The layout structure consists of two columns. The first column is the sidebar and the second is the app content that takes the remaining width. The basic code of the layout structure can be seen below.
@@include('includes/code-examples/layout/basic-structure.html')
The sidebar is the left column of the layout structure and has a header, a navigation and a footer. The template was designed using a sidebar with a maximum of two level of navigation.
The header of the sidebar comes first. It contains the user informations like photo and name.
The single level of navigation is defined by class .sidebar-nav-link
and takes the user to desired page.
You also can add buttons to sidebar wrapped with .sidebar-nav-btn
.
The two level of navigation is defined by class .sidebar-nav-group
and opens the corresponding group of links.
The attribute data-parent
configures the accordion menu
(refer to bootstrap documentation for details).
The sidebar footer contains a set of other buttons like a small toolbar.
The content is the right column of the layout structure and contains the page content that the user navigated to. The content can contain the components of the bootstrap such as navbar, breadcrumb and any other components you may need.
@@include('includes/code-examples/layout/app-content.html')
The template uses the bootstrap breakpoints to define the minimum width to show the sidebar.
When hidden the attributes data-toggle="sidebar"
and data-dismiss="sidebar"
can be used to show and close the sidebar respectively.
An example code for the full setup of the sidebar can be seen below.
@@include('includes/code-examples/layout/starter-template.html')


torch_geometric.data¶
-
class
Data
(x=None, edge_index=None, edge_attr=None, y=None, pos=None, norm=None, face=None, **kwargs)[source]¶ A plain old python object modeling a single graph with various (optional) attributes:
Parameters: - x (Tensor, optional) – Node feature matrix with shape
[num_nodes, num_node_features]
. (default:None
) - edge_index (LongTensor, optional) – Graph connectivity in COO format
with shape
[2, num_edges]
. (default:None
) - edge_attr (Tensor, optional) – Edge feature matrix with shape
[num_edges, num_edge_features]
. (default:None
) - y (Tensor, optional) – Graph or node targets with arbitrary shape.
(default:
None
) - pos (Tensor, optional) – Node position matrix with shape
[num_nodes, num_dimensions]
. (default:None
) - norm (Tensor, optional) – Normal vector matrix with shape
[num_nodes, num_dimensions]
. (default:None
) - face (LongTensor, optional) – Face adjacency matrix with shape
[3, num_faces]
. (default:None
)
The data object is not restricted to these attributes and can be extented by any other additional data.
Example:
data = Data(x=x, edge_index=edge_index) data.train_idx = torch.tensor([...], dtype=torch.long) data.test_mask = torch.tensor([...], dtype=torch.uint8)
-
__call__
(*keys)[source]¶ Iterates over all attributes
*keys
in the data, yielding their attribute names and content. If*keys
is not given this method will iterative over all present attributes.
-
__cat_dim__
(key, value)[source]¶ Returns the dimension for which
value
of attributekey
will get concatenated when creating batches.Note
This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.
-
__cumsum__
(key, value)[source]¶ If
True
,value
of attributekey
is cumulatively summed up when creating batches.Note
This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.
-
__iter__
()[source]¶ Iterates over all present attributes in the data, yielding their attribute names and content.
-
apply
(func, *keys)[source]¶ Applies the function
func
to all tensor attributes*keys
. If*keys
is not given,func
is applied to all present attributes.
-
contiguous
(*keys)[source]¶ Ensures a contiguous memory layout for all attributes
*keys
. If*keys
is not given, all present attributes are ensured to have a contiguous memory layout.
-
is_coalesced
()[source]¶ Returns
True
, if edge indices are ordered and do not contain duplicate entries.
-
keys
¶ Returns all names of graph attributes.
-
num_edges
¶ Returns the number of edges in the graph.
-
num_faces
¶ Returns the number of faces in the mesh.
-
num_features
¶ Returns the number of features per node in the graph.
-
num_nodes
¶ Returns or sets the number of nodes in the graph.
Note
The number of nodes in your data object is typically automatically inferred, e.g., when node features
x
are present. In some cases however, a graph may only be given by its edge indicesedge_index
. PyTorch Geometric then guesses the number of nodes according toedge_index.max().item() + 1
, but in case there exists isolated nodes, this number has not to be correct and can therefore result in unexpected batch-wise behavior. Thus, we recommend to set the number of nodes in your data object explicitly viadata.num_nodes = ...
. You will be given a warning that requests you to do so.
- x (Tensor, optional) – Node feature matrix with shape
-
class
Batch
(batch=None, **kwargs)[source]¶ A plain old python object modeling a batch of graphs as one big (dicconnected) graph. With
torch_geometric.data.Data
being the base class, all its methods can also be used here. In addition, single graphs can be reconstructed via the assignment vectorbatch
, which maps each node to its respective graph identifier.-
static
from_data_list
(data_list, follow_batch=[])[source]¶ Constructs a batch object from a python list holding
torch_geometric.data.Data
objects. The assignment vectorbatch
is created on the fly. Additionally, creates assignment batch vectors for each key infollow_batch
.
-
num_graphs
¶ Returns the number of graphs in the batch.
-
static
-
class
Dataset
(root, transform=None, pre_transform=None, pre_filter=None)[source]¶ Dataset base class for creating graph datasets. See here for the accompanying tutorial.
Parameters: - root (string) – Root directory where the dataset should be saved.
- transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
__getitem__
(idx)[source]¶ Gets the data object at index
idx
and transforms it (in case aself.transform
is given).
-
num_features
¶ Returns the number of features per node in the graph.
-
processed_file_names
¶ The name of the files to find in the
self.processed_dir
folder in order to skip the processing.
-
processed_paths
¶ The filepaths to find in the
self.processed_dir
folder in order to skip the processing.
-
raw_file_names
¶ The name of the files to find in the
self.raw_dir
folder in order to skip the download.
-
raw_paths
¶ The filepaths to find in order to skip the download.
-
class
InMemoryDataset
(root, transform=None, pre_transform=None, pre_filter=None)[source]¶ Dataset base class for creating graph datasets which fit completely into memory. See here for the accompanying tutorial.
Parameters: - root (string) – Root directory where the dataset should be saved.
- transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
__getitem__
(idx)[source]¶ Gets the data object at index
idx
and transforms it (in case aself.transform
is given). Returns a data object, ifidx
is a scalar, and a new dataset in caseidx
is a slicing object, e.g.,[2:5]
, a LongTensor or a ByteTensor.
-
collate
(data_list)[source]¶ Collates a python list of data objects to the internal storage format of
torch_geometric.data.InMemoryDataset
.
-
num_classes
¶ The number of classes in the dataset.
-
processed_file_names
¶ The name of the files to find in the
self.processed_dir
folder in order to skip the processing.
-
raw_file_names
¶ The name of the files to find in the
self.raw_dir
folder in order to skip the download.
-
class
DataLoader
(dataset, batch_size=1, shuffle=True, follow_batch=[], **kwargs)[source]¶ Data loader which merges data objects from a
torch_geometric.data.dataset
to a mini-batch.Parameters: - dataset (Dataset) – The dataset from which to load the data.
- batch_size (int, optional) – How may samples per batch to load.
(default:
1
) - shuffle (bool, optional) – If set to
True
, the data will be reshuffled at every epoch (default:True
) - follow_batch (list or tuple, optional) – Creates assignment batch
vectors for each key in the list. (default:
[]
)
-
class
DataListLoader
(dataset, batch_size=1, shuffle=True, **kwargs)[source]¶ Data loader which merges data objects from a
torch_geometric.data.dataset
to a python list.Note
This data loader should be used for multi-gpu support via
torch_geometric.nn.DataParallel
.Parameters:
-
class
DenseDataLoader
(dataset, batch_size=1, shuffle=True, **kwargs)[source]¶ Data loader which merges data objects from a
torch_geometric.data.dataset
to a mini-batch.Note
To make use of this data loader, all graphs in the dataset needs to have the same shape for each its attributes. Therefore, this data loader should only be used when working with dense adjacency matrices.
Parameters:
-
download_url
(url, folder, log=True)[source]¶ Downloads the content of an URL to a specific folder.
Parameters:
-
extract_tar
(path, folder, mode='r:gz', log=True)[source]¶ Extracts a tar archive to a specific folder.
Parameters:


{% include Head/head.html %}
{% include Navbar/nav.html %} {% include Posts/post-content.html %} {% include Footer/footer.html %} {% include js/js.html %}


Syntax:
wrapsink "program"
Examples:
wrapsink "myexe" wrapsource "myexe -n 3 -o outfile < in.script" wrapsink "ls *"
Description:
Wrapsink is a PHISH minnow that can be used in a PHISH program. In PHISH lingo, a "minnow" is a stand-alone application which makes calls to the PHISH library to exchange data with other PHISH minnows.
The wrapsink minnow is used to wrap a non-PHISH application so that datums can be sent to it from other PHISH minnows as lines it reads from stdin. It is a mechanism for using non-PHISH applications as minnows in a PHISH net.
Ports:
The wrapsink minnow uses one input port 0 and no output ports.
Operation:
When the wrapsink minnow starts, the program argument is treated as a string that is executed as a command by the shell. As in the examples above program can be an executable program name or a shell command. It can include flags or redirection operators. If the string contains spaces, it should be enclosed in quotes in the PHISH input script so that it is treated as a single argument when the script is read by the bait.py tool.
After the wrapsink minnow launches the program command, it calls the phish_loop function. Each time an input datum is received, its single string field is written to the running program with a trailing newline, so that the program reads it as a line of input from stdin. The program may write to the screen or a file as often as it chooses, but its output is not captured by the wrapsink minnow.
The wrapsource minnow shuts down when its input port is closed by receiving a sufficient number of "done" messages. When this occurs, it closes the stdin pipe the running program is reading from, which should cause it to exit.
Data:
The wrapsink minnow must receive single field datums of type PHISH_STRING.
Restrictions:
The C++ version of the wrapsink minnow allocates a buffer of size MAXLINE = 1024 bytes for converting the PHISH_STRING fields of received datums into lines of input read from stdin by the wrapped program. This can be changed (by editing minnow/wrapsink.cpp) if longer lines are needed.
Related minnows:


§431:3-305 Accounts; records. Every insurer shall keep full and adequate accounts and records of its assets, obligations, transactions, and affairs. Every domestic insurer shall maintain said accounts and records at its principal office in this State. [L 1987, c 347, pt of §2]


IJSE POS
Dashboard
Place Orders
Search Orders
Manage Customers
Manage Items
Dashboard
Today Income
Rs. 5,000.00
New Orders +
25
New Customers +
35


Authoritatively fashion synergistic methodologies vis-a-vis unique manufactured products. Credibly scale top-line e-commerce for seamless solutions.


{{ date + " - (" + ground + ")" }}
{{ team1 + " vs " + team2 }}
{{ result }}
Skyline of Overs
{{ "1st Innings: " + firstBattingTeam}}
{{ firstTotalRuns + "-" + firstWickets}}
{{ "2nd Innings: " + secondBattingTeam}}
{{ secondTotalRuns + "-" + secondWickets}}
Timeline for Innings 1 and 2
| Before Over {{ rangeSlider1.minimumOver }} |
After Over {{ finalOver1 }} |
| {{ leftRuns1 + "-" + leftWickets1 }} |
{{ rightRuns1 + "-" + rightWickets1 }} |
Timeline for Inning 1
| Before Over {{ rangeSlider2.minimumOver }} |
After Over {{ finalOver2 }} |
| {{ leftRuns2 + "-" + leftWickets2 }} |
{{ rightRuns2 + "-" + rightWickets2 }} |


This page describes the concepts that you need to add content to your Urubu project.
Website structure
The website structure matches the project directory structure exactly. This feature makes it easy to organize content and to refer to other pages.
Each file with extension .md
is a content file that corresponds to a website
page. Upon a website build, each content file is compiled into a .html
page.
All other files and directories are copied to the website, except those whose
names start with an underscore _
.
Content files
A content file starts with a section between triple dashes ---
. This is the
YAML frontmatter with page variables such as title
and layout
.
After the frontmatter follows the actual content. The format is Markdown with extensions.
Layouts
Each content file uses the layout
variable to specify the look of the
page.
Urubu Quickstart defines a number of layouts. The page
layout yields a page
with a sidebar for the table of contents. If you don't need a sidebar, use the
simple_page
layout.
Reference ids
Urubu resolves reference ids in Markdown content, specified by
the syntax [<reference-id>]
.
Reference ids are names that refer to links. In Urubu, the pathname of a page
or folder (without extension) can be used as its reference id. In addition, the
reflinks
variable in file _site.yml
defines reference ids for external
urls.
Folders
A project subdirectory maps to a folder on the website. The folder content is
defined in the index.md
file. The first option is to list the folder content
explicitly in content
variable, as a list of reference ids. As an example,
see Manual demo. The second option is to specify the content ordering. For
example, for a blog you would set the order
variable to date
, and the
reverse
variable to true
. As an example, see Blog demo.
For index.md
files, you can use the index
layout, which lists the contents
of the folder.
Navbar
The navbar at the top is defined by the content
variable of the top-level
index.md
file.


Welcome guys, you know how to fix the "battlelog offline bug"?
BrainFooLong
Administrator
kameeleon wrote:
Welcome guys, you know how to fix the "battlelog offline bug"?
I don't even know a battlelog offline bug :)
Post edited 1 x times, last by
ORFK
Global Moderator
ORFK
Global Moderator
i know the Battlelog offline bug.
it shows a player as offline instead of online while he is on battlelog.
so you (as a friend) can get chat messages from him but cant answers, because you see him as offline.
and no, we cant fix this. this is a battlelog bug which have to be fixed by EA/DICE/ESN/Origin Team
Better Battlelog doesn't fix connection problems to Battlelog and/or Battlefield. First because this is something what EA/DICE/ESN/Origin Team have to do, second because we simply can't do that.
-edit:-
if you or your friend have this bug and you want to fix it you have to completely reload the page (including to clear the cache). that it should work again.
Brady_The
kameeleon wrote:
Welcome guys, you know how to fix the "battlelog offline bug"?


Friendly ML Tutorial
Contents:
A Complete Guide to Matrix Notation and Linear Regression
Linear Regression with Regularization
Docs
»
Welcome to a Friendly Guide to Machine Learning!
A Complete Guide to Matrix Notation and Linear Regression →
Welcome to a Friendly Guide to Machine Learning!
¶
Just enough theory to get you going.
Contents:
A Complete Guide to Matrix Notation and Linear Regression
The Sum of Squared Error Loss
Going into Matrix Notation
Deriving a Closed-form Solution
A NumPy Example
Using sklearn
Linear Regression with Regularization
The Need for Regularization
L2 and L1 Penalty
Visualizing the Loss Surface with Regularization
Which one to use, L1 or L2?
A Complete Guide to Matrix Notation and Linear Regression →


Which pawn do you want to move?
First
Second
Third
Fourth
Ludo
new game
resume game
score
Press button to roll a dice.
Roll
Active player
PLAYER 1
PLAYER 2
PLAYER 3
PLAYER 4


An extension of pixi.js - a super fast HTML 5 2D rendering engine that uses webGL with canvas fallback
With "PIXI Shape" you can easily create any shapes, like circle, rectangle, star or svg path, and move, resize, change color or animate the shape.


class: center, middle, inverse, title-slide # Lecture 4 ## Static Discrete Choice Models ### Tyler Ransom ### ECON 6343, University of Oklahoma --- # Attribution Many of these slides are based on slides written by Peter Arcidiacono. I use them with his permission. These slides also follow Chapters 1-3 of Train (2009) --- # Plan for the day 1. Describe static discrete choice models 2. Derive logit probabilities 3. Show how to estimate logit and multinomial logit models 4. Cover the IIA property 5. Discuss expected utility and consumer surplus --- # What are discrete choice models? - Discrete choice models are one of the workhorses of structural economics - Deeply tied to economic theory: - utility maximization - revealed preference - Used to model "utility" (broadly defined), for example: - consumer product purchase decisions - firm market entry decisions - investment decisions --- # Example of a discrete choice model - Cities in the Bay Area are interested in how the introduction of rideshare services will impact ridership on Bay Area Rapid Transit (BART) - Questions that cities need to know the answers to: - Is rideshare a substitute for public transit or a complement? - How inelastic is demand for BART? Should fares be `\(\uparrow\)` or `\(\downarrow\)`? - Should BART services be scaled up to compete with rideshares? - Will the influx of rideshare vehicles increase traffic congestion / pollution? - Each of these questions requires making a counterfactual prediction - In particular, need a way to make such a prediction confidently and in a way that is easy to understand --- # Properties of discrete choice models 1. Agents choose from among a .hi[finite] set of alternatives (called the _choice set_) 2. Alternatives in choice set are .hi[mutually exclusive] 3. Choice set is .hi[exhaustive] --- # Notation - Let `\(d_i\)` indicate the choice individual `\(i\)` makes where `\(d_i\in\{1,\ldots, J\}\)`. - Individuals choose `\(d\)` to maximize their utility, `\(U\)`, which generally is written as: `\begin{align} U_{ij}&=u_{ij}+\epsilon_{ij} \end{align}` where: - `\(u_{ij}\)` relates observed factors to the utility individual `\(i\)` receives from choosing option `\(j\)` - `\(\epsilon_{ij}\)` are unobserved to the econometrician but observed to the individual `\begin{align} d_{ij}&=1 \text{ if } u_{ij}+\epsilon_{ij}>u_{ij'}+\epsilon_{ij'}\text{ for all } j'\neq j \end{align}` --- # Probabilities - With the `\(\epsilon\)`'s unobserved, the probability of `\(i\)` making choice `\(j\)` is given by: `\begin{align*} P_{ij}&=\Pr(u_{ij}+\epsilon_{ij}>u_{ij'}+\epsilon_{ij'}\,\,\forall\,\, j'\neq j)\\ &=\Pr(\epsilon_{ij'}-\epsilon_{ij}<u_{ij}-u_{ij'}\,\,\forall\,\, j'\neq j)\\ &=\int_{\epsilon}I(\epsilon_{ij'}-\epsilon_{ij}<u_{ij}-u_{ij'}\,\,\forall\,\, j'\neq j)f(\epsilon)d\epsilon \end{align*}` - Note that, regardless of what distributional assumptions are made on the `\(\epsilon\)`'s, the probability of choosing a particular option does not change when we: 1. Add a constant to the utility of all options (i.e. .hi[only differences in utility matter]) 2. Multiply by a positive number (need to .hi[scale something]; e.g. the variance of the `\(\epsilon\)`'s) --- # Variables - Suppose we have: `\begin{eqnarray*} u_{i1}=\alpha Male_i+\beta_1 X_i + \gamma Z_1\\ u_{i2}=\alpha Male_i+\beta_2 X_i+\gamma Z_2\\ \end{eqnarray*}` - Since only differences in utility matter: `\begin{align*} u_{i1}-u_{i2}&=(\beta_1-\beta_2)X_i+\gamma (Z_1-Z_2) \end{align*}` - We can't tell whether men are happier than women, but can tell whether they more strongly prefer one option - We can only obtain differenced coefficient estimates on `\(X\)`'s - We can only obtain an estimate of a coefficient that is constant across choices if its corresponding variable varies by choice --- # Number of Error Terms - Similar to the `\(X\)`'s, there are restrictions on the number of error terms - This is because only differences in utility matter - Recall that he probability `\(i\)` will choose `\(j\)` is given by: `\begin{align} P_{ij}&=\Pr(u_{ij}+\epsilon_{ij}>u_{ij'}+\epsilon_{ij'}\,\,\forall\,\, j'\neq j)\nonumber\\ &=\Pr(\epsilon_{ij'}-\epsilon_{ij}<u_{ij}-u_{ij'}\,\,\forall\,\, j'\neq j)\label{eq:intprob}\\ &=\int_{\epsilon}I(\epsilon_{ij'}-\epsilon_{ij}<u_{ij}-u_{ij'}\,\,\forall\,\, j'\neq j)f(\epsilon)d\epsilon\nonumber \end{align}` where the integral is `\(J\)`-dimensional --- # Number of Error Terms - Rewriting the last line of \eqref{eq:intprob} as a `\(J-1\)` dimensional integral over the differenced `\(\epsilon\)`'s: `\begin{align} P_{ij}&=\int_{\tilde{\epsilon}}I(\tilde{\epsilon}_{ij'}<\tilde{u}_{ij'} \,\,\forall\,\, j'\neq j)g(\tilde{\epsilon})d\tilde{\epsilon} \end{align}` - This means one dimension of `\(f(\epsilon)\)` is not identified and must therefore be normalized - Arises from only differences in utility mattering (.hi[location normalization]) - The scale of utility also doesn't matter (.hi[scale normalization]) - The scale normalization implies we must place restrictions on the variance of `\(\epsilon\)`'s --- # More on the scale normalization - The need to normalize scale means that we can never estimate the variance of `\(F\left(\tilde{\epsilon}\right)\)` - This contrasts with linear regression models, where we can easily estimate MSE - The scale normalization means our `\(\beta\)`'s and `\(\gamma\)`'s are implicitly divided by an unknown variance term: `\begin{align*} u_{i1}-u_{i2}&=(\beta_1-\beta_2)X_i+\gamma (Z_1-Z_2)\\ &=\tilde{\beta}X_i + \gamma \tilde{Z} \\ &=\frac{\beta^*}{\sigma}X_i + \frac{\gamma^*}{\sigma}\tilde{Z} \end{align*}` - `\(\tilde{\beta}\)` is what we estimate, but we will never know `\(\beta^*\)` because utility is scale-invariant --- # Where does the logit formula come from? - Consider a binary choice set `\(\{1,2\}\)`. The Type 1 extreme value CDF for `\(\epsilon_2\)` is: `\begin{align*} F(\epsilon_2)&=e^{-e^{(-\epsilon_2)}} \end{align*}` - To get the probability of choosing `\(1\)`, substitute in for `\(\epsilon_2\)` with `\(\epsilon_1+u_1-u_2\)`: `\begin{align} \Pr(d_1=1|\epsilon_1)&=e^{-e^{-(\epsilon_1+u_1-u_2)}} \end{align}` - But `\(\epsilon_1\)` is unobserved so we need to integrate it out --- # Logit derivation - Taking the integral over what is random `\((\epsilon_1)\)`: `\begin{align*} \Pr(d_1=1)&=\int_{-\infty}^{\infty}\left(e^{-e^{-(\epsilon_1+u_1-u_2)}}\right)f(\epsilon_1)d\epsilon_1\\ &=\int_{-\infty}^{\infty}\left(e^{-e^{-(\epsilon_1+u_1-u_2)}}\right)e^{-\epsilon_1}e^{-e^{-\epsilon_1}}d\epsilon_1\\ &=\int_{-\infty}^{\infty}\exp\left(-e^{-\epsilon_1}-e^{-(\epsilon_1+u_1-u_2)}\right)e^{-\epsilon_1}d\epsilon_1\\ &=\int_{-\infty}^{\infty}\exp\left(-e^{-\epsilon_1}\left[1+e^{u_2-u_1}\right]\right)e^{-\epsilon_1}d\epsilon_1 \end{align*}` - We can simplify by U-substitution where `\(t=\exp(-\epsilon_1)\)` and `\(dt=-\exp(-\epsilon_1)d\epsilon_1\)` - And adjusting the bounds of integration accordingly, `\(\exp(-\infty)=0\)` and `\(\exp(\infty)=\infty\)` --- # Logit Derivation - Substituting in then yields: `\begin{align*} \Pr(d_1=1)&=\int_{\infty}^0\exp\left(-t\left[1+e^{(u_2-u_1)}\right]\right)(-dt)\\ &=\int_0^{\infty}\exp\left(-t\left[1+e^{(u_2-u_1)}\right]\right)dt\\ &=\left.\frac{\exp\left(-t\left[1+e^{(u_2-u_1)}\right]\right)}{-\left[1+e^{(u_2-u_1)}\right]}\right\vert^{\infty}_{0}\\ &=0-\frac{1}{-\left[1+e^{(u_2-u_1)}\right]}\\ &=\frac{\exp(u_1)}{\exp(u_1)+\exp(u_2)} \end{align*}` --- # Logit Estimation - Consider our model from before: `\begin{align*} u_{i1}-u_{i2}=&(\beta_1-\beta_2)X_i+\gamma (Z_1-Z_2) \end{align*}` - We observe `\(X_i\)`, `\(Z_1\)`, `\(Z_2\)`, and `\(d_i\)` - Assuming `\(\epsilon_1,\epsilon_2 \overset{iid}{\sim} T1EV\)` gives the likelihood of choosing `\(1\)` and `\(2\)` respectively as: `\begin{align*} P_{i1}=&\frac{\exp(u_{i1}-u_{i2})}{1+\exp(u_{i1}-u_{i2})}\\ P_{i2}=&\frac{1}{1+\exp(u_{i1}-u_{i2})} \end{align*}` - Note: if `\(\epsilon_1,\epsilon_2 \overset{iid}{\sim} T1EV\)` then `\(\tilde{\epsilon}_1 \sim Logistic\)`, where `\(\tilde{\epsilon}_1 := \epsilon_1-\epsilon_2\)` --- # Likelihood function - We can view the event `\(d_i = j\)` as a weighted coin flip - This gives us a random variable that follows the Bernoulli distribution - Supposing our sample is of size `\(N\)`, the likelihood function would then be `\begin{align} \mathcal{L}\left(X,Z;\beta,\gamma\right)=&\prod_{i=1}^N P_{i1}^{d_{i1}} P_{i2}^{d_{i2}} \nonumber\\ =&\prod_{i=1}^N P_{i1}^{d_{i1}}\left[1-P_{i1}\right]^{(1-d_{i1})}\label{eq:logitlike} \end{align}` where `\(P_{i1}\)` and `\(P_{i2}\)` are both functions of `\(X,Z,\beta,\gamma\)` --- # Log likelihood function - For many reasons, it's better to maximize the log likelihood function - Taking the log of \eqref{eq:logitlike} gives `\begin{align} \ell\left(X,Z;\beta,\gamma\right)=&\sum_{i=1}^N d_{i1}\log P_{i1} + (1-d_{i1}) \log \left(1-P_{i1}\right)\nonumber\\ =&\sum_{i=1}^N \sum_{j=1}^2 d_{ij}\log P_{ij}\label{eq:logitloglike}\\ =&\sum_{i=1}^N d_{i1}\left[\log \left(\exp (u_{i1}-u_{i2})\right)-\log\left(1 + \exp(u_{i1}-u_{i2})\right)\right] + \nonumber\\ &(1-d_{i1}) \left[\log \left(1\right)-\log \left(1 + \exp (u_{i1}-u_{i2})\right)\right]\nonumber\\ =&\sum_{i=1}^N d_{i1} \left[u_{i1}-u_{i2}\right]-\log\left(1+\exp(u_{i1}-u_{i2})\right)\nonumber \end{align}` --- # Multinomial Logit Estimation .small[ - Adding more choices with i.i.d. Type I extreme value errors yields the .hi[multinomial logit] - Normalizing with respect to alternative `\(J\)` we have (for `\(j\in\{1,\ldots,J-1\}\)`) `\begin{align} u_{ij}-u_{iJ}=&(\beta_j-\beta_J)X_i+\gamma (Z_j-Z_{J}) \end{align}` - We observe `\(X_i, Z_1, \ldots, Z_J\)`, and `\(d_i\)`. The likelihood of choosing `\(j\)` and `\(J\)` respectively is: `\begin{align} P_{ij}&=\frac{\exp(u_{ij}-u_{iJ})}{1+\sum_{j'=1}^{J-1}\exp(u_{ij'}-u_{iJ})},&P_{iJ}=\frac{1}{1+\sum_{j'=1}^{J-1}\exp(u_{ij'}-u_J)} \end{align}` - The log likelihood function we maximize is then: `\begin{align} \ell(X,Z;\beta,\gamma)=&\sum_{i=1}^N\left[\sum_{j=1}^{J-1}(d_{ij}=1)(u_{ij}-u_{iJ})\right]-\ln\left(1+\sum_{j'=1}^{J-1}\exp(u_{ij'}-u_{iJ})\right) \end{align}` ] --- # Independence of Irrelevant Alternatives (IIA) - One of the properties of the multinomial logit model is .hi[IIA] - `\(P_{ij}/P_{ik}\)` does not depend upon what other alternatives are available: `\begin{align*} \frac{P_{ij}}{P_{ik}}&=\frac{e^{u_{ij}}/\sum_{j'}e^{u_{ij'}}}{e^{u_{ik}}/\sum_{j'}e^{u_{ij'}}}\\ &=\frac{e^{u_{ij}}}{e^{u_{ik}}}\\ &=e^{u_{ij}-u_{ik}} \end{align*}` --- # Advantage of IIA - IIA can simplify estimation. Instead of using as our likelihood `\begin{align} P_{ij}=&\frac{\exp(u_{ij})}{\sum_{j'}^J\exp(u_{ij'})}, \end{align}` we can use the _conditional likelihood_ `\(P_i(j|j\in K)\)` where `\(K<J\)`. - The log likelihood function is then (Begg and Gray, 1984): `\begin{align*} L(\beta,\gamma|d_i\in K)&=\sum_{i=1}^N\left[\sum_{j=1}^{K-1}(d_{ij}=1)(u_{ij}-u_{iK})\right]\\ &-\ln\left(1+\sum_{j'=1}^K\exp(u_{ij'}-u_{iK})\right) \end{align*}` --- # Disadvantage of IIA Most famously illustrated by the "red bus/blue bus problem" - Consider a commuter with the choice set `\(\{\text{ride a blue-colored bus}, \text{drive a car}\}\)` - Now add a red-colored bus to the choice set - Assume that the only difference in utility between a red bus and a blue bus is in `\(\epsilon\)` - This will .hi[double] the probability of taking a bus - Why? `\(P(\text{blue bus})/P(\text{car})\)` does not depend upon whether the red bus is available We'll talk later about how to address this disadvantage --- # Expected Utility - It is possible to move from the estimates of the utility function to expected utility - (or at least differences in expected utility) - Individual `\(i\)` is going to choose the best alternative - Thus, expected utility from the best choice, `\(V_i\)`, is given by: `\begin{align*} V_i&=E\max_{j}(u_{ij}+\epsilon_{ij}) \end{align*}` where the expectation is over all possible values of `\(\epsilon _{ij}\)` --- # Expected Utility - For the multinomial logit, this has a closed form: `\begin{align} V_i&=\ln\left(\sum_{j=1}^J\exp{u_{ij}}\right)+C \end{align}` where `\(C\)` is Euler's constant (a.k.a. Euler-Mascheroni constant) - We will use this later when we discuss dynamic discrete choice models --- # Alternative expression for expected utility - Note that we can also express `\(V_i\)` as: `\begin{align*} V_i&=\ln\left(\sum_{j=1}^J\frac{\exp(u_{iJ})\exp(u_{ij})}{\exp(u_{iJ})}\right)+C\\ &=\ln\left(\sum_{j=1}^J\exp(u_{ij}-u_{iJ})\right)+u_{iJ}+C\\ &=\ln\left(1+\sum_{j=1}^{J-1}\exp(u_{ij}-u_{iJ})\right)+u_{iJ}+C \end{align*}` - This representation will become useful later in the course --- # From Expected Utility to Consumer Surplus - We may want to transform utility into dollars to get consumer surplus - We need something in the utility function (such as price) that is measured in dollars - Suppose `\(u_{ij}=\beta_jX_i+\gamma Z_j-\delta p_j\)` - The coefficient on price, `\(\delta\)` then gives the utils-to-dollars conversion: `\begin{align} E(CS_i)=&\frac{1}{\delta}\left[\ln\left(\sum_{j=1}^J\exp{u_{ij}}\right)+C\right] \end{align}` - We can calculate the change in consumer surplus after a policy change as `\(E(CS_{i2})-E(CS_{i1})\)` where the `\(C\)`'s cancel out --- # References .smallest[ Begg, C. B. and R. Gray (1984). "Calculation of Polychotomous Logistic Regression Parameters Using Individualized Regressions". In: _Biometrika_ 71.1, pp. 11-18. DOI: [10.1093/biomet/71.1.11](https://doi.org/10.1093%2Fbiomet%2F71.1.11). Train, K. (2009). _Discrete Choice Methods with Simulation_. 2nd ed. Cambridge; New York: Cambridge University Press. ISBN:<PHONE_NUMBER>555. ]


| PostgreSQL 9.0.10 Documentation | ||||
|---|---|---|---|---|
| Prev | Up | Chapter 41. PL/Perl - Perl Procedural Language | Next |
You can use the global hash %_SHARED to store data, including code references, between function calls for the lifetime of the current session.
Here is a simple example for shared data:
CREATE OR REPLACE FUNCTION set_var(name text, val text) RETURNS text AS $$ if ($_SHARED{$_[0]} = $_[1]) { return 'ok'; } else { return "cannot set shared variable $_[0] to $_[1]"; } $$ LANGUAGE plperl; CREATE OR REPLACE FUNCTION get_var(name text) RETURNS text AS $$ return $_SHARED{$_[0]}; $$ LANGUAGE plperl; SELECT set_var('sample', 'Hello, PL/Perl! How''s tricks?'); SELECT get_var('sample');
Here is a slightly more complicated example using a code reference:
CREATE OR REPLACE FUNCTION myfuncs() RETURNS void AS $$ $_SHARED{myquote} = sub { my $arg = shift; $arg =~ s/(['\\])/\\$1/g; return "'$arg'"; }; $$ LANGUAGE plperl; SELECT myfuncs(); /* initializes the function */ /* Set up a function that uses the quote function */ CREATE OR REPLACE FUNCTION use_quote(TEXT) RETURNS text AS $$ my $text_to_quote = shift; my $qfunc = $_SHARED{myquote}; return &$qfunc($text_to_quote); $$ LANGUAGE plperl;
(You could have replaced the above with the one-liner return $_SHARED{myquote}->($_[0]); at the expense of readability.)
For security reasons, PL/Perl executes functions called by any one SQL role in a separate Perl interpreter for that role. This prevents accidental or malicious interference by one user with the behavior of another user's PL/Perl functions. Each such interpreter has its own value of the %_SHARED variable and other global state. Thus, two PL/Perl functions will share the same value of %_SHARED if and only if they are executed by the same SQL role. In an application wherein a single session executes code under multiple SQL roles (via SECURITY DEFINER functions, use of SET ROLE, etc) you may need to take explicit steps to ensure that PL/Perl functions can share data via %_SHARED. To do that, make sure that functions that should communicate are owned by the same user, and mark them SECURITY DEFINER. You must of course take care that such functions can't be used to do anything unintended.


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Allows to forecast time series using nearest neighbors regression Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J. Rivera (2017) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon multi-step ahead forecasting strategies can be used. The model built is is higher than 1, two autoregressive, that is, it is only based on the observations of the time series. The nearest neighbors used in a prediction can be consulted and plotted.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.4.0) |
| Imports: | ggplot2 (≥ 2.2.1), graphics, stats, utils |
| Suggests: | knitr, rmarkdown, testthat (≥ 2.0.0) |
| Published: | 2018-03-27 |
| Author: | Francisco Martinez [aut, cre] |
| Maintainer: | Francisco Martinez <fmartin at ujaen.es> |
| BugReports: | https://github.com/franciscomartinezdelrio/tsfknn/issues |
| License: | GPL-2 |
| URL: | https://github.com/franciscomartinezdelrio/tsfknn |
| NeedsCompilation: | no |
| Materials: | README |
| In views: | TimeSeries |
| CRAN checks: | tsfknn results |
| Reference manual: | tsfknn.pdf |
| Vignettes: |
Time Series Forecasting with KNN in R: the tsfknn Package |
| Package source: | tsfknn_0.1.0.tar.gz |
| Windows binaries: | r-devel: tsfknn_0.1.0.zip, r-release: tsfknn_0.1.0.zip, r-oldrel: tsfknn_0.1.0.zip |
| OS X binaries: | r-release: tsfknn_0.1.0.tgz, r-oldrel: tsfknn_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=tsfknn to link to this page.


Before Change
ids = list()
pred_annos = list()
gt_annos = list()
unique_labels = dict()
for i, (pred_bbox, pred_label, pred_score, gt_bbox, gt_label,
gt_area, gt_crowded) in enumerate(six.moves.zip(
pred_bboxes, pred_labels, pred_scores,
gt_bboxes, gt_labels, gt_areas, gt_crowdeds)):
After Change
ids = []
pred_annos = []
gt_annos = []
unique_labels = []
for i, (pred_bbox, pred_label, pred_score, gt_bbox, gt_label,
gt_area, gt_crowded) in enumerate(six.moves.zip(
pred_bboxes, pred_labels, pred_scores,
gt_bboxes, gt_labels, gt_areas, gt_crowdeds)):


Upload Collection
Use the Upload Collection component to display a list of uploads and allow the user to perform actions on the files
in that list, such as file name changes or item deletion.
Individual upload collection items emit events when the user performs an action such as deletion or file renaming.
It is the responsibility of the application developer to handle these events properly. These examples simply throw
an alert when the user performs an action. See the fd-upload-collection-item
API documentation for
specific information about these events.
Upload Collection - Small Mode
Use the [small]
input in addition to a narrower wrapping container for the small upload
collection.
Upload Collection - Customization
Functionality of the upload collection can be customized by removing the file edit/delete buttons, or disabling
them. In this example, the first item has no customization options. The second item disables the edit and delete
buttons. The third item only has a edit button with no delete option. The edit/delete customization functionality
can be removed from an upload collection entirely by removing the fd-upload-collection-button-group
and
fd-upload-collection-form-item
directives.
Upload Collection - Complex Example
The upload collection can be used along with a variety of other components such as the file uploader, toolbar,
dialog, button, search input, sorting/filtering functionality etc. to facilitate a smooth user experience. Here in
this example we have added selection and the ability to upload files, or delete multiple files at a time.
Upload Collection - No Data
Use a message page component when the user has yet to upload any files.


Defines a set of annotations that can be used on TypedXmlWriter interfaces.
{@link XmlNamespace} can be used on a package to designate the namespace URI for the whole package.
{@link XmlElement} can be used on TypedXmlWriter-derived interfaces to associate a tag name to that interface.
{@link XmlElement}, {@link XmlAttribute}, or {@link XmlValue} can be used on a method declared on a TypedXmLWriter-derived interface. Those annotations are mutually-exclusive. See their javadoc for details. If none of the above three annotations are specified, {@link XmlElement} is assumed.


Methods
Public Instance methods
Creates a client for accessing remote web services, using the given protocol to communicate with the endpoint_uri.
Example
class MyController < ActionController::Base web_client_api :blogger, :xmlrpc, "http://blogger.com/myblog/api/RPC2", :handler_name => 'blogger' end
In this example, a protected method named blogger will now exist on the controller, and calling it will return the XML-RPC client object for working with that remote service.
options is the set of protocol client specific options (see a protocol client class for details).
If your API definition does not exist on the load path with the correct rules for it to be found using name, you can pass in the API definition class via options, using a key of :api
[ show source ]
# File vendor/rails/actionwebservice/lib/action_web_service/container/action_controller_container.rb, line 34 34: def web_client_api(name, protocol, endpoint_uri, options={}) 35: unless method_defined?(name) 36: api_klass = options.delete(:api) || require_web_service_api(name) 37: class_eval do 38: define_method(name) do 39: create_web_service_client(api_klass, protocol, endpoint_uri, options) 40: end 41: protected name 42: end 43: end 44: end


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{include file="public/head" /}
{include file="public/nav" /}
{$article.title}
点赞数
{$article.user_id}
{$article.published_time}
{$article.article_hits}
{$article.content | raw}
{include file="public/footer" /} {include file="public/scripts" /}


I created a function (partitionVect) that accepts a pivot and a vector and partitions the vector into two vectors according to whether the elements are smaller than or larger than the pivot, and returns the lengths of the parts as well as a proof that the sum of their lengths is equal to the length of the original vector.
Shafil had written an incomplete code that had a merge function to merge to sorted vectors into one sorted vector. I completed the code by creating a mergesort function. The work I did for this is :
commit 8947e9eaf17acafcc833a9d51418d97d1a1d803b
Author: NabarunDeka<EMAIL_ADDRESS>AuthorDate: Mon Feb 25 01:26:18 2019 +0530
Commit: GitHub
CommitDate: Mon Feb 25 01:26:18 2019 +0530
Update my reports file
_reports/nabarun/nabarun.md | 29 +++++++++++++++++++++++------
1 file changed, 23 insertions(+), 6 deletions(-)
commit b59712d9b12ff73fe21e5ed1abf0debb78ac8000
Author: NabarunDeka<EMAIL_ADDRESS>AuthorDate: Mon Feb 25 01:25:03 2019 +0530
Commit: GitHub
CommitDate: Mon Feb 25 01:25:03 2019 +0530
started work on proving that mergesort returns a sorted vector
Code/Merge.idr | 34 +++++++++++++++++++++++++++++++++-
1 file changed, 33 insertions(+), 1 deletion(-)
commit 4b9089bd6eb2b5b17778f70ba189d966ef19beba
Author: NabarunDeka<EMAIL_ADDRESS>AuthorDate: Thu Feb 21 22:05:44 2019 +0530
Commit: NabarunDeka<EMAIL_ADDRESS>CommitDate: Thu Feb 21 22:05:44 2019 +0530
Update Merge.idr
Code/Merge.idr | 168 +++++++++++++++++++++++++++++++++++++++++----------------
1 file changed, 121 insertions(+), 47 deletions(-)
commit d2ddda7c3ba5db45fe0cde6d37192c57160bf0a0
Author: NabarunDeka<EMAIL_ADDRESS>AuthorDate: Thu Feb 21 21:57:02 2019 +0530
Commit: GitHub
CommitDate: Thu Feb 21 21:57:02 2019 +0530
made mergesort almost total.
the proof that (a = b) implies a is LTE b for all natural nos. a,b is not total.
Code/Merge.idr | 146 +++++++++++++++++++++++++++++++++++++++++++--------------
1 file changed, 110 insertions(+), 36 deletions(-)
commit a844527d9bec49923ec52c1d4e19e4b448bebf8c
Author: NabarunDeka<EMAIL_ADDRESS>AuthorDate: Mon Feb 11 22:02:39 2019 +0530
Commit: GitHub
CommitDate: Mon Feb 11 22:02:39 2019 +0530
created file Quicksort with a partition function
Code/Quicksort.idr | 13 +++++++++++++
1 file changed, 13 insertions(+)
commit 8171d1e58823ab87631c5acf5b0b773b21038f44
Author: NabarunDeka<EMAIL_ADDRESS>AuthorDate: Tue Feb 5 16:28:21 2019 +0530
Commit: GitHub
CommitDate: Tue Feb 5 16:28:21 2019 +0530
Add files via upload
Code/Merge.idr | 112 +++++++++++++++++++++++++++++++++++++--------------------
1 file changed, 73 insertions(+), 39 deletions(-)
commit 82c90fbff5e534addcc23a81a560f0781a5be349
Author: NabarunDeka<EMAIL_ADDRESS>AuthorDate: Thu Jan 24 11:48:43 2019 +0530
Commit: GitHub
CommitDate: Thu Jan 24 11:48:43 2019 +0530
Add files via upload
Code/gcd.idr | 20 ++++++++++++++++++++
1 file changed, 20 insertions(+)


Tanwir: I would marry Hazara girls to Pashtoon men if I was in power
by Khaama PressAfghan politician Mohammad Halim Tanwir has said in a gathering, if he was in power he would have married Hazara girls to Pashtoon men for the social equality.
A video clip of Mohammad Halim Tanwir, Afghan politician has gone viral on Social media in which he says he would have married Hazara girls to Pashtoon men if he was in power in order to end the social inequality.
I would not let anyone to rise up in the name of tribe or ethnicity, Tanwir says.
“Most educational resources are taken to a valley and small area, even a football stadium has been constructed where no one yes yet played there, but in the Kabul university entrance examination only 6 people from Paktya province succeed only, pointing to Panjshir province, he says.
“For the rights equality Afghanistan, Dostum, Mohaqqiq and others should be killed”, he adds.
Mohammad Mohaqqiq, a Hazara tribal leader reacted to this statement and asked the UN agencies to investigate this matter.
“The statement by Dr. Tanwir is risky at this very sensitive situation in Afghanistan. The UN agencies must take it serious and run an investigation on such matters”, Mohaqqiq told Khaama Press.
“Tanwir is a member of Mohammad Asrhaf Ghani’s think tank and his statement reflects Ghani’s ideology”, Mohaqqiq said.
Mohammad Bashir Tayanj, spokesperson for Junbesh Party led by General Abdul Rashid Dostum in reaction to Tanwir’s comments, told Khaama Press that Mohammad Haleem Tanwir intends to further spread hypocrisy and divide the nation of Afghanistan.
“Tanwir has been away from the country for a long time, he is not aware of the realities and facts of the Afghanistan community”, Tayanj said.
“Mohammad Halim Tanwir spread hypocrisy to divide the nation of Afghanistan, however, the nation is in need of love, friendship and brotherhood”, he added.
The presidential office has not yet commented on Dr. Tanwir’s statements.
Mohammad Halim Tanwir admits in the video clip that he is a follower and student of Mohammad Gul Khan Mohmand, a man who is popular for his biased behavior during the kingdom of Mohammad Nader Shah, the father of King Zahir Shah.
Mohammad Gul Mohmand was a senior advisor to King Mohammad Nader Shah, who suppressed other ethnicities and tribes, during his kingdom.
Author
-
The Khaama Press News Agency is the leading and largest English news service for Afghanistan with over 3 million hits a month. Independent authors/columnists and experts are welcomed to contribute stories, opinions and editorials. Send stories to news@khaama.com.


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When the user clicks Identify, they will get redirected to your
redirect_uri
page with these query parameters:code
- An identity code that can be exchanged for the user's Minecraft username. These expire after 30 seconds.
state
- The
state
you sent along with the user
-
From your application's server, make a GET request to
https://id.civlabs.com/userinfo?code=
with the code you got from step 2. You will get a JSON response like this: {% highlight 'JSON' %} { "user": "lgp30", "display_name": "LGP30" } {% endhighlight %}user
- The verified canonical Minecraft username in all lowercase.
display_name
- The canonical capitalization of the username. This could be changed by the user.


I am mature professional who was born and raised in one of the coldests parts of this great country, Kenya. I push myself constantly to be the best version of myself that I can possible be in order to set an example for my two little beautiful daughters and my loving wife.
I spend my free time engaging in one a variety of things. Some of my hobbies are as shown in the list below. Please note that they have been arranged in no particular order.


Intermunicipal and Rural Transport Stabilizes in Las Tunas
by Yuset Puig PupoMore fuel availability in the territory has allowed re-establishing the transport to some rural destinations, which in recent months did not have the service.
Las Tunas, Cuba.- The towns of Mejías, Río Blanco, Mastrapa, Villa Nueva, Estrada, Gastón and Dormitorio reconnect with the main municipality through two trips (opening and return) and only on alternate days.
Eulider González, head of the Complex of the State Passenger Base Unit of the Provincial Transport Company, assures 26Digital that the different routes that were interrupted have gradually been reinstated and - at this moment - the service tends to stability, to the satisfaction of a large number of people who travel every day to the different areas of Las Tunas geography.
"We had to face the great challenge of placing resources in the most demanded destinations," says Eulider, but we already guarantee the coverage of the entire territory.
"For the next month we guarantee the daily departure of inter-municipal buses; and the rural service will also be maintained on alternate days, with opening and closure trips."
"Our company received 12 new buses that will be distributed between urban and inter-municipal routes. As more fuel is available, more trips will be added to the different destinations.
"In spite of the limitations with the availability of hydrocarbons, today we can guarantee the stability of the services. In the urban destinations, all the routes of buses are active; only the routes 2, 10, 250, and Kilómetro 7 remain working on alternate days. As long as the resources allow it; the departures and amount of trips will return to normal."


{% extends "base.html" %} {% load staticfiles %} {% block title %}WattTime: In The Press{% endblock %} {% block content %}
14 July 2014
9 July 2014
8 July 2014
4 June 2014
13 November 2013


They'll stone you when you try to take a bath,
Stone you when you're walking on the path,
They'll stone you when you're riding in your seat,
Stone you when you're walking on the street,
Yeah, but I wouldn't feel too all alone
Because everybody must get stoned.
They'll stone you when you're riding in your car,
Stone you when you're standing at the bar,
They'll stone you when you're walking 'cross the floor,
Stone you when you're reaching for the door,
But I wouldn't feel too all alone
Because everybody must get stoned.
They'll stone you when you're driving in your brain,
1
Stone you when you're waiting for a train,
They'll stone you when you're walking on the grass,
Stone you when you're trying to make a pass,
But I wouldn't feel too all alone
Because everybody must get stoned.
They'll stone you and they'll say that it's the end,
They'll stone you and they'll be back again,
They'll stone you when you try to be so brave,
Stone you when you're set down in your grave,
But I wouldn't feel too all alone
Because everybody must get stoned.
1
I have a suspicion that I am actually hearing this line correctly and that it just plain doesn't make sense and that Dylan perhaps stumbles, having to improvise the lyrics. But, then again, these lyrics are so different from the original album version and other versions of the song that I can't be sure that Dylan is even stumbling. The lyrics just seem to be full of odd phrases, wordings that have some quality that is slightly off, i.e., "...when you're walking on the path", "...when you're riding in your seat", "...I wouldn't feel too all alone...", etc.


HTML
|
CSS
|
JavaScript
|
jQuery
Note:
The nav tag is not supported in Internet Explorer 8 and earlier versions.


I’m restraining myself from quoting from every. single. paragraph in John Webster’s {{book.title}}, but wow: this book is packed. The quote I scheduled for tomorrow morning is positively 🔥.


Pokemon Game
Choose your pokemon
Charmander
Squirtle
Bulbasaur
Play!
Your Pokemon:
Rival Pokemon:
Score:
Number of games:
Wins:
Draws:
Losses:


Note that JavaScript uses IEEE-754 floating point arithmetic. This is known to cause errors when performing calculations with decimal numbers. Please see this article for an in-depth explanation of all possible errors.
Drop file here.
Drop file here to replace.


Passenger 'flies plane during scheduled flight as pilot gives instructions'
A worrying video shows the woman sitting in the co-pilot's seat as she's told how to use the controls
by Will Stewart, https://www.mirror.co.uk/authors/will-stewart/Footage of a female passenger apparently flying a plane during a scheduled flight is being investigated by authorities after it appeared on social media.
The woman, reportedly named Anna, appears to be sitting in the co-pilot's seat in the cockpit of the plane as a pilot gives her instructions on how to use the controls.
In the video, 'Anna', who is believed to be in her late 20s and is not thought to possess any flying qualifications, looks like she is flying the plane over Siberia.
Reports say the flight was by Russian airline InAero involving an Antonov-24 flight from Yakutsk to Batagai - which is usually around a one hour 50 minute trip.
The woman enjoyed a “real life flight simulator” on a passenger flight, said one account.
The flight was reportedly made on August 31, but the video has only emerged recently.
'Anna' had posted pictures and video on Instagram after the flight - but now these have been deleted.
She wrote: “This was unbelievably cool” and added: "Thank you!”.
Russian 5-TV channel reported the pilot’s name as Kirill S, and the woman was variously described as his “girlfriend” and a “very close friend”.
The pilot is heard saying: “Back, to the right, now to the left and turn it back," according to The Siberian Times.
She then queries the pilot, pointing at the navigation display: “Why can’t I get there?”
He answers: “Well, I have no idea why you can’t get there.”
If it was a scheduled passenger flight, it is not known how many people were on board.
Transport prosecutors in the region of Yakutia are investigating the video, reported multiple media including The Siberian Times. -
IrAero airlines told a Russian news site that it has begun a “thorough check” into the allegations.
A spokesman said: “There are doubts that these materials have anything to do with our airline’s activities in providing passengers transportation.”
IrAero is an airline based in the Siberian city of Irkutsk operating flights within Russia and internationally.


Types of Forests
Tropical
Tropical forests are characterized by the greatest diversity of species. They occur near the equator, within the area bounded by latitudes 23.5 degrees N and 23.5 degrees S. One of the major characteristics of tropical forests is their distinct seasonality: winter is absent, and only two seasons are present .
tropicalTemperate
Temperate forests occur in eastern North America, northeastern Asia, and western and central Europe. Well-defined seasons with a distinct winter characterize this forest biome. Moderate climate and a growing season of 140-200 days during 4-6 frost-free months distinguish temperate forests.
temperateBoreal forests (taiga)
Boreal forests, or taiga, represent the largest terrestial biome. Occuring between 50 and 60 degrees north latitudes, boreal forests can be found in the broad belt of Eurasia and North America: two-thirds in Siberia with the rest in Scandinavia, Alaska, and Canada in boreal forests is 130 days.
boreal forestsMore about forest
Choose what to know
About 420 million years ago, during the Silurian Period, ancient plants and arthropods began to occupy the land. Over the millions of years that followed, these land colonizers developed and adapted to their new habitat.
Learn by searching
Life on Earth continued to evolve, and in the late Paleozoic, gymnosperms appeared. By the Triassic Period, gymnosperms dominated the Earth's forests. In the Cretaceous Period, the first flowering plants appeared.
Google Map
Present-day forest biomes, biological communities that are dominated by trees and other woody vegetation, can be classified according to numerous characteristics, with seasonality being the most widely used. Distinct forest types also occur within each of these broad groups.


Tcl_RegisterObjType registers a new Tcl object type in the table of all object types that Tcl_GetObjType can look up by name. There are other object types supported by Tcl as well, which Tcl chooses not to register. Extensions can likewise choose to register the object types they create or not. The argument typePtr points to a Tcl_ObjType structure that describes the new type by giving its name and by supplying pointers to four procedures that implement the type. If the type table already contains a type with the same name as in typePtr, it is replaced with the new type. The Tcl_ObjType structure is described in the section THE TCL_OBJTYPE STRUCTURE below.
Tcl_GetObjType returns a pointer to the registered Tcl_ObjType with name typeName. It returns NULL if no type with that name is registered.
Tcl_AppendAllObjTypes appends the name of each registered object type as a list element onto the Tcl object referenced by objPtr. The return value is TCL_OK unless there was an error converting objPtr to a list object; in that case TCL_ERROR is returned.
Tcl_ConvertToType converts an object from one type to another if possible. It creates a new internal representation for objPtr appropriate for the target type typePtr and sets its typePtr member as determined by calling the typePtr->setFromAnyProc routine. Any internal representation for objPtr's old type is freed. If an error occurs during conversion, it returns TCL_ERROR and leaves an error message in the result object for interp unless interp is NULL. Otherwise, it returns TCL_OK. Passing a NULL interp allows this procedure to be used as a test whether the conversion can be done (and in fact was done).
In many cases, the typePtr->setFromAnyProc routine will set objPtr->typePtr to the argument value typePtr, but that is no longer guaranteed. The setFromAnyProc is free to set the internal representation for objPtr to make use of another related Tcl_ObjType, if it sees fit.
typedef struct Tcl_ObjType { char *name; Tcl_FreeInternalRepProc *freeIntRepProc; Tcl_DupInternalRepProc *dupIntRepProc; Tcl_UpdateStringProc *updateStringProc; Tcl_SetFromAnyProc *setFromAnyProc; } Tcl_ObjType;
typedef int (Tcl_SetFromAnyProc) (Tcl_Interp *interp, Tcl_Obj *objPtr);
If an internal representation cannot be created from the string, it returns TCL_ERROR and puts a message describing the error in the result object for interp unless interp is NULL. If setFromAnyProc is successful, it stores the new internal representation, sets objPtr's typePtr member to point to the Tcl_ObjType struct corresponding to the new internal representation, and returns TCL_OK. Before setting the new internal representation, the setFromAnyProc must free any internal representation of objPtr's old type; it does this by calling the old type's freeIntRepProc if it is not NULL.
As an example, the setFromAnyProc for the built-in Tcl list type gets an up-to-date string representation for objPtr by calling Tcl_GetStringFromObj. It parses the string to verify it is in a valid list format and to obtain each element value in the list, and, if this succeeds, stores the list elements in objPtr's internal representation and sets objPtr's typePtr member to point to the list type's Tcl_ObjType structure.
Do not release objPtr's old internal representation unless you replace it with a new one or reset the typePtr member to NULL.
The setFromAnyProc member may be set to NULL, if the routines making use of the internal representation have no need to derive that internal representation from an arbitrary string value. However, in this case, passing a pointer to the type to Tcl_ConvertToType() will lead to a panic, so to avoid this possibility, the type should not be registered.
typedef void (Tcl_UpdateStringProc) (Tcl_Obj *objPtr);
objPtr's bytes member is always NULL when it is called. It must always set bytes non-NULL before returning. We require the string representation's byte array to have a null after the last byte, at offset length, and to have no null bytes before that; this allows string representations to be treated as conventional null character-terminated C strings. These restrictions are easily met by using Tcl's internal UTF encoding for the string representation, same as one would do for other Tcl routines accepting string values as arguments. Storage for the byte array must be allocated in the heap by Tcl_Alloc or ckalloc. Note that updateStringProcs must allocate enough storage for the string's bytes and the terminating null byte.
The updateStringProc for Tcl's built-in double type, for example, calls Tcl_PrintDouble to write to a buffer of size TCL_DOUBLE_SPACE, then allocates and copies the string representation to just enough space to hold it. A pointer to the allocated space is stored in the bytes member.
The updateStringProc member may be set to NULL, if the routines making use of the internal representation are written so that the string representation is never invalidated. Failure to meet this obligation will lead to panics or crashes when Tcl_GetStringFromObj or other similar routines ask for the string representation.
typedef void (Tcl_DupInternalRepProc) (Tcl_Obj *srcPtr, Tcl_Obj *dupPtr);
dupPtr's internal representation is made a copy of srcPtr's internal representation. Before the call, srcPtr's internal representation is valid and dupPtr's is not. srcPtr's object type determines what copying its internal representation means.
For example, the dupIntRepProc for the Tcl integer type simply copies an integer. The built-in list type's dupIntRepProc uses a far more sophisticated scheme to continue sharing storage as much as it reasonably can.
typedef void (Tcl_FreeInternalRepProc) (Tcl_Obj *objPtr);
The freeIntRepProc function can deallocate the storage for the object's internal representation and do other type-specific processing necessary when an object is freed.
For example, the list type's freeIntRepProc respects the storage sharing scheme established by the dupIntRepProc so that it only frees storage when the last object sharing it is being freed.
The freeIntRepProc member can be set to NULL to indicate that the internal representation does not require freeing. The freeIntRepProc implementation must not access the bytes member of the object, since Tcl makes its own internal uses of that field during object deletion. The defined tasks for the freeIntRepProc have no need to consult the bytes member.


PhraSED-ML is a language and a library that provide a text-based way to read, summarize, and create SED-ML files. A basic phraSED-ML script will look like this:
To convert this script to SED-ML, there are a few options. First, you can use the 'phrasedml-convert' program:
mod1 = model "sbml_model.xml"
sim1 = simulate uniform(0,10,100)
task1 = run sim1 on mod1
plot time vs S1
where 'phrased.txt' is the above model. You can also use an API, such as the python bindings:
>phrasedml-convert.exe phrased.txt
These python bindings are used in the 'Tellurium' project (also distributed by our lab), which allow you to use phraSED-ML as part of a larger system that can create a model with antimony, describe the experiment with phraSED-ML, and then perform the experiment:
import phrasedml
phrasedmlStr = '''
mod1 = model "sbml_model.xml"
sim1 = simulate uniform(0,10,100)
task1 = run sim1 on mod1
plot time vs S1
'''
sedml = phrasedml.convertString(phrasedmlStr)
The above Python program will produce the output:
import tellurium as te
antimonyStr = '''
model test()
J0: -> S1; 0.3
S1 = 0
end
'''
phrasedmlStr = '''
mod1 = model "test"
sim1 = simulate uniform(0,10,100)
task1 = run sim1 on mod1
plot "Example plot" time vs S1
'''
exp = te.experiment(antimonyStr, phrasedmlStr)
exp.execute()
In general, the phraSED-ML language follows the SED-ML format, and defines models, simulations, tasks (with repeated tasks), and output.
Each has a corresponding SED-ML construct, but many of the internal complications are elided, allowing a much simpler view of a simulation experiment.
SED-ML models are always models that can be found with a URI (typically a relative path name for a file), or as modifications of previously-defined models. In phraSED-ML, they are defined with the syntax:
model_id = model source with changes
source
: Sources can currently point to local models or to other models defined in the phraSED-ML.model1 = model "C:/The/path/to/the/model.xml"
model2 = model model1
Note that in SED-ML, the model location can be relative ('model.xml
', 'subdir/model.xml
', '../model.xml
', etc.). In fact, it can be any URI. The phraSED-ML library will be able to discover any filename or relative filename that the system can find, but cannot on its own look up URLs to models on the web. The API functions 'setWorkingDirectory' and 'setReferencedSBML' can help with this: see this document's summary of the API or the full online documentation.
changes
: PhraSED-ML supports changing a model element to a single value or a formula. Other possibilities exist in SED-ML, but are not supported at this time.model_id = model source with model_variable_id = formula, model_variable_id2 = formula2, ...
For example:
You can even define local variables to use inside the formulas.model1 = model "C:/The/path/to/the/model.xml"
model2 = model model1 with S1=0.3, S2 = S1+4
Here, S1 and S2 are model variables, and 'local.x' is a local variable (used simply as 'x' in the formulas).model1 = model "C:/The/path/to/the/model.xml"
model2 = model model1 with local.x = 4, S1 = x^2, S2 = x^4
A SED-ML simulation is a description of the type of experiment one wishes to perform. It only describes the experiment itself, and not the model on which it is to be performed (that's the job of the task).
sim_id = simulate simulation_type
The three types of experiments are steady state ('steadystate
'), uniform time course ('uniform
'), and one one-step simulations ('onestep
').
simulation_type 'steadystate'
: A simulation to the steady state can be encoded using the 'steadystate' simulation type:
steadystate
example:
sim_steady = simulate steadystate
simulation_type 'uniform'
: A uniform time course simulation can either be regular ('uniform
') or stochastic ('uniform_stochastic
') and can have either three or four arguments:uniform(start, end, numberOfPoints)
uniform(initialTime, outputStartTime, end, numberOfPoints)
uniform_stochastic(start, end, numberOfPoints)
uniform_stochastic(initialTime, outputStartTime, end, numberOfPoints)
examples:
sim_uniform1 = simulate uniform(0, 10, 100)
sim_uniform2 = simulate uniform(0, 5, 10, 50)
stoch_uniform1 = simulate uniform_stochastic(0, 10, 100)
stoch_uniform2 = simulate uniform_stochastic(0, 5, 10, 50)
simulation_type 'onestep'
: A single simulation step of a given time interval can be encoded using the 'onestep' simulation type. This simulation type is typically used for repeated tasks (see below):onestep(time_interval)
examples:
sim_onestep = simulate onestep(0.5)
In SED-ML, specific simulation types and simulation parameters are set using KiSAO terms (http://co.mbine.org/standards/kisao). In phraSED-ML, these terms can be set by keyword (if the term is known) or by number.
To set the KiSAO term of a simulation type, the algorithm keyword is used:
sim_id.algorithm = keyword
sim_id.algorithm = kisao.kisao_value
For example, both of the following lines would set the algorithm type of simulation 'sim1' to the explicit 4th-order Runge-Kutta method, which has a KiSAO ID of kisao:KISAO_0000032:
sim1.algorithm = rk4
sim1.algorithm = kisao.32
The keywords that phraSED-ML understands that can be used to set the algorithm type (and the equivalent KiSAO ID) are:
In addition, one can set various simulation parameters, again using the algorithm keyword:
sim_id.algorithm.keyword = value
sim_id.algorithm.kisao_value = value
For example, both of the following lines would set the absolute tolerance of simulation 'sim1' (which has a KiSAO id of kisao:KISAO_0000211) to 0.04:
sim1.algorithm.absolute_tolerance = 0.04
sim1.algorithm.211 = 0.04
The keywords that phraSED-ML understands that can be used to set algorithm parameters (and the equivalent KiSAO IDs) are:
Do note that little to no error-checking is performed by phraSED-ML for these terms: impossible values or setting an algorithm parameter for an algorithm to which it does not apply will not be caught by phraSED-ML.
A basic task links a model to a simulation, and means 'run this simulation on that model':
task_id = run simulation_id on model_id
example:
task1 = run sim1 on model1
A repeated task allows multiple simulations to be performed, one after the other. Essentially, this is done by specifying loops.
repeatedtask_id = repeat task_id for target_id in repeat_type
repeat_type
': This may be an explicit vector or a uniform function (either log or linear):
[val1, val2, ...]
: a vector, with explicit listed valuesuniform(start, end, numberOfPoints)
logUniform(start, end, numberOfPoints)
repeat1 = repeat task1 for S1 in [1, 3, 10]
repeat2 = repeat task1 for S2 in uniform(1, 10, 50)
repeat3 = repeat task1 for S3 in logUniform(1, 10, 20)
repeatedtask_id = repeat [task_id1, task_id2, ...] for target_id in repeat_type
or declaring that other elements change at the same time:
or declaring that the model should be reset between repeats (particularly useful for stochastic tasks):repeatedtask_id = repeat task_id for target_id in repeat_type, target_id = repeat_type, ...
repeatedtask_id = repeat task_id for target_id in repeat_type, target_id = formula, ...
repeatedtask_id = repeat task_id for target_id in repeat_type, reset = true, ...
model_id.variable_id
':
repeat1 = repeat task1 for S1 in [1, 3, 5], S2 in uniform(0, 10, 3)
repeat2 = repeat [task1, task2] for X in uniform(0, 10, 100), mod1.S1 = X, mod2.S1 = X+3
repeat3 = repeat task1 for S2 in logUniform(0, 10, 3), reset=true
Plotting will start with plot notation followed by a x variable vs y variables (vs z variables). Each variable will be preceded by the task it is running on, separated with a dot, unless there is only one task/model, in which case the simple variable may be used instead. For simulations with multiple tasks with multiple models, using the notation 'task_id.model_id.variable_id' may be necessary. Functions involving defined elements/attributes for x and y variables are supported..
plot [task.x formula] vs [task.y1 formula], [task.y2 formula], ...
3D plots are also possible:
plot [task.x formula] vs [task.y1 formula] vs [task.z1 formula], ...
Examples:
If all you need is data output, use 'report' instead of plot. Just use commas to delineate what to output:plot task1.time vs task1.S1, task1.S2
plot task2.time vs task2.S1/task2.compartment1, task2.S2/task2.compartment1
plot S1/S2 vs S3/S4
plot task1.time vs task1.S1, task2.time vs task2.S1
plot task1.time vs task1.S1 vs task2.S1
report task1.time, task1.S1, task1.S2
report task2.time, task2.S1/task2.compartment1, task2.S2/task2.compartment1
report S1/S2, S3/S4
report task1.time, task1.S1, task2.time, task2.S1
report task1.time, task1.S1, task2.S1
The API for libphrasedml is relatively simple, and consists of only a dozen functions (eleven for the Python bindings):
convertString(string input)
convertFile(string filename)
These are the main functions in the API. Each takes phraSED-ML or SED-ML input, and outputs SED-ML or phraSED-ML, respectively. If an error occurs, a blank string is returned.
getPhrasedWarnings()
getLastPhrasedError()
getLastPhrasedErrorLine()
When something goes wrong, these functions can be queried to help diagnose what happened.
setWorkingDirectory(string directory)
setReferencedSBML(string URI, string sbmlstring)
clearReferencedSBML()
Both phraSED-ML and SED-ML accept relative file names for referenced models. By default, models will be searched for in the working directory of the program. By using this function, you can set a new working directory in which your models are stored. In some cases, the SBML file may not be retrievable through normal means, but a copy of the file will exist in memory. In those cases, 'setReferencedSBML' can be used to map a URI to a copy of the SBML document itself, and 'clearReferencedSBML' can be used to remove that information again.
getLastPhraSEDML()
getLastSEDML()
These are mostly testing functions for round-tripping phraSED-ML or SED-ML. If you use the 'convert' functions, above, these functions will give you the SED-ML or phraSED-ML versions of the input.
addDotXMLToModelSources()
In some contexts (like when using Antimony and phraSED-ML in concert in Tellurium) a user may wish to declare a model just by its name, not its filename. When constructing the actual SED-ML, however, actual files must be constructed and pointed to. Using this function adds ".xml" to any filename that does not already have ".xml" or ".sbml" in it. The new name is not checked; it is assumed that the files will be created and pointed to appropriately.
freeAllPhrased()
This function will free all the strings (char*'s) that the API has handed out at once. This can only safely be called if the user has not freed any of them by hand. It is not available in the Python API, which handles all memory management separately.
phrasedmlStr = '''
model1 = model "BIOMD0000000021.xml"
model2 = model model1 with V_mT = 0.28, V_dT = 4.8
simulation1 = simulate uniform(0, 380, 1000)
task1 = run simulation1 on model1
task2 = run simulation1 on model2
plot task1.time vs task1.Mt, task2.Mt
plot task1.Cn vs task1.Mt, task2.Cn vs task2.Mt
'''
Here, the Le Loup model (Biomodels model 21) is read in as 'model1', and a copy is made with new initial values for the parameters V_mT and V_dT. The same simulation is run on both, and results for each compared with the other in a series of plots.
phrasedmlStr = '''
model1 = model "oscli.xml"
stepper = simulate onestep(0.1)
task0 = run stepper on model1
task1 = repeat task0 for local.x in uniform(0, 10, 100), J0_v0 = piecewise(8, x<4, 0.1, 4<=x<6, 8)
plot task1.time vs task1.S1, task1.S2, task1.J0_v0
'''
This experiment shows how one might vary a parameter during the course of a run: each time the simulation takes a single time step, a local variable 'x' is incremented. In the meantime, the model variable J0_v0 is set to 8 while x is less than 4, then is reset to 0.1 while x is between 4 and 6, and finally is reset to 8 again after that (until x reaches 10, and the repeated task ends). There is no 'with reset' for this simulation, so this is effectively a single timecourse.
phrasedmlStr = '''
model1 = model "borisejb.xml"
timecourse1 = simulate uniform_stochastic(0, 4000, 1000)
timecourse1.algorithm.seed = 1003
task0 = run timecourse1 on model1
task1 = repeat task0 for local.x in uniform(0, 10, 10), reset=true
plot task1.time vs task1.MAPK, task1.MAPK_P, task1.MAPK_PP, task1.MKK, task1.MKK_P, task1.MKKK, task1.MKKK_P
'''
This experiment repeats a stochastic simulation 10 times, and plots the results. Because 'with reset' is used, these are repeated timecourse simulations with identical starting conditions, but stochastic runs. The seed for the stochastic simulation is set for the first run, so that the exact same run could be produced again on the same computer in the same situation. However, the seed would not be reset for each run of the algorithm; this would not produce useful results.
phrasedmlStr = '''
model1 = model "http://sourceforge.net/p/libsedml/code/119/tree/trunk/Samples/models/oscli.xml?format=raw"
timecourse1 = simulate uniform(0, 20, 1000)
task0 = run timecourse1 on model1
task1 = repeat task0 for J0_v0 in [8, 4, 0.4], reset=true
plot task1.time vs task1.S1, task1.S2
'''
In the above experiment, the model variable 'J0_v0' is set to 8, then 4, and then 0.4, for the three times the timecourse is repeated. All three sets of results are then plotted.
phrasedmlStr = '''
model_3 = model "borisejb.xml"
sim_repeat = simulate steadystate
task_1 = run sim_repeat on model_3
repeatedtask_1 = repeat task_1 for J1_KK2 in [1, 5, 10, 50, 60, 70, 80, 90, 100]
repeatedtask_2 = repeat repeatedtask_1 for J4_KK5 in uniform(1, 40, 100)
plot repeatedtask_2.J4_KK5 vs repeatedtask_2.MKK, repeatedtask_2.MKK_P
'''
In this experiment, model variable J1_KK2 is set to 9 different values for each of 100 values of J4_KK5, evenly spaced between 1 and 40.


let me introduce myself for you.
- My name is eiad nimer alswaidat, i'm 28 years old
- i studied civil engineer at alquds college
- i worked at UAE as a quality engineer for 2 years
- also -as you know- i support real madrid so i'm a madridista
- my favorite player for all time is zidane
- don't forget my lovely meal .. the greatest the mighty the MANSAF !


"x" "1" "ADAM,W, ALBRECHT,O, FEINEIS,E, REUTHER,I, SAHA-MOELLER,CR,SEUFERT-BAUMBACH,P AND WILD,D, BENZOFURAN DIOXETANES, A NEW CLASS OF MUTAGENICAGENTS: SYNTHESIS BY PHOTOOXYGENATION OF BENZOFURAN DERIVATIVES, __ LIEBIGS ANN.CHEM. (1):33-40, 1991"


In this chapter you will learn more about the vector data structure. You will be introduced to some of the vocabulary that we use to describe vectors. You will also be introduced to the geometry of vectors. It is important to recognize that although the illustration of vector geometry is restricted to 2-dimensional space, all of these ideas can be extended to n-dimensions.
A vector is a specifically ordered one-dimensional array of values written as a row or column. Vectors are typically described by the number of elements they have. Here, for example, a is a four-element column vector:
\[ \mathbf{a} = \begin{bmatrix} 5 \\ 4 \\ 7 \\ 2 \end{bmatrix} \]
We might also say that a is a column vector in 4-dimensions.
In statistical or psychometric applications, vectors are how we typically represent and structure data collected on a particular attribute. For example, a might represent test scores for \(n=4\) students. Each element in this vector would correspond to a student’s test score. In this case, \(a_1=5\) would be the test score for the first student recorded in the vector, \(a_2=4\) would be the test score for the second student recorded in the vector, etc.
One important vector operation is transposition. Transposition is an operation in which we replace the ith element of a column vector as the ith element of a row vector, and vice-versa. In other words, we are converting a column vector into a row vector, or, conversely, converting a row vector into a column vector. Notationally, we use a superscripted prime or a superscripted intercalate symbol (looks like a “T”) to denote a vector’s transpose. For example, transposing vector a, whcih we defined earlier:
\[ \mathbf{a} = \begin{bmatrix} 5 \\ 4 \\ 7 \\ 2 \end{bmatrix} \qquad \mathbf{a}^{\prime} = \mathbf{a}^{\intercal} = \begin{bmatrix} 5 & 4 & 7 & 2 \end{bmatrix} \]
Throughout the remainder of the book, row vectors will be denoted using a transpose. So, for example, a will indicate a column vector, and \(\mathbf{a}^{\intercal}\) will indicate a row vector.
Now that we have introduced transposition, we can formalize the general notation for both column and row vectors. The general form of a column vector with n elements is:
\[ \mathbf{x} = \begin{bmatrix} x_1 \\ x_2 \\ x_3 \\ \vdots \\ x_n \end{bmatrix} \]
While, the general form of a row vector with n elements is:
\[ \mathbf{x}^{\intercal} = \begin{bmatrix} x_1 & x_2 & x_3 & \ldots & x_n \end{bmatrix} \]
Vectors can also be thought of as a special case of a matrix in one of the dimensions is equal to 1. For example x could be considered a \(n \times 1\) matrix, and \(\mathbf{x}^{\intercal}\) could be considered a \(1 \times n\) matrix.
A vector can be represented geometrically by using a coordinate system where each element in the vector corresponds to a distance along one of the reference axes defining the coordinate system. Consider the column vector a:
\[ \mathbf{a} = \begin{bmatrix} 2 \\ 3 \end{bmatrix} \]
This vector has a distance of 2 on the first reference axis (R1) and a distance of 3 on the second reference axis (R2).
In Figure 1 the reference coordinate system (R1–R2) define a two-dimensional space. Vector a had two elements and thus resides in a two-dimensional space. In general, an n-dimensional vector resides in an n-dimensional space.
The reference axes always intersect at the origin of the space (i.e., the reference point \((0, 0)\)). Here the reference axes are at right angles to one another, although this is not a requirement. When the reference axes are at right angles to each other, the reference system is referred to as an orthogonal coordinate system.
Geometrically, a vector is displayed as a line segment with an arrowhead at one end of the segment. The end of the vector with the arrowhead is called the head or terminus, and the other end of the vector is called the tail or origin. The head of the vector indicates the direction of the vector.
All vectors have a direction. They also have a length. These two properties completely define a vector within a given reference system. Note that location of the vector in the reference system is not a property of the vector. For example, the red vector and the blue vector below are identical—they have the same direction and length!
Location is a convenience in that we can re-locate any vector within the reference system to make it easier to work with.
The length of a vector (also referred to as the vector’s magnitude or norm) is the distance from the vector’s tip to tail. In an orthogonal reference system, if we locate the tail of the vector at the origin, multiple right triangles with the vector as hypotenuse can be formed with the reference axes. The Pythagorean Theorem can then be applied to determine the length of a vector.
In Figure 3, vector a is the hypotenuse of a right triangle with legs of length 2 and 3. We can use the Pythagorean Theorem to find the length of the vector:
\[ \begin{split} C^2 &= A^2 + B^2 \\[2ex] &= 2^2 + 3 ^2 \\[2ex] &= 13 \\[2ex] C &= \sqrt{13} \end{split} \]
Note that the sum of the squared elements is equal to the square of the length of the vector, and to determine the length of a (denoted as \(\lvert\lvert \mathbf{a} \rvert\rvert\)), we take the square root of that sum. For our two-dimensional vector, this is
\[ \lvert\lvert \mathbf{a} \rvert\rvert = \sqrt{a_1^2 + a_2^2} \]
We can generalize this to finding the length of a vector a with n-dimensions:
\[ \lvert\lvert \mathbf{a} \rvert\rvert = \sqrt{a_1^2 + a_2^2 + a_3^2 + \ldots + a_n^2} \]
A vector’s direction is often expressed as the measure of the angle between the horizontal reference axis and the vector.2 Figure 4 shows this angle (denoted \(\theta\)) for vector a.
To determine this angle, we need to use a trigonometric function. For example, here we can use the fact that the cosine of an angle \(\theta\) is defined as the length of the side of the right triangle which is adjacent to \(\theta\) divided by the length of the hypotenuse of the triangle.3
\[ \cos (\theta) = \frac{\lvert\lvert\mathrm{Adjacent~Side}\rvert\rvert}{\lvert\lvert\mathrm{Hypotenuse}\rvert\rvert} \]
In our example,
\[ \cos (\theta) = \frac{2}{\sqrt{13}} \]
To find \(\theta\), we need to calculate the arc-cosine of \(\frac{2}{\sqrt{13}}\). We can compute the arc-cosine in R using the acos()
function. This function will compute the value of \(\theta\) in radians. We can also convert this to degrees by multiplying by \(\frac{180}{\pi}\) (where \(\pi\) is the mathematical constant approximated as 3.14).
# Find theta using arccosine (radians)
acos(2 / sqrt(13))
[1] 0.9827937
# Find theta using arccosine (degrees)
acos(2 / sqrt(13)) * 180 / pi
[1] 56.30993
We always measure the angle counter-clockwise to the reference axis. So if we measure the direction associated with the vector \(\begin{bmatrix}-2 \\ -2\end{bmatrix}\), the direction is more than \(\pi\) radians (or \(180^\circ\)).
There are several ways to compute this angle. Remember that when we use the trigonometric functions, we are computing the angle in the right triangle formed by the vector and the reference axes (see Figure 6).
To find \(\theta\) we need to find \(\alpha\) using trigonometric functions and then add it to \(\beta\). For example,
\[ \begin{split} \tan (\alpha) &= \frac{\lvert\lvert\mathrm{Opposite~Side}\rvert\rvert}{\lvert\lvert\mathrm{Adjacent~Side}\rvert\rvert} \\[2ex] &= \frac{2}{2} \\[2ex] &= 1 \end{split} \]
Computing the arc-tangent with the atan()
function we find that \(\alpha=0.785\) radians. Adding this to the \(\beta\) value which is \(\pi\) radians, we get the vector’s direction of 3.927 radians (or \(225^\circ\)).
# Compute alpha (in radians)
atan(1)
[1] 0.7853982
# Compute theta = alpha + beta (in radians)
atan(1) + pi
[1] 3.926991
# Compute theta = alpha + beta (in degrees)
atan(1) + pi) * 180 / pi (
[1] 225
Two vectors are said to be equal if they satisfy two conditions:
Consider the following vectors:
\[ \mathbf{a} = \begin{bmatrix} 5 \\ 4 \\ 7 \\ 2 \end{bmatrix} \qquad \mathbf{b} = \begin{bmatrix} 5 \\ 4 \\ 2 \\ 7 \end{bmatrix} \qquad \mathbf{c}^{\intercal} = \begin{bmatrix} 5 & 4 & 7 & 2 \end{bmatrix} \]
Within this set of vectors,
Geometrically, two vectors are equal if they have the same length and direction. (Remember, location in the reference coordinate system is not a property of the vectors, so two equal vectors might be in different locations.)
There are several vectors that are special in that they have unique properties.
One special vector is the zero vector. A zero vector (denoted 0) is a vector in which every element is 0. For example, the following vector is a zero vector:
\[ \mathbf{0} = \begin{bmatrix} 0 \\ 0 \\ 0 \end{bmatrix} \]
The length of a zero vector is 0. (Convince yourself of this!) Furthermore, since geometrically the head and tail of a zero vector coincide (are at the same location in the reference coordinate system) they have an undefinable direction.
Another special vector is referred to as a ones vector. A ones vector is a vector in which every elements is 1. For example, the following vector 1 is a ones vector:
\[ \mathbf{1} = \begin{bmatrix} 1 \\ 1 \\ 1 \end{bmatrix} \]
The length of a ones vector is \(\sqrt{n}\). (Convince yourself of this!) The direction of a ones vector is \(45^\circ\) or 0.785 radians since the each element in the vector is equal. For example, locating the tail of a 2-dimensional ones vector at the origin, it would lie on the \(R1=R2\) line. Figure 7 shows a 2-dimensional ones vector with its tail located at the origin.
Consider the n-element column vector u:
\[ \mathbf{u} = \begin{bmatrix} u_1 \\ u_2 \\ u_3 \\ \vdots \\ u_n \end{bmatrix} \]
u is a unit vector if the length of the vector is 1. For example, each of the following vectors are unit vectors:
\[ \mathbf{u}_1 = \begin{bmatrix} \frac{1}{\sqrt{3}} \\ \frac{1}{\sqrt{3}} \\ \frac{1}{\sqrt{3}} \end{bmatrix} \qquad \mathbf{u}_2 = \begin{bmatrix} \frac{1}{\sqrt{2}} \\ \frac{1}{\sqrt{2}} \\ 0 \end{bmatrix} \qquad \mathbf{u}_3 = \begin{bmatrix} 0 \\ 1 \\ 0 \end{bmatrix} \]
Although the length of a unit vector must be 1, the direction is not specified. Thus there are an infinite number of unit vectors. Figure 8 shows several unit vectors with their tail located at the origin.
An elementary vector is a vector that has one element that is equal to one and the remainder of its elements equal to 0. For example, each of the 3-element column vectors below are elementary vectors.
\[ \mathbf{e}_1 = \begin{bmatrix} 1 \\ 0 \\ 0 \end{bmatrix} \qquad \mathbf{e}_2 = \begin{bmatrix} 0 \\ 1 \\ 0 \end{bmatrix} \qquad \mathbf{e}_3 = \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix} \]
For an n-dimensional vector there are always n elementary vectors. Moreover, every elementary vector is also a unit vector. Geometrically, each elementary vector lies on one of the reference axes. Figure 9 shows the two elementary vectors (with their tail located at the origin) in our 2-dimensional reference coordinate system.
Create a 2-dimensional orthogonal reference system. Add the following vectors to that system.
Compute the following:
Indicate whether the vectors are equal or not equal.
Assume a and b are equal. Find the values for \(a_1\), \(a_3\), and \(b_2\).
While it is common to express the direction as the measure of the angle between the horizontal reference axis, it could also be expressed as the measure of the angle between the vector and any of the reference axes. In more than two-dimensions, the direction would need to include multiple angles to specify the direction; you would need \(n-1\) angles to specify an n-dimensional vector.↩
You could also use the definition for sine or tangent to compute \(\theta\).↩


Numbers in JavaScript A in-depth blog post about how numbers work in JavaScript, and some tips to deal with them.


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Social media and pop psychology perpetuate the myth that happiness equals success, leading many to interpret negative emotions as signs of failure.
In a culture that lacks preventive mental healthcare solutions or open conversations, these myths make it harder for young people to learn how to navigate tough emotions in healthy ways. So how might we turn the quest for happiness into a quest for emotional health?
Talisman is an app that builds emotional fluency one check-in at a time. It guides users to identify emotions, then introduces activities that build resilience and emotional intelligence.
An interactive check-in lets users visualize their feelings. We often talk about our emotions in abstract language like feeling “blue” or “frazzled” or “on edge,” and that’s where Talisman begins.
Based on user inputs during check in, Talisman responds empathetically and suggests activities designed to develop key emotional intelligence (EQ) skills.
Talisman shares social reinforcement in key moments to help users feel less alone and break down barriers to taking productive action.
Talisman tracks emotional intelligence factors rather than good or bad days to encourage a growth mindset.
Here's how the project evolved ↴
Talisman originated from the research my teammates and I did for our graduate thesis project. My role was lead product designer. Over the course of 6 months, we identified a problem and designed a solution to motivate behavior change and better mental health outcomes for young people.
Secondary Research
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After reviewing hundreds of pages of academic research and media, as well as interviewing 17 young people, we began to sketch out the core problem—something I call the repression cycle.
This helped us understand that our goal was not to solve feelings, but to help young people develop the resilience they’d need to handle tough days head-on. Next, a series of card sorts, concept prototypes, and competitive research helped us unearth 3 key insights that would end up driving design:
I built out an impact pathway to better understand the behaviors that we wanted to influence. One key thing that stood out: all the psychological targets related to the adoption of a growth mindset.
A product landscape analysis showed that redirecting focus away from good/bad moods and toward emotional growth factors could be a key differentiator, as well.
Based on our research, I knew that many of the biggest barriers to emotional growth related to a lack of skills.
So through a series of card sorts, I wanted to explore how people thought about challenging emotions. I found that young people use metaphors, colors, and drawings to explain feelings that were uncomfortable.
A series of concept prototypes at progressing levels of fidelity helped to validate this idea and allow us to explore ways to use visualization as a tool for self-awareness.
Finally, an early prototyped exploration of suggested activities taught us that users expect nuanced content personalized to their current mood state.
Based on our research, my team and I developed our problem statement. We needed to build a tool that would guide young people toward emotional fluency.
I developed two personas to better understand our primary user archetypes: the empath, who is tuned into their emotions but not always adept at managing them; and the struggler, who has difficulty identifying or understanding emotions in the first place.
My team and I pitched Talisman at NVC, a venture capital competition at the University of Colorado Boulder, where we advanced to the final round.


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Algorithms View
The Algorithms view shows on four tabs - Algorithms, Analysis, Games and Visualizations - all currently installed crypto plug-ins in JCrypTool. Simply double click on an entry to start the desired wizard or open the plug-in's view. More...
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The Actions view enables you to record most of the cryptographic operations available in JCrypTool. While recording, all required information on the executed cryptographic operations are automatically collected. The generated protocol can be executed again. Additionally it can be saved and shared with other users. More...
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A lot of crypto plug-ins, ranging from Algorithms, Analysis, to Games up to Visualizations, are waiting for you.
Help and Context Help
The JCrypTool help does contain a lot of information for JCrypTool core and most crypto plug-ins. Additionally it provides background information about different cryptographic operations. At the same time more plug-ins are now supporting the context sensitive help view and are providing short context information while executing a certain action or selecting an element in the application. More...


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