Model¶
Base¶
-
class
kospeech.model.base.
BaseRNN
(input_size, hidden_dim, num_layers, rnn_type, dropout_p, bidirectional, device)[source]¶ Applies a multi-layer RNN to an input sequence.
Note
Do not use this class directly, use one of the sub classes.
Parameters: - input_size (int) – size of input
- hidden_dim (int) – the number of features in the hidden state h
- num_layers (int, optional) – number of recurrent layers (default: 1)
- bidirectional (bool, optional) – if True, becomes a bidirectional encoder (defulat: False)
- rnn_type (str, optional) – type of RNN cell (default: gru)
- dropout_p (float, optional) – dropout probability (default: 0)
- device (torch.device) – device - ‘cuda’ or ‘cpu’
Variables: = Dictionary of supported rnns (supported_rnns) –
-
forward
(*args, **kwargs)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
Listener¶
-
class
kospeech.model.encoder.
Listener
(input_size, hidden_dim, device, dropout_p=0.3, num_layers=3, bidirectional=True, rnn_type='lstm', extractor='vgg', activation='hardtanh', mask_conv=False)[source]¶ Converts low level speech signals into higher level features
Parameters: - input_size (int) – size of input
- hidden_dim (int) – the number of features in the hidden state h
- num_layers (int, optional) – number of recurrent layers (default: 1)
- bidirectional (bool, optional) – if True, becomes a bidirectional encoder (defulat: False)
- rnn_type (str, optional) – type of RNN cell (default: gru)
- dropout_p (float, optional) – dropout probability (default: 0)
- device (torch.device) – device - ‘cuda’ or ‘cpu’
- Inputs: inputs, hidden
- inputs: list of sequences, whose length is the batch size and within which each sequence is list of tokens
- hidden: variable containing the features in the hidden state h
- Returns: output
- output: tensor containing the encoded features of the input sequence
-
forward
(inputs, input_lengths)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
Speller¶
-
class
kospeech.model.decoder.
Speller
(num_classes, max_length, hidden_dim, sos_id, eos_id, attn_mechanism='dot', num_heads=4, num_layers=2, rnn_type='lstm', dropout_p=0.3, device=None)[source]¶ Converts higher level features (from listener) into output utterances by specifying a probability distribution over sequences of characters.
Parameters: - num_classes (int) – the number of classfication
- max_length (int) – a maximum allowed length for the sequence to be processed
- hidden_dim (int) – the number of features in the hidden state h
- sos_id (int) – index of the start of sentence symbol
- eos_id (int) – index of the end of sentence symbol
- num_layers (int, optional) – number of recurrent layers (default: 1)
- rnn_type (str, optional) – type of RNN cell (default: gru)
- dropout_p (float, optional) – dropout probability (default: 0)
- device (torch.device) – device - ‘cuda’ or ‘cpu’
- Inputs: inputs, encoder_outputs, teacher_forcing_ratio
- inputs (batch, seq_len, input_size): list of sequences, whose length is the batch size and within which each sequence is a list of token IDs. It is used for teacher forcing when provided. (default None)
- encoder_outputs (batch, seq_len, hidden_dim): tensor with containing the outputs of the listener. Used for attention mechanism (default is None).
- teacher_forcing_ratio (float): The probability that teacher forcing will be used. A random number is drawn uniformly from 0-1 for every decoding token, and if the sample is smaller than the given value, teacher forcing would be used (default is 0).
- Returns: decoder_outputs, ret_dict
- decoder_outputs (seq_len, batch, num_classes): list of tensors containing the outputs of the decoding function.
- ret_dict: dictionary containing additional information as follows {KEY_ATTENTION_SCORE : list of scores representing encoder outputs, KEY_SEQUENCE_SYMBOL : list of sequences, where each sequence is a list of predicted token IDs }.
-
forward
(inputs, encoder_outputs, teacher_forcing_ratio=0.9, language_model=None)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
ListenAttendSpell¶
-
class
kospeech.model.las.
ListenAttendSpell
(listener, speller)[source]¶ Listen, Attend and Spell (LAS) Model
Parameters: - listener (torch.nn.Module) – encoder of seq2seq
- speller (torch.nn.Module) – decoder of seq2seq
- Inputs: inputs, input_lengths, targets, teacher_forcing_ratio
- inputs (torch.Tensor): tensor of sequences, whose length is the batch size and within which each sequence is a list of token IDs. This information is forwarded to the encoder.
- input_lengths (torch.Tensor): tensor of sequences, whose contains length of inputs.
- targets (torch.Tensor): tensor of sequences, whose length is the batch size and within which each sequence is a list of token IDs. This information is forwarded to the decoder.
- teacher_forcing_ratio (float): The probability that teacher forcing will be used. A random number is drawn uniformly from 0-1 for every decoding token, and if the sample is smaller than the given value, teacher forcing would be used (default is 0.90)
- Returns: output
- output (seq_len, batch_size, num_classes): list of tensors containing the outputs of the decoding function.
- Reference:
- Listen Attend and Spell: https://arxiv.org/abs/1508.01211
-
forward
(inputs, input_lengths, targets=None, teacher_forcing_ratio=1.0, language_model=None)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
Attention¶
-
class
kospeech.model.attention.
LocationAwareAttention
(hidden_dim, smoothing=True)[source]¶ Applies a location-aware attention mechanism on the output features from the decoder. Location-aware attention proposed in “Attention-Based Models for Speech Recognition” paper. The location-aware attention mechanism is performing well in speech recognition tasks. We refer to implementation of ClovaCall Attention style.
Parameters: - Inputs: query, value, last_attn
- query (batch, q_len, hidden_dim): tensor containing the output features from the decoder.
- value (batch, v_len, hidden_dim): tensor containing features of the encoded input sequence.
- last_attn (batch_size * num_heads, v_len): tensor containing previous timestep`s attention (alignment)
- Returns: output, attn
- output (batch, output_len, dimensions): tensor containing the feature from encoder outputs
- attn (batch * num_heads, v_len): tensor containing the attention (alignment) from the encoder outputs.
- Reference:
- Attention-Based Models for Speech Recognition: https://arxiv.org/abs/1506.07503
- ClovaCall: https://github.com/clovaai/ClovaCall/blob/master/las.pytorch/models/attention.py
-
forward
(query, value, last_attn)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
class
kospeech.model.attention.
MultiHeadAttention
(hidden_dim, num_heads=4)[source]¶ Applies a multi-headed scaled dot mechanism on the output features from the decoder. Multi-head attention proposed in “Attention Is All You Need” paper.
Parameters: - Inputs: query, value
- query (batch, q_len, hidden_dim): tensor containing the output features from the decoder.
- value (batch, v_len, hidden_dim): tensor containing features of the encoded input sequence.
- Returns: context
- context (batch, output_len, dimensions): tensor containing the attended output features from the decoder.
- Reference:
- Attention Is All You Need: https://arxiv.org/abs/1706.03762
- State-Of-The-Art Speech Recognition with Sequence-to-Sequence Models: https://arxiv.org/abs/1712.01769
- Contributor:
- Soohwan Kim (sooftware)
- Deokjin Seo (qute012)
-
forward
(query, value)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
BeamSearch¶
-
class
kospeech.model.beam_search.
BeamSearchDecoder
(decoder, beam_size)[source]¶ Top-K decoding with beam search.
Parameters: - decoder (e2e.model.speller.Speller) – decoder to which beam search will be applied
- beam_size (int) – size of beam
- Inputs: input_var, encoder_outputs
- input_var : sequence of sos_id
- encoder_outputs : tensor containing the encoded features of the input sequence
- Returns: decoder_outputs
- decoder_outputs : list of tensors containing the outputs of the decoding function.
-
forward
(input_var, encoder_outputs, teacher_forcing_ratio=0.0)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
Conv¶
-
class
kospeech.model.conv.
CNNExtractor
(activation='hardtanh')[source]¶ Provides inteface of convolutional extractor.
Note
Do not use this class directly, use one of the sub classes.
-
forward
(inputs, input_lengths)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
-
class
kospeech.model.conv.
DeepSpeech2Extractor
(in_channels=1, activation='hardtanh', mask_conv=False)[source]¶ DeepSpeech2 extractor for automatic speech recognition described in “Deep Speech 2: End-to-End Speech Recognition in English and Mandarin” paper - https://arxiv.org/abs/1512.02595
-
forward
(inputs, input_lengths)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
-
class
kospeech.model.conv.
MaskConv
(sequential)[source]¶ Masking Convolutional Neural Network
Adds padding to the output of the module based on the given lengths. This is to ensure that the results of the model do not change when batch sizes change during inference. Input needs to be in the shape of (batch_size, channel, hidden_dim, seq_len)
Refer to https://github.com/SeanNaren/deepspeech.pytorch/blob/master/model.py Copyright (c) 2017 Sean Naren MIT License
Parameters: sequential (torch.nn) – sequential list of convolution layer - Inputs: inputs, seq_lengths
- inputs (torch.FloatTensor): The input of size BxCxHxS
- seq_lengths (torch.IntTensor): The actual length of each sequence in the batch
- Returns: output, seq_lengths
- output: Masked output from the sequential
- seq_lengths: Sequence length of output from the sequential
-
forward
(inputs, seq_lengths)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
get_seq_lengths
(module, seq_lengths)[source]¶ Calculate convolutional neural network receptive formula
Parameters: - module (torch.nn.Module) – module of CNN
- seq_lengths (torch.IntTensor) – The actual length of each sequence in the batch
- Returns: seq_lengths
- seq_lengths: Sequence length of output from the module
-
class
kospeech.model.conv.
VGGExtractor
(in_channels=1, activation='hardtanh', mask_conv=False)[source]¶ VGG extractor for automatic speech recognition described in “Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM” paper - https://arxiv.org/pdf/1706.02737.pdf
-
forward
(inputs, input_lengths)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
Language Model¶
-
class
kospeech.model.lm.
RNNLanguageModel
(num_classes, num_layers, rnn_type, hidden_dim, dropout_p, max_length, sos_id, eos_id, device)[source]¶ -
forward
(inputs, teacher_forcing_ratio=1.0)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-


Finch XP Get Started
Get Started
Try out FinchXP today and begin using our APIs in minutes. To get started, simply go to https://console.finchxp.com/signup to create your free account. Once you've received our email with you secure account verification link, simply click on the link and log in at https://console.finchxp.com/login You're now logged in and can access your secure and private API key.
Schemes: https
Version: 1.0.0
Authentication
bearer
Platform
The FinchXP API supports multiple Pay-as-you-go products, depending on your data intelligence requirements.
These include:
- Responsible Lending
- Personal Finance
- Loyalty & Affiliate Marketing
- Digital Banking (see Enterprise Solutions)
Free access
Once your account is created, you’ll be able to select either the Responsible Lending or Personal Finance API to get free monthly access as follows:
- 100 unique users
- 100 calls max. per day
- 5,000 transactions
Hello world
SDK
In addition to our APIs we also have several flexible and easy-to-install SDKs for download, namely:
Mobile:
- iOS
- Android
Web:
- Java
- JS
- Ruby
Enterprise
Finch also provides custom data intelligence solutions for our enterprise-level customers.
These include: Real-time enablement for card controls, instant lending, and more Data mapping and labelling to your specific category or product requirements Other file formats: including JSON, XML, CSV, and parquet.
If you're looking for a custom solution, contact our sales team today<EMAIL_ADDRESS>Introduction
Welcome to the FinchXP API reference documentation.
The FinchXP API provides secure access to data intelligence around Australian transactions - ingesting millions of transactions every month to provide you with industry-leading identification, categorisation, and additional information.
Developer Console
To get access to the API, simply go to https://console.finchxp.com/signup to create your free account. Once you've received our email with you secure account verification link, simply click on the link and log in at https://console.finchxp.com/login
In your console you will find everything you need to get going, including:
- Private API Key
- Client ID?
- Swagger UI
- SDKs
Token
Get the JWT bearer token to be used in all subsequent requests
Get JWT bearer token
undefined
API Key to grab
Request Content-Types: application/json
Request Example
{
"clientId": "string",
"clientSecret": "string"
}
Response Content-Types: application/json
Response Example (200 OK)
{}
Transactions
Parse transactions
undefined
Request Content-Types: application/json
Request Example
{
"transactions": [
{
"transactionId": "61",
"transactionDescription": "Discount Drug Beachme Beachmere AU",
"providerId": "CBAI",
"transactionDate": "2020-11-21",
"transactionAmount": "150.00",
"transactionCurrency": "AUD",
"transactionStatus": "POSTED",
"transactionSource": "Bank",
"userId": "U0001",
"accountNumber": "U0001",
"accountType": "Saving",
"accountSubType": "",
"refreshDate": "2020-11-21",
"balanceDate": "2020-11-21",
"accountBalance": "number",
"availableBalance": "number",
"transactionType": "DEBIT"
}
],
"enrichmentLevel": "BASIC"
}
Parsed transaction response
Response Content-Types: application/json
Response Example (200 OK)
{
"transactions": [
{
"id": "string",
"userId": "string",
"transactionType": "string",
"amount": "number",
"currency": "string",
"merchant": {
"name": "string",
"abn": "string",
"phone": "string",
"email": "string",
"website": "string",
"location": {
"address": "string",
"suburb": "string",
"state": "string",
"postcode": "string",
"longitude": "number",
"latitude": "number"
}
},
"category": "string",
"parentCategory": "string",
"types": {
"atm": "boolean",
"adjustable": "boolean",
"descretionary": "boolean",
"expense": "boolean",
"online": "boolean",
"suitable": "boolean",
"transfer": "boolean"
},
"valueDate": "2020-11-21"
}
]
}
Bulk Processing
Create a new bulk processing job
undefined
Request Content-Types: application/json
Request Example
{
"contentType": "string",
"format": "string",
"processingType": "string",
"enrichmentLevel": "BASIC",
"expiresIn": 86400,
"requireMultipart": false,
"notifiedWebhook": "",
"notifiedEmail": ""
}
Bulk processing job response
Response Content-Types: application/json
Response Example (200 OK)
{
"id": "string",
"status": "string",
"signedPutUrl": "string",
"processingType": "string",
"enrichmentLevel": "BASIC",
"signedPutUrlExpiresAt": "2020-11-21T00:00:00Z",
"created": "2020-11-21T00:00:00Z",
"modified": "2020-11-21T00:00:00Z"
}
Request new part for multi-part upload job
undefined
Request Content-Types: application/json
Request Example
{
"id": "string",
"partNumber": 2,
"expiresIn": 86400
}
Next part details for bulk processing job response
Response Content-Types: application/json
Response Example (200 OK)
{
"id": "string",
"signedPutUrl": "string",
"partNumber": "number",
"signedPutUrlExpiresAt": "2020-11-21T00:00:00Z"
}
Complete job upload
undefined
Request Content-Types: application/json
Request Example
{
"id": "string",
"parts": [
{
"PartNumber": "number",
"ETag": "string"
}
]
}
Mark a job as being completed for uploading
Response Content-Types: application/json
Response Example (200 OK)
{
"id": "string",
"signedPutUrl": "string",
"partNumber": "number",
"signedPutUrlExpiresAt": "2020-11-21T00:00:00Z"
}
Force start job processing (this should happen automatically after upload succeeded)
(no description)
Current job details
Response Content-Types: application/json
Response Example (200 OK)
{
"id": "string",
"status": "string",
"signedPutUrl": "string",
"processingType": "string",
"enrichmentLevel": "BASIC",
"signedPutUrlExpiresAt": "2020-11-21T00:00:00Z",
"created": "2020-11-21T00:00:00Z",
"modified": "2020-11-21T00:00:00Z"
}
Health
Get service health
ServiceHealthModel
Response Content-Types: application/json
Response Example (200 OK)
{
"services": {
"database": "boolean"
},
"isOK": "boolean",
"serverTimestamp": "number"
}
Schema Definitions
AuthTokenAuthRequest: object
- clientId: string
- clientSecret: string
Example
{
"clientId": "string",
"clientSecret": "string"
}
SingleTransactionParseRequest: object
- transactionId: string
-
The transaction number is a unique transaction number as defined by the API Consumer. Each transaction belongs to only one account.
- transactionDescription: string
-
The original description of the transaction as provided by the institution.
- providerId: string
-
Internal provider ID to parse
- transactionDate: string (date-time)
-
The original date of the transaction as provided by the institution.
- transactionAmount: number
-
The signed original amount of the transaction as provided by the institution.
- transactionCurrency: string
-
The currency of the transaction as provided by the institution, default AUD.
- transactionStatus: string POSTED, PENDING
-
The status of the transaction, can be POSTED or PENDING.
- transactionSource: string
-
The source of the transactions, can be aggregator name (i.e. Yodlee, Proviso) or Bank
- userId: string
-
Internal User ID to the API Consumer's platform
- accountNumber: string
-
The account number. This will typically be a masked or partial account number.
- accountType: string
-
An account type represents a financial account held by a financial institution, e.g., a user's checking, savings, or investment account.
- accountSubType: string
-
An account subtype represents the specific type that belongs to an account type, usually loan subtypes.
- refreshDate: string (date-time)
-
The date the account was last refreshed. If the value is empty, null will be returned.
- balanceDate: string (date-time)
-
The last time the balance was refreshed. If the value is empty, null will be returned.
- accountBalance: number
-
The balance in an account, pre-holds or pending transactions. Max length is 14,2. If the value is empty, null will be returned.
- availableBalance: number
-
The available balance in an account, pre-holds or pending transactions. Max length is 14,2. If the value is empty, null will be returned.
- transactionType: string CREDIT, DEBIT
-
A transaction type represents a money movement in or out of an account e.g. money in, income, or credit; money out, expense, or debit.
Example
{
"transactionId": "61",
"transactionDescription": "Discount Drug Beachme Beachmere AU",
"providerId": "CBAI",
"transactionDate": "2020-11-21",
"transactionAmount": "150.00",
"transactionCurrency": "AUD",
"transactionStatus": "POSTED",
"transactionSource": "Bank",
"userId": "U0001",
"accountNumber": "U0001",
"accountType": "Saving",
"accountSubType": "",
"refreshDate": "2020-11-21",
"balanceDate": "2020-11-21",
"accountBalance": "number",
"availableBalance": "number",
"transactionType": "DEBIT"
}
MultipleTransactionParseRequest: object
- transactions: SingleTransactionParseRequest
-
List of transactions to parse
-
SingleTransactionParseRequest - enrichmentLevel: string BASIC, CATEGORISATION, PRO
-
Level of enrichment to be returned. This will control the fields to be returned and the pricing per API call will also change accordingly
Example
{
"transactions": [
{
"transactionId": "61",
"transactionDescription": "Discount Drug Beachme Beachmere AU",
"providerId": "CBAI",
"transactionDate": "2020-11-21",
"transactionAmount": "150.00",
"transactionCurrency": "AUD",
"transactionStatus": "POSTED",
"transactionSource": "Bank",
"userId": "U0001",
"accountNumber": "U0001",
"accountType": "Saving",
"accountSubType": "",
"refreshDate": "2020-11-21",
"balanceDate": "2020-11-21",
"accountBalance": "number",
"availableBalance": "number",
"transactionType": "DEBIT"
}
],
"enrichmentLevel": "BASIC"
}
MerchantLocation: object
- address: string
- suburb: string
- state: string
- postcode: string
- longitude: number
- latitude: number
Example
{
"address": "string",
"suburb": "string",
"state": "string",
"postcode": "string",
"longitude": "number",
"latitude": "number"
}
MerchantDetails: object
- name: string
- abn: string
- phone: string
- email: string
- website: string
- location: MerchantLocation
Example
{
"name": "string",
"abn": "string",
"phone": "string",
"email": "string",
"website": "string",
"location": {
"address": "string",
"suburb": "string",
"state": "string",
"postcode": "string",
"longitude": "number",
"latitude": "number"
}
}
ParsedTransactionType: object
- atm: boolean
- adjustable: boolean
- descretionary: boolean
- expense: boolean
- online: boolean
- suitable: boolean
- transfer: boolean
Example
{
"atm": "boolean",
"adjustable": "boolean",
"descretionary": "boolean",
"expense": "boolean",
"online": "boolean",
"suitable": "boolean",
"transfer": "boolean"
}
ParsedTransaction: object
- id: string
- userId: string
- transactionType: string
- amount: number
- currency: string
- merchant: MerchantDetails
- category: string
- parentCategory: string
- types: ParsedTransactionType
- valueDate: string (date-time)
Example
{
"id": "string",
"userId": "string",
"transactionType": "string",
"amount": "number",
"currency": "string",
"merchant": {
"name": "string",
"abn": "string",
"phone": "string",
"email": "string",
"website": "string",
"location": {
"address": "string",
"suburb": "string",
"state": "string",
"postcode": "string",
"longitude": "number",
"latitude": "number"
}
},
"category": "string",
"parentCategory": "string",
"types": {
"atm": "boolean",
"adjustable": "boolean",
"descretionary": "boolean",
"expense": "boolean",
"online": "boolean",
"suitable": "boolean",
"transfer": "boolean"
},
"valueDate": "2020-11-21"
}
MultipleTransactionParseResponse: object
- transactions: ParsedTransaction
-
List of parsed transactions results
-
ParsedTransaction
Example
{
"transactions": [
{
"id": "string",
"userId": "string",
"transactionType": "string",
"amount": "number",
"currency": "string",
"merchant": {
"name": "string",
"abn": "string",
"phone": "string",
"email": "string",
"website": "string",
"location": {
"address": "string",
"suburb": "string",
"state": "string",
"postcode": "string",
"longitude": "number",
"latitude": "number"
}
},
"category": "string",
"parentCategory": "string",
"types": {
"atm": "boolean",
"adjustable": "boolean",
"descretionary": "boolean",
"expense": "boolean",
"online": "boolean",
"suitable": "boolean",
"transfer": "boolean"
},
"valueDate": "2020-11-21"
}
]
}
JobCreateRequestModel: object
- contentType: string text/csv, text/tab-separated-values, application/x-lzop, application/json, application/gzip, application/x-snappy-framed
- format: string csv, tsv, json, orc, parquet
- processingType: string standard, expedited
-
Definding how job should be processed.
standard
will process it at normal rates,expedited
will do so at faster rates at higher cost - enrichmentLevel: string BASIC, CATEGORISATION, PRO
-
Level of enrichment to be returned. This will control the fields to be returned and the pricing per API call will also change accordingly
- expiresIn: number
-
How long the signed URL to upload job should be valid for. If blank, this will use the system default
- requireMultipart: boolean
-
If True, multipart upload will be initiated. Mandatory for 5GB file upload or above, recommended for 100MB+ uploads. The SignedURL returned will be the link to upload the first part. Call to
getPartUrl
is required for the next part thencompleteUpload
is required after all parts are uploaded. - notifiedWebhook: string
-
Optional WebHook to call when job is completed or failed
- notifiedEmail: string
-
Optional Email to receive notification when the job is completed or failed
Example
{
"contentType": "string",
"format": "string",
"processingType": "string",
"enrichmentLevel": "BASIC",
"expiresIn": 86400,
"requireMultipart": false,
"notifiedWebhook": "",
"notifiedEmail": ""
}
JobModel: object
- id: string
- status: string
- signedPutUrl: string
- processingType: string standard, expedited
-
Definding how job should be processed.
standard
will process it at normal rates,expedited
will do so at faster rates at higher cost - enrichmentLevel: string BASIC, CATEGORISATION, PRO
-
Level of enrichment to be returned. This will control the fields to be returned and the pricing per API call will also change accordingly
- signedPutUrlExpiresAt: string (date-time)
- created: string (date-time)
- modified: string (date-time)
Example
{
"id": "string",
"status": "string",
"signedPutUrl": "string",
"processingType": "string",
"enrichmentLevel": "BASIC",
"signedPutUrlExpiresAt": "2020-11-21T00:00:00Z",
"created": "2020-11-21T00:00:00Z",
"modified": "2020-11-21T00:00:00Z"
}
JobGetNextPartSignedUrlModel: object
- id: string
- partNumber: number
-
Part number (should starts at 2 since the create job request already returned the first signed URL part for upload)
- expiresIn: number
-
How long the signed URL to upload job should be valid for. If blank, this will use the system default
Example
{
"id": "string",
"partNumber": 2,
"expiresIn": 86400
}
JobNextPartModel: object
- id: string
- signedPutUrl: string
- partNumber: number
- signedPutUrlExpiresAt: string (date-time)
Example
{
"id": "string",
"signedPutUrl": "string",
"partNumber": "number",
"signedPutUrlExpiresAt": "2020-11-21T00:00:00Z"
}
JobUploadPartMetadata: object
- PartNumber: number
- ETag: string
Example
{
"PartNumber": "number",
"ETag": "string"
}
JobCompleteUploadModel: object
- id: string
- parts: JobUploadPartMetadata
-
Uploaded part ETag metadata (if multipart upload only)
-
JobUploadPartMetadata
Example
{
"id": "string",
"parts": [
{
"PartNumber": "number",
"ETag": "string"
}
]
}
ServiceHealthModel: object
- services: ServicesStatus
- isOK: boolean
- serverTimestamp: number
Example
{
"services": {
"database": "boolean"
},
"isOK": "boolean",
"serverTimestamp": "number"
}


---
title: "Day 2"
description: "NeIC 2015 conference session program day 2"
layout: menu-wide
---
{% assign event = site.data.neic2015 %}
Conference Program Day 2
Conference Day 2 - {{ event.program[3].day }}
{% assign day = event.program[3] %}
{% include neic2015/conference_schedule.html day=day event=event event_root='/neic2015' %}


Moby Dick
by Herman Melville
Chapter 117: The Whale Watch
Additional Information
- Year Published: 1851
- Language: English
- Country of Origin: United States of America
- Source: Melville H. (1851). Moby Dick.London, England: Richard Bently.
-
Readability:
- Flesch–Kincaid Level: 9.8
- Word Count: 509
- Genre: Adventure
- Keywords: 19th century literature, american literature, herman melville, moby dick
- ✎ Cite This
Downloads
The four whales slain that evening had died wide apart; one, far to windward; one less distant, to leeward; one ahead; one astern. These last three were brought alongside ere nightfall; but the windward one could not be reached till morning; and the boat that had killed it lay by its side all night; and that boat was Ahab’s.
The waif-pole was thrust upright into the dead whale’s spout-hole; and the lantern hanging from its top, cast a troubled flickering glare upon the black, glossy back, and far out upon the midnight waves, which gently chafed the whale’s broad flank, like soft surf upon a beach.
Ahab and all his boat’s crew seemed asleep but the Parsee; who crouching in the bow, sat watching the sharks, that spectrally played round the whale, and tapped the light cedar planks with their tails. A sound like the moaning in squadrons over Asphaltites of unforgiven ghosts of Gomorrah, ran shuddering through the air.
Started from his slumbers, Ahab, face to face, saw the Parsee; and hooped round by the gloom of the night they seemed the last men in a flooded world. “I have dreamed it again,” said he.
“Of the hearses? Have I not said, old man, that neither hearse nor coffin can be thine?”
“And who are hearsed that die on the sea?”
“But I said, old man, that ere thou couldst die on this voyage, two hearses must verily be seen by thee on the sea; the first not made by mortal hands; and the visible wood of the last one must be grown in America.”
“Aye, aye! a strange sight that, Parsee!- a hearse and its plumes floating over the ocean with the waves for the pall-bearers. Ha! Such a sight we shall not soon see.”
“Believe it or not, thou canst not die till it be seen, old man.”
“And what was that saying about thyself?”
“Though it come to the last, I shall still go before thee thy pilot.”
“And when thou art so gone before- if that ever befall- then ere I can follow, thou must still appear to me, to pilot me still?- Was it not so? Well, then, did I believe all ye say, oh my pilot!  I am immortal then, on land and on sea,” cried Ahab, with a laugh of derision;- “Immortal on land and on sea!”
Both were silent again, as one man. The grey dawn came on, and the slumbering crew arose from the boat’s bottom, and ere noon the dead whale was brought to the ship.


THE VANISHING A film review by Daniel Barrett Copyright 1992 Daniel Barrett
THE VANISHING (1991) Starring Bernard-Pierre Donnadieu, Gene Bervoets, and Johanna Ter Steege. Directed by George Sluizer. In French and Dutch with (very readable) subtitles. Distributed on Fox Lorber Home Video.
The videocassette cover of THE VANISHING proudly announces that the movie has won "10 leading international awards" and calls it "the first genuine sleeper of 1991," claiming that the "terrifying conclusion... will haunt you long after the movie ends." I agree wholeheartedly -- this is an extraordinary suspense film with a truly terrifying ending. This review contains *no* spoilers, so you may read it freely.
The premise is simple: Rex, a young man, is baffled when one day his wife Saskia mysteriously disappears. He devotes his life to solving the mystery of what happened to her. Does he find out? Will WE find out? These questions form the basis for the suspense that fills this movie.
And THE VANISHING is a true suspense movie. There is no blood, no gore, and almost no violence (a few punches). The suspense builds VERY subtly; we hardly notice it for the first hour. At that point, Rex is asked to embark on a mysterious journey, and the tension quickly mounts. The final 10 minutes are simply riveting and (to me) very surprising. I will indeed remember the conclusion "long after the movie ends."
THE VANISHING deals with several fundamental questions about human nature. The first question is "What is the nature of innocence?", or more naively, "Why do bad things happen to good people?" What did Rex do, if anything, to deserve this trouble? Was Saskia at all to blame for her disappearance? The film may not necessarily answer these questions, but the viewer will certainly confront them.
The second question is "What are the limits of human decency?", or, "How evil can a person be, yet still remain human?" This is a difficult question to address in a movie that has no blood, gore, nor violence, yet it is handled masterfully. Hidden in this issue is the third important question: "Could *I* have committed such an evil act?"
The fourth question, the one which I found most disturbing, involves a choice that Rex must make: the choice between knowledge or ignorance of what happened to Saskia. Both choices are clearly "the wrong one" to make, and yet he must choose. So the chilling fourth question is what we ask ourselves both before *and* after the event: "If I had been Rex, which would I have chosen?" And after answering it, one can also ask "Am I being honest with myself?"
THE VANISHING has several parallels to one of my favorite suspense films, THE HITCHER. (If you didn't like THE HITCHER, don't let that prevent you from seeing THE VANISHING -- they are very different movies in many respects.) Both films feature an "innocent" protagonist who is thrust into a strange situation and forced to make paradoxical, yet irrevocable choices between equally bad (or unknown) outcomes. There is perhaps nothing more terrifying than making a conscious choice, regretting it, and knowing that the choice can never be undone. But where THE HITCHER is a story of corruption of innocence, THE VANISHING asks "What *is* innocence?"
I should note that the first 15 minutes of the movie are the least interesting -- the acting seems forced, and the plot is slow and almost amateurish. Do not worry; the film improves rapidly after this, and the acting is quite excellent. Although the viewer is never required to like Rex and Saskia personally, nor any of the other characters who appear, all relevant personalities are firmly established. I purposely have not described any other characters because their mysterious actions add greatly to the suspense.
I recommend THE VANISHING very highly to anyone who likes intelligent suspense movies. But BE WARNED: some parts are truly horrifying in a manner I have rarely seen on film. It may well give you nightmares. I haven't been able to stop thinking it about for the past 24 hours.
Dan<EMAIL_ADDRESS>P.S.: I would enjoy corresponding with other people who have seen this movie. Please write and tell me your thoughts! .
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


Some text to show you that I can write text!
My name is Aamer. Your name is: Student. his name is: Another Student.


JavaScript/Express/Handlebars/Bootstrap (Team Project)
JavaScript/Express/Handlebars/Bootstrap
JavaScript/Bulma (Team Project)
JavaScript/CSS


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The following are significant references for anticedents to Common
Lisp or related languages (in some cases being used as working
documents for the ANSI Common Lisp
committee). In some cases, the material presented in these
references is subsummed by current Common Lisp
references or by material in Lisp books.
- ``Common Lisp Object System Specification,''
D. Bobrow, L. DiMichiel, R.P. Gabriel, S. Keene, G. Kiczales, D. Moon,
SIGPLAN Notices V23, September, 1988.
- ``Exceptional Situations in Lisp,''
Kent M. Pitman Proceedings of the First European Conference
on the Practical Application of LISP
(EUROPAL '90),
Churchill College, Cambridge, England,
March 27-29, 1990.
- ``Flavors: A Non-Hierarchical Approach to Object-Oriented Programming,''
Howard I. Cannon, 1982.
- IEEE Standard for the Scheme Programming Language,
IEEE Std 1178-1990,
Institute of Electrical and Electronic Engineers, Inc. (New York), 1991.
- Interlisp Reference Manual Third Revision,
Teitelman, Warren, et al,
Xerox Palo Alto Research Center (Palo Alto, CA), 1978.
- Lisp 1.5 Programmer's Manual
John McCarthy, MIT Press (Cambridge, MA), August, 1962.
- LISP 1.5 Programmer's Manual, 2nd edition
John McCarthy, et al. MIT Press, 1965, ISBN 0-262-13011-4 (paperback).
[Gives five basic functions, CAR, CDR, CONS, EQ, and ATOM.
Using composition, conditional expressions (COND), and
recursion, LAMBDA, and QUOTE, these basic functions may be used
to construct the entire class of computable functions of
S-expressions. Gives the functions EVAL and APPLY in
M-expression syntax.]
- Lisp Machine Manual,
D.L. Weinreb and D.A. Moon,
Artificial Intelligence Laboratory, MIT (Cambridge, MA), July, 1981.
- Often informally referred to as the Chinual.
- Maclisp Reference Manual, Revision 0,
David A. Moon, Project MAC (Laboratory for Computer Science),
MIT (Cambridge, MA), March, 1974.
- Often informally referred to as the Moonual.
- ``NIL---A Perspective,''
JonL White, Macsyma User's Conference, 1979.
- The Revised Maclisp Manual (Saturday Evening Edition),
Kent M. Pitman, Technical Report 295,
Laboratory for Computer Science, MIT (Cambridge, MA), May 1983.
- Often informally referred to as the Pitmanual.
- ``Revised^4 Report on the Algorithmic Language Scheme,''
William Clinger and Jonathan Rees (editors)
Lisp Pointers IV(3):1--55, July--September, 1991.
- (LISP Pointers is a newsletter published by ACM SIGPLAN.)
- ``S-1 Common Lisp Implementation,''
R.A. Brooks, R.P. Gabriel, and G.L. Steele,
Conference Record of the 1982 ACM Symposium on Lisp and Functional Programming,
108-113, 1982.
- Smalltalk-80: The Language and its Implementation,
A. Goldberg and D. Robson, Addison-Wesley, 1983.
- ``Standard LISP Report,''
J.B. Marti, A.C. Hearn, M.L. Griss, and C. Griss,
SIGPLAN Notices V14, #10, October, 1979.
- XP: A Common Lisp Pretty Printing System
R.C. Waters,
Memo 1102a,
Artificial Intelligence Laboratory, MIT (Cambridge, MA), September 1989.
|


Library: Algorithms
Function
Algorithm that counts the number of elements in a range that satisfy a given condition
#include <algorithm> namespace std { template<class InputIterator, class T> typename iterator_traits<InputIterator>::difference_type count(InputIterator start, InputIterator finish, const T& value); template <class InputIterator, class T, class Size> void count(InputIterator start, InputIterator finish, const T& value, Size& n); template<class InputIterator, class Predicate> typename iterator_traits<InputIterator>::difference_type count_if(InputIterator start, InputIterator finish, Predicate pred); template <class InputIterator, class Predicate, class Size> void count_if(InputIterator start, InputIterator finish, Predicate pred, Size& n); }
NOTE -- The second versions of the count() and count_if() functions are not part of the C++ Standard, but are included here as extensions for compatibility purposes. See Appendix B for a complete list of extensions of this implementation.
The count() algorithm compares value to elements in the sequence defined by iterators start and finish. The first version of count() returns the number of matches. The second version, which is provided for backwards compatibility or as an alternative to the first in case the template iterator_traits isn't provided, increments a counting value n each time it finds a match. In other words, count() returns (or adds to n) the number of iterators i in the range [start, finish) for which the following condition holds:
*i == value
Type T must be EqualityComparable.
The count_if() algorithm lets you specify a predicate, and returns the number of times an element in the sequence satisfies the predicate (or increments n that number of times). That is, count_if() returns (or adds to n) the number of iterators i in the range [start, finish) for which the following condition holds:
pred(*i) == true.
Both count() and count_if() perform exactly finish - start applications of the corresponding predicate.
// // count.cpp // #include <algorithm> // for count, count_if #include <functional> // bind2nd, less #include <iostream> // for cout, endl #include <vector> // for vector int main () { // Typedef for convenience. typedef std::vector<short, std::allocator<short> > vector; const vector::value_type a [] = { 1, 2, 3, 4, 5, 5, 7, 8, 9, 10 }; vector::size_type i = 0, j = 0, k = 0; // Set up a vector. const vector v (a + 0, a + 10); std::count (v.begin (), v.end (), 5, i); // Count fives std::count (v.begin (), v.end (), 6, j); // Count sixes // Count all elements less than 8. std::count_if (v.begin(), v.end(), std::bind2nd(std::less<vector::value_type>(), 8), k); std::cout << i << " " << j << " " << k << std::endl; return 0; } Program Output:
2 0 7
If your compiler does not support partial specialization, the first version of both count() and count_if(), the version that returns the count, is not available.
ISO/IEC 14882:1998 -- International Standard for Information Systems -- Programming Language C++, Section 25.1.6


A group needs at least a simple majority of active nodes in order to elect a Master. This means that for a replication group of size two, the failure of a single node means that the group as a whole is no longer available. In some cases, it may be desirable for the application to proceed anyway.
JE HA can explicitly relax the requirement for a simple majority of nodes. This is only possible when the replication group size is two. The application does this by designating one of the two electable nodes as a Primary node. The other node in the group is implicitly the Non-Primary node.
At any given instant in time, exactly one of the two nodes can be designated as the Primary. The application is responsible for ensuring that this is the case.
When the Non-Primary node is not available, the number of nodes required for a simple majority is reduced to one. As a consequence, the Primary is able to elect itself as the Master and then commit transactions that require a simple majority to commit. The Primary is said to be active when it is operating in this state. The transition from a designated Primary to an active Primary happens when the Primary needs to contact the Non-Primary node, but fails to do so for one of the following reasons:
An election is initiated by the Primary to determine a new Master. This might happen because the Primary is just starting up, or because the Primary has lost contact with the Non-Primary. In either case, if the election fails to establish a Master, the Primary is activated and it becomes the Master.
Note that the Primary will attempt to locate a Master until it has hit the retry limit as defined by the ELECTIONS_PRIMARY_RETRIES configuration property. But until the Primary has reached that limit, it will not transition to the active state.
An Environment.beginTransaction() operation is invoked on the Primary while it is in the Master state, and it cannot establish contact with the Non-Primary in the time period specified by the INSUFFICIENT_REPLICAS_TIMEOUT configuration property.
A Transaction.commit() needing a commit acknowledgement is invoked on the Primary while it is in the Master state, and the Primary does not receive the commit acknowledgement within the time period specified by the REPLICA_ACK_TIMEOUT configuration property.
Both the INSUFFICIENT_REPLICAS_TIMEOUT and REPLICA_ACK_TIMEOUT error cases are driven by the durability policy that you are using for your transactions. See Managing Durability for more information.
The three properties described above: ELECTIONS_PRIMARY_RETRIES, INSUFFICIENT_REPLICAS_TIMEOUT and REPLICA_ACK_TIMEOUT impact the time taken by the Primary to become active in the absence of the Non-Primary. Choosing smaller values for the timeouts and election retries will generally result in smaller service disruptions by activating the Primary more rapidly. The downside is that transient network glitches may result in unnecessary transitions to the active state where the Primary is operating with reduced Durability. It's up to the application to make these tradeoffs appropriately based on its operating environment.
When the Non-Primary becomes available again, the Primary becomes aware of it as part of the Master/Replica handshake (see Replica Startup). At that time, the number of nodes required for a simple majority reverts to two. That is, the Primary is no longer in the active state.
Your application must be very careful to not designate two nodes as Primaries. If both nodes are designated as Primaries, and the two nodes cannot communicate with one another for some reason, they could both consider themselves to be Masters and start accepting write transactions. This would violate a fundamental requirement of JE HA that at any given instant in time, there is only one node that is permitted to write to the replicated environment.
The Non-Primary always needs two nodes for a simple majority, and as a result can never become a Master in the absence of the Primary. If the Primary node fails, you can make provisions to swap the Primary and Non-Primary designations, so that the surviving node is now the Primary. The swap must be done carefully to ensure that both nodes are not concurrently designated Primaries. In particular, the failed node must come up as a Non-Primary after it has been repaired.
You designate a node as Primary using the mutable config property DESIGNATED_PRIMARY. You set this property using ReplicationMutableConfig.setDesignatedPrimary(). This property is ignored for groups of size greater than two.
As stated above, this configuration can only be set for one node at a time. This condition is checked during the Master/Replica startup handshake, and if both are designated as Primary then an EnvironmentFailureException is thrown. However, you should not rely on this handshake process to guard against dual Primaries. As stated above, if both nodes are designated Primary at some point after the handshake occurs, and your application experiences a network partition event such that the two nodes can no longer communicate, then both nodes will become Masters. This is error condition that will require you to lose data on at least one of the nodes if writes have occurred on both nodes while the network partition was in progress.


This tool can clip a raster to a boundary, either to a rectangular area or to a shape you define interactively on the screen. You can clip to the extent of the area you currently have displayed on your map or by the study area defined by a polygon.
The input layer to clip.
Choose the extent type to use when clipping the geometry of the layer:
The name of the layer that will be created in My Content and added to the map. The default name is based on the tool name and the input layer name. If the layer already exists, you will be prompted to provide another name.
You can specify the name of a folder in My Content where the result will be saved using the Save result in drop-down box.


| Category | Training (Hours) | Experience (Months) |
|---|---|---|
| Category I | 30 | 6 |
| Category I | CAT I +38 | 18 |
| Category I | CAT II +38 | 36 |
| Category I | CAT II +38 | 60 |
We are GOLD Sponsor Representative in India & Corporate member of MPTI Institute, an organization for scientists, engineers and other professionals representing a broad cross-section of industries to share information about measuring and analyzing vibration.
On Behalf of MPTI Institute , we regularly conduct Category I, II & III Course and Examination in India. Candidates can choose the date of examination to meet their convenience and leave schedule.
Our in-house as well as organized open house vibration Analysis training programs are considered one of the best among our Customers. Our team comprises of renowned technical experts, specialized Plant Engineers, reputed Professors from Academic and Research Institutes and has served the industry and have rich hands-on practical experience in solving vibration related problems from CNC to Large Turbines & Compressors. Our In-Plant Training programme is modeled with real time Case Studies & Exercises to suit each industry.
All our Seminars / Training Programs focus on problem diagnostics and trouble-shooting with priority for Practical Implementation. The subjects covered shall be tailor made to match the customer's requirements. Updating the technical strength, familiarizing the latest technologies and providing practical insight to real life case studies from complex industries are the basic target of these seminars.
Listed below are the minimum hours of formal training required in vibration analysis and the minimum months of experience required in the field of machineryMPTI condition monitoring and diagnostics in order to qualify to sit for (take)MPTI Institute USA examination.
Due to the minimum number of training hour required by the Standard (listed below), many candidates will be required to certify at the Category I level prior to taking the Category II exam. This requirement may be waived if the candidate meets one of the criteria listed below under "Direct Entry to Category II".
| Category | Training (Hours) | Experience (Months) |
|---|---|---|
| Category I | 30 | 6 |
| Category I | CAT I +38 | 18 |
| Category I | CAT II +38 | 36 |
| Category I | CAT II +38 | 60 |


This site is designed to give individuals a clear idea of what their Social Security retirement benefits might be. It uses data provided by the Social Security website (https://ssa.gov/) for an earning's record. From that data, the site produces a detailed social security report.
This site provides clear interactive visuals that let the user investigate how different choices might affect their overall benefit. Scenarios include:
This site does not store any data entered by users. All calculations are made client-side in the browser's JavaScript and are never transmitted over the internet.
While unnecessary for security, this can be confirmed by loading the site in your browser, and then disconnecting your computer before entering any additional data.
This site should be correct, and I will update it as social security rules change. However, there are no guarantees of the accuracy of this information. It should be treated as a useful exploratory tool, but any decisions should be made after researching the subject yourself.
This site is completely free and contains no advertising. I don't collect your email address, subscribe you to SMS services, make a credit report inquiry, kill a dolphin, etc. Feel free to use the site without wondering if there is a catch.
Costs for running the site are minimal, just a few dollars a year, plus my time.
I noticed that most people don't understand their Social Security benefits very well. The online tools at SSA.gov are excellent, but I felt lacking in a few places. In particular, I felt that by simply spitting out a final number, the results failed to educate the user as to how the program works. I thought it would be useful to contribute a tool that helped folks to understand their own social security benefit and how it will help them in the future. Consider it an act of goodwill for a good cause.
If you would like to contribute, the project is on github at: Gregable/social-security-tools


cells cell CDc cKO OpenUrl brain animals mice Cre- priming TMEV H-D Fig infection (N=) CNS class LysM CrossRef model viral MHC antigen time Littermates response specific Theiler’s virus data APCs Abstract D<;sup>;b<;/sup>; antiviral conducted dpi presented relative FREE Immunol. Text live uninfected dendritic mouse DCs Data FITC-albumin epitope flow immune mean previously responses Clone Johnson Mann-Whitney disruption events intracranial (A) (B) CBL/ SEM. following littermates microglia observed presentation test. APC Comparison LoxP MHC-I Virol. b:VP barrier immunodominant peptide spleens transgenic (C) MRI infection. naïve nervous restricted role usage CMV MΦs capable central control early fluorescence injected multiple murine timepoint. tissue total transgene using vascular (N=). (p=.) BBB N>; PIFS Representative Rodriguez Science Web analysis assessed blood-brain course days encephalomyelitis expressing homogenate later mice, normal regions responsible shows study variable virus-specific vivo ANOVA H-D<;sup>;b<;/sup>; ablation capacity changes genotype imaging infection, new novel (D) -way ;<;div CDCD CNS. Comparisons Cre-recombinase Download Experimental However, Open Pirko Tonbo), Unchanged [[#xB]] acute age animals, animals. antibodies brains cells. cytometry data-hide-link-title=""> data-icon-position="" defect deficient domain experiment figure gadolinium gated gender group induce induced intensity macrophages min molecule overt p<;.. p<;..<;/div>;<;/div>;" parenchyma pathogens percentage population post present presenting reported reveals schematic shown solution spinal strain sufficient tab transmembrane xmlns=";http://www.w.org//xhtml";>;<;span C). Daniel’s FITC-conjugated Jin Mice Neuroinflammation. PBS Pavelko ability activated activation activity administration antigen-presenting antigens cells, compensatory consistent critical crossed cytometric day disruption. dpi. equivalent euthanasia euthanized expressed gross impact infiltration knockout lymphoid naive permeability point point. potential prior remains required secondary single subjected subsets taken thymi (E) (MFI) (p=.). B). BioLegend), Chen Conditional Groups H-K Kruskal-Wallis Occludin PBS, Quantitation TCRβ accumulation additional analyses antibody antigen-specific appropriate assay assessing blood cKO, clearing concentration conditional context cre-recombinase delayed demonstrate demyelination depletion described development different dilution disease does dynamics effector entry established experimental expression gene homogenates identity infiltrating injection integrity junction late-arising levels littermates, load median mediated mice. models off-target picornavirus plate plots populations prime priming. promoter protein rapid region repertoire research restoration results robust seven significant standardized subsequently suggests tail tight vein virus.


Ron Howard is taking over the Han Solo 'Star Wars' movie
After days of speculation, the Oscar-winning filmmaker Ron Howard has signed on to take over as director of Disney and Lucasfilm's untitled standalone Han Solo movie.
13/07/2016
After days of speculation, the Oscar-winning filmmaker Ron Howard has signed on to take over as director of Disney and Lucasfilm's untitled standalone Han Solo movie.
13/07/2016
Han Solo got duped into giving a strange old man and cocky young farm boy a ride in the original script for Star Wars: A New Hope
13/07/2017
The auction house Profiles in History said the 43-inch tall unit that was compiled from parts used throughout filming of the original trilogy sold for $2.76 million at an auction Wednesday.
28/06/2017
Our company is comprised of a dedicated team of professional woodworkers, carpenters, designers and furniture manufacturers with one goal in mind – to craft functional and visually flawless furniture pieces that can meet the needs of any whimsical customers. Be it a kitchen furniture, bedroom furniture or just some garden accessories, be sure of the highest quality cobined with the latest trends, fashion, and style. We are commited to exceeding your expectations!
TELEPHONE:+1<PHONE_NUMBER>
FAX:+1<PHONE_NUMBER>


Being the richest man in the cemetery doesn’t matter to me. Going to bed at night saying we’ve done something wonderful… that’s what matters to me
Creativity is just connecting things.”
Have the courage to follow your heart and intuition. They somehow already know what you truly want to become. Everything else is secondary.


{% extends 'base.html' %} {% from 'shared/macros.html' import form_section %} {% block page_title %}{{ _('Upload a Video') }}{% endblock %} {% block content %}
{% trans dropbox='http://dropbox.com', vidly='http://vid.ly' %} Upload your video to a personal server or to a service like Dropbox, then paste the URL for your flick below. We will be reposting all videos to a contest gallery on Vid.ly. Don't forget to give your flick a title, select the video category you are entering it in and choose your region and then press "upload." We will email you when your entry has been processed. {% endtrans %}


HopeRF RFM69CW - wireless transceiver
The RFM69CW is a pin-compatiable replacement for the RFM12B. The module is manufactured by HopeRF with SEMTEC silicon.
With the updated RF12 driver from JeeLabs the RFM69CW can be backwards compatible with the RFM12B
Credit to LowPowerLab.com for comparison table:
Another RFM12B comparison from http://www.mikrocontroller.net/articles/RFM69
The native frequency of the RFM69CW can be identified by the addition of an RF network capacitor on the 433Mhz unit.
Software:
JeeLabs RF12 JeeLib RF12 Arduino Library Driver: https://github.com/jcw/jeelib
JeeLabs Forum RF12 RFM69CW discussion topic: http://jeelabs.net/boards/6/topics/4050
To make existing sketches work with RFM69CW , update to latest JeeLib and add #define RF69_COMPAT 1 before #include <JeeLib.h> . See JeeLib example . (RF69_COMPAT 0 if using RFM12B)
Links:
Hope RF Product Page: http://www.hoperf.com/rf/fsk_module/RFM69CW.htm
RFM69CW Datasheet: http://www.hoperf.com/upload/rf/RFM69CW-V1.1.pdf
Issues
The RFM69CW requires a 10K-100K pull-up resistor on the SEL to de-select it from the SPI bus to allow ISP uploading: https://openenergymonitor.org/emon/node/5345


name: inverse layout: true class: center, middle, inverse --- # STAT 105: Lecture 2 ## Why Engineers Study Statistics ### Chapter 1: Introduction, Continued ### Chapter 2: Data Collection .footnote[Course page: [imouzon.github.io/stat105](https://imouzon.github.io/stat105)] --- # Section 1.2 ## Basic Terminology, Continued --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[ ## Types of Data Structures The most basic way to think about data is to imagine how the the raw observations could be organized once collected. Collected data can be referred to as a **data set**. If the data set is simple enough, we can store it in a **data table** or **flat file**. Traditional data tables store values relating to a single observation/unit/individual as a row of the table. Each column in the table represents a value for some observed characterstic observed. **Example**: Failure time of lightbulbs A single brand and model of lightbulb is being examined for average failure time. Five bulbs were run until they burned out and their lifetime was recorded in hours. The first bult lasted 521.4 hours, the second bulb lasted 501.2 hours, the third bulb lasted 541.8 hours, the fourth bulb lasted 498.1 hours, and the fifth bulb lasted 528.2 hours. ] --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[ ## Types of Data Structures **Example**: Failure time of lightbulbs, continued Assembling the results in a data table could look like this: ```text Bulb Number Failure Time (hours) 1 521.4 2 501.2 3 541.8 4 498.1 5 528.2 ``` Each bulb tested gets its own row - which row is attached to which bulb is identified by the first column. The only feature being observed is failure time - so only one column of observations are recorded for each bulb. Notice: - Failure Time is a **quantitative continuous** variable. - This is a **univariate data set**. ] --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[ ## Types of Data Structures **Example**: Type of bill, date of payment, and payment amount for Mediacom ```text Customer Type Date Amount John Doe Internet 01-05-2015 110.00 John Doe Phone 01-15-2015 10.00 John Doe Internet 02-05-2015 110.00 John Doe Phone 02-15-2015 10.00 John Doe Internet 03-05-2015 110.00 John Doe Phone 03-15-2015 10.00 ... ... ... ... John Doe Internet 01-05-2016 110.00 John Doe Phone 01-15-2016 10.00 Jane Doe Internet 04-12-2015 90.00 Jane Doe Internet 05-12-2015 90.00 ... ... ... ... Jane Doe Internet 01-12-2016 90.00 ``` Notice: - Type of bill is is a **Qualitative** variable. - Amount paid is **quantitative discrete**. - Date is ... ] --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[ ## Types of Data Structures The complexity of our data we gather changes based on our objective. Consider the following scenarios: **Scenario 1: Simple Data Structure** We have designed a less expensive method for cleaning the byproduct of our production process. What constitutes clean is determined by law. - Our data will consist of a simply measure of cleanliness after treatment with the new method. **Scenario 2: Complex Data Structure** Synthesis of a certain chemical can be done in a number of ways. We are considering two sets of substrates, three environments where production can occur, and three chemists to perform the synthesis. Our goal is to get the purest end product. - We must gather data on substrate, environment, the chemist's identity, and the resulting purity. ] --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[ ## Factorial Studies > **Factorial Studies** involve scenarios in which several process variables are indentified as being of interest and data are collected under different settings of these process variables. > We call the process variables **factors** and the possible settings for a process variable its **levels** > **Complete Factorial Studies** are factorial studies where data is collected from each possible combination of the levels of the factors. > **Partial Factorial Studies** are factorial studies where data is collected from some (but not all) possible combinations of the levels of the factors. ] --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[
Factorial Studies Example
> A pair of chemists, Walter and Jessie, are attempting to synthesize a chemical and consider purity to be the most important quality in the product. There are three environments available to them (Winnebago/Basement/Lab) and two precursors (pseudoephedrine/methylamine). They are both willing to take the role of "lead cook" and will try all their options in order to get the best results. - What parts of the synthesis are being treated as variables? - What are the possible values for each of these variables? - How many ways can the variables be combined? ] ??? lead cook - Walter, Jessie environment - winnebago, basement, lab precursor - pseudo, methylamine 2 x 3 x 2 = 12 WRITE OUT THE LEVELS AFTER SOME TIME FOR THEM TO WRITE THEM OUT --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[
Factorial Studies Example, cont
Here are all the possible combinations of the factors: $$ \scriptsize{ (\text{# of Cooks}) \cdot (\text{# of Environments}) \cdot (\text{# of Precursors}) = 2 \cdot 3 \cdot 2 = 12} $$ ```text cook environment precursor walter winnebago psuedoephedrine walter winnebago methylamine walter basement psuedoephedrine walter basement methylamine walter lab psuedoephedrine walter lab methylamine jessie winnebago psuedoephedrine jessie winnebago methylamine jessie basement psuedoephedrine jessie basement methylamine jessie lab psuedoephedrine jessie lab methylamine ``` ] ??? --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[
Factorial Studies Example, cont
After testing each scenario, Walter and Jessie decide that the best combination to use is Walt as cook in the lab with methylamine. However, a new "chemist" Victor has joined the group and is going to try to be the cook and "follow the recipe" in the lab. Jessie also tries a new environment, South America, where only methylamine is available. - If we consider the all the past combinations to be part of this new study, how many combinations of factor levels are now possible? - Victor never works in the Winnebago, the basement, or South America. Walter never works in South America. What kind of study is this? ] --- layout:false .left-column[ ## What and Why ## Terms ### Types of Studies ### Types of Data ### Ex: Machine Parts ### Uni/Multi/Repeat ### Structures ] .right-column[
Factorial Studies Example, cont
``` cook env precursor 1. walt winne pseudo 2. walt winne methylamine 3. walt basement pseudo 4. walt basement methylamine 5. walt lab pseudo 6. walt lab methylamine 7. jessie winne pseudo 8. jessie winne methylamine 9. jessie basement pseudo 10. jessie basement methylamine 11. jessie lab pseudo 12. jessie lab methylamine 13. jessie so. am. methylamine 14. victor lab methylamine ``` ] ??? --- name: inverse layout: true class: center, middle, inverse --- # Section 1.3 ## Measurement: It's Importance and Difficulty --- layout:false .left-column[ ## What and Why ## Terms ## Measure ### Key Words ] .right-column[ ## If You Can't Measure, You Can't Do Statistics ### Or Engineering For That Matter - **Validity**: faithfully representing the aspect of interest - **Precision**: the amount of variation in repeated measures - **Accuracy**: aka "unbiasedness"; how close a measurement is to the true value "on average" We **calibrate** to improve accuracy ] ??? --- name: inverse layout: true class: center, middle, inverse --- # Section 1.4 ## Mathematical Models --- layout:false .left-column[ ## What and Why ## Terms ## Measure ## Math Models ] .right-column[ ## Mathematical Models and Data Analysis > **Mathematical Model**: A description of a physical system using mathematical concepts and language. Identifying mathematical relationships between parts of a system allows us to describe complexity in simple terms. **Example**: Height of an Object in Projectile Motion We can describe the relationship between height of a projectile \\(y\\) and time \\(t\\) as \\[ y = h_0 + v_h \cdot t - \frac{1}{2} g t^2, \text{ } t \ge 0, \\] where - \\(h_0\\) is the initial height, - \\(v_h\\) is the initial vertical velocity, and - \\(g\\) is the (constant) acceleration due to gravity ] ??? --- layout:false .left-column[ ## What and Why ## Terms ## Measure ## Math Models ] .right-column[ **Example**: Height of an Object in Projectile Motion, cont. \\[ y = h_0 + v_h \cdot t - \frac{1}{2} g t^2, \text{ } t \ge 0, \\] However, this is not what we see in real life for a variety of reasons. This model assumes 1. \\(g\\) is constant as the ball falls, while \\(g\\) is actually a function of distance between the objects which changes, 2. \\(g\\) is a known, while the actual acceleration due to gravity is it is actually estimated and approximated, 3. Gravity is the only force acting on the object, ignoring drag force, electrical attractions, etc. 4. There are no other changes in the system (for instance, changes in air pressure) We can fix these by writing a better relationship *or* we can accept that some things won't be known and use a **stochastic model** - a mathematical model that specifically allows for variation (or "randomness"). Understanding how these **stochastic models** work is a major focus of this course. ]


Mindfulness Conversation
Learnings:
- Listening - I'm a bit of an introvert at times which I think intrinsically makes
me a good listener. I found this bit really easy.
- Dipping - As humans we attach a lot of meaning onto the things people say to us.
I have a habit of drawing conclusions or making assumptions and reading into the things
people (especially friends and family) say to me. It's easy to misinterpret these messages
so I found it extremely helpful to take a mindful moment to check in with myself to ensure
I wasn't making meaning out of nothing.
- Looping - I reckon looping could have saved a lot of my past failed relationships! Feeding
back to your loved one to confirm what you heard aligned with what they intended is crucial
to avoid any misunderstandings.
I subtly tested this on a love interest and I think it made him feel heard and understood.
Sustaining my Practice
Take one breath a day
I chose this because of its simplicity. Sometimes we get lost in the mind's chatter and we need to
check in. When we become aware of the mind's distractions, we can take one breath, similar to a reset
button. During a busy day we can give ourself the gift of a mindful breath as an act of kindness
towards ourselves.


Buildings with original texture
Show commercial buildings
Emphasize commercial buildings
Desaturate texture
Remove texture


This guide will walk you through configuring the logging of Baleen.
Logging is a key tool for diagnosing problems and fixing bugs. It also provides an audit trail (to a certain extent) of what Baleen has done. Configuring it so that it provides relevant and useful information, is therefore an important task.
In this guide, we will be looking at the configuration options available for the logging framework in Baleen.
Logging for Baleen, and a number of other options, is configured in the Baleen configuration file. This is the file you pass to Baleen from the command line, for example:
java -jar baleen-2.1.0.jar config.yaml
In the above example, config.yaml is the name of the configuration file and this is the name we will refer to it by during this guide. Calling it config.yaml isn't required though, and you can call it whatever you want so long as that's the file you pass to Baleen when you start it up.
The configuration file is a YAML file, and uses YAML markup to configure Baleen. If you're not familiar with YAML markup, you should look at some online tutorials as we will not cover the syntax in this guide.
The configuration file is made up of a number of sections, and the logging section is indicated by the logging
property.
We then define a list of loggers using the loggers
property. For example, here's a simple config.yaml to log things to the console.
logging: loggers: - name: console
The logger name is indicated with the name
property, as shown above.
The name console is a special case to log to the standard console, if a different name is provided it will log to file (by default a file with the same name as the name value you provided).
Multiple loggers can be specified by adding further list items to the loggers
key. If specifying a file name, the log file will be written to the current running directory.
The name
property isn't the only option you can specify, and there are a number of properties you can specify to control things such as
the file name, the maximum file size, the logging levels to log, the packages to log, and more.
These are all documented in the JavaDoc (see BaleenLogging to get started),
so rather than go through them in detail here we will just provide some common 'recipes' from which you can start configuring your logging to suit your needs.
logging: loggers: - name: errors.log minLevel: WARN
logging: loggers: - name: warnings.log minLevel: WARN maxLevel: WARN - name: errors.log minLevel: ERROR
logging: loggers: - name: ignoreJetty file: baleen.log excludeLoggers: - org.eclipse.jetty
logging: loggers: - name: baleen.log size: 32
logging: loggers: - name: baleen.log daily: true history: 7


{% extends 'frontend/layouts/base.html' %} {% block content %} {% load static %} {% load humanize %} {% load widget_tweaks %} {% include 'frontend/layouts/header_ad.html' %}
Your password has been set. You may go ahead and login in now.


CachingPlayerItem
public final class CachingPlayerItem : AVPlayerItem
AVPlayerItem subclass that supports caching while playing.
-
Useful for keeping relevant model associated with CachingPlayerItem instance. This is a strong reference, be midful not to create a retain cycle.
Declaration
Swift
public var passOnObject: Any?
-
Undocumented
Declaration
Swift
public weak var delegate: CachingPlayerItemDelegate?
-
Play and cache remote media on a local file.
saveFilePath
is radomly generated. Requiresurl.pathExtension
to not be empty otherwise the player will fail playing.Declaration
Swift
public convenience init(url: URL)
Parameters
url
URL referencing the media file.
-
Play and cache remote media on a local file.
saveFilePath
is radomly generated.Declaration
Swift
public convenience init(url: URL, customFileExtension: String)
Parameters
url
URL referencing the media file.
customFileExtension
Media file extension. E.g. mp4, mp3. This is required for the player to work correctly with the intended file type.
-
Play and cache remote media.
Declaration
Swift
public init(url: URL, saveFilePath: String, customFileExtension: String?)
Parameters
url
URL referencing the media file.
saveFilePath
The desired local save location. E.g. “video.mp4”. Must be a unique file path that doesn’t already exist. If a file exists at the path than it’s required to be empty (contain no data).
customFileExtension
Media file extension. E.g. mp4, mp3. This is required for the player to work correctly with the intended file type.
-
Play remote media without caching.
Declaration
Swift
public init(nonCachingURL url: URL)
Parameters
nonCachingURL
URL referencing the media file.
-
Play from data.
Throws
An error in the Cocoa domain, if there is an error writing to the
URL
.Declaration
Swift
public convenience init(data: Data, customFileExtension: String) throws
Parameters
data
Media file represented in data.
customFileExtension
Media file extension. E.g. mp4, mp3. This is required for the player to work correctly with the intended file type.
-
Play from file.
Declaration
Swift
public init(filePathURL: URL, fileExtension: String? = nil)
Parameters
filePathURL
The local file path of a media file.
fileExtension
Media file extension. E.g. mp4, mp3. Required if
filePathURL.pathExtension
is empty. -
Not implemented
Declaration
Swift
override public init(asset: AVAsset, automaticallyLoadedAssetKeys: [String]?)
-
Downloads the media file. Works only with the initializers intended for play and cache.
Declaration
Swift
public func download()
-
Undocumented
Declaration
Swift
public override func observeValue(forKeyPath keyPath: String?, of object: Any?, change: [NSKeyValueChangeKey : Any]?, context: UnsafeMutableRawPointer?)


Key-value based environment variables
Strings, booleans and integers you define will be injected as variables to every source-code of the language you prefer using designated THiNX header or config file.
You can use this to inject SSID and PASSWORD of your WiFi in case you don't want to store those in the GIT repository (e.g. in case where your repo is public).
|
{{enviro}}
|


{% extends "emails/BaseEmail-en.html" %} {% block title %} SportySpots - Signup Magic Link. {% endblock title %} {% block preheader %} Thanks for trying out SportySpots. {% endblock preheader %} {% block email_body %}
Thanks for trying out SportySpots. We’re thrilled to have you on board.
To get started, please confirm your email:
|


GOODFELLAS (1990)
Starring: Ray Liotta (Henry Hill), Joe Pesci (Tommy DeVito), Lorraine Bracco (Karen Hill), Robert DeNiro (James Conway), Paul Sorvino (Paul Cicero)
Directed by: Martin Scorsese, Written by: Nicholas Pileggi and Martin Scorsese, based on the novel WISEGUY written by Nicholas Pileggi
Reviewed by Doug Skiles
"As far back as I can remember, I always wanted to be a gangster." - Henry Hill
Martin Scorsese's GOODFELLAS is one of the great mafia movies. Every part is acted so well, with every directorial touch perfectly done (including the longest stedicam shot on record), every plot detail is so relevant, that in the end, the only thing irrelevant are the reviews like this one. What is there to say about a movie like this?
If you had to make a complaint, it would be that there's not really any characters in this movie worth liking. There's too many scumbags. Sure, at first you like all of 'em, just like Henry. But soon you see the truth. Then again, why would you want someone to like in a story like this? It would go against the entire message.
Henry is a kid who grows up in New York, and dreams of being in the mob. He gets in, and we follows his life for decades as things go from bad to worse for him.
His life in the mob starts enjoyable enough, and we're enjoying it with him. Everybody is a smiling face, everybody wants to help. Sure, they're all criminals, but they're nice criminals, who're always willing to stick out their neck for a friend.
But, as Henry warns us, in the mafia, your murderers come with smiles. They're all just as willing to have their friends brutally murdered as they are to give them a hand. If you don't do what you're supposed to in the mob, you wind up "whacked", sometimes by a gun, sometimes a knife, sometimes hanging, frozen, in a meat locker, gutted. Just as fast as things seemed fun, they turn evil, dark, and sinister. Life in the mob looks glamorous on the outside, in the movies of Hollywood's Golden Age. But Henry Hill soon learns that the reality is far, far harsher. Life in the mob isn't heaven, it's hell.
GOODFELLAS is a true story, and that only further drives home what we're learning here. The mob isn't a colorful crowd of wacky criminals like in "Dick Tracy" - it's a brutal underworld of murder and deceit. And sure, that's not really a lesson - we all knew it, inside. But it's rarely showed to us quite so clearly how one can be drawn in by the glamour, only to end up fearing death at every turn.
Rating: ****
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


Adjust settings associated with your profile.
Providing a detailed profile helps us perform more accurate searches for your records.
The information contained here can also be helpful for doctors in emergency situations.
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{% extends 'reporting_base.html' %} {% block content %}
Welcome to the Edinburgh Genomics Reporting App. Here, you can view QC data for pipeline runs, sequencing runs, samples and projects. Select a view from the toolbar above.


Model-Based RL
Here is the summary of the lecture
The paradigm of model-based RL is to learn a parameterized model which can be easily trained through supervised learning and can be used to generate experience. There are two reasons to prefer model-based over model-free RL:
- Training a model is in general easier than learning an action-value function (in fact, it is easier to learn the reward for a state-action pair than learning the return for a state-action pair). Arguably, you can use policy gradient algorithms.
- If the learnt model is good, then you can sample a experience from it, thus learning more efficiently.
Typically, we assume conditional independence between the reward and the next state, namely
$$P(R_{t+1},S_{t+1}|A_t,S_t)=P(R_{t+1}|A_t,S_t)P(S_{t+1}|A_t,S_t)$$Therefore, the model approximates the two conditional densities.
$$\begin{align} P(R_{t+1}|A_t,S_t)&\approx P(R_{t+1}|A_t,S_t,\eta_1) \\ P(S_{t+1}|A_t,S_t)&\approx P(S_{t+1}|A_t,S_t,\eta_2) \end{align}$$Different models can be used, namely:
- Nonparametric table lookup, viz. store tuples $<S_t,A_t,R_{t+1},S_{t+1}>$
- Table lookup, viz. estimate $P_{ss'}^a$ and $R_s^a$ for all $s,a,s'$.
- Linear models
- Nonliner models
IT'S IMPORTANT TO LEARN A GOOD MODEL (PROBLEM OF LEARNING A MODEL). Once we have a model, we can generate experience. But, how can we exploit the sampled experience? (PROBLEM OF USING SAMPLED EXPERIENCE)
How to Use Simulated Experience
A natural way to exploit the sampled experience is to use model-free RL. Here, we provide the pseudo-code for the most-general algorithm
1. s <- initial state
2. Initialize Model M
3. Initialize policy/value functions
4. loop until termination
5. a <- Act in the real world
6. r <- get reward
7. s' <- sample next state
8. Learn policy/value functions on real experience (s,a,r,s')
9. Update model M based on s,a,r,s'
10 Sample experience from model M and learn policy/value functions on simulated experience (MODEL-FREE RL)
11 s <- s'
In the pseudo-code there are two ways to learn policy/value functions. We can use either real experience or simulated experience or both. Furthemore, we can use different strategies to sample simulated experience. All these degrees of freedom allow to specify a wide variety of RL algorithms. In the next subsections, we overview some of them.
Sample-Based Planning
In sample-based planning we don't learn from real experience (no line 8 in the pseudo code). Furthermore, the sampling of experience from model M is performed in a random way (random states, random actions).
Dyna
In Dyna, we learn from both real and simulated experience. Furthermore, the sampling of experience from model M is performed in a random way (random states, random actions from previous observed ones). Dyna-Q combines model-based and value-based RL (viz. using the action-value function).
Simulation-Based Search
In simulation-based search we learn only from simulated experience. The main difference with respect to previous cases lies in the sampling of experience. Instead of sampling randomly, we condition on the current state and we perform a search for the most promising experiences. Practically speaking, the differences consists of (i) setting the initial state of each simulated experience to the current one instead of choosing it randomly and (ii) using a simulation policy instead of a random one, to guide the search.

In general, there are different ways to perform search. There are classical approaches, based on uninformed search (e.g. breadth-first search, depth-first search) or informed search (e.g. best-first search, $A^*$ search), and non classical ones, based for example on adversarial search. For more details, please see the book of Russell and Norvig. In this subsection, we focus on best-first search strategies.
Simple Monte-Carlo Search
In simple Monte-Carlo search, line 10 in the pseudo-code is specified as follows:
Given fixed simulation policy
a. for each action a
b. sample K episodes of experience using the fixed policy based on initial state s and action a
c. Q(s,a) <- Monte-Carlo estimate on K episodes
Characteristics:
- Simulation policy is fixed
- The action-value function is estimated for all actions, but only for the current state
- At each iteration of the for loop (line 4 in the pseudo-code), the action-value function is re-estimated
Monte-Carlo Tree Search
Monte-Carlo tree search modifies the first two characteristics of simple Monte-Carlo search. The main idea is to incrementally update the simulation policy at each episode. Furthermore, the action-value function is estimated for a subset of states and actions (namely those ones close to the root). Line 10 in the pseudo-code is specified as follows:
a. Initialize tree by setting the root to current state
b. for K
b. Selection: traverse tree using tree policy (e.g. epsilon-greedy using estimated action-value function) until reaching an unexplored action a (leaf)
c. Expansion: expand the tree with state s' sampled from model M (from last visited state s and unexplored action a)
d. Rollout: take actions using rollout policy (e.g. random policy) until termination of episode
e. Backup: update all state-action pairs involved in the episode contained in the tree using Monte-Carlo estimate
Characteristics:
- Simulation policy consists of tree and rollout policy (the idea is to behave randomly in new cases)
- Simulation policy changes at each episode
- The action-value function is estimated for a subset of actions and states (namely those ones contained in the tree)
- At each iteration of the for loop (line 4 in the pseudo-code), the action-value function is re-estimated
The drawback of this method is that it is high variance.
TD Search
To reduce variance, you can update the action value function at each time step of each episode.
Characteristics:
- Simulation policy changes at each step of each episode
The drawback is that it has higher bias. Therefore, we can use TD($\lambda$) search.
Dyna-2
We learn from both real and simulated experience. In particular, we combine TD learning on real experience and TD($\lambda$) search on simulated experience. For more details, check 2008 paper.
Summary
| Name | Learning from real experience | Sampling (initial state) | Sampling (simulation policy) | Update value/policy | Bias | Variance |
|---|---|---|---|---|---|---|
| Sample-based planning | No | Random | Random | Each real action | Low | Very High |
| Dyna | Yes | Random | Random | Each real action | Low | Very High |
| Simulation-based search (Simple Monte-Carlo) | No | Current state | Fixed simulation policy | Each real action | Low | High |
| Simulation-based search (Monte-Carlo Tree Search) | No | Current state | simulation policy (tree and rollout policies) | Each simulated episode | Low | Medium |
| Simulation-based search (TD Search) | No | Current state | simulation policy | Each step of simulated episode | Medium | Low |
| Simulation-based search (TD($\lambda$) Search) | No | Current state | simulation policy | Each step of simulated episode | Medium/Low (tradeoff) | Medium/Low (tradeoff) |
| Dyna-2 | Yes | Current state | simulation policy (TD($\lambda$) Search) | Each real action | Medium/Low (tradeoff) | Medium/Low (tradeoff) |
For more updated reference papers about model-based RL check OPENAI website.


Test your understanding
Quiz Guide
To begin, click the start button
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Use the Prev and Next buttons to move through the questions. To go back to the beginning, click restart.


Helping borrowers find ways to stay afloat
A path forward to spur affordable repayment options
Earlier this year, we asked what you thought should be done to help borrowers find affordable options to pay for their private student loans. Nearly 30,000 of you responded with personal stories, ideas, and solutions. Here is some of what we heard.
We also published an op-ed discussing the potential impact of student debt on the economy. Read the entire op-ed here.
We also published a report highlighting the ways in which student loan debt can be a roadblock to a full financial life for consumers. The report identifies options for policymakers and market participants to consider when helping student borrowers manage their debt. Check out the report.
- Download a spreadsheet of the comments as an .xls sheet or in .csv format.
- Read comments submitted by consumer groups, financial institutions and nonprofit organizations.
Check out our blog for more information about this effort.
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With the CRUD methods you're able to create, update, delete or read records from Needle. In this case we add (create) a new item and then change (update) all the active keys to true.
The original data that was loaded in the original state before manipulating.


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layout: post
title: "Prediction Time! UFC 145: Jones vs. Evans"
tags:
- Prediction Posts
status: publish
type: post
published: true
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PRELIMINARY CARD
Marcus Brimage vs. Maximo Blanco
Haven't seen much of Blanco except for his Strikeforce loss to Pat Healy. Brimage hasn't impressed me, but he's fighting close to home and may have extra fuel in the tank because of it. I'm not loving this pick, but let's go with the Bama Beast. BRIMAGE by DECISION
Keith Wisniewski vs. Chris Clements
Wisniewski has got the huge experience advantage and was stopped by cuts when he faced Josh Neer last October. Clements is gracing the Octagon for the first time and even though he took care o' bidness against Rich Clementi the smart money here is probably on the Polish dude. But not much money, Clements has some KO power. WISNIEWSKI by SUBMISSION
Mac Danzig vs. Efrain Escudero
Yuck. Barf. Meh. Glarg. Wish I could abstain on this one. DANZIG by SUBMISSION
Anthony Njokuani vs. John Makdessi
Should be a good fight. I think Njokuani's reach proves too much. NJOKUANI by DECISION
Stephen Thompson vs. Matt Brown
Careful what you wish for, Matt Brown... WONDERBOY by TKO, (or late submission set up by strikes.)
 Although I hate to doubt Griggs, I think Browne's gonna come away with it. BROWNE by DECISION
Miguel Tores vs. Michael McDonald
As long as the kid doesn't get caught sleeping and wind up in a desperate last ditch submission, I think McDonald is gonna prove he's ready for the big(ger) guns at 135. Taking it to the Streets with MCDONALD by DECISION
MAIN CARD
John Alessio vs. Mark Bocek
Love Alessio. Don't love the two weeks notice. BOCEK by SUBMISSION
Mark Hominick vs. Eddie Yagin
No way Hominick drops three in a row. Speaking of dropping, I'd wager Yagin tries the 135 circuit after this fight. HOMINICK by TKO
Brendan Schaub vs. Ben Rothwell
Schaub's a tough fighter to predict. He SHOULD be able to mix it up on the feet against Rothwell. He SHOULD be able to take Rothwell down and keep him there. I just hope this isn't one of those fights where he underestimates the guy and ends up getting his bell rung by some Rothwell high kick. Ugh. Ok, pal. Giving you a chance here. SCHAUB by DECISION
Rory MacDonald vs. Che Mills
Fight of the night candidate and one of those fights where you really gotta tip your hat to Joe Silva. Real difficult fight to call. Canada vs Britain. I think the kid takes it. MACDONALD by SUBMISSION
Jon Jones (champ) vs. Rashad Evans
I've seen nothing in Jon Jones' fights that deter me from backing him here. Seriously, are you going to say that part in Round 1 against Machida we saw Jones at his most vulnerable and THAT'S the reason you're picking against him? Or that Evans trained with him and "knows his weakness"? Please. While Evans has talked a good (somewhat convincing) game that he's gonna be the one that "exposes" Jones and can a) take him down or b) land the big right hand, I just don't see him getting inside to do it. Jones is a special fighter, someone we haven't seen in this sport EVER and yes, I'm including Anderson Silva in that statement. I think Evans' work with the Blackzillians should have his submission defense on point and he'll squirm out of a couple close calls but in the end though, I think Jones can and will dictate how this fight goes. JONES by TKO
Last Card Results
8-4. Officially off the Papi Abedi hype train by the way. Any of you guys still hanging on? Also, didn't expect Siver to hang tough at 145, go figure. Even though he lost, be sure to remember the name Yoislandy Izquierdo. Kid looked sharp in defeat.
47 - 24 on the year.


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My name is Zachary! I am a 25 year old Front End Web Developer who LOVES to learn and has a knack for solving problems! Thanks so much for stopping by and taking a look. If you are interested in some of my work please feel free to contact me down below!
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