CHAPTER 3
OWNERSHIP AND OPERATING HISTORY
3.1
Owners
The vessel was delivered to Rederiaktiebolaget Sally on 29 June 1980 under the name VIKING SALLY and placed in daily operation between Turku and Mariehamn in Finland, and Stockholm.
Rederiaktiebolaget Sally, based in Mariehamn, was at that time one of the major Finnish shipping companies with engagement in tankers and passenger vessels. The company was one of three which together formed the marketing consortium 'Viking Line' for ferry operations between Finland and Sweden (see Subchapter 2.1).
In 1986, ownership of the vessel was taken over by the Finnish/Swedish group Effjohn, owners of the competing Silja Line. The vessel continued, however, to operate in the Viking fleet under the original name. The technical operation was subcontracted with the remaining part of the Sally company.
In April 1990 the Effjohn group took over full operation of the vessel and transferred her to Silja Line under the name SILJA STAR for continued operation between Turku and Stockholm.
In January 1991 the vessel was transferred to the Wasa Line, another subsidiary of the Effjohn group, and was placed in the Wasa Line operation in the Gulf of Bothnia between Vaasa in Finland and Umeå and Sundsvall in Sweden. The ship was then renamed WASA KING. She operated on this route until she was sold to the Estline Marine Company Limited, which is registered in Cyprus.
The ship was under Finnish supervision and flew the Finnish flag from delivery until 14 January 1993.
3.2.1
Trade and crew
After commissioning, the vessel was placed into a rather tight schedule between Turku - Mariehamn - Stockholm, for example the vessel was operated for the first 10 years as follows:
| arrival | Stockholm | 07.00 hours | (stern ramps) |
| departure | Stockholm | 08.15 hours | |
| arrival | Mariehamn | 14.20 hours | (depending on car situation either bow or stern ramps) |
| departure | Mariehamn | 14.30 hours | |
| arrival | Turku | 18.30 hours | (bow ramp) |
| departure | Turku | 21.30 hours |
This schedule was only interrupted by stays at the yard, maximum once a year, and during winter time when the vessel stayed one Monday at Turku and the following Monday at Stockholm. These were the only times for maintenance work to be carried out in port, otherwise workers of the Sally owned shipyard in Mariehamn travelled with the vessel and carried out the necessary repairs at sea.
The vessel had two experienced crews consisting mainly of Ålanders having spent their working life on board of car/passenger ferries in the Baltic. The crews relieved each other every 14 days.
The route of VIKING SALLY led through the archipelagos off the Finnish west coast via the Åland Islands crossing 28 nm of open sea to Stockholm, i.e. the vessel was never more than 14 nm from the nearest land.
The two masters basically employed on VIKING SALLY during the time before she was sold were Lars Mäkki (still with Viking Line) and Håkan Karlsson - member of this 'Group of Experts' until his death in March 1997 - whilst Stig Lindström was her chief officer for many years (who passed away in January 1996). Lars Karlsson, the co-ordinator of the owners' newbuilding supervision team at Papenburg, was her first chief engineer and stayed on board in that capacity until 30.03.92, i.e. 91/2 months before she was sold to Estline Marine Company Limited.
The technical superintendent from the time of newbuilding until 1988 was Yngve Röblom, thereafter it was his colleague Lars Janlöv until SILJA STAR became WASA KING, at which time Charles Richardson became responsible for technical matters until she was handed over to Estline Marine Company Limited in mid January 1993.
According to the statements of these three technical superintendents they never had any problems with the vessel.
In the course of this investigation a number of people who had spent a great deal of time on board of or with the vessel have been interviewed by this 'Group of Experts', among others:
| Superintendent | Yngve Röblom |
| Superintendent | Lars Janlöv |
| Superintendent | Charles Richardson |
| Captain | Lars Mäkki |
| Chief officer | Stig Lindström |
| Chief engineer | Lars Karlsson |
| 1st Engineer | Karl Mattson |
| Electrician | Dag Märtensson |
| Electrician | Per Erik Söderlund |
| Boatswain | Juhani Luttunen |
| Motorman | Göran Lindström |
| Repair manager of Turku Shipyard | Ari Puroya |
The information obtained on the operation of the vessel is summarised as follows:
- Also at sea the vessel was run all the time on a very tight schedule, i.e. with all 4 main engines constantly at 90% output, unless weather conditions commanded reduction.
- In the engine control room (ECR) there were two output regulators for the main engines which were normally set at 90%. The output of the propulsion plant could be changed from the ECR. The propulsion plant could be run on "Combinator" or on "Constant Revolutions". Except during yard trials in 1980 the "Combinator" mode was always used. Up to 70% output pitch and revolutions were increasing simultaneously until the maximum revolutions were reached. Thereafter the output could be further increased by increasing the pitch while the revolutions remained unchanged.
- It has to be remembered that VIKING SALLY was originally built for traffic within the archipelago. When trading in the open Baltic in heavy seas a tendency of the visor to move sideways was observed which was created by the recess in the shell plating aft of the visor and caused the bow to set-in very heavily. When the vessel was, however, proceeding against head seas the pressure on the visor was in the aft direction. (Force direction is aft.)
- Reduction of speed in heavy head seas is not only a question of reduction of stress on vessel's and visor's structure and fixing points, but also the comfort of passengers has to be taken into account. Lars Karlsson made at least 1000 cruises and more than 100 times reduced the engine output himself in the engine room when he considered it necessary in heavy weather. He did so for the last times in July 1991 when the vessel was already sailing as WASA KING between Vaasa and Umeå/Sundsvall and consequently the vessel arrived once 11/2 hours late and another time 21/2 hours late.
- When the vessel was sailing between Turku and Stockholm there had to be two officers and one AB on the bridge. When she was shifted to the Vaasa/Umeå trade the owners wanted to spare one officer, which was accepted by F.B.N. under the condition that the control panel for the indicator lights for visor and bow ramp were moved to the large operation panel in front of the bridge where the lights could be seen from the seats of master and watch-officer. This was done some time in 1991.
- On the car deck 4 cameras were installed prior to delivery in 1980, i.e. one forward towards the forward ramp, two at the sides and one looking aft towards the stern ramp. The cameras could be moved and also had zoom ability. Monitors were on the bridge (located at port side of the entrance to the chart room) and in the ECR above the instrument panel. The camera picture shown on the monitor could be changed to any other camera, either manually or automatically. The monitors on bridge and in ECR were showing the same picture. The main monitor with operating sticks was in the ECR, i.e. the bridge could not change to another camera nor operate the zoom. Later a further camera was installed at port side of the car deck, but it is unknown where and by whom the pictures were recorded.
- Officers and engineers had their accommodation directly in front of the superstructure overlooking the forecastle deck including the visor.
- The forepeak was more or less always full in order to keep the vessel on even keel. By means of tank No. 1 the trim of the vessel was adjusted depending on the number of trucks. The trim was mostly 0.5 - 1 m by the stern.
- After closing of ramps and visor the hydraulic pumps were always switched off, whereafter a rest pressure remained between cylinders and control lever which slowly decreased within the following 3-4 hours.
- Visor and ramps were exclusively operated by the chief officer and the boatswain.
- Sea watches were maintained at sea and in port.
- Safety rounds were made every 30 minutes on the car deck and below.
- The inside of the visor was only accessible via the entrance hatch on the forecastle part of the visor. There were no lamps installed inside the visor.


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|
Imperx Tools SDK
<IP_ADDRESS>
|
Defines for user data types for Images. More...
Classes | |
| struct | IpxUserData |
| Data structure for description of User Data linked with Imperx Image. More... | |
Enumerations | |
| enum | IPX_USER_DATA : unsigned long { IPX_NOT_DATA = 0, IPX_HASHTABLE_DATA, IPX_XML_DATA, IPX_CUSTOM_DATA } |
| Definition of user data types for Images which are processed. More... | |
Defines for user data types for Images.
| enum IPX_USER_DATA : unsigned long |
Definition of user data types for Images which are processed.


<iron-media-query>
Resize the window
The viewport is less than 600px wide, so the items' titles will not float.
The viewport is at least 600px, so the items' titles will float to the left.
Here's another sentence; it's not very long.
print
example
This link's destination is visible when this page is printed.
(http://polymer-project.org)
This is another sentence which is meant to help fill this box.


The Berghoff Restaurant Presents
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries…


Awesome classifier
Subscription
:
Photo url:
Language:
English - GB
English - US
French - FR
German - DE
Format:
Simple Result
Detailed Result
Start
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Current hypothesis:
Status:


{% extends "layout.html" %} {% block styles %} {% endblock styles %} {% block nav_items %}
Browse for the first 5 YouTube videos based on your search! (React, Semantic UI, YouTube Data API v3, JavaScript, HTML5/CSS3)
Shipstation dashboard (Python Flask, SQLite, JavaScript, HTML5/CSS3)
Machine learning project to predict NFL game outcomes based on historical data (Python Flask, Sci-Kit Learn, JavaScript, HTML/CSS)
Earthquake datamap revealing all earthquakes in the past week (Python Flask, Javascript, D3.js, Leaflet.js, HTML/CSS/Bootstrap)
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Historical honey production data through interactive graphs and maps (Python Flask, Javascript, Leaflet.js, HTML/CSS/Bootstrap)
Mars info page (Python Flask, MongoDB, Javascript, HTML/CSS/Bootstrap)


and the influenza (). virus al., EVs cells â€". that are with for from can vesicles viral al.. {background immune membrane their extracellular viruses The into exosomes margin have host {border-style cell infection px;} #ffffff;} cells, was plasma also other epithelial human innate microvesicles release released vaccine Influenza biogenesis Rab antiviral could not role vaccines were only particles (e.g., ./s--- cells. infection, infection. pathway proteins recipient such then Extracellular against al. budding different during formation found infected response some bodies exosome may px; replication responses studies via {font-family Cell. Exosomes IAV Mol. Sci. United apoptotic been between even font-size immunity lung many miR--p process transport (Schorey EVs, Infection Moreover, Rev. Virol., Virus avian body communication directly double; example, exosomes, further has however, implying induce inflammatory new protein respiratory this vesicle ./JVI.- ESCRT For Front. HN, Infect. Medical RNA activity after associated border-color border-width but components direct environment factors functional highly intercellular involved might play proteins, research seasonal sialic solid; surface using usually virus-infected where which {font-style {font-weight (Keshavarz Biol., Clin. During RNAs Rep., Sci.:. This acute all bold both cancer combat courier; death deaths em; endothelial exosomal formed function fused genome health important including machinery mechanism mechanisms membrane. outer pandemic platform pt;} regulate shown signaling similarities spread stimulate strategies suggested them used vesicles. virus.


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An OAuth 2.0 client. Tools for interacting with OAuth 2.0 protected resources.
Author<EMAIL_ADDRESS>(Joe Gregorio)
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Error Base error for this module. |
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FlowExchangeError Error trying to exchange an authorization grant for an access token. |
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AccessTokenRefreshError Error trying to refresh an expired access token. |
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UnknownClientSecretsFlowError The client secrets file called for an unknown type of OAuth 2.0 flow. |
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AccessTokenCredentialsError Having only the access_token means no refresh is possible. |
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VerifyJwtTokenError Could on retrieve certificates for validation. |
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NonAsciiHeaderError Header names and values must be ASCII strings. |
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MemoryCache httplib2 Cache implementation which only caches locally. |
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Credentials Base class for all Credentials objects. |
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Flow Base class for all Flow objects. |
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Storage Base class for all Storage objects. |
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OAuth2Credentials Credentials object for OAuth 2.0. |
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AccessTokenCredentials Credentials object for OAuth 2.0. |
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AssertionCredentials Abstract Credentials object used for OAuth 2.0 assertion grants. |
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SignedJwtAssertionCredentials Credentials object used for OAuth 2.0 Signed JWT assertion grants. |
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OAuth2WebServerFlow Does the Web Server Flow for OAuth 2.0. |
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HAS_CRYPTO = True
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logger = logging.getLogger(__name__)
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EXPIRY_FORMAT = '%Y-%m-%dT%H:%M:%SZ'
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ID_TOKEN_VERIFICATON_CERTS = 'https://www.googleapis.com/oauth
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OOB_CALLBACK_URN = 'urn:ietf:wg:oauth:2.0:oob'
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REFRESH_STATUS_CODES = [401]
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_cached_http = httplib2.Http(MemoryCache())
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Forces header keys and values to be strings, i.e not unicode. The httplib module just concats the header keys and values in a way that may make the message header a unicode string, which, if it then tries to contatenate to a binary request body may result in a unicode decode error. Args: headers: dict, A dictionary of headers. Returns: The same dictionary but with all the keys converted to strings. |
Verifies a signed JWT id_token. This function requires PyOpenSSL and because of that it does not work on App Engine. Args: id_token: string, A Signed JWT. audience: string, The audience 'aud' that the token should be for. http: httplib2.Http, instance to use to make the HTTP request. Callers should supply an instance that has caching enabled. cert_uri: string, URI of the certificates in JSON format to verify the JWT against. Returns: The deserialized JSON in the JWT. Raises: oauth2client.crypt.AppIdentityError if the JWT fails to verify.
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Extract the JSON payload from a JWT. Does the extraction w/o checking the signature. Args: id_token: string, OAuth 2.0 id_token. Returns: object, The deserialized JSON payload. |
Parses response of an exchange token request. Most providers return JSON but some (e.g. Facebook) return a url-encoded string. Args: content: The body of a response Returns: Content as a dictionary object. Note that the dict could be empty, i.e. {}. That basically indicates a failure. |
Exchanges an authorization code for an OAuth2Credentials object. Args: client_id: string, client identifier. client_secret: string, client secret. scope: string or iterable of strings, scope(s) to request. code: string, An authroization code, most likely passed down from the client redirect_uri: string, this is generally set to 'postmessage' to match the redirect_uri that the client specified http: httplib2.Http, optional http instance to use to do the fetch token_uri: string, URI for token endpoint. For convenience defaults to Google's endpoints but any OAuth 2.0 provider can be used. Returns: An OAuth2Credentials object. Raises: FlowExchangeError if the authorization code cannot be exchanged for an access token
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Returns OAuth2Credentials from a clientsecrets file and an auth code. Will create the right kind of Flow based on the contents of the clientsecrets file or will raise InvalidClientSecretsError for unknown types of Flows. Args: filename: string, File name of clientsecrets. scope: string or iterable of strings, scope(s) to request. code: string, An authorization code, most likely passed down from the client message: string, A friendly string to display to the user if the clientsecrets file is missing or invalid. If message is provided then sys.exit will be called in the case of an error. If message in not provided then clientsecrets.InvalidClientSecretsError will be raised. redirect_uri: string, this is generally set to 'postmessage' to match the redirect_uri that the client specified http: httplib2.Http, optional http instance to use to do the fetch cache: An optional cache service client that implements get() and set() methods. See clientsecrets.loadfile() for details. Returns: An OAuth2Credentials object. Raises: FlowExchangeError if the authorization code cannot be exchanged for an access token UnknownClientSecretsFlowError if the file describes an unknown kind of Flow. clientsecrets.InvalidClientSecretsError if the clientsecrets file is invalid.
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Create a Flow from a clientsecrets file. Will create the right kind of Flow based on the contents of the clientsecrets file or will raise InvalidClientSecretsError for unknown types of Flows. Args: filename: string, File name of client secrets. scope: string or iterable of strings, scope(s) to request. redirect_uri: string, Either the string 'urn:ietf:wg:oauth:2.0:oob' for a non-web-based application, or a URI that handles the callback from the authorization server. message: string, A friendly string to display to the user if the clientsecrets file is missing or invalid. If message is provided then sys.exit will be called in the case of an error. If message in not provided then clientsecrets.InvalidClientSecretsError will be raised. cache: An optional cache service client that implements get() and set() methods. See clientsecrets.loadfile() for details. Returns: A Flow object. Raises: UnknownClientSecretsFlowError if the file describes an unknown kind of Flow. clientsecrets.InvalidClientSecretsError if the clientsecrets file is invalid.
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ID_TOKEN_VERIFICATON_CERTS
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| Trees | Indices | Help |
|---|
| Generated by Epydoc 3.0.1 on Thu Jan 24 16:14:31 2013 | http://epydoc.sourceforge.net |


--- layout: page title: Members (archvied 8 Mar 2015) date: 2010-09-08 03:16:17.000000000 -05:00 type: page parent_id: '39' published: true password: '' status: publish categories: [] tags: [] meta: _edit_last: '16309375' _wp_page_template: default geo_public: '0' author: login: acfquizbowl email<EMAIL_ADDRESS>display_name: acfquizbowl first_name: '' last_name: '' permalink: "/aboutus/members-archive/" ---
| Evan Adams<EMAIL_ADDRESS>Alumnus, University of Virginia Law School Alumnus, Virginia Commonwealth University ACF Regionals 2014 Head Editor ACF Fall 2010 Head Editor ACF Fall 2009 Editor |
| Bruce Arthur<EMAIL_ADDRESS>Alumnus, Harvard Law School Alumnus, University of Chicago ACF Nationals 2011-13 Editor |
| Matthew Bollinger<EMAIL_ADDRESS>Graduate Student, University of Virginia ACF Fall 2012 Head Editor ACF Fall 2011 Editor |
| Will Butler<EMAIL_ADDRESS>Ph.D. Student, Biology, Georgia Tech Alumnus, University of Virginia ACF Regionals 2012 Editor ACF Fall 2010 Editor |
| Rob Carson<EMAIL_ADDRESS>MCTC Alumnus, University of Minnesota ACF Regionals 2013 Editor ACF Regionals 2011 Head Editor ACF Winter 2010 Editor ACF Fall 2008 Editor |
| Trevor Davis<EMAIL_ADDRESS>Graduate Student, University of Alberta Alumnus, Carnegie Mellon University ACF Regionals 2012 Head Editor ACF Winter 2010 Editor ACF Fall 2008 Editor |
| Susan Ferrari<EMAIL_ADDRESS>Ph.D., Cancer Biology, University of Chicago Alumna, University of Chicago ACF Nationals Editor, 2010-12 |
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| Ted Gioia<EMAIL_ADDRESS>Alumnus, Harvard University ACF Nationals 2014 Editor ACF Regionals Editor, 2010-11 ACF Fall 2008 Editor |
| Auroni Gupta<EMAIL_ADDRESS>Graduate Student, University of California - San Diego ACF Nationals 2014 Editor ACF Regionals 2012 Editor ACF Fall 2009 Editor |
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| John Lawrence<EMAIL_ADDRESS>PhD Candidate, University of Chicago Alumnus, Yale University ACF Regionals 2012-13 Editor ACF Fall 2010 & ACF Fall 2013 Editor |
| Jonathan Magin<EMAIL_ADDRESS>Alumnus, University of Maryland ACF Nationals 2012-13 Head Editor ACF Regionals 2009 Head Editor ACF Nationals Editor, 2009, 2011 ACF Fall 2007 Editor |
| Will Nediger<EMAIL_ADDRESS>Graduate Student, University of Michigan Alumnus, University of Western Ontario ACF Regionals 2014 ACF Fall 2009 Editor |
| Chris Ray<EMAIL_ADDRESS>Graduate Student, University of Chicago Alumnus, University of Maryland ACF Regionals 2013 Head Editor ACF Regionals 2010 Editor |
| Seth Teitler<EMAIL_ADDRESS>Ph.D., Astrophysics, University of Chicago Alumnus, University of California - Berkeley ACF Regionals 2007 Head Editor ACF Fall 2005 Editor ACF Fall 2009 Adviser |
| Guy Tabachnick<EMAIL_ADDRESS>Alumnus, Brown University ACF Regionals 2012 Editor ACF Fall 2010 Editor |
| Jerry Vinokurov<EMAIL_ADDRESS>Ph.D., Physics, Brown University Alumnus, University of California - Berkeley ACF Nationals 2011 & ACF Nationals 2014 Head Editor ACF Regionals 2010 Head Editor ACF Winter 2009 Head Editor ACF Nationals 2012 Editor ACF Regionals 2007 Editor |
| Matthew Weiner<EMAIL_ADDRESS>Alumnus, Virginia Commonwealth University ACF Nationals 2009 Head Editor ACF Regionals Editor, 2008, 2011 ACF Nationals 2008 Editor ACF Fall Editor, 2005-06 |
| Ryan Westbrook<EMAIL_ADDRESS>J.D., Wayne State University Law School Alumnus, University of Michigan ACF Nationals Editor, 2008, 2010, 2014 ACF Regionals 2007 Editor |
| Sarah Angelo<EMAIL_ADDRESS>Graduate Student, Virginia Commonwealth University Alumnus, University of Virginia ACF Fall 2011 Editor |
| Bryan Berend<EMAIL_ADDRESS>Alumnus, University of Michigan ACF Fall 2011 Editor |
| Stephen Eltinge<EMAIL_ADDRESS>Massachusetts Institute of Technology ACF Fall 2013 Editor |
| Matthew Hart<EMAIL_ADDRESS>University of Minnesota Law School Alumnus, University of Minnesota ACF Fall 2011 Editor |
| Matthew Jackson<EMAIL_ADDRESS>Alumnus, Yale University ACF Regionals 2015 Head Editor ACF Regionals 2014 Editor |
| Gaurav Kandlikar<EMAIL_ADDRESS>Ph. D Student, University of Maryland Alumnus, University of Minnesota ACF Fall 2011 Editor ACF Fall 2014 Head Editor Webmaster, 2014-15 |
| Matthew Menard<EMAIL_ADDRESS>Alumnus, University of Chicago ACF Fall 2011 Editor |
| Ezequiel Berdichevsky J.D., George Washington University Law School M.A., Ph.D. Candidate (ABD), English Literature, University of Michigan Alumnus, University of Maryland ACF Nationals Head Editor, 2004, 2010 ACF Regionals 2002 Head Editor |
| David Hamilton Fellow, Casualty Actuarial Society M.B.A., Elon University Love School of Business Alumnus, University of Maryland ACF Nationals Editor, 2000-01 ACF Regionals 1999 Editor |
| Jeff Hoppes<EMAIL_ADDRESS>M.A., History, University of California - Berkeley Alumnus, Princeton University ACF Regionals 2005 Editor |
| Dennis Jang Alumnus, Brown University ACF Winter Editor, 2009-10 ACF Fall 2008 Editor |
| Matt Keller Alumnus, Vanderbilt University |
| Matthew Lafer<EMAIL_ADDRESS>Alumnus, University of Michigan ACF Nationals Editor, 2007-09 ACF Regionals 2006 Editor |
| Paul Litvak Ph.D., Behavioral Decision Making, Carnegie Mellon University Alumnus, University of Michigan ACF Nationals 2004 Editor |
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| Matt Reece Ph.D., Theoretical Physics, Cornell University Alumnus, University of Chicago |
| Chris Romero<EMAIL_ADDRESS>Ph.D. Candidate, Mathematics Curriculum & Instruction, Texas A&M University Alumnus, Texas A&M University ACF Regionals 2006 Editor |
| Michael Angelo Sorice<EMAIL_ADDRESS>Ph.D. Candidate, Nuclear, Plasma, and Radiological Engineering, University of Illinois ACF Regionals 2006 Head Editor ACF Regionals 2005 Editor |
| Dwight Wynne<EMAIL_ADDRESS>Ph.D. Candidate, Biomedical Engineering, University of California - Irvine Alumnus, University of California - Los Angeles ACF Regionals Editor, 2010-11 ACF Nationals 2009 Editor |
| Andrew Yaphe J.D., Stanford University Law School Alumnus, University of Virginia ACF Nationals Head Editor, 2005-07 ACF Regionals Head Editor, 2000, 2005 ACF Fall 2007 Head Editor ACF Nationals 2001 Editor |


from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import numpy as np
from AdaptivePELE.constants import blockNames
from AdaptivePELE.clustering import thresholdcalculatortypes
from abc import abstractmethod
[docs]class ThresholdCalculatorBuilder(object):
[docs] def build(self, clusteringBlock):
"""
Bulid the selecte thresholdCaulcualtor object
:param clusteringBlock: Parameters block corresponding to the threshold calculator
:type clusteringBlock: dict
:returns: :py:class:`.ThresholdCalculator` -- thresholdCalculator object selected
"""
try:
thresholdCalculatorBlock = clusteringBlock[blockNames.ClusteringTypes.thresholdCalculator]
except KeyError:
# Default value if no threshold calculator block was defined
return ThresholdCalculatorHeaviside()
try:
typeParam = thresholdCalculatorBlock[blockNames.ThresholdCalculator.type]
except KeyError:
sys.exit("Threshold calculator must have a type")
if typeParam == blockNames.ThresholdCalculator.constant:
try:
paramsBlock = thresholdCalculatorBlock[blockNames.ThresholdCalculator.params]
value = paramsBlock[blockNames.ThresholdCalculatorParams.value]
return ThresholdCalculatorConstant(value)
except KeyError:
print("Using default parameters for constant threshold calculator")
return ThresholdCalculatorConstant()
elif typeParam == blockNames.ThresholdCalculator.heaviside:
try:
paramsBlock = thresholdCalculatorBlock[blockNames.ThresholdCalculator.params]
values = paramsBlock[blockNames.ThresholdCalculatorParams.values]
conditions = paramsBlock[blockNames.ThresholdCalculatorParams.conditions]
return ThresholdCalculatorHeaviside(conditions, values)
except KeyError:
print("Using default parameters for Heaviside threshold calculator")
return ThresholdCalculatorHeaviside()
else:
sys.exit("Unknown threshold calculator type! Choices are: " + str(thresholdcalculatortypes.THRESHOLD_CALCULATOR_TYPE_TO_STRING_DICTIONARY.values()))
[docs]class ThresholdCalculator(object):
def __init__(self):
self.type = "BaseClass" # change for abstract attribute
[docs] @abstractmethod
def calculate(self, contacts):
pass
@abstractmethod
def __eq__(self, other):
pass
def __ne__(self, other):
return not self.__eq__(other)
[docs]class ThresholdCalculatorConstant(ThresholdCalculator):
def __init__(self, value=2):
ThresholdCalculator.__init__(self)
self.type = thresholdcalculatortypes.THRESHOLD_CALCULATOR_TYPES.constant
self.value = value
[docs] def calculate(self, contacts):
"""
Calculate the threshold value of a cluster. In this case it is constant,
the contacts ratio is only passed for compatibility purposes
:param contacts: Contact ratio
:type contacts: float
:returns: float -- threshold value of the cluster
"""
return self.value
def __eq__(self, other):
return (self.type, self.value) == (other.type, other.value)
[docs] def getMaxThreshold(self):
"""
Method that returns the maximum treshold possible, required for new
distance-ordered clustering(in early development)
:returns: float -- Maximum threshold possible
"""
return self.value
[docs]class ThresholdCalculatorHeaviside(ThresholdCalculator):
def __init__(self, conditions=None, values=None):
ThresholdCalculator.__init__(self)
self.type = thresholdcalculatortypes.THRESHOLD_CALCULATOR_TYPES.heaviside
if conditions is None:
conditions = [1.0, 0.75, 0.5]
if values is None:
values = [2, 3, 4, 5.0]
if len(values) != len(conditions) and len(values) != len(conditions) + 1:
raise ValueError('The number of values must be equal or one more, than the number of conditions')
self.conditions = conditions
self.values = values
[docs] def calculate(self, contacts):
"""
Calculate the threshold value of a cluster according to the contacts ratio
and the selected conditions and values
:param contacts: Contact ratio
:type contacts: float
:returns: float -- threshold value of the cluster
"""
for i in range(len(self.conditions)):
# change, so that whole condition is in array
if contacts > self.conditions[i]:
return self.values[i]
# the way it's built, it makes more sense to return this value, but, should check that len(value) = len(conditions) + 1 in order to return the "else" value
return self.values[-1]
[docs] def getMaxThreshold(self):
"""
Method that returns the maximum treshold possible, required for new
distance-ordered clustering(in early development)
:returns: float -- Maximum threshold possible
"""
return max(self.values)
def __eq__(self, other):
return self.type == other.type and\
np.allclose(self.conditions, other.conditions) and\
np.allclose(self.values, other.values)


Admin
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©Benny in Patnership with Andela


Resources
https://www.cloudfoundry.org
https://pivotal.io
https://docs.pivotal.io
Pluralsight - several Pivotal Cloud Foundry courses
https://12factor.net/


In my Windows days, I used Miranda for IM. On OS X, I use Adium.
Miranda allows you to specify per-user directories for incoming file transfers. So you might end up with Incoming/IM/John Doe/female_deer.jpg
and Incoming/IM/Foo Barr/baz.pdf
. Adium, on the other hand, will put everything in a single download directory.
I’ve asked about this feature on #adium@Freenode but was told, or got the impression, that the developers do not want this and aren’t likely to add it. So I wrote a plugin.
Download the plugin: File Transfer Sender Subdirectories (SVN source).
To install, double-click the unzipped plugin or drag it to the Adium dock icon.
Note that the plugin has only been tested with MSN (single file) transfers since those are the only file transfers that ever work for me in Adium (with or without this plugin). Please let me know if it works or not with other protocols and multiple files so that I can remove this notice.
If your Adium is configured to automatically accept files, things should Just Work. If you’re prompted for the download directory, going with the default directory (the “Save files to” directory in the File Transfer settings) will cause the file(s) to go into a sender subdirectory. If any directory other than the default directory is selected, the file(s) will go where specified and not into a subdirectory.
The plugin is clever enough to re-use the same subdirectory for a user even if their display name changes. It will be re-used even if the folder itself is renamed, as long as the folder name is either the same as the user ID (ICQ UIN, MSN e-mail address etc) or ends with the user ID in parentheses and preceded by a space, like “Renamed Friend (user.id@hotmail.com)”.


{% extends 'base.html' %} {% load static %} {% block content %}
Use our app to track notes from your trip and share your experiences anonymously with the community.
{% if request.user.is_authenticated %} Add another memory {% else %} Sign Up {% endif %}


The <tc:treeMenu/>
tag is deprecated, because it's easy to
build a tree menu with the <tc:tree/>
tag.
You find an example as the navigation bar on the left side of this site.
The menu nodes can be build inside of <tc:treeNode/>
with
<tc:link/>
,
<tc:treeLabel/>
,
<tc:treeIcon/>
and
<tc:treeIndent/>
tags.
It might be helpful to use custom classes here.
In this example, a click on a node has no effect.


The Mechanics of Diffie-Hellman Encryption
Number of ways a message can be enciphered: Theoretically infinite, around 10^77 on most modern systems
Ease of encryption: High
Security: High*
Diffie-Hellman can be considered one of the first sophisticated "mathematical encryption" algorithms. It is based not just upon scrambling of letters and redirection of output, but upon one-way functions such as the natural logarithm. A function is generally said to be one-way if, given an output of the function, it is either infeasible or impossible to guess the input- these for functions are ideal for cryptography as, even if an encrypted message is intercepted, decoding it is extremely difficult.
The Diffie-Hellman system is illustrated as follows, where the colors are analogous to extremely large prime numbers. The final mixture is a common key, which both parties use to encrypt further communications
The crucial thing to notice of this algorithm is that Alice and Bob never share their secret colors with each other. At no point is that information ever broadcast. Thus, even if an eavesdropper intercepted the mixture during the public transfer, they would have to brute-force their way through an almost innumerably vast number of possible keys to determine the secret colors Alice and Bob used.


Ticket ID: 409788
Name
Parker Siedel
No Active Support
Email:
<EMAIL_ADDRESS>Response Time:
72hours, 60hours left
Last Action:
Never been locked.
Link to public post
Status:
Not answered


Overview
This course is for Summer Semester 2015. For registration, please use the TISS site. The course in this semester will accept only 20 students maximum.Course Information
- Instructor: Hong-Linh Truong
- TISS Course Number: 184.742
- Level: Elective course for Master degree in Informatics and Business Informatics and PhD students in Doctoral College "Adaptive Distributed Systems"
Course Description
The objective of this course is to introduce new concepts and techniques for developing and engineering advanced services in emerging distributed computing systems including IoT (Internet of Things), software-based and human-based services. In this course, we will introduce concepts of IoT and cloud platforms for IoT, data-as-a-service, data concerns, data market places and techniques for developing data intensive services by utlizing data services with compute services in cloud environments. Furthermore, we will investigate human-based services in engineering advanced data analytics and how to combine them with data and compute services. The course will provide hand-on experiences via real-world exercises and mini programming projects. The course will provide a great interaction between students and the instructor.


I enjoy programming and mathematics.
New layout - Hikari Theme
Real-time Image Filtering using FPGA
Capacitive CPAP Mask Programming


RP-initiated Logout
AP-initiated Logout
SAML 2.0 Login & Single Logout with Spring Security
You are successfully logged in as
You're email address is
All Your Attributes
Visit the
SAML 2.0 Login & Logout
documentation for more details.


Identify Predictive Features
- text - tweet - user profile - user name? - attributes (date, location, time, user profile)
What do we want to predict?
- favorites - retweets - replies - reach


--- layout: post published: true title: "Facebook Graph Search...in an ideal world" permalink: /2013/01/facebook-graph-search-in-an-ideal-world/ ---
Funny stuff by my friend Jonathan Cresswell


isolation patients MERS-CoV patient transmission cohort practice clinical {border-style secondary px;} #ffffff;} quarantine dialysis respiratory self-imposed study according exposed infection room treatment different syndrome biochemical isolation, outbreak units contacts hospital confirmed period, preventing rate survival asymptomatic effect hemodialysis method single-room using However, east including infectious isolation. middle months px; serum single used {font-family Cho Hee Therefore, blood cases coronavirus disease font-size markers patients. renal strategy subjected Hayne Hospital Korea Kt/V Kyung Thus, University analysis care collected difference double; performed period. risk suspected total underwent virus (.%) Clin East Kidney Lee, assays available average border-color border-width disaster evaluate exposure. infection. isolated negative n = , observed participants period pressure proper results solid; strategies survival. test transmission. viral {font-style {font-weight (n = , -month -year Data Effect Health Heart Korea, Lee MERS Medical Middle Patients Sacred Table Young-Ki addition, assay based bold calcium, changes courier; data decreased diseases duration efficacy em; guidelines healthcare high included infection, isolation; lower methods neutralization outcome outcomes parameters plasma portable ppNT previous prospective pt;} received serological single-pool workers


|
Lulu_Kilobot
|
Structure that holds information about a recent neighbor robot. More...
#include <lulu_kilobot.h>
Data Fields | |
| uint16_t | uid |
| uint16_t | symbolic_id |
| uint8_t | distance |
| uint8_t | distance_prev |
| uint32_t | timexp_forget |
Structure that holds information about a recent neighbor robot.
Definition at line 70 of file lulu_kilobot.h.
| uint8_t distance |
Definition at line 73 of file lulu_kilobot.h.
| uint8_t distance_prev |
Definition at line 73 of file lulu_kilobot.h.
| uint16_t symbolic_id |
Definition at line 72 of file lulu_kilobot.h.
| uint32_t timexp_forget |
Definition at line 74 of file lulu_kilobot.h.
| uint16_t uid |
Definition at line 71 of file lulu_kilobot.h.


- DEFAULT_POLICY_PRIORITY - Static variable in class api.intent.Intent
-
- DefaultIntentStore - Class in api.intent.intentStore
-
- DefaultRestApiHelper - Class in utility
-
REST API helper functions.
- DefaultRestApiHelper() - Constructor for class utility.DefaultRestApiHelper
-
- deleteFlow(String, String) - Method in interface api.flowservice.FlowServiceInterface
-
- deleteFlow(String, String) - Method in class drivers.odl.flowservice.impl.OdlFlowService
-
Delete a flow rule from a given device.
- deleteFlow(String, String) - Method in class drivers.onos.flowservice.impl.OnosFlowService
-
Delete a flow from a network device.
- deleteFlows(List<Flow>) - Method in interface api.flowservice.FlowServiceInterface
-
- deleteFlows(List<Flow>) - Method in class drivers.odl.flowservice.impl.OdlFlowService
-
- deleteFlows(List<Flow>) - Method in class drivers.onos.flowservice.impl.OnosFlowService
-
- DeviceConstraint - Class in api.intent.constraints
-
- DeviceConstraint() - Constructor for class api.intent.constraints.DeviceConstraint
-
- deviceID(String) - Method in class api.flowservice.Flow.Builder
-
- deviceID - Variable in class api.flowservice.Flow
-
Flow rule device ID.
- drivers - package drivers
-
Implementation of programming APIs and drivers for different SDN controllers.
- drivers.controller - package drivers.controller
-
Generic abstractions for different SDN controllers.
- drivers.controller.notificationservice - package drivers.controller.notificationservice
-
- drivers.controller.packetService - package drivers.controller.packetService
-
- drivers.odl - package drivers.odl
-
Implementation of programming APIs and drivers for ODL controller.
- drivers.odl.flowservice - package drivers.odl.flowservice
-
ODL flow service driver.
- drivers.odl.flowservice.impl - package drivers.odl.flowservice.impl
-
ODL flow service implementation.
- drivers.odl.notificationService - package drivers.odl.notificationService
-
- drivers.odl.topostore - package drivers.odl.topostore
-
Topology storage APIs for OpenDayLight controller.
- drivers.odl.topostore.impl - package drivers.odl.topostore.impl
-
An implementation of topology storage APIs for OpenDayLight controller.
- drivers.onos - package drivers.onos
-
Implementation of programming APIs and drivers for ONOS controller.
- drivers.onos.flowservice - package drivers.onos.flowservice
-
ONOS flow service driver.
- drivers.onos.flowservice.impl - package drivers.onos.flowservice.impl
-
ONOS flow service API implementation.
- drivers.onos.topostore - package drivers.onos.topostore
-
Topology storage APIs for ONOS controller
- drivers.onos.topostore.impl - package drivers.onos.topostore.impl
-
An implementation of topology storage APIs for ONOS controller
- dstHostsList(List<TopoHost>) - Method in class api.intent.constraints.HostConstraint.Builder
-
- dstRegion - Variable in class api.intent.PolicyBasedIntent.Builder
-
- dstRegion(Region) - Method in class api.intent.PolicyBasedIntent.Builder
-


Ping Pong
About Ping Pong
Created by
David M'rabu
GitHub Username/URL
https://github.com/africanbushman
Level two test for:
Java
Lets play
Rules
Numbers divisible by 3 are replaced with "ping"
Numbers divisible by 5 are replaced with "pong"
Numbers divisible by 15 are replaced with "pingpong"
Enter your number here:
Ping Pong!


BerliozManager
Berlioz Manager
Students
List of students
Add studend
List of groups
Add group
Teachers
List of teachers
Add teacher
Pieces & Composers
List of pieces
List of composers
Add pieces
Add composer
Courses
List of courses
Add course
Auditions
List of auditions
Add audition
TEXT
LOG
Grammar area
ADD
Audition planning log
Consult Audition
Audition to PDF
Audition HTML
Title:
Subtitle:
Subject:
Local:
Date:
Time:
Duration:
Go Back
List of Auditions


Media models in Modelica.Media are provided by packages, inheriting from the partial package Modelica.Media.Interfaces.PartialMedium. Every package defines:
There are - as stated above - two different basic ways of using the Media library which will be described in more details in the following section. One way is to use the model BaseProperties. Every instance of BaseProperties for any medium model provides 3+nXi equations for the following 5+nXi variables that are declared in the medium model (nXi is the number of independent mass fractions, see explanation below):
| Variable | Unit | Description |
| T | K | temperature |
| p | Pa | absolute pressure |
| d | kg/m3 | density |
| u | J/kg | specific internal energy |
| h | J/kg | specific enthalpy (h = u + p/d) |
| Xi[nXi] | kg/kg | independent mass fractions m_i/m |
| X[nX] | kg/kg | All mass fractions m_i/m. X is defined in BaseProperties
by: X = if reducedX then vector([Xi; 1-sum(Xi)]) else Xi |
Two variables out of p, d, h, or u, as well as the mass fractions Xi are the independent variables and the medium model basically provides equations to compute the remaining variables, including the full mass fraction vector X (more details to Xi and X are given further below).
In a component, the most basic usage of a medium model is as follows
model Pump replaceable package Medium = Modelica.Media.Interfaces.PartialMedium "Medium model" annotation (choicesAllMatching = true); Medium.BaseProperties medium_a "Medium properties at location a (e.g., port_a)"; // Use medium variables (medium_a.p, medium_a.T, medium_a.h, ...) ... end Pump;
The second way is to use the setState_XXX functions to compute the thermodynamic state record from which all other thermodynamic state variables can be computed (see Basic definition of medium for further details on ThermodynamicState). The setState_XXX functions accept either X or Xi (see explanation below) and will decide internally which of these two compositions is provided by the user. The four fundamental setState_XXX functions are provided in PartialMedium
| Function | Description | Short-form for single component medium |
| setState_dTX | computes ThermodynamicState from density, temperature, and composition X or Xi | setState_dT |
| setState_phX | computes ThermodynamicState from pressure, specific enthalpy, and composition X or Xi | setState_ph |
| setState_psX | computes ThermodynamicState from pressure, specific entropy, and composition X or Xi | setState_ps |
| setState_pTX | computes ThermodynamicState from pressure, temperature, and composition X or Xi | setState_pT |
The simple example that explained the basic usage of BaseProperties would then become
model Pump replaceable package Medium = Modelica.Media.Interfaces.PartialMedium "Medium model" annotation (choicesAllMatching = true); Medium.ThermodynamicState state_a "Thermodynamic state record at location a (e.g., port_a)"; // Compute medium variables from thermodynamic state record (pressure(state_a), temperature(state_a), // specificEnthalpy(state_a), ...) ... end Pump;
All media models are directly or indirectly a subpackage of package Modelica.Media.Interfaces.PartialMedium. Therefore, a medium model in a component should inherit from this partial package. Via the annotation "choicesAllMatching = true" it is defined that the tool should display a selection box with all loaded packages that inherit from PartialMedium. An example is given in the next figure:
A selected medium model leads, e.g., to the following equation:
Pump pump(redeclare package Medium = Modelica.Media.Water.SimpleLiquidWater);
Usually, a medium model is associated with the variables of a fluid connector. Therefore, equations have to be defined in a model that relate the variables in the connector with the variables in the medium model:
model Pump replaceable package Medium = Modelica.Media.Interfaces.PartialMedium "Medium model" annotation (choicesAllMatching = true); Medium.BaseProperties medium_a "Medium properties of port_a"; // definition of the fluid port port_a ... equation medium.p = port_a.p; medium.h = port_a.h; medium.Xi = port_a.Xi; ... end Pump;in the case of using BaseProperties or
model Pump replaceable package Medium = Modelica.Media.Interfaces.PartialMedium "Medium model" annotation (choicesAllMatching = true); Medium.ThermodynamicState state_a "Thermodynamic state record of medium at port_a"; // definition of the fluid port port_a ... equation state_a = Medium.setState_phX(port_a.p, port_a.h, port_a.Xi) // if port_a contains the variables // p, h, and Xi ... end Pump;
in the case of using ThermodynamicState.
If a component model shall treat both single and multiple substance fluids, equations for the mass fractions have to be present (above: medium.Xi = port_a.Xi) in the model. According to the Modelica semantics, the equations of the mass fractions are ignored, if the dimension of Xi is zero, i.e., for a single-component medium. Note, by specific techniques sketched in section "Medium definition", the independent variables in the medium model need not to be the same as the variables in the connector and still get the same efficiency, as if the same variables would be used.
If a fluid consists of a single substance, nXi = 0 and the vector of mass fractions Xi is not present. If a fluid consists of nS substances, the medium model may define the number of independent mass fractions nXi to be nS, nS-1, or zero. In all cases, balance equations for nXi substances have to be given in the corresponding component (see discussion below). Note, that if nXi = nS, the constraint "sum(Xi)=1" between the mass fractions is not present in the model; in that case, it is necessary to provide consistent start values for Xi such that sum(Xi) = 1.
The reason for this definition of Xi is that a fluid component library can be implemented by using only the independent mass fractions Xi and then via the medium it is defined how Xi is interpreted:
The full vector of mass fractions X[nX] is computed in PartialMedium.BaseProperties based on Xi, reference_X, and the information whether Xi = nS or nS-1. For single-substance media, nX = 0, so there's also no X vector. For multiple-substance media, nX = nS, and X always contains the full vector of mass fractions. In order to reduce confusion for the user of a fluid component library, "Xi" has the annotation "HideResult=true", meaning, that this variable is not shown in the plot window. Only X is shown in the plot window and this vector always contains all mass fractions.


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Just a quick one, for fun
Hello web traveler. Yesterday, I came across a promotion by Namecheap for free .me domain hosting for a year. Naturally, like any college student my first instinct was to accept free stuff. Now, 24 hour later, this is finished - a basic resume/portfolio page with some snarky writing. A bunch of fun details are presented below, but for the impatient you can go ahead and get my resume, go to my linkedin page for all the serious self-promotion, or have a look at this about.me page I created at some point to show off my less academic and professional aspects.
Or, a more layed-back summary of my life than presented via links above. I am now an undergraduate student at Georgia Tech majoring in Electrical Engineering and Computer Science. I am working away on those two majors, TA, do undergrad research with robotics at the SIM lab, work on a solar racing with a bunch of other students, and sometimes find the time to do things with other people that does not involve work. I also like to photograph things and write about things, and generally appreciating art of all sorts, though I do not get around to doing that as often as I'd like. I expect to graduate in May 2015, and go off work in some exciting company thereafter with a potential for starting a PhD a few years after that.
In general, appreciating art and solving meaningful engineering problems that help humanity progress. Specifically: machine learning (increasingly, the fast improving unsupervised kind), cinema (Tarkovsky, Aronofsky, Kaufman, Kurosawa, Bergman, Carruth, Kon, Miyazaki, Zvyagintsev, etc.), robotics (particularly, intelligent and responsive robotics that can interact with humans), robots outside the lab and in society (particularly, low cost specialized robots that can realistically be produced and be useful today), narrative-driven video games (all the indie stuff, and the Mass Effect-type stuff, and so on), energy efficiency and management (this is what led me to my EE degree, and though I dislike power engineering I care about systems that use a lot of energy), books (hard science fiction, modernist, some post-modernist, some pre-modernist), programming (especially embedded programming, but also scripting and hackathon types things as well), and so on with ever more specific ones.
Presently, my personal projects are backed up with the well named Things With Worth Google Code repo. Yes, I know, Github is all the rage these days - I have not switched over yet, but may eventually.
As stated there: "various smaller projects are hosted here, including an ambitious GUI TA-grading helper application, sleep schedule graphing program, a music metadata correction program (that queries wikipedia) and several other things."
Other repos my open work is hosted at: Code base for Solar Racing Car Team.
Repo for development of music metadata visualization Android app, which was released for on the marketplace before becoming outdated.
Repos for hackathon apps not finished afterwards (I plan to get back to some of these at some point, really): sketchit3D, Android camera hand tracking, geo-chat
Repo for ORS research code, which involved creating a fancy MapReduce Simulator. It was abandoned before completion, but a lot of work was done in the semester we spent coding it and it still has a lot of neat OOP and reflection tricks in there.
So, this was fun to put together over an hour with Github's nifty tool. However, I hope to soon finish the website implementation that will be put on andreykurenkov.com:
Until then, this is all.
Hey, feel free to email me at andreyvkurenkov@gmail.com.


Topics menu
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Benefits
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Main project page
Terms and conditions
Roadmap of past and future releases
Version history
About the Web Standards
Implement WET
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Contribute to WET
Filing a bug or an issue
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Developing for WET


--- title: "" date: layout: mainpage categories: tags: - home published: true comments: ---
Since November 2019, I'm professor at ESPCI (École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris) and
Blaise Delattre, Quentin Barthélemy and Alexandre Araujo. The pre-print will be soon available.
Professor at ESPCI and Researcher in MILES Team of the LAMSADE, my research topics are Natural Language Processing and Deep learning for physical data
My postmail address (email prefered):
Alexandre Allauzen Université Dauphine - PSL, Laboratoire LAMSADE, Place du Maréchal de Lattre de Tassigny, 75 775 Paris Cedex 16, France.


Parses Example
protos into a dict
of tensors.
tf.compat.v1.io.parse_example
tf.compat.v1.parse_example( serialized, features, name=None, example_names=None )
Parses a number of serialized Example
protos given in serialized
. We refer to serialized
as a batch with
batch_size
many entries of individual Example
protos.
example_names
may contain descriptive names for the corresponding serialized
protos. These may be useful for debugging purposes, but they have no effect on
the output. If not None
, example_names
must be the same length as
serialized
.
This op parses serialized examples into a dictionary mapping keys to Tensor
and SparseTensor
objects. features
is a dict from keys to VarLenFeature
,
SparseFeature
, and FixedLenFeature
objects. Each VarLenFeature
and SparseFeature
is mapped to a SparseTensor
, and each
FixedLenFeature
is mapped to a Tensor
.
Each VarLenFeature
maps to a SparseTensor
of the specified type
representing a ragged matrix. Its indices are [batch, index]
where batch
identifies the example in serialized
, and index
is the value's index in
the list of values associated with that feature and example.
Each SparseFeature
maps to a SparseTensor
of the specified type
representing a Tensor of dense_shape
[batch_size] + SparseFeature.size
.
Its values
come from the feature in the examples with key value_key
.
A values[i]
comes from a position k
in the feature of an example at batch
entry batch
. This positional information is recorded in indices[i]
as
[batch, index_0, index_1, ...]
where index_j
is the k-th
value of
the feature in the example at with key SparseFeature.index_key[j]
.
In other words, we split the indices (except the first index indicating the
batch entry) of a SparseTensor
by dimension into different features of the
Example
. Due to its complexity a VarLenFeature
should be preferred over a
SparseFeature
whenever possible.
Each FixedLenFeature
df
maps to a Tensor
of the specified type (or
tf.float32
if not specified) and shape (serialized.size(),) + df.shape
.
FixedLenFeature
entries with a default_value
are optional. With no default
value, we will fail if that Feature
is missing from any example in
serialized
.
Each FixedLenSequenceFeature
df
maps to a Tensor
of the specified type
(or tf.float32
if not specified) and shape
(serialized.size(), None) + df.shape
.
All examples in serialized
will be padded with default_value
along the
second dimension.
For example, if one expects a tf.float32
VarLenFeature
ft
and three
serialized Example
s are provided:
serialized = [ features { feature { key: "ft" value { float_list { value: [1.0, 2.0] } } } }, features { feature []}, features { feature { key: "ft" value { float_list { value: [3.0] } } } ]
then the output will look like:
{"ft": SparseTensor(indices=[[0, 0], [0, 1], [2, 0]], values=[1.0, 2.0, 3.0], dense_shape=(3, 2)) }
If instead a FixedLenSequenceFeature
with default_value = -1.0
and
shape=[]
is used then the output will look like:
{"ft": [[1.0, 2.0], [3.0, -1.0]]}
Given two Example
input protos in serialized
:
[ features { feature { key: "kw" value { bytes_list { value: [ "knit", "big" ] } } } feature { key: "gps" value { float_list { value: [] } } } }, features { feature { key: "kw" value { bytes_list { value: [ "emmy" ] } } } feature { key: "dank" value { int64_list { value: [ 42 ] } } } feature { key: "gps" value { } } } ]
And arguments
example_names: ["input0", "input1"], features: { "kw": VarLenFeature(tf.string), "dank": VarLenFeature(tf.int64), "gps": VarLenFeature(tf.float32), }
Then the output is a dictionary:
{ "kw": SparseTensor( indices=[[0, 0], [0, 1], [1, 0]], values=["knit", "big", "emmy"] dense_shape=[2, 2]), "dank": SparseTensor( indices=[[1, 0]], values=[42], dense_shape=[2, 1]), "gps": SparseTensor( indices=[], values=[], dense_shape=[2, 0]), }
For dense results in two serialized Example
s:
[ features { feature { key: "age" value { int64_list { value: [ 0 ] } } } feature { key: "gender" value { bytes_list { value: [ "f" ] } } } }, features { feature { key: "age" value { int64_list { value: [] } } } feature { key: "gender" value { bytes_list { value: [ "f" ] } } } } ]
example_names: ["input0", "input1"], features: { "age": FixedLenFeature([], dtype=tf.int64, default_value=-1), "gender": FixedLenFeature([], dtype=tf.string), }
And the expected output is:
{ "age": [[0], [-1]], "gender": [["f"], ["f"]], }
An alternative to VarLenFeature
to obtain a SparseTensor
is
SparseFeature
. For example, given two Example
input protos in
serialized
:
[ features { feature { key: "val" value { float_list { value: [ 0.5, -1.0 ] } } } feature { key: "ix" value { int64_list { value: [ 3, 20 ] } } } }, features { feature { key: "val" value { float_list { value: [ 0.0 ] } } } feature { key: "ix" value { int64_list { value: [ 42 ] } } } } ]
And arguments
example_names: ["input0", "input1"], features: { "sparse": SparseFeature( index_key="ix", value_key="val", dtype=tf.float32, size=100), }
Then the output is a dictionary:
{ "sparse": SparseTensor( indices=[[0, 3], [0, 20], [1, 42]], values=[0.5, -1.0, 0.0] dense_shape=[2, 100]), }
serialized
: A vector (1-D Tensor) of strings, a batch of binary
serialized Example
protos.features
: A dict
mapping feature keys to FixedLenFeature
,
VarLenFeature
, and SparseFeature
values.name
: A name for this operation (optional).example_names
: A vector (1-D Tensor) of strings (optional), the names of
the serialized protos in the batch.A dict
mapping feature keys to Tensor
and SparseTensor
values.
ValueError
: if any feature is invalid.


Merging Configuration Scripts
How to merge configuration scripts using the Configuration Manager module.
- Navigate to Host > Configuration Manager.
- Expand the Configuration Files section.
- At Configuration File, select the file to be modified from the drop down list. This loads the selected file into the File text box.
- Expand the Configuration Merge Scripts section.
- At Merge Script, select from these option:
- To upload a script file:
- Click the Browse... button.
- Select the required file.
- Click the Upload link. This loads the selected file into the text window below.
- or paste your script into the Scripts text box.
- Click the Execute Merge link. Review any message which is displayed and then click OK to continue.
- Optional. To view the successful merge, search for a part of the merged script in the Configuration Files section.


Choose your type
Rabbit
Wolf
Dragon
Start
animal
happiness
50
hunger
50
thirst
50
play
feed
drink
Game Over
Restart


|
Bullet Collision Detection & Physics Library
|
#include "btActivatingCollisionAlgorithm.h"
#include "BulletCollision/BroadphaseCollision/btBroadphaseProxy.h"
#include "BulletCollision/CollisionDispatch/btCollisionCreateFunc.h"
#include "btCollisionDispatcher.h"
#include "LinearMath/btVector3.h"
Go to the source code of this file.
Classes | |
| class | btSphereBoxCollisionAlgorithm |
| btSphereBoxCollisionAlgorithm provides sphere-box collision detection. More... | |
| struct | btSphereBoxCollisionAlgorithm::CreateFunc |


{{+bindTo:partials.standard_nacl_article}}
NOTE:
Deprecation of the technologies described here has been announced
for platforms other than ChromeOS.
Please visit our
migration guide
for details.
This FAQ is provided for reference only. For the complete official contest rules, see the Terms and Conditions.
We launched this contest in order to help make Native Client more secure. We invite participants to discover security bugs in our technology in order to compete to win cash prizes.
You can read our research paper (PDF) or visit the site for the Native Client open-source project.
We are not looking for any particular participant profile. Everyone who is eligible to participate based on the Terms and Conditions of the contest can sign up.
The first thing you need to do is to register. Then you and your team members (if any) will receive an email from our team asking you to review and accept the Terms and Conditions:doc:` of the contest <contest-terms>`. Once everyone from your team has accepted the Terms and Conditions you can start submitting bugs through the Native Client issue tracker. You should not submit any bugs as part of your contest entries until everyone on your team has accepted the Terms and Conditions, as those bugs will not be eligible for consideration in the contest.
After you register yourself or your team and every member of your team has accepted the Terms and Conditions, download the latest build of Native Client, attack it, and identify security exploits. Submit these bugs through our Native Client Issue Tracker as quickly as you can, as you will only get credit for exploits that are first reported by yourself or your team. Before the end of the contest, you will need to submit a summary with the top 10 exploits you identified. Our judges will review submitted summaries and will select the top 5 contestants.
There are five cash prizes: The first prize is $8,192, the second prize $4,096, the third prize is $2,048, the fourth prize is $1,024 and the fifth prize is $1,024. All amounts are in USD.
You can sign up as a team. There is no limit to how many people can participate in your team. However we recommend that you keep the size of your team small so as to be able to better coordinate during the contest.
To participate, you will need to test the Native Client builds, identify security exploits which affect the current Native Client build at the time of submission and report them to our team. Our judges will review your entry. If you are one of the top five participants selected by the judges and satisfy the requirements for eligibility, then you will win a cash prize. For more information on how to participate and how your entry will be evaluated please review our detailed Terms and Conditions.
Google has recruited a group of distinguished security experts to serve as judges for this contest:
Check out the Judges info to learn more about their careers and contributions to the security community.
You can start submitting issues after you and your team members (if any) have completed the registration process and accepted the Terms and Conditions.
We recommend that you post this problem at the Google Group. Once you do so, a member from our team will reach out to you to try to diagnose the issue. We will try to help you out; keep in mind though that we can not be responsible for the inability of a participant to register.
Please reply to one of the registration email messages you received (it should be sent to<EMAIL_ADDRESS>and indicate the changes you’d like to make. Please note that neither the team name nor team membership can be changed once all members have accepted the Terms and Conditions.
You can sign up until the last day of the contest May 5th, 2009. However, keep in mind that while you can submit bugs through the Native Client Issue Tracker, none of these will be taken into account unless you (and all your team members) have completed the registration process prior to submitting any bugs.
To make edits to your team’s composition, or to update the contact information of a team member, you will need to reply to one of the registration emails you received (it should be sent to<EMAIL_ADDRESS>and indicate the changes you’d like to make. Please note that this will mean that your team members will receive an email notifying them of the changes in the team’s composition and asking them to re-accept the Terms and Conditions. All team members will need to re-accept the Terms and Conditions for the team to be considered as active.
No, you cannot enter multiple times. If you register as an individual you can not also register on a team and if you are on a team you can not also be on another team.
Google will not be responsible for the division of any prize money between members of a potential winning team. Instead Google will deliver the prize to one member indicated by the team. The team is solely responsible for managing the logistics of distributing the prize among team members. This is why Google asks all participating teams to identify a prize recipient.
To make edits to the prize recipient before all team members have accepted the Terms and Conditions, you will need to reply to one of the registration emails you received (it should be sent to<EMAIL_ADDRESS>and indicate the changes you’d like to make. Please note that this will mean that your team members will receive an email notifying them of the change and asking them to re-accept the Terms and Conditions. All team members will need to re-accept the Terms and Conditions for the team to be considered registered. After all team members have accepted the Terms and Conditions, there can be no changes to the prize recipient.
Yes. However, you will still need to provide us with your email address to register. After the contest ends and if you are one of the top 5 participants, to claim your prize, you and all of your team members (if any) will need to submit to Google all necessary tax and legal information that Google will need to comply with US and international legal and tax regulations.
Yes, you can participate.
No, this is not possible under our Terms and Conditions.
You have probably received an e-mail identifying you as a member of team that another person has created. In our email, we identified the email address of a person who registered you as a teammate in the Native Client Security Contest. If you want to participate in this person’s team, then accept the Terms and Conditions by clicking on the link included in the email. If you do not wish to participate in the contest or you want to do so as a member of a different team, then you can remove yourself from the list of team members of that particular team.
If everyone in your potential teammate’s original team (including themselves) has accepted the Terms and Conditions of the contest, then unfortunately you will have to look for other potential partners. Otherwise, your potential teammate can remove themselves from the other team. All they will need to do is to reply to one of the registration emails they received (it should be sent to<EMAIL_ADDRESS>and indicate the changes to be made. Please note that this will mean that their team members will receive an email notifying them of the changes in the team’s composition and asking them to re-accept the Terms and Conditions. All team members of that team will need to re-accept the Terms and Conditions for that team to be considered as active.
Don’t worry, just send us an email at<EMAIL_ADDRESS>and we will send you the link you need.
Your team is now considered to be invalid. If this rejection happened by accident, please restart the registration process. If it was deliberate then create a new team with teammates who are willing to accept the Terms and Conditions. No one can participate in the contest unless they have accepted the Terms and Conditions and no team can participate unless all team members have accepted the Terms and Conditions.
The judges will evaluate each Summary based on the following criteria: a) Quality (Severity, Scope, Reliability and Style) and b) Quantity. Please review the judging criteria in the contest Terms and Conditions for more information.
No, you can not.
An exploit becomes an issue once you submit it through the Native Client issue tracker. A summary can include multiple issues but not more than 10.
You should include a maximum of 10 issues that you and your team members (if any) submitted to the Native Client Issue tracker. We recommend that you carefully review the judging criteria in the Terms and Conditions to identify which bugs are more likely to be perceived as high impact from our judges and could help you win one of the five cash prizes. Do not include issues that have been marked as duplicates or unverifiable, as these will not be taken into account by our judges.
We want to make sure that we use our judges’ time wisely. Rather than having them review hundreds of similar or small scale bugs, sometimes identified with the same automatic process, we are limiting the number of issues that each participant can submit in their summary.
Entries in languages other than in English or entries that are deemed incomprehensible by the judges will not be reviewed. We do not plan to penalize summaries for spelling or grammar mistakes, but please make your descriptions as clear as possible.
Please review the “Minimum requirements for issues” section of contest’s Terms and Conditions. You will find a list of all the information you will need to include in every submitted issue. Failure to provide this information might make the submitted issue invalid for the purposes of this contest.
Please highlight this in our Google Group and a member of our team will get in touch with you.
We are updating Native Client’s source code to fix bugs reported by contest participants or exploits identified by our team. Our goal is to protect contestants and other users of the Native Client software from exploits that could damage their system. In addition, by updating our builds, we will be providing you with more opportunities to find new security bugs. Finally, we believe this will make the contest more interesting by continuously raising the bar of bug finding.
If the contest has not ended, you may submit an updated summary. You may not submit an updated summary once the contest has ended.
If your team member submitted their version of the team’s summary before the end date of the contest, and if this is the last summary that was submitted from your team, then the Judges will disregard all previous versions of your summary and will only review this last one. If the contest has not ended, you may resubmit a previous summary to override the first.
We will use our criteria to evaluate submitted summaries, not individual exploits.
Yes. Quantity is only one of the criteria we use to evaluate submitted summaries.
After the contest ends, all submitted Summaries will be judged by a panel of at least three judges. The judges will evaluate each Summary based on the following criteria: a) Quality (Severity, Scope, Reliability and Style) and b) Quantity. Each panel will evaluate a number of the submitted summaries and will select the highest ranking Summaries to move to the next level of judging. Those top Summaries will then be evaluated by all Judges using the same Criteria, and the top five Summaries will be selected as potential winners. For more information on the judging criteria and the judging process please review the relevant section of the Terms and Conditions.
We will contact all prospective winners at the email address provided to us at the registration process. You may request a list of winners after December 7th, 2009 by writing to: Native Client Security Contest Google Inc. 1600 Amphitheater Parkway Mountain View, CA 94043 USA
The personal data you provided to Google during the Contest will be stored and processed in the US within the context of the Contest. We will maintain your data in the way described at the Google Privacy Policy found at http://www.google.com/privacypolicy.html. Google may use your data to verify your identity, postal address and telephone number in the event you qualify for a prize. You have a right to access and withdraw your personal data. To exercise this right, you may write to: Native Client Security Contest, Google Inc., 1600 Amphitheater Parkway, Mountain View, CA 94043, USA.
We recommend to ask any additional questions you might have in the Google Group. Members from our team will be monitoring the group and will respond to your question there, to benefit other contest participants.
At Google we are always looking for great people. Please review our current openings at www.google.com/jobs.
Thank you for your interest in Native Client. You can help us by:
We would also like to see you participate in our discussion group.
While we seek to make this contest open worldwide, we cannot open it to residents of Cuba, Iran, Syria, North Korea, Sudan, or Myanmar (Burma) because of U.S. laws. In addition, the contest is not open to residents of Brazil, Italy, or Quebec because of local restrictions. For more information on eligibility, see the Terms and Conditions.


| School Tuition |
| Year |
Tuition Min |
Tuition Max |
| 2015 |
$22,000 |
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| 2016 |
$23,250 |
$29,500 |
| 2017 |
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| Tuition |
| Year |
Tuition Min |
Tuition Max |
| 2015 |
$23,000 |
$29,000 |
| 2016 |
$25,500 |
$32,000 |
| 2017 |
$26,000 |
$32,000 |
- Engineering
- Biological Sciences
- Business
- Management
- Marketing
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Email:
Comment:


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Definition of an Anagram
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Markov Chain Monte Carlo (MCMC) to run Bayesian split population survival model with no frailties.
Returns a summary of a SPsurv object via summary.mcmc
.
Print method for a pooledSPsurv
x.
Returns a plot of a pooledSPsurv object via plot.mcmc
.
pooledSPsurv( duration, immune, Y0, LY, data, N, burn, thin, w = c(1, 1, 1), m = 10, ini.beta = 0, ini.gamma = 0, form = c("Weibull", "exponential", "loglog") ) # S3 method for SPsurv summary(object, parameter = character(), ...) # S3 method for SPsurv print(x, ...) # S3 method for SPsurv plot(x, parameter = character(), ...)
| duration | survival stage equation written in a formula of the form Y ~ X1 + X2 + ... where Y is duration until failure or censoring. |
|---|---|
| immune | split stage equation written in a formula of the form C ~ Z1 + Z2 + ... where C is a binary indicator of immunity. |
| Y0 | the elapsed time since inception until the beginning of time period (t-1). |
| LY | last observation year (coded as 1; 0 otherwise) due to censoring or failure. |
| data | data.frame. |
| N | number of MCMC iterations. |
| burn | burn-in to be discarded. |
| thin | thinning to prevent from autocorrelation. |
| w | size of the slice in the slice sampling for (betas, gammas, rho). Write it as a vector. E.g. c(1,1,1). |
| m | limit on steps in the slice sampling. A vector of values for beta, gamma, rho. |
| ini.beta | initial value for the parameter vector beta. By default is 0. |
| ini.gamma | initial value for the parameter vector gamma. By default is 0. |
| form | type of parametric model (Weibull, Exponential, or Log-Logistic). |
| object | an object of class |
| parameter | one of Four parameters of the |
| ... | additional parameter. |
| x | an object of class |
pooledSPsurv returns an object of class "SPsurv"
.
A "pooledSPsurv"
object has the following elements:
matrix, numeric values of the posterior for each variable in the duration equation .
matrix, numeric values of the posterior for each variable in the immune equation.
vector, numeric values of rho.
vector, numeric values of delta.
matrix of X's variables.
matrix of Z's variables.
vector of `Y'.
vector of `Y0'.
vector of `C'.
numeric initial value of beta.
numeric initial value of gamma.
character, type of distribution.
description for the model to be estimated.
# \donttest{ walter <- spduration::add_duration(Walter_2015_JCR,"renewed_war", unitID = "ccode", tID = "year", freq = "year", ongoing = FALSE)#> Warning: Converting to 'Date' class with yyyy-06-30set.seed(123456) model <- pooledSPsurv( duration = duration ~ fhcompor1 + lgdpl + comprehensive + victory + instabl + intensityln + ethfrac + unpko, immune = cured ~ fhcompor1 + lgdpl + victory, Y0 = 't.0', LY = 'lastyear', data = walter, N = 100, burn = 10, thin = 10, w = c(1,1,1), m = 10, form = "Weibull" ) print(model)#> Call: #> pooledSPsurv(duration = duration ~ fhcompor1 + lgdpl + comprehensive + #> victory + instabl + intensityln + ethfrac + unpko, immune = cured ~ #> fhcompor1 + lgdpl + victory, Y0 = "t.0", LY = "lastyear", #> data = walter, N = 100, burn = 10, thin = 10, w = c(1, 1, #> 1), m = 10, form = "Weibull") #> #> #> Iterations = 1:9 #> Thinning interval = 1 #> Number of chains = 1 #> Sample size per chain = 9 #> #> Empirical mean and standard deviation for each variable, #> plus standard error of the mean: #> #> #> Duration equation: #> Mean SD Naive SE Time-series SE #> (Intercept) 1.69150492 1.13842191 0.37947397 0.72836286 #> fhcompor1 -0.95879645 0.71976455 0.23992152 0.23992152 #> lgdpl -0.01711938 0.10324308 0.03441436 0.03308852 #> comprehensive -0.88064889 0.48338727 0.16112909 0.16112909 #> victory 0.36350782 0.58901831 0.19633944 0.19633944 #> instabl 0.68810472 0.60268434 0.20089478 0.20089478 #> intensityln 0.10202337 0.05913569 0.01971190 0.01202436 #> ethfrac -0.21938349 0.70877869 0.23625956 0.23625956 #> unpko 0.84936611 0.65902802 0.21967601 0.21967601 #> #> Immune equation: #> Mean SD Naive SE Time-series SE #> (Intercept) 0.7645582 1.878438 0.6261459 1.2225668 #> fhcompor1 3.4480967 4.610149 1.5367162 2.8349533 #> lgdpl -3.1381824 2.568191 0.8560636 0.8560636 #> victory -0.6346364 3.068650 1.0228834 1.0228834 #>#> #> Iterations = 1:9 #> Thinning interval = 1 #> Number of chains = 1 #> Sample size per chain = 9 #> #> 1. Empirical mean and standard deviation for each variable, #> plus standard error of the mean: #> #> Mean SD Naive SE Time-series SE #> (Intercept) 1.69150 1.13842 0.37947 0.72836 #> fhcompor1 -0.95880 0.71976 0.23992 0.23992 #> lgdpl -0.01712 0.10324 0.03441 0.03309 #> comprehensive -0.88065 0.48339 0.16113 0.16113 #> victory 0.36351 0.58902 0.19634 0.19634 #> instabl 0.68810 0.60268 0.20089 0.20089 #> intensityln 0.10202 0.05914 0.01971 0.01202 #> ethfrac -0.21938 0.70878 0.23626 0.23626 #> unpko 0.84937 0.65903 0.21968 0.21968 #> #> 2. Quantiles for each variable: #> #> 2.5% 25% 50% 75% 97.5% #> (Intercept) -0.13664 1.06496 1.84080 2.326544 3.27727 #> fhcompor1 -2.08731 -1.58665 -0.80098 -0.589260 -0.05586 #> lgdpl -0.14048 -0.07796 -0.02347 -0.003296 0.15663 #> comprehensive -1.69718 -1.12879 -0.88757 -0.601159 -0.27984 #> victory -0.44365 0.25385 0.28800 0.321501 1.45898 #> instabl -0.11008 0.28032 0.66443 1.119573 1.59026 #> intensityln 0.01332 0.06036 0.12142 0.140956 0.17080 #> ethfrac -1.13836 -0.55928 -0.44751 0.427076 0.84619 #> unpko -0.15069 0.51824 0.93883 1.298319 1.71663 #># plot(model) # }


--- layout: default title: "UI Pattern Library" order: 1 --- {% assign categories = site.docs | group_by:"category" %} {% for category in categories %}