Networks¶
The networks module contains pieces of neural network that combine multiple layers.
NLP¶
sequence_conv_pool¶
-
paddle.trainer_config_helpers.networks.
sequence_conv_pool
(*args, **kwargs) Text convolution pooling layers helper.
Text input => Context Projection => FC Layer => Pooling => Output.
Parameters: - name (basestring) – name of output layer(pooling layer name)
- input (LayerOutput) – name of input layer
- context_len (int) – context projection length. See context_projection’s document.
- hidden_size (int) – FC Layer size.
- context_start (int or None) – context projection length. See context_projection’s context_start.
- pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
- context_proj_layer_name (basestring) – context projection layer name. None if user don’t care.
- context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute. None if user don’t care.
- fc_layer_name (basestring) – fc layer name. None if user don’t care.
- fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
- fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias, None if user don’t care.
- fc_act (BaseActivation) – fc layer activation type. None means tanh
- pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care. False if no bias.
- fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
- context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
- pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
Returns: output layer name.
Return type: LayerOutput
text_conv_pool¶
-
paddle.trainer_config_helpers.networks.
text_conv_pool
(*args, **kwargs) Text convolution pooling layers helper.
Text input => Context Projection => FC Layer => Pooling => Output.
Parameters: - name (basestring) – name of output layer(pooling layer name)
- input (LayerOutput) – name of input layer
- context_len (int) – context projection length. See context_projection’s document.
- hidden_size (int) – FC Layer size.
- context_start (int or None) – context projection length. See context_projection’s context_start.
- pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
- context_proj_layer_name (basestring) – context projection layer name. None if user don’t care.
- context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute. None if user don’t care.
- fc_layer_name (basestring) – fc layer name. None if user don’t care.
- fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
- fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias, None if user don’t care.
- fc_act (BaseActivation) – fc layer activation type. None means tanh
- pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care. False if no bias.
- fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
- context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
- pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
Returns: output layer name.
Return type: LayerOutput
Images¶
img_conv_bn_pool¶
-
paddle.trainer_config_helpers.networks.
img_conv_bn_pool
(*args, **kwargs) Convolution, batch normalization, pooling group.
Parameters: - name (basestring) – group name
- input (LayerOutput) – layer’s input
- filter_size (int) – see img_conv_layer’s document
- num_filters (int) – see img_conv_layer’s document
- pool_size (int) – see img_pool_layer’s document.
- pool_type (BasePoolingType) – see img_pool_layer’s document.
- act (BaseActivation) – see batch_norm_layer’s document.
- groups (int) – see img_conv_layer’s document
- conv_stride (int) – see img_conv_layer’s document.
- conv_padding (int) – see img_conv_layer’s document.
- conv_bias_attr (ParameterAttribute) – see img_conv_layer’s document.
- num_channel (int) – see img_conv_layer’s document.
- conv_param_attr (ParameterAttribute) – see img_conv_layer’s document.
- shared_bias (bool) – see img_conv_layer’s document.
- conv_layer_attr (ExtraLayerOutput) – see img_conv_layer’s document.
- bn_param_attr (ParameterAttribute.) – see batch_norm_layer’s document.
- bn_bias_attr – see batch_norm_layer’s document.
- bn_layer_attr – ParameterAttribute.
- pool_stride (int) – see img_pool_layer’s document.
- pool_padding (int) – see img_pool_layer’s document.
- pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer’s document.
Returns: Layer groups output
Return type: LayerOutput
img_conv_group¶
-
paddle.trainer_config_helpers.networks.
img_conv_group
(*args, **kwargs) Image Convolution Group, Used for vgg net.
TODO(yuyang18): Complete docs
Parameters: - conv_batchnorm_drop_rate –
- input –
- conv_num_filter –
- pool_size –
- num_channels –
- conv_padding –
- conv_filter_size –
- conv_act –
- conv_with_batchnorm –
- pool_stride –
- pool_type –
Returns:
simple_img_conv_pool¶
-
paddle.trainer_config_helpers.networks.
simple_img_conv_pool
(*args, **kwargs) Simple image convolution and pooling group.
Input => conv => pooling
Parameters: - name (basestring) – group name
- input (LayerOutput) – input layer name.
- filter_size (int) – see img_conv_layer for details
- num_filters (int) – see img_conv_layer for details
- pool_size (int) – see img_pool_layer for details
- pool_type (BasePoolingType) – see img_pool_layer for details
- act (BaseActivation) – see img_conv_layer for details
- groups (int) – see img_conv_layer for details
- conv_stride (int) – see img_conv_layer for details
- conv_padding (int) – see img_conv_layer for details
- bias_attr (ParameterAttribute) – see img_conv_layer for details
- num_channel (int) – see img_conv_layer for details
- param_attr (ParameterAttribute) – see img_conv_layer for details
- shared_bias (bool) – see img_conv_layer for details
- conv_layer_attr (ExtraLayerAttribute) – see img_conv_layer for details
- pool_stride (int) – see img_pool_layer for details
- pool_padding (int) – see img_pool_layer for details
- pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details
Returns: Layer’s output
Return type: LayerOutput
vgg_16_network¶
-
paddle.trainer_config_helpers.networks.
vgg_16_network
(input_image, num_channels, num_classes=1000) Same model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8
Parameters: - num_classes –
- input_image (LayerOutput) –
- num_channels (int) –
Returns:
Recurrent¶
LSTM¶
lstmemory_unit¶
-
paddle.trainer_config_helpers.networks.
lstmemory_unit
(*args, **kwargs) Define calculations that a LSTM unit performs in a single time step. This function itself is not a recurrent layer, so that it can not be directly applied to sequence input. This function is always used in recurrent_group (see layers.py for more details) to implement attention mechanism.
Please refer to Generating Sequences With Recurrent Neural Networks for more details about LSTM. The link goes as follows: .. _Link: https://arxiv.org/abs/1308.0850
\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t & = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t & = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]The example usage is:
lstm_step = lstmemory_unit(input=[layer1], size=256, act=TanhActivation(), gate_act=SigmoidActivation(), state_act=TanhActivation())
Parameters: - input (LayerOutput) – input layer name.
- name (basestring) – lstmemory unit name.
- size (int) – lstmemory unit size.
- param_attr (ParameterAttribute) – Parameter config, None if use default.
- act (BaseActivation) – lstm final activiation type
- gate_act (BaseActivation) – lstm gate activiation type
- state_act (BaseActivation) – lstm state activiation type.
- mixed_bias_attr (ParameterAttribute|False) – bias parameter attribute of mixed layer. False means no bias, None means default bias.
- lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer. False means no bias, None means default bias.
- mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
- lstm_layer_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
- get_output_layer_attr (ExtraLayerAttribute) – get output layer’s extra attribute.
Returns: lstmemory unit name.
Return type: LayerOutput
lstmemory_group¶
-
paddle.trainer_config_helpers.networks.
lstmemory_group
(*args, **kwargs) lstm_group is a recurrent layer group version Long Short Term Memory. It does exactly the same calculation as the lstmemory layer (see lstmemory in layers.py for the maths) does. A promising benefit is that LSTM memory cell states, or hidden states in every time step are accessible to for the user. This is especially useful in attention model. If you do not need to access to the internal states of the lstm, but merely use its outputs, it is recommended to use the lstmemory, which is relatively faster than lstmemory_group.
NOTE: In PaddlePaddle’s implementation, the following input-to-hidden multiplications: \(W_{xi}x_{t}\) , \(W_{xf}x_{t}\), \(W_{xc}x_t\), \(W_{xo}x_{t}\) are not done in lstmemory_unit to speed up the calculations. Consequently, an additional mixed_layer with full_matrix_projection must be included before lstmemory_unit is called.
The example usage is:
lstm_step = lstmemory_group(input=[layer1], size=256, act=TanhActivation(), gate_act=SigmoidActivation(), state_act=TanhActivation())
Parameters: - input (LayerOutput) – input layer name.
- name (basestring) – lstmemory group name.
- size (int) – lstmemory group size.
- reverse (bool) – is lstm reversed
- param_attr (ParameterAttribute) – Parameter config, None if use default.
- act (BaseActivation) – lstm final activiation type
- gate_act (BaseActivation) – lstm gate activiation type
- state_act (BaseActivation) – lstm state activiation type.
- mixed_bias_attr (ParameterAttribute|False) – bias parameter attribute of mixed layer. False means no bias, None means default bias.
- lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer. False means no bias, None means default bias.
- mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
- lstm_layer_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
- get_output_layer_attr (ExtraLayerAttribute) – get output layer’s extra attribute.
Returns: the lstmemory group.
Return type: LayerOutput
simple_lstm¶
-
paddle.trainer_config_helpers.networks.
simple_lstm
(*args, **kwargs) Simple LSTM Cell.
It just combine a mixed layer with fully_matrix_projection and a lstmemory layer. The simple lstm cell was implemented as follow equations.
\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t & = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t & = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]Please refer Generating Sequences With Recurrent Neural Networks if you want to know what lstm is. Link is here.
Parameters: - name (basestring) – lstm layer name.
- input (LayerOutput) – input layer name.
- size (int) – lstm layer size.
- reverse (bool) – whether to process the input data in a reverse order
- mat_param_attr (ParameterAttribute) – mixed layer’s matrix projection parameter attribute.
- bias_param_attr (ParameterAttribute|False) – bias parameter attribute. False means no bias, None means default bias.
- inner_param_attr (ParameterAttribute) – lstm cell parameter attribute.
- act (BaseActivation) – lstm final activiation type
- gate_act (BaseActivation) – lstm gate activiation type
- state_act (BaseActivation) – lstm state activiation type.
- mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
- lstm_cell_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
Returns: lstm layer name.
Return type: LayerOutput
bidirectional_lstm¶
-
paddle.trainer_config_helpers.networks.
bidirectional_lstm
(*args, **kwargs) A bidirectional_lstm is a recurrent unit that iterates over the input sequence both in forward and bardward orders, and then concatenate two outputs form a final output. However, concatenation of two outputs is not the only way to form the final output, you can also, for example, just add them together.
Please refer to Neural Machine Translation by Jointly Learning to Align and Translate for more details about the bidirectional lstm. The link goes as follows: .. _Link: https://arxiv.org/pdf/1409.0473v3.pdf
The example usage is:
bi_lstm = bidirectional_lstm(input=[input1], size=512)
Parameters: - name (basestring) – bidirectional lstm layer name.
- input (LayerOutput) – input layer.
- size (int) – lstm layer size.
- return_seq (bool) – If set False, outputs of the last time step are concatenated and returned. If set True, the entire output sequences that are processed in forward and backward directions are concatenated and returned.
Returns: LayerOutput object accroding to the return_seq.
Return type: LayerOutput
GRU¶
gru_unit¶
-
paddle.trainer_config_helpers.networks.
gru_unit
(*args, **kwargs) Define calculations that a gated recurrent unit performs in a single time step. This function itself is not a recurrent layer, so that it can not be directly applied to sequence input. This function is almost always used in the recurrent_group (see layers.py for more details) to implement attention mechanism.
Please see grumemory in layers.py for the details about the maths.
Parameters: - input (LayerOutput) – input layer name.
- name (basestring) – name of the gru group.
- size (int) – hidden size of the gru.
- act (BaseActivation) – type of the activation
- gate_act (BaseActivation) – type of the gate activation
- gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
Returns: the gru output layer.
Return type: LayerOutput
gru_group¶
-
paddle.trainer_config_helpers.networks.
gru_group
(*args, **kwargs) gru_group is a recurrent layer group version Gated Recurrent Unit. It does exactly the same calculation as the grumemory layer does. A promising benefit is that gru hidden sates are accessible to for the user. This is especially useful in attention model. If you do not need to access to any internal state, but merely use the outputs of a GRU, it is recommanded to use the grumemory, which is relatively faster.
Please see grumemory in layers.py for more detail about the maths.
The example usage is:
gru = gur_group(input=[layer1], size=256, act=TanhActivation(), gate_act=SigmoidActivation())
Parameters: - input (LayerOutput) – input layer name.
- name (basestring) – name of the gru group.
- size (int) – hidden size of the gru.
- reverse (bool) – whether to process the input data in a reverse order
- act (BaseActivation) – type of the activiation
- gate_act (BaseActivation) – type of the gate activiation
- gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
- gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
Returns: the gru group.
Return type: LayerOutput
simple_gru¶
-
paddle.trainer_config_helpers.networks.
simple_gru
(*args, **kwargs) You maybe see gru_step_layer, grumemory in layers.py, gru_unit, gru_group, simple_gru in network.py. The reason why there are so many interfaces is that we have two ways to implement recurrent neural network. One way is to use one complete layer to implement rnn (including simple rnn, gru and lstm) with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But, the multiplication operation \(W x_t\) is not computed in these layers. See details in their interfaces in layers.py. The other implementation is to use an recurrent group which can ensemble a series of layers to compute rnn step by step. This way is flexible for attenion mechanism or other complex connections.
- gru_step_layer: only compute rnn by one step. It needs an memory as input and can be used in recurrent group.
- gru_unit: a wrapper of gru_step_layer with memory.
- gru_group: a GRU cell implemented by a combination of multiple layers in recurrent group. But \(W x_t\) is not done in group.
- gru_memory: a GRU cell implemented by one layer, which does same calculation with gru_group and is faster than gru_group.
- simple_gru: a complete GRU implementation inlcuding \(W x_t\) and gru_group. \(W\) contains \(W_r\), \(W_z\) and \(W\), see formula in grumemory.
The computational speed is that, grumemory is relatively better than gru_group, and gru_group is relatively better than simple_gru.
The example usage is:
gru = simple_gru(input=[layer1], size=256)
Parameters: - input (LayerOutput) – input layer name.
- name (basestring) – name of the gru group.
- size (int) – hidden size of the gru.
- reverse (bool) – whether to process the input data in a reverse order
- act (BaseActivation) – type of the activiation
- gate_act (BaseActivation) – type of the gate activiation
- gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
- gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
Returns: the gru group.
Return type: LayerOutput
simple_attention¶
-
paddle.trainer_config_helpers.networks.
simple_attention
(*args, **kwargs) Calculate and then return a context vector by attention machanism. Size of the context vector equals to size of the encoded_sequence.
\[ \begin{align}\begin{aligned}a(s_{i-1},h_{j}) & = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j})\\e_{i,j} & = a(s_{i-1}, h_{j})\\a_{i,j} & = \frac{exp(e_{i,j})}{\sum_{k=1}^{T_x}{exp(e_{i,k})}}\\c_{i} & = \sum_{j=1}^{T_{x}}a_{i,j}h_{j}\end{aligned}\end{align} \]where \(h_{j}\) is the jth element of encoded_sequence, \(U_{a}h_{j}\) is the jth element of encoded_proj \(s_{i-1}\) is decoder_state \(f\) is weight_act, and is set to tanh by default.
Please refer to Neural Machine Translation by Jointly Learning to Align and Translate for more details. The link is as follows: https://arxiv.org/abs/1409.0473.
The example usage is:
context = simple_attention(encoded_sequence=enc_seq, encoded_proj=enc_proj, decoder_state=decoder_prev,)
Parameters: - name (basestring) – name of the attention model.
- softmax_param_attr (ParameterAttribute) – parameter attribute of sequence softmax that is used to produce attention weight
- weight_act (Activation) – activation of the attention model
- encoded_sequence (LayerOutput) – output of the encoder
- encoded_proj (LayerOutput) – attention weight is computed by a feed forward neural network which has two inputs : decoder’s hidden state of previous time step and encoder’s output. encoded_proj is output of the feed-forward network for encoder’s output. Here we pre-compute it outside simple_attention for speed consideration.
- decoder_state (LayerOutput) – hidden state of decoder in previous time step
- transform_param_attr (ParameterAttribute) – parameter attribute of the feed-forward network that takes decoder_state as inputs to compute attention weight.
Returns: a context vector
Miscs¶
dropout_layer¶
-
paddle.trainer_config_helpers.networks.
dropout_layer
(*args, **kwargs) @TODO(yuyang18): Add comments.
Parameters: - name –
- input –
- dropout_rate –
Returns:
outputs¶
-
paddle.trainer_config_helpers.networks.
outputs
(layers, *args) Declare the outputs of network. If user have not defined the inputs of network, this method will calculate the input order by dfs travel.
Parameters: layers (list|tuple|LayerOutput) – Output layers. Returns:


---
layout: post
title: Galen's view of science
date: '2007-12-25T22:30:00.000-05:00'
author: RyGuy
tags:
- inquiry
- science
modified_time: '2008-07-05T13:28:28.590-04:00'
blogger_id: tag:blogger.com,1999:blog-2767138550438691941.post-7467286681096149866
blogger_orig_url: https://muchtoilingant.blogspot.com/2007/12/galens-view-of-science.html
---
"One must be daring and approach the truth: for even if we may not grasp it completely, yet we will get closer to it than we are now" - Galen
[In light of this it appears that the prototype modern scientist enjoys tenets of Epicurean, Utilitarian, Thomist, reductive materialist, and Galenic thought. Contrast with what the modern scientist is not: Kantian, Platonic (most, at least), Aristotelian, Humean, ...]


{{define "cp_streaming"}} {{template "cp_head" .}}
This plugin extends the built in streaming status of discord
Note: The bot needs to have a role higher than the one you are trying to assign to streamers!
Available template data is {{template "template_helper_user"}} and {{"{{"}}.URL {{"}}"}} (The stream link)
{{"{{"}}.URL {{"}}"}}
Filter out the people streaming by their game name (prepend with (?i) for case insensitivity)
(?i)
Assign a role to people currently streaming, so you can for example hoist it up where people can see
Only care about the people with this role
Ignore people with this role
Filter out the people streaming by the stream title (prepend with (?i) for case insensitivity)


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|
Standard Reference Data Program |
Data Gateway |
Chemistry WebBook |
NIST subscription sites provide data under the NIST Standard Reference Data Program, but require an annual fee to access. The purpose of the fee is to recover costs associated with the development of data collections included in such sites. Your institution may already be a subscriber. Follow the links above to find out more about the data in these sites and their terms of usage.
Go To: Top, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data compiled by: K.P. Huber and G. Herzberg
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Unclassified band system in the red; no details. | ||||||||||||
| Loomis and Watson, 1935 | ||||||||||||
| B 2Π3/2 | x2 + 28113.8 | 539.1 1 H | 6.15 | [0.3195] | [5.0E-07] | [1.9474] | B → X V | 28137.3 1 H | ||||
| missing citation | ||||||||||||
| B 2Π1/2 | x1 + 28072.3 | 535.7 1 H | 6.34 | [0.3178] 1 2 | [0.00000051] 1 | [1.9526] | B → X V | 28094.2 1 H | ||||
| missing citation | ||||||||||||
| A 2Π3/2 | x2 + 24540 | [241.1] 1 H | 3 | [0.2816] 3 | [0.00000171] | [2.074] | A → X R | 24416.0 1 H | ||||
| missing citation | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| A 2Π1/2 | x1 + 24370 | [237.3] 1 H | 3 | [0.2812] 2 3 | [0.00000168] | [2.076] | A → X R | 24244.5 1 H | ||||
| missing citation | ||||||||||||
| X 2Π3/2 | x2 4 | 490.5 1 H | 2.86 | 0.3036 | 0.0026 | 5.0E-07 | 1.9977 | |||||
| X 2Π1/2 | x1 4 | 490.2 1 H | 3.06 | 0.3020 2 | 0.0025 | 0.00000045 | 2.0030 |
| 1 | For the natural isotopic mixture 107Ag/109Ag. |
| 2 | The Ω-type doubling observed in the 1/2 → 1/2 transitions of A → X and B → X is approximately given by Be= 0.029(J+1/2)-... and Be= 0.022(J+1/2) - ..., respectively. |
| 3 | Predissociation above v=0, J=66 1/2 in A 2Π1/2 and above v=0, J=73 1/2 in A 2Π3/2. No bands involving v'=2 have been observed. |
| 4 | Not quite certain that this is the ground state. It appears to be regular with A ~+135 cm-1, contrary to expectations; see Cheetam and Barrow, 1967. |
| 5 | Thermochemical value (mass-spectrom.) Smoes, Mandy, et al., 1972. |
Go To: Top, Constants of diatomic molecules, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Loomis and Watson, 1935
Loomis, F.W.; Watson, T.F.,
Band spectra of AgO and CuO,
Phys. Rev., 1935, 48, 280. [all data]
Cheetam and Barrow, 1967
Cheetam, C.J.; Barrow, R.F.,
Adv. High Temp. Chem., 1967, 1, 7. [all data]
Smoes, Mandy, et al., 1972
Smoes, S.; Mandy, F.; Vander Auwera-Mahieu, A.; Drowart, J.,
Determination by the mass spectrometric Knudsen cell method of the dissociation energies of the group IB chalcogenides,
Bull. Soc. Chim. Belg., 1972, 81, 45. [all data]
Go To: Top, Constants of diatomic molecules, References
Copyright for NIST Standard Reference Data is governed by the Standard Reference Data Act.
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StockQuoter Procedures
# stockQuoter-procs.tcl # The namespace alias will be stockquoter: set sqns "stockquoter" #### Simple Types # Trade Symbol ::wsdl::types::simpleType::restrictByEnumeration $sqns symbol tcl::string {MSFT WMT XOM GM F GE } # Verbose ::wsdl::types::simpleType::new $sqns verbose tcl::boolean # Quote ::wsdl::types::simpleType::new $sqns quote tcl::double # Timestamp ::wsdl::types::simpleType::new $sqns dateOfChange tcl::dateTime # Trend ::wsdl::types::simpleType::restrictByEnumeration $sqns trend tcl::integer {-1 0 1} # DailyMove ::wsdl::types::simpleType::new $sqns dailyMove tcl::double # LastMove ::wsdl::types::simpleType::new $sqns lastMove tcl::double # Name ::wsdl::types::simpleType::new $sqns name tcl::string #### Elements (complexTypes): # Note that the command returns a script. eval executes it. eval [::wsdl::elements::modelGroup::sequence::new $sqns StockRequest { {Symbol stockquoter::symbol} {Verbose stockquoter::verbose 1}}] eval [::wsdl::elements::modelGroup::sequence::new $sqns StockQuote { {Symbol stockquoter::symbol } {Quote stockquoter::quote } {DateOfChange stockquoter::dateOfChange 0 } {Name stockquoter::name 0 } {Trend stockquoter::trend 0 } {DailyMove stockquoter::dailyMove 0 } {LastMove stockquoter::lastMove 0 } }] #### Messages: eval [::wsdl::messages::new $sqns StockRequest StockRequest] eval [::wsdl::messages::new $sqns StockQuote StockQuote] #### Operations: eval [::wsdl::operations::new $sqns StockQuoteOperation {::sq::mystockquote {Symbol Value Verbose Value}} \ {input StockRequest} {output StockQuote}] #### PortTypes: ::wsdl::portTypes::new $sqns StockQuotePortType {StockQuoteOperation} #### Bindings (SOAP to Operations): ::wsdl::bindings::soap::documentLiteral::new $sqns StockQuotePortType \ StockQuoteSoapBind \ http://stockquoter.com/StockQuote StockQuoteOperation #### Bindings (Port to PortType): ::wsdl::ports::new StockQuotePort StockQuoteSoapBind "/StockQuote" #### Services: ::wsdl::services::new StockQuoteService {StockQuotePort} # Example Wrapped Tcl API: namespace eval ::sq { } proc ::sq::mystockquote { symbol {verbose 1} } { set StockValue [format %0.2f [expr 25.00 + [ns_rand 4].[format %0.2d [ns_rand 99]]]] if {$verbose} { return [list $symbol $StockValue 2006-04-11T00:00:00Z Microsoft 1 0.75 0.10] } else { return [list $symbol $StockValue] } }
StockQuoter Initialization
# stockQuoter-init.tcl #### Server: ::wsdl::server::new StockQuoter "http://stockquoter.com/stockquoter" {StockQuoteService} #### Configure Host header to Server: set ::wsdb::servers::StockQuoter::hostHeaderNames [list "maria:8080" "<IP_ADDRESS>:8080"] #### Listen for requests: ::wsdl::server::listen StockQuoter
The above code will add a service to the tWSDL DB database. When the AOLserver instance is restarted, the tWSDL Core will start the web service.
Once started, the tWSDL client can access the WSDL and generate the Client API.
The following is example code for this process:
Client code has not been written yet. (Aug. 31, 2006)
# Assuming library code is loaded, prefer that the following # code could be run from a Tcl command line until indicated. set wsdl_file_url "http://stockquoter.com/stockquoter.wsdl" set wsdlService "StockQuoteService" set serviceNamespace "::myStockQuoteService" # getURL returns the content of the requested url set wsdl_file [::wsdl::http::getURL $wsdl_file_url] # validate WSDL and write Client API ::wsdl::client::new $serviceNamespace $wsdl_file # list available Services: ::wsdl::client::services {::myStockQuoteService} #(returns ::myStockQuoteService and any others available as tcl list) # list operations for StockQuoteService: ::wsdl::client::operations ::myStockQuoteService {StockQuoteOperation} # show operation signature: ::wsdl::client::signature ::myStockQuoteService StockQuoteOperation {::myStockQuoteService::StockQuoteOperation {StockRequest} {StockQuote}} # show input message signature: ::wsdl::client::signature ::myStockQuoteService StockRequest {::myStockQuoteService::StockRequest {Symbol Verbose} {StockRequest}} # show output message signature: ::wsdl::client::signature ::myStockQuoteService StockQuote {::myStockQuoteService::StockQuote {StockQuote} {Symbol Quote DateOfChange Name Trend DailyMove LastMove}} ## The following is what would end up in an actual application (once the client API ## is created. # Invoke Service and get Quote set stockRequest [::myStockQuoteService::StockRequest "MSFT" "1"] set stockQuote [::myStockQuoteService::StockQuoteOperation $stockRequest] set QuoteList [::myStockQuoteService::StockQuote $stockQuote] # maybe ns_return?? puts $QuoteList {MSFT 27.04 2006-04-11T00:00:00Z Microsoft 0 0.00 0.10}
Notice that we cannot simply feed in the Symbol and Verbose flag from and automatically generated API to invoke the service. The Request Message must be generated first. There should be one API for each type (global, message, operation, etc.) so that complex types can be constructed and sent, and/or received and decomposed.
However, also note that this is a simple example. There will likely be cases where something other than a list is needed, so the API above is not complete.
A more complete application would likely validate the input values prior to creating the request, or possibly validate the stockQuote document once it is constructed.


Other than your home layout, in order of preference, what are your three favourite courses in ?
Other than your home layout and three favourites in already selected, in order of preference, what are your two favourite LOCAL courses (within three hours drive time from your home)?
In no particular order, pick up to three "Hidden Gems" anywhere in that you think deserve higher recognition:
Please provide your PGA of BC ID# and your Town of Residence during the golf season. If your ID# is not available, your name can be used. Note: This information is only used to identify who has taken the survey and is kept independently of your selections.


--- title: Quick'n'dirty ELB healthcheck date: 2014-12-05 18:29:51.000000000 +02:00 categories: - ops - programming - tech tags: - autoscaling - aws - elb - monitoring - shell script - systemd status: publish type: post published: true meta: _edit_last: '600927' geo_public: '0' publicize_twitter_url: http://t.co/wBy1YbQrpD publicize_twitter_user: koukopoulos _wpas_done_14391: '1' _publicize_done_external: a:1:{s:7:"twitter";a:1:{i:13858422;b:1;}} author: login: kouk email<EMAIL_ADDRESS>display_name: Konstantinos Koukopoulos first_name: '' last_name: '' excerpt: !ruby/object:Hpricot::Doc options: {} ---
Sometimes you have a service you are running on multiple instances in an AWS autoscaling group, and you want to monitor the application health with ELB so that if an instance become unhealthy your autoscaling group will launch a new instance. But if the service doesn't expose any ports, such as a web server would, ELB health-checks cannot work because they rely on connecting via TCP or HTTP to monitor the health of it's instances. So? If you can check the health of your service with some local command or script then all you need is the files in this gist and you're off!
The idea is to use (or abuse) systemd
's socket activation feature to run a script whenever ELB asks for a certain URL on a certain port. Ideally this port should not be accessible from the public internet or really anywhere else than where ELB will be connecting from. But this is outside the scope of this document (you are running your instances in a VPC right?).
So, assuming /path/to/somecommand
is a an executable that will succeed or fail depending on the health of your service, you need the following script to respond to ELB health checks:
{% gist kouk/d12753757d506f97e13f status.sh %}
Then you configure the status.socket
and<EMAIL_ADDRESS>unit files (usually in /usr/lib/systemd/system/
). And then prof.. ehm test it first!.
This is perhaps just a hack, but it works OK in many cases.


CAREERS
Fliter
Search
iOS
iOS Developer
UI
UI Developer
ASP
Senior ASP.Net Developer
IS
Inside Sales Executive
PS
Senior Executive - Presales


utter_greet:
- text: "Hello 😀"
dispatcher.utter_message(text="Hello 😀")
utter_cheer_up:
- text: "Here is something to cheer you up 😉 "
image: "https://i.imgur.com/nGF1K8f.jpg"
dispatcher.utter_message(text="Here is something to
cheer you up 😉",
image="https://i.imgur.com/nGF1K8f.jpg")
utter_greet:
- text: "Hey! How are you?"
buttons:
- title: "great"
payload: "great"
- title: "super sad"
payload: "super sad"
button_resp=[
{
"title": "great",
"payload": "great"
},
{
"title": "super sad",
"payload": "super sad"
}
]
dispatcher.utter_message(text="Hey! How are you?", buttons=button_resp)
utter_greet:
- text: "Check this video"
attachment: {
"type":"video",
"payload":{
"src": "https://youtube.com/embed/9C1Km6xfdMA" }
}
msg = {
"type": "video",
"payload": {
"title": "Link name",
"src": "https://youtube.com/9C1Km6xfdMA"
}
}
dispatcher.utter_message(text="Check this video",attachment=msg)
utter_menu:
- text: "Please select a option"
custom:
payload: dropDown
data:
- label: option1
value: "/inform{'slot_name':'option1'}"
- label: option2
value: "/inform{'slot_name':'option2'}"
- label: option3
value: "/inform{'slot_name':'option3'}"
data=[
{"label":"option1","value":"/inform{'slot_name':'option1'}"},
{"label":"option2","value":"/inform{'slot_name':'option2'}"},
{"label":"option3","value":"/inform{'slot_name':'option3'}"}
]
message={"payload":"dropDown","data":data}
dispatcher.utter_message(text="Please select a option",json_message=message)
utter_cuisine:
- text: "Please choose a cuisine"
custom:
payload: quickReplies
data:
- title: chip1
payload: chip1_payload
- title: chip2
payload: chip2_payload
- title: chip3
payload: chip3_payload
data= [
{ "title":"chip1", "payload":"chip1_payload" },
{ "title":"chip2", "payload":"chip2_payload" },
{ "title":"chip3", "payload":"chip3_payload" } ]
message={"payload":"quickReplies","data":data}
dispatcher.utter_message(text="Please choose a cuisine",json_message=message)
utter_askLeaveTypes:
- text: "You can apply for below leaves"
custom:
payload: "collapsible"
data:
- title: Sick Leave
description: Sick leave is time off from work that workers
can use to stay home to address their health and safety
needs without losing pay.
- title: Earned Leave
description: 'Earned Leaves are the leaves which are
earned in the previous year
and enjoyed in the preceding years. '
- title: Casual Leave
description: Casual Leave are granted for certain unforeseen
situation or were you are require to go for one or two days
leaves to attend to personal matters and
not for vacation.
- title: Flexi Leave
description: Flexi leave is an optional leave which one can
apply directly in system
at lease a week before.
data= [
{ "title": "Sick Leave",
"description": "Sick leave is time off from work that workers can
use to stay home to address their health and safety needs without
losing pay." },
{ "title": "Earned Leave",
"description": "Earned Leaves are the leaves which are earned in
the previous year and enjoyed in the preceding years. " },
{ "title": "Casual Leave",
"description": "Casual Leave are granted for certain unforeseen
situation or were you are require to go for one or two days
leaves to attend to personal matters and not for vacation." },
{ "title": "Flexi Leave",
"description": "Flexi leave is an optional leave which one
can apply directly in system at lease a week before." }
]
message={ "payload": "collapsible", "data": data }
dispatcher.utter_message(text="You can apply for below leaves",json_message=message)
utter_askLeaveBalance:
- text: "Here are your leave balance details"
custom:
payload: chart
data:
title: Leaves
labels:
- Sick Leave
- Casual Leave
- Earned Leave
- Flexi Leave
backgroundColor:
- "#36a2eb"
- "#ffcd56"
- "#ff6384"
- "#009688"
- "#c45850"
chartsData:
- 5
- 10
- 22
- 3
chartType: pie
displayLegend: 'true'
data={ "title": "Leaves",
"labels": [ "Sick Leave", "Casual Leave", "Earned Leave", "Flexi Leave" ],
"backgroundColor": [ "#36a2eb", "#ffcd56", "#ff6384", "#009688", "#c45850" ],
"chartsData": [ 5, 10, 22, 3 ],
"chartType": "pie", "displayLegend": "true" }
message={ "payload": "chart", "data": data }
dispatcher.utter_message(text="Here are your leave balance
details",json_message=message)
utter_ask_location::
- text: "Sure, please allow me to access your location 🧐"
custom:
payload: location
message={"payload":"location"}
dispatcher.utter_message("Sure, please allow me to access your
location 🧐",json_message=message)
utter_menu:
- text: "Showing Restauarants (just display no slotfilling)"
custom:
payload: cardsCarousel
data:
- name: Aditya's Restauarant
ratings: 4.3
image:"url_image1"
- name: Azhar's Restauarant
ratings: 5
image:"url_image2"
- name: Naaniz's Kitchen
ratings: 4.5
image:"url_imag3"
data=[
{"name":"Aditya's Restauarant","ratings":4.3,"image":"url_image1"},
{"name":"Azhar's Restauarant","ratings":5,"image":"url_image2"},
{"name":"Naaniz's Kitchen","ratings":4.5,"image":"url_image3"}
]
message={"payload":"cardsCarousel","data":data}
dispatcher.utter_message(text="Showing Restauarants
(just display no slotfilling)",json_message=message)
utter_ask_pdf:
- text: "Here's the PDF 😀"
custom:
payload: pdf_attachment
data:
custom:
url:"url_pdf"
title:"pdf_title"
data={
"custom":{
"url":"url_pdf",
"title":"pdf_title"
}
}
message={"payload":"pdf_attachment","data":data}
dispatcher.utter_message(text="Here's the PDF 😀",
json_message=message)
utter_show_cart:
- text: "Showing Cart"
custom:
payload: cartCarousels
data:
- title: Dosa
price: 30
quantity:3
restauarant:Naaniz
item_id:gjhgsajgsjb
image: url
location: Hyderabad
category: tiffins
- title: Dosa
price: 30
quantity:3
restauarant:Naaniz
item_id:gjhgsajgsjb
image: url
location: Hyderabad
category: tiffins
dispatcher.utter_message(text="Showing Cart")
data = [
{
"title":"Dosa",
"price":30,
"quantity":2,
"restauarant":"Naaniz",
"item_id":"dhsh212hjkhiyhffhgs",
"location":"Hyderabad",
"image":"https://thumbs.dreamstime.com/
b/indian-dosa-special-
food-made-rice-lentils-south-india-
84751483.jpg",
"category":"tiffins"
},
{
"title":"Dosa",
"price":30,
"quantity":2,
"restauarant":"Naaniz",
"item_id":"sh212ffhgsdhhjkhiyh",
"image":"https://thumbs.dreamstime.com/
b/indian-dosa-special-
food-made-rice-lentils-south-india-
84751483.jpg",
"location":"Hyderabad",
"category":"tiffins"
},
{
"title":"Dosa",
"price":30,
"quantity":2,
"restauarant":"Naaniz",
"item_id":"ffhjkhihgsdhsh212yh",
"image":"https://thumbs.dreamstime.com/
b/indian-dosa-special-
food-made-rice-lentils-south-india-
84751483.jpg",
"location":"Hyderabad",
"category":"tiffins"
},
{
"title":"Dosa",
"price":30,
"quantity":2,
"restauarant":"Naaniz",
"item_id":"fhjkhifgsdhsh212hyh",
"image":"https://thumbs.dreamstime.com/
b/indian-dosa-special-
food-made-rice-lentils-south-india-
84751483.jpg",
"location":"Hyderabad",
"category":"tiffins"
}
]
message={ "payload": "cartCarousels", "data": data }
dispatcher.utter_message(text="Showing your cart",
json_message=message)
I can help you place an order.


In
1973, the Yaussy-Winkler dairy farm was bought at public auction from the
Winkler family following the death of Herman Winkler. The dairy barn and all of the equipment was purchased for around
$2,500 by two Monroe County citizens on behalf of the Monroe County Historical
Society. Later, the two gentlemen were
reimbursed by the Monroe County Park District and the dairy barn and all of the
equipment was moved to a site behind the Parry Museum. Also
standing behind the Parry Museum, is the Byers/Oak Ridge schoolhouse.
Originally, the one-room schoolhouse was located in Washington Township. In 1986, the school was donated to the MCHS by
Mr. and Mrs. Daniel Jacobs. In 1987,
the school was moved, stone-by-stone to the Parry Park grounds, where it stands
today.


Date
8-4-2016
22-4-2016
Time
3.00pm - 6.00pm
Cost
Free
Venue
BK 9 and 10
For Beginner
Git is a widely used source code management system for software development.
It is a distributed revision control system with an emphasis on speed, data integrity,
and support for distributed, non-linear workflows.
Git was initially designed and developed in 2005 by Linux kernel developers (including Linus Torvalds) for Linux kernel development.
Version control is an essential tool if you want to be successful in today's web & software world. In real world,
You won't find a top programmer, web developer, or web designer who doesn't use version control.
Because it helps you produce better results and makes collaboration easy.
And because it can save your life when things go wrong...
In this course, we will introduce about git as well as the way of install git and use it. Furthermore, We will also guide you how to
work with Github where you work remotely with your team member.
1. Install Text Editor in ur machine. e.g Sublime Text or
Atom
2. Download Git on your PC
3. Follow these steps to complete the Installations
4. Register yourself a GitHub Account
5. Invite your friend to join the event
In the end of this course, you'll understand the how to use Git and also work together with GitHub.
No syllabus for this course
Loh Jia Jing
BITI student, hate coding, love design. Hope to be Front end developer
He like to bully junior!!!
Image too handsome, cannot be displayed
Leong Jun Jie
Apa games also can be found in his laptop, A classic BITE student a.k.a Gamer
In the same time, also like to explore new technique and technology
P.S ... Don't stare on his gaming laptop


Video Comments
Managing Video Comments
Managing video comments is done by accessing the Videos > Video Comments > Search Video Comments function in the control panel menu.
From this interface you can add new video comments and search, edit, or delete the existing video comments. For an overview of searching,
adding, editing, and deleting items, please see the Control Panel Basics section of this software manual.
Database Fields
Video Comments have the following fields associated with them:
Individual Functions
-
Username - The username of the account which submitted this comment.
Date - The date and time the comment was added.
Status - The status of the comment
Pending
The comment is still waiting to be approved by an administrator and will not appear on your site.
Active
The comment is active and will appear on your site.
Comment - The comment itself.
Besides the Common Functions available for most items, video comments also have some custom functions that can be run. The icons that appear for each comment in
the Search Comments interface are described below.
Bulk Functions
-
- View the video associated with this comment. This will take you to the Search Videos interface which will display
only the video that is associated with this comment.
- Approve this comment. This will only be available for comments with Pending status. Approving a comment will change it's status to Active and the comment will then be visible on your site.
- Reject this comment. This will only be available for comments with Pending status. Rejecting a comment will delete the comment from the database permanently.
Besides the Common Functions available for most items, video comments also have some custom functions that can be run. The icons that appear in the toolbar
which can be run on multiple comments at once are covered below.
-
- Approve the checked or matching comments. Only comments with Pending status will be affected by this function.
Approving a comment will change the comments's status to Active and make it visible on your site
- Reject the checked or matching comments. Only comments with Pending status will be affected by this function. Rejecting a comment will delete the comment from the database permanently.


These are requirements for the PAL Mediator, which is the
mid-layer that connects the Bridge with Lumen and LAPDOG.
- Messaging semantics
- Call-type semantics, where the caller expects a return value or
possibly an Exception
- Execution request Bridge -> Lumen
- Execution request Lumen -> Bridge
- Learning request Bridge -> LAPDOG
- Action model changes (sent to some authoritative Action
Model from a client)
- Notification semantics, which don't expect any response
- Execution status updates for procedures and actions
- These could be single-threaded or multi-threaded with
respect to each client.
- These must be delivered in chronological order to each
client, based on dependencies between the notifications. If event B
depends on event A, the client cannot be notified of B until its event
handler method which receives A has returned.
- "Sub-task start" depends on "parent task start."
- "Task end" depends on "task start."
- "Parent task end" depends on "sub-task end."
- Action model changes (sent to all the clients from the
Mediator)
- These must be single-threaded with respect to each client.
A client should never be handling more than one of these at a time.
- These must be delivered in chronological order to each
client.
- Action model.
- Contains types. Types include:
- Atomic types. Can be represented by java.lang.String.
- List/Set/Bag/PermutableList. These are types that take a
single parameter, e.g., "List of addresses"
- Map. Takes two parameters. Maps one type to another type.
- Tuple. Fixed-length, ordered list of items, where each item
has a distinct type.
- Contains actions and procedures. A procedure is a special type
of action, which the action model doesn't necessarily have to
distinguish between.
- Each type or action has a unique name.
- Can retrieve a type/action by name.
- Can list all types/actions.
- Can add a type/action.
- Can remove a type/action.
- Can attach arbitrary string key/value metadata to a type/action.
- Locking. Any client should be able to try to get either type of
lock:
- Exclusive, distributed locks. Exclusive in that no other
party
can get a lock while an exclusive lock is already held. Distributed
meaning multiple resources (Action Model, Lumen, LAPDOG) are all locked
at the same time.
- Action model additions
- Action model removals
- Non-exclusive, distributed locks. Non-exclusive meaning that
multiple lockers can lock the same resources at the same time without
intefering with each other. "Don't change the action model while I'm
running this procedure."
- Procedure execution
- Procedure learning
- Procedure editing?
- The easiest way to meet this requirement is not through
distributed locks, but instead by having only one copy of the action
model. Any party wishing to do something with an action first acquires
a read lock on the action in the action model. Any party wishing to
delete an action checks that there are no read locks on it, then
acquires a write lock to exclude race conditions involving readers.
Adding new actions should use a write lock also. Changing an existing
action is not supported.
- Action model garbage collection. When an action is no longer in
use, we need to detect that condition and remove the action from all
action models.
- Softening requirement: We don't have to detect it immediately.
- Softening requirement: We can tolerate a few false postiives,
as long as we have locking implemented correctly. The removal will only
happen when there are no procedures running, so no procedures will
break. And the next time somebody executes the procedures, we'll load
it on demand.
- How do we handle inherited actions? If A inherits from B, does
A
include all of B's fields, or does the client have to retrieve B to
fill in those fields?
- All clients receive notification when a type is added or
removed from the action model.
- The client which initiated the add or remove only needs to
hold its lock until the action has been added or removed from the
action model. That means the remove command from the client to the
Mediator must be synchronous.
- The add/remove notifications from the Mediator to the clients
should not be synchronous; otherwise one bad client could deadlock the
whole system.
- Add and remove notifications should be single-threaded as far
as any client is concerned. In other words, there should never be both
an add notification and a remove notification being handled by one
client at the same time.
- Action execution. This also applies to procedures, which are a
special type of action.
- Any client can request that an action be executed. The
requesting client doesn't need to know what other client is responsible
for executing the action.
- The execution request contains the name or some reference to
the action in the action model.
- The execution request may contain values for the input
parameters. It's up to the implementing client whether to execute an
action with no input values specified. Input values need to be of the
appropriate type, but the Mediator doesn't have to do any type checking.
- If no client is responsible for handling the execution request,
the Mediator should throw an Exception.
- The execution request should be handled asynchronously. The
requesting client thread should not block until the execution is
complete.
- Each execution request needs to be uniquely identified.
- All clients should receive status updates as the execution
request proceeds.
- Start. This notification should include input parameters.
- Successful completion. This notification should include
output parameters.
- Error. This should include the Exception which caused the
error.
- Cancel. Needs no additional information.
- (optional) Only the client which is executing the request
should be allowed to update the status of the execution.
- (optional) It would be nice if the requester could get the
request ID before any status messages are delivered.
- (optional) It would be nice if the requester could listen for
status updates of only one particular request.
- A client needs a way to advertise that it has started a new
execution which it is handling. (As opposed to handling a request from
another client.) This is what happens when Lumen executes a sub-task.
- Each execution request has an optional parent execution
request. Top-level executions have a null parent.
- Ready signal.
- Each client needs a way to signal when it's fully initialized
and ready to handle requests.
- Clients are not considered ready just because they connect to
the Mediator. The must signal readiness.
- The client will not receive any execution requests or execution
status notifications until it signals readiness.
- Should a client receive action model changes before it signals
readiness? If not, should those notifications be queued up until the
client is ready?
- Arbitrary client-to-client calls.
- Any client needs to be able to publish an RMI interface that
can be used by other clients. The consumer needs a way to get the
appropriate RMI stub object based on the name of the producer.
- RMIException should be hidden in this interface, to allow for
future transports which would not be based on RMI.
- Keep off the net. Our communications need to exclusively use the
loopback interface, not the external network interface. For RMI, this
means we need to set the system property java.rmi.server.hostname to
"<IP_ADDRESS>".
- No cross-JVM class loading. RMI wants to do this, and it has to
be disabled.
- Type information. We need to represent many kinds of types and
actions.
- Simple types, which have a Java class backing them and a
distinct name.
- Aggregate types, which all fit the pattern of
java.util.Collection<T>, where T is essentially another type.
- List
- Permutable list
- Set
- Bag
- Maps, which are analogous to java.util.Map<K,V>.
- Tuples, which are fixed-length lists of items, each of which
has a distinct type.
- Actions, which look a lot like tuples, but don't necessarily
have to be represented the same way.
- Procedures, which look a lot like actions.
- Has to be sendable from one JVM to another. This probably means
Serializable.
Interfaces between Bridge and ITL
code
This is a list of the spots where the Bridge depends on ITL
functionality. In other words, these are the things we're going to have
to support int he new middleware layer.
- ActionExecAdapter
- Receives exec requests from Lumen, asking for
CPOF actions to be executed. Needs to decode the calling
ActionInvocation,
the called ActionDef, and the input params. Then create a new
ActionInvocation
for the called action, and invoke it. When invocation finishes,
re-encode output params and invocation status and send them back.
- ActionInvocation
- When its status changes, it broadcasts the new status along with
output params. Is this duplicating what ActionExecAdapter already does?
- ActionModel
- Gets types out of the ITL type system, and puts new types into
ITL. Needs to work with TypeTranslator, described below.
- Bridge
- Connects to ITL. Signals ITL that the Bridge is ready. Connects
to the Shell.
- ITLExecutionStatusListener
- Receives notification of ActionInvocations that have started,
ended, and failed. Decodes input/output parameters and either finds a
cached ActionInvocation or creates a new one.
- ITLSystemManager
- Starts and stops LAPDOG, Lumen, and ITL Server.
- Learner
- Connects to the LAPDOG Mediator.
- SparkProcedureExecutor
- Connects to Lumen. Requests procedure execution. Encodes
procedure name and input parameters. Adds procedures to Lumen
(add_tm_tasks). Receives procedure completion message from Lumen.
Decodes output parameters.
- TaskExecutor.
- Chooses between SPARK and Lumen. Can probably be deleted.
- TypeTranslator
- Two-way translator between ITL type representations and Bridge
type representations.
How does the Lumen Mediator interact with ITL?
- ITLTypeChangeListener
- Receives notification of added types and sends the info to
LumenClient.
- ITLTypeRemovalListener
- Receives notification of removed types and sends the info to
LumenClient.
- LumenClient
- Adds and removes actions and procedures from Lumen.
- LumenTaskExecutorListener
- Receives procedure execution requests for Lumen. Publishes status
updates.
- LumenTaskResultListener
- Sends procedure execution results. Encodes status and output
parameters.
- SubTaskExecutionListener
- Sends subtask execution results by calling into
ServerConnectionImpl.
- LumenCallServiceImpl
- RMI class which accepts calls from other ITL clients and makes
calls into Lumen.
- LumenProcess
- Responsible for starting the Lumen service.
- ServerConnectionImpl
- Evil god class. Does that make it a devil class?
- ConvertTask
- Converts task representations between Lumen and ITL forms.
How does the LAPDOG Mediator interact with ITL?
- ITL_ServerFacadeImpl
- Allows the LAPDOG Mediator to make calls to ITL. Subscribes to
type addition and removal. Checks a type to see if it's an action.
Retrieves types by name. Makes LAPDOG available by registering
LapdogClientCallServiceImpl with ITL.
- LapdogClient
- Connects to ITL. Accepts type addition and removal events and
delegates them to TypePublicationFacade.
- LapdogClientCallServiceImpl
- Accepts LAPDOG calls from other ITL clients. Learn and cancel.
- LapdogClientProcessManager
- Responsible for starting and stopping LAPDOG.
- TypePublicationFacade
- Converts ITL types to LAPDOG types, and pushes them to LAPDOG via
LapdogFacade.
Stuff to save from ITL
- Stuff used to start and stop modules of the system.
- ITLModule
- ITLServerProcessManager - with changes
- ModuleManager
- Mechanism to differentiate between a failed action and a canceled
one.
- Anything that uses System.getProperty and friends.
- RMI_MX_PORT - used in RmiMXConnectionFactory to choose a port
number for the RMI registry.
- PAL.disableFinalizer - used in Bridge to determine whether to
run the action finalizer.
- itl.module.*.startup - used in ITLModule to control how a
module is started. Probably don't need to preserve this.
- run-with-rmi - used in XPSServerMain to disable use of RMI.
It's hard to imagine using this.


Organizations on One Heart share in the goal of building a strong involved community engaged in cooperation and support. Check out all of these groups dedicated to the enrichment and growth of Rock Hill and the surrounding area.
This is not only an opportunity to connect with your local community, but a opportunity to help others in new ways. Read about the involvment of other people on the Experiences page and let their ideas inspire to get involved in new ways. Thank you for helping make our community great!
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc eu ornare erat. Morbi at ante et amet.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc eu ornare erat. Morbi at ante et amet.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc eu ornare erat. Morbi at ante et amet.


ChooseWifi lets the user select a WiFi network in his range.
title1 and title2 are the titles for the scanning and the selection process. They default to “Scanning” and “Select WiFi”.
The callback function returns the name and the ssid of the selected network.
You can set the force option so that the dialog cannot be closed until the user has selected an item.
Finally you can add extra items as pipe “|” separated string - ie. to add a default “[None]” entry to select no wifi network.
See Also: WifiConnect
Example - Choose WiFi Network
function OnStart()
{
lay = app.CreateLayout( "linear", "VCenter,FillXY" );
btnChoose = app.CreateButton( "Choose WiFi", 0.5, 0.1 );
btnChoose.SetOnTouch( btnChoose_OnTouch );
lay.AddChild( btnChoose );
app.AddLayout( lay );
}
function btnChoose_OnTouch()
{
app.ChooseWifi( "", "", OnWifiChoose );
}
function OnWifiChoose( ssid )
{
app.ShowPopup( "User selected " + ssid );
}


If you are in TV Production 3 or 4 your name will be listed here, but I need to have a list of everyone first.


Configuration¶
PyPackProxy uses an INI-style configuration file. All paths must be absolute!
Section [pypackproxy]¶
- index-url
The base URL of the package repository (optional; default: https://pypi.org/simple). Can be disabled with
index-url=false
.- project-url
The web-interface can show links to project pages on the startpage. The poject-url must contain the placeholder
{}
which will be replaced with the lower case project name (default: https://pypi.org/project/{}/). To show no links:project-url=false
.- storage-path
The path to the directory where the package files are stored (required; must exist).
- admin-pass
The password for the admin user (min. 8 characters; required). The admin login can be disabled with
admin-pass=false
.- admin-expire
The time in minutes after which the admin session expires (optional; default: 30)
- timeout
The timeout in seconds for requests to the package index (optional; default: 30.0).
- retries
Number of retries for connection attempts to the package index (optional; default: 5).
Section [server]¶
- host
Host and port of the server, e.g. example.com:80 (required).
- daemonize
Whether to daemonize the server process (optional; default: yes).
- pidfile
Path to the PID file (optional).
- user
The user under which to run the server when started as root, e.g. when using a port < 1024 (optional).
- group
Group for user (optional; default: primary group of user).
- ssl_certificate
Path to the certificate file (required for HTTPS).
- ssl_private_key
Path to the private key file (required for HTTPS).
- ssl_certificate_chain
Path to the certificate chain file (optional).
Section [proxy]¶
- proxy-url
The URL of the HTTP-Proxy (optional).
- proxy-user
The user for proxy authentication (optional).
- proxy-pass
The password for proxy authentication (optional).
Section [logging]¶
The application has two log files: An access log, which logs the HTTP requests and uses the common log format, and a message log which logs all other messages.
Logging is disabled by default.
The application can do a rollover of the log files at a predetermined size of *-size bytes. It will keep up to *-count backups of older files. If *-size or *-count are 0 rollover will never occur and the files will grow indefinitely.
‘M’ or ‘K’ can be appended to the *-size options for Mibi and KiBi bytes respectively.
- access-file
Path to access log file (optional; directory must exist).
- message-file
Path to message log file (optional; directory must exist).
- access-size
Max. access log size in bytes (optional; default: 0).
- message-size
Max. message log size in bytes (optional; default: 0).
- access-count
Number of max. backups of access log files (optional; default: 1).
- message-count
Number of max. backups of message log files (optional; default: 1).


--- layout: page_legal title: Mentions légales permalink: /mentions/ --- {%-comment-%} main class=container @show _layout/page_legal is mentions & CGV template. Starts the for loop at a specific index to ignore title in loop @see https://help.shopify.com/en/themes/liquid/tags/iteration-tags#offset {%-endcomment-%}
{{ item.content }}
{%- endfor -%}


{% comment %}
Aim: List out individual posts related to the project of the current page
IMPORTANT: ONLY render html if at least ONE valid entry exists!
Not happy with the solution below for efficiency, but it should work nicely
Specifically, I am not keen on the idea of looping through all the posts
twice: 1. To see if any posts exist with a matching project
2. To do the actual work
to minimise this: searching from latest post to earliest post
exit loop with posts_found = true when the first post is found
While not pretty, this might help to neaten up downstream logic.
This all had to be done because I wasn't prepared to live through
the night mare that was project_related_series.html!
{% endcomment %}
{% assign posts_found = false %}
{% for post in site.posts %}
{% if page.project == post.project and page.project != nil
%}
{% assign posts_found = true %}
{% break %}
{% endif %}
{% endfor %}
{% if page.show_project_posts and posts_found == true %}


Project Volucris: (Xenimus Arena System) Enhanced.
Posted: Fri Aug 03, 2007 3:34 pm
I know this is probably a long shot and i hope this doesnt go down to bad, i spent alot of time working this out and actually thinking about the arena itself.
Basically it is the simple team battle game we hae all played any enjoyed, flag capture. I'm sure your wondering how does this work in xenimus, well let me explain.
Obviously you have 2 sides, There will be a minimum of 3 players each side to a maximum of 6 players each side each event. Now once we have 6 players join the arena, they are all asigned to a team (randomly if possible) so now we have an equally chosen 3v3.
Q.
A. Using models that were available with the map editor i have made what i believe to bea suitable method of recreating the flag, see screenshots lower down .
Q. How can a xenimus model be shon carrying a flag?
A. they don't. however i was thinking when a character is in possession of the flag their Character name is constanly visible below the feet of the character as if you had right clicked it (Right click to display character name will be disabled in arena) the name could show up in a golden yellow colour or any better way that programming expertise can distinguish flag carrier.
Q. What do you benefit from flag capture arena?
A. Well i think there should be a reward system of which I am yet to think up, i have been extremely ill over the past few days, my head is pounding right now a si speak. so if anyoe has any reward scheme process's they are more than welcome. Untill i maybe figure one out myself at a later date and let you all know.
Right hope iv not bored you too much, heres some shots of the arena. Hope its not that bad to you all.
Flag Posts
Inside and around Northern Building
Trees and Bushes, Ideal for Hiding Characters.
Inside and Around Southern Cave
Basically it is the simple team battle game we hae all played any enjoyed, flag capture. I'm sure your wondering how does this work in xenimus, well let me explain.
Obviously you have 2 sides, There will be a minimum of 3 players each side to a maximum of 6 players each side each event. Now once we have 6 players join the arena, they are all asigned to a team (randomly if possible) so now we have an equally chosen 3v3.
Q.
A. Using models that were available with the map editor i have made what i believe to bea suitable method of recreating the flag, see screenshots lower down .
Q. How can a xenimus model be shon carrying a flag?
A. they don't. however i was thinking when a character is in possession of the flag their Character name is constanly visible below the feet of the character as if you had right clicked it (Right click to display character name will be disabled in arena) the name could show up in a golden yellow colour or any better way that programming expertise can distinguish flag carrier.
Q. What do you benefit from flag capture arena?
A. Well i think there should be a reward system of which I am yet to think up, i have been extremely ill over the past few days, my head is pounding right now a si speak. so if anyoe has any reward scheme process's they are more than welcome. Untill i maybe figure one out myself at a later date and let you all know.
Right hope iv not bored you too much, heres some shots of the arena. Hope its not that bad to you all.
Flag Posts
Inside and around Northern Building
Trees and Bushes, Ideal for Hiding Characters.
Inside and Around Southern Cave


Visit our Repository and use the search bar to narrow your search. Keywords should be separated by white spaces and can mix words from different fields (e.g. 'machine learning London' or 'neuron computational modeling'). You can filter results to display only profiles from under-represented countries (list based on the World's Bank list of Low, Lower-Middle and Upper-Middle income economies as of August 2017).
Look at the profile provided by the researcher and Google them for other profiles (e.g. LinkedIn, institution, ResearchGate, …). Read the recommendations if any, making sure they are sound (e.g. if Recommender A says 'great talk at OHBM 2017', check the OHBM 2017 program and maybe watch the video if available). Important note: the researchers are sole responsible for their profiles and we cannot guarantee authenticity.
It is a vicious circle: researchers need to be known to get invited as speakers but they need to be invited as speaker to get known. This cycle can be broken by taking a chance and inviting someone less recognized. Don't forget that younger scientists also have things to say and they might be more approachable/committed.
If you cannot identify potential speakers for your event, check out these other great resources:
Visit the Repository and enter the researcher's name in the search bar or keywords related to your field. Click on 'View Profile' to view the detailed profile of a scientist you would like to recommend. You can then click the 'Recommend' button at the top right corner or click the 'write her one' link in the Recommendation section. Anyone can submit a recommendation, male or female. Recommendations must be positive. When recommending a researcher, please make sure to include all relevant information, e.g. whether you have seen this person talk at an event before, and where and when that was (as videos of the event might be available). Also include any potential conflicts of interest, disclosing any personal relationship with the scientist (e.g. colleague, close friend, student, ...). At the moment, it is NOT possible to edit a recommendation, but contact us for related queries.
To enter your name on the repository, fill in the form in the Create profile page. Make sure you agree with our data storing terms (see here).
At the moment, editing your information can be done by contacting Women in Neuroscience Repository, mentioning the exact information to modify.
We store the data provided (by you) only for display on this website, with the intent to increase the visibility of women in neuroscience. We do not distribute it to third-parties within or outside the EU under any circumstances. You have the right to access your data, as well as for the data to be erased from our database at any time. To do so, simply email us at Women in Neuroscience Repository. To know more about your rights as users of this website, please refer to the European Commission data protection policies.


WeShouldLearn is a video and text tutorial site. Videos are short, project focussed where possible, and try to stay funny and friendly.


The fourth day of festivals is celebrated in the form of Govardhan puja or Vishkarma puja all across India. And in 2020, it is celebrated on 15 November.
It is believed that on this day, Lord Krishna defeated Indra, the god of thunder and rain. In Gujarat, the day is celebrated as the start of the New year. People on this day worship instruments, arms, and machinery to appease Lord Vishwakarma.
शुभम करोति कल्याणम,
अरोग्यम धन संपदा,
शत्रु-बुद्धि विनाशायः,
दीपःज्योति नमोस्तुते ।।
********************


--- layout: post title: The Recurring Beauty date: 2009-12-27 19:38:57.000000000 +02:00 categories: - Computer Science tags: - Computer Science - recursion status: publish type: post published: true meta: _edit_last: '10173910' _wpas_done_twitter: '1' _wp_old_slug: the-recuring-beauty author: login: saherneklawy email<EMAIL_ADDRESS>display_name: saherneklawy first_name: '' last_name: '' excerpt: !ruby/object:Hpricot::Doc options: {} ---
Recently, I stared reading one of the essential books for a computer scientists, namely Concrete Mathematics. After the first chapter, it really got me thinking "Oh My!! Recurrences could solve anything." This is of course not matched to the thrill of the challenge in funding recurring patterns in a structure.
Along my college years, colleges tend to step away from recurrences. In fact, many cases it is the easier approach to tackle. Furthermore, it could be in some cases the more efficient approach. This reminds me of many algorithm techniques that largely depend on finding substructures or small solutions that are used for generalizations. The most vivid example comes to me in the form of memoization in dynamic programming (DP). For me the corner stone of DP is finding the "magical" recurrence. Thus, from super slow, to super fast (lots of memory use though :D).
Other examples of how recursive thinking makes complex problems elegantly simple could be seen in graph theory. Just by examining some properties on nodes, arcs, degrees, huge complexities just boil down to some elegantly beautiful properties that are easily generalizable.
Such beauty could be easily seen when recurrences could end up in something that is visually breath taking. An example are Fractals, a stunning example of how simple math with recursion lead to amazing imagery.


Just put your image size (width & height) after our URL and you'll get a placeholder.
https://picsum.photos/200/300
To get a square image, just put the size you want.
https://picsum.photos/200
Get a random image by appending ?random
to the end of the url.
https://picsum.photos/200/300/?random
Use the /g/
path to greyscale the image.
https://picsum.photos/g/200/300
Get a list of images by using the /list
endpoint.
https://picsum.photos/list
[
{
"format": "jpeg",
"width": 5616,
"height": 3744,
"filename": "51492538696.jpg",
"id": 0,
"author": "Alejandro Escamilla",
"author_url": "https://alejandroescamilla.com/",
"post_url": "https://unsplash.com/post/51492538696/download-by-alejandro-escamilla"
}
]
Get a specific image by appending ?image
to the end of the url.
https://picsum.photos/200/300?image=0
You can find a list of all images here.
Get a blurred image by appending ?blur
to the end of the url.
https://picsum.photos/200/300/?blur
Select the cropping gravity by adding ?gravity
to the end of the url.
Valid options are: north, east, south, west, center
https://picsum.photos/200/300/?gravity=east


Registartion Form:
First Name:
Email:
Required
Enter correct format
Password:
Required
too short!!!
too long!!!
Gender:
Female
Male
Telephone:
Enter Correct pattern
DOB:
Enter Correct DATE
{{message}}


Through Deloitte Digital I worked directly with the team at Texas Health and Human Services on their mobile app for iOS and Android called YourTexasBenefits. The goal of the app was to make it easy for people to apply and manage their benefits for different social services. Not an easy thing given the rigorous documentation and process required for government projects.
I helped this team create recommendations for app improvements and lead UX research. Much of our research involved hands-on mobile user testing at service locations, many of which had very little mobile experience or did not speak english.
We would develop testing plans to try and validate ideas for improvements and work hands-on to record and document interactions in the field. Each part of the process always had to be heavily documented and approved. We would take new ideas, mock them into functional prototypes in Invision and record tests in the field.
While the progress was often slower then we might like, it was great to see how a simple thing like making it easy to file a change to your benefits on mobile could save thousands of people hours out of their day instead of visiting an office.
Given my time and experience with benefit and insurance programs I also went on the road to consult and guide discussions around features and needs of other states for similar programs.
From Washington state to Pennsylvania I worked directly with program stakeholders, using UX tactics like card sorting, to hear their concerns, turn them into feature ideas, rank them and make sure everyones voices were heard.
In the end we would provide outlines for new product ideas and improvements for each state based on the unique needs of their clients and existing tools.


Barham Ink
Welcome to the online home of Barham Ink, a self-publishing company established with quality in mind every step of the way. It all starts with a carefully crafted story. Then from book design to cover art to physical copy, everything we and our professional, independent contractors do is scrutinized with a critical eye. When you pick up or download your first book from Barham Ink, it should feel every bit as high-quality as a product from a larger traditional publishing house.


The outline-width property specifies the width of the outline, and can have one of the following values: thin (typically 1px) medium (typically 3px) thick (typically 5px) A specific size (in px, pt, cm, em, etc)
Thin Outline of 1px using keyword
Medium Outline of 3px using keyword
Thick Outline of 5px using keyword
Random outline of 10px with specification


Steps
It is possible to update your version of MNE between releases for bugfixes or new features.
Warning
In between releases, function and class APIs can change without warning.
You can use pip
for a one-time update:
$ pip install --upgrade --no-deps git+https://github.com/mne-tools/mne-python.git
Or, if you prefer to be set up for frequent updates, you can use git
directly:
$ git clone git://github.com/mne-tools/mne-python.git
$ cd mne-python
$ python setup.py develop
A feature of python setup.py develop
is that any changes made to
the files (e.g., by updating to latest master
) will be reflected in
mne
as soon as you restart your Python interpreter. So to update to
the latest version of the master
development branch, you can do:
$ git pull origin master
and MNE will be updated to have the latest changes.
If you plan to contribute to MNE, please continue reading how to How to contribute to MNE.
Some routines can utilize NVIDIA CUDA GPU processing to speed up some operations (e.g. FIR filtering) by roughly an order of magnitude. To use CUDA, first ensure that you are running the NVIDIA proprietary drivers on your operating system, and then do:
$ conda install cupy
$ MNE_USE_CUDA=true python -c "import mne; mne.cuda.init_cuda(verbose=True)"
Enabling CUDA with 1.55 GB available memory
If you receive a message reporting the GPU’s available memory, CuPy is woriking properly. To permanently enable CUDA in MNE, you can do:
>>> mne.utils.set_config('MNE_USE_CUDA', 'true')
You can then test MNE CUDA support by running the associated test:
$ pytest mne/tests/test_filter.py -k cuda
If the tests pass, then CUDA should work in MNE. You can use CUDA in methods
that state that they allow passing n_jobs='cuda'
, such as
mne.io.Raw.filter()
and mne.io.Raw.resample()
,
and they should run faster than the CPU-based multithreading such as
n_jobs=8
.
In Jupyter, we strongly recommend using the Qt matplotlib backend for fast and correct rendering:
$ ipython --matplotlib=qt
On Linux, for example, QT is the only matplotlib backend for which 3D rendering will work correctly. On Mac OS X for other backends certain matplotlib functions might not work as expected.
To take full advantage of MNE-Python’s visualization capacities in combination with IPython notebooks and inline displaying, please explicitly add the following magic method invocation to your notebook or configure your notebook runtime accordingly:
In [1]: %matplotlib inline
If you use another Python setup and you encounter some difficulties please report them on the MNE mailing list or on github to get assistance.
If you run into trouble when visualizing source estimates (or anything else
using mayavi), you can try setting the ETS_TOOLKIT
environment variable:
>>> import os
>>> os.environ['ETS_TOOLKIT'] = 'qt4'
>>> os.environ['QT_API'] = 'pyqt5'
This will tell Traits that we will use Qt with PyQt bindings.
For more information, see http://docs.enthought.com/mayavi/mayavi/building_applications.html.
On remote systems, it might be possible to use MESA software rendering
(such as llvmpipe
or swr
) for 3D visualization with some tweaks.
For example, on CentOS 7.5 you might be able to use the environment variable
to force MESA to use modern OpenGL by using this before executing
spyder
or python
:
$ export MESA_GL_VERSION_OVERRIDE=3.3
Also, it’s possible that different software rending backends might perform
better than others, such as using the llvmpipe
backend rather than swr
.


Azure Storage Explorer doesn't just access Azure Storage. It can also access data in Azure Cosmos DB and Azure Data Lake.
You've worked through the basics of connecting Storage Explorer to your Azure account. In the CRM system, your developers use Azure Cosmos DB for day-to-day operations, and Data Lake for big data storage. You want to use Storage Explorer to connect to both of these databases.
Note
The Azure Cosmos DB integration with Storage Explorer has been deprecated. Any existing functionality will not be removed for a minimum of one year from this release. You should use the Azure Portal, Azure Portal desktop app or the standalone Azure Cosmos explorer instead. The alternative options contain many new features that aren’t currently supported in Storage Explorer.
You can use Storage Explorer to connect to Azure Cosmos DB and do useful activities like creating and deleting databases, collections, documents, and graphs.
There are multiple ways to connect to Azure Cosmos DB from Storage Explorer. The primary method is to use a connection string. A connection string contains all the information that you need to find and connect to a particular database in Azure Cosmos DB. The connection string has a specific composition, as shown here:
AccountEndpoint=https://<YOUR-COSMOS-DB-NAME>.documents.azure.com:443/;AccountKey=<PRIMARY-MASTER-KEY>;
In this template, replace <YOUR-COSMOS-DB-NAME>
with the name of your database. <PRIMARY-MASTER-KEY>
is a unique key for the database.
There are two ways to obtain the connection string.
You can use the Azure portal to find the database that you want to connect to. Look for the Keys menu item and make a copy of the primary connection string.
The other way is to use the Azure CLI. This method gives you the primary master key for the Cosmos DB database. Use this command:
az cosmosdb keys list \
--name COSMOS-DB-NAME \
--resource-group myresourcegroup \
--subscription "My Subscription" \
--type keys
By using the connection string template, add the name of the database, along with the primary master key returned from the preceding command.
Now that you have a connection string, you can use Storage Explorer to connect to your database. You can create or delete a database, create or delete collections, and create or delete documents.
Azure Data Lake is a service used for storing and analyzing large data sets. It supports large data workloads. It's well suited to capture data of any type or size, and at any speed. Data Lake supports all the expected enterprise-grade capabilities like security, scalability, reliability, manageability, and availability.
There are two types of Azure Data Lake: Gen1 and Gen2. Both types are supported in Storage Explorer.
You can use Storage Explorer to connect to Data Lake accounts. Just like storage accounts, you can use it to:
Let's go through creating Azure Cosmos DB and Data Lake storage, and then use Storage Explorer to connect to them.


Search for a city:
City Name
Country Name
Submit
Weather:
Weather:
Forecast:
Forecast:
This is a modified jumbotron that occupies the entire horizontal space of its parent.


Methods
createQuestion(quizMode, numberOfOptions) → {object}
Creates a single question for the selected quiz mode.
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
quizMode |
string
|
flag-to-country | Selects quiz mode (i.e. types of questions and options). Possible values: flag-to-country, flag-to-capital, country-to-flag, country-to-capital, capital-to-flag or capital-to-country. |
numberOfOptions |
number
|
4 | Number of options (i.e. possible answers) for this question. |
Returns:
- Type:
-
object
Question object with the question, the answers and the options.
createQuiz(quizMode, numberOfQuestions, numberOfOptions) → {object}
Creates a new quiz with random questions.
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
quizMode |
string
|
flag-to-country | Selects quiz mode (i.e. types of questions and options). Possible values: flag-to-country, flag-to-capital, country-to-flag, country-to-capital, capital-to-flag, capital-to-country or mixed. |
numberOfQuestions |
number
|
5 | Number of questions in this quiz. |
numberOfOptions |
number
|
4 | Number of options (i.e. possible answers) per question. |
Returns:
- Type:
-
object
Quiz object with the selected quiz mode and an array of questions. If quiz mode is 'mixed' then the quiz mode for every question in the quiz is randomly selected.
createRandomQuestion(numberOfOptions) → {object}
Creates a single question with random quiz mode.
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
numberOfOptions |
number
|
4 | Number of options (i.e. possible answers) for this question. |
Returns:
- Type:
-
object
Question object with the quiz mode, question, the answer and the options.


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Idle Ant 2 is an incremental game about ants. The game uses Angular 6 and Clarity Design System list of used package.


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diff --git a/README.md b/README.md index 63aac5c..21fffa2 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ This is a Django app for running puzzlehunts, written by several members of ✈ + The site is set up to use a `db.sqlite3` file in the root of this repository as its database. If this doesn't exist, Django will create a new empty database when you run `./manage.py migrate`. It's perfectly fine to start with this, but you won't have any puzzles populated and you almost certainly want to create a superuser. - If you just want to try out the website quickly with some sample data, you can run `./manage.py loaddata sample.yaml` (after `./manage.py migrate`) to load a sample hunt, an admin account (username and password `admin`), and a test account with a team (username and password `test`). You can view the templates used to render the puzzles in the [puzzles/templates/puzzle_bodies](puzzles/templates/puzzle_bodies) and [puzzles/templates/solution_bodies](puzzles/templates/solution_bodies) folders, which you can also base your puzzles/solutions off of. + If you just want to try out the website quickly with some sample data, you can run `./manage.py loaddata sample.yaml` (after `./manage.py migrate`) to load a sample hunt, an admin account (username and password `admin`), a test account with a team (username and password `test`), and some sample hint requests. You can view the templates used to render the puzzles in the [puzzles/templates/puzzle_bodies](puzzles/templates/puzzle_bodies) and [puzzles/templates/solution_bodies](puzzles/templates/solution_bodies) folders, which you can also base your puzzles/solutions off of. - ...be a superuser? diff --git a/puzzles/fixtures/sample.yaml b/puzzles/fixtures/sample.yaml index 556052e..b63ce79 100644 --- a/puzzles/fixtures/sample.yaml +++ b/puzzles/fixtures/sample.yaml @@ -79,3 +79,96 @@ user: 2 team_name: test creation_time: "9001-01-01T00:00:00+00:00" +- model: puzzles.Hint + pk: 1 + fields: + team: 2 + puzzle: 1 + submitted_datetime: "9001-01-01T00:00:00+00:00" + hint_question: > + I have solutions for most of the subpuzzles from a friend, except for the two longest clues, which they weren't able to solve. I'm hoping I can get by without them, because I don't know how they got their answers and they're busy right now, so I put in placeholders for the unsolved clues and hope they'll be resolved later. + + + I didn't trust the ordering I got up to this point, so I decided to sort the things I had extracted, first by length and then lexicographically to break any ties. And there were quite a few ties, which were between things of even length. + + + Finally, I guessed that the numbers in this puzzle might suggest that there were some kind of Caesar shifts involved. I don't know what the shift amount is, but I thought that might be okay because I could just try all of them. I'm super demotivated and want something soft to cuddle now... +- model: puzzles.Hint + pk: 2 + fields: + team: 2 + puzzle: 2 + submitted_datetime: "9001-01-01T00:00:00+00:00" + hint_question: > + I noticed that the last digit repeats a lot, so I tried concatenating the numbers into groups by that, and then of course discarding that last digit, since we used it. (So I got two groups of 17 and 8 digits respectively.) + + + Then, I noticed that we had a square grid (in this puzzle, just by putting the 25 digits in row-major order), and a square grid makes me want to play Boggle on it, so I tried tracing some paths in the grid. Some of the numbers I got by tracing: 14134, 14534, 26953, 31341, 31641, 43213, 43413, 72764, 87221. I was pretty sure that only one of the paths I found would be relevant (in this puzzle I thought 31641 was the most compelling), and that this wouldn't be the last step even if I got a word I might want. + + + Finally, I discovered that I can just randomly anagram what I got into 31416, but PI isn't the answer. I would really like some help, in any form! +- model: puzzles.Hint + pk: 3 + fields: + team: 2 + puzzle: 3 + submitted_datetime: "9001-01-01T00:00:00+00:00" + hint_question: > + So I think I figured out the first part, what the puzzle is cluing (STOP and TIME). That's cool because I can make a grid with the letters (it's just 2x4), but after I couldn't find anything in it I tried flipping the grid vertically, or equivalently, reversing the order of the rows. + + + After that, I realized I haven't used the blanks at the bottom, so I decided to just keep only the first few of the letters, as many as needed to fill the blanks, and delete all the rest. + + + Although I think the puzzle is definitely suggesting that I should submit the contents of the box as the answer, what I have there currently isn't a word. But I thought I could try guessing just the last ⅔ of the letters, since that's a plausible answer (kind of... sounds like something musical?), but it was incorrect. How could I have been prevented from solving this puzzle? +- model: puzzles.Hint + pk: 4 + fields: + team: 2 + puzzle: 4 + submitted_datetime: "9001-01-01T00:00:00+00:00" + hint_question: > + I looked at the clues and thought that the first step would probably be to reorder them, so I did that and got an ordering that ends STRO?UHR, INC?SOR, ECZ?MA. It's kinda cute that the last letter of the alphabet is in the last clue in this ordering as confirmation, although I think that's too obvious and just having a lot of very late letters in the last clue would be fine. + + + For each clue, I tried to solve it while ignoring the ? in the middle for now, which I thought might be used later. Then, I tried to find a word that fit in the bottom blanks that could be clued by "eczma" or any of the other words I got. + + + When that didn't work, I remembered that numbers and letters are interchangeable in puzzlehunts, like A=1, B=2, and so on, so I decided to sum the things I had. I would have really liked it if the numbers were small enough to convert back to letters (but they weren't: the first few numbers I got were 161 and 175, so I'm totally stuck). I think I need to find more structure in this puzzle to solve it. +- model: puzzles.Hint + pk: 5 + fields: + team: 2 + puzzle: 5 + submitted_datetime: "9001-01-01T00:00:00+00:00" + hint_question: > + I heard your hunt had a lot of puzzles about video games, so I tried to see if there were any famous video games, probably one of those considered best of all time or something, that might be clued by the puzzle title because they start with the same, say, three or four letters. But those letters shouldn't be pronounced the same way in the title and the puzzle, since that would be too obvious. (I found Diablo and Diablo II, but was hoping for something much more recent...) + + + That didn't get me anywhere, so I looked at the rest of the puzzle and noticed that there were a lot of numbers, so I guessed they might be indexes and extracted some letters that way (I got D, R, A, M, D, I...) For the numbers that were too big, of course I just wrapped around (so 2016 became index 3, or A), and so on. I thought it seemed promising that the second to fourth letters form a word. + + + When that didn't reveal anything, I looked at the puzzle again and realized I hadn't used the clues yet. I could use the numbers to index into those instead. But I tried that too, and didn't get anywhere. When will I be able to exclaim victory over this puzzle? +- model: puzzles.Hint + pk: 6 + fields: + team: 2 + puzzle: 6 + submitted_datetime: "9001-01-01T00:00:00+00:00" + hint_question: > + I looked at all these numbers and couldn't see anything meaningful in them, so I decided to transform them, digit by digit, into new numbers (so like the first line became (7,7)—(15,15) and the last line became (19,31)—(31,27)). You get the idea. + + + I didn't get anywhere after staring at these numbers some more, but I thought I could try a little harder, so I added 1 to all of them. I like the power of 2 I got at the start. + + + Alas, my data overall still looked like garbage, but I concatenated it all together and tried to find an answer anyway. Maybe there was an ancient non-English name of something that matched what I had? +- model: puzzles.Hint + pk: 7 + fields: + team: 2 + puzzle: 7 + submitted_datetime: "9001-01-01T00:00:00+00:00" + hint_question: I noticed what the answers have in common, but I can't figure out what order to put them in. I'll probably just keep them in the provided order right now, even though I know it's alphabetical by puzzle title, and hope that I can get an answer out of that. My teammates are also saying different things about what they think the answers should be. What's going on? + status: ANS + response: Based on all the hint requests you've made, we think the most helpful thing to try would be to have your teammates all try working on different puzzles than the one they're working on right now.


Evolving from single-celled yellow organisms at the dawn of time, Minions live to serve, but find themselves working for a continual series of unsuccessful masters, from T. Rex to Napoleon. Without a master to grovel for, the Minions fall into a deep depression. But one minion, Kevin, has a plan; accompanied by his pals Stuart and Bob, Kevin sets forth to find a new evil boss for his brethren to follow. Their search leads them to Scarlet Overkill, the world's first-ever super-villainess.