Enabling MySQL 4-byte support¶
Note
This feature is currently experimental.
In order to use Emojis on your Nextcloud server with a MySQL database, the installation needs to be tweaked a bit.
Update your Nextcloud server to Nextcloud 11 or later.
Make sure the following InnoDB settings are set on your MySQL server:
[mysqld] innodb_large_prefix=true innodb_file_format=barracuda innodb_file_per_table=true
Restart the MySQL server in case you changed the configuration in step 2.
Change your databases character set and collation:
ALTER DATABASE nextcloud CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci;
Set the
mysql.utf8mb4
config to true in your config.php:$ sudo -u www-data occ config:system:set mysql.utf8mb4 --type boolean --value="true"
Convert all existing tables to the new collation by running the repair step:
$ sudo -u www-data occ maintenance:repair
Now you should be able to use emojis like 🎉
in your file names, calendar events, comments and many more.
MariaDB support¶
Note
This is even more experimental.
Follow MySQL steps 1, 2 and 3
Figure out whether the file formate was changed to Barracuda:
MariaDB> SELECT NAME, SPACE, FILE_FORMAT FROM INFORMATION_SCHEMA.INNODB_SYS_TABLES WHERE NAME like "nextcloud%";
If the file format is “Barracuda” for every single table, nothing is left to do. Continue with the MySQL instructions. While testing, all tables’ file format was “Antelope”.
The tables needs to be migrated to “Barracuda” manually, one by one. SQL commands can be created easily, however:
MariaDB> USE INFORMATION_SCHEMA; MariaDB> SELECT CONCAT("ALTER TABLE `", TABLE_SCHEMA,"`.`", TABLE_NAME, "` ROW_FORMAT=DYNAMIC;") AS MySQLCMD FROM TABLES WHERE TABLE_SCHEMA = "nextcloud";
This will return an SQL command for each table in the nextcloud database. The rows can be quickly copied into a text editor, the “|”s replaced and the SQL commands copied back to the MariaDB shell. If no error appeared (in doubt check step 2) all is done and nothing is left to do here. It can be proceded with the MySQL steps.
It is possible, however, that some tables cannot be altered. The operations fails with: “ERROR 1478 (HY000): Table storage engine ‘InnoDB’ does not support the create option ‘ROW_FORMAT’”. In that case the failing tables have a SPACE value of 0 in step 2. It basically means that the table does not have an index file of its own, which is required for the Barracuda format. This can be solved with a slightly different SQL command:
MariaDB> ALTER TABLE `nextcloud`.`oc_tablename` ROW_FORMAT=DYNAMIC, ALGORITHM=COPY;
Replace oc_tablename with the failing table. If there are too many (did not happen here), SQL commands can be generated in a batch (task for the reader).
Now everything should be fine and the MySQL instructions should be proceeded.


Search for your favourite movies using the search bar below. To generate search results, press ENTER on your keyboard, or the search button. To add movies to your nomination list, press NOMINATE in the movie card. Your list of nominations will be automatically saved. To dismiss this dialog, press the button on the top right corner.


private suspend fun getItems(path: String): List<ShopItem>
Get shop items from a certain path in the database
path - The path to the items (should be the a constant string defined in Constants)
path
Return a list of ShopItem objects
See Also
ShopItem
Constants.CHARADES_PATH
Constants.TRUTH_OR_DARE_PATH


A new equipment request has been made by = request.email ?>.
Employee name: = request['Employee name'] ?>
Desk location name: = request['Desk location'] ?>
Due date: = request['Due date'] ?>
Laptop model: = request['Laptop'] ?>
Desktop model: = request['Desktop'] ?>
Monitor(s): = request['Monitor'] ?>


WATCH | Clinton shames UK for not publishing Russian meddling report
Former US presidential candidate Hillary Clinton says Britain's failure to publish a report into allegations of Russian meddling is "shameful".
Former US presidential candidate Hillary Clinton says Britain's failure to publish a report into allegations of Russian meddling is "shameful".


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You will see some information about the workers to help you make the hiring decision.
These workers have already participated in two rounds of tasks.
These workers were required to answer 5 easy SAT math questions in Round 1 and another 5 hard SAT math questions in Round 2. Workers were incentivized to answer as many correct questions as possible. In other words, they were paid more if they answered more questions correctly. They had five minutes available for each round of the task, and were not allowed to use a calculator.
You may or may not receive information on how well the workers performed in Round 1, your job is to predict who performed the best in Round 2. And your payment will be determined by how the workers you choose actually performed in Round 2.
To have a better understanding of the workers’ task , please see the sample question they were asked below.
To have a better understanding of the task, please see the sample question they were asked below.
|
|
To test your basic understanding of workers' task, please answer the following question:
1
. How many questions they need to answer in each round?


[1]
staffs seem to briing it 's still cooking as an excuse and unhelpful.
[2]
though the soup was so when they come to serve we had the worst lasagna ever here.


Constructor
new MongooseStream(opts)
Create a Transform stream which bulkWrite to mongo based on the itemWaterMark. model (mongoose Model) is a required field.
Parameters:
| Name | Type | Description |
|---|---|---|
opts |
MongooseStreamSettings | Configuration for MongoStream. Default value for |
- Source:
Fires:
Example
var stream2mongo = new MongooseStream({mode: SomeMongooseModel});
someReadableStreamFromArray([{text: 'abc'}, {text: 'efg'}])
.pipe(stream2mongo) // writes to mongo (while stream are also passthrough)
.pipe(response); // stream same results back to some request
Extends
- stream.Transform


Shan Ye Portfolio
Map Portfolio
I make maps for various purposes!
2019-nCoV Virus
NA Dinosaurs
River Typography
Devil's Lake
Glacier Field Trip
Wisconsin Glaciation
Study Site of 8.2-ka Event
TU Campus
Where Are Geoscientists?
Tree Town Ann Arbor
GSP 2014
African River Drainages
MDG-2: Education in Detroit
2014 Mineralogy Trip
Imperial Exams Statistics
Chinese History
Silk Road
Company Towns of PA


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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object | +--org.objectstyle.cayenne.access.DataContext
Class that provides applications with access to Cayenne persistence features. In most cases this is the only access class directly used in the application.
Most common DataContext use pattern is to create one DataContext per session. "Session" may be a an HttpSesession in a web application, or any other similar concept in a multiuser application.
DataObjects are registered with DataContext either implicitly when they are fetched via
a query, or read via a relationship from another object, or explicitly via calling
createAndRegisterNewObject(Class)
during new DataObject creation. DataContext
tracks changes made to its DataObjects in memory, and flushes them to the database when
commitChanges()
is called. Until DataContext is committed, changes made to its
objects are not visible in other DataContexts.
Each DataObject can belong only to a single DataContext. To create a replica of an
object from a different DataContext in a local context, use
localObjects(java.util.List)
method.
For more information see Cayenne User Guide.
| Field Summary | |
static EventSubject |
DID_COMMIT
|
static EventSubject |
DID_ROLLBACK
|
protected java.lang.String |
lazyInitParentDomainName
Stores the name of parent DataDomain. |
protected ObjectStore |
objectStore
|
protected QueryEngine |
parent
|
protected static java.lang.ThreadLocal |
threadDataContext
A holder of a DataContext bound to the current thread. |
protected boolean |
usingSharedSnaphsotCache
|
protected boolean |
validatingObjectsOnCommit
|
static EventSubject |
WILL_COMMIT
|
| Constructor Summary | |
DataContext()
Default constructor that creates a DataContext that has no association with a DataDomain. |
|
DataContext(QueryEngine parent)
Deprecated. since 1.1 use DataContext(QueryEngine, ObjectStore) |
|
DataContext(QueryEngine parent,
ObjectStore objectStore)
Creates a DataContext with parent QueryEngine and a DataRowStore that should be used by the ObjectStore. |
| Method Summary | |
static void |
bindThreadDataContext(DataContext context)
Binds a DataContext to the current thread. |
protected void |
clearFlattenedUpdateQueries()
Deprecated. Since 1.1 this method is not used in Cayenne. All flattened relationship logic was moved to the ObjectStore |
void |
commitChanges()
Synchronizes object graph with the database. |
void |
commitChanges(org.apache.log4j.Level logLevel)
Synchronizes object graph with the database. |
DataObject |
createAndRegisterNewObject(java.lang.Class objectClass)
Instantiates new object and registers it with itself. |
DataObject |
createAndRegisterNewObject(java.lang.String objEntityName)
Instantiates new object and registers it with itself. |
static DataContext |
createDataContext()
Factory method that creates and returns a new instance of DataContext based on default domain. |
static DataContext |
createDataContext(boolean useSharedCache)
Factory method that creates and returns a new instance of DataContext based on default domain. |
static DataContext |
createDataContext(java.lang.String domainName)
Factory method that creates and returns a new instance of DataContext using named domain as its parent. |
static DataContext |
createDataContext(java.lang.String domainName,
boolean useSharedCache)
Creates and returns new DataContext that will use a named DataDomain as its parent. |
ObjectId |
createPermId(DataObject anObject)
Deprecated. Since 1.1 this method is no longer used. |
DataRow |
currentSnapshot(DataObject anObject)
Creates or gets from cache a DataRow reflecting current object state. |
DataNode |
dataNodeForObjEntity(ObjEntity objEntity)
Returns a DataNode that handles database operations for a specified ObjEntity . |
java.util.Collection |
deletedObjects()
Returns a list of objects that are registered with this DataContext and have a state PersistenceState.DELETED |
void |
deleteObject(DataObject anObject)
Notifies data context that a registered object need to be deleted on next commit. |
java.util.Collection |
getDataMaps()
Returns a collection of DataMaps associated with this QueryEngine. |
DataContextDelegate |
getDelegate()
Returns a delegate currently associated with this DataContext. |
EntityResolver |
getEntityResolver()
Returns EntityResolver object used to resolve and route queries. |
ObjectStore |
getObjectStore()
Returns ObjectStore associated with this DataContext. |
QueryEngine |
getParent()
Returns parent QueryEngine object. |
DataDomain |
getParentDataDomain()
Note: currently nested DataContexts are not supported, so this method simply calls "getParent()". |
static DataContext |
getThreadDataContext()
Returns the DataContext bound to the current thread. |
boolean |
hasChanges()
Returns true if there are any modified, deleted or new objects
registered with this DataContext, false otherwise. |
void |
invalidateObject(DataObject dataObject)
Deprecated. Since 1.1, use invalidateObjects(Collections.singletonList(dataObject))
to invalidate a single object. |
void |
invalidateObjects(java.util.Collection dataObjects)
"Invalidates" a Collection of DataObject. |
boolean |
isTransactionEventsEnabled()
|
boolean |
isUsingSharedSnapshotCache()
Returns true if the ObjectStore uses shared cache of a parent
DataDomain. |
boolean |
isValidatingObjectsOnCommit()
Returns whether this DataContext performs object validation before commit is executed. |
java.util.List |
localObjects(java.util.List objects)
Creates a list of DataObjects local to this DataContext from a list of DataObjects coming from a different DataContext. |
DataNode |
lookupDataNode(DataMap dataMap)
Returns a DataNode that should hanlde queries for all DataMap components. |
java.util.Collection |
modifiedObjects()
Returns a list of objects that are registered with this DataContext and have a state PersistenceState.MODIFIED |
java.util.Collection |
newObjects()
Returns a list of objects that are registered with this DataContext and have a state PersistenceState.NEW |
DataObject |
objectFromDataRow(java.lang.Class objectClass,
DataRow dataRow,
boolean refresh)
Creates a DataObject from DataRow. |
DataObject |
objectFromDataRow(ObjEntity objEntity,
java.util.Map dataRow,
boolean refresh)
Deprecated. Since 1.1 |
DataObject |
objectFromDataRow(java.lang.String entityName,
java.util.Map dataRow)
Deprecated. Since 1.1 use objectFromDataRow(Class,DataRow,boolean) . |
java.util.List |
objectsFromDataRows(java.lang.Class objectClass,
java.util.List dataRows,
boolean refresh,
boolean resolveInheritanceHierarchy)
Converts a list of DataRows to a List of DataObject registered with this DataContext. |
java.util.List |
objectsFromDataRows(ObjEntity entity,
java.util.List dataRows,
boolean refresh,
boolean resolveInheritanceHierarchy)
Converts a list of data rows to a list of DataObjects. |
ResultIterator |
performIteratedQuery(GenericSelectQuery query)
Performs a single database select query returning result as a ResultIterator. |
int[] |
performNonSelectingQuery(Query query)
Performs a single database query that does not select rows. |
int[] |
performNonSelectingQuery(java.lang.String queryName)
Performs a named mapped query that does not select rows. |
int[] |
performNonSelectingQuery(java.lang.String queryName,
java.util.Map parameters)
Performs a named mapped non-selecting query using a map of parameters. |
void |
performQueries(java.util.Collection queries,
OperationObserver observer)
Delegates queries execution to parent QueryEngine. |
void |
performQueries(java.util.Collection queries,
OperationObserver resultConsumer,
Transaction transaction)
Delegates queries execution to parent QueryEngine. |
java.util.List |
performQuery(GenericSelectQuery query)
Performs a single selecting query. |
void |
performQuery(Query query,
OperationObserver operationObserver)
Deprecated. Since 1.1 use performQueries(List, OperationObserver). This method is redundant and doesn't add value. |
java.util.List |
performQuery(java.lang.String queryName,
boolean refresh)
Returns a list of objects or DataRows for a named query stored in one of the DataMaps. |
java.util.List |
performQuery(java.lang.String queryName,
java.util.Map parameters,
boolean refresh)
Returns a list of objects or DataRows for a named query stored in one of the DataMaps. |
void |
prefetchRelationships(SelectQuery query,
java.util.List objects)
Performs prefetching. |
protected DataObject |
readOnlyObjectFromDataRow(ObjEntity objEntity,
java.util.Map dataRow,
boolean refresh)
Deprecated. Since 1.1 use objectsFromDataRows(ObjEntity,List,boolean,boolean) instead. |
DataObject |
refetchObject(ObjectId oid)
Refetches object data for ObjectId. |
DataObject |
registeredObject(ObjectId oid)
Returns an object for a given ObjectId. |
void |
registerFlattenedRelationshipDelete(DataObject source,
ObjRelationship relationship,
DataObject destination)
Deprecated. Since 1.1 this method is not used in Cayenne. All flattened relationship logic was moved to the ObjectStore |
void |
registerFlattenedRelationshipInsert(DataObject source,
ObjRelationship relationship,
DataObject destination)
Deprecated. Since 1.1 this method is not used in Cayenne. All flattened relationship logic was moved to the ObjectStore |
void |
registerNewObject(DataObject dataObject)
Registers a new object (that is not yet persistent) with itself. |
void |
registerNewObject(DataObject dataObject,
java.lang.String objEntityName)
Deprecated. since 1.1 this method is deprecated. It is misleading to think that Cayenne supports more than one class per ObjEntity. Use registerNewObject(DataObject) instead. |
void |
rollbackChanges()
Reverts any changes that have occurred to objects registered with DataContext. |
void |
setDelegate(DataContextDelegate delegate)
Sets a DataContextDelegate for this context. |
void |
setParent(QueryEngine parent)
Sets direct parent of this DataContext. |
void |
setTransactionEventsEnabled(boolean flag)
Enables or disables posting of transaction events by this DataContext. |
static void |
setTransactionEventsEnabledDefault(boolean flag)
Sets default for posting transaction events by new DataContexts. |
void |
setValidatingObjectsOnCommit(boolean flag)
Sets the property defining whether this DataContext should perform object validation before commit is executed. |
java.util.Map |
takeObjectSnapshot(DataObject anObject)
Deprecated. Since 1.1 use "currentSnapshot" |
void |
unregisterObject(DataObject dataObject)
Deprecated. Since 1.1, use unregisterObjects(Collections.singletonList(dataObject))
to invalidate a single object. |
void |
unregisterObjects(java.util.Collection dataObjects)
Unregisters a Collection of DataObjects from the DataContext and the underlying ObjectStore. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
public static final EventSubject WILL_COMMIT
public static final EventSubject DID_COMMIT
public static final EventSubject DID_ROLLBACK
protected static final java.lang.ThreadLocal threadDataContext
protected boolean usingSharedSnaphsotCache
protected boolean validatingObjectsOnCommit
protected ObjectStore objectStore
protected transient QueryEngine parent
protected transient java.lang.String lazyInitParentDomainName
| Constructor Detail |
public DataContext()
public DataContext(QueryEngine parent)
DataContext(QueryEngine, ObjectStore)
public DataContext(QueryEngine parent, ObjectStore objectStore)
parent
- parent QueryEngine used to communicate with the data source.objectStore
- ObjectStore used by DataContext.| Method Detail |
public static DataContext getThreadDataContext() throws java.lang.IllegalStateException
java.lang.IllegalStateException
- if there is no DataContext bound to the current
thread.WebApplicationContextProvider
public static void bindThreadDataContext(DataContext context)
getThreadDataContext()
. Using
null parameter will unbind currently bound DataContext.
public static DataContext createDataContext()
createDataContext(String)
must be used instead. ObjectStore associated
with created DataContext will have a cache stack configured using parent domain settings.
public static DataContext createDataContext(boolean useSharedCache)
createDataContext(String, boolean)
must be used instead.
ObjectStore associated with newly created DataContext will have a cache
stack configured according to the specified policy, overriding a parent domain setting.
public static DataContext createDataContext(java.lang.String domainName)
public static DataContext createDataContext(java.lang.String domainName, boolean useSharedCache)
public QueryEngine getParent()
public DataDomain getParentDataDomain()
public void setParent(QueryEngine parent)
public void setDelegate(DataContextDelegate delegate)
public DataContextDelegate getDelegate()
public ObjectStore getObjectStore()
public boolean hasChanges()
true
if there are any modified, deleted or new objects
registered with this DataContext, false
otherwise.
public java.util.Collection newObjects()
public java.util.Collection deletedObjects()
public java.util.Collection modifiedObjects()
public DataObject registeredObject(ObjectId oid)
public DataRow currentSnapshot(DataObject anObject)
public java.util.Map takeObjectSnapshot(DataObject anObject)
public java.util.List localObjects(java.util.List objects)
public java.util.List objectsFromDataRows(ObjEntity entity, java.util.List dataRows, boolean refresh, boolean resolveInheritanceHierarchy)
public java.util.List objectsFromDataRows(java.lang.Class objectClass, java.util.List dataRows, boolean refresh, boolean resolveInheritanceHierarchy)
objectsFromDataRows(ObjEntity,List,boolean,boolean)
.
DataRow
,
DataObject
public DataObject objectFromDataRow(java.lang.Class objectClass, DataRow dataRow, boolean refresh)
objectsFromDataRows(Class,java.util.List,boolean,boolean)
.
DataRow
,
DataObject
public DataObject objectFromDataRow(java.lang.String entityName, java.util.Map dataRow)
objectFromDataRow(Class,DataRow,boolean)
.
public DataObject objectFromDataRow(ObjEntity objEntity, java.util.Map dataRow, boolean refresh)
objectFromDataRow(Class, DataRow, boolean)
instead.
protected DataObject readOnlyObjectFromDataRow(ObjEntity objEntity, java.util.Map dataRow, boolean refresh)
objectsFromDataRows(ObjEntity,List,boolean,boolean)
instead.
public DataObject createAndRegisterNewObject(java.lang.String objEntityName)
Note: preferred way to create new objects is via
createAndRegisterNewObject(Class)
method. It works exactly the same way,
but makes the application type-safe.
createAndRegisterNewObject(Class)
public DataObject createAndRegisterNewObject(java.lang.Class objectClass)
public void registerNewObject(DataObject dataObject, java.lang.String objEntityName)
registerNewObject(DataObject)
instead.
dataObject
- new object that we want to make persistent.objEntityName
- a name of the ObjEntity in the map used to get persistence
information for this object.public void registerNewObject(DataObject dataObject)
dataObject
- new object that we want to make persistent.public void unregisterObject(DataObject dataObject)
unregisterObjects(Collections.singletonList(dataObject))
to invalidate a single object.
public void unregisterObjects(java.util.Collection dataObjects)
public void invalidateObject(DataObject dataObject)
invalidateObjects(Collections.singletonList(dataObject))
to invalidate a single object.
public void invalidateObjects(java.util.Collection dataObjects)
public void deleteObject(DataObject anObject)
anObject
- data object that we want to delete.public DataObject refetchObject(ObjectId oid)
PersistenceState.HOLLOW
. It can also be
used to refresh certain objects.
CayenneRuntimeException
- if object id doesn't match any records, or if there
is more than one object is fetched.public DataNode dataNodeForObjEntity(ObjEntity objEntity)
lookupDataNode(DataMap)
since queries are not
necessarily based on an ObjEntity. Use Entity.getDataMap()
to obtain DataMap from ObjEntity.
QueryEngine
ObjEntity
.
dataNodeForObjEntity
in interface QueryEngine
public DataNode lookupDataNode(DataMap dataMap)
lookupDataNode
in interface QueryEngine
public void rollbackChanges()
public void commitChanges() throws CayenneRuntimeException
CayenneRuntimeException
public void commitChanges(org.apache.log4j.Level logLevel) throws CayenneRuntimeException
logLevel
- if logLevel is higher or equals to the level set for QueryLogger,
statements execution will be logged.
CayenneRuntimeException
public int[] performNonSelectingQuery(Query query)
public int[] performNonSelectingQuery(java.lang.String queryName)
public int[] performNonSelectingQuery(java.lang.String queryName, java.util.Map parameters)
public ResultIterator performIteratedQuery(GenericSelectQuery query) throws CayenneException
CayenneException
public void performQueries(java.util.Collection queries, OperationObserver observer)
performQueries
in interface QueryEngine
public void performQueries(java.util.Collection queries, OperationObserver resultConsumer, Transaction transaction)
performQueries
in interface QueryEngine
OperationObserver
,
Transaction
public void prefetchRelationships(SelectQuery query, java.util.List objects)
WARNING: Currently supports only "one-step" to one relationships. This is an arbitrary limitation and will be removed eventually.
public void performQuery(Query query, OperationObserver operationObserver)
performQuery
in interface QueryEngine
public java.util.List performQuery(GenericSelectQuery query)
GenericSelectQuery.isFetchingDataRows()
.public java.util.List performQuery(java.lang.String queryName, boolean refresh)
queryName
- a name of a GenericSelectQuery defined in one of the DataMaps. If
no such query is defined, this method will throw a
CayenneRuntimeException.refresh
- A flag that determines whether refresh is required in case a query
uses caching.public java.util.List performQuery(java.lang.String queryName, java.util.Map parameters, boolean refresh)
queryName
- a name of a GenericSelectQuery defined in one of the DataMaps. If
no such query is defined, this method will throw a
CayenneRuntimeException.parameters
- A map of parameters to use with stored query.refresh
- A flag that determines whether refresh is required in case a query
uses caching.public ObjectId createPermId(DataObject anObject) throws CayenneRuntimeException
anObject
. Object must already have a
temporary ObjectId.
This method is called when we are about to save a new object to the database. Primary key columns are populated assigning values in the following sequence:
CayenneRuntimeException
public EntityResolver getEntityResolver()
getEntityResolver
in interface QueryEngine
public static void setTransactionEventsEnabledDefault(boolean flag)
public void setTransactionEventsEnabled(boolean flag)
public boolean isTransactionEventsEnabled()
public boolean isUsingSharedSnapshotCache()
true
if the ObjectStore uses shared cache of a parent
DataDomain.
public boolean isValidatingObjectsOnCommit()
public void setValidatingObjectsOnCommit(boolean flag)
public java.util.Collection getDataMaps()
QueryEngine
getDataMaps
in interface QueryEngine
protected void clearFlattenedUpdateQueries()
public void registerFlattenedRelationshipDelete(DataObject source, ObjRelationship relationship, DataObject destination)
public void registerFlattenedRelationshipInsert(DataObject source, ObjRelationship relationship, DataObject destination)
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Talent Aquisition Partner,
Ovoko
2022-01—now
Ovoko is an online marketplace where car parts sellers, auto dismantlers, and recyclers meet car dealers, mechanics, and enthusiasts
- Staff recruitment for Tech, Marketing, Support, Data departments
- Screening and shortlisting candidates
- Facilitating interviews and hiring process
- Coaching hiring managers
- Contributing to Ovoko Hiring strategy
- Preparing information and input for the salaries budget
- Nurturing the healthy atmosphere in organization
Recruitment coordinator,
Freelancer
2016-05—2022-01 (with 2 maternity leaves)
- Consulting hiring managers
- Sourcing candidates
- Screening candidates
- Conducting interviews (online/offline)
- Coordinating interviews
2012-07—2015-01 (Maternity leave from 2014-05)
Seriously, Inc. is a Marketing and Design digital agency.
We engage with consumers through a variety of channels (Web, Social Media, Mobile, Email, HORECA, Event, Retail, POP) to help brands make conversation.
- Staff recruitment
- Screening and shortlisting candidates
- Sourcing and selection of recruitment agencies, further collaboration with them
- Facilitating interviews and hiring process
- Company representation in IT conferences and events
- Development of the HR strategy and management plan
- Preparing information and input for the salaries budget
- Ensuring compliance to the approved salaries budget
- Facilitating staff appraisals and promotions
- Nurturing the healthy atmosphere in organization
- Coordinating organization's finances with accountants
- Management of legal entity's journals and documents
2012-11—2014-01
S2 is a startups incubator bringing accelerating growth in technology companies. Focused on early-stage technology companies, S2 provides direct investment and/or consulting services for equity.
- Consulting Start-ups on legal issues
- Assisting with strategic growth
- HR management
- Preparing of investment projects
- Project management and support on attracting European Union and other funds
- Promotion of private investments
- Assisting with business plans
Senior administrator / marketing assistant,
Wavin Baltic
2012-04—2012-07
Wavin is part of Mexichem, the global leader in plastic pipe systems and the chemical and petrochemical industries in Latin America. Organized in four European regions, the Head Office is located in Zwolle, the Netherlands
- Administration
- Staff recruitment
- Facilitating job interviews
- Documents management: employment contracts, licenses, management of legal entity's journals, employee cards, job descriptions, work schedules, time-sheets, requisitions, work and fire safety registration
- Facilitating meetings
- Improvement of business processes and systematization of Office Administration Department
- Organizing of Company employees business trips and meetings with foreign partners
Senior administrator / HR specialist ,
FL Technics
2010-01—2012-04
FL Technics is a global provider of aircraft maintenance, repair and overhaul services.
FL Technics acts as one-stop-shop aircraft maintenance solutions’ provider, serving customers from Europe, the CIS, and Asia-Pacific. With representative offices in Lithuania, Indonesia, the United Kingdom, Thailand and Russia, the company provides services in Base Maintenance facilities in Lithuania and Indonesia, as well as through an extensive network of Line Maintenance support across Europe, Asia Pacific and the CIS.
- Administration
- Staff recruitment
- Documents management: employment contracts, licenses, management of legal entity's journals, employee cards, job descriptions, work schedules, time-sheets, requisitions, work and fire safety registration
- Facilitating meetings
- Improvement of business processes and systematization of Office Administration Department
- Organizing of Company employees business trips and meetings with foreign partners
2008-01—2010-12
Helios group is a real estate and city infrastructure developer and electronics retailer
- Administration & staff recruitment
- Documents management: employment contracts, licenses, management of legal entity's journals, employee cards, job descriptions, work schedules, time-sheets, requisitions, work and fire safety registration
- Facilitating meetings
- Contracts management, communication with customer, real estate sales and consulting, working with primary documents, customer support.
- Interest and commissions calculations
- Cooperation with bailiffs and debt collectors
2007-01—2008-01
Baltix mobile is a marketing and advertising agency.
- Data Administration
- Testing of Information system
- Translations from/to English
- Creation of surveys for Forum Cinemas, and Philip Morris
2006-09—2007-03
SBC is A-class business center. It is a perfect place to start your business activities in Vilnius. For your successful start it offers you a wide choice of business services ranging from furnished A-class office space to company formation
- Company's documentation and information management
- Management of personnel documents
- Communication with customers, customer support and reception


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Samuel
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Description:
This instruction performs 8-bit × 8-bit → 16-bit unsigned multiplication and shifts the result one bit left.
|
Rd |
Rr |
R1 |
R0 | ||
|---|---|---|---|---|---|
|
Multiplicand |
× |
Multiplier |
-> |
Product High |
Product Low |
|
8 |
8 |
16 |
Let (N.Q) denote a fractional number with N binary digits left of the radix point, and Q binary digits right of the radix point. A multiplication between two numbers in the formats (N1.Q1) and (N2.Q2) results in the format ((N1+N2).(Q1+Q2)). For signal processing applications, the format (1.7) is widely used for the inputs, resulting in a (2.14) format for the product. A left shift is required for the high byte of the product to be in the same format as the inputs. The FMUL instruction incorporates the shift operation in the same number of cycles as MUL.
The (1.7) format is most commonly used with signed numbers, while FMUL performs an unsigned multiplication. This instruction is therefore most useful for calculating one of the partial products when performing a signed multiplication with 16-bit inputs in the (1.15) format, yielding a result in the (1.31) format. Note: the result of the FMUL operation may suffer from a 2's complement overflow if interpreted as a number in the (1.15) format. The MSB of the multiplication before shifting must be taken into account, and is found in the carry bit. See the following example.
The multiplicand Rd and the multiplier Rr are two registers containing unsigned fractional numbers where the implicit radix point lies between bit 6 and bit 7. The 16-bit unsigned fractional product with the implicit radix point between bit 14 and bit 15 is placed in R1 (high byte) and R0 (low byte).
This instruction is not available in all devices. Refer to the device specific instruction set summary.
Operation:
R1:R0 ← Rd × Rr(unsigned (1.15) ← unsigned (1.7) × unsigned (1.7))
Syntax: Operands: Program Counter:
FMUL Rd,Rr 16 ≤ d ≤ 23, 16≤ r ≤ 23 PC ← PC + 1
16-bit Opcode:
|
0000 |
0011 |
0ddd |
1rrr |
Status Register (SREG) and Boolean Formula:
|
I |
T |
H |
S |
V |
N |
Z |
C |
|---|---|---|---|---|---|---|---|
|
- |
- |
- |
- |
- |
- |
⇔ |
⇔ |
C: R16
Set if bit 15 of the result before left shift is set; cleared otherwise.
Z:
Set if the result is $0000; cleared otherwise.
R (Result) equals R1,R0 after the operation.
Example:
;****************************************************************************** ;* DESCRIPTION ;*Signed fractional multiply of two 16-bit numbers with 32-bit result. ;* USAGE ;*r19:r18:r17:r16 = ( r23:r22 * r21:r20 ) << 1 ;****************************************************************************** fmuls16x16_32: clr r2 fmuls r23, r21 ;((signed)ah * (signed)bh) << 1 movw r19:r18, r1:r0 fmul r22, r20 ;(al * bl) << 1 adc r18, r2 movw r17:r16, r1:r0 fmulsu r23, r20 ;((signed)ah * bl) << 1 sbc r19, r2 add r17, r0 adc r18, r1 adc r19, r2 fmulsu r21, r22 ;((signed)bh * al) << 1 sbc r19, r2 add r17, r0 adc r18, r1 adc r19, r2
Words: 1 (2 bytes)
Cycles: 2


encoders¶
This package contains various encoders. An encoder typically takes data and produces representation.
encoder¶
-
class
encoders.encoder.
Encoder
(params, model, name='encoder', mode='train')[source]¶ Bases:
object
Abstract class from which all encoders must inherit.
-
__init__
(params, model, name='encoder', mode='train')[source]¶ Encoder constructor. Note that encoder constructors should not modify TensorFlow graph, all graph construction should happen in the
self._encode()
method.Parameters: - params (dict) – parameters describing the encoder.
All supported parameters are listed in
get_required_params()
,get_optional_params()
functions. - model (instance of a class derived from
Model
) – parent model that created this encoder. Could be None if no model access is required for the use case. - name (str) – name for encoder variable scope.
- mode (str) – mode encoder is going to be run in. Could be “train”, “eval” or “infer”.
Config parameters:
- initializer — any valid TensorFlow initializer. If no initializer is provided, model initializer will be used.
- initializer_params (dict) — dictionary that will be passed to
initializer
__init__
method. - regularizer — and valid TensorFlow regularizer. If no regularizer is provided, model regularizer will be used.
- regularizer_params (dict) — dictionary that will be passed to
regularizer
__init__
method. - dtype — model dtype. Could be either
tf.float16
,tf.float32
or “mixed”. For details see mixed precision training section in docs. If no dtype is provided, model dtype will be used.
- params (dict) – parameters describing the encoder.
All supported parameters are listed in
-
_cast_types
(input_dict)[source]¶ This function performs automatic cast of all inputs to encoder dtype.
Parameters: input_dict (dict) – dictionary passed to self._encode()
method.Returns: same as input_dict, but with all Tensors cast to encoder dtype. Return type: dict
-
_encode
(input_dict)[source]¶ This is the main function which should construct encoder graph. Typically, encoder will take raw input sequence as an input and produce some hidden representation as an output.
Parameters: input_dict (dict) – dictionary containing encoder inputs. If the encoder is used with
models.encoder_decoder
class,input_dict
will have the following content:{ "source_tensors": data_layer.input_tensors['source_tensors'] }
Returns: dictionary of encoder outputs. Return all necessary outputs. Typically this will be just: { "outputs": outputs, "state": state, }
Return type: dict
-
encode
(input_dict)[source]¶ Wrapper around
self._encode()
method. Here name, initializer and dtype are set in the variable scope and thenself._encode()
method is called.Parameters: input_dict (dict) – see self._encode()
docs.Returns: see self._encode()
docs.
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
mode
¶ Mode encoder is run in.
-
name
¶ Encoder name.
-
params
¶ Parameters used to construct the encoder (dictionary).
-
ds2_encoder¶
-
class
encoders.ds2_encoder.
DeepSpeech2Encoder
(params, model, name='ds2_encoder', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
DeepSpeech-2 like encoder.
-
__init__
(params, model, name='ds2_encoder', mode='train')[source]¶ DeepSpeech-2 like encoder constructor.
See parent class for arguments description.
Config parameters:
dropout_keep_prop (float) — keep probability for dropout.
conv_layers (list) — list with the description of convolutional layers. For example:
"conv_layers": [ { "kernel_size": [11, 41], "stride": [2, 2], "num_channels": 32, "padding": "SAME", }, { "kernel_size": [11, 21], "stride": [1, 2], "num_channels": 64, "padding": "SAME", }, { "kernel_size": [11, 21], "stride": [1, 2], "num_channels": 96, "padding": "SAME", }, ]
activation_fn — activation function to use.
num_rnn_layers — number of RNN layers to use.
rnn_type (string) — could be “lstm”, “gru”, “cudnn_gru”, “cudnn_lstm” or “layernorm_lstm”.
rnn_unidirectional (bool) — whether to use uni-directional or bi-directional RNNs.
rnn_cell_dim (int) — dimension of RNN cells.
row_conv (bool) — whether to use a “row” (“in plane”) convolutional layer after RNNs.
row_conv_width (int) — width parameter for “row” convolutional layer.
n_hidden (int) — number of hidden units for the last fully connected layer.
data_format (string) — could be either “channels_first” or “channels_last”. Defaults to “channels_last”.
bn_momentum (float) — momentum for batch norm. Defaults to 0.99.
bn_epsilon (float) — epsilon for batch norm. Defaults to 1e-3.
-
_encode
(input_dict)[source]¶ Creates TensorFlow graph for DeepSpeech-2 like encoder.
Parameters: input_dict (dict) – input dictionary that has to contain the following fields:
input_dict = { "source_tensors": [ src_sequence (shape=[batch_size, sequence length, num features]), src_length (shape=[batch_size]) ] }
Returns: dictionary with the following tensors: { 'outputs': hidden state, shape=[batch_size, sequence length, n_hidden] 'src_length': tensor, shape=[batch_size] }
Return type: dict
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
w2l_encoder¶
-
class
encoders.w2l_encoder.
Wave2LetterEncoder
(params, model, name='w2l_encoder', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
Wave2Letter like encoder. Fully convolutional model
-
__init__
(params, model, name='w2l_encoder', mode='train')[source]¶ Wave2Letter like encoder constructor.
See parent class for arguments description.
Config parameters:
dropout_keep_prop (float) — keep probability for dropout.
convnet_layers (list) — list with the description of convolutional layers. For example:
"convnet_layers": [ { "type": "conv1d", "repeat" : 5, "kernel_size": [7], "stride": [1], "num_channels": 250, "padding": "SAME" }, { "type": "conv1d", "repeat" : 3, "kernel_size": [11], "stride": [1], "num_channels": 500, "padding": "SAME" }, { "type": "conv1d", "repeat" : 1, "kernel_size": [32], "stride": [1], "num_channels": 1000, "padding": "SAME" }, { "type": "conv1d", "repeat" : 1, "kernel_size": [1], "stride": [1], "num_channels": 1000, "padding": "SAME" }, ]
activation_fn — activation function to use.
data_format (string) — could be either “channels_first” or “channels_last”. Defaults to “channels_last”.
normalization — normalization to use. Accepts [None, ‘batch_norm’]. Use None if you don’t want to use normalization. Defaults to ‘batch_norm’.
bn_momentum (float) — momentum for batch norm. Defaults to 0.90.
bn_epsilon (float) — epsilon for batch norm. Defaults to 1e-3.
-
_encode
(input_dict)[source]¶ Creates TensorFlow graph for Wav2Letter like encoder.
Parameters: input_dict (dict) – input dictionary that has to contain the following fields:
input_dict = { "source_tensors": [ src_sequence (shape=[batch_size, sequence length, num features]), src_length (shape=[batch_size]) ] }
Returns: dictionary with the following tensors: { 'outputs': hidden state, shape=[batch_size, sequence length, n_hidden] 'src_length': tensor, shape=[batch_size] }
Return type: dict
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
rnn_encoders¶
RNN-based encoders
-
class
encoders.rnn_encoders.
BidirectionalRNNEncoderWithEmbedding
(params, model, name='bidir_rnn_encoder_with_emb', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
Bi-directional RNN-based encoder with embeddings. Can support various RNN cell types.
-
__init__
(params, model, name='bidir_rnn_encoder_with_emb', mode='train')[source]¶ Initializes bi-directional encoder with embeddings.
Parameters: params (dict) – dictionary with encoder parameters Must define:
- src_vocab_size - data vocabulary size
- src_emb_size - size of embedding to use
- encoder_cell_units - number of units in RNN cell
- encoder_cell_type - cell type: lstm, gru, etc.
- encoder_layers - number of layers
- encoder_dp_input_keep_prob -
- encoder_dp_output_keep_prob -
- encoder_use_skip_connections - true/false
- time_major (optional)
- use_swap_memory (optional)
- mode - train or infer
… add any cell-specific parameters here as well
Returns: encoder_params
-
_encode
(input_dict)[source]¶ Encodes data into representation. :param input_dict: a Python dictionary.
- Must define:
- *src_inputs - a Tensor of shape [batch_size, time] or
- [time, batch_size] (depending on time_major param)
- src_lengths - a Tensor of shape [batch_size]
Returns: - encoder_outputs - a Tensor of shape
- [batch_size, time, representation_dim]
or [time, batch_size, representation_dim] * encoder_state - a Tensor of shape [batch_size, dim] * src_lengths - (copy ref from input) a Tensor of shape [batch_size]
Return type: a Python dictionary with
-
enc_emb_w
¶
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
src_emb_size
¶
-
src_vocab_size
¶
-
-
class
encoders.rnn_encoders.
GNMTLikeEncoderWithEmbedding
(params, model, name='gnmt_encoder_with_emb', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
Encoder similar to the one used in GNMT model: https://arxiv.org/abs/1609.08144. Must have at least 2 layers
-
__init__
(params, model, name='gnmt_encoder_with_emb', mode='train')[source]¶ Encodes data into representation.
Parameters: params (dict) – a Python dictionary. Must define:
- src_inputs - a Tensor of shape [batch_size, time] or
- [time, batch_size] (depending on time_major param)
- src_lengths - a Tensor of shape [batch_size]
Returns: - encoder_outputs - a Tensor of shape
- [batch_size, time, representation_dim]
or [time, batch_size, representation_dim] * encoder_state - a Tensor of shape [batch_size, dim] * src_lengths - (copy ref from input) a Tensor of shape [batch_size]
Return type: a Python dictionary with
-
enc_emb_w
¶
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
src_emb_size
¶
-
src_vocab_size
¶
-
-
class
encoders.rnn_encoders.
GNMTLikeEncoderWithEmbedding_cuDNN
(params, model, name='gnmt_encoder_with_emb_cudnn', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
Encoder similar to the one used in GNMT model: https://arxiv.org/abs/1609.08144. Must have at least 2 layers. Uses cuDNN RNN blocks for efficiency
-
__init__
(params, model, name='gnmt_encoder_with_emb_cudnn', mode='train')[source]¶ Encodes data into representation
Parameters: params (dict) – a Python dictionary. Must define:
- src_inputs - a Tensor of shape [batch_size, time] or
- [time, batch_size] (depending on time_major param)
- src_lengths - a Tensor of shape [batch_size]
Returns: - encoder_outputs - a Tensor of shape
- [batch_size, time, representation_dim]
or [time, batch_size, representation_dim] * encoder_state - a Tensor of shape [batch_size, dim] * src_lengths - (copy ref from input) a Tensor of shape [batch_size]
Return type: a Python dictionary with
-
enc_emb_w
¶
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
src_emb_size
¶
-
src_vocab_size
¶
-
-
class
encoders.rnn_encoders.
UnidirectionalRNNEncoderWithEmbedding
(params, model, name='unidir_rnn_encoder_with_emb', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
Uni-directional RNN decoder with embeddings. Can support various RNN cell types.
-
__init__
(params, model, name='unidir_rnn_encoder_with_emb', mode='train')[source]¶ Initializes uni-directional encoder with embeddings.
Parameters: params (dict) – dictionary with encoder parameters Must define:
- src_vocab_size - data vocabulary size
- src_emb_size - size of embedding to use
- encoder_cell_units - number of units in RNN cell
- encoder_cell_type - cell type: lstm, gru, etc.
- encoder_layers - number of layers
- encoder_dp_input_keep_prob -
- encoder_dp_output_keep_prob -
- encoder_use_skip_connections - true/false
- time_major (optional)
- use_swap_memory (optional)
- mode - train or infer
… add any cell-specific parameters here as well
-
_encode
(input_dict)[source]¶ Encodes data into representation.
Parameters: input_dict – a Python dictionary. Must define:
- src_inputs - a Tensor of shape [batch_size, time] or
- [time, batch_size] (depending on time_major param)
- src_lengths - a Tensor of shape [batch_size]
Returns: Return type: a Python dictionary with - encoder_outputs - a Tensor of shape
- [batch_size, time, representation_dim]
or [time, batch_size, representation_dim] * encoder_state - a Tensor of shape [batch_size, dim] * src_lengths - (copy ref from input) a Tensor of shape [batch_size]
-
enc_emb_w
¶
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
src_emb_size
¶
-
src_vocab_size
¶
-
transformer_encoder¶
-
class
encoders.transformer_encoder.
TransformerEncoder
(params, model, name='transformer_encoder', mode='train')[source]¶ Bases:
open_seq2seq.encoders.encoder.Encoder
Transformer model encoder
convs2s_encoder¶
Conv-based encoder
-
class
encoders.convs2s_encoder.
ConvS2SEncoder
(params, model, name='convs2s_encoder_with_emb', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
Fully convolutional Encoder of ConvS2S
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
src_emb_size
¶
-
src_vocab_size
¶
-
static
resnet_encoder¶
-
class
encoders.resnet_encoder.
ResNetEncoder
(params, model, name='resnet_encoder', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
resnet_blocks¶
Contains definitions for Residual Networks.
Residual networks (‘v1’ ResNets) were originally proposed in: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
The full preactivation ‘v2’ ResNet variant was introduced by: [2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv: 1603.05027
The key difference of the full preactivation ‘v2’ variant compared to the ‘v1’ variant in [1] is the use of batch normalization before every weight layer rather than after.
-
encoders.resnet_blocks.
batch_norm
(inputs, training, data_format, regularizer, momentum, epsilon)[source]¶ Performs a batch normalization using a standard set of parameters.
-
encoders.resnet_blocks.
block_layer
(inputs, filters, bottleneck, block_fn, blocks, strides, training, name, data_format, regularizer, bn_regularizer, bn_momentum, bn_epsilon)[source]¶ Creates one layer of blocks for the ResNet model.
Parameters: - inputs – A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format.
- filters – The number of filters for the first convolution of the layer.
- bottleneck – Is the block created a bottleneck block.
- block_fn – The block to use within the model, either building_block or bottleneck_block.
- blocks – The number of blocks contained in the layer.
- strides – The stride to use for the first convolution of the layer. If greater than 1, this layer will ultimately downsample the input.
- training – Either True or False, whether we are currently training the model. Needed for batch norm.
- name – A string name for the tensor output of the block layer.
- data_format – The input format (‘channels_last’ or ‘channels_first’).
Returns: The output tensor of the block layer.
-
encoders.resnet_blocks.
bottleneck_block_v1
(inputs, filters, training, projection_shortcut, strides, data_format, regularizer, bn_regularizer, bn_momentum, bn_epsilon)[source]¶ A single block for ResNet v1, with a bottleneck.
Similar to _building_block_v1(), except using the “bottleneck” blocks described in:
- Convolution then batch normalization then ReLU as described by:
- Deep Residual Learning for Image Recognition https://arxiv.org/pdf/1512.03385.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
Parameters: - inputs – A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format.
- filters – The number of filters for the convolutions.
- training – A Boolean for whether the model is in training or inference mode. Needed for batch normalization.
- projection_shortcut – The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input).
- strides – The block’s stride. If greater than 1, this block will ultimately downsample the input.
- data_format – The input format (‘channels_last’ or ‘channels_first’).
Returns: The output tensor of the block; shape should match inputs.
-
encoders.resnet_blocks.
bottleneck_block_v2
(inputs, filters, training, projection_shortcut, strides, data_format, regularizer, bn_regularizer, bn_momentum, bn_epsilon)[source]¶ A single block for ResNet v2, without a bottleneck.
Similar to _building_block_v2(), except using the “bottleneck” blocks described in:
- Convolution then batch normalization then ReLU as described by:
- Deep Residual Learning for Image Recognition https://arxiv.org/pdf/1512.03385.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
- Adapted to the ordering conventions of:
- Batch normalization then ReLu then convolution as described by:
- Identity Mappings in Deep Residual Networks https://arxiv.org/pdf/1603.05027.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.
Parameters: - inputs – A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format.
- filters – The number of filters for the convolutions.
- training – A Boolean for whether the model is in training or inference mode. Needed for batch normalization.
- projection_shortcut – The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input).
- strides – The block’s stride. If greater than 1, this block will ultimately downsample the input.
- data_format – The input format (‘channels_last’ or ‘channels_first’).
Returns: The output tensor of the block; shape should match inputs.
-
encoders.resnet_blocks.
building_block_v1
(inputs, filters, training, projection_shortcut, strides, data_format, regularizer, bn_regularizer, bn_momentum, bn_epsilon)[source]¶ A single block for ResNet v1, without a bottleneck.
- Convolution then batch normalization then ReLU as described by:
- Deep Residual Learning for Image Recognition https://arxiv.org/pdf/1512.03385.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
Parameters: - inputs – A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format.
- filters – The number of filters for the convolutions.
- training – A Boolean for whether the model is in training or inference mode. Needed for batch normalization.
- projection_shortcut – The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input).
- strides – The block’s stride. If greater than 1, this block will ultimately downsample the input.
- data_format – The input format (‘channels_last’ or ‘channels_first’).
Returns: The output tensor of the block; shape should match inputs.
-
encoders.resnet_blocks.
building_block_v2
(inputs, filters, training, projection_shortcut, strides, data_format, regularizer, bn_regularizer, bn_momentum, bn_epsilon)[source]¶ A single block for ResNet v2, without a bottleneck.
- Batch normalization then ReLu then convolution as described by:
- Identity Mappings in Deep Residual Networks https://arxiv.org/pdf/1603.05027.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.
Parameters: - inputs – A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format.
- filters – The number of filters for the convolutions.
- training – A Boolean for whether the model is in training or inference mode. Needed for batch normalization.
- projection_shortcut – The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input).
- strides – The block’s stride. If greater than 1, this block will ultimately downsample the input.
- data_format – The input format (‘channels_last’ or ‘channels_first’).
Returns: The output tensor of the block; shape should match inputs.
-
encoders.resnet_blocks.
conv2d_fixed_padding
(inputs, filters, kernel_size, strides, data_format, regularizer)[source]¶ Strided 2-D convolution with explicit padding.
-
encoders.resnet_blocks.
fixed_padding
(inputs, kernel_size, data_format)[source]¶ Pads the input along the spatial dimensions independently of input size.
Parameters: - inputs – A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format.
- kernel_size – The kernel to be used in the conv2d or max_pool2d operation. Should be a positive integer.
- data_format – The input format (‘channels_last’ or ‘channels_first’).
Returns: A tensor with the same format as the input with the data either intact (if kernel_size == 1) or padded (if kernel_size > 1).
cnn_encoder¶
This module contains classes and functions to build “general” convolutional neural networks from the description of arbitrary “layers”.
-
class
encoders.cnn_encoder.
CNNEncoder
(params, model, name='cnn_encoder', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
General CNN encoder that can be used to construct various different models.
-
__init__
(params, model, name='cnn_encoder', mode='train')[source]¶ CNN Encoder constructor.
See parent class for arguments description.
Config parameters:
cnn_layers (list) — list with the description of “convolutional” layers. For example:
"conv_layers": [ (tf.layers.conv2d, { 'filters': 64, 'kernel_size': (11, 11), 'strides': (4, 4), 'padding': 'VALID', 'activation': tf.nn.relu, }), (tf.layers.max_pooling2d, { 'pool_size': (3, 3), 'strides': (2, 2), }), (tf.layers.conv2d, { 'filters': 192, 'kernel_size': (5, 5), 'strides': (1, 1), 'padding': 'SAME', }), (tf.layers.batch_normalization, {'momentum': 0.9, 'epsilon': 0.0001}), (tf.nn.relu, {}), ]
Note that you don’t need to provide “regularizer”, “training”, “data_format” and “axis” parameters since they will be automatically added. “axis” will be derived from “data_format” and will be
1 if data_format == "channels_first" else 3
.fc_layers (list) — list with the description of “fully-connected” layers. The only different from convolutional layers is that the input will be automatically reshaped to 2D (batch size x num features). For example:
'fc_layers': [ (tf.layers.dense, {'units': 4096, 'activation': tf.nn.relu}), (tf.layers.dropout, {'rate': 0.5}), (tf.layers.dense, {'units': 4096, 'activation': tf.nn.relu}), (tf.layers.dropout, {'rate': 0.5}), ],
Note that you don’t need to provide “regularizer”, “training”, “data_format” and “axis” parameters since they will be automatically added. “axis” will be derived from “data_format” and will be
1 if data_format == "channels_first" else 3
.data_format (string) — could be either “channels_first” or “channels_last”. Defaults to “channels_first”.
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
-
encoders.cnn_encoder.
build_layer
(inputs, layer, layer_params, data_format, regularizer, training, verbose=True)[source]¶ This function builds a layer from the layer function and it’s parameters.
It will automatically add regularizer parameter to the layer_params if the layer supports regularization. To check this, it will look for the “regularizer”, “kernel_regularizer” and “gamma_regularizer” names in this order in the
layer
call signature. If one of this parameters is supported it will pass regularizer object as a value for that parameter. Based on the same “checking signature” technique “data_format” and “training” parameters will try to be added. Finally, “axis” parameter will try to be specified with axis =1 if data_format == 'channels_first' else 3
. This is required for automatic building batch normalization layer.Parameters: - inputs – input Tensor that will be passed to the layer. Note that layer has to accept input as the first parameter.
- layer – layer function or class with
__call__
method defined. - layer_params (dict) – parameters passed to the
layer
. - data_format (string) – data format (“channels_first” or “channels_last”) that will be tried to be passed as an additional argument.
- regularizer – regularizer instance that will be tried to be passed as an additional argument.
- training (bool) – whether layer is built in training mode. Will be tried to be passed as an additional argument.
- verbose (bool) – whether to print information about built layers.
Returns: Tensor with layer output.
tacotron2_encoder¶
-
class
encoders.tacotron2_encoder.
Tacotron2Encoder
(params, model, name='tacotron2_encoder', mode='train')[source]¶ Bases:
encoders.encoder.Encoder
Tacotron-2 like encoder.
Consists of an embedding layer followed by a convolutional layer followed by a recurrent layer.
-
__init__
(params, model, name='tacotron2_encoder', mode='train')[source]¶ Tacotron-2 like encoder constructor.
See parent class for arguments description.
Config parameters:
dropout_keep_prop (float) — keep probability for dropout.
src_emb_size (int) — dimensionality of character embedding.
conv_layers (list) — list with the description of convolutional layers. For example:
"conv_layers": [ { "kernel_size": [5], "stride": [1], "num_channels": 512, "padding": "SAME" }, { "kernel_size": [5], "stride": [1], "num_channels": 512, "padding": "SAME" }, { "kernel_size": [5], "stride": [1], "num_channels": 512, "padding": "SAME" } ]
activation_fn (callable) — activation function to use for conv layers.
num_rnn_layers — number of RNN layers to use.
rnn_cell_dim (int) — dimension of RNN cells.
rnn_type (callable) — Any valid RNN Cell class. Suggested class is lstm
rnn_unidirectional (bool) — whether to use uni-directional or bi-directional RNNs.
zoneout_prob (float) — zoneout probability. Defaults to 0.
use_cudnn_rnn (bool) — need to be enabled in rnn_type is a Cudnn class.
data_format (string) — could be either “channels_first” or “channels_last”. Defaults to “channels_last”.
bn_momentum (float) — momentum for batch norm. Defaults to 0.1.
bn_epsilon (float) — epsilon for batch norm. Defaults to 1e-5.
-
_encode
(input_dict)[source]¶ Creates TensorFlow graph for Tacotron-2 like encoder.
Parameters: input_dict (dict) – dictionary with inputs. Must define:
- source_tensors - array containing [
- source_sequence: tensor of shape [batch_size, sequence length]
- src_length: tensor of shape [batch_size]
]
Returns: - A python dictionary containing:
- outputs - tensor containing the encoded text to be passed to the attention layer
- src_length - the length of the encoded text
Return type: dict
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-


--- layout: default title: Design Projects | Silvia Fuentes permalink: /projects ---
I’m a Product designer, previosly @ Bestow who likes:
I’m looking to join a team of nice folks this August 2022, if you’re working on something cool and thoughtful, reach out to me via email<EMAIL_ADDRESS>View my resume


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Yue Eunice Wong
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source code
Every Western institution was unprepared for the coronavirus pandemic, despite many prior warnings. This monumental failure of institutional effectiveness will reverberate for the rest of the decade, but it’s not too early to ask why, and what we need to do about it.
Many of us would like to pin the cause on one political party or another, on one government or another. But the harsh reality is that it all failed — no Western country, or state, or city was prepared — and despite hard work and often extraordinary sacrifice by many people within these institutions. So the problem runs deeper than your favorite political opponent or your home nation.
Part of the problem is clearly foresight, a failure of imagination. But the other part of the problem is what we didn’t *do* in advance, and what we’re failing to do now. And that is a failure of action, and specifically our widespread inability to *build*.
We see this today with the things we urgently need but don’t have. We don’t have enough coronavirus tests, or test materials — including, amazingly, cotton swabs and common reagents. We don’t have enough ventilators, negative pressure rooms, and ICU beds. And we don’t have enough surgical masks, eye shields, and medical gowns — as I write this, New York City has put out a desperate call for rain ponchos to be used as medical gowns. Rain ponchos! In 2020! In America!
We also don’t have therapies or a vaccine — despite, again, years of advance warning about bat-borne coronaviruses. Our scientists will hopefully invent therapies and a vaccine, but then we may not have the manufacturing factories required to scale their production. And even then, we’ll see if we can deploy therapies or a vaccine fast enough to matter — it took scientists 5 years to get regulatory testing approval for the new Ebola vaccine after that scourge’s 2014 outbreak, at the cost of many lives.
In the U.S., we don’t even have the ability to get federal bailout money to the people and businesses that need it. Tens of millions of laid off workers and their families, and many millions of small businesses, are in serious trouble *right now*, and we have no direct method to transfer them money without potentially disastrous delays. A government that collects money from all its citizens and businesses each year has never built a system to distribute money to us when it’s needed most.
Why do we not have these things? Medical equipment and financial conduits involve no rocket science whatsoever. At least therapies and vaccines are hard! Making masks and transferring money are not hard. We could have these things but we chose not to — specifically we chose not to have the mechanisms, the factories, the systems to make these things. We chose not to *build*.
You don’t just see this smug complacency, this satisfaction with the status quo and the unwillingness to build, in the pandemic, or in healthcare generally.
You see it in housing and the physical footprint of our cities. We can’t build nearly enough housing in our cities with surging economic potential — which results in crazily skyrocketing housing prices in places like San Francisco, making it nearly impossible for regular people to move in and take the jobs of the future. We also can’t build the cities themselves anymore. When the producers of HBO’s “Westworld” wanted to portray the American city of the future, they didn’t film in Seattle or Los Angeles or Austin — they went to Singapore. We should have gleaming skyscrapers and spectacular living environments in all our best cities at levels way beyond what we have now; where are they?
You see it in education. We have top-end universities, yes, but with the capacity to teach only a microscopic percentage of the 4 million new 18 year olds in the U.S. each year, or the 120 million new 18 year olds in the world each year. Why not educate every 18 year old? Isn’t that the most important thing we can possibly do? Why not build a far larger number of universities, or scale the ones we have way up? The last major innovation in K-12 education was Montessori, which traces back to the 1960s; we’ve been doing education research that’s never reached practical deployment for 50 years since; why not build a lot more great K-12 schools using everything we now know? We know one-to-one tutoring can reliably increase education outcomes by two standard deviations (the Bloom two-sigma effect); we have the internet; why haven’t we built systems to match every young learner with an older tutor to dramatically improve student success?
You see it in manufacturing. Contrary to conventional wisdom, American manufacturing output is higher than ever, but why has so much manufacturing been offshored to places with cheaper manual labor? We know how to build highly automated factories. We know the enormous number of higher paying jobs we would create to design and build and operate those factories. We know — and we’re experiencing right now! — the strategic problem of relying on offshore manufacturing of key goods. Why aren’t we building Elon Musk’s “alien dreadnoughts” — giant, gleaming, state of the art factories producing every conceivable kind of product, at the highest possible quality and lowest possible cost — all throughout our country?
You see it in transportation. Where are the supersonic aircraft? Where are the millions of delivery drones? Where are the high speed trains, the soaring monorails, the hyperloops, and yes, the flying cars?
Is the problem money? That seems hard to believe when we have the money to wage endless wars in the Middle East and repeatedly bail out incumbent banks, airlines, and carmakers. The federal government just passed a $2 trillion coronavirus rescue package in two weeks! Is the problem capitalism? I’m with Nicholas Stern when he says that capitalism is how we take care of people we don’t know — all of these fields are highly lucrative already and should be prime stomping grounds for capitalist investment, good both for the investor and the customers who are served. Is the problem technical competence? Clearly not, or we wouldn’t have the homes and skyscrapers, schools and hospitals, cars and trains, computers and smartphones, that we already have.
The problem is desire. We need to *want* these things. The problem is inertia. We need to want these things more than we want to prevent these things. The problem is regulatory capture. We need to want new companies to build these things, even if incumbents don’t like it, even if only to force the incumbents to build these things. And the problem is will. We need to build these things.
And we need to separate the imperative to build these things from ideology and politics. Both sides need to contribute to building.
The right starts out in a more natural, albeit compromised, place. The right is generally pro production, but is too often corrupted by forces that hold back market-based competition and the building of things. The right must fight hard against crony capitalism, regulatory capture, ossified oligopolies, risk-inducing offshoring, and investor-friendly buybacks in lieu of customer-friendly (and, over a longer period of time, even more investor-friendly) innovation.
It’s time for full-throated, unapologetic, uncompromised political support from the right for aggressive investment in new products, in new industries, in new factories, in new science, in big leaps forward.
The left starts out with a stronger bias toward the public sector in many of these areas. To which I say, prove the superior model! Demonstrate that the public sector can build better hospitals, better schools, better transportation, better cities, better housing. Stop trying to protect the old, the entrenched, the irrelevant; commit the public sector fully to the future. Milton Friedman once said the great public sector mistake is to judge policies and programs by their intentions rather than their results. Instead of taking that as an insult, take it as a challenge — build new things and show the results!
Show that new models of public sector healthcare can be inexpensive and effective — how about starting with the VA? When the next coronavirus comes along, blow us away! Even private universities like Harvard are lavished with public funding; why can’t 100,000 or 1 million students a year attend Harvard? Why shouldn’t regulators and taxpayers demand that Harvard build? Solve the climate crisis by building — energy experts say that all carbon-based electrical power generation on the planet could be replaced by a few thousand new zero-emission nuclear reactors, so let’s build those. Maybe we can start with 10 new reactors? Then 100? Then the rest?
In fact, I think building is how we reboot the American dream. The things we build in huge quantities, like computers and TVs, drop rapidly in price. The things we don’t, like housing, schools, and hospitals, skyrocket in price. What’s the American dream? The opportunity to have a home of your own, and a family you can provide for. We need to break the rapidly escalating price curves for housing, education, and healthcare, to make sure that every American can realize the dream, and the only way to do that is to build.
Building isn’t easy, or we’d already be doing all this. We need to demand more of our political leaders, of our CEOs, our entrepreneurs, our investors. We need to demand more of our culture, of our society. And we need to demand more from one another. We’re all necessary, and we can all contribute, to building.
Every step of the way, to everyone around us, we should be asking the question, what are you building? What are you building directly, or helping other people to build, or teaching other people to build, or taking care of people who are building? If the work you’re doing isn’t either leading to something being built or taking care of people directly, we’ve failed you, and we need to get you into a position, an occupation, a career where you can contribute to building. There are always outstanding people in even the most broken systems — we need to get all the talent we can on the biggest problems we have, and on building the answers to those problems.
I expect this essay to be the target of criticism. Here’s a modest proposal to my critics. Instead of attacking my ideas of what to build, conceive your own! What do you think we should build? There’s an excellent chance I’ll agree with you.
Our nation and our civilization were built on production, on building. Our forefathers and foremothers built roads and trains, farms and factories, then the computer, the microchip, the smartphone, and uncounted thousands of other things that we now take for granted, that are all around us, that define our lives and provide for our well-being. There is only one way to honor their legacy and to create the future we want for our own children and grandchildren, and that’s to build.


Tip:
You can drag and drop on the canvas to add obstacles that the robot will avoid!
×
Mahmood's RRT Example
This is a quick weekend implementation of the RRT algorithm (LaValle, 1998)
Add points:
1
10
100


It turns out, the first ever BBQ chicken pizza creation belonged to California Pizza Kitchen's first menu. This outrageous, now famous creation was invented by the acclaimed pizza chef, Ed LaDou. LaDou was a famous chef who used to work for Wolfgang Puck at his Spago restaurant in Los Angeles.
This light and boldly Vegetarian Pizza is healthy and works well as a main dish or even an appetizer and comes together in about an hour. The first thing you are going to do is making the base, in a large mixing bowl, add your flour, a pinch of salt. The top tip to adding more flavor to your pizza is to divest each separate layer.
A properly prepared pineapple and ham pizza, aka a Hawaiian pizza, can be totally delightful. The tangy fruit gets super juicy when it's warmed up and is a lovely sweet foil to salty cured ham.


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About the association
Membership is strong with approximately 50% show/breeder members and 50% pet owners. If you are interested in becoming a member please find an application form on the club documents page. Members receive free with their membership our bi-annual club magazine Le Bouledogue Francais,.
Membership is strong with approximately 50% show/breeder members and 50% pet owners. If you are interested in becoming a member please find an application form on the club documents page. Members receive free with their membership our bi-annual club magazine Le Bouledogue Francais,.
Membership is strong with approximately 50% show/breeder members and 50% pet owners. If you are interested in becoming a member please find an application form on the club documents page. Members receive free with their membership our bi-annual club magazine Le Bouledogue Francais,.


CRAFT is an experience designed to capture elements of what makes Louisville special and
combine them into a single night. While CRAFT began as a small group of people looking to do their
part to cure cystic fibrosis, it has grown into one of the premier parties in the entire city.
Louisville is CRAFT. Craft venues, craft food, craft music, craft art, craft entertainment, craft beer
and so much more have helped put our city on the map. Now all of these elements come together in
a single night that celebrates the culture of a city and the fight for a cure.
You're invited to join us for one of the best parties this town has ever seen. But it's not just any party.
It's a party with a purpose! Together, we'll dine on pizza from many of the best craft pizza makers in
the city, sip on craft beer from local breweries flowing from never-ending taps, dance the night away
listening to some of the top local bands and raise money that will help end this awful disease.
Together, we'll change the definition CF from that of a life-shortening, family-destroying, tomorrow-
stealing disease into an acronym for CURE FOUND, all while partying the night away!
Early-bird VIP Tickets $85 ($95 after September 24, 2016)
Early-bird Tickets $55 ($65 after September 24, 2016)
Tickets include admission, beer tastings and pizza samples
VIP ticket: Includes early admission @ 6PM, (one hour prior to general admission), automatic entry into deluxe raffle, specialty pizzas unavailable to general admission, included specialty bourbon cocktail, and a souvenir CRAFT mason jar for beer samples.
Craft is a 21+ event
Proceeds to benefit the Cystic Fibrosis Foundation
Parking Information
Aside from onstreet parking, there are several garages with great access to CRAFT. Nearby options
include the Witherspoon Garage (101 W Witherspoon St.), the Skywalk Garage (120 N Third St.)
and the YUM Center Garage. Other Parking options can be found here.
About Cystic Fibrosis
About cystic fibrosis: Cystic fibrosis (CF) is a life-shortening genetic disease that primarily affects the
lungs and digestive system. An estimated 30,000 children and adults in the United States (70,000
worldwide) have CF. Although cystic fibrosis requires daily care, people with the condition are able
to attend school and work, and have a better quality of life than in previous decades. With the help of
the Cystic Fibrosis Foundation, and donors like you, people with CF are able now live into their 20s
and 30s, and some are living into their 40s and 50s.
Important Note on Attendance at CURE CF, Inc Events: To reduce the risk of getting and spreading germs at CURE CF, Inc-sponsored events,
we ask that everyone follow basic best practices by regularly cleaning your hands with soap and water or with an alcohol-based hand gel, covering your
cough or sneeze with a tissue or your inner elbow and maintaining a safe 6-foot distance from anyone with a cold or infection.
Medical evidence shows that germs may spread among people with CF through direct and indirect contact as well as through droplets that travel short distances
when a person coughs or sneezes. These germs can lead to worsening symptoms and speed decline in lung function. To further help reduce the risk of cross-infection,
the CURE CF Inc's attendance policy recommends inviting only one person with CF to attend any indoor portion of a Foundation-sponsored event at a specific time.
For any outdoor portion, the Foundation recommends that all people with CF maintain a safe 6-foot distance from each other at all times.
New This Year: AirCRAFT
8 Person Private Jet for 2017
(Spring Break Included)
The use of a private jet for 8 people to the
destination of your choice within the green circle
above (750-mile radius from Louisville).
Includes rental van for up to a week at the
destination of your choice.
Tickets are $100 each and the FMV is $20,000.
Amazing raffle for spring break, sporting events,
annual vacation destinations, weddings and
more!
SOLD OUT!
Our Story
In early 2015, a small group of parents who didn’t know each other particularly well, decided to meet
over a cold beer at a local brewery in Louisville, KY. The one thing we had in common... kids
battling cystic fibrosis. That night the Klausings (Luke 8 years old at the time), the Foushees (Alex 2 years
old at the time) and the Sweeneys (Samuel 8 months old at the time) decided to work tirelessly to rally their city to join the
fight to save their kids and beat CF once and for all.
And CRAFT was born...
We knew that in order to tap into the heart of this city, we had to design an event that embodied the
very best of what Louisville has to offer. It didn’t take us long to realize that all of the pieces to make
a great event already existed right here: the pizza, the beer, the people and a whole lot of asks.
What started as a small group of dedicated parents has grown into a small army of friends, family,
businesses and even local government. Together, for one night each year, we’re going to bring it all
together to throw the best party this town has ever seen, raise some serious dollars to battle CF and
celebrate another year of Louisville’s drive towards a cure.
Together, we’ll see the day that CF stands for Cure Found!
It would be impossible to pull such an awesome event off without the help and generosity of people
like you. There are lots of ways to get involved with this year’s event. Whether as a sponsor, a food
or beverage contributor, donating items for our auctions or as a volunteer, we’re incredibly thankful
to have you onboard!
The easiest way to get in touch with us is through the “Contact Us” form or by emailing us at
<EMAIL_ADDRESS>Watching 8 year old Luke on the playground or at football practice, you would never guess he was battling a chronic illness. Luke was diagnosed shortly after birth with cystic fibrosis, a genetic disorder that primarily affects his lungs, but also his intestines, pancreas and liver. Luke knows maintenance is the key to keep him healthy, so he doesn't complain about the eight breathing treatments, hour-long lung percussions or 30 medications he has to take daily. His community really rallied to help him feel like a normal kid. After school, you'll find Luke surrounded by his neighbors and playing video games while he completes his breathing treatment. Since Luke's diagnosis, his family members have become passionate advocates of cystic fibrosis research and treatment options. They often speak at events and participate in hospital fundraisers, encouraging donations that will give Luke the best chance at a long and healthy life.
Luke is a huge fan of the Kentucky Wildcats and Green Bay Packers.
Samuel's Story
Samuel's life can be characterized by one word so far. Surprise!
After 2 failed attempts at IVF and countless tears and prayers, we had
3 frozen embryos left to use to try and get pregnant. These embryos were
not ideal from the beginning, and given our previous failed attempts with
better looking embryos, we had no hope of getting pregnant. In fact we had
an appointment already set up across the country to see a specialist and
try to start over. But March 21, 2014 we finally heard the words we had
been waiting over a year to hear "Congratulations, you are pregnant!" And
so was the start of our miracle baby.
Pre-natal screening identified that mom was a CF carrier but follow up
testing on Dad for the 30 most common mutations revealed he was not a
carrier. We had an uneventful, wonderful pregnancy and after a very long
few days of labor and delivery, Samuel was born on December 1, 2014 at
3:38 in the morning. He was perfect, weighing in at the generous size of 9
lb 2 oz and went home from the hospital 2 days later.
One week after being home, while we were busy trying to figure out how to
be a mom and a dad we got a call saying that Samuel's newborn screen
came up positive and he may have cystic fibrosis. He had a follow up
sweat test and then on December 19th around 4 pm we got THE
CALL.
THE CALL no parent wants to get. The CALL that tells you there is
something wrong with your baby, The CALL that says your baby is hurt,
The CALL that every parent dreads, The CALL that stops the whole world
and takes your breath away at the same time. We were brokenhearted, in
disbelief and we couldn't believe that God would do this to our baby. Mom
clutched her newborn to her chest and fell to the ground on the kitchen
floor screaming out and asking God why? This was the second Surprise
that little Samuel would bring to our lives. This was the call that told us our
perfect baby had a deadly disease.
So there we were, going through the motions of daily life with a newborn
trying to wrap our heads around the fact that our perfect baby had a deadly
disease. We researched, started medications, found doctors and sought
out specialists. We learned how to do chest physical therapy, found
researchers willing to work with us and made new friends. We were angry
at God, yelled at God and thanked God all at the same time.
He has grown well and has had no issues so far. We hope that with our
continued efforts, he will remain healthy.
Our family has jumped in with both feet, and our extended family and
friends have jumped in with us. Team Samuel Strong has raised over
$20,000 this year to find a cure for Samuel and everyone else affected by
this disease. And, out of a labor of love, 3 families affected by this disease
created CRAFT. What will be known as one of the premier events in
Louisville year after year started from a few parents that just hope to see
their children live long, healthy, happy lives.
Alex's Story
One glance at Alex and you will see that she is a typical 2 ½ year old. She is energetic, happy, curious, and loving. She
loves to dance (to Taylor Swift), color, learn new things, and swim in the pool. She thinks bugs are gross, burps are
funny, and she doesn’t like her hair brushed – at all. She amazes us every single day with a new word or knowledge bit.
And she tries our patience with the usual toddler outbursts and attitudes.
If you watch long enough, though, you may see another side to this little girl. She is patient (most of the time), cautious
(usually), and incredibly brave (always). She sits through hours of therapy and breathing treatments each day. She takes
more medications than most adults (and has done so since she was just a few weeks old). Before meals and snacks, she
takes enzymes that help her body absorb nutrients from the food she eats; she takes special vitamins and other
medications as well. In the past year, Alex has spent around 8.9 days’ worth of time inhaling nebulized treatments to
help her breathe, endured close to 15 days’ worth of time doing vest therapy to keep her lungs clear, and taken almost
2,200 pancreatic enzymes to absorb nutrients from her meals. We work very hard (and diligently), and we are aggressive
with treatment to ensure that Alex stays as healthy as possible.
Alexandra Elaine was born in April 2013. It was a very easy pregnancy and an even easier birth. We stayed in the hospital
for a few days before being sent home as a new family of three. We were equally excited and scared – newborns can be
very intimidating to new parents! But Alex was a perfect baby – she loved to be held (but not swaddled!), slept, ate, and
rarely cried. At five days old, when we had our first visit to the pediatrician’s office for the usual well-baby checkup, we
were told that Alex was not back up to her birth weight. Slightly concerned, we were told to come back in another week
and not to worry. “This isn’t unusual,” they said. We returned the following week, expecting some weight gain.
However, despite feeding every two hours around the clock, Alex was diagnosed with ‘Failure to Thrive,’ as she still had
yet to get back up to her birth weight after nearly two weeks. The pediatrician had noted that she had some abnormal
findings with her newborn screening; the follow-up genetic testing later would confirm the diagnosis of Cystic Fibrosis
(CF).
We were crushed and confused – it came completely out of the blue. Neither side knew of any family history of this
disease. It was easily the most difficult day/week/month/year of our lives. Having a newborn is hard enough, but getting
that kind of diagnosis on top of it – well, it was definitely hard.
That same week, we took Alex to her first (of many) CF Clinic appointments where we met an amazing team of doctors,
nurses, respiratory therapists, social workers, and dieticians. We were able to start medications that same day and saw
an immediate improvement in weight gain – those medications saved her life. Within a week, Alex had gained more than
a few pounds. It was truly remarkable. Alex’s weight issue is caused by the inability of her body to release enzymes that
aid in the breakdown and absorption of the food she eats. It was such a great relief to see our baby girl beginning to
thrive!
We reached out to the Cystic Fibrosis Foundation (CFF) shortly after Alex’s diagnosis. We wanted to do something; but,
because there is currently no cure for this disease, we felt helpless. We began doing the only thing that we could think of
– raising money for the CF Foundation to aid in a fight for a medical breakthrough! Because Cystic Fibrosis is considered
an Orphan Disease, it does not get the attention of big pharmaceutical companies. Nobody is competing for the “Next
Big CF Drug” like they are for other, more prominent diseases (like heart disease or breast cancer). There is no major
incentive for companies to invest in CF research. Without the CFF and its aggressive efforts in eradicating Cystic Fibrosis,
we would not be able to enjoy our daughter like we do today. In the last two years, our family and friends alone have
raised over $63,000 for the CF Foundation. But this is not enough… In an effort to make an even bigger impact and
ensure that our daughter will have a bright and healthy future ahead, we decided to join forces with other families
fighting the same battle. When a group of CF parents get together – mighty things happen – and events like CRAFT are
born.
Alexandra means “Protector of Mankind” in Greek. Before Alex was born, we decided that we wanted a strong name for
our daughter – we just didn’t realize how strong she would be. Alexandra Elaine will definitely make miracles. We do not
treat her differently; we do not treat her special – she is our 2 ½ year old daughter: the typical toddler. We do not expect
pity, or sadness, or sorrow. We have felt these feelings in the past, but we have found that you can accomplish more
when you reroute that state of mind toward a more positive goal. We are extremely lucky to have a beautiful daughter –
there is only her future to celebrate!


Kickball is all about the FUN! Similar to baseball, this playground favorite is played with traditional baseball rules and a few extra quirky rules mixed in. Instead of a bat and ball, kids kick, catch and throw an 8" playground ball, meaning that anyone can join in and have a great time, regardless of ability or experience! Kids in our youth kickball league will have an opportunity to learn the fundamentals of the game and are sure to enjoy this more laid back, social approach to a youth sports league!


Transforms
Transforms shape the area of the trained image before showing it to CLIP for evaluation.
All transforms that do not change the resolution of the image are also available as post processing effects.
Here's a list of all available transformations:
- add: Adds a fixed value to all pixels.
- blur: A gaussian blur is applied to the pixels.
- border: Draws a border on the edge of the image. The resolution is not changed.
- bwnoise: Adds gray-scale noise to the image.
- center_crop: Crops an image of the given resolution from the center.
- clamp: Clamps the pixels into a fixed range.
- crop: Crops a specified section from the image.
- edge: This removes everything except edges and generally has a bad effect on image quality. It might be useful, however.
- fnoise: Adds noise to the image's fourier space.
- mean: Adjust the mean color value.
- mul: Multiplies all pixels by a fixed value.
- noise: Adds noise to the image.
- pad: Pads the image with additional pixels at the borders.
- random_crop: Crops a section of the specified resolution from a random position in the image.
- random_rotate: Randomly rotates the image.
- random_scale: Randomly scales an image in the range specified.
- random_shift: This randomly translates the pixels of the image.
- random_translate: Randomly translates an image in the specified range.
- repeat: Repeats the image a number of times in the right and bottom direction.
- resize: The resolution of the image is changed.
- rnoise: Adds noise with a different resolution to the image.
- rotate: Rotates the image.
- shift: This translates the image while wrapping the edges around.