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Integrating local supervision (patterns and dependency paths) and global supervision(embedding vectors of entities and relations) for Open Information Extraction, more than 4% MAP gained on news and twitter
ReHession combines multiple types of weak supervision in a competing way. Specifically, it infers instance-wise trustworthiness of different supervision source, i.e. domain heuristics and distant supervision
My primary responsibility is developing a general pose machine, which can apply to multiple objects key point detection and pose estimation. We explore convolutional pose machine trained on synthetic images only.


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We exclusively and deliberately focus on mid-cap companies. There is a growing deal flow from emerging entrepreneurs, generational change and pre-IPO companies that are constrained by limited access to capital. We see significant investment opportunities to support these mid-sized companies.
Once we invest in a business, we are an active partner committed to our portfolio companies’ growth. We build strategic relations with management teams to provide operational and financial guidance. We support the execution of each portfolio company’s growth plan including scaling existing business, expanding product offerings or add-on acquisitions. We focus on governance and reporting to meet the highest international standards. We utilize our network of relationships to accelerate growth for our portfolio companies.
We build leading middle-market businesses in partnership with strong management teams to maximize shareholder value. During our investment horizon, complete alignment with our management team is critical to success.


CodeUs is a project created to write code together with friends and colleagues, or for educational purposes. The idea was born from the need to write code with our friends in real time without the use of usbdrive, the cloud, or any other support to share files. And so with this site we can have a personal page to create projects, share and write and accessible in any part of the world.


Assemble the geometry of the measured object using primitives such as nodes, lines and triangles. Alternatively, import geometry from excel or uff.
Acquire data directly or import it from uff. National Instruments hardware is supported, more to come.
Perform modal analysis using state-of-the-art methods.
Visualize and animate results.
OpenModal is written in Python and developed on GitHub.
Experimental modal analysis of a beam structure.
Full video from the ICoEV conference in Ljubljana, Slovenia.


|
libpqxx 4.0
|
Binary data corresponding to PostgreSQL's "BYTEA" binary-string type. More...
Functions | |
| std::string | pqxx::escape_binary (const std::string &bin) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::escape_binary (const char bin[]) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::escape_binary (const char bin[], size_t len) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::escape_binary (const unsigned char bin[]) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::escape_binary (const unsigned char bin[], size_t len) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::connection_base::esc (const char str[]) |
| Escape string for use as SQL string literal on this connection. | |
| std::string | pqxx::connection_base::esc (const char str[], size_t maxlen) |
| Escape string for use as SQL string literal on this connection. | |
| std::string | pqxx::connection_base::esc (const std::string &str) |
| Escape string for use as SQL string literal on this connection. | |
| std::string | pqxx::connection_base::esc_raw (const unsigned char str[], size_t len) |
| Escape binary string for use as SQL string literal on this connection. | |
| std::string | pqxx::connection_base::quote_raw (const unsigned char str[], size_t len) |
| Escape and quote a string of binary data. | |
| std::string | pqxx::connection_base::quote_name (const std::string &identifier) |
| Escape and quote an SQL identifier for use in a query. | |
| template<typename T > | |
| std::string | pqxx::connection_base::quote (const T &t) |
| Represent object as SQL string, including quoting & escaping. | |
| std::string | pqxx::connection_base::quote (const binarystring &) |
| std::string | pqxx::transaction_base::esc (const char str[]) const |
| Escape string for use as SQL string literal in this transaction. | |
| std::string | pqxx::transaction_base::esc (const char str[], size_t maxlen) const |
| Escape string for use as SQL string literal in this transaction. | |
| std::string | pqxx::transaction_base::esc (const std::string &str) const |
| Escape string for use as SQL string literal in this transaction. | |
| std::string | pqxx::transaction_base::esc_raw (const unsigned char str[], size_t len) const |
| Escape binary data for use as SQL string literal in this transaction. | |
| std::string | pqxx::transaction_base::esc_raw (const std::string &) const |
| Escape binary data for use as SQL string literal in this transaction. | |
| template<typename T > | |
| std::string | pqxx::transaction_base::quote (const T &t) const |
| Represent object as SQL string, including quoting & escaping. | |
| std::string | pqxx::transaction_base::quote_raw (const unsigned char str[], size_t len) const |
| Binary-escape and quote a binarystring for use as an SQL constant. | |
| std::string | pqxx::transaction_base::quote_raw (const std::string &str) const |
| std::string | pqxx::transaction_base::quote_name (const std::string &identifier) const |
| Escape an SQL identifier for use in a query. | |
| std::string | pqxx::binarystring::escape_binary (const std::string &bin) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::binarystring::escape_binary (const char bin[]) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::binarystring::escape_binary (const char bin[], size_t len) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::binarystring::escape_binary (const unsigned char bin[]) |
| Escape binary string for inclusion in SQL. | |
| std::string | pqxx::binarystring::escape_binary (const unsigned char bin[], size_t len) |
| Escape binary string for inclusion in SQL. |
Binary data corresponding to PostgreSQL's "BYTEA" binary-string type.
This class represents a binary string as stored in a field of type bytea. The raw value returned by a bytea field contains escape sequences for certain characters, which are filtered out by binarystring.
Internally a binarystring is zero-terminated, but it may also contain zero bytes, just like any other byte value. So don't assume that it can be treated as a C-style string unless you've made sure of this yourself.
The binarystring retains its value even if the result it was obtained from is destroyed, but it cannot be copied or assigned.
To convert the other way, i.e. from a raw series of bytes to a string suitable for inclusion as bytea values in your SQL, use the transaction's esc_raw() functions.
Use these functions to "groom" user-provided strings before using them in your SQL statements. This reduces the chance of failures when users type unexpected characters, but more importantly, it helps prevent so-called SQL injection attacks.
To understand what SQL injection vulnerabilities are and why they should be prevented, imagine you use the following SQL statement somewhere in your program:
TX.exec("SELECT number,amount " "FROM accounts " "WHERE allowed_to_see('" + userid + "','" + password + "')");
This shows a logged-in user important information on all accounts he is authorized to view. The userid and password strings are variables entered by the user himself.
Now, if the user is actually an attacker who knows (or can guess) the general shape of this SQL statement, imagine he enters the following password:
x') OR ('x' = 'x
Does that make sense to you? Probably not. But if this is inserted into the SQL string by the C++ code above, the query becomes:
SELECT number,amount FROM accounts WHERE allowed_to_see('user','x') OR ('x' = 'x')
Is this what you wanted to happen? Probably not! The neat allowed_to_see() clause is completely circumvented by the "<tt>OR ('x' = 'x')</tt>" clause, which is always true
. Therefore, the attacker will get to see all accounts in the database!
To prevent this from happening, use the transaction's esc() function:
TX.exec("SELECT number,amount " "FROM accounts " "WHERE allowed_to_see('" + TX.esc(userid) + "', " "'" + TX.esc(password) + "')");
Now, the quotes embedded in the attacker's string will be neatly escaped so they can't "break out" of the quoted SQL string they were meant to go into:
SELECT number,amount FROM accounts WHERE allowed_to_see('user', 'x'') OR (''x'' = ''x')
If you look carefully, you'll see that thanks to the added escape characters (a single-quote is escaped in SQL by doubling it) all we get is a very strange-looking password string--but not a change in the SQL statement.
| string pqxx::connection_base::esc | ( | const char | str[] | ) |
Escape string for use as SQL string literal on this connection.
| string pqxx::connection_base::esc | ( | const std::string & | str | ) |
Escape string for use as SQL string literal on this connection.
| std::string pqxx::transaction_base::esc | ( | const std::string & | str | ) | const |
Escape string for use as SQL string literal in this transaction.
| string pqxx::connection_base::esc | ( | const char | str[], |
| size_t | maxlen | ||
| ) |
Escape string for use as SQL string literal on this connection.
| std::string pqxx::transaction_base::esc | ( | const char | str[] | ) | const |
Escape string for use as SQL string literal in this transaction.
| std::string pqxx::transaction_base::esc | ( | const char | str[], |
| size_t | maxlen | ||
| ) | const |
Escape string for use as SQL string literal in this transaction.
| string pqxx::connection_base::esc_raw | ( | const unsigned char | str[], |
| size_t | len | ||
| ) |
Escape binary string for use as SQL string literal on this connection.
References pqxx::internal::PQAlloc< T, DELETER >::get().
| std::string pqxx::transaction_base::esc_raw | ( | const unsigned char | str[], |
| size_t | len | ||
| ) | const |
Escape binary data for use as SQL string literal in this transaction.
Raw, binary data is treated differently from regular strings. Binary strings are never interpreted as text, so they may safely include byte values or byte sequences that don't happen to represent valid characters in the character encoding being used.
The binary string does not stop at the first zero byte, as is the case with textual strings. Instead, they may contain zero bytes anywhere. If it happens to contain bytes that look like quote characters, or other things that can disrupt their use in SQL queries, they will be replaced with special escape sequences.
| string pqxx::transaction_base::esc_raw | ( | const std::string & | str | ) | const |
Escape binary data for use as SQL string literal in this transaction.
| string pqxx::escape_binary | ( | const unsigned char | bin[] | ) |
Escape binary string for inclusion in SQL.
References pqxx::escape_binary().
| std::string escape_binary | ( | const unsigned char | bin[], |
| size_t | len | ||
| ) | [related] |
Escape binary string for inclusion in SQL.
| string pqxx::escape_binary | ( | const char | bin[] | ) |
Escape binary string for inclusion in SQL.
References pqxx::escape_binary().
| string pqxx::escape_binary | ( | const unsigned char | bin[], |
| size_t | len | ||
| ) |
Escape binary string for inclusion in SQL.
References pqxx::internal::PQAlloc< T, DELETER >::get().
| std::string escape_binary | ( | const char | bin[], |
| size_t | len | ||
| ) | [related] |
Escape binary string for inclusion in SQL.
| std::string escape_binary | ( | const std::string & | bin | ) | [related] |
Escape binary string for inclusion in SQL.
| std::string escape_binary | ( | const unsigned char | bin[] | ) | [related] |
Escape binary string for inclusion in SQL.
| string pqxx::escape_binary | ( | const char | bin[], |
| size_t | len | ||
| ) |
Escape binary string for inclusion in SQL.
References pqxx::escape_binary().
| std::string escape_binary | ( | const char | bin[] | ) | [related] |
Escape binary string for inclusion in SQL.
| string pqxx::escape_binary | ( | const std::string & | bin | ) |
Escape binary string for inclusion in SQL.
Referenced by pqxx::escape_binary().
| std::string pqxx::transaction_base::quote | ( | const T & | t | ) | const |
Represent object as SQL string, including quoting & escaping.
Nulls are recognized and represented as SQL nulls.
| std::string pqxx::connection_base::quote | ( | const T & | t | ) |
Represent object as SQL string, including quoting & escaping.
Nulls are recognized and represented as SQL nulls.
References pqxx::to_string().
| string pqxx::connection_base::quote | ( | const binarystring & | b | ) |
References pqxx::binarystring::data(), and pqxx::binarystring::size().
| std::string pqxx::transaction_base::quote_name | ( | const std::string & | identifier | ) | const |
Escape an SQL identifier for use in a query.
| string pqxx::connection_base::quote_name | ( | const std::string & | identifier | ) |
Escape and quote an SQL identifier for use in a query.
References pqxx::internal::PQAlloc< T, DELETER >::get().
| string pqxx::transaction_base::quote_raw | ( | const std::string & | str | ) | const |
| std::string pqxx::transaction_base::quote_raw | ( | const unsigned char | str[], |
| size_t | len | ||
| ) | const |
Binary-escape and quote a binarystring for use as an SQL constant.
| string pqxx::connection_base::quote_raw | ( | const unsigned char | str[], |
| size_t | len | ||
| ) |
Escape and quote a string of binary data.


Vector Ruler Generator
Download a laser cuttable ruler to etch into your belongings and belts.
Start:
Width:
Height:
Units:
Inches
Centimeters
Fractional
Decimal
Subdivisions:
Zero
One
Two
Three
Four
Five
Six
Smaller tick length:
0.1
0.2
0.3
0.4
0.5 - Default
0.6
0.7
0.8
0.9
1.0 - All ticks same size
Draw redundant lines
Filename:


Cause the beautiful thing does not ask for attention Bacon ipsum dolor amet meatball tri-tip venison cupim, brisket pancetta chicken porchetta short loin turducken shank drumstick ham picanha.  Kevin meatloaf ground round, cupim pancetta corned beef short loin shoulder venison strip steak pork belly chicken. Meatball spare ribs shoulder hamburger, fatback ground round kevin ball tip tenderloin tri-tip short ribs chicken tail kielbasa meatloaf. Ground round hamburger pancetta frankfurter chicken rump sausage ribeye shank shoulder.
Fatback bacon kielbasa kevin shank. Biltong picanha shoulder, alcatra cupim fatback short loin meatloaf pork drumstick. Turducken frankfurter short ribs chuck chicken, strip steak kevin ham. Tenderloin biltong pig, fatback ham hock tail jerky drumstick pork belly prosciutto corned beef frankfurter flank meatloaf pastrami. Doner leberkas jerky meatball biltong pancetta. Fatback salami boudin strip steak cow bacon.


Before Change
if self.goal_distance < self.goal_radius:
goal_reward, done = self.goal_reward, True
else:
goal_reward, done = 0.0, False
// Add control cost
if self.ctrl_cost_coeff > 0:
After Change
done = self.goal_distance < self.goal_radius
if self._reward_type == "dense":
goal_reward = -self.goal_distance * self.goal_reward_weight
elif self._reward_type == "sparse":
goal_reward = int(done) * self.goal_reward
// Add control cost
if self.ctrl_cost_coeff > 0:
lb, ub = self.action_bounds
scaling = (ub - lb) * 0.5
ctrl_cost = 0.5 * self.ctrl_cost_coeff * np.sum(


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
This budget version of a spicy Thai-style curry is packed with healthy veggies so uses just a little chicken.
Stir the lime leaves in to the stock and set aside to infuse.
Meanwhile, half-fill a large saucepan with water and bring to the boil. Add the rice, stir well and return to the boil, then cook according to the packet instructions.
Heat the oil in a large non-stick frying pan or wok over a high heat. Add the chicken, red pepper, green beans and baby sweetcorn and stir-fry for 2-3 minutes.
Add the curry paste and cook for a further 1-2 minutes.
Add the stock, lime leaves and fish sauce, bring the mixture to the boil. Reduce the heat and simmer for 2-3 minutes, or until the vegetables are just tender.
In a small bowl, whisk the cornflour with 1 tbsp cold water to form a smooth paste. Whisk into the curry and continue to simmer for 4-5 minutes, or until the curry sauce has thickened slightly.
Stir in the spinach and continue to simmer until just wilted. Stir in the basil leaves just before serving. Discard the lime leaves.
To serve, drain the rice and spoon it onto serving plates. Top each portion with a ladleful of the curry.


|
orx
1.x dev
Portable Game Engine
|
Macros | |
| #define | orxSPAWNER_KU32_FLAG_AUTO_DELETE 0x00000001 |
| #define | orxSPAWNER_KU32_FLAG_AUTO_RESET 0x00000002 |
| #define | orxSPAWNER_KU32_FLAG_CLEAN_ON_DELETE 0x00000200 |
| #define | orxSPAWNER_KU32_FLAG_INTERPOLATE 0x00000400 |
| #define | orxSPAWNER_KU32_FLAG_NONE 0x00000000 |
| #define | orxSPAWNER_KU32_FLAG_USE_ALPHA 0x00000004 |
| #define | orxSPAWNER_KU32_FLAG_USE_COLOR 0x00000008 |
| #define | orxSPAWNER_KU32_FLAG_USE_RELATIVE_SPEED_OBJECT 0x00000040 |
| #define | orxSPAWNER_KU32_FLAG_USE_RELATIVE_SPEED_SPAWNER 0x00000080 |
| #define | orxSPAWNER_KU32_FLAG_USE_ROTATION 0x00000010 |
| #define | orxSPAWNER_KU32_FLAG_USE_SCALE 0x00000020 |
| #define | orxSPAWNER_KU32_FLAG_USE_SELF_AS_PARENT 0x00000100 |
| #define | orxSPAWNER_KU32_MASK_USE_RELATIVE_SPEED 0x000000C0 |
| #define | orxSPAWNER_KU32_MASK_USER_ALL 0x000004FF |
Typedefs | |
| typedef struct __orxSPAWNER_t | orxSPAWNER |
Enumerations | |
| enum | orxSPAWNER_EVENT { orxSPAWNER_EVENT_SPAWN = 0, orxSPAWNER_EVENT_CREATE, orxSPAWNER_EVENT_DELETE, orxSPAWNER_EVENT_RESET, orxSPAWNER_EVENT_EMPTY, orxSPAWNER_EVENT_WAVE_START, orxSPAWNER_EVENT_WAVE_STOP, orxSPAWNER_EVENT_NUMBER, orxSPAWNER_EVENT_NONE = orxENUM_NONE } |
Spawner module Allows to spawn orxSPAWNERS Spawners derived from structures
| #define orxSPAWNER_KU32_FLAG_AUTO_DELETE 0x00000001 |
Auto delete flag
Definition at line 58 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_AUTO_RESET 0x00000002 |
Auto delete flag
Definition at line 59 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_CLEAN_ON_DELETE 0x00000200 |
Clean on delete flag
Definition at line 70 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_INTERPOLATE 0x00000400 |
Interpolate flag
Definition at line 71 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_NONE 0x00000000 |
Spawner flagsNo flags
Definition at line 56 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_USE_ALPHA 0x00000004 |
Use alpha flag
Definition at line 60 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_USE_COLOR 0x00000008 |
Use color flag
Definition at line 61 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_USE_RELATIVE_SPEED_OBJECT 0x00000040 |
Use relative speed (object) flag
Definition at line 65 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_USE_RELATIVE_SPEED_SPAWNER 0x00000080 |
Use relative speed (spawner) flag
Definition at line 66 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_USE_ROTATION 0x00000010 |
Use rotation flag
Definition at line 62 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_USE_SCALE 0x00000020 |
Use scale flag
Definition at line 63 of file orxSpawner.h.
| #define orxSPAWNER_KU32_FLAG_USE_SELF_AS_PARENT 0x00000100 |
Use self as parent flag
Definition at line 69 of file orxSpawner.h.
| #define orxSPAWNER_KU32_MASK_USE_RELATIVE_SPEED 0x000000C0 |
Use relative speed mask
Definition at line 67 of file orxSpawner.h.
| #define orxSPAWNER_KU32_MASK_USER_ALL 0x000004FF |
All mask
Definition at line 73 of file orxSpawner.h.
| typedef struct __orxSPAWNER_t orxSPAWNER |
Internal spawner structure
Definition at line 96 of file orxSpawner.h.
| enum orxSPAWNER_EVENT |
Event enum
Definition at line 78 of file orxSpawner.h.
| orxDLLAPI orxSPAWNER* orxFASTCALL orxSpawner_Create | ( | ) |
Creates an empty spawner
| orxDLLAPI orxSPAWNER* orxFASTCALL orxSpawner_CreateFromConfig | ( | const orxSTRING | _zConfigID | ) |
Creates a spawner from config
| [in] | _zConfigID | Config ID @ return orxSPAWNER / orxNULL |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_Delete | ( | orxSPAWNER * | _pstSpawner | ) |
Deletes a spawner
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI void orxFASTCALL orxSpawner_Enable | ( | orxSPAWNER * | _pstSpawner, |
| orxBOOL | _bEnable | ||
| ) |
Enables/disables a spawner
| [in] | _pstSpawner | Concerned spawner |
| [in] | _bEnable | Enable / disable |
| orxDLLAPI orxU32 orxFASTCALL orxSpawner_GetActiveObjectCount | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner active object count
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxU32 orxFASTCALL orxSpawner_GetActiveObjectLimit | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner active object limit
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxFRAME* orxFASTCALL orxSpawner_GetFrame | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner frame
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI const orxSTRING orxFASTCALL orxSpawner_GetName | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner name
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxFLOAT orxFASTCALL orxSpawner_GetNextWaveDelay | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner next wave delay
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxVECTOR* orxFASTCALL orxSpawner_GetObjectSpeed | ( | const orxSPAWNER * | _pstSpawner, |
| orxVECTOR * | _pvObjectSpeed | ||
| ) |
Gets spawner object speed
| [in] | _pstSpawner | Concerned spawner |
| [in] | _pvObjectSpeed | Speed applied to every spawned object |
| orxDLLAPI orxSTRUCTURE* orxFASTCALL orxSpawner_GetParent | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner parent
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxVECTOR* orxFASTCALL orxSpawner_GetPosition | ( | const orxSPAWNER * | _pstSpawner, |
| orxVECTOR * | _pvPosition | ||
| ) |
Get spawner position
| [in] | _pstSpawner | Concerned spawner |
| [out] | _pvPosition | Spawner position |
| orxDLLAPI orxFLOAT orxFASTCALL orxSpawner_GetRotation | ( | const orxSPAWNER * | _pstSpawner | ) |
Get spawner rotation
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxVECTOR* orxFASTCALL orxSpawner_GetScale | ( | const orxSPAWNER * | _pstSpawner, |
| orxVECTOR * | _pvScale | ||
| ) |
Get spawner scale
| [in] | _pstSpawner | Concerned spawner |
| [out] | _pvScale | Spawner scale vector |
| orxDLLAPI orxU32 orxFASTCALL orxSpawner_GetTotalObjectCount | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner total object count
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxU32 orxFASTCALL orxSpawner_GetTotalObjectLimit | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner total object limit
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxFLOAT orxFASTCALL orxSpawner_GetWaveDelay | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner wave delay
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxU32 orxFASTCALL orxSpawner_GetWaveSize | ( | const orxSPAWNER * | _pstSpawner | ) |
Gets spawner wave size
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxVECTOR* orxFASTCALL orxSpawner_GetWorldPosition | ( | const orxSPAWNER * | _pstSpawner, |
| orxVECTOR * | _pvPosition | ||
| ) |
Get spawner world position
| [in] | _pstSpawner | Concerned spawner |
| [out] | _pvPosition | Spawner world position |
| orxDLLAPI orxFLOAT orxFASTCALL orxSpawner_GetWorldRotation | ( | const orxSPAWNER * | _pstSpawner | ) |
Get spawner world rotation
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxVECTOR* orxFASTCALL orxSpawner_GetWorldScale | ( | const orxSPAWNER * | _pstSpawner, |
| orxVECTOR * | _pvScale | ||
| ) |
Gets spawner world scale
| [in] | _pstSpawner | Concerned spawner |
| [out] | _pvScale | Spawner world scale |
Inits the spawner module
| orxDLLAPI orxBOOL orxFASTCALL orxSpawner_IsEnabled | ( | const orxSPAWNER * | _pstSpawner | ) |
Is spawner enabled?
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI void orxFASTCALL orxSpawner_Reset | ( | orxSPAWNER * | _pstSpawner | ) |
Resets (and disables) a spawner
| [in] | _pstSpawner | Concerned spawner |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetActiveObjectLimit | ( | orxSPAWNER * | _pstSpawner, |
| orxU32 | _u32ActiveObjectLimit | ||
| ) |
Sets spawner active object limit
| [in] | _pstSpawner | Concerned spawner |
| [in] | _u32ActiveObjectLimit | Active object limit, 0 for unlimited |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetNextWaveDelay | ( | orxSPAWNER * | _pstSpawner, |
| orxFLOAT | _fWaveDelay | ||
| ) |
Sets spawner next wave delay (without affecting the normal wave delay)
| [in] | _pstSpawner | Concerned spawner |
| [in] | _fWaveDelay | Delay before next wave / -1 for the current full wave delay |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetObjectSpeed | ( | orxSPAWNER * | _pstSpawner, |
| const orxVECTOR * | _pvObjectSpeed | ||
| ) |
Sets spawner object speed
| [in] | _pstSpawner | Concerned spawner |
| [in] | _pvObjectSpeed | Speed to apply to every spawned object / orxNULL to not apply any speed |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetParent | ( | orxSPAWNER * | _pstSpawner, |
| void * | _pParent | ||
| ) |
Sets spawner parent
| [in] | _pstSpawner | Concerned spawner |
| [in] | _pParent | Parent structure to set (object, spawner, camera or frame) / orxNULL |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetPosition | ( | orxSPAWNER * | _pstSpawner, |
| const orxVECTOR * | _pvPosition | ||
| ) |
Sets spawner position
| [in] | _pstSpawner | Concerned spawner |
| [in] | _pvPosition | Spawner position |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetRotation | ( | orxSPAWNER * | _pstSpawner, |
| orxFLOAT | _fRotation | ||
| ) |
Sets spawner rotation
| [in] | _pstSpawner | Concerned spawner |
| [in] | _fRotation | Spawner rotation (radians) |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetScale | ( | orxSPAWNER * | _pstSpawner, |
| const orxVECTOR * | _pvScale | ||
| ) |
Sets spawner scale
| [in] | _pstSpawner | Concerned spawner |
| [in] | _pvScale | Spawner scale vector |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetTotalObjectLimit | ( | orxSPAWNER * | _pstSpawner, |
| orxU32 | _u32TotalObjectLimit | ||
| ) |
Sets spawner total object limit
| [in] | _pstSpawner | Concerned spawner |
| [in] | _u32TotalObjectLimit | Total object limit, 0 for unlimited |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetWaveDelay | ( | orxSPAWNER * | _pstSpawner, |
| orxFLOAT | _fWaveDelay | ||
| ) |
Sets spawner wave delay
| [in] | _pstSpawner | Concerned spawner |
| [in] | _fWaveDelay | Delay between two waves / -1 for deactivating wave mode |
| orxDLLAPI orxSTATUS orxFASTCALL orxSpawner_SetWaveSize | ( | orxSPAWNER * | _pstSpawner, |
| orxU32 | _u32WaveSize | ||
| ) |
Sets spawner wave size
| [in] | _pstSpawner | Concerned spawner |
| [in] | _u32WaveSize | Number of objects to spawn in a wave / 0 for deactivating wave mode |
| orxDLLAPI orxU32 orxFASTCALL orxSpawner_Spawn | ( | orxSPAWNER * | _pstSpawner, |
| orxU32 | _u32Number | ||
| ) |
Spawns items
| [in] | _pstSpawner | Concerned spawner |
| [in] | _u32Number | Number of items to spawn, if orxU32_UNDEFINED, the spawner's wave size will be used instead |


Container support for standard STL containers is handled automatically
by the standard descriptors. To extend the support to custom
containers, it is necessary to inform classdesc that your custom type
is a container, and that is done via two type traits:
is_sequence
and
is_associative_container
,
which handle these two concepts from the standard library. The
standard sequence containers are vector, deque and list, and the
standard associative containers are set, map, and the multi_ and
unordered_ variants of those.
To enable support for your custom container, eg one that conforms to
the sequence concept, define a type trait for your custom container
somewhere prior to classdesc\_epilogue.h
(See
§1.1) being included. For example:
namespace classdesc { template <> struct is_sequence<MyContainer>: public true_type {}; }
If you wish to support containers in your own custom descriptor, you
should ideally not refer to specific types, but use these type traits
to write truly generic code. This involves quite advance
metaprogramming skills, but you can use the file pack_stl.h
as
an exemplar.


--- layout: post status: publish published: true title: Episode 5.17 - "Cubs Stank" author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 8531 wordpress_url: http://ivyenvy.com/?p=8531 date: '2013-07-15 22:57:05 +0000' date_gmt: '2013-07-16 03:57:05 +0000' categories: - Podcast tags: [] comments: [] ---
We pause at the All Star Break to reflect on the first half of the 2013 season and look ahead to the final 2 1/2 months. It's been a pretty eventful first half of the season, especially in regards to the roster. We talk about some recent moves the Cubs have made and then assess the team's performance to this point. What players have surprised us? Who stands out? What is the front office trying to do and is it working?
We make a few predictions for the second half of the season and answer some listener questions. We have two scary stats of the week for you and then we pick a winner for this week's photo caption contest.


'Bows fall 66-53 to Illinois on the road
by Kainoa CarlsonHONOLULU, Hawaii (HawaiiNewsNow) - The University of Hawai’i men’s basketball team gave the Illinois Fighting Illini everything they could handle, before falling, 66-53, Monday night at the State Farm Center.
UH fell to 3-2 following its first road game of the season.
The game was tied at halftime and UH held the lead early in the second half, however Illinois (3-1) went on a game-changing 14-2 run to take a double-digit lead, early into the second half.
Freshman Kofi Cockburn led Illinois (3-1) with 15 points and 14 rebounds while Trent Frazier added 13.
UH’s Zigamrs Raimo finished with a team-high 16 points and six rebounds for UH and newly minted Big West Conference Player of the Week Eddie Stansberry, chipped in 10 points on a tough shooting night.
Both teams struggled offensively to start the game, going a combined 4-of-24 from the floor. UH held the lead midway through the first half, before the Illini went on a 9-0 run to go-ahead.
The game eventually warmed up on the offensive end, as the Warriors evened the game up by halftime, 26-26.
The 'Bows led 32-30 early in the second half, but Illinois scored 14 of the next 16 points to take a 44-34 advantage.
Following the Fighting Illini 10-point lad UH struggled to mount a comeback and would get no closer than five points thereafter.
Illinois shot 50 percent in the second half after shooting just 33 percent in the first half. UH finished the game shooting 38 percent, including just 3-of-22 on three-pointers.
UH now returns home for its next three games at home, beginning with a meeting versus New Orleans on Sunday, Nov. 24. The game will tipoff at 5:00 p.m., following the women’s game at 2:30 p.m.
Copyright 2019 Hawaii News Now. All rights reserved.


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These lace frontal wigs blend seamlessly with your natural hairline to give you the most natural look. Shop your favorite brands like Vivica Fox and Motown tress and find the lace look you'll love today!


I am certified and proficient in multiple programming languages and technologies for the development and maintenance of various undertaken projects.
I complete my projects with meticulous attention to detail and this has helped to optimize customer satisfaction with all my reporting clients.
My innovative approach to my work has led me to design numerous modules and functionalities that have been a great benefit to previous organizations.
I completed my undergraduate degree - Bachelor of Engineering in Information Technology - from SIES Graduate School of Technology. The great mentors and fellow students I met here, sparked my interest in computer programming and development.
Started my professional career at Quinnox Inc. What started out as doubt and apprehension towards programming, turned to love thanks to the amazing peers and seniors that I was extremely fortunate to work under. Constantly challenged and always learning, I look back fondly on the experience that has taught me the most in my professional life.
Not content with the similar sort of challenges I was facing, I ventured out looking for greater ones. Rolta India Limited. proved to be a perfect match for me, with its bigger scale projects and the opportunity to be able to work for truly revolutionary projects in the national defense field.
But I was never content, I wanted to learn more. Grow more. I realized that I needed to get back to studies and that I needed to give my complete effort. I decided to pursue my graduate degree - Master of Science in Computer Science - at the University of Texas at Arlington.
After 2 years of learning, months of job hunting, all the uncertainties and worries, I believe that I have landed at the right place. The work is exciting and the people motivated. This is the start to something completely new and I look forward to it with enthusiasm :)


Preparation time
over 2 hours
Cooking time
30 mins to 1 hour
Serves
Serves 4
This easy, make-ahead dessert will create an impressive but stress-free end to a dinner party.
For this recipe you will need four attractive glasses such as Martini glasses to serve the passion fruit cream.
For the passion fruit cream, bring the double cream and 150g/5oz of the sugar gently to the boil in a large saucepan, then remove from the heat. Whisk in the lime juice and half of the sieved passion fruit pulp.
Pour the cream mixture into four large Martini glasses (do not fill to the top) and chill in the fridge for at least two hours, or until set.
Meanwhile, drain the gelatine leaves and shake dry. Bring 100ml/3½fl oz of water and the remaining sugar to the boil in a large saucepan. Add the gelatine leaves and whisk until completely dissolved.
Add the remaining sieved passion fruit pulp to the sugar syrup mixture, then set aside to cool.
When the gelatine mixture has cooled and the cream has set, pour one-quarter of the gelatine mixture on top of each portion of set cream to form a thin layer about 0.5cm/¼in thick. Return to the fridge for another hour, or until set.
Meanwhile, for the biscotti, preheat the oven to 180C/350F/Gas 4. Line a baking tray with baking parchment.
Place all of the biscotti ingredients into a large bowl and stir until the mixture comes together as a soft, sticky dough.
Divide the dough in half and roll each portion out into a sausage shape on a lightly floured work surface.
Place the pieces of dough onto the prepared baking tray and bake for 20-30 minutes, or until golden-brown. Remove from the oven and set aside for 10 minutes to cool and firm up.
Using a serrated knife, slice 1cm/½in-thick discs from the pieces of dough, slicing them at an angle. Lay the discs onto the same baking tray (use an extra baking tray if necessary). Return the biscotti to the oven and cook for 6-8 minutes, then turn the slices over and cook for a further 10-15 minutes, or until crisp and pale golden-brown on both sides. Remove from the oven and transfer to wire racks to cool.
Serve the passion fruit creams with the biscotti alongside.


Immunization Tracker: Our Team
Back
Edward Reeseg
Speciality: Back-End Development
Dionuta (Tay) Burton
Speciality: Front-End Development
Shota Kikozashvili
Speciality: Front-End Development
Eric Sarragalugo
Speciality: UI Development
Sachin Benny
Speciality: UI Development
Shobhita Vaishnav
Speciality: UI Development


Two for One
Learn a more efficient way to fire your arrows which allows you to rapidly shoot two arrows in sequence.{^n}{^y}Requires a bow.
Bow
${value}% Chance to be Used
8 10 13 15 18 20 21 23 24 25
Launches ${value} Projectile(s)
1
MaxLevel
6
UltimateLevel
10


|
Instructions for setting up an alert:
- Define a ROI (see right).
- Add additional criteria if desired.
- Click "Save Alert" button.
|
Instructions For Defining ROI:
- Click on the map to insert a vertex.
- Right-click on a vertex to remove it.
- Drag a vertex to move it.
|


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vignettes/microshades-CMD.Rmd
microshades-CMD.Rmd
For this tutorial, we will be exploring human microbiome profiles from the Curated Metagenomic Data library. We will use microshades functions to examine stool samples of school age children and compare samples from different locations.
This data is available in the curatedMetagenomicData library. To
download use
BiocManager::install("curatedMetagenomicData")
.
Learn more about the Curated Metagenomic Data here.
# Load the CMD data
britol = BritoIL_2016.metaphlan_bugs_list.stool()
ps_britol = ExpressionSet2phyloseq (britol)
## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
HMP = HMP_2012.metaphlan_bugs_list.stool()
ps_HMP = ExpressionSet2phyloseq (HMP)
The ps_britol
object contains 112 samples, and
ps_HMP
contains 141 samples. To focus on a smaller sample
size, use phyloseq function subset_samples()
. In this
scenario, only schoolage subjects samples will be included.
# Subset the samples
ps_britol_sub <- subset_samples(ps_britol,age_category == "schoolage")
ps_HMP_sub <- subset_samples(ps_HMP,age_category == "schoolage")
Now ps_britol_sub
object contains 18 samples, and
ps_HMP_sub
contains 6 samples. Begin using microshades
functions to evaluate abundance and apply advanced color
organization.
To orient the shades from the top darkest to lightest instead of from
the bottom darkest to lightest, use the
top_orientation = TRUE
parameter in the
create_color_dfs()
# Use microshades function prep_mdf to agglomerate, normalize, and melt the phyloseq object
mdf_britol_pre <- prep_mdf(ps_britol_sub)
# Create a color object for the specified data
color_objs_britol <- create_color_dfs(mdf_britol_pre, top_orientation = TRUE)
# Extract
mdf_britol<- color_objs_britol$mdf
cdf_britol <- color_objs_britol$cdf
The dataframe mdf_britol
contains sample data and
abundance info. The dataframe cdf_britol
stores the color
mapping information used for plotting.
plot_microshades
produces a stacked barplot with ordered
subgroup taxonomy. The darkest shade is the most abundant, and the
lightest shade is the least abundant. (excluding the “other” subgroup
from each of the selected groups)
plot_britol <- plot_microshades(mdf_britol, cdf_britol)
plot_britol + scale_y_continuous(labels = scales::percent, expand = expansion(0)) +
theme(legend.key.size = unit(0.2, "cm"), text=element_text(size=10)) +
theme(axis.text.x = element_text(size= 6))
To change the colors assignment, use the function
color_reassign
to specify the groups and colors in order of
reassignment. For example, we can change the color assignment of
Bacteroidetes to micro_green
and the color assignment of
Proteobacteria to micro_blue
new_cdf_britol <- color_reassign(cdf_britol,
group_assignment = c("Bacteroidetes", "Proteobacteria"),
color_assignment = c("micro_green", "micro_blue"))
new_plot_britol <- plot_microshades(mdf_britol, new_cdf_britol)
new_plot_britol + scale_y_continuous(labels = scales::percent, expand = expansion(0)) +
theme(legend.key.size = unit(0.2, "cm"), text=element_text(size=10)) +
theme(axis.text.x = element_text(size= 6))
# Use microshades function prep_mdf to agglomerate, normalize, and melt the phyloseq object
mdf_HMP_pre <- prep_mdf(ps_HMP_sub)
# Create a color object for the specified data
color_objs_HMP <- create_color_dfs(mdf_HMP_pre)
# Extract
mdf_HMP <- color_objs_HMP$mdf
cdf_HMP <- color_objs_HMP$cdf
plot_HMP <- plot_microshades(mdf_HMP, cdf_HMP)
plot_HMP + scale_y_continuous(labels = scales::percent, expand = expansion(0)) +
theme(legend.key.size = unit(0.2, "cm"), text=element_text(size=10)) +
theme(axis.text.x = element_text(size= 6))
Examine/Compare Legends
HMP_legend <- get_legend(plot_HMP)
britol_legend <- get_legend(new_plot_britol)
# Plot The HMP legend on the left, and the britol legend on the right
plot_grid(HMP_legend, britol_legend)
Each legend is configured to reflect the dataset of the plot. Notice that the HMP and Britol legends are similar, but not the exact same, due to different top abundances for genera in each phylum.
If the plots are being compared, it may be confusing visually if the
genus shading colors account for different genera between the two plots.
In this case, the function match_cdf()
can apply the color
factoring information from one processed mdf to a different, unprocessed
mdf.
To use the Britol color legend with the HMP data, apply the Britol color factoring to the HMP data.
mdf_HMP_matched <- match_cdf(mdf_HMP_pre, mdf_britol)
# Use the Britol cdf for color reference
plot_HMP_matched <- plot_microshades(mdf_HMP_matched, new_cdf_britol)
plot_HMP_matched + scale_y_continuous(labels = scales::percent, expand = expansion(0)) +
theme(legend.key.size = unit(0.2, "cm"), text=element_text(size=10)) +
theme(axis.text.x = element_text(size= 6))


In order to support autonomy and belonging, we’re going to practice offer reflections.
Why
By offering reflections, you paraphrase back what the client is thinking and feeling. You express your understanding of their thoughts and feelings back to them. Reflections help us bridge the gap between what the client is communicating and the coach hears, and allow coaches to check for their understanding of what was said.
How
Take a guess at how your client is feeling. Then say that.
You might use the following preambles to start your reflections to let your client know you’re about to take a guess.
- It sounds like…
- I get the sense that…
- It feels as though…
- You seem to be saying…
After saying one of those preambles, you could then add the reflection. For example, if you wanted to offer a reflection about a client feeling frustrated, it might look like:
- You seem to be saying… you’re not happy with how you’re handling social events
When
To get into the habit of offering reflections, it’s a good idea to figure out a trigger ahead of time for when you’ll make them. That way you’ll remember to offer them.
What specific situation do you want to offer reflections? For example, you could offer a reflection after a client answers an OEQ. Other situations you may want to offer reflections include when you hear clients express frustration, or when they express reasons to change.


14
%{
#include <stdio.h>
int yylex(void);
void yyerror(char *);
%}
%token INTEGER
%%
program:
program expr '\n'
{ printf("%d\n", $2); }
|
;
expr:
INTEGER
{ $$ = $1; }
| expr '+' expr
{ $$ = $1 + $3; }
| expr '-' expr
{ $$ = $1 - $3; }
;
%%
void yyerror(char *s) {
fprintf(stderr, "%s\n", s);
}
int main(void) {
yyparse();
return 0;
}
The rules section resembles the BNF grammar discussed earlier. The left-hand side of a
production, or nonterminal, is entered left-justified and followed by a colon. This is followed by the
right-hand side of the production. Actions associated with a rule are entered in braces.
With left-recursion, we have specified that a program consists of zero or more expressions. Each
expression terminates with a newline. When a newline is detected we print the value of the
expression. When we apply the rule
expr: expr '+' expr
{ $$ = $1 + $3; }
we replace the right-hand side of the production in the parse stack with the left-hand side of the
same production. In this case we pop “
expr '+' expr
” and push “
expr
”. We have reduced the stack
by popping three terms off the stack and pushing back one term. We may reference positions in
the value stack in our C code by specifying “
$1
” for the first term on the right-hand side of the
production, “
$2
” for the second, and so on. “
$$
” designates the top of the stack after reduction has
taken place. The above action adds the value associated with two expressions, pops three terms
off the value stack, and pushes back a single sum. As a consequence the parse and value stacks
remain synchronized.


Chapter 5 Project G: Handwriting (& Signatures)
The handwriting project has four major focuses:
- data collection
- computational tools
- statistical analysis
- glyph clustering
- closed set modeling for writer identification
- communication of results
5.1 Data Collection
We are conducting a large data collection study to gather handwriting samples from a variety of participants across the world (most in the Midwest). Each participant provides handwriting samples at three sessions. Session packets are prepared, mailed to participants, completed, and mailed back. Once recieved, we scan all surveys and writing samples. Scans are loaded, cropped, and saved using a Shiny app. The app also facilitates survey data entry, saving that participant data to lines in an excel spreadsheet.
As of September 2019, Marc and Anyesha are the primary contacts for the study. Phase 2 recruiting is underway.
The first 90 complete writers were published: > Crawford, Amy; Ray, Anyesha; Carriquiry, Alicia; Kruse, James; Peterson, Marc (2019): CSAFE Handwriting Database. Iowa State University. Dataset. https://doi.org/10.25380/iastate.10062203.
Anyesha and I worked on a variety of data quality checks and batch processing prior to publication. Scanner cuts a portion of the left side of the documents off sporadically. Survey date checks. Batch removal of header.We hit a bump with the ISU Office for Responsible Research (ORR). They had concerns with the demographic variables we collected on the surveys and wanted to publish. We "negotiated’’ and settled on the following survey information.
A data article was accepted at Data in Brief.
Crawford, A., Ray, A., & Carriquiry, A. (2020). A database of handwriting samples for applications in forensic statistics. Data in brief, 28, 105059.
5.2 Computational Tools
handwriter
is a developmental R package hosted at https://github.com/CSAFE-ISU/handwriter. It is our major computational tool for the project. The package takes in scanned handwritten documents and the following are performed.
- Binarize. Turn the image to pure black and white.
- Skeletonize. Reduce writing to a 1 pixel wide skeleton.
- Break. Connected writing is decomposed into small manageable pieces called glyphs . Glyphs are graphical structures with nodes and edges that often, but not always, correspond to Roman letters, and are the smallest unit of observation we consider for statistcal modelling.
- Measure. A variety of measurements are taken on each glyph. See Section 4.2.1.
For an input document, functions in the package give back a list of glyphs with path and node location information, adjacency grouping assignment, centroid locations, measurements (such as slope, pictured below), among other things. More on measurements in Section 4.2.1.
5.2.1 Graph measurements (extracted in RCpp by James)
The goal is to extract features that will discrimiate between writers within clusters.
1. Compactness measurements
\[\sum_{Num.\: Pixels}\frac{d(pixel,centroid)^2}{Num.\: Pixels -1}\] While informative for comparing like-sized graphs, this measurement suffers from the inherit spacial dependence between pixels and (unfortunately) does not scale well for modelling.
2. Rotation angle measurements
The first principal component for each graph, or “letter”, is calculated through an eigendecomposition of the covariance matrix. The first principal component enjoys the property of having two invariant directions, and so we take the vector that lies in the upper half plane. The rotation angle of the graph is defined as the angle between the vector (0,1), and the unit principal component vector on the upper half plane. The angle is measured in radians and is considered a feature of the graph defined on the interval (0,\(\pi\)).
3. Loop measurements
5.3 Statistical Analysis
5.3.1 Clustering
Paper submitted to ASA Statistical Analysis and Data Mining.
Rather than impose rigid grouping rules (the previously used ‘’adjacency grouping’’) we consider a more robust, dynamic \(K-\)means type clustering method that is focused on major glyph structural components.
Clustering algorithms require:
- A distance measure. For us, a way to measure the discrepancy between glyphs.
- A measure of center. A glyph-like structure that is the exemplar representation of a group of glyphs.
Glyph Distance Measure
We begin by defining edge to edge distances. Edge to edge distances are subsequently combined for an overall glyph to glyph distance.
Edge to edge distances:
Consider the following single edge glyphs, \(e_1\) and \(e_2\). Make \(3\) edits to \(e_1\) to match \(e_2\). The combined magnitude of each edit make the edge distance.- Shift. Anchor to the nearest endpoint by shifting.
- Stretch. Make the endpoints the same distance apart.
- Shape. Bend and twist the edge using \(7\) shape points. Shape points are ‘’matched’’ and the distance between them is averaged to obtain the shape contribution to the distance measure.
Combine the three measurements for a final edge to edge distance. D\((e_1, e_2) = 1.4 + 9.9 + 8.4 = 19.7\).
For multi-edge glyphs get pairwise edge distances. Minimize total glyph distance using linear programming to match edges (sudoku). Down-weight edge distance contributions based on edge lengths. There are nuances associated with all of this. We must handle glyphs with differing number of edges, and consider the direction in which edges are compared (endpoint labels are arbitrary). All of this is addressed in detail in the paper.
Measure of glyph centers
Take the weighted average of endpoints, \(7\) shape points, and edge length.The mean of a set can be iteratively calculated by properly weighting each newly introduced glyph. For stability, the \(K-\)means algorithm finds the glyph nearest the mean and uses that as measure of cluster center.
\(K-\)means Algorithm for Glyphs
Implement a standard \(K-\)means12 with handling of outlier observations.3
Begin with fixed \(K\) and a set of exemplars. Iterate between the following steps until cluster assignments do not change:
- Assign each glyph to the exemplar it is nearest to, with respect to the glyph distance measure.
- Calculate each cluster mean as defined. Find the exemplar nearest the cluster mean and use it as the new cluster center.
Use one document from each training writer to cluster and obtain a template. Make cluster assignments for the remaining documents by finding the template exemplar each glyph is nearest to. Below is an example of data that arises from the clustering method.
<IP_ADDRESS> Outliers
During clustering, outliers are considered glyphs that are at least \(T_o\) distance units from cluster exemplars. The algorithm sets a ceiling \(n_o\) on the allowable number of outliers. Initially, we were going to ignore the outliers, but now they are considered the \(K+1\)th cluster and contribute information about writer.
<IP_ADDRESS> CVL + CSAFE + IAM Template Development
Template development on:
1. CSAFE Handwriting Database, 25 documents.
2. Computer Vision Lab (CVL) Database, 25 documents.
3. IAM Handwriting Database, 50 documents.
With the new template, we’ve made a few adjustments to the original algorithm and measurement tools:
- Starting values.
- Changes to the distance measurement to emphasize shape, and focus less on character size.
- \(\frac{1}{2}\times\) Stretch (straight line distance component, ~ size)
- 1 \(\times\) Shift (nearest endpoint)
- 2 \(\times\) Shape
- (ghost edge comparisons)\(^2\)
- A sample template is picture below.
- 27 CVL documents (6 unique), 73 CSAFE documents (2 unique)
- Currently working on 20+ algorithm runs with a variety of starting values to select final template.
The final template.
5.3.2 Statistical Modeling
Model #1, Straw Man
Let,
- \(\boldsymbol{Y}_{w(d)} = \{Y_{w(d),1}, \dots, Y_{w(d),K}\}\) be the number of glyphs assigned to each cluster for document within writer, \(w(d),\) and
- \(\boldsymbol{\pi}_w = \{\pi_{w,1}, \dots, \pi_{w,K} \}\) be the rate at which a writer emits glyphs to each cluster.
- Here, \(K = 41\), \(d = 1,2,...,5\), \(w = 1,...,27\).
Then a hierarchical model for the data is \[\begin{array}{rl} \mathbf{Y}_{w(d)}\: &\stackrel{ind}{\sim} \:Multinomial(\boldsymbol\pi_{w}), \\ \boldsymbol{\pi}_w \:&\stackrel{ind}{\sim}\: Dirichlet(\boldsymbol{\alpha}),\\ \alpha_{1}, \dots, \alpha_{K}\: &\stackrel{iid}{\sim}\: Gamma(2, \:0.25). \label{model_line3} \end{array}\]
Posterior Predictive Analysis
For a holdout document of unknown source, \(w^*\),
- Extract glyphs with and assign groups,
- \(\boldsymbol{Y}^*_{w^*}=\{Y_{w^*,1}, ... Y_{w^*,K}\}\)
- Assess the posterior probability of writership under each known writer \(\boldsymbol\pi_{w}\) vectors.
- i.e. \(p(w^* = w^\prime|\boldsymbol{Y^*}, \boldsymbol{Y})\) for each writer \(w^\prime\) in the training data.
The posterior probability that the questioned document \(\boldsymbol{Y}^*_{w^*}\) belongs to writer \(w^\prime\) with respect to the closed set is, \[\begin{array}{rl} p(w^* = w^\prime|\boldsymbol{Y}^*, \boldsymbol{Y}) & \propto p(\boldsymbol{Y}^*| w^* = w^\prime, \boldsymbol{Y}) p(w^*=w^\prime | \boldsymbol{Y})\nonumber\\ & \propto p(\boldsymbol{Y}^*| w^* = w^\prime, \boldsymbol{Y}) \nonumber\\ & = \int p(\boldsymbol{Y}^*| \boldsymbol{\pi}_{w^\prime}) p(\boldsymbol{\pi}_{w^\prime}| \boldsymbol{Y}) d \boldsymbol{\pi}_{w^\prime}\nonumber\\ & \approx \frac{1}{M} \sum_{m=1}^M p(\boldsymbol{Y}^*| \boldsymbol{\pi}^{(m)}_{w^\prime}) , \mbox{ where } \boldsymbol{\pi}^{(m)}_{w^\prime} \sim p(\boldsymbol{\pi}_{w^\prime}|\boldsymbol{Y}) \nonumber \end{array}\]
for MCMC iterations \(m = 1, \dots, M\). Then, for a given iteration \(m\),
\[ p(\boldsymbol{Y}^*| \boldsymbol{\pi}^{(m)}_{w^\prime}) = p_{w^\prime}^{(m)} = \frac{Mult(\boldsymbol{Y}^*; \boldsymbol{\pi}_{w^\prime}^{(m)})}{\sum_{w_i = 1}^{27}Mult(\boldsymbol{Y}^*; \boldsymbol{\pi}_{w_i}^{(m)}) }. \nonumber \] Calculate the quantitity for each known writer \(w^\prime = w_1, \dots, w_{27}\) in training set to get \[ \boldsymbol{p}^{(m)} = \{p_{w_1}^{(m)}, ..., p_{w_{27}}^{(m)}\}, \] and compute summaries over the MCMC draws, \[ \boldsymbol{\bar{p}} = \{\bar{p}_{w_1},...,\bar{p}_{w_{27}}\}. \]
Results
If we use a single holdout document for each writer (doc 4). The \(\boldsymbol{\bar{p}}\) vector as given above is shown graphically for each holdout document (the rows).
We can use a cross validation routine to estimate error. For each writer, shuffle the order of their body of documents. For the first fold we holdout the first document for testing, for the second fold we hold out the second document for testing, etc.. This yields 6 results analogous to that of the figure above.
Evaluating Over-/Under-dispersion (Writer Variability Index)
With a count data model it is important to investigate the presence of over- and under-dispersion. An index of intra-writer variability with respect to a model.
- Multivariate Generalized Dispersion Index (GDI) of Kokonendji and Puig4.
- Relative Dispersion Index (RDI) = \(\frac{\mbox{writer data GDI}}{\mbox{model simulated GDI}}.\)
- High RDI \(\implies\) data over-dispersion and high sample to sample variability.
High RDI \(\implies\) data over-dispersion & high sample to sample variability (in the sense of glyph cluster membership rates).
Model #2, Mixture
There seem to be greatly varying shapes in the relative frequency of cluster fill. We think maybe a more flexible Dirichlet distribution is warranted.Add flexibility to the original model by including a mixture component in the Dirichlet parameter space.
\[\begin{array}{rl} \mathbf{Y}_{w(d)} | \boldsymbol\pi_{w}, w\: &\stackrel{ind}{\sim} \:Multinomial(\boldsymbol\pi_{w}), \\ \boldsymbol{\pi}_w|\boldsymbol{\alpha},\boldsymbol{\beta}, \rho_w \:&\stackrel{ind}{\sim}\:Dirichlet(\rho_w \: \boldsymbol{\alpha}+ (1-\rho_w) \: \boldsymbol{\beta}), \\ \alpha_{k}, \: \beta_{k} \: &\stackrel{iid}{\sim}\: Gamma(2, \: 0.25) \:\: \mbox{ for } k > 3, \\ \rho_w\: & \stackrel{iid}{\sim}\: Beta(1,1) \end{array}\]
Label Switchin Issues.
The model is non-identifiabile. We need to either place constraints on the parameter space to exclude the possibility of a second mode apriori, or estimate the posterior in both modes and post-process for label reassignment after.
This has been resolved (?) by placing constraints on the first three elements of \(\alpha\) and \(\beta\) with moderately informative priors.
\(\mathbf{\alpha_1 < \beta_1}\), \(\quad\) \(\alpha_1 \sim Gamma(2, 0.25) \:\: \& \:\: \beta_1 \sim Gamma(4, 0.25)\)
\(\mathbf{\alpha_2 > \beta_2}\) , \(\quad\) \(\alpha_2 \sim Gamma(4, 0.25) \:\: \& \:\: \beta_2 \sim Gamma(1, 0.25)\)
\(\mathbf{\alpha_3 > \beta_3}\) , \(\quad\) \(\alpha_3 \sim Gamma(4, 0.25) \:\: \& \:\: \beta_3 \sim Gamma(1, 0.25)\)
Results
The mixing component gives insights into writing style.<IP_ADDRESS> Model #2, CSAFE + CVL (not most recent) Template
The following analysis was done for the first 20 CSAFE writers.
- Training docs: s01_pLND_r01, s01_pLND_r03, s01_pWOZ_r02, s01_pPHR_r03
- Testing doc: s01_pLND_r02
Not surprising. Working on:
- More writers (need to go through handwriter)
- Testing on the phrase prompt
- Including not yet seen CVL writers in analysis.
Model #3, Normal Slopes. Work in progress.
Adding slope measurements into the model.
Let,
- \(\boldsymbol{Y}_{w(d)} = \{Y_{w(d),1}, \dots, Y_{w(d),K}\}\) be the number of glyphs assigned to each group for document within writer, \(w(d),\)
- \(\boldsymbol{\bar{S}}_{w(d)} = \{\bar{S}_{w(d),1}, \dots, \bar{S}_{w(d),K}\}\), be the average slope in each group for document \(w(d)\)
- \(G_{w(d),j}\) be the \(j^{th}\) glyph in document \(w(d)\),
- \(g_{w(d), j} = 1, \dots, 41\) be the available cluster assignments for the \(j^{th}\) glyph in the document,
- \(S_{w(d),j}\) be the slope of the \(j^{th}\) glyph in the document.
- Here, \(K = 41\), \(d = 1,2,...,5\), \(w = 1,...,27\), \(j= 1, \dots,\mathrm{J}_{w(d)}.\)
\[ \bar{S}_{w(d),k}= \frac{1}{Y_{w(d),k}} \sum_{j \ni \mathrm{I}[g_j = k]}S_{w(d),j} \quad \& \quad Y_{w(d),k} = \sum_{j = 1}^{\mathrm{J}_{w(d)}} \mathrm{I}[g_j = k] \]
Then a hierarchical model for the data is
\[\begin{array}{rl} \mathbf{Y}_{w(d)} | \boldsymbol\pi_{w}, w\: &\stackrel{ind}{\sim} \:Multinomial(\boldsymbol\pi_{w}), \\ \boldsymbol{\pi}_w|\boldsymbol{\alpha} \:&\stackrel{ind}{\sim}\:Dirichlet(\boldsymbol{\alpha}), \\ \alpha_k\: &\stackrel{iid}{\sim}\: Gamma(a = 2, \: b= 0.25), \\ \bar{S}_{w(d),k} | Y_{w(d),k}, w, \mu_{w, k}, \sigma^2_{k} \: & \stackrel{iid}{\sim}\: N \left(\mu_{w, k}, \frac{\sigma^2_{k}}{Y_{w(d),k}}\right) \\ \mu_{w, k} | \mu_{k} \: & \stackrel{iid}{\sim}\: N(\mu_{k}, 3) \\ \mu_{k} \: & \stackrel{iid}{\sim}\: N(0, 3) \\ 1/\sigma^2_{k}\: & \stackrel{iid}{\sim}\: Gamma(0.5, 0.1) \end{array}\]
Consider a new document with cluster fill counts \(\boldsymbol{Y}^*\) and average slope measurements \(\boldsymbol{\bar{S}}^*\) from unknown writer \(w^*\).
Posterior Predictive Analysis
Posterior probability that the questioned document belongs to writer \(w^\prime\) with respect to the closed set is: \begin{array}{rl} &p(w^* = w|* ^, ) \ &p(^ | w^* = w^, ) \ & _{m=1}^{M} Mult(^*; _{w}{(m)}) \end{array} for MCMC samples \(m = 1, \dots, M\).
Results
Evaluate the posterior probability of writership for each of the holdout documents.Modeling Summary
| Model | Data | LogLoss |
|---|---|---|
| #1 | Adjacency Grouping (not presented) | 0.5413 |
| #1 | Cluster Grouping | 0.2013 |
| #2 | Cluster Grouping | 0.2078 |
| #3 | Cluster Grouping & Glyph Slopes | 0.0883 |
<IP_ADDRESS> Modeling update (Feb 17)
90 writers, Session #1
- Train: 3 London Letters
- A few writers only had 2 (working on this)
- Test: 1 Wizard of Oz
Recall rotation angle measurements:
A NAIVE Model for Rotation Angles
Naively we can treat the rotation angle values as if they are linear on the interval (0,\(\pi\)). This is of course not the case, since a value of 0 is extremely similar to a value of \(\pi\), both corresponding to letters that do not lean a great deal to the left or right, and are wider than they are tall. This can be seen in Figure 2, where the unimodal density ends abuptly at zero and “wraps around” in the higher values near \(\pi\). Nonetheless, we can specify a disribution on the linear, or unwrapped, rotation angles as follows: \[\begin{align} \nonumber \mathbf{Y}_{w(d)} | \boldsymbol\pi_{w}, w \: &\stackrel{ind}{\sim} \:Multinomial(\boldsymbol\pi_{w}), \\ \nonumber \boldsymbol{\pi}_w|\boldsymbol{\gamma} \: &\stackrel{ind}{\sim}\:Dirichlet(\boldsymbol{\gamma}), \\ \nonumber \gamma_k\: &\stackrel{iid}{\sim}\: Gamma(a = 2, \: b= 0.25), \\ \nonumber & \quad \\ \nonumber SRA_{w,k} | w,k \: & \stackrel{iid}{\sim}\: Beta \left( \alpha_{w,k}, \beta_{w,k} \right) \\ \nonumber & \quad \\ \nonumber \mu_{w,k} &= \frac{\alpha_{w,k}}{\alpha_{w,k}+\beta_{w,k}} \: \sim Beta(2,2) \\ \nonumber variance_{w,k} &= \frac{\mu_{w,k}(1 - \mu_{w,k})}{\alpha_{w,k} + \beta_{w,k} + 1} = \frac{\mu_{w,k}(1 - \mu_{w,k})}{\kappa_{w,k}+1} \\ \nonumber \kappa_{w,k} &= \alpha_{w,k} + \beta_{w,k} \: \sim Gamma(shape = 0.1, rate = 1) \end{align}\]
Where \(SRA_{w,k,i}, \: \:i = 1, \dots, n_{w,k}\), are rotation angles from cluster \(k\), and writer \(w\), scaled to the beta density support of (0,1). For a given writer and cluster, \(\mu_{w,k}\) is the mean of the data distribution, expressed as a functions of \(\alpha_{w,k}\) and \(\beta_{w,k}\). We place a weak prior, \(Beta(2,2)\), on all \(\mu_{w,k}\). Similarly, \(\kappa_{w,k}\) is expressed as a function of \(\alpha_{w,k}\) and \(\beta_{w,k}\) and is a sort of “sample size”, or “precision” parameter. Notice that \(\kappa_{w,k}+1\propto\frac{1}{variance_{w,k}}\).
For a different writer who does not tend to lean one way or another…
And we can look at a handful of beta distributions that arise from \(w=1\) and \(k=16\)…
Why am I showing you histograms and MCMC samples from clusters #29 and #16?? Becasue these are great examples of where the problems occur with the naive approach to modeling the “unwrapped” angles.
These are clusters that tend to hold the letters \(n\) and \(u\) and thus, are generally wider than they are tall. When a writer emits one of these nearly straight up and down, but slightly right leaning, the rotation angle will be just above zero. If the graph is very slightly left leaning, then the rotation angle will be just under \(\pi\).
While the beta distribution can take on a \(U\)-shape (like the Writer 1, Cluster 16 data in Figure 4), this is still undesirable, becuase we want to express the fact that values near \(0\) and \(\pi\) are actually very similar. For example, a mean near \(\pi/2\) does not do a good job of expressing the center of those rotation angles, and a traditional “unwrapped” variance will overstate the variability in data that is actually quite similar.
Consider the following distribution of cluster #16 rotation angles from writer #85’s questioned document. The largest observation is 3.129, which scales to 0.996, the next largest is 3.024, which scales to 0.963. These are going to accumulate a huge amount of likelihood when evaluated under the beta parameters for Writer #1, displayed in Figure 6. The rest of the rotation angles are relatively spread out over the \(x\)-axis, and not necessarily reflective of the Writer #1 distribution in Cluster #16.
QD evaluation under beta distributed rotation angles using \(k=1,\dots,20\), the \(20\) most populated clusters. Notice the probability for questioned writer #85 that is assigned to known writer #1. This error is explained by the behavior depicted above. Not all off-diagonal probability assignment can be attributed to similar behavior, but much of it can.
A Wrapped Model for Rotation Angles
A more appropriate approach to the problem is to consider the rotation angles in the polar coordinate system and treat them as spanning the full circle. So we map the upper half plane values to \((0,2\pi)\), where values above the \(x-\)axis indicate a right leaning graph and below the \(x-\)axis indicate left leaning graphs. Graphs that are relatively straight up and down will have values near \(0/2\pi\) if they are wider than they are tall, and near \(\pi\) if they are taller than they are wide.
We consider two distributions that are appropriate for circular data. First, the von Mises distribution is a close approximation to the wrapped normal distribution, which is the circular analouge of the normal. This is the “go-to” unimodal wrapped distribution, like the normal is the traditional “go-to” unimodal distribution. It is specified through the mean, \(\mu\), and concentration, \(\kappa\) (\(\frac{1}{\kappa}\) analogous to \(\sigma^2\)).
The second is the wrapped Cauchy distribution, which is the wrapped version of the Caucy distribution. Similar to the traditional Cauchy, this distribution is symmetric, unimodel, and specified by a location parameter, \(\mu\), and concentration parameter, \(\rho\). This distribution is also heavy-tailed in the sense that it will place more density on the “back” of the circle opposite the peak of the distribution.
Now, consider \[\begin{align} \nonumber \mathbf{Y}_{w(d)} | \boldsymbol\pi_{w}, w \: &\stackrel{ind}{\sim} \:Multinomial(\boldsymbol\pi_{w}), \\ \nonumber \boldsymbol{\pi}_w|\boldsymbol{\gamma} \: &\stackrel{ind}{\sim}\:Dirichlet(\boldsymbol{\gamma}), \\ \nonumber \gamma_k\: &\stackrel{iid}{\sim}\: Gamma(a = 2, \: b= 0.25), \\ \nonumber & \quad \\ \nonumber RA_{w,k} | w,k \: & \stackrel{iid}{\sim}\: Wrapped \: Cauchy \left( \mu_{w,k}, \phi_{w,k} \right) \\ \nonumber \mu_{w,k} &\sim Uniform(0,2\pi) \\ \nonumber \phi{w,k} &\sim Uniform(0,1) \\ \nonumber \end{align}\]
Where \(RA_{w,k,i}, \: \:i = 1, \dots, n_{w,k}\), are rotation angles from cluster \(k\), and writer \(w\), on the full circle \((0,2\pi)\). For a given writer and cluster, \(\mu_{w,k}\) and \(\phi_{w,k}\) are the location and concentration parameters, respectively. We place non-informative uniform priors on each. Again, there is no borrowing set up in the model for rotation angles. Each writer/cluster combination gets its own estimated rotation angle distribution.
QD evaluation under wrapped Cauchy distributed rotation angles, using \(k=1,\dots,20\), the \(20\) most populated clusters. Notice the probability for questioned writer #85 that was previously assigned to known writer #1 is no longer an issue.
5.3.3 Interesting documents
<IP_ADDRESS> Where do we miss?
We tend to do worse on writers who only had 2 training documents.
Writers 32 and 133.Samples:
Writer 32 Test
Writer 133 Train
<IP_ADDRESS> Writer 4 Data
London Letter #1
London Letter #2
London Letter #3
<IP_ADDRESS> You all!
Wolfgang
Soyoung
Other writer
5.4 Communication of Results
Presenting author is in bold.
5.4.1 Papers
- “A Clustering Method for Graphical Handwriting Components and Statistical Writership Analysis”
- Authors: Nick Berry and Amy Crawford
- Submitted to The Annals of Applied Statistics in September 2019.
- “A Database of Handwriting Samples for Applications in Forensic Statistics”
- Authors: Anyesha Rey, Amy Crawford, and Alicia Carriquiry
- Submitted to Data in Brief in October 2019.
- “Bayesian Hierarchical Modeling for Forensic Handwriting Analysis”
- Authors: Amy Crawford, Alicia Carriquiry, and Danica Ommen
- In preparation for submission to PNAS.
5.4.2 Talks
- “Statistical Analysis of Handwriting for Writer Identification”
- August 2019
- Authors: Amy Crawford, Nick Berry, Alicia Carriquiry, Danica Ommen
- American Society of Questioned Document Examiners (ASQDE) Annual Meeting in Cary, NC.
- “A Bayesian Hierarchical Mixture Model with Applications in Forensic Handwriting Analysis”
- July 2019
- Authors: Amy Crawford, Nick Berry, Alicia Carriquiry Danica Ommen
- Joint Statistical Meetings (JSM) in Denver, CO.
- “Forensic Analysis of Handwriting”
- July 2019
- Authors: Alicia Carriquiry, Amy Crawford, Nick Berry, Danica Ommen
- VI Latin American Meeting on Bayesian Statistics (VI COBAL), Lima, Peru.
- “Exploratory Analysis of Handwriting Features: Investigating Numeric Measurements of Writing”
- February 2019
- Authors: Amy Crawford, Nick Berry, Alicia Carriquiry, Danica Ommen
- American Academy of Forensic Sciences (AAFS) Annual Meeting in Baltimore, MD.
- “Toward a Statistical and Algorithmic Approach to Forensic Handwriting Comparison”
- August 2018
- Authors: Amy Crawford and Alicia Carriquiry
- American Society of Questioned Document Examiners (ASQDE) Annual Meeting in Park City, UT.
- “A Bayesian Approach to Forensic Handwriting Evidence”
- July 2018
- Authors: Amy Crawford and Alicia Carriquiry
- Joint Statistical Meetings (JSM) in Vancouver, BC, Canada.
- July 2018
- “Bringing Statistical Foundations to Forensic Handwriting Analysis”
- May 2018
- Authors: Amy Crawford and Alicia Carriquiry
- American Bar Association, 9th Annual Prescription for Criminal Justice Forensics Program in New York, NY.
5.4.3 Posters
- “A Bayesian Hierarchical Model for Forensic Writer Identification”
- September 2019
- Authors: Amy Crawford, Alicia Carriquiry, Danica Ommen
- 10th International Workshop on Statistics and Simulation in Salzburg, Austria
- 1st Springer Poster Award
Click for Poster Image
- “Statistical Analysis of Handwriting”
- May 2019
- Authors: Amy Crawford and Nick Berry
- CSAFE Annual All-Hands Meeting in Ames, IA
- “Statistical Analysis of Letter Importance for Document Examination”
- February 2018
- Authors: Amy Crawford and Alicia Carriquiry
- American Academy of Forensic Sciences in Seattle, WA
- YFSF Best Poster Award
- (Presented AAFS 2018 Poster for a Second Time)
- May 2018
- Authors: Amy Crawford and Alicia Carriquiry
- CSAFE Annual All-Hands Meeting in Ames, IA
5.5 People involved
5.5.1 Faculty
- Alicia Carriquiry
- Hal Stern (UCI, Project G PI)
- Danica Ommen
5.5.2 Graduate Students
- Amy Crawford
5.5.3 Undergraduates
- Anyesha Ray (data collection)
- James Taylor (feature extraction)
Forgy, E. (1965). Cluster Analysis of Multivariate Data: Efficiency vs. Interpretability of Classifications. Biometrics, 21:768–780.↩
Lloyd, S. (1982). Least Squares Quantization in PCM. IEEE Trans. on Information Theory, 28(2):129–137.↩
Gan, G. and Ng, M. K.-P. (2017). K-means clustering with outlier removal. Pattern Recog. Letters, 90:8–14.↩
Kokonendji, C. C. and Puig, P. (2018). Fisher Dispersion Index for Multivariate Count Distributions. Journal of Multivariate Analysis, v165 p180-193.↩


"IBLEAM "people-waster, a city assigned to Manasseh (Joshua 17:11), from which the Israelites, however, could not expel the Canaanites (Judges 1:27). It is also called Bileam (1 Chronicles 6:70). It was probably the modern Jelamah, a village 2 1/2 miles north of Jenin.
"IBLEAM "people-waster, a city assigned to Manasseh (Joshua 17:11), from which the Israelites, however, could not expel the Canaanites (Judges 1:27). It is also called Bileam (1 Chronicles 6:70). It was probably the modern Jelamah, a village 2 1/2 miles north of Jenin.


Ohio State, Penn State Players Get Into It During Warmups
by Matt LombardiOhio State vs. Penn State is set to kick off at Noon ET, but it looks like players on both sides are ready for the contest to start a little early. FOX just aired some interesting footage during warmups on the field.
In the clip, you can see players on both sides jawing at each other about an hour and a half before kickoff. It looks like both teams are ready to go for the all-important contest.
The game was big enough to draw both ESPN College GameDay and FOX’s Big Noon Kickoff. It’s a huge contest for both the Big Ten and the College Football Playoff race.
Here’s video of the encounter. It doesn’t look like anyone got physical – just a lot of posturing.
Ohio State can win the Big Ten East with a victory. Penn State needs to win and defeat Rutgers next week to do the same.
Ohio State sits at No. 2 in the College Football Playoff rankings, while Penn State checks in at No. 8. That said, this is essentially an elimination game.
We’re an hour away from kickoff.


%[@ IncludeFile "Code/Lang.vbs" ]%> <%[@ IncludeFile "Code/Util.vbs" ]%> <%[If Session("Book") Then Report.AbortTemplate]%> <%[ Set doc = ReportGenerator.document strTitle = Session("Title") ' Provide a mechanism for the viewer to contact the author of the report. This contact mechanism is only available if ' the report is published to http://familytrees.genopro.com/ fContactAuthor = (Left(ReportGenerator.PathOutputHttp, 31) = "http://familytrees.genopro.com/") ]%>


localhost
localhost
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localhost
localhost
h1 tag
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pre tag line 2 line 3 line 4


Tc.Connector Class
Base class for the different connectors.
Item Index
Methods
init
(
-
connectorId
Initializes the Connector.
Parameters:
-
connectorId
StringThe unique connector ID
notify
(
Boolean
-
origin
-
state
-
data
Notifies all registered components about a state change This can be be overriden in the specific connectors.
Parameters:
-
origin
ModuleThe module that sends the state change
-
state
StringThe component's state
-
data
ObjectContains the state relevant data (if any)
Returns:
Boolean:
Indicates whether the default action should be excuted or not
registerComponent
(
-
component
Registers a component.
Parameters:
-
component
ModuleThe module to register
unregisterComponent
(
-
component
Unregisters a component.
Parameters:
-
component
ModuleThe module to unregister


--- layout: post title: 'New gem: Mixable Engines' date: '2011-09-04T12:47:17-04:00' tags: [] tumblr_url: http://timothymorton.com/post/9793673037/new-gem-mixable-engines ---
Just published a new gem for my day job: Mixable Engines.
In the beginning, there was the engines plugin. This was back before Rails 2.3, when men were men and there was no “engines” functionality built into rails. The plugin would mix together the controller classes from your engines and your application.
In those days, my colleagues and I at the ASEE wrote lots of engines, and lots of apps that used those engines, and all of it depending on this mixed-together architecture. Now as we upgrade our apps to Rails 3, this gem allows us to keep the existing code and engines working without rewriting everything.


Logged in as John Doe
Status:
Player:
There are no trophies.
There are no ranking.
There are no screenshots.


I am a High School Student, currently taking these classes: Digital Media II, Graphics, and Advanced Animation. Last summer I took a coding bootcamp for html, css, and javascript. I plan on being a peer tutor over next summer.
I have a YouTube that I use Premiere Pro, Photoshop, and Illustrator all the time. I also make websites for my school and for cometitions. For example I made this site and I also made this website. The first for a TSA competition and the second for my Digital Media II class.


Plugin Information |
|---|
View Curseforge Publisher on the plugin site for more information. |
What does it do?
This plugin allows users to upload build artifacts to CurseForge as mod releases.
Plugin Information |
|---|
View Curseforge Publisher on the plugin site for more information. |
This plugin allows users to upload build artifacts to CurseForge as mod releases.


Select Fractal:
Sierpinski Circles
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Scratchpad
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WITNESS LIST Appropriations-S/C on Health & Human Services Committee March 3, 2003-7:30A Budget Against: van Hell, Sylvia Y. (Self) Brewer, Sister Helen (Texas Hospital Association; Seton Health Care Network) Camacho, Jose E. (Texas Association of Community Health Centers, Inc.) Cranston, Ron (ADAPT of Texas) Donovan, Jim (AMERIGROUP Texas) Dunkelberg, Anne (Center for Public Policy Priorities) Graves, Tim (Texas Health Care Association) Greebon, Karen (Self and ADAPT) Harracksingh, Rachel B. (Texas Ambulance Association; Life Ambulance) Hill, Heidi R. (Self) Horton, Colleen (Self and Texas Center for Disability Studies) Johnson, Karen R. (United Ways of Texas) Lachman, MD, MPH, Dr. Barry S. (Texas Association of Health Plans) Lilley, Stacy (Medical Staffing Network) Litzinger, Amy (Self) Litzinger, Linda (Self) Lovelace, Joe (National Alliance for the Mentally Ill (NAMI) Texas) McPhail, Jennifer (ADAPT) Metz, Albert (ADAPT of Texas) Moe, Dianne (Gentiva Health Services) Pena, Angela (Self and Special Kids Care; Pediatric Home Health Services) Peterson, Dr. Mary Dale (Driscoll Children's Health Plan; Texas Association of Health Plans) Prejean, Caldwell M. (Catholic Charities of Central Texas; Diocese of Austin) Saenz, Danny (ADAPT of Texas) Stellar, Terri (Self) Stubbs, Sr. Mary Lou (Diocese of Austin) Vasek, Heather (Texas Association for Home Care) On: Briones, Felix (Self) Burch, Jr., Curtis (Health and Human Services Commission) Cooke, Jason (Health and Human Services Commission) Coombes, Phyllis (Comptroller of Public Accounts) Counihan, Will (Comptroller of Public Accounts) Ford, Ruthie (Comptroller of Public Accounts) Gonzales, David (Texas Pharmacy Association) Hawkins, Albert (Health and Human Services Commission) Holcomb, MD, John R. (Self and Texas Medical Association) Hose, Rev. Samuel L. (Catholic Charities of Central Texas) Kimbrough, Jay (Office of the Attorney General) LeBrun, Aurora F. (Health and Human Services Commission) Lorenzen, Steve (Health and Human Services Commission) Murphree, Susan (Advocacy Inc.) Palmer, David (Health and Human Services Commission) Spelce, Bennett (AARP) Stephenson, Scott E. (Office of the Attorney General) Suehs, Thomas (Health and Human Services Commission) Vordenbaumen, Tim L. (Long Term Care Pharmacy Alliance; Omnicare, Inc.) Welch, Ken (Comptroller of Public Accounts)