Rails 4.0.1 (November 01, 2013)
-
Fix
has_secure_password
to honor bcrypt-ruby's cost attribute.T.J. Schuck
-
inclusion
/exclusion
validations with ranges will only use the fasterRange#cover
for numerical ranges, and the more accurateRange#include?
for non-numerical ones.Fixes range validations like
:a..:f
that used to pass with values like:be
.Fixes #10593.
Charles Bergeron
Rails 4.0.0 (June 25, 2013)
-
Fix regression in has_secure_password. When a password is set, but a confirmation is an empty string, it would incorrectly save.
Steve Klabnik and Phillip Calvin
-
Add
ActiveModel::Errors#full_messages_for
, to return all the error messages for a given attribute.Example:
class Person include ActiveModel::Validations attr_reader :name, :email validates_presence_of :name, :email end person = Person.new person.valid? # => false person.errors.full_messages_for(:name) # => ["Name can't be blank"]
Volodymyr Shatsky
-
Added a method so that validations can be easily cleared on a model. For example:
class Person include ActiveModel::Validations validates_uniqueness_of :first_name validate :cannot_be_robot def cannot_be_robot errors.add(:base, 'A person cannot be a robot') if person_is_robot end end
Now, if someone runs
Person.clear_validators!
, then the following occurs:Person.validators # => [] Person._validate_callbacks.empty? # => true
John Wang
-
has_secure_password
does not fail the confirmation validation when assigning empty String topassword
andpassword_confirmation
. Fixes #9535.Example:
# Given User has_secure_password. @user.password = "" @user.password_confirmation = "" @user.valid?(:update) # used to be false
Yves Senn
-
validates_confirmation_of
does not override writer methods for the confirmation attribute if no reader is defined.Example:
class Blog def title=(new_title) @title = new_title.downcase end # previously this would override the setter above. validates_confirmation_of :title end
Yves Senn
-
Add
ActiveModel::Validations::AbsenceValidator
, a validator to check the absence of attributes.class Person include ActiveModel::Validations attr_accessor :first_name validates_absence_of :first_name end person = Person.new person.first_name = "John" person.valid? # => false person.errors.messages # => {:first_name=>["must be blank"]}
Roberto Vasquez Angel
-
[attribute]_changed?
now returnsfalse
after a call toreset_[attribute]!
.Renato Mascarenhas
-
Observers was extracted from Active Model as
rails-observers
gem.Rafael Mendonça França
-
Specify type of singular association during serialization.
Steve Klabnik
-
Fixed length validator to correctly handle
nil
. Fixes #7180.Michal Zima
-
Removed dispensable
require
statements. Make sure to requireactive_model
before requiring individual parts of the framework.Yves Senn
-
Use BCrypt's
MIN_COST
in the test environment for speedier tests when usinghas_secure_password
.Brian Cardarella + Jeremy Kemper + Trevor Turk
-
Add
ActiveModel::ForbiddenAttributesProtection
, a simple module to protect attributes from mass assignment when non-permitted attributes are passed.DHH + Guillermo Iguaran
-
ActiveModel::MassAssignmentSecurity
has been extracted from Active Model and theprotected_attributes
gem should be added to Gemfile in order to useattr_accessible
andattr_protected
macros in your models.Guillermo Iguaran
-
Due to a change in builder,
nil
and empty strings now generate closed tags, so instead of this:<pseudonyms nil=\"true\"></pseudonyms>
it generates this:
<pseudonyms nil=\"true\"/>
Carlos Antonio da Silva
-
Inclusion/exclusion validators accept a method name passed as a symbol to the
:in
option.This allows to use dynamic inclusion/exclusion values using methods, besides the current lambda/proc support.
Gabriel Sobrinho
-
ActiveModel::Validation#validates
ability to pass custom exception to the:strict
option.Bogdan Gusiev
-
Changed
ActiveModel::Serializers::Xml::Serializer#add_associations
to by default propagate:skip_types, :dasherize, :camelize
keys to included associations. It can be overridden on each association by explicitly specifying the option on one or more associationsAnthony Alberto
-
Changed
ActiveModel::Serializers::JSON.include_root_in_json
default value to false. Now, AM Serializers and AR objects have the same default behaviour. Fixes #6578.class User < ActiveRecord::Base; end class Person include ActiveModel::Model include ActiveModel::AttributeMethods include ActiveModel::Serializers::JSON attr_accessor :name, :age def attributes instance_values end end user.as_json => {"id"=>1, "name"=>"Konata Izumi", "age"=>16, "awesome"=>true} # root is not included person.as_json => {"name"=>"Francesco", "age"=>22} # root is not included
Francesco Rodriguez
-
Passing false hash values to
validates
will no longer enable the corresponding validators.Steve Purcell
-
ConfirmationValidator
error messages will attach to:#{attribute}_confirmation
instead ofattribute
.Brian Cardarella
-
Added
ActiveModel::Model
, a mixin to make Ruby objects work with AP out of box.Guillermo Iguaran
-
AM::Errors#to_json
: support:full_messages
parameter.Bogdan Gusiev
-
Trim down Active Model API by removing
valid?
anderrors.full_messages
.José Valim
-
When
^
or$
are used in the regular expression provided tovalidates_format_of
and the:multiline
option is not set to true, an exception will be raised. This is to prevent security vulnerabilities when usingvalidates_format_of
. The problem is described in detail in the Rails security guide.Jan Berdajs + Egor Homakov
Please check 3-2-stable for previous changes.


| Product | Item | Quantity | Price |
|---|---|---|---|
| Silver Necklace and Earring Set | 1 | $12.10 | |
| Gold and Diamond Hand Adornment | 1 | $12.50 | |
| Navy Blue Floral Dress | 1 | $32.50 | |
| Sleeveless Floral Romper | 1 | $27.50 | |
| White/Black Bracelet | 1 | $8.50 | |
| Shimmery Sliver | 1 | $20.24 |
| Items: 6 | TOTAL: $113.34 | |


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makeEcModel Expands a conventional genome-scale model (in RAVEN format) with enzyme information and prepares the reactions for integration of enzyme usage coefficients. This function contains all the steps that need to be done to get a basic ecModel, without incorporating any kcat values or constraints yet. This function should only have to be run once for a model. Input: model a model in RAVEN format geckoLight true if a simplified GECKO light model should be generated. (Optional, default is false). modelAdapter a loaded model adapter (Optional, will otherwise use the default model adapter). Ouput: model an ecModel in GECKO 3 format, with a model.ec structure where enzyme and kcat information are stored. Protein pseudo- metabolites and their draw reactions are added to the model, but their usage is not yet implemented (due to absent kcat values at this stage). noUniprot genes for which no information could be found in the Uniprot database The function goes through the following steps: 1. Remove gene associations from pseudoreactions. 2. Invert irreversible backwards reactions. 3. Correct 'rev' vector to match lb and ub vectors. 4. Convert to irreversible model (splits reversible reactions). 5. [Skipped with geckoLight:] Expand model to split reactions with 'OR' in grRules (each reaction is then catalyzed by one enzyme (complex). 6. Make empty model.ec structure, that will contain enzyme and kcat information. One entry per reaction, where isozymes have multiple entries. This model.ec structure will later be populated with kcat values. For geckoLight the structure is different, where each reaction can have multiple isozymes. 7. Add enzyme information fields to model.ec structure: MW, sequence. 8. Populate model.ec structure with information from each reaction. 9. [Skipped with geckoLight:] Add proteins as pseudometabolites. 10. Add prot_pool pseudometabolite. 11. [Skipped with geckoLight:] Add usage reactions for the protein pseudometabolites, replenishing from the protein pool (default, can be changed to consider proteomics data at later stage) 12. Add protein pool reaction, without upper bound. Note that while protein pseudometabolites, draw & pool reactions might be added to the model, the enzyme usage is not yet incorporated in each metabolic reaction, so enzymes will not be used. applyKcatConstraints incorporates protein pseudometabolites in reactions as enzyme usages by applying the specified kcats as constraints. The EC structure looks as follows Attributes: geckoLight: 0 if full model, 1 if light rxns: reaction identifiers that correspond to model.rxns kcat: kcat values - not set here source: specifies where the kcats come from - not set here notes: notes that can be set by downstream functions - not set here eccodes: enzyme codes for each enzyme - not set here genes: the genes involved in the kcats - not necessarily the same as model.genes, since some genes may not be found in databases etc. enzymes: Uniprot protein identifiers for the genes mw: molecular weights of the enzymes sequence: sequence of the genes/enzymes concs: concentrations of the enzymes - not set here rxnEnzMat: matrix of enzymes and rxns The full model is split on all ORs in the GPRs, meaning that the reactions will be duplicated for each isozyme. Only the rxns with genes are added. The fields rxns, eccodes, kcat, source and notes will therefore have one entry per reaction. The fields genes, enzymes, mw, sequence and concs will have one entry per gene. The rxnEnzMat is a matrix with reactions and genes, mapping which genes are connected to which reaction (where isozymes have different reactions). The light model works a bit differently. The model has the same number of rxns as the original model, but expanded since it is reversible + one the extra prot maintenance rxn and one extra prot_pool rxn. However, the ec fields rxns, eccodes, kcat, source and notes are duplicated for each isozyme, sorted the same way as model.rxns. So, in model.ec.rxns, the same reaction will appear several times after one another, one entry per izozyme, with corresponding values for that isozyme. These fields therefore have the same length as for the full model. The fields genes, enzymes, mw, sequence and concs are the same here as in the full model. The rxnEnzMat maps the model.ec.rxns entries to genes and is therefore of the same size as for the full model. Usage: [model, noUniprot] = makeEcModel(model, geckoLight, modelAdapter)
0001 function [model, noUniprot] = makeEcModel(model, geckoLight, modelAdapter) 0002 % makeEcModel 0003 % Expands a conventional genome-scale model (in RAVEN format) with enzyme 0004 % information and prepares the reactions for integration of enzyme usage 0005 % coefficients. This function contains all the steps that need to be done 0006 % to get a basic ecModel, without incorporating any kcat values or 0007 % constraints yet. This function should only have to be run once for a 0008 % model. 0009 % 0010 % Input: 0011 % model a model in RAVEN format 0012 % geckoLight true if a simplified GECKO light model should be generated. 0013 % (Optional, default is false). 0014 % modelAdapter a loaded model adapter (Optional, will otherwise use the 0015 % default model adapter). 0016 % 0017 % Ouput: 0018 % model an ecModel in GECKO 3 format, with a model.ec structure where 0019 % enzyme and kcat information are stored. Protein pseudo- 0020 % metabolites and their draw reactions are added to the model, 0021 % but their usage is not yet implemented (due to absent kcat 0022 % values at this stage). 0023 % noUniprot genes for which no information could be found in the 0024 % Uniprot database 0025 % 0026 % The function goes through the following steps: 0027 % 1. Remove gene associations from pseudoreactions. 0028 % 2. Invert irreversible backwards reactions. 0029 % 3. Correct 'rev' vector to match lb and ub vectors. 0030 % 4. Convert to irreversible model (splits reversible reactions). 0031 % 5. [Skipped with geckoLight:] Expand model to split reactions with 0032 % 'OR' in grRules (each reaction is then catalyzed by one enzyme 0033 % (complex). 0034 % 6. Make empty model.ec structure, that will contain enzyme and kcat 0035 % information. One entry per reaction, where isozymes have multiple 0036 % entries. This model.ec structure will later be populated with kcat 0037 % values. For geckoLight the structure is different, where each 0038 % reaction can have multiple isozymes. 0039 % 7. Add enzyme information fields to model.ec structure: MW, sequence. 0040 % 8. Populate model.ec structure with information from each reaction. 0041 % 9. [Skipped with geckoLight:] Add proteins as pseudometabolites. 0042 % 10. Add prot_pool pseudometabolite. 0043 % 11. [Skipped with geckoLight:] Add usage reactions for the protein 0044 % pseudometabolites, replenishing from the protein pool (default, can 0045 % be changed to consider proteomics data at later stage) 0046 % 12. Add protein pool reaction, without upper bound. 0047 % 0048 % Note that while protein pseudometabolites, draw & pool reactions might 0049 % be added to the model, the enzyme usage is not yet incorporated in each 0050 % metabolic reaction, so enzymes will not be used. applyKcatConstraints 0051 % incorporates protein pseudometabolites in reactions as enzyme usages by 0052 % applying the specified kcats as constraints. 0053 % 0054 %The EC structure looks as follows 0055 % Attributes: 0056 % geckoLight: 0 if full model, 1 if light 0057 % rxns: reaction identifiers that correspond to model.rxns 0058 % kcat: kcat values - not set here 0059 % source: specifies where the kcats come from - not set here 0060 % notes: notes that can be set by downstream functions - not set 0061 % here 0062 % eccodes: enzyme codes for each enzyme - not set here 0063 % genes: the genes involved in the kcats - not necessarily the 0064 % same as model.genes, since some genes may not be found in 0065 % databases etc. 0066 % enzymes: Uniprot protein identifiers for the genes 0067 % mw: molecular weights of the enzymes 0068 % sequence: sequence of the genes/enzymes 0069 % concs: concentrations of the enzymes - not set here 0070 % rxnEnzMat: matrix of enzymes and rxns 0071 % 0072 % The full model is split on all ORs in the GPRs, meaning that the 0073 % reactions will be duplicated for each isozyme. Only the rxns with genes 0074 % are added. The fields rxns, eccodes, kcat, source and notes will 0075 % therefore have one entry per reaction. The fields genes, enzymes, mw, 0076 % sequence and concs will have one entry per gene. The rxnEnzMat is a 0077 % matrix with reactions and genes, mapping which genes are connected to 0078 % which reaction (where isozymes have different reactions). 0079 % 0080 % The light model works a bit differently. The model has the same number of 0081 % rxns as the original model, but expanded since it is reversible + one the 0082 % extra prot maintenance rxn and one extra prot_pool rxn. However, the ec 0083 % fields rxns, eccodes, kcat, source and notes are duplicated for each 0084 % isozyme, sorted the same way as model.rxns. So, in model.ec.rxns, the 0085 % same reaction will appear several times after one another, one entry per 0086 % izozyme, with corresponding values for that isozyme. These fields 0087 % therefore have the same length as for the full model. The fields genes, 0088 % enzymes, mw, sequence and concs are the same here as in the full model. 0089 % The rxnEnzMat maps the model.ec.rxns entries to genes and is therefore of 0090 % the same size as for the full model. 0091 % 0092 % Usage: 0093 % [model, noUniprot] = makeEcModel(model, geckoLight, modelAdapter) 0094 0095 if nargin<2 0096 geckoLight=false; 0097 elseif ~islogical(geckoLight) && ~(geckoLight == 0) && ~(geckoLight == 1) 0098 error('geckoLight should be either true or false') 0099 end 0100 0101 if nargin < 3 || isempty(modelAdapter) 0102 modelAdapter = ModelAdapterManager.getDefault(); 0103 if isempty(modelAdapter) 0104 error('Either send in a modelAdapter or set the default model adapter in the ModelAdapterManager.') 0105 end 0106 end 0107 params = modelAdapter.getParameters(); 0108 compartmentID = strcmp(model.compNames,params.enzyme_comp); 0109 if ~any(compartmentID) 0110 error(['Compartment ' params.enzyme_comp ' (specified in params.enzyme_comp) '... 0111 'cannot be found in model.compNames']) 0112 end 0113 compartmentID = model.comps(compartmentID); 0114 0115 if geckoLight 0116 ec.geckoLight=true; 0117 else 0118 ec.geckoLight=false; 0119 end 0120 0121 %Check if model is in RAVEN format 0122 if any(isfield(model,{'rules','modelID'})) 0123 error(['The model is likely loaded using COBRA Toolbox readCbModel(). Instead, use ' ... 0124 'RAVEN Toolbox importModel(). Alternatively, you can also convert the ', ... 0125 'model in MATLAB using ravenCobraWrapper().']) 0126 end 0127 0128 %Check for conflicting reaction and metabolite identifiers 0129 conflictId = startsWith(model.mets,'prot_'); 0130 if any(conflictId) 0131 error('The identifiers in model.mets are not allowed to start with ''prot_''.') 0132 end 0133 conflictId = startsWith(model.rxns,{'usage_prot_','prot_pool'}) | ... 0134 endsWith(model.rxns,'_REV') | ... 0135 ~cellfun(@isempty,regexp(model.rxns,'_EXP_\d+$')); 0136 if any(conflictId) 0137 error(['The identifiers in model.rxns are not allowed to start with ''usage_prot'' ' ... 0138 'or ''prot_pool'', or end with ''_REV'' or ''_EXP_[digit]''.']) 0139 end 0140 0141 uniprotDB = loadDatabases('both', modelAdapter); 0142 uniprotDB = uniprotDB.uniprot; 0143 0144 %1: Remove gene rules from pseudoreactions (if any): 0145 if exist(fullfile(params.path,'data','pseudoRxns.tsv'),'file') 0146 fID = fopen(fullfile(params.path,'data','pseudoRxns.tsv')); 0147 fileData = textscan(fID,'%s %s','delimiter','\t'); 0148 fclose(fID); 0149 pseudoRxns = fileData{1}; 0150 pseudoRxns = ismember(model.rxns,pseudoRxns); 0151 else 0152 pseudoRxns = false(numel(model.rxns),1); 0153 end 0154 pseudoRxns = find(pseudoRxns | contains(model.rxnNames,'pseudoreaction')); 0155 model.grRules(pseudoRxns) = {''}; 0156 model.rxnGeneMat(pseudoRxns,:) = zeros(numel(pseudoRxns), numel(model.genes)); 0157 0158 %2: Swap direction of reactions that are defined to only carry negative flux 0159 to_swap=model.lb < 0 & model.ub == 0; 0160 model.S(:,to_swap)=-model.S(:,to_swap); 0161 model.ub(to_swap)=-model.lb(to_swap); 0162 model.lb(to_swap)=0; 0163 0164 %3: Correct rev vector: true if LB < 0 & UB > 0, or it is an exchange reaction: 0165 model.rev = false(size(model.rxns)); 0166 for i = 1:length(model.rxns) 0167 if (model.lb(i) < 0 && model.ub(i) > 0) || sum(model.S(:,i) ~= 0) == 1 0168 model.rev(i) = true; 0169 end 0170 end 0171 0172 %4: Make irreversible model (appends _REV to reaction IDs to indicate reverse 0173 %reactions) 0174 [~,exchRxns] = getExchangeRxns(model); 0175 nonExchRxns = model.rxns; 0176 nonExchRxns(exchRxns) = []; 0177 model=convertToIrrev(model, nonExchRxns); 0178 0179 %5: Expand model, to separate isozymes (appends _EXP_* to reaction IDs to 0180 %indicate duplication) 0181 if ~geckoLight 0182 model=expandModel(model); 0183 end 0184 % Sort reactions, so that reversible and isozymic reactions are kept near 0185 if ~geckoLight 0186 model=sortIdentifiers(model); 0187 end 0188 0189 %6: Make ec-extension structure, one for gene-associated reaction. 0190 % The structure is different for light and full models 0191 rxnWithGene = find(sum(model.rxnGeneMat,2)); 0192 if ~geckoLight 0193 ec.rxns = model.rxns(rxnWithGene); 0194 emptyCell = cell(numel(rxnWithGene),1); 0195 emptyCell(:) = {''}; 0196 emptyVect = zeros(numel(rxnWithGene),1); 0197 0198 ec.kcat = emptyVect; 0199 ec.source = emptyCell; % Strings, like 'dlkcat', 'manual', 'brenda', etc. 0200 ec.notes = emptyCell; % Additional comments 0201 ec.eccodes = emptyCell; 0202 ec.concs = emptyVect; 0203 else 0204 %Different strategy for GECKO light: Each reaction can exist multiple times in 0205 %ec.rxns and similar fields - one time per isozyme. The number of copies is 0206 %the number of ORs in the GPR + 1 0207 numOrs = count(model.grRules(rxnWithGene), ' or '); 0208 cpys = numOrs + 1; 0209 prevNumRxns = length(numOrs); 0210 cpyIndices = repelem(rxnWithGene, cpys).'; %.' only matter when number of rxns is 1 0211 %loop through and add a prefix with an isozyme index to the rxns 0212 %we just give a fixed-length number as prefix, and assume that 999 is enough 0213 tmpRxns = model.rxns(cpyIndices); %now they have no prefix 0214 newRxns = tmpRxns; 0215 0216 %add the prefix 0217 nextIndex = 1; 0218 for i = 1:numel(model.rxns) 0219 localRxnIndex = 1; 0220 if nextIndex <= length(tmpRxns) && strcmp(model.rxns(i), tmpRxns(nextIndex)) 0221 while true 0222 tmp = compose('%03d_',localRxnIndex); 0223 newRxns{nextIndex} = [tmp{1} tmpRxns{nextIndex}]; 0224 localRxnIndex = localRxnIndex + 1; 0225 if (localRxnIndex >= 1000) 0226 error('Increase index size to 10000 - error in the code.'); %this should never happen, we don't have > 999 isozymes 0227 end 0228 nextIndex = nextIndex + 1; 0229 if nextIndex > length(tmpRxns) || ~strcmp(model.rxns(i), tmpRxns(nextIndex)) 0230 break; 0231 end 0232 end 0233 end 0234 end 0235 0236 ec.rxns = newRxns; 0237 0238 emptyCell = cell(numel(ec.rxns),1); 0239 emptyCell(:) = {''}; 0240 emptyVect = zeros(numel(ec.rxns),1); 0241 0242 ec.kcat = emptyVect; 0243 ec.source = emptyCell; % Strings, like 'dlkcat', 'manual', 'brenda', etc. 0244 ec.notes = emptyCell; % Additional comments 0245 ec.eccodes = emptyCell; 0246 ec.concs = emptyVect; 0247 end 0248 0249 %7: Gather enzyme information via UniprotDB 0250 uniprotCompatibleGenes = modelAdapter.getUniprotCompatibleGenes(model.genes); 0251 [Lia,Locb] = ismember(uniprotCompatibleGenes,uniprotDB.genes); 0252 0253 uniprot = modelAdapter.getUniprotIDsFromTable(uniprotCompatibleGenes); 0254 if ~isequal(uniprot,uniprotCompatibleGenes) 0255 uniprot(cellfun(@isempty,uniprot)) = {''}; 0256 [Lia,Locb] = ismember(uniprot,uniprotDB.ID); 0257 end 0258 noUniprot = uniprotCompatibleGenes(~Lia); 0259 if ~isempty(noUniprot) 0260 printOrange(['WARNING: The ' num2str(numel(noUniprot)) ' gene(s) reported in noUniprot cannot be found in data/uniprot.tsv, these will\n' ... 0261 'not be enzyme-constrained. If you intend to use different Uniprot data (e.g. from a\n'... 0262 'different proteome, make sure you first delete the existing data/uniprot.tsv file.\n']) 0263 end 0264 ec.genes = model.genes(Lia); %Will often be duplicate of model.genes, but is done here to prevent issues when it is not. 0265 ec.enzymes = uniprotDB.ID(Locb(Lia)); 0266 ec.mw = uniprotDB.MW(Locb(Lia)); 0267 ec.sequence = uniprotDB.seq(Locb(Lia)); 0268 %Additional info 0269 ec.concs = nan(numel(ec.genes),1); % To be filled with proteomics data when available 0270 0271 %8: Only parse rxns associated to genes 0272 if ~geckoLight 0273 ec.rxnEnzMat = zeros(numel(rxnWithGene),numel(ec.genes)); % Non-zeros will indicate the number of subunits 0274 for r=1:numel(rxnWithGene) 0275 rxnGenes = model.genes(find(model.rxnGeneMat(rxnWithGene(r),:))); 0276 [~,locEnz] = ismember(rxnGenes,ec.genes); % Could also parse directly from rxnGeneMat, but some genes might be missing from Uniprot DB 0277 if locEnz ~= 0 0278 ec.rxnEnzMat(r,locEnz) = 1; %Assume 1 copy per subunit or enzyme, can be modified later 0279 end 0280 end 0281 else 0282 %For light models, we need to split up all GPRs 0283 ec.rxnEnzMat = zeros(numel(ec.rxns),numel(ec.genes)); % Non-zeros will indicate the number of subunits 0284 nextIndex = 1; 0285 %For full model generation, the GPRs are controlled in expandModel, but 0286 %here we need to make an explicit format check 0287 indexes2check = findPotentialErrors(model.grRules,model); 0288 if ~isempty(indexes2check) 0289 printOrange('Run standardizeGrRules(model) for a more detailed warning.\n') 0290 printOrange('For Human-GEM, these reactions can be corrected using simplifyGrRules.\n'); 0291 end 0292 0293 for i=1:prevNumRxns 0294 %ind is the index in the model, not to confuse with the index in the ec struct (i), 0295 %which only contains reactions with GPRs. 0296 ind = rxnWithGene(i); 0297 %Get rid of all '(' and ')' since I'm not looking at complex stuff 0298 %anyways 0299 geneString=model.grRules{ind}; 0300 geneString=strrep(geneString,'(',''); 0301 geneString=strrep(geneString,')',''); 0302 geneString=strrep(geneString,' or ',';'); 0303 0304 if (numOrs(i) == 0) 0305 geneNames = {geneString}; 0306 else 0307 %Split the string into gene names 0308 geneNames=regexp(geneString,';','split'); 0309 end 0310 0311 %Now loop through the isozymes and set the rxnGeneMat 0312 for j = 1:length(geneNames) 0313 %Find the gene in the gene list If ' and ' relationship, first 0314 %split the genes 0315 fnd = strfind(geneNames{j},' and '); 0316 if ~isempty(fnd) 0317 andGenes=regexp(geneNames{j},' and ','split'); 0318 ec.rxnEnzMat(nextIndex,ismember(ec.genes,andGenes)) = 1; %should be subunit stoichoimetry 0319 else 0320 ec.rxnEnzMat(nextIndex,ismember(ec.genes,geneNames(j)))=1;%should be subunit stoichoimetry 0321 end 0322 nextIndex = nextIndex + 1; 0323 end 0324 end 0325 end 0326 0327 %9: Add proteins as pseudometabolites 0328 if ~geckoLight 0329 [proteinMets.mets, uniprotSortId] = sort(ec.enzymes); 0330 proteinMets.mets = strcat('prot_',proteinMets.mets); 0331 proteinMets.metNames = proteinMets.mets; 0332 proteinMets.compartments = compartmentID; 0333 if isfield(model,'metMiriams') 0334 proteinMets.metMiriams = repmat({struct('name',{{'sbo'}},'value',{{'SBO:0000252'}})},numel(proteinMets.mets),1); 0335 end 0336 if isfield(model,'metCharges') 0337 proteinMets.metCharges = zeros(numel(proteinMets.mets),1); 0338 end 0339 proteinMets.metNotes = repmat({'Enzyme-usage pseudometabolite'},numel(proteinMets.mets),1); 0340 model = addMets(model,proteinMets); 0341 end 0342 0343 %10: Add protein pool pseudometabolite 0344 pool.mets = 'prot_pool'; 0345 pool.metNames = pool.mets; 0346 pool.compartments = compartmentID; 0347 pool.metNotes = 'Enzyme-usage protein pool'; 0348 model = addMets(model,pool); 0349 0350 %11: Add protein usage reactions. 0351 if ~geckoLight 0352 usageRxns.rxns = strcat('usage_',proteinMets.mets); 0353 usageRxns.rxnNames = usageRxns.rxns; 0354 usageRxns.mets = cell(numel(usageRxns.rxns),1); 0355 usageRxns.stoichCoeffs = cell(numel(usageRxns.rxns),1); 0356 for i=1:numel(usageRxns.mets) 0357 usageRxns.mets{i} = {proteinMets.mets{i}, 'prot_pool'}; 0358 usageRxns.stoichCoeffs{i} = [-1,1]; 0359 end 0360 usageRxns.lb = zeros(numel(usageRxns.rxns),1) - 1000; 0361 usageRxns.ub = zeros(numel(usageRxns.rxns),1); 0362 usageRxns.rev = ones(numel(usageRxns.rxns),1); 0363 usageRxns.grRules = ec.genes(uniprotSortId); 0364 model = addRxns(model,usageRxns); 0365 end 0366 0367 %12: Add protein pool reaction (with open UB) 0368 poolRxn.rxns = 'prot_pool_exchange'; 0369 poolRxn.rxnNames = poolRxn.rxns; 0370 poolRxn.mets = {'prot_pool'}; 0371 poolRxn.stoichCoeffs = {-1}; 0372 poolRxn.lb = -1000; 0373 poolRxn.ub = 0; 0374 poolRxn.rev = 1; 0375 model = addRxns(model,poolRxn); 0376 0377 model.ec=ec; 0378 end 0379 0380 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 0381 %Function that gets the model field grRules and returns the indexes of the 0382 %rules in which the pattern ") and (" is present. 0383 %Copied from standardizeGrRules 0384 % TODO: Make this an accessible function in a separate file in RAVEN and remove this 0385 %implementation. 0386 function indexes2check = findPotentialErrors(grRules,model) 0387 indxs_l = find(~cellfun(@isempty,strfind(grRules,') and ('))); 0388 indxs_l_L = find(~cellfun(@isempty,strfind(grRules,') and'))); 0389 indxs_l_R = find(~cellfun(@isempty,strfind(grRules,'and ('))); 0390 indexes2check = vertcat(indxs_l,indxs_l_L,indxs_l_R); 0391 indexes2check = unique(indexes2check); 0392 0393 if ~isempty(indexes2check) 0394 textToPrint = 'WARNING: Potentially problematic ") AND (" in the grRules for reaction(s):\n'; 0395 for i=1:numel(indexes2check) 0396 textToPrint=[textToPrint '\t' model.rxns{indexes2check(i)} '\n']; 0397 end 0398 printOrange(textToPrint); 0399 end 0400 end


The city of San Francisco needs to watch out for larger rent increases where shuttle stops and AirBnB listings are more densely populated. This visualization shows that there is a strong positive correlation between bus stops, rent petitions, and AirBnB listings. This is true especially in the downtown area and around the Castro/Mission area.


In this HIT, you will be shown a short 3-sentence story. For the middle sentence, there will be 5 options. There are two parts to the task.
The task requires you to:
-
Pick the middle sentence which makes the most plausible story
-
Check all middle options that form plausible stories.
Examples
The most plausible middle sentence is in bold and each option is marked as correct/incorrect.
1
|
|
|
|
Rob wanted a Sega Genesis.
|
1: Rob sold one to Dan for $20 (INCORRECT) |
Dan hated to see Rob sad so he paid the $20. |
2: Rob couldn't afford one, however. (CORRECT) / (BEST) |
3: Rob wanted to play tennis with Dan. (INCORRECT) |
4: Rob did not know where to find one. (CORRECT) |
5: Rob asked Dan for money. (CORRECT) |
2
|
|
|
|
Amy got a flat tire on the way home from work.
|
1: Amy called someone to fix the flat tire. (CORRECT) |
Amy drove away, happy there were still good samaritans in the world. |
2: A few people drove-by and saw her broken down. (INCORRECT) |
3: Amy's brother told her how to replace the tire. (INCORRECT) |
4: A passerby stopped and helped her change tires. (CORRECT) / (BEST) |
5: Amy did not have a spare tire. (INCORRECT) |
Great! You are now ready to start the following questions!


---
layout: default
title: Research
permalink: research.html
---
Research
Journal Papers
- Label-free and portable field-effect sensor for monitoring RT-LAMP products to detect SARS-CoV-2 in wastewater,
Talanta, 2023. DOI: 10.1016/j.talanta.2022.124060
- Effective Capacitance from Equivalent Electrical Circuit as a Tool for Monitoring Non-Adherent Cell Suspensions at Low Frequencies,
Bioengineering, 2022. DOI: 10.3390/bioengineering9110697
- Loop-mediated isothermal amplification-based electrochemical sensor for detecting SARS-CoV-2 in wastewater samples,
Journal of Environmental Chemical Engineering, 2022. DOI: 10.1016/j.jece.2022.107488
- Data-driven modeling of impedance biosensors: a subspace approach,
Measurement Science and Technology, 2021. DOI: 10.1088/1361-6501/ac0b15
- Nonlinear finite impulse response estimation using regularized neural networks,
IFAC-PapersOnLine, 2021. DOI: 110.1016/j.ifacol.2021.08.354
- Time-Constant-Domain Spectroscopy: An Impedance-Based Method for Sensing Biological Cells in Suspension,
IEEE Sensors Journal, 2020. DOI: 10.1109/JSEN.2020.3014569
- A framework for high-resolution frequency response measurement and parameter estimation in microscale impedance applications,
Measurement, 2019. DOI: 10.1016/j.measurement.2019.106913
- Bioimpedance Parameter Estimation using Fast Spectral Measurements and Regularization,
IFAC-PapersOnLine, 2018. DOI: 10.1016/j.ifacol.2018.09.198
- Ex-vivo biological tissue differentiation by the Distribution of Relaxation Times method applied to Electrical Impedance Spectroscopy,
Electrochimica Acta, 2018. DOI: 10.1016/j.electacta.2018.04.167
Conference Papers
- Sensing Micro-colloid Concentration by Spectral Impedance Measurements and Relaxation Times Analysis,
2019 IEEE Sensors Applications Symposium (SAS), 2019.
DOI: 10.1109/SAS.2019.8706080
- Analysis of Impedance Spectroscopy Measurements of Biological Tissue using the Distribution of Relaxation Times Method,
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies, 2017,
DOI: 10.5220/0006253902240228


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CHAPTER 8
HOLIDAYS AND PERIODS OF RECOGNITION AND OBSERVANCE
Section
8-1 Holidays designated
8-1.5 Discoverers' Day
8-2 Observance of holidays falling on Sundays and Saturdays
8-3 Banking holidays
8-3.4 Civil liberties and the Constitution Day
8-3.6 Gold Star Family Day
8-3.5 Patriot Day
8-4 Buddha Day
8-4.5 Bahai New Year's Day
8-4.6 Asian Lunar New Year Commemoration Week
8-5 King Kamehameha celebration commission
8-5.5 Queen Liliuokalani Day
8-6 Repealed
8-7 Arbor Day
8-8 Saint Damien de Veuster Day
8-8.2 Saint Marianne Cope Day
8-9 Respect for Our Elders Day
8-10 Bodhi Day
8-11 Children and Youth Day and Month
8-12 World Ocean Day
8-13 Lei Day
8-14 Financial Literacy Month
8-15 Kupuna Recognition Day
8-16 Peace Day
8-16.2 Mohandas Karamchand Gandhi Day
8-17 Caregiver Recognition Day
8-18 Filipino-American History Month
8-18.5 Sakada Day
8-19 Makahiki Commemoration Day
8-20 Language Access Month
8-21 Domestic Violence Awareness Month
8-22 Kalo Appreciation Month
8-23 Homelessness Awareness Month
8-24 Ōlelo Hawaii Month
8-25 Human Trafficking Awareness Month
8-28 Outdoor Heritage Month
8-29 Farm to School Month in Hawaii
Note
Chapter heading amended by L 2015, c 35, §1.


Lot of opportunities awaits you. 😎
The online aptitude test will be conducted online on Hacker Earth Portal. The test will be consisting of 45 questions. Each student will be given 40 minutes. There is no particular syllabus for the test, questions can be asked from any topic like numerical or verbal reasoning to check your ability as the name suggests. If More than 1 Student gains the Top marks then Fastest finger first Round would be conducted to choose Top 3 among the qualified students. In the Fastest Finger First round, Students Securing top positions will be asked verbal questions and the student answering correctly first will be decided a winner.
A panel of 2 faculties will take your interview. Students will be tested on their technical aspects. There'll be profile and branch-specific questions. Students will be asked questions according to their year, adhering to the subjects they studied. Everyone will get a participation certificate from Microsoft Technical Community and the best candidate will be awarded with a goodie.
In the session, you'll be taught about the introduction, future and the scope of ethical hacking, virus creation, execution, and prevention, installation of Kali on Virtual Machine. You'll get to learn how to hack and secure both wifi and wired networks, how to crack passwords. You'll be performing SQL Injection along with the instructor on your own systems. At the last, you'll end up gaining a lot of knowledge and confidence in ethical hacking. You all will be provided with software toolkits along with study material, cool laptop stickers and a certificate from Microsoft. here will be a quiz in the last of the workshop and the best performers will be awarded goodies.
The workshop will cover a lot of noteworthy topics that will enhance your skills in the fields like Web designing basics, Cloud fundamentals, Microsoft Azure, Domain Connections, Google listings, etc. You will get to know about how to host a website, activating azure services, file transfer protocols and a lot more. All the participants would be given free Microsoft Azure credits worth Rs 6500/- including cool laptop stickers and a certificate from Microsoft. There will be a quiz in the last of the workshop and the winners will be awarded goodies.
The session will help you to make your CV look more presentable. All of your myths regarding CV building would be cleared. You'll get to know what the employers are looking for and what are the things that you've to avoid while building your CV. The session will consist of various topics like prioritizing the different CV sections, correct formatting of the CV and a proper structure for your CV. At the end of the workshop, you'll be having a presentable and updated CV of your own along with some cool laptop stickers and a certificate from Microsoft. There will be a quiz in the last of the workshop and the winners will be awarded goodies.
The group discussion will help in the batches of 10-10 students. Each batch will get the time duration of 5-10 minutes to speak. Any general, technical or current affair topic can be given. The marking scheme will be explained on the spot. And causing interruptions and using foul language will result in marks deduction. Everyone would get a certificate from Microsoft and cool laptop stickers. Best speakers would be awarded goodies.
For more details please contact Sarvagya Tripathi +91 79051 00938 or Rishabh Arya+91 81719 47770


---
layout: post
status: publish
published: true
title: iPod + iTunes, Update Together
author: Leask
author_login: leask
author_email<EMAIL_ADDRESS>author_url: https://leaskh.com
wordpress_id: 511
wordpress_url: http://leaskh.wordpress.com/2006/06/30/ipod-itunes-update-together
date: '2006-06-30 06:00:19 +0800'
date_gmt: '2006-06-30 06:00:19 +0800'
categories:
- iPod + iTunes
tags: []
comments: []
---
- iPod Updater 2006-06-28 includes iPod Software 1.1.2 for iPod, iPod Software 1.2 for iPod nano, and iPod Software 1.1.4 for iPod shuffle. iPod Updater 2006-06-28 contains the same software versions as iPod Updater 2006-03-23 for all other iPod models.
- What's new in iPod Updater 2006-06-28:
1.Nike + iPod support for iPod nano
2.Volume limit for iPod shuffle
3.Bug fixes
- Download(Mac + PC):
http://www.apple.com/ipod/download/
- With iTunes 6, you can preview, buy, and download music videos, short films, and hit TV programs on the iTunes Music Store, and sync your music and purchased videos with iPod to enjoy on the go.
- iTunes 6.0.5 allows you to sync your Nike + iPod workout data to nikeplus.com, where you can easily track your progress, set training goals, challenge others, and much more. Visit the Nike Sport Music area on the iTunes Music Store to download workout mixes, Athlete Inspiration playlists, Nike podcasts, and more.
- Note: After purchasing music from the iTunes Music Store with iTunes 6 or later, you also need to upgrade your other computers that purchase music from the iTunes Music Store to the latest version of iTunes.
- Download(Mac + PC):
http://www.apple.com/itunes/download/


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GE taps Maersk's Carolina Dybeck Happe as new CFO
General Electric Co has named Carolina Dybeck Happe, currently finance chief at shipper AP Moller-Maersk as its chief financial officer, bringing in more new blood at the top of the struggling U.S. industrial conglomerate.
REUTERS: General Electric Co has named Carolina Dybeck Happe, currently finance chief at shipper AP Moller-Maersk as its chief financial officer, bringing in more new blood at the top of the struggling U.S. industrial conglomerate.
Happe will start in early 2020 and will replace Jamie Miller, whose transition was previously announced, the company said on Monday.
(Reporting by Dominic Roshan K.L. in Bengaluru; Editing by Sriraj Kalluvila)


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© Made By Shantanu Pathak


OsmAnd Trip recording plugin is an essential tool for runners, sportsmen and tourists. It allows you to record your movement using your phone's GPS (and through other networks optionally). If you are going for a run, need to see your entire route after a walk around the city or measure the distance you covered, the plugin will help you.
You have to enter the Plugins menu, then select Trip recording and tap Enable. That's pretty much it. To start recording, you need to tap on the GPX widget in the upper-right corner of the map screen. Also, you can choose the logging interval which determines the time between recording moments in your track. The plugin is working now. You can start your trip and see the distance you've walked in the specific widget on the right. To view your track during recording, please tap 'Show on map'.
There's also an opportunity to add new waypoints to the track manually. Just tap on the location on map, choose Add GPX waypoint, name it the way you want. Then go to My places-> My tracks or to My Tracks dashboard menu and hit the save button. Your waypoint is added to the most recent track automatically.
You can easily pick the color and thickness of your GPX line. To do that, please go to Configure map-> GPX track-> Appearance. You can add as many tracks as you need to the map by checking them in Configure map-> 'GPX track' menu.
Please see full video guide below:
To get the plugin, just go to Maps & Resources - Plugins - Trip recording. Then tap the checkbox next to it. That's it. Just tap the widget on the map screen to start recording, set the recording interval and start your trip. When done, just tap the widget again. You can also start a new segment or view track info in the same menu.
You can view your GPX tracks in OsmAnd or in other software. Also, the track itself can be created by you or imported from an external source. For instance, from openstreetmap.org, your local hiking forum or any other place.
The simplest way to view a track you've downloaded is to tap on it in your device's file manager and choose to open it in OsmAnd. After that, you'll see the track in My places-> My tracks or in the Dashboard-> My tracks. To view it the next time, just enter 'My tracks' tap 'Show on map' on those you'd like to see. You can read more about details of the recorded GPX routes here
To view it in Google Earth, go to File > Open > file type GPS (*.gpx, . . .) Navigate to your track and open.
Because OsmAnd GPX format differs from the Garmin one, you will need to convert your OsmAnd track into the Garmin format.
Download and install GPSBabel from gpsbabel.org
To be safe, make a copy of the GPX file to work with.
Open GPSBabel and use the following settings:
Open the converted file in Garmin software (!MapSource, !BaseCamp) in your usual way.
Download RouteConverter from routeconverter.de.
RouteConverter can edit and convert GPX tracks and tracks from many other formats. It can be used to create a route and save that as a GPX track, as well.
You can also save your every navigation trip as a GPX track automatically. To do that, go to Plugins-> Trip recording-> Settings and choose 'Auto-record track during navigation'. After you do that, your every navigation trip will be saved as a GPX track in the tracks folder.
Take OsmAnd Trip recording plugin with you on your trip to make an informative track.
CAUTION Recording a track impacts the battery life: even if the screen is off, it continues to run. Check it using the notification that OsmAnd is running in the background in your status bar.Increasing battery life
Hint: Before putting the device to sleep mode on a long hike, press the home button so the OsmAnd app goes to the background. If OsmAnd remains in the foreground and the device is often turned on even briefly (maybe even accidentally, like by shifting in your pocket or backpack), then GPS would be activated each time with very noticeable impact on your battery life. In remote areas with bad or no cell reception, you can also set the device to "Flight mode" (but with GPS on) to save the battery by preventing the cell phone using high transmission power to look for transmission towers. You can get good 20 hours of recording GPX tracks on hikes with the above parameters.
Split your GPX tracks into intervals by distance (20 m - 10 km) or by time (15 seconds - 60 minutes) to view the details of each interval. View the ascent and descent of each part of your track, the maximum and average speed, as well as moving time.
Use our OsmAnd online GPS Tracker. Our application is multifunctional, it works including in the zone of unstable Internet connection. All you need is to have a Telegram account, additionally install the OsmAnd online GPS Tracker and now you can watch the movement of each member of your group, and they will be able to see you on the map in real time. More information: what it is and how to use it, read here.
You can save your navigation route as a GPX track. It will be done automatically every time you use navigation. Just go to Plugins-> Trip recording-> Settings. Choose 'Auto-record track during navigation'. You'll see your tracks in 'My tracks' section and in your osmand/tracks folder. GPX file will be keep all information about traffic lanes and complete turn instructions.
On iOS devices the way to save track as GPX is a bit different from one on Android. Having recent iOS versions with Files app, you can export GPX directly on device with "Save to Files" option in "Export" menu. To export any legacy trip track from your iOS OsmAnd application, choose My Places from side menu, select Trips tab, switch from "Active" to "All trips", tap on a trip you want to export. Tap "three dots button" on top of the screen and choose Export.
Sometimes when you record you seem to miss points/segments, like in the screenshot below (blue arrows).
The most common reason for this is the "Minimum Accuracy" in the Trip Recording settings (see below).
It is very tempting to set the "Minimum Accuracy" to someting like 5 or 10 meter, but if your have your phone in the pocket of your trousers or blouse, the accuracy is often > 5 meter, so choosing an accuracy of >10 meter is better. Otherwise the points will not be recorded. The accuracy is the "68% chance that the coordinate falls within the circle with radius x meters.
The most possible reason for this is "Battery Optimisation". On your phone go to Settings-> Battery. Select the triple-dot menu in the top right and choose "Battery Optimisation". OsmAnd should not be optimised. If it is optimised it can be stopped/paused by Android.
Note: Switching battery optimisation off can/will shorten battery life.
OsmAnd at Facebook, Twitter, and Reddit!
Join us at our groups of Telegram (EN), (IT), (FR), (DE), (RU), (UA).


Before Change
batch_shape = self.batch_shape
time_shape = self.event_shape[:1]
init = self.initial_dist.expand(batch_shape).rsample(sample_shape)
trans = self.transition_dist.expand(batch_shape + time_shape).rsample(sample_shape)
obs = self.observation_dist.expand(batch_shape + time_shape).rsample(sample_shape)
mat = self.transition_matrix.expand(batch_shape + time_shape + (self.hidden_dim, self.hidden_dim))
z = _linear_integrate(init, mat, trans)
return (z.unsqueeze(-2) @ self.observation_matrix).squeeze(-2) + obs
def filter(self, value):
Compute posterior over final state given a sequence of observations.
After Change
def rsample(self, sample_shape=torch.Size()):
sample_shape = torch.Size(sample_shape)
obs_dim = self.obs_dim
hidden_dim = self.hidden_dim
obs = self._obs.marginalize(right=self.obs_dim).event_pad(left=self.hidden_dim)
z = _sequential_gaussian_filter_sample(self._init, self._trans + obs, sample_shape)
perm = torch.cat([torch.arange(hidden_dim, hidden_dim + obs_dim, device=z.device),
torch.arange(hidden_dim, device=z.device)])
x = self._obs.event_permute(perm).condition(z).rsample()
return x
def filter(self, value):
Compute posterior over final state given a sequence of observations.


sparknlp.annotator.graph_extraction
#
Contains classes for GraphExtraction.
Module Contents#
Classes#
Extracts a dependency graph between entities. |
- class GraphExtraction(classname='com.johnsnowlabs.nlp.annotators.GraphExtraction', java_model=None)[source]#
Extracts a dependency graph between entities.
The GraphExtraction class takes e.g. extracted entities from a
NerDLModel
and creates a dependency tree which describes how the entities relate to each other. For that a triple store format is used. Nodes represent the entities and the edges represent the relations between those entities. The graph can then be used to find relevant relationships between words.Both the
DependencyParserModel
andTypedDependencyParserModel
need to be present in the pipeline. There are two ways to set them:Both Annotators are present in the pipeline already. The dependencies are taken implicitly from these two Annotators.
Setting
setMergeEntities()
toTrue
will download the default pretrained models for those two Annotators automatically. The specific models can also be set withsetDependencyParserModel()
andsetTypedDependencyParserModel()
:>>> graph_extraction = GraphExtraction() \ ... .setInputCols(["document", "token", "ner"]) \ ... .setOutputCol("graph") \ ... .setRelationshipTypes(["prefer-LOC"]) \ ... .setMergeEntities(True) >>> #.setDependencyParserModel(["dependency_conllu", "en", "public/models"]) >>> #.setTypedDependencyParserModel(["dependency_typed_conllu", "en", "public/models"])
Input Annotation types
Output Annotation type
DOCUMENT, TOKEN, NAMED_ENTITY
NODE
- Parameters:
- relationshipTypes
Paths to find between a pair of token and entity
- entityTypes
Paths to find between a pair of entities
- explodeEntities
When set to true find paths between entities
- rootTokens
Tokens to be consider as root to start traversing the paths. Use it along with explodeEntities
- maxSentenceSize
Maximum sentence size that the annotator will process, by default 1000. Above this, the sentence is skipped
- minSentenceSize
Minimum sentence size that the annotator will process, by default 2. Below this, the sentence is skipped.
- mergeEntities
Merge same neighboring entities as a single token
- includeEdges
Whether to include edges when building paths
- delimiter
Delimiter symbol used for path output
- posModel
Coordinates (name, lang, remoteLoc) to a pretrained POS model
- dependencyParserModel
Coordinates (name, lang, remoteLoc) to a pretrained Dependency Parser model
- typedDependencyParserModel
Coordinates (name, lang, remoteLoc) to a pretrained Typed Dependency Parser model
See also
GraphFinisher
to output the paths in a more generic format, like RDF
Examples
>>> import sparknlp >>> from sparknlp.base import * >>> from sparknlp.annotator import * >>> from pyspark.ml import Pipeline >>> documentAssembler = DocumentAssembler() \ ... .setInputCol("text") \ ... .setOutputCol("document") >>> sentence = SentenceDetector() \ ... .setInputCols(["document"]) \ ... .setOutputCol("sentence") >>> tokenizer = Tokenizer() \ ... .setInputCols(["sentence"]) \ ... .setOutputCol("token") >>> embeddings = WordEmbeddingsModel.pretrained() \ ... .setInputCols(["sentence", "token"]) \ ... .setOutputCol("embeddings") >>> nerTagger = NerDLModel.pretrained() \ ... .setInputCols(["sentence", "token", "embeddings"]) \ ... .setOutputCol("ner") >>> posTagger = PerceptronModel.pretrained() \ ... .setInputCols(["sentence", "token"]) \ ... .setOutputCol("pos") >>> dependencyParser = DependencyParserModel.pretrained() \ ... .setInputCols(["sentence", "pos", "token"]) \ ... .setOutputCol("dependency") >>> typedDependencyParser = TypedDependencyParserModel.pretrained() \ ... .setInputCols(["dependency", "pos", "token"]) \ ... .setOutputCol("dependency_type") >>> graph_extraction = GraphExtraction() \ ... .setInputCols(["document", "token", "ner"]) \ ... .setOutputCol("graph") \ ... .setRelationshipTypes(["prefer-LOC"]) >>> pipeline = Pipeline().setStages([ ... documentAssembler, ... sentence, ... tokenizer, ... embeddings, ... nerTagger, ... posTagger, ... dependencyParser, ... typedDependencyParser, ... graph_extraction ... ]) >>> data = spark.createDataFrame([["You and John prefer the morning flight through Denver"]]).toDF("text") >>> result = pipeline.fit(data).transform(data) >>> result.select("graph").show(truncate=False) +-----------------------------------------------------------------------------------------------------------------+ |graph | +-----------------------------------------------------------------------------------------------------------------+ |[[node, 13, 18, prefer, [relationship -> prefer,LOC, path1 -> prefer,nsubj,morning,flat,flight,flat,Denver], []]]| +-----------------------------------------------------------------------------------------------------------------+
- setRelationshipTypes(value)[source]#
Sets paths to find between a pair of token and entity.
- Parameters:
- valueList[str]
Paths to find between a pair of token and entity
- setEntityTypes(value)[source]#
Sets paths to find between a pair of entities.
- Parameters:
- valueList[str]
Paths to find between a pair of entities
- setExplodeEntities(value)[source]#
Sets whether to find paths between entities.
- Parameters:
- valuebool
Whether to find paths between entities.
- setRootTokens(value)[source]#
Sets tokens to be considered as the root to start traversing the paths.
Use it along with explodeEntities.
- Parameters:
- valueList[str]
Sets Tokens to be consider as root to start traversing the paths.
- setMaxSentenceSize(value)[source]#
Sets Maximum sentence size that the annotator will process, by default 1000.
Above this, the sentence is skipped.
- Parameters:
- valueint
Maximum sentence size that the annotator will process
- setMinSentenceSize(value)[source]#
Sets Minimum sentence size that the annotator will process, by default 2.
Below this, the sentence is skipped.
- Parameters:
- valueint
Minimum sentence size that the annotator will process
- setMergeEntities(value)[source]#
Sets whether to merge same neighboring entities as a single token.
- Parameters:
- valuebool
Whether to merge same neighboring entities as a single token.
- setMergeEntitiesIOBFormat(value)[source]#
Sets IOB format to apply when merging entities.
- Parameters:
- valuestr
IOB format to apply when merging entities. Values IOB or IOB2
- setIncludeEdges(value)[source]#
Sets whether to include edges when building paths.
- Parameters:
- valuebool
Whether to include edges when building paths
- setDelimiter(value)[source]#
Sets delimiter symbol used for path output.
- Parameters:
- valuestr
Delimiter symbol used for path output
- setPosModel(value)[source]#
Sets Coordinates (name, lang, remoteLoc) to a pretrained POS model.
- Parameters:
- valueList[str]
Coordinates (name, lang, remoteLoc) to a pretrained POS model


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This Div will fill 6 columns when the screen is bigger than 1024px.
Then untill the screen is bigger than 768px it will only fill 4 columns
In screen sizes between 767px and 480px it will fill 5 columns
For really small phones it will ocupy 12 columns
This Div will fill 6 columns when the screen is bigger than 1024px.
Then untill the screen is bigger than 768px it will fill 8 columns
In screen sizes between 767px and 480px it will fill 7 columns
For really small phones this div will not appear
This element has an offset of four columns
This element has 4 columns


{% extends "layout_unbranded.html" %} {% block page_title %} sportplaces.uk {% endblock %} {% block content %} {% include "includes/header_sports_places.html" %} Back When do you want to play badminton? How far can you travel to do sport? In the next day In the next week In the next month In more than a month {% endblock %}


Analyze and visualize results of studies performed with the analysis tools in 'JAMES', a modern object-oriented Java framework for discrete optimization using local search metaheuristics (see http://www.jamesframework.org).
| Version: | 1.0.1 |
| Depends: | R (≥ 2.10), graphics (≥ 2.15.3) |
| Imports: | rjson (≥ 0.2.14), naturalsort (≥ 0.1.2) |
| Suggests: | testthat (≥ 0.9.1) |
| Published: | 2015-06-18 |
| Author: | Herman De Beukelaer |
| Maintainer: | Herman De Beukelaer <Herman.DeBeukelaer at UGent.be> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | james.analysis results |
| Reference manual: | james.analysis.pdf |
| Package source: | james.analysis_1.0.1.tar.gz |
| Windows binaries: | r-devel: james.analysis_1.0.1.zip, r-release: james.analysis_1.0.1.zip, r-oldrel: james.analysis_1.0.1.zip |
| OS X binaries: | r-release: james.analysis_1.0.1.tgz, r-oldrel: james.analysis_1.0.1.tgz |
| Old sources: | james.analysis archive |
Please use the canonical form https://CRAN.R-project.org/package=james.analysis to link to this page.


Endeavour, Fortuner, Kodiaq, Alturas G4, Tiguan, CR-V – October 2019 Sales Comparison
by Sahil KukrejaThe full-sized premium SUV segment saw a decline in sales, with only the Ford Endeavour and Volkswagen Tiguan flaring
Even after the hefty discounts and offers provided by carmakers during the festive period, the premium SUV segment has seen a 12% YoY decline in sales last month as compared to October 2018.
The Toyota Fortuner was still untouched by any of its competitors. Even after witnessing a YoY decline in sales, it regained its four-digit sales last month after a hiatus in September 2019, and Toyota managed to ship a total of 1,302 Fortuners, making it the undisputed segment leader.
Its arch-rival Ford Endeavour, on the other hand, registered a YoY growth by 24%, which is the highest in the segment, thanks to a facelift being launched earlier this year which saw the seven-seat SUV get some additional features along with the festive deals Ford offered.
| Model | October 2019 Sales | October 2018 Sales |
| Toyota Fortuner | 1,302 | 1,842 |
| Ford Endeavour | 700 | 565 |
| Skoda Kodiaq | 180 | 190 |
| Volkswagen Tiguan | 155 | 133 |
| Honda CR-V | 152 | 228 |
| Mahindra Alturus G4 | 101 | – |
| Hyundai Tucson | 83 | 90 |
The Skoda Kodiaq took the third spot in the list, thanks to the off-roading centric variant ‘Scout’ being launched last month. Skoda sold 180 units of Kodiaq, which is still 10 units less than what the Czech carmaker sold in October 2018.
Apart from the Ford Endeavour, Volkswagen’s Tiguan was the only premium SUV to have registered a YoY growth last month. The German brand’s sole SUV offering in the Indian market sold a total of 155 units in October 2019, which resulted in a 17% YoY growth for the car.
On the fifth spot came the Honda CR-V, which was at the receiving end of the biggest YoY sales decline in the segment. Honda shipped 152 units of CR-V, 33% less than the 228 units it sold in October 2018. The CR-V recently received a facelift globally, but don’t expect it to come to India any time before late 2020.
The Mahindra Alturas G4 sold just 101 units in October 2019, which puts it at the 6th spot. The Alturas was highly anticipated before its launch in November last year but has failed to make a name for itself in the segment.
Just behind the Alturas G4 was the Hyundai Tucson, which was the only premium SUV with two-digit sales last month. Hyundai could only sell 83 units of the Tucson, but in terms of YoY sales, that is just 7 units lesser as compared to October 2018.


| test_file {testthat} | R Documentation |
Helper, setup, and teardown files located in the same directory as the test will also be run.
test_file(path, reporter = default_compact_reporter(), package = NULL, ...)
path |
Path to file. |
reporter |
Reporter to use to summarise output. Can be supplied
as a string (e.g. "summary") or as an R6 object
(e.g. See Reporter for more details and a list of built-in reporters. |
package |
If these tests belong to a package, the name of the package. |
... |
Additional parameters passed on to |
A list (invisibly) containing data about the test results.
There are two types of .R
file that have special behaviour:
Test files start with test
and are executed in alphabetical order.
Setup files start with setup
and are executed before tests. If
clean up is needed after all tests have been run, you can use
withr::defer(clean_up(), teardown_env())
. See vignette("test-fixtures")
for more details.
There are two other types of special file that we no longer recommend using:
Helper files start with helper
and are executed before tests are
run. They're also loaded by devtools::load_all()
, so there's no
real point to them and you should just put your helper code in R/
.
Teardown files start with teardown
and are executed after the tests
are run. Now we recommend interleave setup and cleanup code in setup-
files, making it easier to check that you automatically clean up every
mess that you make.
All other files are ignored by testthat.
Each test is run in a clean environment to keep tests as isolated as possible. For package tests, that environment that inherits from the package's namespace environment, so that tests can access internal functions and objects.
path <- testthat_example("success") test_file(path) test_file(path, reporter = "minimal")


|
Name here
V1.0
Summary here
|
Sample synthesize request More...
Classes | |
| class | InputOptions |
| Inputs Options for the TTS Service. More... | |
Public Member Functions | |
| Synthesize () | |
| Initializes a new instance of the Synthesize class. More... | |
| async Task< Stream > | Speak (CancellationToken cancellationToken, InputOptions inputOptions) |
| Sends the specified text to be spoken to the TTS service and saves the response audio to a file. More... | |
| string | ConvertVoiceNametoString (VoiceName voicename) |
| Converts a specific VoioceName enum option into its string counterpart as expected by the API when building the SSML string that is sent to Cognitive Services. Make sure that each option in the enum is included in the switch below. More... | |
| string | GetVoiceLocale (VoiceName voicename) |
Sample synthesize request
Definition at line 327 of file TTSClient.cs.
| CognitiveServicesTTS.Synthesize.Synthesize | ( | ) |
Initializes a new instance of the Synthesize class.
Definition at line 362 of file TTSClient.cs.
| string CognitiveServicesTTS.Synthesize.ConvertVoiceNametoString | ( | VoiceName | voicename | ) |
Converts a specific VoioceName enum option into its string counterpart as expected by the API when building the SSML string that is sent to Cognitive Services. Make sure that each option in the enum is included in the switch below.
| voicename |
Definition at line 421 of file TTSClient.cs.
| string CognitiveServicesTTS.Synthesize.GetVoiceLocale | ( | VoiceName | voicename | ) |
Definition at line 494 of file TTSClient.cs.
| async Task<Stream> CognitiveServicesTTS.Synthesize.Speak | ( | CancellationToken | cancellationToken, |
| InputOptions | inputOptions | ||
| ) |
Sends the specified text to be spoken to the TTS service and saves the response audio to a file.
| cancellationToken | The cancellation token. |
Definition at line 380 of file TTSClient.cs.


Cognitive Limitations
Introduction
Most cognitive deficits have biological or physiological roots. The link between biology and psychological processes is most obvious in cases of traumatic brain injury and genetic disorders, but more subtle cognitive disorders also stem from brain structures or chemical reactions. People with severe cognitive impairment need help in almost every aspect of their daily lives. However, people with mild learning disabilities may be able to function fully, even to the extent that the disability is never diagnosed (©WebAIM, 2020).
Functional Classifications
Functional categories focus on the user's abilities and challenges, regardless of medical or behavioral reasons. Multiple functional impairments can stem from a clinical diagnosis: people with memory loss may also have difficulty concentrating or solving problems ("Web Content Accessibility Guidelines (WCAG) 2.1", 2020 ).
Functional categories of cognitive disabilities mainly include difficulties with:
- Memory
- Problem-solving
- Attention
Functional categories are useful in Web accessibility because they align with the concerns of Web designers and developers. Telling a developer that certain people have autism only makes sense if the developer knows what obstacles autistic people may face with Web content. On the other hand, telling developers that certain people have difficulty understanding math provides a meaningful environment for developers. Developers just need to understand and consider the range of abilities of their users (©WebAIM, 2020).
The Toolkit to Help Cognitive Disability People
-
Accommodating Memory Deficits:
Reminders of the overall website context can help those with memory deficiencies. Long or complex processes (such as online browsing/searching and buying products) should be kept as simple and short as possible. To focus users' attention on specific tasks, break interactions into separate pages with progress indicators such as "Step 2 of 4" to help them keep track of what they've done and what's left to do. Links should say not only "Previous" and "Next", but also "Return to payment and shipping information" and "Next: Review order"("Web Content Accessibility Guidelines (WCAG) 2.1", 2020).
-
Accommodating Problem-Solving Deficits:
Anyone can click on the wrong link, misspell a word or leave a required form field blank. In some people, this trend is exaggerated. The error message should explain what is incorrect and show how to fix the problem. If the original spelling is suspicious or no results are returned, the search function should suggest alternative spellings to the user. The user should be warned to take significant action, such as deleting a file or making an irreversible financial transaction. Instructions at the beginning of the task help prevent user error.
Avoid accidental changes in the context of the site. Functionality should be as predictable as possible, with warnings or interpreter bias. Authentication processes that require the completion of cognitive function tests (e.g., completing a CAPTCHA puzzle) should include another authentication method, such as two-factor authentication (©WebAIM, 2020).
-
Accommodating Attention Deficits:
Help users focus their attention. Use visual cues to highlight key points or sections of displayed content. If possible, eliminate animations, ads, and sponsored links. Use headlines to draw attention to the focus and outline of the content. Avoid distracting background noise or images. Instead, use them to draw the user's attention to the content (©WebAIM, 2020).


... for considering to contribute directly! By building, using, posting, and talking about Hangprinter, you're already doing what the Hangprinter Project is all about.
The maintainers are super grateful if you make a pull request (in Gitlab called merge request), and submit it to the repo. See for example here for help with doing that.
If your improvement can't be structured into a pull request, the second best options are GitLab issues, and the forum.
Monthly donations can be set up via the Bountysource Salt Campaign.
Merchandise can be bought here: Mechadise Sweden. And here: Mechadise US.
All kinds of donations via Brave Browser are enabled. If you get Brave Browser via this link, then tobben will receive a small kickback.
Bitcoin donations are accepted on: 1BwobkC5Tb7psWkzCugtcH21ufj6Lc9mgY
... or in QR format:


 Franzen lumbersexual single-origin coffee, live-edge 3 wolf moon farm-to-table tilde fam unicorn. Synth iPhone ethical, celiac raw denim bitters snackwave yuccie unicorn bespoke trust fund tofu venmo 90's ramps.
Roof party chicharrones subway tile, messenger bag pok pok fap post-ironic master cleanse microdosing. Franzen lumbersexual single-origin coffee, live-edge 3 wolf moon farm-to-table tilde fam unicorn. Synth iPhone ethical, celiac raw denim bitters snackwave yuccie unicorn bespoke trust fund tofu venmo 90's ramps.
Roof party chicharrones subway tile, messenger bag pok pok fap post-ironic master cleanse microdosing. Franzen lumbersexual single-origin coffee, live-edge 3 wolf moon farm-to-table tilde fam unicorn. Synth iPhone ethical, celiac raw denim bitters snackwave yuccie unicorn bespoke trust fund tofu venmo 90's ramps.  Tbh fixie hell of blue bottle typewriter.
black locust farm | gathering together farm | wobbly cart farm | ayers creek farm | groundwork organics | briar rose creamery | denoble farms | camas country mill | central milling | square peg farm | point reyes creamery | cascadia creamery | realy good food | la quercia | cowbell cheesemonger | sauvie island organics | straus family creamery | bee local & more...


We Are Coming Soon ...
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don't look even believable.
Super Services
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
24/7 Support
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s.
Cloud Upload
The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested.


Widgets are a common feature of Ajax frameworks. While not technically Ajax, meaning they generally don’t use the XMLHTTP request object, widgets are called “Ajax” referring to next generation web UI. The Spry team is designing the framework in such a way that widgets are easy to understand and build. We hope to enable the community to start building and sharing their own widgets.
A widget is a block of HTML, CSS and JavaScript that encapsulates a piece of advanced user interface. Common widgets are accordions, trees and tabbed interfaces. These objects have been difficult to build, requiring advanced coding skills. The benefit of prebuilt widgets is that the difficult coding is taken care of and users can easily incorporate these advanced interface objects into their pages.
The Spry team, as a subset of the Dreamweaver team, has taken advantage of our insight and research into web page development and applied it to the Spry framework. We have developed a set of guiding principles in creating the Spry framework.
The benefits of the above 2 items is that Spry Widgets should be easy to edit. This model is familiar to designers and editing: To change the look and feel, simply change the CSS. To add a new accordion panel simply copy and paste an existing code block.
Many widgets are ‘disclosure’ objects, meaning that they hide and show content per user interactions. Examples of disclosure widgets are:
Other widgets embody common patterns in a discrete package:
It is easy to talk about code, but it’s even easier to understand by looking at code. Simple 3 panel accordion looks like:

Panel Header 1

Panel 1 Content

Panel Header 2

Panel 2 Content

Panel Header 3

Panel 3 Content

Using class names above, it is easy to figure out how to build an accordion. There is a:
The classes shown here are for clarity only. They are not required for functionality. That being said, CSS can be used to create accordions with very different styles. In the browser, the simply changing the ‘Header’ class code can give you these varieties:
There is a JavaScript object associated with a widget. It attaches behaviors and handles the user interaction. This JavaScript is kept in a external file.
It is critical to the usability of the widget that it be accessible when following established web navigation conventions. We can’t assume a mouse and therefore we take steps to ensure that all aspects of our widgets are accessible through the keyboard. In the accordion, up and down arrow keys can be used to open panes. We encourage all widget builders to build in this functionality.
We also can’t control the end user’s environment. We build our widgets to ensure that when JavaScript is turned off, all the content of the widget is available on the screen. While this will most likely affect the page layout, it is more important that the content of the widget be available, especially with disclosure widgets. We ensure that default CSS states do not set visibility to ‘hidden’ and markup is not positioned off the screen.
One of the goals of Spry is to enable the user community to build and share widgets. We have a set of guidelines that we use when authoring widgets for public consumption. We are publishing these guidelines in the hope that all widgets will have consistent base functionality.
Keeping to these guidelines will help ensure that widgets are easy to understand and use, plus consistency strengthens the framework for everyone.
Using standard markup is important because users know it already and it increases learnability. It also makes it easy to use these widgets within WYSIWYG editors.
CSS is used in some widgets to show and hide content by toggling the visibility rule in CSS. This would be a required use of CSS. That’s acceptable because that is the obvious mechanism for showing and hiding content. CSS that is pure styling should not be required. The widget should function without styling. Document required CSS rules with comments in the CSS file and if further documentation is provided, mention it there.
Most widgets are fired off with a single line of JavaScript just below the actual widget markup. Try to keep the arguments to a minimum. Widths and heights of widgets should be set in CSS, no JavaScript, unless there is no other way.
Keyboard Navigation and accessibility are important to users and to Spry. Write keyboard navigation so that users can use common workflow (arrow keys, space bar) to access all parts of your widget. Use things like tab order where appropriate.
It is vital that content not be hidden in non-scripting environments.
With Spry, Adobe is hoping to make Ajax accessible to designers and those that have been daunted by the steep learning curve. Remembering that the goal of computers is to do heavy calculations that would be difficult or tedious to do by hand, Spry does the heavy lifting and allows web page designers to focus on designing. With widgets, it will be easy to add advanced functionality to all pages. Sticking to the widget model, all Spry widgets should have a similar ease of use that will make Spry a strong and stable framework for designing next generation web pages.
Copyright © 2006. Adobe Systems Incorporated. All rights reserved.


Problem 6. Most frequent number
Write a script that finds the most frequent number in an array.
Push F12


Market Update
Crypto Markets Showing Mild Signs of Recovery, BTC Stalls At $7,200
by Joeri CantMonday, Nov. 25 — Cryptocurrency markets are showing mild signs of recovery, while Bitcoin (BTC) is fighting to stay above the $7,100 price mark.
Cryptocurrency market daily overview. Source: Coin360
Bitcoin started the day just above the $7,100 price mark, at which point the world’s largest crypto coin bounced off a local low of $6,560, before moving up again to its current trading price near $7,150. After a tumultuous 24 hours, BTC is showing a minor gain of just under 1% on the day.
BTC is down around 12% from the weekly open of $8,100, while, over the month, investors have lost around $1,800 per coin.
Bitcoin daily price chart. Source: Coin360
Bitcoin could spend months trading at $5,500
Commodities trader and technician Peter Brandt recently revealed his belief that BTC could spend months trading at $5,500 before it recovers. The veteran trader became best known as being one of the first analysts who accurately predicted the end of the bull run in December 2017.
“My target of $5,500 is not far below today's low. But I think the surprise might be in the duration and nature of the market,” he wrote. Brandt continued:
“I am thinking about a low in July 2020. That will wear out bulls quicker than a price correction.”
Well-known statistician and analyst Willy Woo, together with crypto trader Tone Vays, echoed similar sentiments when they said that the threat still remains that Bitcoin will hit $4,500.
Regular Cointelegraph contributor Filb Filb added his opinion to the mix of negative sentiments, saying that for him now was a time to be risk-off:
“I’m totally back in cash now. There doesn’t seem to be obvious sign of reversal. Going to sit on the sidelines until there is more indication.”
Ether (ETH), meanwhile, saw a painful drop in sync with BTC during recent days. The number-one altcoin bounced of its daily low at $134, before moving up to its current trading price of $147 per coin. Ether is showing a mild gain of around 0.7% on the day.
Ether 7-day price chart. Source: Coin360
XRP, the third-largest coin by market capitalization, continues to lose ground and currently sits at $0.220 per coin, showing a loss of more than 3% at press time.
XRP 7-day price chart. Source: Coin360
Mixed signals among the top 20
Many of the top 20 coins are showing mild signs of recovery, with Cosmos (ATOM) taking the lead, up more than 4% on the day. Monero (XMR) comes in second place with a gain of around 2.2% at press time. Tezos (XTZ) takes the title of biggest loser, seeing a loss of nearly 9% on the day.
The overall cryptocurrency market cap currently sits around $195.9 billion, with Bitcoin making up 66.4% of the total.
Keep track of top crypto markets in real time here


| GetRosstat | R Documentation |
Download data based on indicator and aggregation level from Rosstat Regional Statistics
GetRosstat(indicator = "mortality_rate", level = "federal_district")
indicator |
Character string. Select indicator from
the |
level |
Character string. Defines the administrative level of aggregation
More than one level is NOT allowed. |
data frame with 7 columns
region
Region name in Russian
region_en
Region name
in English
year
Year of observation
value
Value of the indicator
level
Aggregation level
id_shape
id number for
mergin with shapefile from GADM
indicator
name of the indicator
Markus Kainu <markuskainu(at)gmail.com>
# dat <- GetRosstat("life_expectancy_men","federal_district")


I'm A Celebrity star Ian Wright explains why Match of the Day is so poignant to him
Ian Wright has been a regular on Match of the Day since his playing career ended but the BBC show has extra poignancy to him as he prepares to enter the I'm a Celebrity jungle
by Alex SmithIan Wright has explained that appearing on Match of the Day is his "Graceland" after his abusive step-father wouldn't let him watch the football highlights show.
Arsenal legend Wright, who is set to enter the I'm a Celebrity...Get Me Out of Here! jungle on Sunday night, opened up on how the famous jaunty theme music would leave him with a pain in his chest.
Wright’s father absconded and left mother Nesta to raise her family in a one-bedroom house in Brockley, South London.
His step-dad was cruel to Wright, with the ex-England striker sharing an emotional story about how he was only allowed to listen, but not watch, Match of the Day on Saturday nights.
"He was a weed-smoking, gambling, coming-home-late, gambling-his-wages, womanising kind of guy,” Wright told the Players' Tribune.
 And I don’t know why, but he didn’t like me in particular.
"One of the few things my brother and I looked forward to in the house was Match of the Day, and my stepdad used to take that away from us – just because he could.
"Depending on what mood he was in, he’d come into the bedroom just before it started and he’d say, ‘Turn around. Turn around to the wall.’
"We had to face the wall the whole time Match of the Day was on. And the really cruel thing was that we could still hear everything. It was awful. I would cry myself to sleep whenever he did it.
"I carried that with me for years. Whenever I heard the Match of the Day theme music come on, I would feel that pain in my chest. And I’ll be honest with you, it still gets to me every now and again."
Wright would go on to become a regular on the BBC program, following his successful playing career, but it is his first appearance, under the stewardship of Des Lynam that left him almost in tears.
He added: "The first time I went on the show as a presenter, Des Lynam walked up to me and said, ‘Ian Wright, welcome to Match of the Day'.
"I nearly broke down crying. I told Des, 'This is my Graceland.'"


[NEWS!] We will host HACS Temporal Action Localization Challenge at CVPR'21 International Challenge on Activity Recognition Workshop. You are welcome to participate in our action localization challenge. The challenge guideline is HERE, and the evaluation server is open now!
This project introduces a novel video dataset, named HACS (Human Action Clips and Segments). It consists of two kinds of manual annotations. HACS Clips contains 1.55M 2-second clip annotations; HACS Segments has complete action segments (from action start to end) on 50K videos. The large-scale dataset is effective for pretraining action recognition and localization models, and also serves as a new benchmark for temporal action localization. (*SLAC dataset is now part of HACS dataset.)
HACS Clips includes:
HACS Segments includes:
HACS annotation pipeline:
Each row shows the sampled clips from one video, their corresponding start and end times (start, end), and the annotations (Positive or Negative).
Play the videos to check segment annotations, which are shown in the timelines below.
If you find our work helpful, please cite the following paper:
@article{zhao2019hacs, title={HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization}, author={Zhao, Hang and Yan, Zhicheng and Torresani, Lorenzo and Torralba, Antonio}, journal={arXiv preprint arXiv:1712.09374}, year={2019} }


This challenge focuses on classes in JavaScript using the new class
keyword.
Follow these steps to set up and work on your project:
<firstName-lastName>
.<firstName-lastName>
branch, committing changes regularly.<firstName-lastName>
.Follow these steps for completing your project.
You already pretty much know all about classes but you’re used to seeing them built in the following context:
function Person(personAttributes) {
this.name = personAttributes.name;
this.age = personAttributes.age;
this.location = personAttributes.location;
}
const fred = new Person({
name: 'Fred',
age: 37,
location: 'Bedrock'
});
prototype-refactor
- Take existing code and make it modern.Challenge:
Convert all of your constructors into ES6 Classes using the class
and extends
keywords. You should be able to run your same logs and they should build out the proper expected behaviors.lambda-classes
- We need a roster of Lambda School personnel. Build it!people
.
const fred = new Instructor({
name: 'Fred',
location: 'Bedrock',
age: 37,
favLanguage: 'JavaScript',
specialty: 'Front-end',
catchPhrase: `Don't forget the homies`
});
base-class
name
age
location
all as propsspeak
as a method.Hello my name is Fred, I am from Bedrock
where name
and location
are the object’s own propsspecialty
what the Instructor is good at i.e. ‘redux’favLanguage
i.e. ‘JavaScript, Python, Elm etc.’catchPhrase
i.e. Don't forget the homies
demo
receives a subject
string as an argument and logs out the phrase ‘Today we are learning about {subject}’ where subject is the param passed in.grade
receives a student
object and a subject
string as arguments and logs out ‘{student.name} receives a perfect score on {subject}’previousBackground
i.e. what the Student used to do before Lambda SchoolclassName
i.e. CS132favSubjects
. i.e. an array of the student’s favorite subjects [‘Html’, ‘CSS’, ‘JavaScript’]listsSubjects
a method that logs out all of the student’s favoriteSubjects one by one.PRAssignment
a method that receives a subject as an argument and logs out that the student.name has submitted a PR for {subject}
sprintChallenge
similar to PRAssignment but logs out student.name has begun sprint challenge on {subject}
gradClassName
: i.e. CS1favInstructor
: i.e. SeanstandUp
a method that takes in a slack channel and logs {name} announces to {channel}, @channel standy times!
debugsCode
a method that takes in a student object and a subject and logs out {name} debugs {student.name}'s code on {subject}


reveal.js-plugins
reveal.js-plugins
This is a collection of plugins for Reveal.js - a framework for easily creating beautiful presentations using HTML. Example presentations and demos for these plugins can be found and added here.
This collections includes the following plugins:
- Anything (Demo): A plugin for adding plots, charts, animated SVGs,or anything else inside an HTML object using a JSON string and a javascript function.
- Audio slideshow (Demo): A plugin for audio playback and recording.
- Broadcast (Demo): An extension of the multiplex plugin allowing to broadcast audio and video.
- Chalkboard (Demo): A plugin adding a chalkboard and slide annotation.
- Chart (Demo): A plugin for using Chart.js v2.0.
- Custom controls (Demo): A plugin for customization of controls.
- Embed tweet (Demo): A plugin allowing to easily embed tweets in your slides.
- Fullscreen (Demo): A simple plugin allowing to use fullscreen slides.
- MathSVG: An extension of the math.js plugin allowing to render LaTeX expressions in SVG.
- Menu (Demo): A plugin by Greg Denehy for adding a slideout menu.
- Spreadsheet (Demo): A plugin for adding Excel-like spreadsheets with formulas.
Getting started
The source code of the demos can be found here: https://github.com/rajgoel/reveal.js-demos.
Download
You can download the plugins into the bower_components
folder using
bower install reveal.js-plugins
or manually copy this repository next to the folder of your reveal.js presentation.
Please note that the menu-plugin is a submodule and has to be downloaded separately.
License
MIT licensed
Copyright (C) 2017 Asvin Goel


Purchase
Dealer Registration
Sales
Register New Item
Logout
Logged in as {{full_name}}
Purchase Page
{{message}}
{% csrf_token %}
{% for item_number in "1234567" %}
{% for item_name in item_names %}
{{item_name}}
{% endfor %}
{% endfor %}


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New
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Garden
BOTANICAL
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Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


--- layout: default title: Welcome to Kotlin By Example ---
Learn Kotlin with small simple annotated examples. Click on the left navigation menu to go to a specific sample.
If you'd like to contribute, please see the `examples` folder. More instructions coming soon.
Copyright MIT (c) 2017 JetBrains


0.0
HOURS OF
DEEP WORK
DAILY
more >
+0.0
hours from yesterday!
WEEKLY
more >
+0.0
hours from last week!
START
ROTATE YOUR DEVICE!


{% load define_action %} {% if fixed == "true" %} {% assign "" as root %} {% assign "" as activeModifiers %} {% assign "" as responsiveModifiers %} {% assign "" as closingTag %} {% assign " nav-link" as navlinkModifier %} {% else %} {% assign "


The Peacemaker - A Real American Hero by Homer Yen (c) 1997
This anticipated inaugural film by the newly created Dreamworks SKG production company is not a tremendous offering given the hype that has preceded its release. However, it accomplishes three improbable but ultimately satisfying things. It re-energizes the somewhat stalled career of leading lady Nicole Kidman. It gives George Clooney, that medical hearthrob from television's ER, a chance to really flex his cinematic muscles. And, it refreshes an increasingly stale plotline of stolen nuclear weapons and America as the target.
As the crisis unfolds, we are introduced to Dr. Julia Kelly (Nicole Kidman). As the acting head of the White House Nuclear Smuggling Group, she is the expert that's leading the investigation and the operation of recovering the stolen nuclear warheads. With a PhD from Harvard under her belt, she is adamant about following rules and protocols, and is the perennial cautious planner that builds in contingencies for everything. As she briefs the top brass on the intelligence that has been gathered so far, she is constantly interrupted by Thomas Devoe (George Clooney). He is an intelligence officer with the U.S. Army's special forces who believes profiteers will sell these weapons to rogue governments. Not the typical soldier, he is brash, bends rules, and takes great liberties to get what he wants including heavy bribes and savage in-your-face beatings. However, when it comes to things military, he is smart, clever, and a man of action. He gets to the point and on people's nerves too...especially those of Dr. Kelly's.
It doesn't take a degree in nuclear physics to figure out that these two are eventually paired up by the powers that be to save the world. But fortunately, the writers are able to give us two likable characters that can overcome the cliches of hardened Russians, sophisticated spy satellites, and bombs with digital readouts. The two work in unison to overcome their distaste for one another's personality and skillfully negotiate their way towards the location of the warheads. But as with all bombs with digital readouts, the audience knows that time is running out for the heroes.
The Peacemaker, which was filmed in various locales around the world, evokes the look and feel of Tom Clancy's books-turned-movies. A strong score helps to elevate the unfolding drama and the tension is constant throughout. Clooney is perfectly cast as the virtuous and honorable military fighter. He is macho. He is action-hungry. He is GI Joe. Kidman also provides good balance as they race to save the world. But despite the slick look of the movie and the constant level of excitement that is generated by the chase scenes, you'll come out with the feeling that most of what you saw was done in dozens of action movies in the recent past. Also, at times the movie seems overly complicated as if simply adding more events and chases would somehow add to the film's weight. Still, this kind of entertainment is scarce during the autumn, and you'll still be able to make peace with yourself for seeing it.
Grade: B
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: post title: Happy people are less well-connected to reality date: 2005-01-21 00:00:00 category: play ---
"[T]he data support other empirical research showing that individuals in a positive mood (here, happiness) tend to process information more superficially than those in a negative mood (here, anxiety)": Happy people are less well-connected to reality


Connect
Ed
New Class
My Classes
New Question Set
My Question Sets
Welcome to ConnectEd!
Students:
Sit tight and follow your teacher's instructions to join his or her class.
Instructors:
Get started by
creating a class
and inviting your students to join.


>>
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{% extends "layout.html" %} {% include 'navigation_bar.html' %} {% block body %}
"We hope you may like this phone."


Freedom Bridge is a full-range iOS app development company that covers complete iOS development cycles spanning from conception to deployment.
Freedom Bridge is a digital agency with design and development expertise. We are focusing on Product design, UX/UI and iOS Development.


| ↑ Home |
Answers to questions that are asked frequently or might be asked frequently in the future.
Moss is a dynamically typed programming language, which allows functional and object oriented programming.
The program moss
, written in lower case, consists
of a small compiler and a larger byte code interpreter. It is also
available as a library, which could be integrated into another
program.
Moss is meant to be a general purpose programming language, but it is certainly not suitable for everything. Most well suited would it be for scripting or as an interface language to performant programs.
For example, Moss is developed together with a scientific library, a matrix caculator and a plotting library, serving purposes similar to SciPy, Matlab/Octave, Gnuplot and Matplotlib.
Moss is not suitable for programs that should run performant on the bare metal cold or for large complex systems, due to its lack of a static type system and optimizations.
Be aware that callback functions written in a dynamically typed language might slow down systems written in a performant language. If callback functions occur at critical locations, it might be necessary to reformulate the whole procedure in the performant language.
The project goals are so far:
Have a look on these micro benchmarks:
→ Benchmarks.
Asynchronous functions are currently not implemented, but planned for the future. It is easy to implement async-await as a syntactic sugar, once the coroutine/continuation system is completed.
Alternatively, to be more minimalistic, async
might
be provided as a library module, dispensing with syntacic sugar,
as coroutine syntax is versatile enough. Currently possible is the
following toy implementation of async
:
async_list = [] function async(f) return fn|*a| async_list.push(f(*a)) end end function scheduler for fp in cycle(async_list) fp() end end f = async(|text| fn*|| while true print(text) yield end end) g = async(|text| fn*|| while true print(text) print(text) yield end end) f("la") g("dup") scheduler()