Layer 7 routing
Estimated reading time: 7 minutesThese are the docs for UCP version 3.0.16
To select a different version, use the selector below.
When you deploy a Kubernetes application, you may want to make it accessible to users using hostnames instead of IP addresses.
Kubernetes provides ingress controllers for this. This functionality is specific to Kubernetes. If you’re trying to route traffic to Swarm-based applications, check layer 7 routing with Swarm.
Use an ingress controller when you want to:
- Give your Kubernetes app an externally-reachable URL.
- Load-balance traffic to your app.
Kubernetes provides an NGINX ingress controller that you can use in Docker EE without modifications. Learn about ingress in Kubernetes.
Create a dedicated namespace
- Navigate to the Namespaces page, and click Create.
- In the Object YAML editor, append the following text.
metadata: name: ingress-nginx
The finished YAML should look like this.
apiVersion: v1 kind: Namespace metadata: name: ingress-nginx
- Click Create.
-
In the ingress-nginx namespace, click the More options icon, and in the context menu, select Set Context.
Create a grant
The default service account that’s associated with the ingress-nginx
namespace needs access to Kubernetes resources, so create a grant with
Restricted Control
permissions.
- From UCP, navigate to the Grants page, and click Create Grant.
- Within the Subject pane, select Service Account. For the Namespace select ingress-nginx, and select default for the Service Account. Click Next.
- Within the Role pane, select Restricted Control, and then click Next.
- Within the Resource Set pane, select the Type Namespace, and select the Apply grant to all existing and new namespaces toggle.
- Click Create.
Ingress and role-based access control
Docker EE has an access control system that differs from Kubernetes RBAC. If your ingress controller has access control requirements, you need to create corresponding UCP grants. Learn to migrate Kubernetes roles to Docker EE authorization.
Deploy NGINX ingress controller
The cluster is ready for the ingress controller deployment, which has three main components:
- a simple HTTP server, named
default-http-backend
, - an ingress controller, named
nginx-ingress-controller
, and - a service that exposes the app, named
ingress-nginx
.
Navigate to the Create Kubernetes Object page, and in the Object YAML editor, paste the following YAML.
apiVersion: apps/v1beta2
kind: Deployment
metadata:
name: default-http-backend
labels:
app: default-http-backend
namespace: ingress-nginx
spec:
replicas: 1
selector:
matchLabels:
app: default-http-backend
template:
metadata:
labels:
app: default-http-backend
annotations:
seccomp.security.alpha.kubernetes.io/pod: docker/default
spec:
terminationGracePeriodSeconds: 60
containers:
- name: default-http-backend
# Any image is permissable as long as:
# 1. It serves a 404 page at /
# 2. It serves 200 on a /healthz endpoint
image: gcr.io/google_containers/defaultbackend:1.4
livenessProbe:
httpGet:
path: /healthz
port: 8080
scheme: HTTP
initialDelaySeconds: 30
timeoutSeconds: 5
ports:
- containerPort: 8080
resources:
limits:
cpu: 10m
memory: 20Mi
requests:
cpu: 10m
memory: 20Mi
---
apiVersion: v1
kind: Service
metadata:
name: default-http-backend
namespace: ingress-nginx
labels:
app: default-http-backend
spec:
ports:
- port: 80
targetPort: 8080
selector:
app: default-http-backend
---
kind: ConfigMap
apiVersion: v1
metadata:
name: nginx-configuration
namespace: ingress-nginx
labels:
app: ingress-nginx
---
kind: ConfigMap
apiVersion: v1
metadata:
name: tcp-services
namespace: ingress-nginx
---
kind: ConfigMap
apiVersion: v1
metadata:
name: udp-services
namespace: ingress-nginx
---
apiVersion: apps/v1beta2
kind: Deployment
metadata:
name: nginx-ingress-controller
namespace: ingress-nginx
spec:
replicas: 1
selector:
matchLabels:
app: ingress-nginx
template:
metadata:
labels:
app: ingress-nginx
annotations:
prometheus.io/port: '10254'
prometheus.io/scrape: 'true'
seccomp.security.alpha.kubernetes.io/pod: docker/default
spec:
initContainers:
- command:
- sh
- -c
- sysctl -w net.core.somaxconn=32768; sysctl -w net.ipv4.ip_local_port_range="1024 65535"
image: alpine:3.6
imagePullPolicy: IfNotPresent
name: sysctl
securityContext:
privileged: true
containers:
- name: nginx-ingress-controller
image: quay.io/kubernetes-ingress-controller/nginx-ingress-controller:0.17.1
args:
- /nginx-ingress-controller
- --default-backend-service=$(POD_NAMESPACE)/default-http-backend
- --configmap=$(POD_NAMESPACE)/nginx-configuration
- --tcp-services-configmap=$(POD_NAMESPACE)/tcp-services
- --udp-services-configmap=$(POD_NAMESPACE)/udp-services
- --annotations-prefix=nginx.ingress.kubernetes.io
env:
- name: POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: POD_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
ports:
- name: http
containerPort: 80
- name: https
containerPort: 443
livenessProbe:
failureThreshold: 3
httpGet:
path: /healthz
port: 10254
scheme: HTTP
initialDelaySeconds: 10
periodSeconds: 10
successThreshold: 1
timeoutSeconds: 1
readinessProbe:
failureThreshold: 3
httpGet:
path: /healthz
port: 10254
scheme: HTTP
periodSeconds: 10
successThreshold: 1
timeoutSeconds: 1
---
apiVersion: v1
kind: Service
metadata:
name: ingress-nginx
namespace: ingress-nginx
spec:
type: NodePort
ports:
- name: http
port: 80
targetPort: 80
protocol: TCP
- name: https
port: 443
targetPort: 443
protocol: TCP
selector:
app: ingress-nginx
Check your deployment
The default-http-backend
provides a simple service that serves a 404 page
at /
and serves 200 on the /healthz
endpoint.
-
Navigate to the Controllers page and confirm that the default-http-backend and nginx-ingress-controller objects are scheduled.
Scheduling latency
It may take several seconds for the HTTP backend and the ingress controller’s
Deployment
andReplicaSet
objects to be scheduled. -
When the workload is running, navigate to the Load Balancers page and click the ingress-nginx service.
-
In the details pane, click the first URL in the Ports section.
A new page opens, displaying
default backend - 404
.
Check your deployment from the CLI
From the command line, confirm that the deployment is running by using
curl
with the URL that’s shown on the details pane of the ingress-nginx
service.
curl -I http://<ucp-ip>:<ingress port>/
This command returns the following result.
HTTP/1.1 404 Not Found
Server: nginx/1.13.8
Test the server’s health ping service by appending /healthz
to the URL.
curl -I http://<ucp-ip>:<ingress port>/healthz
This command returns the following result.
HTTP/1.1 200 OK
Server: nginx/1.13.8


A presentation library with intuitive, scroll-based navigation.
Built with d3.js.
Sneak a peek at the next slide by scrolling down and back.
Scroll many slides at once with a flick of the wrist.
Or, grab the scrollbar on the slide and jump around.
If you stop between slides, the stack will drift to the closest slide.
If you scroll up, the previous slide is immediately visible.
If you scroll down, the next slide is immediately visible.
(This would be more robust if WebKit supported touch events!)
up / down / left / right
page up / page down
home / end
space / shift-space
Press up or down multiple times, and it does the right thing!
Zoom-out overview?
Anchor fragments.
Auto-loading iframes for resource-expensive slides.
iOS & non-WebKit browser testing.


What's their goal? To meet the god of the shrine!
Join the spirit as they answer people's prayers and scare off other spirits to rebuild the shrine!
(use mouse to drag and tap on objects and spirits, and arrow keys to move the spirit!)
The source code can be found here."
tags: Mac Singleplayer
# tag options: "Windows", "Mac", "Linux",
# "Singleplayer", "Multiplayer",
# "Controller-Compatible", "VR",
# "Game-Jam"
project_members: {
Flora Cheng: "Team Lead, Programming, Art",
Emily Guo: "Team Lead, Programming",
Allison Wivagg: "Programming",
Grayson Ticer: Art,
Nandini Kuppa-Apte: Art
}
stage_game_path: shrine_spirit/
builds: {
Mac: ShrineSpiritBuild_macOS_fixed.zip
}
thumbnail: banner.jpeg
top-img: banner.jpeg
screenshots: [
1.png
]
# Optional
awards: [
Exec's Choice
]
top-img-fit: cover
# top-img-fit options:
# "cover" - Cover the entire top area with the image, without squashing/stretching
# "fill" - Fit the FULL image into the top area, squashing/stretching as necessary
---


Jedi: Fallen Order PC performance is decent so far, but limited
by Tyler WildeAfter tinkering with the graphics options a bit, I'm able to play Jedi: Fallen Order at 60 fps or higher at 2560x1080 on medium settings. This is with a GTX 980 and a Core i7-6700K at 4GHz, and I'd say it looks and runs about as I expected. Even on medium, it's a slick-looking game, and my five-year-old graphics card is holding up well enough.
Occasionally there'll be stuttering when entering and leaving the menus, but that's the only issue I've encountered after lowering the settings. With the settings higher, stuttering is far more frequent. I can counter it by turning on resolution scaling, but who wants to do that? It looks better with lower settings and resolution scaling turned off.
Tom Senior is having a similar experience with slightly lower specs: a GTX 970 and Core i5-4670k. He's using the high settings preset. "FPS is really high and stable ordinarily, but with major stutters, particularly when loading into new areas," he said. "I've also had big half second pauses during fights that have killed me."
If you're running a 900-series Nvidia card, I'd recommend the medium preset.
Jarred has been doing his usual suite of testing, with a variety of GPUs all running on an overclocked i7-8700K. Here's how things look right now, at 1080p medium (the minimum quality setting), 1080p epic (the maximum quality), plus 1440p and 4K at epic. If you want to run the same test sequence as Jarred, here's a video of the 60 second run on Dathomir (clear out the enemies first).
Image 1 of 4
Image 2 of 4
Image 3 of 4
Image 4 of 4
At medium quality and 1080p, multiple Nvidia GPUs are sometimes bumping into the 144 fps framerate cap. There's still some stuttering, which is why all of the fastest GPUs hit around 75-85 fps on the 97 percentile minimums, but a fast CPU does help. The 2080 Ti doesn't lose much performance stepping up to the maximum quality option, and even a 1060 6GB still averages 65 fps, but tweaking a few settings is probably a good idea.
1440p and 4K at epic quality will require a bit more muscle than midrange GPUs can provide. The 2060 Super still does 60+ fps at 1440p, but only the 2080 Ti is likely to clear 60 fps at 4K, and even then minimums are going to be lower. Also note that going from epic to medium quality only improves performance by around 50 percent if you're GPU limited.
That's because the graphics options are fairly limited. You can alter settings like anti-aliasing and texture quality separately, but you only get three options: medium, high, and epic. Yes, 'medium' is the lowest possible setting so that us mid-range folks don't feel bad. Though as I said, it's still impressive looking at that setting.
We'd like to see the stuttering fully resolved, and for high-end PCs to be able to go above 144 fps if they're able.
Oddly, Jarred had some initial testing results where the 2080 Ti couldn't even break 90 fps, but a driver reinstall and reboot seems to have fixed that. A Steam forum poster noticed the same issue Jarred was having, that their 2080 Ti is under performing even when they change the settings, but otherwise I haven't seen widespread reports of serious problems.
Obviously we've only tested a few setups, and no AMD cards. Jarred will be putting together a much more comprehensive performance analysis in the coming days, but that'll take a bit of time for benchmarking. Tom will have a review of Fallen Order next week.


Duotone JS
Creates a dual-tone image from any other image
NOTE: Only works in good browsers (currently just Firefox)
Usage:
Specify the colours you wish to use by adding a data-highlight and data-lowlight attribute to your images. Include the Duotone JS script somewhere in the page and apply the effect with "new Duotone(Selector);", where "Selector" matches any other image. Your images must have width and height attributes.
<img class="duo" data-highlight="#0CF" data-lowlight="#013" width="927" height="1246" src="example/dave-ruck-1075684-unsplash.jpg" alt="example" />
<script src="duotone/duotone.0.2.js"></script>
<script>
(function(){
new Duotone('.duo');
})();
</script>
Examples
Photo by Dave Ruck on Unsplash
Photo by Chris Murray on Unsplash
Photo by Ferenc Horvath on Unsplash
Gradients
V0.2 introduces the ability to use gradients instead of just flat colours. To use, specify the angle and each colour in the gradient in the data-lowlight or data-highlight attributes (e.g. data-highlight="90,#91b6f5,#cff58b")
Photo by Karsten Wurth on Unsplash


Restore old photos
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Scratched photo
Force CPU
Reformat
Go
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Close


|
Bullet Collision Detection & Physics Library
|
#include <btPointCollector.h>
Public Member Functions | |
| btPointCollector () | |
| virtual void | setShapeIdentifiersA (int partId0, int index0) |
| setShapeIdentifiersA/B provides experimental support for per-triangle material / custom material combiner More... | |
| virtual void | setShapeIdentifiersB (int partId1, int index1) |
| virtual void | addContactPoint (const btVector3 &normalOnBInWorld, const btVector3 &pointInWorld, btScalar depth) |
| Public Member Functions inherited from btDiscreteCollisionDetectorInterface::Result | |
| virtual | ~Result () |
Public Attributes | |
| btVector3 | m_normalOnBInWorld |
| btVector3 | m_pointInWorld |
| btScalar | m_distance |
| bool | m_hasResult |
Definition at line 21 of file btPointCollector.h.
|
inline |
Definition at line 29 of file btPointCollector.h.
|
inlinevirtual |
Implements btDiscreteCollisionDetectorInterface::Result.
Definition at line 45 of file btPointCollector.h.
|
inlinevirtual |
setShapeIdentifiersA/B provides experimental support for per-triangle material / custom material combiner
Implements btDiscreteCollisionDetectorInterface::Result.
Definition at line 34 of file btPointCollector.h.
|
inlinevirtual |
Implements btDiscreteCollisionDetectorInterface::Result.
Definition at line 39 of file btPointCollector.h.
| btScalar btPointCollector::m_distance |
Definition at line 25 of file btPointCollector.h.
| bool btPointCollector::m_hasResult |
Definition at line 27 of file btPointCollector.h.
| btVector3 btPointCollector::m_normalOnBInWorld |
Definition at line 23 of file btPointCollector.h.
| btVector3 btPointCollector::m_pointInWorld |
Definition at line 24 of file btPointCollector.h.


We provide excellent baked goods for that beauifult smile of yours.
| Product | Quantity | Subtotal(Rs.) |
|---|---|---|
|
Birthday1 cake price: rs. 500.00 |
||
|
Birthday cake price: rs. 600.00 |
||
|
Birthday cake price: rs. 100.00 |
||
|
Birthday cake price: rs. 200.00> |
||
|
Birthday cake price: rs. 200.00 |
||
|
Birthday cake price: rs. 200.00 |
||
|
Birthday cake price: rs. 300.00 |
||
|
Birthday cake price: rs. 200.00 |
| Sub-Total(Rs.) | |
| Delivery charge(Rs.) | |
| Total(Rs.) |
(a short paragraph describing the bakery and the service it provides.)
(explanation)
address:(bakery location)
contact number:
e-mail:


This super simple server provides a nice little look into how GCM notifications work, and how they can be sent with node-gcm. It lets you send notifications super simply, which you could probably also use for testing purposes or whatever.
The following values are all, with the exception of recipient(s), to
, turned into a JSON object, data
.
To write the equivalent in code, using node-gcm, do this:
var gcm = require("node-gcm");
var msg = new gcm.Message(data);
var sender = new gcm.Sender("api key");
sender.send(msg, to, callback);
| key | value | type |
|---|---|---|
| string (required) | ||
| comma separated string | ||
| string | ||
| number | ||
| boolean | ||
| boolean | ||
| number | ||
| string | ||
| boolean | ||
JSON (will be run through JSON.parse ) |
||
JSON (will be run through JSON.parse ) |
||
|


Not a formal entry port unless arranged beforehand and you pay the extra expenses for the Customs & Immigration officials to come from Suva. This is done sometimes by rallies since the extra expense is then shared by many yachts. If coming from another Fijian port, one needs only to have a cruising permit. Often this permit has to be showed to the village chiefs when doing sevusevu.


You have installed a new version which requires an update of your database. Please back up your database just in case and once you have done this proceed with the installation.


How long is I'm A Celebrity on for? New series of award winning show kicks off on ITV
A new mix of famous faces are bedding down in camp for the new series of I'm A Celebrity Get Me Out of Here
by Simon DukeThere will be no more shouts of “let’s get ready to juuuungle” as the new series of I'm A Celebrity is now underway.
The ITV ratings winner, won by North East lasses Vicky Pattison and Scarlett Moffatt in 2015 and 2016 respectively returns to our screens on Sunday night for its 19th series - we’re all of a sudden feeling very old!
As ever, this year’s I'm A Celebrity lineup unites a very eclectic mix of famous faces with everyone from Ian Wright to Caitlyn Jenner.
Other celebs in the mix for 2018 include Nadine Coyle, Kate Garraway and Adele Roberts.
As we’ve come to realise, the return of I’m A Celebrity means that Christmas is just around the corner but how long will the new series last?
How long is I’m A Celebrity on for?
While the exact end date is still being kept under wraps, I’m A Celebrity traditionally runs for three weeks, meaning we should have our new King or Queen of the Jungle by December 7.
The series launches with a bumper episode on Sunday, November 17 and is set to run for either an hour or 90 minutes every night.
When do people start getting voted out?
Typically, the celeb campers have around two weeks to settle in before being given the boot, so expect the first elimination to come towards the end of next week.


Enable "Try it!" mode
Allow "Try it!" for anonymous users
Base URL used to try the API
Show the URL to download the content
Display the operationId in the operations list
Expands only the tagsExpands the tags and operationsExpands nothing
Add a top bar to filter content
Display vendor extension (X-) fields and values for Operations, Parameters, and Schema
Display extensions (pattern, maxLength, minLength, maximum, minimum) fields and values for Parameters
Limits the number of tagged operations displayed to at most this many (-1 means show all operations).


|
OpenMesh
|
This class provides the composite subdivision rules for the uniform case. More...
#include <OpenMesh/Tools/Subdivider/Uniform/Composite/CompositeT.hh>
Classes | |
| struct | Coeff |
| Abstract base class for coefficient functions. More... | |
Public Types | |
| typedef RealType | real_t |
| typedef MeshType | mesh_t |
| typedef SubdividerT< mesh_t, real_t > | parent_t |
| Public Types inherited from OpenMesh::Subdivider::Uniform::SubdividerT< MeshType, RealType > | |
| typedef MeshType | mesh_t |
| typedef RealType | real_t |
Public Member Functions | |
| CompositeT (MeshType &_mesh) | |
| virtual const char * | name (void) const override=0 |
| Return name of subdivision algorithm. | |
| Public Member Functions inherited from OpenMesh::Subdivider::Uniform::SubdividerT< MeshType, RealType > | |
| virtual | ~SubdividerT () |
| Destructor (calls detach()) | |
| SubdividerT (void) | |
| Constructor to be used with interface 2. More... | |
| SubdividerT (MeshType &_m) | |
| Constructor to be used with interface 1 (calls attach()) More... | |
| bool | operator() (MeshType &_m, size_t _n, const bool _update_points=true) |
Subdivide the mesh _m _n times. More... | |
| bool | attach (MeshType &_m) |
Attach mesh _m to self. More... | |
| bool | operator() (size_t _n, const bool _update_points=true) |
Subdivide the attached _n times. More... | |
| void | detach (void) |
| Detach an eventually attached mesh. More... | |
Protected Member Functions | |
| bool | prepare (MeshType &_m) override |
| Prepare mesh, e.g. add properties. | |
| bool | subdivide (MeshType &_m, size_t _n, const bool _update_points=true) override |
Subdivide mesh _m _n times. | |
| bool | cleanup (MeshType &_m) override |
| Cleanup mesh after usage, e.g. remove added properties. | |
| virtual void | apply_rules (void)=0 |
| Assemble here the rule sequence, by calling the constructor of the wanted rules. More... | |
| void | commit (MeshType &_m) |
| Move vertices to new positions after the rules have been applied to the mesh (called by subdivide()). More... | |
| void | corner_cutting (HalfedgeHandle _heh) |
| Corner Cutting. | |
| VertexHandle | split_edge (HalfedgeHandle _heh) |
| Split Edge. | |
Uniform composite subdivision rules | |
| void | Tvv3 () |
| Split Face, using Vertex information (1-3 split) | |
| void | Tvv4 () |
| Split Face, using Vertex information (1-4 split) | |
| void | Tfv () |
| Split Face, using Face Information. | |
| void | FF () |
| Face to face averaging. | |
| void | FFc (Coeff &_coeff) |
| Weighted face to face averaging. | |
| void | FFc (scalar_t _c) |
| Weighted face to face averaging. | |
| void | FV () |
| Face to vertex averaging. | |
| void | FVc (Coeff &_coeff) |
| Weighted face to vertex Averaging with flaps. | |
| void | FVc (scalar_t _c) |
| Weighted face to vertex Averaging with flaps. | |
| void | FE () |
| Face to edge averaging. | |
| void | VF () |
| Vertex to Face Averaging. | |
| void | VFa (Coeff &_coeff) |
| Vertex to Face Averaging, weighted. | |
| void | VFa (scalar_t _alpha) |
| Vertex to Face Averaging, weighted. | |
| void | VV () |
| Vertex to vertex averaging. | |
| void | VVc (Coeff &_coeff) |
| Vertex to vertex averaging, weighted. | |
| void | VVc (scalar_t _c) |
| Vertex to vertex averaging, weighted. | |
| void | VE () |
| VE Step (Vertex to Edge Averaging) | |
| void | VdE () |
| Vertex to edge averaging, using diamond of edges. | |
| void | VdEc (scalar_t _c) |
| Weighted vertex to edge averaging, using diamond of edges. | |
| void | VdEg (Coeff &_coeff) |
| Weigthed vertex to edge averaging, using diamond of edges for irregular vertices. More... | |
| void | VdEg (scalar_t _gamma) |
| Weigthed vertex to edge averaging, using diamond of edges for irregular vertices. More... | |
| void | EF () |
| Edge to face averaging. | |
| void | EV () |
| Edge to vertex averaging. | |
| void | EVc (Coeff &_coeff) |
| Weighted edge to vertex averaging. | |
| void | EVc (scalar_t _c) |
| Weighted edge to vertex averaging. | |
| void | EdE () |
| Edge to edge averaging w/ flap rule. | |
| void | EdEc (scalar_t _c) |
| Weighted edge to edge averaging w/ flap rule. | |
Overload theses methods |
This class provides the composite subdivision rules for the uniform case.
To create a subdivider derive from this class and overload the functions name() and apply_rules(). In the latter one call the wanted rules.
For details on the composite scheme refer to
|
protectedpure virtual |
Assemble here the rule sequence, by calling the constructor of the wanted rules.
Implemented in OpenMesh::Subdivider::Uniform::CompositeLoopT< MeshType, RealType >, and OpenMesh::Subdivider::Uniform::CompositeSqrt3T< MeshType, RealType >.
|
inlineprotected |
Move vertices to new positions after the rules have been applied to the mesh (called by subdivide()).
|
protected |
Weigthed vertex to edge averaging, using diamond of edges for irregular vertices.
|
protected |
Weigthed vertex to edge averaging, using diamond of edges for irregular vertices.


Background
What is your main focus/concept/direction?
In this
What motivates and inspires you to carry out this research?
What are your ambitions - what do you want the project to do? Why?
Research
What are your influences & inspirations? (Specifically, list a few projects, papers, theories)
What are the main issues and debates in this subject area?
Which are the three most important papers that relate to your topic?
What is the relevance of your work to other researchers?
What is the relevance (if any) of your work to practitioners?
Critique & Reflection
Summarise the key findings from your literature research so far.
Which of these findings are the most interesting to you? Why?
Which do you most disagree with? Why?
Which are the best three examples you have found (that you didn't know before) of work related to what you want to do?

Methodology
How have you approached this project?
Do you have any design/research principles, guidelines, rules, etc. in place?
What considerations (targets, practical aspects, ethics, how would people engage with your project etc.) do you need to take into account?
What constraints are there on you/the project (if any)?
How will you evaluate your work?
Implementation
What is your plan for implementing your project (design and/or research)?
What alternatives have you considered (if any)? What are their strengths/weaknesses?
Working from your methodology, (how) might things change? Why?
What needs to be done next?


Company designs 'creepy' robots that can be made into clones of anyone in the world
Manufacturer Promobot claims it can make androids that look like anyone - and one order has already come in from a customer who wants robot versions of their parents
by Dave BurkeMounted on a tabletop in Russia, an eerily human-looking robot smiles, squints and even frowns when he gets angry.
The bionic man, named Android Robo-C, is the first produced by a manufacturer which boasts it can make robot clones to look like anyone in the world.
He may have to practice his frown though, after the venture was branded "creepy" and questions were raised about the legality.
This week the robot was unveiled by Russian startup company Promobot - which claims one customer has already ordered replicas of their parents to greet guests at their home.
Critics say the project opens the door for clones to be created of anyone without their knowledge.
Promobot chiefs say they can make realistic likenesses of well known figures that can be used to hawk goods.
It said the service robots could replace staff in public-facing roles in places such as post offices, banks and council buildings.
Aleksei Iuzhakov, chairman of the company's board of directors, said in a statement: "Everyone will now be able to order a robot with any appearance — for professional or personal use.
"Thus, we open a huge market in service, education and entertainment. Imagine a replica of Michael Jordan selling basketball uniforms and William Shakespeare reading his own texts in a museum?"
And a person's mannerisms and turn of phrase will also be replicated by the bots.
Mr Iuzhakov continued: "We can build a linguistic model based on popular phrases of a particular person – the robot will communicate and answer questions by analyzing frequent expressions of the 'original' and using a certain context of knowledge of this person."
Company founder Oleg Kivokurtsev told CNBC that one family have ordered replicas of their mum and dad to greet guests at their home.
He added: "Now we have more than 10 orders from companies and private clients from around the world.”
But unsurprisingly it has made plenty of people uneasy, with many branding the idea "creepy".
And it could also present legal problems if the person the android is based on finds out.
In a blog post on Hot Air , Jazz Shaw wrote: "It seems to me that if I ordered a robot that looks and sounds exactly like Chuck Schumer, for example, and started posting embarrassing videos of its performances online, he might be able to sue me.
"Of course, we’re talking about a Russian company here, so I’m not sure how much they care about such laws.
"But the customer still might wind up getting in trouble."
The manufacturers say they envisage the androids being used as companion robots, or performing customer service roles.
But it will need to be recharged every eight hours.


THE BEST FOOD BLOG ON THE WEB
PRINT
POSTED ON 15 DEC 2013 / DESSERTS
For the fig-swirl: Melt butter over medium heat in a saucepan. Add brown sugar and sir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stirring frequently, until you have a chunky-jammy mixture. Add salt with one or two stirs, set aside and let cool
Ice cream: In a small pot over medium heat, combine milk, and granulated sugar until sugar is completly dissolved and the milk is just barely lukewarm. Whisk in the egg yolks. set mixture in the fridge and wait until the fig mixture is cooled.
Using an ice cream machine, pour liquids into the frozen basin and process according to manufacturer instructions, i.e., let spin and thicken for 20 minutes before adding mascarpone, fig jam mixture, and the nuts. Continue to process for +/- 10 minutes. Pour semi-frozen mixture into a pyrex dish or glass tupperware. Freeze for at least two hours before serving.
Food enthusiast, photography fan. Add a pinch of raw foodism and that's pretty much who I am.


|
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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object domosaics.model.arrangement.DomainArrangement
public class DomainArrangement
Class DomainArrangement represents a data structure do describe domain
arrangements of proteins.
Basically this structure stores the domains of a protein in their linear
order within a DomainVector
.
A Sequence can be assigned to a domain arrangement, which is allowed to
contain gap characters. The assignment of a sequence is for instance
necessary to visualize domain arrangements in MSA view,mark subsequences of
it and export sequences of domains.
| Constructor Summary | |
|---|---|
DomainArrangement()
Constructor for a domain arrangement. |
| Method Summary | |
|---|---|
void |
addDomain(Domain dom)
Adds a domain to the arrangement. |
java.lang.Object |
clone()
Clones an domain arrangement object. |
boolean |
contains(Domain dom)
Check if a specified domain is contained by the arrangement. |
boolean |
contains(DomainFamily domFamily)
Check if a specified domain family is contained by the arrangement. |
int |
countDoms()
Counts the number of domains. |
java.lang.String |
getDesc()
|
Domain |
getDomain(int index)
Returns the domain at the specified position. |
java.util.Iterator<Domain> |
getDomainIter()
Returns an iterator over all domain objects stored in the arrangement. |
DomainVector |
getDomains()
Returns the domain vector of the arrangement. |
int |
getIndexOf(Domain dom)
Returns the index of a domain within the arrangement. |
int |
getLen(boolean gaps)
Returns the length of the protein sequence. |
java.lang.String |
getName()
Returns the proteins name. |
SequenceI |
getSequence()
Returns the underlying sequence data for this arrangement, if it was assigned. |
boolean |
hasOverlap(Domain dom)
|
void |
setDesc(java.lang.String desc)
|
void |
setName(java.lang.String name)
Sets the name of the protein, which is gained from the xdom file. |
void |
setSeqLen(int len)
Sets the sequence length of the arrangement, which might be stored in the xdom file |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public DomainArrangement()
| Method Detail |
|---|
public java.lang.Object clone() throws java.lang.CloneNotSupportedException
clone
in class java.lang.Object
java.lang.CloneNotSupportedException
public SequenceI getSequence()
public boolean contains(Domain dom)
dom
- the domain which should be checked for containment
public boolean contains(DomainFamily domFamily)
domFamily
- the domain family which should be checked for containment
public int getIndexOf(Domain dom)
dom
- the domain from which the index is requested
public DomainVector getDomains()
public int getLen(boolean gaps)
gaps
- flag if the length is allowed to contain gaps
public int countDoms()
public java.util.Iterator<Domain> getDomainIter()
public Domain getDomain(int index)
index
- position within the arrangement
public java.lang.String getName()
public void setName(java.lang.String name)
name
- proteins namepublic void setSeqLen(int len)
len
- length of the arrangementpublic void addDomain(Domain dom)
dom
- the domain to addpublic void setDesc(java.lang.String desc)
public java.lang.String getDesc()
public boolean hasOverlap(Domain dom)
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The assignment is out of 100 marks. It is 50% of the course weight.
In your groups of 3, you will plan, develop and test a user interface design. The project will consist of the following components:
Please create a persona and a scenario or a sketch or storyboard. You can create the persona, scenario or a sketch or storyboard in any format that you wish, but you must include the following components:
Please create a Task Flow.
Please see the example posted, complete your readings and get instructor's feedback before submitting your work.


| < Day Day Up > |
5.4 Testing Squid in a Terminal WindowOnce you've initialized the cache directories, you should run Squid in a terminal window with logging to stderr. This way, you can easily spot any errors or problems and make sure that Squid successfully starts. Use the -N option to keep Squid in the foreground and the -d1 option to display level 1 debugging on stderr: % squid -N -d1 You should see output like this: 2003/09/29 12:57:52| Starting Squid Cache version 2.5.STABLE4 for i386-unknown-freebsd4.8... 2003/09/29 12:57:52| Process ID 294 2003/09/29 12:57:52| With 1064 file descriptors available 2003/09/29 12:57:52| DNS Socket created on FD 4 2003/09/29 12:57:52| Adding nameserver <IP_ADDRESS> from /etc/resolv.conf 2003/09/29 12:57:52| Adding nameserver <IP_ADDRESS> from /etc/resolv.conf 2003/09/29 12:57:52| Unlinkd pipe opened on FD 9 2003/09/29 12:57:52| Swap maxSize 102400 KB, estimated 7876 objects 2003/09/29 12:57:52| Target number of buckets: 393 2003/09/29 12:57:52| Using 8192 Store buckets 2003/09/29 12:57:52| Max Mem size: 8192 KB 2003/09/29 12:57:52| Max Swap size: 102400 KB 2003/09/29 12:57:52| Rebuilding storage in /usr/local/squid/var/cache (DIRTY) 2003/09/29 12:57:52| Using Least Load store dir selection 2003/09/29 12:57:52| Set Current Directory to /usr/local/squid/var/cache 2003/09/29 12:57:52| Loaded Icons. 2003/09/29 12:57:52| Accepting HTTP connections at <IP_ADDRESS>, port 3128, FD 11. 2003/09/29 12:57:52| Accepting ICP messages at <IP_ADDRESS>, port 3130, FD 12. 2003/09/29 12:57:52| WCCP Disabled. 2003/09/29 12:57:52| Ready to serve requests. If you see an error message, you need to fix it before proceeding. Be sure to check the first few lines of output for warning messages. The most common errors are file/directory permissions and configuration file syntax errors. If you see an error message that doesn't make sense, have a look at Chapter 16 for advice and information on troubleshooting Squid. If that doesn't help, check the Squid FAQ, or search the mailing list archives for an explanation. Once you see the Ready to serve requests message, test Squid with a few HTTP requests. You can do this by configuring your browser to use Squid as a proxy and then open a web page. If Squid is working correctly, the page should load as quickly as it would without using Squid. Alternatively, you can use the squidclient program that comes with Squid: % squidclient http://www.squid-cache.org/ If this works, Squid's home page HTML file will scroll across your terminal window. Once you're confident that Squid works okay, you can interrupt the Squid process (i.e., with Ctrl-C) and run Squid as a daemon. |
| < Day Day Up > |


com.lib.pygmy
Interfaces
Game
GameEntity
GameLevel
GameRule
GameUniverse
OverlapProcessor
OverlapRulesApplier
Classes
BoardType
BuildConfig
GameMove
OverlapProcessorImpl
PygmyGame
PygmyGameContext
PygmyGameEntity
PygmyGameLevel
PygmyGameRule
PygmyGameUniverse
PygmyOverlapRulesApplier
R
R.attr
R.drawable
R.string
R.style
Tile
Enums
EntityType
Exceptions
IllegalMoveException


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
learning_rate = tf.placeholder(tf.float32, None, name='learning_rate')
return inputs_real, inputs_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).
def default_gan_activation(x,alpha=0.2):
#return tf.nn.elu(x)
return tf.maximum(alpha * x, x)
def discriminator_valid_padding(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
x = images
with tf.variable_scope('discriminator', reuse=reuse):
x1 = tf.layers.conv2d(x, 64, 6, strides=2, padding='valid')
relu1 = default_gan_activation(x1)
x2 = tf.layers.conv2d(relu1, 128, 4, strides=2, padding='valid')
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = default_gan_activation(bn2)
x3 = tf.layers.conv2d(relu2, 256, 3, strides=2, padding='valid')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = default_gan_activation(bn3)
relu3_size = int(relu3.shape[1]*relu3.shape[2]*relu3.shape[3])
flat = tf.reshape(relu3, (-1, relu3_size))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator_valid_padding, tf)
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
x = images
d = 0
with tf.variable_scope('discriminator', reuse=reuse):
x1 = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')
x1 = tf.layers.dropout(x1,d)
relu1 = default_gan_activation(x1)
x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
x2 = tf.layers.dropout(x2,d)
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = default_gan_activation(bn2)
x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
x3 = tf.layers.dropout(x3,d)
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = default_gan_activation(bn3)
relu3_size = int(relu3.shape[1]*relu3.shape[2]*relu3.shape[3])
flat = tf.reshape(relu3, (-1, relu3_size))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator_valid_padding(z, out_channel_dim, is_train=True,debug = False):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
if(debug):
print('-----verify generator inputs:-----')
[print(i) for i in zip(['z', 'out_channel_dim', 'is_train'],[z, out_channel_dim, is_train])]
# TODO: Implement Function
(z, output_dim, reuse, training) = (z, out_channel_dim, not(is_train), is_train)
default_padding = 'same'
default_kernel_size = 5
with tf.variable_scope('generator', reuse=reuse):
a=2
x1 = tf.layers.dense(z, a*a*512)
x1 = tf.reshape(x1, (-1, a, a, 512))
x1 = tf.layers.batch_normalization(x1, training=training)
x1 = default_gan_activation(x1)
x2 = tf.layers.conv2d_transpose(x1, 256, 3, strides=2, padding='valid')
x2 = tf.layers.batch_normalization(x2, training=training)
x2 = default_gan_activation(x2)
x3 = tf.layers.conv2d_transpose(x2, 128, 4, strides=2, padding='valid')
x3 = tf.layers.batch_normalization(x3, training=training)
x3 = default_gan_activation(x3)
logits = tf.layers.conv2d_transpose(x3, output_dim, 6, strides=2, padding='valid')
out = tf.tanh(logits)
if(debug):
print('-----verify generator processing:-----')
[print(i) for i in zip(['x1','x2','x3'],[x1,x2,x3])]
print('-----verify generator outputs:-----')
print(out)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator_valid_padding, tf)
def generator(z, out_channel_dim, is_train=True,debug = False):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
if(debug):
print('-----verify generator inputs:-----')
[print(i) for i in zip(['z', 'out_channel_dim', 'is_train'],[z, out_channel_dim, is_train])]
# TODO: Implement Function
(z, output_dim, reuse, training) = (z, out_channel_dim, not(is_train), is_train)
default_padding = 'same'
default_kernel_size = 5
with tf.variable_scope('generator', reuse=reuse):
a=2
x1 = tf.layers.dense(z, a*a*512)
x1 = tf.reshape(x1, (-1, a, a, 512))
x1 = tf.layers.batch_normalization(x1, training=training)
x1 = default_gan_activation(x1)
x2 = tf.layers.conv2d_transpose(x1, 256, default_kernel_size, strides=2, padding='valid')
x2 = tf.layers.batch_normalization(x2, training=training)
x2 = default_gan_activation(x2)
x3 = tf.layers.conv2d_transpose(x2, 128, default_kernel_size, strides=2, padding=default_padding)
x3 = tf.layers.batch_normalization(x3, training=training)
x3 = default_gan_activation(x3)
logits = tf.layers.conv2d_transpose(x3, output_dim, default_kernel_size, strides=2, padding=default_padding)
out = tf.tanh(logits)
if(debug):
print('-----verify generator processing:-----')
[print(i) for i in zip(['x1','x2','x3'],[x1,x2,x3])]
print('-----verify generator outputs:-----')
print(out)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
output_dim = out_channel_dim
g_model = generator(input_z, output_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
smoothing = 1
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# Get weights and bias to update
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
print('-----verify train inputs:-----')
[print(i) for i in zip(['epoch_count', 'batch_size', 'z_dim', 'learning_rate', 'beta1', 'get_batches', 'data_shape', 'data_image_mode'],\
[epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode])]
print_every = max(batch_size//16,10)
show_every = print_every*10
n_images = show_n_images
filename_suffix = '_' + data_image_mode + '_' + str(z_dim)
saver_path = './checkpoints_generator'+filename_suffix+'.ckpt'
sample_path = './samples'+filename_suffix+'.pkl'
tf.reset_default_graph()
# MODEL INPUT
input_real, input_z, input_learning_rate = model_inputs(*data_shape[1:], z_dim)
# MODEL
d_loss, g_loss = model_loss(input_real, input_z, data_shape[-1])
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print('variables initialized')
saver = tf.train.Saver()
try:
saver.restore(sess, saver_path)
print('variables restored')
except:
pass
steps = 1
samples, losses = [], []
for e in range(epoch_count):
#for x, y in dataset.batches(batch_size):
for batch_x in get_batches(min(steps,batch_size)): ## incriasing batchsize to optimise trainingspeed (and time) over iterations, partly simular to a learning rate declay
steps += 1
#print(steps,end='')
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
batch_x *= 2
# Run optimizers
for _ in range(1):
_ = sess.run(d_train_opt, feed_dict={input_real : batch_x,
input_z : batch_z,
input_learning_rate: learning_rate})
for _ in range(3):
_ = sess.run(g_train_opt, feed_dict={input_real : batch_x,
input_z : batch_z,
input_learning_rate: learning_rate})
if steps % print_every == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_x})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(e+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps % show_every == 0:
show_generator_output(sess, n_images, input_z, data_shape[-1], data_image_mode)
saver.save(sess, saver_path)
with open(sample_path, 'wb') as f:
pkl.dump(samples, f)
fig, ax = pyplot.subplots()
losses = np.array(losses)
pyplot.plot(losses.T[0], label='Discriminator', alpha=0.5)
pyplot.plot(losses.T[1], label='Generator', alpha=0.5)
pyplot.title("Training Losses")
pyplot.legend()
return losses, samples
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = .5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = .5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.AppConfig @import forms.agent.OtherSourcesOfIncomeForm @import forms.submapping.YesNoMapping @import models.YesNo @import play.api.data.Form @import play.api.i18n.Messages @import play.api.mvc.{Call, Request} @import views.html.helpers.injected.{ContinueButton, RadioHelper} @import views.html.templates.AgentMainTemplate @this( mainTemplate: AgentMainTemplate, radioHelper: RadioHelper, continueButton: ContinueButton, form: FormWithCSRF, govukDetails:GovukDetails, hmrcPageHeading: HmrcPageHeading ) @(otherSourcesOfIncomeForm: Form[YesNo], postAction: Call, clientName : String, clientNino : String, backUrl: String)(implicit request: Request[_], messages: Messages, appConfig: AppConfig) @inputContent = {
@messages("agent.other-sources-of-income.not-include")


GIT-Ignore Builder
Usage: Select the tools, environments, and frameworks you will be using, and copy/paste the output.
This does not provide custom declarations, but leaves a space at the bottom for your own settings.
Usage: Select the tools, environments, and frameworks you will be using, and copy/paste the output.
This does not provide custom declarations, but leaves a space at the bottom for your own settings.


The symptoms of depression can be complex and vary widely between people. If you're depressed, you may feel sad, hopeless and lose interest in things you used to enjoy.

There are many other symptoms of depression and you're unlikely to have all of those listed on this page.
disturbed sleep – for example, finding it difficult to fall asleep at night or waking up very early in the morning
Social symptoms
The social symptoms of depression include:
avoiding contact with friends and taking part in fewer social activities
neglecting your hobbies and interests
having difficulties in your home, work or family life
Severities of depression
Depression can often come on gradually, so it can be difficult to notice something is wrong. Many people try to cope with their symptoms without realising they're unwell. It can sometimes take a friend or family member to suggest something is wrong.
Doctors describe depression by how serious it is:
mild depression – has some impact on your daily life
moderate depression – has a significant impact on your daily life
severe depression – makes it almost impossible to get through daily life; a few people with severe depression may have psychotic symptoms
Grief and depression
It can be difficult to distinguish between grief and depression. They share many of the same characteristics, but there are important differences between them.
Grief is an entirely natural response to a loss, while depression is an illness.
People who are grieving find their feelings of sadness and loss come and go, but they're still able to enjoy things and look forward to the future.
In contrast, people who are depressed constantly feel sad. They find it difficult to enjoy anything or be positive about the future.
There are different types of depression, and some conditions where depression may be one of the symptoms. These include:
postnatal depression – sometimes new mothers, fathers or partners develop depression after they have a baby; this is known as postnatal depression and it's treated in a similar way to other types of depression, with talking therapies and antidepressant medicines
bipolar disorder – also known as "manic depression", in bipolar disorder there are spells of both depression and excessively high mood (mania); the depression symptoms are similar to clinical depression, but the bouts of mania can include harmful behaviour, such as gambling, going on spending sprees and having unsafe sex
seasonal affective disorder (SAD) – also known as "winter depression", SAD is a type of depression with a seasonal pattern usually related to winter


Maintain Normal Blood Presure
know that one abnormal blood pressure reading does not mean hypertension.Your body only needs about 500 milligrams of salt a day, but if you are like the average American you may be consuming up to 9,000 mg a day. Studies show that high salt leads to high blood pressure.
Exercise. One reason exercise is so effective at controlling blood pressure is because it stimulates your body. Being overweight increases your risk for heart disease and diabetes, too.bad habits to avoid if you want to keep a normal blood pressure.


Before Change
Generate a url to view this image and overlay in a workstation
user = context.request.user
session = (
Session.objects.filter(creator=user)
.exclude(status__in=[Session.QUEUED, Session.STOPPED])
.order_by("-created")
.first()
)
if session:
url = session.workstation_url
else:
After Change
@register.simple_tag(takes_context=True)
def workstation_url(context, image, overlay=None):
Generate a url to view this image and overlay in a workstation
session = context.get("workstation_session")
if session:
url = session.workstation_url
else:


BEFORE THE NATIONAL GREEN TRIBUNAL SOUTHERN ZONE, CHENNAI Appeal No. 42 of 2013 (SZ) In the matter of: M/s. Gokulam Blue Metals Rep. by its Partner Mr K. Raj Kumar S.F.No. 3, Palathurai Village Coimbatore South Taluk Coimbatore District ..... Appellant versus 1. Tamil Nadu Pollution Control Board Represented by its Chairman Tamil Nadu Pollution Control Board 100, Anna Salai, Guindy Chennai - 600 032. 2. The District Environmental Engineer Tamil Nadu Pollution Control Board 42d, SNR College Road Peelamedu, Coimbatore South Coimbatore 3. Superintending Engineer TANGEDCO Tamil Nadu Electricity Board Tatabad, Coimbatore South Coimbatore District. .... Respondents
Counsel for the appellant M/s.K. Rajasekaran, and N. Thangavel advocates 1 Counsel for the respondents:
Smt. H. Yasmeen Ali, for respondent Nos. 1 and 2, Shri P. Gnanasekaran for Responent No. 3
ORDER/JUDGEMENT PRESENT :
Hon'ble Mr. Justice Dr. P. Jyothimani Hon'ble Prof. Dr. R. Nagendran ______________________________________________________________________________ Dated: July 12, 2013 ______________________________________________________________________________ Justice Dr. P. Jyothimani, Judicial Member
1. We heard the learned counsel for the appellant as well as the counsel for the respondents at length. We have also perused the report filed by the single member Expert Committee appointed by this Tribunal presided by Dr. R. Sivacoumar, Principal Scientist, CSIR-NEERI.
2. This appeal has been filed by the appellant, who is engaged in the business of blue metals involving the process of stone crushing, against the order of the Tamil Nadu Pollution 2 Control Board dated 25.4.2013 by the which the Pollution Control Board by exercising the powers conferred under Section 31(A) of the Air (Prevention and Control of Pollution) Act 1981 has directed the immediate closure of the stone crushing plant of the appellant on various grounds., that all the air pollution control measures namely closed cover for the jaw crusher and rotary screen were found to be in damaged condition, that no water sprinkler was provided to suppress the dust emission both at the jaw crusher and other vulnerable points in the premises, that conveyors were not provided with cover, that the unit has expanded its activity by providing Hot Mix Plant and Ready Mix Concrete Plant without obtaining Consent of the Board, and that complaints were received against the operation of the unit causing air pollution.
3. It is not in dispute that the hot mix plant and the ready mix concrete plant of the appellant unit were completely removed and therefore the complaint made by the Tamil Nadu Pollution Board in that regard has become infructuous. The Appellant who is engaged in the business of crushing blue metals is stated to have established its industry during 1990 as a small scale industry with nearly 500 employees. Thereafter, in 3 the year 2000, after purchasing the adjacent business of stone crushing which was carried on in the name of M/s. Ponni Maan Blue Metals, the Appellant has applied for consent and it is stated that it has been functioning for the past 23 years without any complaint either under air or water related pollution.
4. It is the case of the Appellant that the consent to renewal to operate was granted to the Appellant under Section 21 of the Air (Prevention and Control of Pollution) Act, 1981 only on 3.12.2012 which was renewed till 30.6.2013 and the application for further extension of renewal is stated to be pending before the Tamil Nadu Pollution Control Board. It is also stated by the Appellant that the Appellant has planted around 3000 trees around the crushing unit. While so, the impugned order has been passed by the Tamil Nadu Pollution Control Board abruptly ordering the closure of the unit without even giving opportunity to the Appellant. In fact, the Appellant has chosen to state that the reason adduced for the abrupt closure of the unit by the Tamil Nadu Pollution Control Board were that in the Appellant unit the pollution control equipments are not functioning properly, the water sprinkling unit has 4 exceeded its production capacity, that the Appellant unit is operating hot mix plant and concrete mixing plant without the consent of the Board, that the Appellant unit is operating in violation of various conditions imposed by the 1st and the 2nd respondents resulting in the pollution of agriculture lands in and around the unit and complaints from public have been received, etc. Therefore, the appeal has been filed challenging the impugned order on various grounds including that the impugned order is in violation of the principle of natural justice, that the impugned order has been passed ignoring the fact that the consent for the unit has been granted by the same Tamil Nadu Pollution Control Board which is in force till 9/2013 and that the further action for renewal of consent is also pending and no order has been passed.
5. That apart, it is the case of the Appellant that the Tamil Nadu Pollution Control Board has failed to find out any non- compliance of various conditions and requirements of the Tamil Nadu Pollution Control Board itself and therefore, the impugned order has been affected by total non-application of mind. As stated above, where all the alleged violations relating to the hot mix plant and the concrete mix plant are concerned, 5 since the same were closed, we are to consider about the other requirements.
6. After hearing the learned counsel, the Tribunal, in order to find out the correct factual position, appointed a Single Member Expert Committee presided by the Dr. R. Sivacoumar, Principal Scientist, CSIR-NEERI, who after a thorough inspection, has filed his report before this Tribunal. The report, which has taken note of each and every minute detail regarding the functioning of the unit with reference to the terms and references made by the Tribunal, considered various issues at length, as to whether the unit has installed all the pollution control equipment in the stone crushing unit.
7. While considering each one of the issues, the expert has stated that in so far as the installation of air pollution control equipment, the same has been complied with by the Appellant unit. That apart, while answering the same, the expert has also stated that the Appellant has planted nearly 3111 trees of various species. Again, while referring to the next question on proper functioning of air pollution control equipments, the Expert has given his report positively. Relating to the measuring of air ambient quality available in and around the 6 plant, it is stated by the Expert that the scheme of measuring the ambient air quality is not available in and around the plant and neither Tamil Nadu Pollution Control Board nor M/s. Gokulam Blue Metals has any scheme of measuring the ambient air quality in and around the plant. The non-providing of such a facility would not mean that the project proponent has exceeded the limit as far as the ambient air quality is concerned.
8. While answering the next question about the unit whether it has installed water sprinkling to control quarry dust emanating during the operation, again the Expert has given a positive reply. That was so in respect of the next question of production. Since the change in the ownership of the unit, there is no change in the sanctioned capacity and production during the time of inspection of the unit according to the Expert.
9. Further, while answering the next question on whether the unit has any hot mix plant without approval, the expert has also concurred with the Appellant and there is no operation of hot mix plant during the time of inspection. As stated above, the project proponent, namely the Appellant, has removed the 7 said two schemes namely the hot mix plant and the ready mix concrete plant and he also undertakes that he has no proposal of reintroducing the same in future.
10. The Expert, while answering for any visible observations on the impact of this crusher unit on abutting agricultural lands, has clearly stated that there is not much visible dust emanating from this stone crushing unit on the abutting agricultural lands around the crusher unit. He has also answered many other observations and has given the following recommendations:
1. Construction of metalled / paved road within the premises leading to bunker near jaw crusher;
2. Crusher sanctioned capacity may be stipulated on daily basis 55MT/day for 24 working days per month) instead of monthly basis so as to utilise optimally the assimilative capacity of the air shed;
3. A buffer zone of at least 200 m may be provided by the Government / Authorities around M/s. Gokulam Blue Metals so as to contain any dust in case of strong winds;
4. It is recommended that M/s. Gokulam Blue Metals may be asked to replace the wind breaking net in the west direction from where the complaint is originating, with a 8 permanent structure - either steel with proper support or masonry wall up to a height of 15 m.
11. The learned counsel for the appellant, while reiterating his stand, submits that the impugned order is not innocuous in the sense that the same has been passed in total violation of the principles of natural justice and that the order is in ignorance of the existing factual position and it is to be set aside on the ground of total non-application of mind. The learned counsel would fairly submit that the unit is prepared to comply with the recommendations given by the learned Expert and if necessary, abide by any other conditions which may be imposed by this Tribunal also.
12. On going through the recommendations of the learned Expert, we are of the view that there is no reason to go against the report of the Expert except, of course, a few minor modifications which are based on the existing facts and also on the statutory and other circulars issued by the Tamil Nadu Pollution Control Board. Accordingly, we dispose the appeal with the following directions.
9
1) Construction of metal / paved road within the premises leading to bunker jaw crusher;
2) Relating to the second recommendation of crusher capacity which has to be stipulated on daily basis instead of monthly basis, so as to utilise optimally the assimilative capacity of the air shed; while reiterating that the unit shall follow the same, we also make it clear that the Tamil Nadu Pollution Control Board shall consider the effect of the sanctioning capacity on daily basis and monthly basis in respect of the other concerned units in the State by appointing an Action Group to make a thorough study about the same and if necessary, file a report before this Tribunal to enable the Tribunal to give guidance in future.
3) Relating to the recommendation on the buffer zone, the Expert has stated that at least 200 m to be provided by the Government or Authorities around the Appellant unit so as to contain any dust in case in case of strong wind.
10
13. The learned counsel appearing for the Tamil Nadu Pollution Control Board has brought to the notice of this Tribunal about the proceedings of the Board under B.P.Ms. No. 4 dated 2.7.2004 which has streamlined the norms for the location for stone crushing industries based on the orders of the Appellate Authority of Pollution Control constituted under Water Act and Air Act. In this proceedings, in paragraph 1.0. the Board has framed criteria for the existing stone crushing units which are based on the recommendations of NEERI. The distance criteria which has been stated in respect of the existing stone crushing units is set under paragraph 1.1 and is as follows:
1.No stone crushers units should be located within 500 m from any NH or SH or primary residential area or mixed residential area or places of public and religious importance;
2.The minimum distance between two stone crushers should be 1 km to avoid dust pollution influence of one over the other.
14. Since the Tamil Nadu Pollution Control Board has already assessed and issued proceedings, admittedly the Appellant unit is an existing stone crushing unit, as rightly submitted by the 11 learned counsel appearing for the Tamil Nadu Pollution Control Board. In as much as it relates to the Appellant unit, the said Board's proceedings being statutory in nature as provided by law, shall be the guiding factor. Accordingly, in respect of the suggestions made by the learned counsel, we direct that the Tamil Nadu Pollution Control Board proceedings as mentioned in its proceedings dated 2.7.2004 and contained in paragraph 1.0 and 1.1 shall be made applicable to the Appellant unit and the Appellant unit shall make necessary arrangements to implement them.
15. Relating to the next suggestion that the Appellant unit, shall be asked to replace the wind breaking net in the west direction from where the complaint is originating by a permanent structure either steel sheet with proper support or masonry wall up to a height of 15 m, the learned counsel appearing for the Appellant unit would submit that putting up a masonry wall to the extent of 15 mts will be absolutely impossible and that would also affect the free flow of air and therefore, he has suggested that the Appellant unit may be directed to put up a masonry wall to the height of 3 m and thereafter another 4 m by way of dust arresting net, instead. 12
16. We find reason in such a contention raised by the learned counsel for the Appellant and therefore we are of the view that instead of directing the Appellant unit to construct a wall of 15 m height, we direct the Appellant unit that the wall shall be in combination of masonry and dust arresting net as proposed by the learned counsel for the Appellant unit to the extent of 3 m height and thereafter for another 4 m of dust arresting net shall be put up by the Appellant unit, thereby the total barrier shall be 7 m in height. We are of the view that providing such arrangement will be sufficient to prevent air pollution that arises on the western side.
17. On the compliance of the above said directions, the Appellant unit should be entitled to run with its full capacity, of course in accordance with all requirements in law and as per the directions of the Tamil Nadu Pollution Control Board.
18. We also make it clear that it is always up to the Tamil Nadu Pollution Control Board to have the overall provision of supervisory powers over the unit to see as to whether the Appellant unit has complied with the above said directions within a period of 4 months and ensure the compliance 13 accordingly. With the above said directions, the Appeal stands closed. We also record our appreciation for the involvement and work done by the Expert Dr. R. Sivacoumar and the suggestions made and the efforts taken by the learned counsel for the Appellant unit Shri K. Rajasekaran and the able assistance rendered by the learned counsel appearing the Tamil Nadu Pollution Control Board Smt. H. Yasmeen Ali. The parties shall bear their own costs.
Justice Dr. P. Jyothimani Judicial Member Prof. Dr. R. Nagendran Expert Member Chennai July 12, 2013 14


Load the required assembly files and data types.
We'll need to import libraries to help with data processing and visualization. Import numpy
and matplotlib
libraries by the following:
#load "../Initialize.csx" #load "../QuantConnect.csx" using QuantConnect; using QuantConnect.Data; using QuantConnect.Data.Market; using QuantConnect.Algorithm; using QuantConnect.Research; using System; using System.Linq; using Accord.Statistics;
import numpy as np from matplotlib import pyplot as plt


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© 2019
| ampelinus ampelina ampelinum | of the vine | |||
| ampelinus | ampelin | adj | αμπελινοϛ of the vine | |
| Rubus ampelinus Focke | ||||
| ampeloprasum ampeloprasa | a wild leek found in Greek vineyards | |||
| ampelos | ampel | noun/f | αμπελοϛ a vine; Clematis maritima | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| prason | pras | noun/n | πρασον leek | |
| um | noun/n | Latinizing nounal suffix | ||
| Allium ampeloprasum L. | ||||
| amphianthus amphiantha amphianthum | flowers on all sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| anthos | anth | noun/n | ανϑοϛ flower | |
| us | adj | Latinizing suffix | ||
| Trifolium amphianthum Torr. & Gray | ||||
| amphibius amphibia amphibium | growing in and out of water | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| bios | bi | noun/m | βιοϛ life, mode of life | |
| us | adj | Latinizing suffix | ||
| Persicaria amphibia (L.) S.F. Gray p.p. Polygonum amphibium L. Lupinus amphibius Suksdorf | ||||
| amphibolus amphibola amphibolum | ambiguous | |||
| amphibolus | amphibol | adj | αμφιβολοϛ ambiguous | |
| Acarospora amphibola Wedd. Allium amphibolum Ledeb. | ||||
| amphicarpos amphicarpos amphicarpon | seeds on all sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| carpos | carp | noun/m | καρποϛ fruit | |
| os | adj | οϛ adjectival suffix | ||
| Amphicarpum amphicarpon (Pursh) Nash | ||||
| amphicarpus amphicarpa amphicarpum | seeds on all sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| carpos | carp | noun/m | καρποϛ fruit | |
| us | adj | Latinizing suffix | ||
| Vicia amphicarpa L. | ||||
| amphichlorus amphichlora amphichlorum | colored on all sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| chloros | chlor | adj | χλοροϛ pale green, bright green, green, yellow | |
| us | adj | Latinizing suffix | ||
| amphidasys amphidasys amphidasys | hairy on both sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| dasys | dasy | adj | δασυϛ hairy, bushy, thick grown | |
| Rubus amphidasys Focke ex Diels | ||||
| amphidoxus amphidoxa amphidoxum | doubtful | |||
| amphidoxos | amphidox | adj | αμφιδοξοϛ fo two meanings: undecided, doubtful | |
| us | adj | Latinizing suffix | ||
| Photinia amphidoxa (C. K. Schneid.) Rehder & E. H. Wilson | ||||
| amphigenus amphigeni amphigenum | amphigenous, growing on two sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| genus | gen | adj/ | in botanical Latin: born or originating in a place or condition | |
| Carex amphigena (Fern.) Mackenzie | ||||
| amphilobus amphiloba amphilobum | lobes on both sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| lobus | lob | noun/m | λοβοϛ Late L Greek: lobe of the ear; late Latin: husk, pod | |
| us | adj | Latinizing suffix | ||
| Stylocline amphiloba (Gray) J.T. Howell Helenium amphilobum Gray | ||||
| amphioxys amphioxys amphioxys | sharp on both sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| oxys | oxy | adj | οξυϛ sharp, pointed; dazzling; shrill; pungent | |
| Astragalus amphioxys Gray | ||||
| amphitrichus amphitricha amphitrichum | hairy on both sides | |||
| amphi | amphi | prep | αμφι on both or all sides | |
| thrix | trich | noun/f | ϑριξ, τριχοϛ hair, wool, bristle | |
| us | adj | Latinizing suffix | ||
| Sasa amphitricha Koidz. Ranunculus amphitrichus Colenso | ||||
| amphoratus amphorata amphoratum | amphora like | |||
| amphora | amphor | noun/f | αμφορευϛ an amphora | |
| atus | at | adj | adjectival suffix for nouns: possessive of or likeness of something (with, shaped, made)/ for verb participles: a completed action, -ed | |
| amplectens amplectens amplectens | clasping | |||
| amplectens | amplectent | apar | amplector to wind around, surround | |
| Allium amplectens Torr. | ||||
| amplexicaulis amplexicaulis amplexicaule | stems clasped | |||
| amplexus | amplex | ppar | amplector to wind around, surround | |
| i | i | cnct | connective vowel used by botanical Latin | |
| caulis | caul | adj/ | καυλοϛ stem of a plant; usual spelling was colis or coles | |
| Heliotropium amplexicaule Vahl Ranunculus amplexicaulis L. | ||||
| amplexifolius amplexifolia amplexifolium | leaves clasped | |||
| amplexus | amplex | ppar | amplector to wind around, surround | |
| i | i | cnct | connective vowel used by botanical Latin | |
| folius | foli | adj | folium leaf | |
| Arnica amplexifolia Rydb. Lepuropetalon amplexifolium (Sternb.) Ser. Streptopus amplexifolius (L.) DC. | ||||
| amplexus amplexa amplexum | clasped | |||
| amplexus | amplex | ppar | amplector to wind around, surround | |
| Tortula amplexa (Lesq.) Steere | ||||
| ampliatus ampliata ampliatum | widened, enlarged | |||
| ampliatus | ampliat | padj | amplio L. to enlarge, increase, amplify | |
| Oncidium ampliatum Lindl. Mimulus ampliatus A.L. Grant | ||||
| amplibracteatus amplibracteata amplibracteatum | large bracts | |||
| amplus | ampl | adj | large, great, ample, spacious | |
| i | i | cnct | connective vowel used by botanical Latin | |
| bractea | bracte | noun/f | thin plate of metal, gold-leaf | |
| atus | at | adj | adjectival suffix for nouns: possessive of or likeness of something (with, shaped, made)/ for verb participles: a completed action, -ed | |
| Delphinium amplibracteatum Woot. | ||||
| ampliceps amplicaputis amplicapute | large head | |||
| amplus | ampl | adj | large, great, ample, spacious | |
| i | i | cnct | connective vowel used by botanical Latin | |
| ceps | caput | adj | suffix: headed | |
| Acacia ampliceps Maslin | ||||
| ampliflorus ampliflora ampliflorum | large flowered | |||
| amplus | ampl | adj | large, great, ample, spacious | |
| i | i | cnct | connective vowel used by botanical Latin | |
| florus | flor | ppar | floreo to bloom, to flower | |
| Rubus ampliflorus H. Lev. & Vaniot | ||||
| amplifolius amplifolia amplifolium | large leaved | |||
| amplus | ampl | adj | large, great, ample, spacious | |
| i | i | cnct | connective vowel used by botanical Latin | |
| folius | foli | adj | folium leaf | |
| Carex amplifolia Boott Aster amplifolius (Woot. & Standl.) Kittell | ||||
| amplifrons amplifrondes | large leaf | |||
| amplus | ampl | adj | large, great, ample, spacious | |
| i | i | cnct | connective vowel used by botanical Latin | |
| frons | frond | noun/f | leaf, foliage | |
| Licuala amplifrons Miq. | ||||
| ampliretis ampliretis amplirete | large network | |||
| amplus | ampl | adj | large, great, ample, spacious | |
| i | i | cnct | connective vowel used by botanical Latin | |
| rete | ret | noun/n | net, snare | |
| is | adj | adjectival suffix | ||
| amplisquamus amplisquama amplisquamum | large scale | |||
| amplus | ampl | adj | large, great, ample, spacious | |
| i | i | cnct | connective vowel used by botanical Latin | |
| squama | squam | noun/f | a scale | |
| us | adj | Latinizing suffix | ||
| Carex amplisquama F.J. Herm. | ||||
| amplissimus amplissima amplissimum | largest | |||
| superlative of amplus | ampl | adj | large, great, ample, spacious | |
| issimus | issim | adj | superlative suffix; most so, to the greatest degree; most-, -est (e.g. largest, prettiest, whitest) | |
| Bidens amplissima Greene | ||||
| amplus ampla amplum | large | |||
| amplus | ampl | adj | large, great, ample, spacious | |
| Agrostis ampla A.S. Hitchc. Acer amplum Rehder Lupinus amplus Greene | ||||
| ampullaceus ampullacea ampullaceum | flask like, bottle shaped | |||
| ampulla | ampull | noun/f | container with a narrow neck used for liquids | |
| aceus | ace | adj | adjective suffix for nouns: resembles, of..., ...like, mixture using, fragrance of; borrowed from nouns declined like ...ax, ...acis + eus | |
| Citrus ampullacea hort. ex Tanaka Eriogonum ampullaceum J.T. Howell Senecio ampullaceus Hook. | ||||
| amygdalifer amygdalifera amygdaliferum | almond bearing | |||
| amygdala | amygdal | noun/f | αμυγδαλη L. an almond | |
| i | i | cnct | connective vowel used by botanical Latin | |
| fer | fer | apar | fero to bear, carry, bring | |
| Caryocar amygdaliferum Mutis | ||||
| amygdaliformis amygdaliformis amygdaliforme | almond shaped | |||
| amygdala | amygdal | noun/f | αμυγδαλη L. an almond | |
| i | i | cnct | connective vowel used by botanical Latin | |
| formis | form | adj | in botanical Latin a substantive suffix for nouns derived from 'forma': to be in the form or shape of... | |
| Pyrus amygdaliformis Vill. | ||||
| amygdalinus amygdalina amygdalinum | of almonds | |||
| amygdalinos | amygdalin | adj | αμυγδαλινοϛ of almonds | |
| us | adj | Latinizing suffix | ||
| Clathroporina amygdalina sensu Fink Mecranium amygdalinum (Desr.) C. Wright ex Sauvalle Astragalus amygdalinus Bunge | ||||
| amygdalo-persicus amygdalo-persica amygdalo-persicum | persian almond | |||
| amygdala | amygdal | noun/f | αμυγδαλη L. an almond | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| - | - | cnct | ||
| persicus | persic | adj | Persicus Persian | |
| Amygdalus amygdalo-persica Weston | ||||
| amygdaloides amygdaloides amygdaloides | almond like | |||
| amygdala | amygdal | noun/f | αμυγδαλη L. an almond | |
| oides | oid | adj | -οειδεϛ adjective suffix for nouns: like, resemble | |
| Salix amygdaloides Anderss. | ||||
| amygdalus amygdala amygdalum | almond like | |||
| amygdala | amygdal | noun/f | αμυγδαλη L. an almond | |
| us | adj | Latinizing suffix | ||
| Prunus amygdalus Batsch |


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Open Source DIY Kit¶
This is the documentation site for the Desktop Stir Plate and Colorimeter Stir Plate. The stir plate kits are easy to assemble - no soldering is required and tools are included with the kits. See Assembly Instructions beginning with Step 1: Assembly of motor and magnet hub. Each kit also includes a suitable stir bar so you can start using the stir plates right away. A full list of kit contents can be found on the Kit Contents - checklist page.
Desktop Stir Plate Kit¶
- Part # STIR-01
- Build a stir plate suitable for many general purpose lab applications such as buffer, reagent and media preparation.
- 4.5” x 4.5” surface area for beakers, flasks, cylinders and other glassware. The stir plate surface is a smooth, white material (delrin/acetal).
- For use with glassware with a base diameter of up to 11mm max (typically up to and including 1L size flasks).
- Stir plate height on the benchtop is 2.5”. Multiple color options are available for the base.
- The stir plate is designed so that the magnet hub sits close to the stir bar (approx. 1/8” distance between magnets and bottom of flask).
- Stir-plate has a variable speed control to allow for a range of speed from very slow to very rapid mixing.
- A blue LED indicator displays whether the stir-plate is on.
Colorimeter Stir Plate¶
- Part # STIR-09
- Build a stir plate for use with the colorimeter to add cuvette stirring during measurement. Useful when carrying out titrations,enzyme assays and growth curves.
- Dimensions: 4.50” x 4.25” x 2.25”.
- The stir plate is designed so that the magnet hub sits directly underneath the cuvette in the colorimeter.
- Stir-plate has a variable speed control to allow for a range of speed from very slow to very rapid mixing.
- A blue LED indicator displays whether the stir-plate is on.
License¶
This is an open source hardware project licensed under the Creative Commons Attribution 3.0 License.
Design files¶
- KiCad PCB design and Gerber files - https://bitbucket.org/iorodeo/basic_speed_control_3v3.
- Mechanical designs - https://bitbucket.org/iorodeo/stir_plate_mechanics.


| Category: allocators | Component type: function |
template <class T> pair<T*, ptrdiff_t> get_temporary_buffer(ptrdiff_t len, T*);
The first argument to get_temporary_buffer specifies the requested size of the temporary buffer, and the second specifies the type of object that will be stored in the buffer. That is, get_temporary_buffer(len, (T*) 0) requests a buffer that is aligned for objects of type T and that is large enough to hold len objects of type T. [1]
The return value of get_temporary_buffer is a pair P whose first component is a pointer to the temporary buffer and whose second argument indicates how large the buffer is: the buffer pointed to by P.first is large enough to hold P.second objects of type T. P.second is greater than or equal to 0 [2], and less than or equal to len [1]. Note that P.first is a pointer to uninitialized memory, rather than to actual objects of type T; this memory can be initialized using uninitialized_copy, uninitialized_fill, or uninitialized_fill_n.
As the name suggests, get_temporary_buffer should only be used to obtain temporary memory. If a function allocates memory using get_temporary_buffer, then it must deallocate that memory, using return_temporary_buffer [3], before it returns.
Note: get_temporary_buffer and return_temporary_buffer are only provided for backward compatibility. If you are writing new code, you should instead use the temporary_buffer class.
int main() { pair<int*, ptrdiff_t> P = get_temporary_buffer(10000, (int*) 0); int* buf = P.first; ptrdiff_t N = P.second; uninitialized_fill_n(buf, N, 42); int* result = find_if(buf, buf + N, bind2nd(not_equal_to<int>(), 42)); assert(result == buf + N); return_temporary_buffer(buf); }
[1] The argument len is a request, rather than a requirement. The intention is that get_temporary_buffer will return as large a buffer as can be allocated without hurting performance. Note that determining this maximum size is quite difficult: it depends on cache size, physical versus virtual memory, heap fragmentation, and so on. A good implementation of get_temporary_buffer must be nonportable.
[2] If P.second is 0, this means that get_temporary_buffer was unable to allocate a temporary buffer at all. In that case, P.first is a null pointer.
[3] It is unspecified whether get_temporary_buffer is implemented using malloc, or ::operator new, or some other method. The only portable way to return memory that was allocated using get_temporary_buffer is to use return_temporary_buffer.


Thoughts of a trainee web developer
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An utopic game which will blow your mind with amazing superheroes, wonderful graphics and a revolutionary gameplay!
Rules
Play the game!


This tool provides information for users who want to research parcels located in the Kansas City metropolitan area for real estate development and other purposes.
To use the tool you can click the polygon representing the parcel you are interested in to see a summary of information about it. Click the more info button to see detailed information about the parcel and the available tools.
If there is a particular parcel you are looking for you can enter an address associated with it in the search bar to locate it.


For those who like wonders.
Are you ready for read the most scary tales?
The most beautiful storys are here.
Be afraid! Prepare to not go to sleep at night.
Dream about princesses and princes and elves and fairies.
Stories of heroic wars and legendary facts.
Several tales specific from some countrys.
For funny moments funny tales.


| ping_port {pingr} | R Documentation |
Check if a port of a server is active, measure response time
is_up()
checks if a web server is up.
ping_port( destination, port = 80L, continuous = FALSE, verbose = continuous, count = 3L, timeout = 1 ) is_up( destination, port = 80, timeout = 0.5, fail_on_dns_error = FALSE, check_online = TRUE )
destination |
Host name or IP address. |
port |
Port. |
continuous |
Logical, whether to keep pinging until the user interrupts. |
verbose |
Whether to print progress on the screen while pinging. |
count |
Number of pings to perform. |
timeout |
Timeout, in seconds. How long to wait for a ping to succeed. |
fail_on_dns_error |
If |
check_online |
Whether to check first if the computer is online.
Otherwise it is possible that the computer is behind a proxy, that
hijacks the HTTP connection to |
Vector of response times, in milliseconds.
NA
means no response within the timeout.
ping_port("r-project.org") is_up("google.com") is_up("google.com", timeout = 0.01)