class: center, middle, title-slide count: false # Deep Learning on Graphs
# an Introduction
.bold[Marc Lelarge] .bold[[www.dataflowr.com](https://mlelarge.github.io/dataflowr-web/)] --- # Node embedding ## Language model ### one fixed graph, no signal. Ex: community detection -- count: false # Signal processing on graphs ## Fourier analysis on graphs ### one fixed graph, various signals. Ex: classification of signals -- count: false # Inductive representations on graphs ## Graph isomorphism problem: Weisfeiler-Lehman test ### various graphs. Ex: classification of graphs --- # Node embedding ## Language model ### one fixed graph, no signal. Ex: community detection - [DeepWalk](https://arxiv.org/abs/1403.6652) - hierarchical softmax - [node2vec](https://snap.stanford.edu/node2vec/) - negative sampling --- # Language model - goal: to estimate the likelihood of a specific sequence of words appearing in a corpus. - formally: to maximize $p(w\_{n} \| w\_{1}, ... , w\_{n-1})$ where $w\_i$ are in the vocabulary $V$. -- ## skip-gram model - from the text, construct $D$ the multiset of all word and context pairs: $(w,c)$. - goal: maximize $\prod\_{(w,c)\in D} p(c\| w ; \theta)$ in the parameter $\theta$. - parametrization of the conditional probability thanks to a softmax: $$ p(c\|w; \theta) = \frac{e^{u\_c \cdot u\_w}}{\sum\_{c'\in C}e^{u\_{c'} \cdot u\_w}}, $$ where $u\_c$ and $u\_w$ are vector representations for $c$ and $w$ and $C$ is the set of all available contexts. --- # Hierarchical softmax - Assign the contexts to the leaves of a binary tree and use the parametrization: $$ p(c\|w; \theta) = \prod\_{b\in \pi(c)} \sigma(h\_b \cdot u\_w), $$ where $\pi(c)$ is the path from the root to the leaf $c$ and $\sigma$ is the sigmoid function: $$ \sigma(x) = \frac{1}{1+e^{-x}}. $$ - reduce computational complexity from $O(|C|)$ to $O(\log|C|)$. - speed up: assign shorter paths to frequent contexts using Huffman coding. --- # DeepWalk - a sentence = a random walk on the graph -- count: false
--- # Negative sampling - introduce $B = \mathbf{1}((w,c)\in D)$ with the parameterization $$ p(b=1|w,c,\theta) = \sigma(u\_w \cdot u\_c)=\frac{1}{1+e^{-u\_w \cdot u\_c}} . $$ - instead of maximizing $\prod\_{(w,c)\in D} p(c\| w ; \theta)$, we now maximize $\prod\_{(w,c)\in D} p(b=1 |c, w ; \theta)$ -- count: false Problem, this has a trivial solution! We only have positive examples! -- count: false - create negative examples $D'$ and maximize in $\theta$: $$ \sum\_{(w,c)\in D}\log \sigma(u\_w \cdot u\_c) + \sum_{(w,c)\in D'}\log \sigma(-u\_w \cdot u\_c) $$ - in [Mikolov et al.](http://papers.nips.cc/paper/5021-distributed-representations-of-words-andphrases) $D'$ is $k$ times larger than $D$, and for each $(w,c)\in D$, we construct $k$ samples $(w,c\_1) ... (w,c\_k)$ where $c\_i$ is drawn according to its empirical distribution raised to the $3/4$ power. --- # node2vec - parameterization of the skip-gram model approximated thanks to negative sampling - notion of context obtained thanks to biased random walk. .center.width-50[![](images/node2vec_rw.png)] --- # some results .center.width-40[![](images/node2vec_miserables.png)] --- # some results In the multi-label classification setting, every node is assigned one or more labels from a finite set. During training phase, a certain fraction of nodes with all their labels is observed. The task is to predict the labels for the remaining nodes. .center.width-50[![](images/node2vec_res.png)] --- # Signal processing on graphs ## Fourier analysis on graphs ### one fixed graph, various signals. Ex: classification of signals ## Problem: how to implement a band-pass filter on a grah? We first need to define a notion of frequency domain for graphs. This will allow us to define convolutions on graphs. --- # Recap: Fourier analysis For (smooth) $f:\mathbb{R}^d \to \mathbb{C}$, we define the Fourier transform: $$ \hat{f} (\xi) = \int f(x) e^{-2i\pi x\cdot \xi} dx, $$ so that we have $f(x) = \int \hat{f}(\xi) e^{2i\pi x\cdot \xi} d\xi$. The Fourier transform is a systematic way to decompose ''generic'' functions into a superposition of ''symmetric'' functions. Here, the ''symmetric'' fucntions are plane waves with frequency $\xi$ defined by $e^{2i\pi x\cdot \xi}$ and can be seen as the eigenfunctions of the Laplacian on $\mathbb{R}^d$: $$ f\mapsto \Delta f = \sum\_{i=1}^d\frac{\partial^2 f}{\partial x\_i^2}. $$ Indeed $\Delta e^{2i\pi x\cdot \xi} = -4\pi^2|\xi|^2 e^{2i\pi x\cdot \xi}$. The Fourier transform allows us to write an arbitrary function as a superposition of eigenfunctions of the Laplacian. This apporach works for general graphs! --- # Spectral graph theory For a graph $G=(V,E)$, we denote by $A$ its adjacency matrix and we define its Laplacian by $L=D-A$ where $D = \text{diag}(A 1)$ is the diagonal matrix of (weighted) degrees. ## Basics of $L$ We have $v^{T} L v = \sum\_{i < j} A\_{i,j}(v\_i -v\_j)^2$, $L 1=0$ and: let $\lambda\_1=0\leq \lambda\_2\leq ... \leq \lambda\_n$ be the eigenvalues of the Laplacian, then $\lambda\_2>0$ if and only if $G$ is connected. -- count: false ## Fiedler's nodal domain Theorem Let $\lambda\_1=0< \lambda\_2\leq ... \leq \lambda\_n$ be the eigenvalues of the Laplacian of a connected graph $G$ and let $(u\_1,..., u\_n)$ be the corresponding eigenvectors. For any $k\geq 2$, let $$ W\_k = \\{ i \in V: u_k(i) \geq 0 \\}. $$ Then, the graph induced by $G$ on $W\_k$ has at most $k-1$ connected components. --- # Graph Fourier transform For a connected graph $G$ with $n$ vertices, we write the spectral decomposition of $L = U\Lambda U^T$, where $$ \Lambda = \text{diag}(\lambda_1=0,\lambda_2,..., \lambda_n), \text{ and } U = (u\_1, ..., u\_n), $$ with $U^TU = U U^T = Id$. For a signal $f\in \mathbb{R}^n$, we define by $\hat{f}(\ell) = u\_{\ell}^T f$ its Fourier mode associated with frequency $\lambda\_{\ell}$. The Fourier transform of $f$ is given by $$ \hat{f} = U^T f, $$ so that we get $$ f = U\hat{f} = \sum\_{\ell} \hat{f}(\ell) u\_{\ell}. $$ --- # Filtering ##convolution = product in spectral domain .center.width-60[![](images/fourier_ox.png)] .citation[slide by Andrew Zisserman] --- # Filtering on graphs For a kernel function $k:\mathbb{R}^+ \to \mathbb{R}^+$ acting on the frequency domain, we define the operator $T\_k$ acting on a given signal $f$ as: $$ \widehat{T\_k f}(\ell) = k(\lambda\_{\ell} )\hat{f}(\ell). $$ In matrix form, we write (with $\odot$ the componentwise product): $$ \widehat{T\_k f} = k(\Lambda) \odot \hat{f}. $$ -- count: false Going back in the spatial domain, we get: $$\begin{aligned} T\_k f & = U (k(\Lambda) \odot (U^T f))\\\\ &= U \text{diag}(k(\Lambda)) U^T f \\\\ & = k(L) f. \end{aligned}$$ --- # Learning a localized kernel In order to be able to learn our kernel, we will parametrize them and use polynomial kernels: $$ k\_{\theta}(L) = \sum\_{k=0}^K \theta\_k L^k, $$ where the parameter $\theta\in \mathbb{R}^K$ will be learned. -- count: false Note that if $d\_G(i,j)>K$, then we have $(L^K)\_{i,j} =0$, hence our kernel is $K$-localized. Moreover, to compute the output of our filter, we need to evaluate: $$ T\_k f = \sum\_{k=0}^K \theta\_k L^k f. $$ This requires $K$ multiplication by a (sparse) matrix $L$ with a cost $O(K\|E\|)<< n^2$. We defined a convolutional layer for graphs! --- # Polynomial approximation We are dealing with polynomial kernels. [Wavelets on graphs via spectral graph theory](https://arxiv.org/abs/0912.3848) suggests to use Chebyshev polynomial approximation. The Chebyshev polynomials $T\_k(x)$ are generated by the recurrence: $$ T\_k(x) = 2xT\_{k-1}(x) - T\_{k-2}(x), $$ with $T\_0=1$ and $T\_1=x$. They are an orthogonal basis for $L^2([-1,1],dx/\sqrt{1-x^2})$ so that $$ k(x) = c\_0/2 +\sum\_{k=1}^\infty c\_k T\_k(x), $$ with Chebyshev coefficients $$ c\_k = \frac{2}{\pi} \int\_{-1}^1 \frac{T\_k(x)k(x)}{\sqrt{1-x^2}}dx. $$ --- # Polynomial approximation .center.width-60[![](images/low-pass.png)] Note: we shift the domain $[-1,1]$ to $[0,\lambda\_{\max}]$ using the transformation $y = \frac{\lambda\_{\max}(x+1)}{2}$. With the shifted Chebyshev polynomials $\overline{T}\_k(y) = T\_k(\frac{2y-\lambda\_{\max}}{\lambda\_{\max}})$, we parametrize the kernel by: $$ k\_\theta(y) = \theta\_0 +\sum\_{k=1}^\infty \theta\_k \overline{T}\_k(y). $$ .citation[source [Fast Eigenspace Approximation using Random Signals](https://arxiv.org/abs/1611.00938)] --- # Convolutional neural networks on graphs The paper [CNN on graphs with fast localized spectral filtering](https://arxiv.org/abs/1606.09375) introduces an additional pooling layer: .center.width-60[![](images/pooling.png)] -- count: false ### Performances on MNIST Underlying graph: 8-NN graph of the 2D grid of size $28\times 28$ with weight $W\_{i,j} = e^{-\||z\_i-z\_j\||^2/\sigma^2}$, where $z\_i$ is the 2D coordinate of pixel $i$. .center.width-60[![](images/mnist.png)] --- # Semi-supervised learning for the karate club .center.width-70[![](images/KIDS-KARATE.jpg)] --- # Graph Convolutional Neural Networks .center[
] A two-layer GCN forward model is (with $\tilde{L}$ a renormalized version of the Laplacian): $$ Z = f(X,A) = \text{softmax}(\tilde{L}\text{ ReLU}(\tilde{L}XW^{0})W^{1}), $$ with $\tilde{L}\_{i,j} = 1/\sqrt{(d\_i+1)(d\_j+1)}$ for $i\sim j$ or $i=j$, and zero otherwise. .citation[source [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907)] --- # Graph Convolutional Neural Networks .center[
] .citation[source [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907)] --- # Inductive representations on graphs ## Graph isomorphism problem: Weisfeiler-Lehman test ### various graphs. Ex: classification of graphs -- count: false Result of seeing an image where nodes are pixels and where we replace the grid by the complete graph: .center.width-80[![](images/permuted_image.png)] --- # Invariant and equivariant linear operators For an order-$k$ tensor $T\in \mathbb{R}^{n^k}$, we define for $\sigma\in \mathcal{S}\_n$: $$ (\sigma \star T)\_{\sigma(i\_1),...,\sigma(i\_k)} = T\_{i\_1,...,i\_k}. $$ A graph $G$ can be seen as a $2$-tensor. Two graphs $G\_1, G\_2$ are said isomorphic if there is a permutation $\sigma$ such that $G\_1=\sigma\star G\_2$. -- count: false A function $f:\mathbb{R}^{n^k}\to\mathbb{R}$ is said to be invariant if $f(\sigma \star T) = f(T)$ for every permutation $\sigma$. A function $f:\mathbb{R}^{n^k}\to\mathbb{R^{n^\ell}}$ is said to be equivariant if $f(\sigma \star T) = \sigma\star f(T)$. -- count: false There is a basis of $b(k+\ell)$ possible equivariant linear operators $f:\mathbb{R}^{n^k}\to\mathbb{R^{n^\ell}}$. The dimension of this space does not depend on the number of nodes $n$. This allows applying the same network on graphs of different sizes! .citation[[Invariant and Equivariant Graph Networks](https://arxiv.org/abs/1812.09902)] --- # Invariant graph neural networks A linear invariant function $f:\mathbb{R}^{n}\to\mathbb{R}$ is of the form $f(x) = \alpha 1^Tx$. A linear equivariant function $f:\mathbb{R}^{n}\to\mathbb{R^n}$ is of the form $f(x) = [\alpha Id + \beta 11^T]x$. A linear invariant function $f:\mathbb{R}^{n\times n}\to\mathbb{R}$ is of the form -- count: false $$ f(A) = \alpha\sum\_{ i =\not j } A\_{i,j} + \beta \sum\_{i=j}A\_{i,j}. $$ In order to get more expressive layer, we need to use tensor of higher order. [On the universality of invariant networks](https://arxiv.org/abs/1901.09342) shows that the minimal required order is $k\geq n^2$ in order to be able to approximate any invariant function. This is of little practical value... we need another approach! --- # The Weisfeiler-Lehman graph isomorphism test .center.width-70[![](images/WL.png)] --- # Graph Neural Networks - start with initial node features $h_v^{(0)}$ for $v\in G$. - after $k$ iterations, a node's representation captures the structural information within $k$-hop network neighborhood by: $$ h_v^{(k)} = \phi\left(h_v^{(k-1)}, f \left( h_u^{(k-1)}, u\in\mathcal{N}(v) \right) \right), $$ where: - $f$ is called the (local) aggregate function and is **permutation invariant** - $\phi$ is the combine funtion. --- # Representations used for prediction - For node classification, the node representationh $h_v^{(K)}$ of the final iteration is used for prediction. - For graph classification, we add a $\text{READOUT}$ function such that prediction is made thanks to $$ h_G = \text{READOUT}\left( h_v^{(K)}, v\in G\right), $$ where $\text{READOUT}$ is **permutation invariant** -- count: false ### Property: 2 isomorphic graphs will have the same representation. --- # Learning procedure The functions $\phi$, $f$ and $\text{READOUT}$ are parametrized by neural networks (with the constraint of being permutation invariant for $f$ and $\text{READOUT}$). For a given task, a loss is defined and the parameters of the functions $\phi$, $f$ and $\text{READOUT}$ are learned with a standard stochastic gradient procedure. -- count: false ### Example: Graph Convolutional Networks (seen previously) $$ h_v^{(k)} = \text{ReLU}\left( W \cdot \text{MEAN}\left( h_u^{(k-1)}, u\in\mathcal{N}(v)\cup {v}\right)\right) $$ -- count: false ### Example: Graph Isomorphism Network (GIN) $$ h\_v^{(k)} = \text{MLP}^{(k)}\left( (1+\epsilon^{(k)}) h\_v^{(k-1)} + \sum\_{u\in\mathcal{N}(v)} h_u^{(k-1)}\right) $$ .citation[ [How Powerful are Graph Neural Networks?](https://arxiv.org/abs/1810.00826)] --- # Results .center.width-80[![](images/result_GIN.png)] --- ## Implementation with [DGL](https://docs.dgl.ai/en/latest/index.html) .center.width-20[![](images/Petersen.png)] You can create a DGL graph from networkx: ``` import networkx as nx import dgl g_nx = nx.petersen_graph() g_dgl = dgl.DGLGraph(g_nx) import torch g.ndata['h'] = torch.randn(10,3) # assign one 3D vector to each node ``` --- ## Convolutional layer ``` import torch.nn as nn # Define the message & reduce function def gcn_message(edges): # The argument is a batch of edges. # This computes a (batch of) message called 'msg' using the source node's feature 'h'. return {'msg' : edges.src['h']} def gcn_reduce(nodes): # The argument is a batch of nodes. # This computes the new 'h' features by summing received 'msg' in each node's mailbox. return {'h' : torch.sum(nodes.mailbox['msg'], dim=1)} # Define the GCNLayer module class GCNLayer(nn.Module): def __init__(self, in_feats, out_feats): super(GCNLayer, self).__init__() self.linear = nn.Linear(in_feats, out_feats) def forward(self, g, inputs): # g is the graph and the inputs is the input node features # first set the node features g.ndata['h'] = inputs # trigger message passing on all edges g.send(g.edges(), gcn_message) # trigger aggregation at all nodes g.recv(g.nodes(), gcn_reduce) # get the result node features h = g.ndata.pop('h') # perform linear transformation return self.linear(h) ``` --- ## A 2-layer Graph Convolutional Network for node classification ``` # Define a 2-layer GCN model class GCN(nn.Module): def __init__(self, in_feats, hidden_size, num_classes): super(GCN, self).__init__() self.gcn1 = GCNLayer(in_feats, hidden_size) self.gcn2 = GCNLayer(hidden_size, num_classes) def forward(self, g, inputs): h = self.gcn1(g, inputs) h = torch.relu(h) h = self.gcn2(g, h) return h # The first layer transforms input features of size of 34 to a hidden size of 5. # The second layer transforms the hidden layer and produces output features of # size 2, corresponding to the two groups. net = GCN(34, 5, 2) ``` --- ## Semi-supervised learning for the karate club .center.width-20[![](images/karate_net.jpg)] ``` inputs = torch.eye(34) # one-hot vectors to initialize the node features labeled_nodes = torch.tensor([0, 33]) # only the instructor and the president nodes are labeled labels = torch.tensor([0, 1]) # their labels are different optimizer = torch.optim.Adam(net.parameters(), lr=0.01) all_logits = [] for epoch in range(30): logits = net(G, inputs) logp = F.log_softmax(logits, 1) # we only compute loss for labeled nodes loss = F.nll_loss(logp[labeled_nodes], labels) optimizer.zero_grad() loss.backward() optimizer.step() print('Epoch %d | Loss: %.4f' % (epoch, loss.item())) ``` --- ## Results .center[
] .citation[source: Tutorial [DGL at a Glance](https://docs.dgl.ai/en/latest/tutorials/basics/1_first.html)] --- # Node embedding ## Language model ### one fixed graph, no signal. Ex: community detection # Signal processing on graphs ## Fourier analysis on graphs ### one fixed graph, various signals. Ex: classification of signals # Inductive representations on graphs ## Graph isomorphism problem: Weisfeiler-Lehman test ### various graphs. Ex: classification of graphs --- class: end-slide, center count: false The end.


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src/utils/createEEG.js
import { interval } from "rxjs/observable/interval";
import { map } from "rxjs/operators";
import museCsv from "../../dataset/muse-lsl.csv";
import { sample } from "rxjs/operator/sample";
// Code splitting: dynaically importing the csv so it is not included in the main bundle
// @TODO: not currently working, webpack bug: https://twitter.com/castillo__io/status/975585584486916096
export const getMuseCsv = async () =>
await import(/* webpackChunkName: "museCsv" */
"../../dataset/muse-lsl.csv").then(
csv => csv.map(row => row.slice(1, 5)) // filter channel data
);
const NUM_CHANNELS = 4;
const SAMPLE_RATE = 256;
let sinCounter = 0;
// filter channel data
const museData = museCsv.map(row => row.slice(1, 5));
const createMockRow = length => Array.from({ length }, () => Math.random());
const createSineRow = (length, freq, sampleRate) => {
sinCounter++;
return Array.from(
{ length },
() => Math.sin(sinCounter / (sampleRate / freq / (Math.PI * 2))) * 100
);
};
const getCsvRow = (csv, interval) => csv[interval % csv.length];
const injectNaNs = (data, [min, max]) =>
data.map(value => (value > min && value < max ? NaN : value));
/*
* Creates a stream of pre-recorded EEG data that can be used for testing
* Default data was collected from Muse 2016 with muse-lsl: staring at software
*/
export const createEEG = ({
channels = NUM_CHANNELS,
sampleRate = SAMPLE_RATE,
mock = false,
NaNRange = [0, 0],
sine = false,
csv = museData
} = {}) =>
interval(1000 / sampleRate).pipe(
map(interval => {
const timestamp = Date.now();
let row = [];
if (sine) {
row = createSineRow(channels, sine, sampleRate);
} else {
row = mock ? createMockRow(channels) : getCsvRow(csv, interval);
}
const data = injectNaNs(row, NaNRange);
return { timestamp, data };
})
);


{{ class1 }} Hmmm. This quest says to find {{ number }} {{ item }}, but they aren't on the map.
{{ class2 }} That is weird for sure. Have you tried looking here?
{{ class1 }} Thanks, I'll try that.


Arnold Lvl 50
Curling - Level 50
Cardio - Level 40
Bench Press - Level 20
Swimming - Level 40
Swimming - Level 40 40%


--- layout: post title: Smartclient RestDatasource (ListGrid )using JAX-RS as JSON Data service date: 2014-07-23 03:06:54.000000000 +01:00 type: post published: true status: publish categories: - RestDatasource - smartclient tags: - listgrid - restdatasource - smartclient meta: _edit_last: '36016338' _wpcom_is_markdown: '1' geo_public: '0' _publicize_pending: '1' author: login: velmuruganv email<EMAIL_ADDRESS>display_name: velmuruganv first_name: '' last_name: '' ---
This is for beginners who likes to understand the smartclient RIA using JAVA as JSON service.
Smartclient LPGL version does not ship with smartclient server , so we need to manually create server side datasources for all of our Databound UI components.
Say I have downloaded the smartclient LPGL version from isomorphic website. Now I would like to add ListGrid library to my web application. Here is sample grid we are going to build here. The tutorial application is hosted in herokuapp.com. Click here to access it
[caption id="attachment_163" align="aligncenter" width="620"] smartclient-listgrid-jax-rs-json-datasource-tutorial[/caption]
Here are the few things i did to make this work.
The complete source code for this grid (UI) and Server side implementation(JAX-RS) for CRUD operations are available in the github here. git@heroku.com:vel-smartclient-tutorial.git


#include "arduino.h" // ***************ACTIVITIES TO CHECK WHILE GRADING BEGIN******************* // // 1. LED’s 2 & 9, 3 & 10, 4 & 11, and so on up through LED’s 8 & 15 blink on and off // 2. The sequence repeats five times. // 3.  Verify that nested for loops are used // 5. Verify that some sort of math like otherLedNum=ledNum+7; is being used to turn on the second LED. // 6. Verify that variables are used in the delays. For example: delay(waitTime); instead of delay(100); // // ***************ACTIVITIES TO CHECK WHILE GRADING END********************* void setup() { int ledNumber; for(ledNumber = 2; ledNumber <= 15; ledNumber++) { pinMode(ledNumber, OUTPUT); // Enable pins connected to LEDs 2-15 to be outputs } } void loop() { int count; int ledNumber; int otherLedNum; int delayTime; delayTime=100; for(count = 1; count <= 5; count++) // this repeats the sequence five times { for(ledNumber = 2; ledNumber <= 8; ledNumber++) // LED’s 2 and 9 blink on and off together, then 3 and 10 together, then 4 and 11 together and so on up through LED’s 8 and 15 together. { otherLedNum=ledNumber+7; digitalWrite(ledNumber, HIGH); // Turns on LED2, then 3, then 4, 5, 6, 7, 8 digitalWrite(otherLedNum,HIGH); // Turns on LED9, then 10, then 11, 12, 13, 14, 15 delay(delayTime); digitalWrite(ledNumber, LOW); // Turns off LED2, then 3, then 4, 5, 6, 7, 8 digitalWrite(otherLedNum,LOW); // Turns off LED9, then 10, then 11, 12, 13, 14, 15 } } delay(delayTime*40); }


how about 🤔
Edit this list, add your own ideas! Your list will only be saved in your current browser; nowhere surveillable, nowhere permanent.
Want to keep different lists, maybe one for family/friends night, one for date night? Use different browsers!


|
cds
2.3.1
|
CuckooSet and CuckooMap related definitions. More...
Data Structures | |
| class | empty_refinable_stat |
| Empty refinable internal statistics. This is typedef for intrusive::cuckoo::empty_refinable_stat. More... | |
| class | empty_stat |
| Cuckoo empty statistics.This is typedef for intrusive::cuckoo::empty_stat. More... | |
| class | empty_striping_stat |
| Empty striping internal statistics. This is typedef for intrusive::cuckoo::empty_striping_stat. More... | |
| struct | make_traits |
| Metafunction converting option list to CuckooSet/CuckooMap traits. More... | |
| struct | probeset_type |
| Probe set type option. More... | |
| class | refinable |
| Refinable concurrent access policy. This is typedef for intrusive::cuckoo::refinable template. More... | |
| class | refinable_stat |
| Refinable internal statistics. This is typedef for intrusive::cuckoo::refinable_stat. More... | |
| class | stat |
| Cuckoo statistics. This is typedef for intrusive::cuckoo::stat. More... | |
| struct | store_hash |
| Option specifying whether to store hash values in the node. More... | |
| class | striping |
| Lock striping concurrent access policy. This is typedef for intrusive::cuckoo::striping template. More... | |
| class | striping_stat |
| Striping internal statistics. This is typedef for intrusive::cuckoo::striping_stat. More... | |
| struct | traits |
| Type traits for CuckooSet and CuckooMap classes. More... | |


nscd - name service cache daemon
nscd is a daemon that provides a cache for the most common name service
requests. The default configuration file, /etc/nscd.conf, determines
the behavior of the cache daemon. See nscd.conf(5).
nscd provides caching for accesses of the passwd(5),
group(5), hosts(5) services(5) and netgroup
databases through standard libc interfaces, such as getpwnam(3),
getpwuid(3), getgrnam(3), getgrgid(3),
gethostbyname(3), and others.
There are two caches for each database: a positive one for items
found, and a negative one for items not found. Each cache has a separate TTL
(time-to-live) period for its data. Note that the shadow file is
specifically not cached. getspnam(3) calls remain uncached as a
result.
- --help
- will give you a list with all options and what they do.
The daemon will try to watch for changes in configuration files appropriate for
each database (e.g., /etc/passwd for the passwd database or
/etc/hosts and /etc/resolv.conf for the hosts database),
and flush the cache when these are changed. However, this will happen only
after a short delay (unless the inotify(7) mechanism is available and
glibc 2.9 or later is available), and this auto-detection does not cover
configuration files required by nonstandard NSS modules, if any are specified
in /etc/nsswitch.conf. In that case, you need to run the following
command after changing the configuration file of the database so that
nscd invalidates its cache:
$ nscd -i <database>
nscd.conf(5), nsswitch.conf(5)
This page is part of release 5.05 of the Linux man-pages project. A
description of the project, information about reporting bugs, and the latest
version of this page, can be found at https://www.kernel.org/doc/man-pages/.


When you place a node into service, there is a set of information that you must provide which will be unique to each and every node. The application development team may or may not have provided defaults for some or all of these values, so you should check with them to see exactly what you need to override.
This information can be provided to the application in two different ways. One is by using JE API calls. Typically you will pass the information to those calls using command line parameters. Again, how you do this is specific to your application.
In addition, you can provide this information to the application
using the je.properties
file. Note that the
information provided in this file is handled as if it is a
default setting. Therefore, if you also
provide conflicting information using the JE APIs (again,
usually passed to a production application using command line
parameters), then the information provided directly to the APIs
takes priority over whatever might be found in the
je.properties
file.
No matter how it is done, there are three pieces of information that you must provide every JE replicated application:
Group Name
This is the replication's group name. This value must be the same for every node in a given replication group. This name must be made up of alpha numeric characters and must not be zero length.
JE developers can provide this information to the
application using the
ReplicationConfig.GROUP_NAME
field. In the je.properties
file,
it is defined using the
je.rep.group.name
parameter.
Node Name
This is the name of the node. This name must be unique within the group. This name combined with the group name uniquely identifies the node.
JE developers can provide this information to the
application using the
ReplicationConfig.NODE_NAME
field. In the je.properties
file,
it is defined using the
je.rep.node.name
parameter.
Node Host
This is the hostname and port pair that is used by other nodes in the replication group to communicate with this node. The node uses this property to establish a TCP/IP socket for communication with other members of the group.
The string that you provide to this property takes the form:
hostname[:port]
The hostname provided to this property must be reachable by the other nodes in the replication group.
The port number is optional for this property because a
default port can be defined using the
je.properties
file (you use the
je.rep.defaultPort
property to do
this). However, if a port is provided explicitly to this
property, then je.rep.defaultPort
is
ignored.
Be careful to ensure that the port you identify for the node does not conflict with ports used by other applications (including other nodes, if any) currently running on the local machine.
Note that monitor nodes will use the socket identified by this property so that they can be kept informed of the results of elections, and so they can keep track of changes in group composition.
Electable nodes use this socket to:
Hold elections
Supply commit acknowledgements
Both electable and secondary nodes use this socket to:
Establish replication streams between the Master and its Replicas
Support network-based JE HA utility services, such as JE's network restore utility. (See Restoring Log Files for details on this utility.)
You can change the hostname and/or port number for an existing electable or monitor node using the DbGroupAdmin.updateAddress() or ReplicationGroupAdmin.updateAddress() methods. Hostnames and port numbers for secondary nodes can be changed by restarting the nodes with the desired values.
The properties discussed here are simply the bare-bones minimum properties required to configure a JE node. For a complete description of all the replication properties available to a JE application, see the ReplicationConfig and ReplicationMutableConfig class descriptions.


t
Posted on February 26, 2017
The Sixth Blog Post!
What did you do this past week?
This week I spent doing some review for the class, as the test is coming up on only a few days. I admit I've done less reviewing than I should, but thankfully Professor Downing posts all of the code (rather than slides) up on the website with the schedule. I don't have any real idea what will be on the test, not because the content of the class is unclear, but because we haven't discussed the format of the test at all so far. Will it be multiple choice? Short answer? Coding questions? Who knows? I must prepare for it all.
What will you do next week?
MORE PREPARATION. I plan to review all of the coding, and review the canvas questions (If they're available anywhere?), and reread all of the articels if I have time. The textbook readings are of questionable worthiness, if the pattern of questions is similar to the quizzes. There seems to be only one question out of the question per week, which pales in comparison to the questions from the articles and the class content itself. I don't know if there will be any content from the labs on test, but I feel like I have a pretty good understanding of their contents regardless.
What's in the way?
Honestly the only thing in the way of my preparation for this test this week is my own tendency to procrastinate and do as little work as possible. If I do not perform well on this test, it will be my own fault.
What's my experience in the class?
This was a light week, likely because I was supposed to be studying for the upcoming exam rather than laying around doing nothing. It's nice to have a week off though, the labs are surprisingly time consuming due to the strict turnin requirements.
What's the tip of the week?
Be smarter than me. Do not procrastinate. I'm going to be doing some heavy studying for the next few days, especially due to some surprises this week that are going to take up extra time (Interviews! Friends coming in to town!).


I have built e-commerce websites which is shown below .
I personly develop websites and web applications centered on all device use.
All my web sites are secured and thats why I recommend to my clients the use of frameworks like laravel and django which have many inbuilt security.
This is the year I started on the journey am on of becameing the best software engineer of my time. This dream is being actualized in a phased way through starting school at Aptech Compute Education.
This was the day I was beggat into this earthly realm by my dear mother.
Janaruary 2019 I start at Aptech Computer Eduacation in hopes of becaming a full stack dev.
I graduate out of Aptech haveing scored highly I moved to Isbat University that same year to achieve a honors degree in software engineering.
Thanks for the support
The community of wise people that need forward thinkers.
The african continet is full of wise people that is why I make platform like moonpostcto share wisdom between each for our aggregate growth.
The Uganda and the rest of the african continet in a few years is not to be as is so my dream is to work with forward thinking companies and individuals that see past themselves to reach and help other people through technology.


So, I've had some more time to dork around with this, and I'm adding instructions! The last time I made a website was in, like, 2005, so this is going to be sort of rough. But I want to provide us with tools to build our intuition about triangles. In passing, this is inspired by Brett Victor's work, especially the ladder of abstraction. You can click and drag on the blue numbers in the following paragraph to change their values.
Consider a right triangle whose hypotenuse is units long and θ is degrees.


Talkatoo!
Generates a list of moons to collect while playing through Super Mario Odyssey
Random seed
Any%
Dark Side
Darker Side
Include story moons
(Gives exactly 124 moons, unless moon requirements are ignored)
(Gives exactly 250 moons, unless moon requirements are ignored)
(Gives exactly 500 moons, unless moon requirements are ignored)
Include seed moons
Include warp painting moons
Include hint art moons
Ignore moon requirements
(Likely need world peace in almost every kingdom)
Generate!
Share:
{{ shareLink }}
{{ kingdom.name }}


Feyenoord vs Ajax
Competition: Eredivisie
Sunday, 31 March 14:30
Location: Amsterdam ArenA
Score:
FEY 0 - 0 AJA
Enable notification


WITNESS LIST Corrections Committee March 27, 2003-8:00A HB 1273 For: DeLuca, Stuart (TIFA) Against: Levy, Rick (TX AFL-CIO) HB 1331 Registering, but not testifying: On: Ninaud, Christian (Sunset Advisory Commission) HB 1480 On: Fabelo, Tony (Criminal Justice Policy Council) Registering, but not testifying: For: Shaw, Jim (Civigenics) Against: Foster, Arlan (AFSCME Local 3921) Merrell, Sam (Self) Olsen, Brian (AFSCME) Reich, Jerry (AFSME Local 3920) Simpson, Dee (AFSCME - Corrections employees) Stewart, Ray (AFSME Local 3920) Tobias, Toby (AFSCME/CEC 7) On: Deitch, Michele (Self) Rayfield, Penny (OnShore Resources, Advisory Council/Austin State Jail) HB 1669 On: Fabelo, Tony (Criminal Justice Policy Council) Registering, but not testifying: For: Shaw, Jim (Civigenics) Against: Foster, Arlan (AFSCME Local 3921) Merrell, Sam (Self) Olsen, Brian (AFSCME) Reich, Jerry (AFSME Local) Stewart, Ray (AFSME Local) Tobias, Toby (AFSCME/CEC 7) Williams, Robin (Houston Food Bank) On: Anderson, Gary (Texas Public Employees Assn) Deitch, Michele (Self) HB 1852 On: Fabelo, Tony (Criminal Justice Policy Council) Registering, but not testifying: For: Shaw, Jim (Civigenics) Against: Merrell, Sam (Self) Simpson, Dee (AFSCME - Corrections employees) HB 1856 Registering, but not testifying: Against: Simpson, Dee (AFSCME - Corrections employees) HB 2190 On: Fabelo, Tony (Criminal Justice Policy Council) Registering, but not testifying: For: Shaw, Jim (Civigenics) Against: Merrell, Sam (Self) Simpson, Dee (AFSCME - Corrections employees) On: Anderson, Gary (Texas Public Employees Assn) Deitch, Michele (Self)


Start planning your graduation route in NUS! No more worrying about the many pre-requisites and preclusions when bidding for your modules
About Path of NUS
This is done by Team Daedalus for Orbital over the Summer of 2014.
Orbital is the School of Computing's self-driven programming summer experience.
It is designed to give first-year students the opportunity to : 1. self-learn 2. build something useful. It is designed as a 4 modular credit (MC) module that is taken pass/fail (CS/CU) over the summer.


Package view
Class MoviegoerView
- java.lang.Object
-
- view.MoviegoerView
-
public class MoviegoerView extends java.lang.Object
This View class displays the page for the moviegoer, and takes in input from the moviegoer and passes them to the Controller class for processing.
-
-
Constructor Summary
Constructors Constructor Description MoviegoerView()
-
Method Summary
Modifier and Type Method Description java.lang.String
getTid(java.lang.String cineplex, java.util.Date now)
This method generates the Transaction ID (TID) when booking made by the moviegoer is successful.boolean
insertBooking(ShowTime show_times)
This method inserts booking based on the showtime selected by the moviegoer.void
mainPage()
void
mainPage2()
void
mainPage3()
This method displays the third main page for the moviegoer.void
showAvailableSeats()
This method shows the current seating arrangement of the showtime for the moviegoer.
-
-
-
Method Detail
-
mainPage
public void mainPage() throws java.io.IOException
- Throws:
java.io.IOException
- This method displays the main page for the moviegoer. Movie Goer User Interface (main page) to perform following actions: Search/list movies, view booking history, book ticket, show available seats, give review/rating, view rating/review for movies, logging out some options will go into other mainPages under this main page Creating objects from MoviegoerListMovieController, Moviegoersearchcontroller and MovieBookingController to be used within the mainPage
-
mainPage2
public void mainPage2() throws java.io.IOException
- Throws:
java.io.IOException
- This method displays the second main page for the moviegoer. Movie Goer User Interface (main page 2) to perform following actions: Search movies, view movie by rating, view movie by sales, return back to main page, Creating objects from MoviegoerListMovieController and Moviegoersearchcontroller to be used within the mainPage2
-
mainPage3
public void mainPage3()
This method displays the third main page for the moviegoer. Movie Goer User Interface (main page 3) to perform following actions: search by movies, search by cineplex, going back to mainpage. Creating objects from MovieBookingController and Moviegoersearchcontroller to be used within the mainPage3
-
insertBooking
public boolean insertBooking(ShowTime show_times)
This method inserts booking based on the showtime selected by the moviegoer. Movie goer booking the ticket will need to input his/her name, email, mobile number, number of ticket to purchase, age of person viewing the movie, seat of the cinema in that particular showtime final price for the person going to the movie will be calculated and displayed before user chooses his/her seat (after inputing the age) A 2-D array with "O"s (not occupied) and "X"s (occupied) will printed for the showtime user selected row and column number and screen will be displayed when selecting the seats too movie goer can choose to book tickets halfway and decide not to book, if everything is decided before making the payment- Parameters:
show_times
- showtime given- Returns:
- true if book successfully, false if not
-
showAvailableSeats
public void showAvailableSeats()
This method shows the current seating arrangement of the showtime for the moviegoer. Movie Goer User Interface for showing available seats,user can choose to perform following actions:(viewing showtime seats by) search by movie, search by cineplex, going back to mainpage. A 2-D array with "O"s (not occupied) and "X"s (occupied) will printed for the showtime user selected row and column number and screen will be displayed too Creating objects from MovieBookingController and Moviegoersearchcontroller to be used within the mainPage2
-
getTid
public java.lang.String getTid(java.lang.String cineplex, java.util.Date now)
This method generates the Transaction ID (TID) when booking made by the moviegoer is successful. Method to get the Tid for booking, by concatenating of the first 2 letter, first letter after whitespace of the cineplex name, then uppercasing them, and concatenating the date-time ("yyyyMMddkkmm") to it- Parameters:
cineplex
- cineplex namenow
- Date object- Returns:
- String of the TID
-
-


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Contributing to the Coral Project
Welcome! We’re delighted to have you contribute to the Coral Project. Before you get started, be sure to review our Code of Conduct, which governs all development and project contributions.
There are a number of ways that you can contribute to the Coral Project: you don’t have to be a developer to help out.
Contributing without programming
We need help with a number of non-programming tasks:
Contributing by coding
If you are a developer, especially if you know Go or Node.js, we would love your help in fixing bugs and developing new features and plug-ins.
Contributing to the community
- Join us in our community forum to discuss online communities, comment sections, and journalism. Offer suggestions, ask questions!
- Sign up to our newsletter.
- We are also on Twitter.
- Play our Cards Against Community card game!


Introduction
Investment process "3-legged stool"
Securities research
Portfolio management
Securities trading
Traders are tasked with executing desired trades quickly, without error, and at favorable prices.
Market Microstructure
Definition: The market structures and processes that affect how the manager's interest in buying or selling an asset is translated into executed trades
Order Types
Market order
Limit order
Market-not-held order, participate order, best efforts order, undiscolsed limit order, market on open/close order
Principal trade vs. portfolio trade
A market order is an instruction to execute an order promptly in the public markets at the best available price. It places a premium on the immediacy of execution at the cost of price uncertainty. A limit order is an instruction to trade at the best price available but only if the price is at least as good as the limit price specified in the order. It places an emphasis on price while risking execution uncertainty. A principal trade is a trade with a broker in which the broker commits capital to facilitate the prompt execution of the trader's order. Portfolio trades involve orders that require the purchase/sale of a specified basket of securities at as close to the same time as possible.
Types of Markets
Quote-driven markets - traders trade with dealers
Order-driven markets - traders trade directly with one another
Brokered markets - traders rely on a broker to find the other side of a trade
Markets are organized to provide liquidity, transparency, and assurity of completion.
Quote-Driven Markets
Rely on dealers to esablish firm prices at which securities can be bought and sold
Bid-ask spread and size indicate liquidity
Effective spread
The effective spread is two times the deviation of the actual execution price from the midpoint of the market quote at the time an order is entered. It captures price improvement and market impact.
Order-Driven Markets
Electronic crossing networks (ECNs)
Auction markets
Periodic vs. batch vs. continuous
Automated auctions (electronic limit-order markets)
Order-driven markets are markets in which transaction prices are established by public limit orders to buy or sell a security at specified prices. ECNs are markets in which buy and sell orders are batched (accumulated) and crossed at a specific point in time (no price discovery). Auction markets are markets in which the orders of multiple buyers compete for execution. Automated markets are markets are computer based auctions that operate continuously within the day using a specified set of rules to execute orders.
Brokered Markets
Definition: A market in which transactions are largely effected through a search-brokerage mechanism away from public markets
Block order
Position a trade
Front-run
A block order is an order to buy or sell in a quantity that is large relative to the liquidity ordinarily available from dealers in the security or in other markets. Positioning a trade occurs when a broker takes the opposide side of a trade as a principal. To front-run a trade is to trade ahead of the initiator, exploiting priviliged information in the process. Hybrid markets are combinations/permutations of the previously described markets.
Roles of Brokers and Dealers
As the agent of an investor, brokers should:
Represent the order
Find the opposite side of a trade
Supply market information
Provide discretion and security
Provide other supporting investment services
Support the market mechanism
Market Quality
Characteristics of a liquid market
Relatively low bid-ask spreads
Deep
Resilient
Contribution factors
Many buyers and sellers
Diversity of opinion, information, and investment needs
Convenience
Market integrity (pre- and post-trade transparency)
Depth refers to the number of shares available for purchase or sale at the quoted bid and ask prices. Resilient markets are those in which discrepancies between the market price and intrinsic value tend to be small and quickly corrected.
Transaction Cost Components
Explicit costs
Implicit costs
Bid-ask spread
Market impact
Missed trade opportunity costs
Delay costs
Volume-weighted average price (VWAP)
Implementation shortfall
Econometric cost models (pretrade analysis)
Explicit costs are the direct costs of trading, such as broker commissions, taxes, and exchange fees. Implicit costs are indirect trading costs. Implementation shortfall is the difference between the money return on a notional portfolio in which positions are established at the prevailing price when the decision to trade is known and the actual portfolio's return (explicit costs, realized profit/loss, delay costs, missed trade opportunities)
VWAP vs. Implementation Shortfall
Types of Traders
There is a trade-off between the urgency of a trade and its certainty of execution.
Trading Objectives
Algorithmic Trading
Simple logical participation strategies allow institutional investors to participate in overall market volumes without being overly visible. Implementation shortfall strategies solve for the optimal trading strategy that minimizes trading costs as measured by the implementation shortfall method. Opportunistic strategies involve passive trading combined with the opportunistic seizing of liquidity.


0; else noChartData">
Date
Completed clients
{{row['completedClients']}}
= 0"
[matTooltip]="'Out of all scheduled clients: ' + totalClients">
({{ row['completedClientsPct'] }}%)
Scheduled clients
{{row['scheduledClients']}}
= 0"
[matTooltip]="'Out of all scheduled clients: ' + totalClients">
({{ row['scheduledClientsPct'] }}%)
There is no hunt progress data to show.


--- layout: post title: Krystal³ date: '2013-09-14T17:31:12+02:00' categories: - Sketches - Character picture_url: https://film-Storyboards.tumblr.com/post/61244883891/krystal3-rough-sketch ---
Krystal³ - character sketch.


|
Veiebrygge
|
Classes | |
| class | App |
| Interaction logic for App.xaml More... | |
| class | centerOfGravity |
| Interaction logic for centerOfGravity.xaml More... | |
| class | dashboard |
| Interaction logic for dashboard.xaml More... | |
| class | forsvaretDataSet |
| Represents a strongly typed in-memory cache of data. /summary> More... | |
| class | helpPage |
| Interaction logic for helpPage.xaml More... | |
| class | helpPage_RunTest |
| Interaction logic for helpPage_RunTest.xaml More... | |
| class | helpPage_Tests |
| Interaction logic for helpPage_Tests.xaml More... | |
| class | helpPageVehicles |
| Interaction logic for helpPageVehicles.xaml More... | |
| class | index |
| class | LidatVisualization |
| Interaction logic for LidatVisualization.xaml More... | |
| class | Login |
| Interaction logic for Login.xaml More... | |
| class | MainWindow |
| Interaction logic for MainWindow.xaml More... | |
| class | read_from_arduino |
| Interaction logic for read_from_arduino.xaml More... | |
| class | regnr |
| class | regnr2 |
| Interaction logic for regnr2.xaml More... | |
| class | RunTest |
| Interaction logic for RunTest.xaml More... | |
| class | Tests |
| Interaction logic for Tests.xaml More... | |
| class | write_to_arduino |
| Interaction logic for arduino.xaml More... | |


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Wade Antenna Inc
Mailing Address:
29 Sharp RdBrantford, Ontario
N3T 5L8
Location Address:
29 Sharp RdBrantford, Ontario
N3T 5L8
Tel.:<PHONE_NUMBER>
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Logo
Company Profile
Wade Antenna strives to be a global leader delivering unique and innovative technological solutions for applications in both demanding and challenging environments for the communications industry. Our engineered solutions and superior proven products provide a positive customer experience. With progressive leadership that emphasizes service through sales, marketing, research and innovation, WADE and TACO deliver unrivalled service and satisfaction alongside leading edge technology.
Founded in 1987 when our Chairman, J. Patric (Ric) Murphy, purchased the rights to the Wade Log Periodic Antenna line from Geoff Wade, Wade Antenna specializes in the commercial VHF/UHF cable television antennas for cable operators in Canada and the United States. For many years our experienced team have been providing antenna solutions for TV broadcast signal reception and interference issues. The Wade Log Antenna was developed over 60 years ago and when tested by the Grand Alliance in 1996, was found to be the optimum antenna for digital off-air reception.
We strengthened our market position, distribution channels, and customer service by acquiring TACO Antenna and Delhi Antenna Products in 2001. TACO Antenna was founded in 1934 in New York City as manufacturer of AM/FM couplers. The company later moved into making VHF and UHF antennas for civil and military aviation and shipboard communications, and this has been its focus for several decades. Delhi specialized in light duty towers and mounting products, and that tradition continues very successfully to this day. In 2006, Wade Antenna acquired the product rights to Lindsay Antenna, a competitor in the cable television industry.
Customers include the Federal Aviation Administration, several branches of the US Military (DoD), NAV Canada, Canadian Forces (DND), Rockwell Collins, IBM, Park Air Systems, Raytheon, Northrop Grumman, ARINC, Harris, Department of Homeland Security, General Dynamics and many other system integrators. TACO and Wade Antennas can be found in over 60 countries around the globe such as the United Kingdom, Germany, Spain, France, Norway, Sweden, Denmark, Poland, Italy, Estonia, Bulgaria, Romania, Cyprus, Israel, Saudi Arabia, Afghanistan, Oman, Turkey, Japan, S. Korea, Singapore, Australia, Taiwan, Thailand, Bermuda, Peru, Chile, South Africa, the United States and Canada.
Wade Antenna will continue to provide secure and reliable communication solutions for organizations worldwide that value security and peace of mind.
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
Recently, TACO Antenna won a product innovation award with Brant County for its new portable UHF SATCOM Antenna SAT-MP-320. The SAT-MP-320 is designed for rapid deployment for both UFO and MUOS satellite communications. The antenna comes with a single antenna and tripod combination.
5895-Miscellaneous Communication Equipment
5920-Fuses, Arresters, Absorbers, and Protectors
5985-Antennas, Waveguide, and Related Equipment
5995-Cable, Cord, and Wire Assemblies: Communication Equipment
Market profile
Industry sector market interests:
- Information Technology and Telecommunications
- Mining/Petroleum/Gas
- Defence
Geographic markets:
Export experience:
- Australia
- Bermuda
- Cayman Islands
- Cyprus
- France
- Germany
- Greece
- Hungary
- Iceland
- Japan
- Malaysia
- Poland
- South Africa
- Turkey
- United Kingdom
- United States
- Alabama
- Alaska
- Arkansas
- California
- Florida
- Georgia
- Indiana
- Louisiana
- Maine
- Massachusetts
- Mississippi
- New York
- Oklahoma
- Pennsylvania
- South Carolina
- Tennessee
- Texas
Sector information
Total plant size:
Unique applications:
Our 2.4 GHz Sectoral Antenna, P250-2.4, was installed on a Parasailing boat to record a video feed from the helmut cam on the customer. There were several sold to tourism areas throughout the United States.Key / Major clients:
Our customers range from Time Warner, Cox Communications, Charter, Roger's Cable, Cogeco, Mountain Cable, City of Fredericton, University of Toronto, Dofasco, Suncor, IBM, Via Rail, SaskTel, NBC, FOX, and others.Success stories:
We had our 2.4 GHz Sectoral antennas installed for the City of Fredericton's Wi-Fi network in 2004. We also have had great success with the Wade Log Antennas with the advance of HDTV in the United States.Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
Products, services and licensing
Product / Service / Licensing
Recently, TACO Antenna won a product innovation award with Brant County for its new portable UHF SATCOM Antenna SAT-MP-320. The SAT-MP-320 is designed for rapid deployment for both UFO and MUOS satellite communications. The antenna comes with a single antenna and tripod combination.
5895-Miscellaneous Communication Equipment
5920-Fuses, Arresters, Absorbers, and Protectors
5985-Antennas, Waveguide, and Related Equipment
5995-Cable, Cord, and Wire Assemblies: Communication Equipment
Market
Market profile
Industry sector market interests:
- Information Technology and Telecommunications
- Mining/Petroleum/Gas
- Defence
Geographic markets:
Export experience:
- Australia
- Bermuda
- Cayman Islands
- Cyprus
- France
- Germany
- Greece
- Hungary
- Iceland
- Japan
- Malaysia
- Poland
- South Africa
- Turkey
- United Kingdom
- United States
- Alabama
- Alaska
- Arkansas
- California
- Florida
- Georgia
- Indiana
- Louisiana
- Maine
- Massachusetts
- Mississippi
- New York
- Oklahoma
- Pennsylvania
- South Carolina
- Tennessee
- Texas


Troll (and others now) Quirk Generator 3.0
Generator for Homestuck/Hiveswap typing quirks. Created by QuaternionMark.
Want to check out the source or report issues? See the
GitHub
.
The quick brown fox jumps over the lazy dog. :D
Input Text
Light Mode
Enable All
Disable All
Toggle Copy-to-Clipboard:
Hide Labels:
Select a category:


Home
About
Contact
Contact Us!
We accept messages by carrier pigeon, smoke signals, and deep thoughts (by Jack Handy).


2 Chronicles 20:1
It came to pass after this also, that the children of Moab, and the children of Ammon, and with them other beside the Ammonites, came against Jehoshaphat to battle.


Shen
Veis
One Tensor
region
Grid-Step
Wt
D min
D max
D step
OR set:
D parameter
cut excess stations
generate statistics
RUN
Strain parameters:
Velocities
Principal Axis
Rotational Rates
Shear Strain
Shear Strain Axes
Second Invariant
Statistics:
Stations per cell
D optimal
sigma value
ADD to Map
Create GMT Maps:
Options:
plot stations
add labels
add delaunay triangles
Scales:
velocities
Strains
Rotational
Outputs:
Pre/Suf-fix
GMT Map


Four classes are provided for quick alignment of text: text-left
, text-right
, text-center
, and text-justify
.
This text is left-aligned.
This text is centered.
This text is right-aligned.
This text is right-aligned. It's going to take a few sentences to really illustrate what's going on with justified text. Now I'm just rambling.

This text is left-aligned.

This text is right-aligned.

This text is centered.

This text is justified.


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classes(source).add(instruction)
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def invoked(id: Int) = {
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id
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def instructionCoverage(instructions: Iterable[MeasuredInstruction]): Double =
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def instructionCoverage: Double = instructions.values.count(_.count > 0) / instructions.size
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case class MeasuredClass(name: String) {
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case class MeasuredInstruction(source: String, id: Int, start: Int, line: Int, var end: Int = -1) {
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Friday’s Insider Report: CEO accumulates shares of this beaten-down stock
by Jennifer DowtyFeatured below are companies that have experienced recent insider trading activity in the public market through their direct and indirect ownerships, including accounts they have control or direction over. The list features insider transaction activity; it does not convey total ownership information as an insider may hold numerous accounts.
Keep in mind, when looking at transaction activities by insiders, purchasing activity may reflect perceived value in a security. Selling activity may or may not be related to a stock's valuation; perhaps an insider needs to raise money for personal reasons or perhaps shares may be sold to address tax implications when options and rights are exercised. An insider's total holdings should be considered because a sale may, in context, be insignificant if this person has a large remaining position in the company. In addition, I tend to put great weight on insider transaction activity when I see multiple insiders trading a company's shares or units. Furthermore, it can be of value to note if an insider’s share or unit balance (investment in the company) is trending higher or lower.
Listed below are two stocks that have had recent buying activity reported by insiders.
Emera Inc. (EMA-T)
On Nov. 18, Richard Sergel, who sits on the board of directors, invested over $163,000 in shares of the company. He acquired 3,000 shares at a cost per share of $54.37, increasing his account balance to 6,500 shares.
In a news release issued on Sept. 27, the company announced that its board of directors approved a dividend increase, and raised its dividend growth rate target to 5 per cent from 4 per cent. The current annualized dividend yield is 4.45 per cent.
Sierra Wireless Inc. (SW-T)
On Nov. 15, president and chief executive officer Kent Thexton purchased 10,000 shares at a price per share of $10.45, lifting his account’s holdings to 53,278 shares.
Year-to-date, the share price is down 39 per cent.
**
Listed below are two stocks that have had recent selling activity in the public market reported by insiders.
Air Canada (AC-T)
On Nov. 18, chief commercial officer Lucie Guillemette exercised her options, receiving 19,531 shares at an average cost per share of roughly $10.71, and sold 19,531 shares at an average price per share of approximately $49.24, leaving 19,311 shares in her account. Net proceeds, excluding trading fees, totalled over $752,000.
Computer Modelling Group Ltd. (CMG-T)
On Nov. 15, chief technology officer and vice-president of research and development Long Nghiem sold 10,000 shares at a price per share of $8.48, trimming his portfolio’s position to 809,302 shares.


By Bruce Changlong Xu
今天我想分享一下我们公司特别珍重的价值观，就是真诚和稳当。虽然我们的世界和社会日新 月异，我们一定要信守诺言，毕竟一诺千金。自知自明，如人饮水、冷暖自知。我喜欢数学， 物理和科学的原因就是从这些课程学到的概念古今中外不会改变，而学了以后、会改变自己的 世界透视，一生不会忘记。虽然这些科目不容易，这种纯正想了解世界以及提高自己能力的志 向我非常喜欢。做生意时，常常会遇到失败，但只有通过失败才能够学到新知识，想办法去把 自己的制品改进，让顾客更开心。
Today I would like to touch upon a topic that drives the way humans interact, businesses operate, and generational trends arise and fall – namely the issue of constancy and continuity. In Avengers Endgame, Thor (Chris Hemsworth) aptly claims that “The only permanence is impermanence”. In a world that changes in the blink of an eye, our constants are often frighteningly undefined, we change more than we would like to admit, and for every inkling of knowledge that we master there are infinitely many concepts that we do not understand – our competence is a small island embedded within a vast sea of incompetence. Despite this sobering fact, having a sense of continuity and constancy is vital to our survival.
投资管理的公司主要依靠人与人之间的关系去增长收益，因此在这种行业中一定要知 道怎样赢得他人的信任、把握人事管理。在金融业中，信赖就是黄金。如果某一个公 司能够日复一日地帮助客户、这个公司将来就会有更多客户、更多机会去证明自己的 可靠性 --- 是一个正加固的循环。虽然如此，没有任何一个公司或者系统是完美的，所 以我们要保持一种灵活、适应性强的态度。
The way investment management and boutique firms operate is through client interactions, being able to maintain strong relationships with your clients is crucial to keep the deals flowing in and out of the firm. Being reliable and able to deliver on your promises is a musthave in this industry, being unreliable is a recipe for disaster. Caution must be exercised when you say one thing and do another, as this will slowly lead to a decline in trust and the previous deposit of relationship “currency”.
一个可靠的人在哪儿都能够生存的，而是我自己生活中最注重的性格特点。只有通过 尊重自己和其他人的时间才能够稳定地往前走。很多人会因为追求名望、财富忘记自 己的核心价值。快乐就是知道你尽力而为地把自己的事情做好。无论今天挂台风而明 天打雷雨、太阳后天仍然会从黑云中闪耀。大部分的公司和社会流行会消失、但自己 的知识经验是持久的。好人、有本事、可靠的人在哪儿都会快乐、都会取得自己定义 的成功。话说 Jamie Dimon 被 Sandy Weill 遣散的原因就是 年轻的 Dimon 那时候太雄 心，而 Weill 不想让他成为 CEO。但其实 Dimon 这样的的人潜力无限，一到 JPMorgan Chase 之后就立刻又开始表示自己的权限。
Being reliable is a personality trait that anyone should be proud of, and I hope a trait that I am able to demonstrate in my own life. Maintaining that sense of constancy is the only way an individual can develop any semblance of continuity, and the latter gives the former purpose. Thunderstorms happen, and clouds will come and go, but the sun and stars never stop shining in the day and night. Organizations disassemble and movements collapse, but great people will continue to do great things. Prestige means nothing, prestige is simply a byproduct of great professionals carrying out their commitments and should never be the focus.
为东南亚洲带来世界一流的教育机会可不容易，而可能需要一代一代耐心的进展。就 像我在心目中能够看到数学各种不同领域中的链接，我对中国、美国和香港多样人口 和移民，忠诚、自豪以及相信我们能够一起创造一个更好的未来。我们公司里面的人 对香港的感恩和债务实无法衡量，因此真正地想通过多样的方法去改进我们家乡的教 育基础设施。谢谢你们的兴趣知识！
Bringing a world-class higher education, research and teachers to South East Asia is not easy and requires a shift in mentality that could very well take more than a few generations. Just as I visualize and dream about the thriving connections between Ergodic Theory, Number Theory and Combinatorics, I see threads of continuity running through the diverse populations that coexist in societies of Hong Kong, America and China. Never before has the motto “Strength from Diversity” (West Island School) rang so true in my mind. I am optimistic for a bright future for the people of Hong Kong, if only because it is my home and a city that I owe the world to. Presque Partout Sino will be constant, will be continuous, and will be the infinitely differentiable forcing function that will catalyze South East Asia’s growth. Our next step is to release our first two courses PPCS01, PPECON01 that will be our first test towards a minimally viable data-driven educational product. Our team has expertise that ranges from Business to Computer Science to Economics and a diverse global perspective, and we hope to blend this together to create an educational experience that is unmatched by any other company in South East Asia. How hard we work to make this a reality will speak for itself.


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Working with molecular PAH properties
The 'AmesPAHdbIDLSuite_Species'-object exposes the molecular PAH properties.
pahs = pahdb->getSpeciesByUID( $
pahdb->Search("c<=20 neutral n=2 neutral"))
The 'AmesPAHdbIDLSuite_Species'-object's 'Print'-method will print out the associated molecular properties for each PAH species.
pahs->Print
Optionally, the 'Str'-keyword can be given to the 'Print'-method, which will return the molecular PAH properties for each species as an array of strings.
pahs->Print,Str=Str


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| NVelocity |
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About Community Docs Related Sites |
NAnt TasksNAnt is a build tool built specifically for .Net applications and like NVelocity is based on work done by a Java open source proejct. It offers the ability to extend its functionality by the definition of new tasks. The following tasks were Ant tasks ported from Velocity and are now availbe as NAnt tasks: |
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Al Pacino and Jordan Peele to become Nazi hunters in new Amazon series
A teaser trailer for 'Hunters' was also released to coincide with the show's announcement
Al Pacino and Jordan Peele are set to team up next year for a new Amazon series about Nazi hunters.
Titled Hunters, it arrives in 2020, and follows a pack of Nazi hunters from 1970s New York.
A synopsis for the new show reads: “Hunters follows a diverse band of Nazi hunters living in 1977 New York City. The Hunters, as they’re known, have discovered that hundreds of high-ranking Nazi officials are living among us and conspiring to create a Fourth Reich in the U.S.

The series is also set to feature Logan Lerman (The Perks of Being A Wallflower), Josh Radnor (How I Met Your Mother), Kate Mulvany (The Great Gatsby) and more.
Watch a short teaser trailer for ‘Hunters’ below:
Alongside Peele, the show was created by David Weil, who also executive produces and serves as a showrunner alongside executive producer Nikki Toscano.
Meanwhile, Al Pacino appears alongside long-time collaborator Robert De Niro in new Martin Scorsese film The Irishman. In a new interview, though, De Niro says the pair might not make movies together in the future.
“I don’t see us putting on a movie like this,” he said. “I hope we do other films together, but like this? Not likely. This is it.”


Before Change
// get indices which have the value True = not yet calculated
g_ind_calc = self.grid_indices[calc_ind]
r0 = self.r0
rm = self.rm
kj = array(2j*pi*self.freq/self.c)[newaxis]
steerVecFormulation = steerVecTranslation(self.steer)
if self.calcmode == "single":
for ind in g_ind_calc:
ac[:,ind] = calcPointSpreadFunction(steerVecFormulation, (r0, rm, kj, [ind]), self.precision)[0,:,0]
gp[ind] = True
elif self.calcmode == "full":
gp[:] = True
ac[:] = calcPointSpreadFunction(steerVecFormulation, (r0, rm, kj, arange(r0.shape[0])), self.precision)[0,:,:]
else: // "block"
hh = calcPointSpreadFunction(steerVecFormulation, (r0, rm, kj, g_ind_calc), self.precision)
indh = 0
After Change
precisionTuple = _precision(self.precision)
// check wether self.freq is part of SteeringVector.f
freqInSteerObjFreq = isclose(array(self.steer_obj.f), self.freq)
if freqInSteerObjFreq.any():
freqInd = flatnonzero(freqInSteerObjFreq)
else:
warn("PointSpreadFunction.freq was appended to PointSpreadFunction.steer_obj.f, "\
"as it was not an element of the original list! "\
"You should check possible implications on beamformer results, etc.", Warning, stacklevel = 2)
self.steer_obj.f.append(self.freq)
freqInd = int(-1)
// get the cached data, or, if non-existing, create new structure
if not fr in self.h5f.root:
if self.calcmode == "readonly":
raise ValueError("Cannot calculate missing PSF (freq %s) in \"readonly\" mode." % fr)


pyhpeimc.plat.device module¶
This module contains functions for working with the device capabilities of the HPE IMC NMS platform using the RESTful API
-
pyhpeimc.plat.device.
HEADERS
= {'Content-Type': 'application/json', 'Accept': 'application/json', 'Accept-encoding': 'application/json'}¶ This section contains functions which operate at the system level This whole section has been moved to pyhpeimc.plat.system - functions left here for legacy. Intention is to remove by version 1.0.60 or greater. Please modify any scripts using functions in this section to use the new functions in the new module
-
pyhpeimc.plat.device.
get_all_devs
(auth, url, network_address=None)[source]¶ Takes string input of IP address to issue RESTUL call to HP IMC
Parameters: - auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
:param network_address= str IPv4 Network Address
Returns: dictionary of device details Return type: dict >>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> dev_list = get_all_devs( auth.creds, auth.url, network_address= '10.11.')
>>> assert type(dev_list) is list
>>> assert 'sysName' in dev_list[0]
-
pyhpeimc.plat.device.
get_all_interface_details
(auth, url, devid=None, devip=None)[source]¶ function takes the devId of a specific device and the ifindex value assigned to a specific interface and issues a RESTFUL call to get the interface details file as known by the HP IMC Base Platform ICC module for the target device.
Parameters: - auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
- devid – int or str value of the devId of the target device
- devip – ipv4 address of the target device
Returns: list of dict objects which contains the details of all interfaces on the target device
Retype: list
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> all_interface_details = get_all_interface_details( auth.creds, auth.url, devId='10')
>>> all_interface_details = get_all_interface_details( auth.creds, auth.url, devip='<IP_ADDRESS>')
>>> assert type(all_interface_details) is list
>>> assert 'ifAlias' in all_interface_details[0]
-
pyhpeimc.plat.device.
get_dev_details
(ip_address, auth, url)[source]¶ Takes string input of IP address to issue RESTUL call to HP IMC
Parameters: - ip_address – string object of dotted decimal notation of IPv4 address
- auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: dictionary of device details
Return type: dict
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> dev_1 = get_dev_details('<IP_ADDRESS>', auth.creds, auth.url)
>>> assert type(dev_1) is dict
>>> assert 'sysName' in dev_1
>>> dev_2 = get_dev_details('<IP_ADDRESS>', auth.creds, auth.url) Device not found
>>> assert type(dev_2) is str
-
pyhpeimc.plat.device.
get_dev_interface
(auth, url, devid=None, devip=None)[source]¶ Function takes devid as input to RESTFUL call to HP IMC platform and returns list of device interfaces
Parameters: - devid – optional devid as the input
- devip – optional devip as input
- auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: list object which contains a dictionary per interface
Return type: list
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> dev_interfaces = get_dev_interface(auth.creds, auth.url, devid='15')
>>> dev_interfaces = get_dev_interface(auth.creds, auth.url, devip='<IP_ADDRESS>')
>>> assert type(dev_interfaces) is list
>>> assert 'ifAlias' in dev_interfaces[0]
-
pyhpeimc.plat.device.
get_dev_mac_learn
(auth, url, devid=None, devip=None)[source]¶ function takes devid of specific device and issues a RESTFUL call to gather the current IP-MAC learning entries on the target device.
Parameters: - devid – int value of the target device
- devip – ipv4 address of the target device
- auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: list of dict objects which contain the mac learn table of target device id
Return type: list
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> dev_mac_learn = get_dev_mac_learn( auth.creds, auth.url, devid='10')
>>> dev_mac_learn = get_dev_mac_learn( auth.creds, auth.url, devip='<IP_ADDRESS>')
>>> assert type(dev_mac_learn) is list
>>> assert 'deviceId' in dev_mac_learn[0]
-
pyhpeimc.plat.device.
get_dev_run_config
(auth, url, devid=None, devip=None)[source]¶ function takes the devId of a specific device and issues a RESTFUL call to get the most current running config file as known by the HP IMC Base Platform ICC module for the target device.
Parameters: - devid – int or str value of the target device
- devip – str of ipv4 address of the target device
- auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: str which contains the entire content of the target device running configuration. If the device is not
currently supported in the HP IMC Base Platform ICC module, this call returns a string of “This feature is not supported on this device”
Return type: str >>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> run_config = get_dev_run_config(auth.creds, auth.url, devid='10')
>>> run_config = get_dev_run_config(auth.creds, auth.url, devip='<IP_ADDRESS>')
>>> assert type(run_config) is str
-
pyhpeimc.plat.device.
get_dev_start_config
(auth, url, devid=None, devip=None)[source]¶ function takes the devId of a specific device and issues a RESTFUL call to get the most current startup config file as known by the HP IMC Base Platform ICC module for the target device.
Parameters: - auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
- devid – optional int or str value of the target device
- devip – optional ipv4 address of the target device
Returns: str which contains the entire content of the target device startup configuration. If the device is not
currently supported in the HP IMC Base Platform ICC module, this call returns a string of “This feature is not supported on this device”
Retype: str >>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> start_config = get_dev_start_config(auth.creds, auth.url, devId='10')
>>> start_config = get_dev_start_config(auth.creds, auth.url, devip='<IP_ADDRESS>')
>>> assert type(start_config) is str
-
pyhpeimc.plat.device.
get_interface_details
(ifindex, auth, url, devid=None, devip=None)[source]¶ function takes the devId of a specific device and the ifindex value assigned to a specific interface and issues a RESTFUL call to get the interface details file as known by the HP IMC Base Platform ICC module for the target device.
Parameters: - ifindex – int or str value of the ifIndex of the target interface
- auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
- devid – int or str value of the devId of the target device
- devip – str of ipv4 address of the target device
Returns: dict which contains the details of the target interface”
Retype: dict
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> interface_details = get_interface_details('1', auth.creds, auth.url, devId = '10')
>>> interface_details = get_interface_details('1', auth.creds, auth.url, devip = '<IP_ADDRESS>')
>>> assert type(interface_details) is dict
>>> assert 'ifAlias' in interface_details
-
pyhpeimc.plat.device.
get_system_category
(auth, url)[source]¶ Takes string no input to issue RESTUL call to HP IMC
Parameters: - auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: list of dictionaries where each dictionary represents a single device category
Return type: list
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> categories = get_system_category(auth.creds, auth.url)
>>> assert type(categories) is list
>>> assert 'name' in categories[0]
-
pyhpeimc.plat.device.
get_system_device_models
(auth, url)[source]¶ Takes string no input to issue RESTUL call to HP IMC
Parameters: - auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: list of dictionaries where each dictionary represents a single device model
Return type: list
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> device_models = get_system_device_models(auth.creds, auth.url)
>>> assert type(device_models) is list
>>> assert 'virtualDeviceName' in device_models[0]
-
pyhpeimc.plat.device.
get_system_series
(auth, url)[source]¶ Takes string no input to issue RESTUL call to HP IMC
Parameters: - auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: list of dictionaries where each dictionary represents a single device series
Return type: list
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> series = get_system_series(auth.creds, auth.url)
>>> assert type(series) is list
>>> assert 'name' in series[0]
-
pyhpeimc.plat.device.
get_system_vendors
(auth, url)[source]¶ Takes string no input to issue RESTUL call to HP IMC
Parameters: - auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: list of dictionaries where each dictionary represents a single vendor
Return type: list
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> vendors = get_system_vendors(auth.creds, auth.url)
>>> assert type(vendors) is list
>>> assert 'name' in vendors[0]
-
pyhpeimc.plat.device.
run_dev_cmd
(cmd_list, auth, url, devid=None, devip=None)[source]¶ Function takes devid of target device and a sequential list of strings which define the specific commands to be run on the target device and returns a str object containing the output of the commands. :param devid: int devid of the target device
Parameters: - cmd_list – list of strings
- auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
- devip – str of ipv4 address of the target device
Returns: str containing the response of the commands
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> cmd_list = ['display version']
>>> cmd_output = run_dev_cmd( cmd_list, auth.creds, auth.url, devid ='10')
>>> cmd_output = run_dev_cmd( cmd_list, auth.creds, auth.url, devip='<IP_ADDRESS>')
>>> assert type(cmd_output) is dict
>>> assert 'cmdlist' in cmd_output
>>> assert 'success' in cmd_output
-
pyhpeimc.plat.device.
set_inteface_up
(ifindex, auth, url, devid=None, devip=None)[source]¶ function takest devid and ifindex of specific device and interface and issues a RESTFUL call to “undo shut” the spec ified interface on the target device.
Parameters: - devid – int or str value of the target device
- devip – ipv4 address of the target devices
- ifindex – int or str value of the target interface
- auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: HTTP status code 204 with no values.
Rype: int
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> int_down_response = set_interface_down( '9', auth.creds, auth.url, devid = '10') 204
>>> int_up_response = set_inteface_up( '9', auth.creds, auth.url, devid = '10')
>>> int_down_response = set_interface_down( '9', auth.creds, auth.url, devid = '10') 204
>>> int_up_response = set_inteface_up('9', auth.creds, auth.url, devip = '<IP_ADDRESS>')
>>> assert type(int_up_response) is int
>>> assert int_up_response is 204
-
pyhpeimc.plat.device.
set_interface_down
(ifindex, auth, url, devid=None, devip=None)[source]¶ function takest devid and ifindex of specific device and interface and issues a RESTFUL call to ” shut” the specifie d interface on the target device. :param devid: int or str value of the target device
Parameters: - devip – ipv4 address of the target devices
- ifindex – int or str value of the target interface
- auth – requests auth object #usually auth.creds from auth pyhpeimc.auth.class
- url – base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
Returns: HTTP status code 204 with no values.
:rtype:int
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.device import *
>>> auth = IMCAuth("http://", "<IP_ADDRESS>", "8080", "admin", "admin")
>>> int_up_response = set_inteface_up('9', auth.creds, auth.url, devip = '<IP_ADDRESS>')
>>> int_down_response = set_interface_down( '9', auth.creds, auth.url, devid = '10') 204
>>> int_up_response = set_inteface_up('9', auth.creds, auth.url, devip = '<IP_ADDRESS>')
>>> int_down_response = set_interface_down( '9', auth.creds, auth.url, devip = '<IP_ADDRESS>') 204
>>> assert type(int_down_response) is int
>>> assert int_down_response is 204
>>> int_up_response = set_inteface_up('9', auth.creds, auth.url, devip = '<IP_ADDRESS>')


11 hours ago
The delivery vehicle is back to the depot and ready for next delivery.
Options:
If the Threshold is reached, invoke the Rick Mitigation Strategy. This is manually decided by logistics manager on the status page.
Track al lthe shipments using GPS systems and escalte the deliveries that are late but are within threshold level.
Pick the sorted and labeled goods up from the Warehouse and start for the destination. Calculate the delivery time estimate and track the location of the truck via GPS.
The Goods are sorted and vehicles are allocated as per availablilty and space complexity.
Current date is the delivery date and the goods are at the warehouse.


Each song is by a singer/artist who shares a name with a song. For example, “Woman in Love” is by Barbra Streisand, and “Barbra Streisand” is by Duck Sauce. Buddy Holly, Barbra Streisand, and maybe Alex Chilton may be break-ins to this step.
The second song's singer/artist fits into one of the lines of blanks.
CHARLIE [P]UTH B[A]RENAKED LADIES GOR[I]LLAZ DUCK [S]AUCE THE REP[L]ACEMENTS W[E]EZER TA[Y]LOR SWIFT
Read the boxed blanks to get the answer, PAISLEY.


Cookies
Peanutbutter / Chocolate / Oasts / Honey / Butter / Milk
(Please do fill in the date and time you sent the request).
This date may change as the site can be completed before or after the estimated deadline.
Every tradition has it's own methods,
cooking/baking/smoking/frying and braai are all the same methods but the techniques are different and unique.
Sweats,Juices,Baked and Dried.
There are so many ways to enjoy Deserts of all kinds.
I may not be able to respond within seconds but i will respond to all texts.
(Please note that i will be currently running all communication via email and cell networks)


About the author
- Bruno P. Kinoshita, Brazilian, Software Quality Engineer.
- Developed some software (corporate and oss).
- Wrote some articles.
- Got some certifications.
- Currently writing his Master thesis.
- http://kinoshita.eti.br
Introducing the tools
- TestLink – Open Source Test Management tool (PHP) – http://www.teamst.org
- Hudson – Open Source (?) Continuous Integration Server (Java) – http://www.hudson-ci.org
- Jenkins – Open Source Continuous Integrationg Server (Java) – http://www.jenkins-ci.org
- Enable xml-rcp external API in TestLink
- Generate a devkey
- Install the plug-in
- Configure the plug-in
- Create and configure a job
- Have fun!
Pros
- Easy integration with CI
- No need to train users on a new Test Management tool
- Keep using your existing automated tests
Cons
- Works only with TestLink and Hudson
- Better results if you are using TAP
- Requires some technical knowledge
The objectives of this presentation
- How to manage your automated tests with TestLink and Hudson?
- What is TestLink Hudson Plug-in?
- What is TAP?
- What is the future of the plug-in?
- What is "legal" (in pt-BR)?


A function returning a value in a dependent type
Define a function with a dependent type that returns
true
for natural numbers of the form
4n+1
,
false
for numbers of the form
4n+3
and
n
for numbers of the form
2n
.
Solution
This file


| # | Title | Added On | Due Date | Attempted On | Time (min) | Obtained Marks | Total Marks | Status | Controls |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Project Charter | 2012/01/01 | 2012/01/01 | 2012/01/01 | 15 | 8 | 10 | Attemped |
|
| 1 | Project Charter | 2012/01/01 | 2012/01/01 | 2012/01/01 | 15 | 8 | 10 | Pending |
|
| # | Title | Made On | Added On | Due Date | Time (min) | Average Marks | Total Marks | Status | Attemped By | Controls |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Project Charter | 2012/01/01 | 2012/01/01 | 2012/01/01 | 15 | 8.5 | 10 | Not Uploaded | N/A |
|
| 1 | Project Charter | 2012/01/01 | 2012/01/01 | 2012/01/01 | 15 | 8.5 | 10 | Uploaded | 10/50 |
|


torchfunc.module¶
This module provides functionalities related to torch.nn.Module instances (e.g. freezing parameters).
For performance analysis of torch.nn.Module
please see subpackage performance
.
-
class
torchfunc.module.
Snapshot
(*modules: torch.nn.modules.module.Module)[source]¶ Save module snapshots in memory and/or disk.
Next modules can be added with
+
or+=
and their state or whole model saved to disk with appropriate methods.All added modules are saved unless removed with
pop()
method.Additionally, self-explainable methods like
len
,__iter__
or item access are provided (although there is no__setitem__
as it’s discouraged to mutate contained modules).Example:
snapshot = torchfunc.module.Snapshot() snapshot += torch.nn.Sequential(torch.nn.Linear(784, 10), torch.nn.Sigmoid()) snapshot.save("models") # Save all modules to models folder
- Parameters
*modules (torch.nn.Module) – Var args of PyTorch modules to be kept.
-
pop
(index: int = -1)[source]¶ Remove module at
index
from memory.- Parameters
index (int, optional) – Index of module to be removed. Default: -1 (last module)
- Returns
module – Module removed by this operation
- Return type
torch.nn.Module
-
save
(folder: pathlib.Path = None, remove: bool = False, *indices: int) → None[source]¶ Save module to disk.
Snapshot(s) will be saved using the following naming convention:
module_{index}_{timestamp}.pt
See PyTorch’s docs for more information.
- Parameters
folder (pathlib.Path, optional) – Name of the folder where model will be saved. It has to exist. Defaults to current working directory.
remove (bool, optional) – Whether module should be removed from memory after saving. Useful for keeping only best/last model in memory. Default:
False
*indices (int, optional) – Possibly empty varargs containing indices of modules to be saved. Negative indexing is supported. If empty, save all models.
-
save_state
(folder: pathlib.Path = None, remove: bool = False, *indices: int) → None[source]¶ Save module’s state to disk.
Snapshot(s) will be saved with using the following naming convention:
module_{index}_{timestamp}.pt
See PyTorch’s docs about state_dict for more information.
- Parameters
folder (pathlib.Path, optional) – Name of the folder where model will be saved. It has to exist. Defaults to current working directory.
remove (bool, optional) – Whether module should be removed from memory after saving. Useful for keeping only best/last model in memory. Default: False
*indices (int, optional) – Possibly empty varargs containing indices of modules to be saved. Negative indexing is supported. If empty, save all models.
-
torchfunc.module.
freeze
(module: torch.nn.modules.module.Module, weight: bool = True, bias: bool = True) → torch.nn.modules.module.Module[source]¶ Freeze module’s parameters.
Sets
requires_grad
toFalse
for specified parameters in module. If bias and weight are specified, ALL parameters will be frozen (even if their names are not matched byweight
and bias).If you want to freeze only those whose names contain
bias
orweight
, call the function twice consecutively (once withbias=True
andweight=False
and vice versa).Example:
logistic_regression = torch.nn.Sequential( torch.nn.Linear(784, 10), torch.nn.Sigmoid(), ) # Freeze only bias in logistic regression torchfunc.freeze(logistic_regression, weight = False)
- Parameters
module (torch.nn.Module) – Module whose weights and biases will be frozen.
weight (bool, optional) – Freeze weights. Default: True
bias (bool, optional) – Freeze bias. Default: True
- Returns
module – Module after parameters were frozen
- Return type
torch.nn.Module
-
torchfunc.module.
unfreeze
(module: torch.nn.modules.module.Module, weight: bool = True, bias: bool = True) → torch.nn.modules.module.Module[source]¶ Unfreeze module’s parameters.
Sets
requires_grad
toTrue
for all parameters in module. Works as complementary function to freeze, see it’s documentation.- Parameters
module (torch.nn.Module) – Module whose weights and biases will be unfrozen.
weight (bool, optional) – Freeze weights. Default: True
bias (bool, optional) – Freeze bias. Default: True
- Returns
module – Module after parameters were unfrozen
- Return type
torch.nn.Module


Tiffany Aching
Tiffany Aching is a fictional character in Terry Pratchett's satirical Discworld series of fantasy novels. Her name in Nac Mac Feegle is Tir-far-thóinn or "Land Under Wave".
Tiffany is a trainee witch whose growth into her job forms one of the many arcs in the Discworld series. She is the main character in The Wee Free Men, A Hat Full of Sky, Wintersmith, I Shall Wear Midnight and The Shepherd's Crown. Tiffany grows up over the course of the series, from nine years old in The Wee Free Men to being in her late teens in The Shepherd's Crown.


The DIMENSIONS record contains the minimum and maximum bounds of the sheet. It provides a concise indication of the sheet size.
Note : both the rwMac and colMac fields are greater by 1 than the actual last row and column. For example, a worksheet that exists between cells B3 and D6 would have the following dimensions in the dimensions record (rows and columns are 0-based in BIFF files in which row 1 and column A are both coded as 0):
|
rwMic=2 |
indicates that 3 is the first row |
|
colMic=1 |
indicates that B is the first column |
|
rwMac=6 |
indicates that 6 is the last row |
|
colMac=4 |
indicates that D is the last column |
Record Data — BIFF8
|
Offset |
Field Name |
Size |
Contents |
|
4 |
rwMic |
4 |
First defined row on the sheet |
|
8 |
rwMac |
4 |
Last defined row on the sheet, plus 1 |
|
12 |
colMic |
2 |
First defined column on the sheet |
|
14 |
colMac |
2 |
Last defined column on the sheet, plus 1 |
|
16 |
(Reserved) |
2 |
Reserved; must be 0 (zero) |
Record Data — BIFF7 and earlier
|
Offset |
Field Name |
Size |
Contents |
|
4 |
rwMic |
2 |
First defined row on the sheet |
|
6 |
rwMac |
2 |
Last defined row on the sheet, plus 1 |
|
8 |
colMic |
2 |
First defined column on the sheet |
|
10 |
colMac |
2 |
Last defined column on the sheet, plus 1 |
|
12 |
(Reserved) |
2 |
Reserved; must be 0 (zero) |


--- layout: page title: About header-img: "img/about-bg.png" ---
Information Technology professional and coder with advice for newer software developers.