Hotfix for the installation of new languages.
Version 1.1.16
- Hotfix for the camera integration. The camera was not started on some phones.
Version 1.1.15
- Support for the FV-5 Camera app.
- Automatically add a small border around the image. This improves text recognition a bit.
- Text that is directly add the edges gets no longer cut off.
- Fixed crash when downloading language data after the tess directory was changed.
- Fixed crash when Text to Speech is unavailable on the phone.
- Fixed crash when image format is unsupported.
Version 1.1.12
- New feedback page.
- Fix crash on Android 2.3 devices.
- Fix column selection. Columns were overlapping each other.
- Small graphical adjustments.
Version 1.1.11
- Navigation Drawer.
- App can be moved to sd card. This is an experimental feature though. Please use only at your own risk.
- Fix crash when using Hindi language.
- Fix for using the camera app on sony phones.
- Small graphical adjustments.
Version 1.1.10
- Bugfix for languages that have non latin characters (e.g. Chinese).
- Option to send the text directly to other apps.
- Text-to-speech to let the app read the text to you.
- PDF files are now searchable.
Version 1.1.9
- During cropping the image will be scaled to fit the screen.
- The cropping rectangle will only grow where you touch it.
- Application size reduced by 4Mb.
- Text copied into clip board is now plain text so that other apps can deal with it.
- Crash fixed that occurred sometimes when accepting images sent from other apps.
Version 1.1.8
- New option to copy complete text into the clipboard.
- Other apps can now send images directly to the text fairy.


Shipment ID
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| Railway Origination |
Railway Destination |
Container No. |
Fleet |
Plate No. |
Trucker |
Loading TOC |
Trans Plan No. |
Rail ETS |
Station TOA |
Rail ATS |
|
| Railway Origination |
Railway Destination |
Container No.
|
Trucker |
Container No. |
Trucker |
Loading TOC |
Trans Plan No. |
Rail TOA |
Station TOA |
Rail ATS |
|


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{% extends 'base.html' %} {% block styles%} {{ super() }} {% endblock %} {% block content %}
Come share our great posts. Add yours to the mix as well.
{{quote.quote}}
Posted by :{{post.author.username}}
Published: {{ moment(post.posted).fromNow() }}


Section 1.10.33 of "de Finibus Bonorum et Malorum", written by Cicero in 45 BC
"At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium
voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi sint occaecati
cupiditate non provident, similique sunt in culpa qui officia deserunt mollitia animi, id est
laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio. Nam libero
through weakness of will, which is the same as saying through shrinking from toil and pain. These
accepted. The wise man therefore always holds in these matters to this principle of selection: he
rejects pleasures to secure other greater pleasures, or else he endures pains to avoid worse pains."


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PuTTY is a free SSH, Telnet, Rlogin, and SUPDUP client for Windows systems.
If you already know what SSH, Telnet, Rlogin, and SUPDUP are, you can safely skip on to the next section.
SSH, Telnet, Rlogin, and SUPDUP are four ways of doing the same thing: logging in to a multi-user computer from another computer, over a network.
Multi-user operating systems, typically of the Unix family (such as Linux, MacOS, and the BSD family), usually present a command-line interface to the user, much like the ‘Command Prompt’ or ‘MS-DOS Prompt’ in Windows. The system prints a prompt, and you type commands which the system will obey.
Using this type of interface, there is no need for you to be sitting at the same machine you are typing commands to. The commands, and responses, can be sent over a network, so you can sit at one computer and give commands to another one, or even to more than one.
SSH, Telnet, Rlogin, and SUPDUP are network protocols that allow you to do this.  The network connection carries your keystrokes and commands from the client to the server, and carries the server's responses back to you.
These protocols can also be used for other types of keyboard-based interactive session. In particular, there are a lot of bulletin boards, talker systems and MUDs (Multi-User Dungeons) which support access using Telnet. There are even a few that support SSH.
You might want to use SSH, Telnet, Rlogin, or SUPDUP if:
You probably do not want to use SSH, Telnet, Rlogin, or SUPDUP if:
This list summarises some of the differences between SSH, Telnet, Rlogin, and SUPDUP.
The Internet is a hostile environment and security is everybody's responsibility. If you are connecting across the open Internet, then we recommend you use SSH. If the server you want to connect to doesn't support SSH, it might be worth trying to persuade the administrator to install it.
If your client and server are both behind the same (good) firewall, it is more likely to be safe to use Telnet, Rlogin, or SUPDUP, but we still recommend you use SSH.
If you want to provide feedback on this manual or on the PuTTY tools themselves, see the Feedback page.
[PuTTY release 0.78]


Compute orientation selectivity weights for ENDOR and ESEEM simulations of disordered systems.
orisel(Sys,Par) orisel(Sys,Par,Opt) Weights = ... [Weights,Trans] = ...
This function calculates weights for orientation selectivity in powders
and frozen solutions (disordered systems). At a given
magnetic field not all, but only centres with certain orientations relative to the
external magnetic field have transitions that are on resonance with the spectrometer
frequency and can be excited. orisel
determines which orientations are excited
and how strongly.
Weights
is a vector with the selectivity weights for a set of
orientations. The larger the weight, the stronger the selectivity. The weights
are not normalized. The maximum values for resonant excitation of a transition
depend on the number of spins and the g tensor(s). E.g., for an S=1/2 spin
system with an isotropic g factor, a value of 1231 results if the magnetic
field is centered on the EPR line.
Trans
is an array listing the transitions included in the computation,
with one transition per row. A transition is specified by the numbers of the two
levels participating, with the lowest-energy level having label 1, etc.
The orientations are computed internally, but are not returned
by orisel
.
To obtain the orientations associated with the weights, call
sphgrid with the symmetry
and number of knots given in the command window output of orisel
.
These weights can directly be used in the options structure in salt.
If the function is called with no output argument, the weights are plotted as a function of orientation.
Sys
is a spin system structure.
Be sure to include the EPR line width in the field HStrain
,
since it is used in the selectivity computation.
Par
is the parameter structure containing the following mandatory
fields.
Field
mwFreq
In addition, there are a few optional parameters.
ExciteWidth
Orientations
[phi theta]
giving the polar angles that define the orientations. This is an optional field. If not given, orientations
are computed internally using sphgrid with the values of Symemtry
and nKnots
given in the Options
structure.
Opt
is a structure containing optional settings
nKnots
Symmetry
The total effective excitation bandwidth is the combination of Par.ExciteWidth
and the anisotropic EPR line width Sys.HStrain
. Strains (gStrain
, AStrain
and DStrain
) are not included in the computation.
Orientation selection in the spin system
Sys = struct('S',1/2,'g',[2.1 2.2 2],'HStrain',[10 10 10]); Sys = nucspinadd(Sys,'1H',[5 4 6]*1e2);
can be computed using
Exp = struct('mwFreq',9.5,'Field',325,'ExciteWidth',100); Weights = orisel(Sys,Exp);
The orientation selection can easily be visualised by omitting the output argument
orisel(Sys,Exp);
If the full orientational sphere should be plotted, set the
symmetry manually to C1
Opt = struct('Symmetry','C1'); orisel(Sys,Exp,Opt);


The web is a bunch of computers working together. When someone uses a browser and opens up a webpage, the computer sends a http request to a server. Servers are big computers on which data is stored. There are millions of servers around the world. After the request is recognized, it sends the information to the webbrowser and the browser shows the user the requested information on his or her screen.
HTML stands for HyperText Markup Language. These texts are documents on the internet and they form the most content on the web. Beside HTML you have pictures, video and many more things! HTML documents contain text, which describe what the user sees.
HTML is made up of elements. There are a lot of tags to form elements and you do not need to learn them by head, mainly because you will learn them automatically by looking them up and applying them. It's really easy to find all the tags. I found mine on this website. Beside following this course, use the internet to learn more if you don't understand and don't go asking inmediatly. The easy way out isnt always the most learnful!
Void tags are tags that don't need a closure tag.
These are for example:


{% extends 'header_footer.html' %} {% block title %}
Please go away. You shouldn't be here.
Contact us if you think you did something wrong.Send a message to: {{ admin_username }}