Description
In this task, you will have a conversation with another person.
You both see a character descriptions that we randomly assign to one of you;
that person will assume that personality for the duration of this chat.
The other person has access to internet search and can use that to find
information about the topics that interest the first person (given their personality description).
The goal of this task is to go into depth about the topics that interest the first person,
based on their assigned personality.
Please chitchat pretending you live in a world WITHOUT COVID.
Sample Character description for Person 1
I love board games.
Sample Conversation
Person 1: Hi! I spent yesterday finding a new board game to try.
Person 2: Oo, what type of board games?
Person 1: I like strategy games, especially ones that are sci-fi
Person 2: I love Risk, but it takes place on earth, so not sci-fi,
and it takes forever
Person 1: Right? How do you feel about cards against humanity?
Person 2: Cards against humanity is fun but a little too risque for me
Reward/Bonus
We will reward engaging and knowledgeable chats with a bonus.
Close Window/Timeout/Return HIT
Once the conversation has started, close window/timeout or return HIT during the
chat will result in
HIT EXPIRED to you and NO reward paid.
You need to continue the conversation for at least 5 rounds.
Important Notice
1. Be aware the conversations you have will be made public, so act as you
would e.g. on a public social network like Twitter.
2. Please do not send long messages: messages cannot exceed 30 words.
3. Please do not reference the task or MTurk itself during the conversation,
but speak naturally to the other person.
4. Please do not send any message that could make others uncomfortable,
including any level of discrimination, racism, sexism and offensive
religious/politics comments, otherwise the submission will be rejected.
If you are ready, please click "Accept HIT" to start this task.


50
Israel's Sin and the Servant's Obedience
1 Thus says the LORD: “Where is your mother's certificate of divorce,
with which I sent her away?
Or which of my creditors is it
to whom I have sold you?
Behold, for your iniquities you were sold,
and for your transgressions your mother was sent away.
2 Why, when I came, was there no man;
why, when I called, was there no one to answer?
Is my hand shortened, that it cannot redeem?
Or have I no power to deliver?
Behold, by my rebuke I dry up the sea,
I make the rivers a desert;
their fish stink for lack of water
and die of thirst.
3 I clothe the heavens with blackness
and make sackcloth their covering.”
4 The Lord GOD has given me
the tongue of those who are taught,
that I may know how to sustain with a word
him who is weary.
Morning by morning he awakens;
he awakens my ear
to hear as those who are taught.
5 The Lord GOD has opened my ear,
and I was not rebellious;
I turned not backward.
6 I gave my back to those who strike,
and my cheeks to those who pull out the beard;
I hid not my face
from disgrace and spitting.
7 But the Lord GOD helps me;
therefore I have not been disgraced;
therefore I have set my face like a flint,
and I know that I shall not be put to shame.
8 He who vindicates me is near.
Who will contend with me?
Let us stand up together.
Who is my adversary?
Let him come near to me.
9 Behold, the Lord GOD helps me;
who will declare me guilty?
Behold, all of them will wear out like a garment;
the moth will eat them up.
10 Who among you fears the LORD
and obeys the voice of his servant?
Let him who walks in darkness
and has no light
trust in the name of the LORD
and rely on his God.
11 Behold, all you who kindle a fire,
who equip yourselves with burning torches!
Walk by the light of your fire,
and by the torches that you have kindled!
This you have from my hand:
you shall lie down in torment.


I am a passionate front-end developer with few years of experience in front end development. I was involved in some backend development (.Net framework) as well. I am highly organized and disciplined. I easily adapt to new environment and value working with new people. I love using recent technologies and eager to learn.
As Front End Developer, I was responsible for designing the layout in photoshop and coding it in HTML/CSS/JavaScript (for the business logic), I can provide in case required.
· As Front-End Developer /UX designer, I was responsible for designing the UX journey/designing the web pages /coding HTML-CSS-Bootstrap- JavaScript-jQuery.
As a member of IT team, I was responsible for designing the Assets (Icons -Banner -Logo – presentation(ppt)) Managing the content and data entry also maintaining the daily back up.
Hardware Installation /Graphic Design such as Business Cards, Posters, Stands, …
Apart from being a web developer, I enjoy most of my time being outdoors. In the winter, I am an avid skiier and novice ice climber. During the warmer months here in Colorado, I enjoy mountain biking, free climbing, and kayaking.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technolgy advancements in the front-end web development world.


Ionic is an HTML5 mobile app development framework targeted at building hybrid mobile apps.
Ionic Framework Docs


skupper init
Initialise skupper installation
Setup a router and other supporting objects to provide a functional skupper installation that can then be connected to other skupper installations
skupper init --[option]
- site-name
-
Provide a specific name for this skupper installation (string)
- ingress
-
Setup Skupper ingress to one of: [route|loadbalancer|nodeport|nginx-ingress-v1|contour-http-proxy|ingress|none]. If not specified route is used when available, otherwise loadbalancer is used. (string)
- ingress-host
-
Hostname or alias by which the ingress route or proxy can be reached (string)
- router-mode
-
Skupper router-mode (default "interior") (string)
- labels
-
Labels to add to skupper pods (strings)
- router-logging
-
Logging settings for router. 'trace', 'debug', 'info' (default), 'notice', 'warning', and 'error' are valid values. (string)
- router-debug-mode
-
Enable debug mode for router ('asan' or 'gdb' are valid values) (string)
- routers
-
Number of router replicas to start (int)
- enable-console
-
Enable skupper console (default true) (bool)
- create-network-policy
-
Create network policy to restrict access to skupper services exposed through this site to current pods in namespace (bool)
- console-auth
-
Authentication mode for console(s). One of: 'openshift', 'internal', 'unsecured' (string)
- console-user
-
Skupper console user. Valid only when --console-auth=internal (string)
- console-password
-
Skupper console user. Valid only when --console-auth=internal (string)
- console-ingress
-
Determines if/how console is exposed outside cluster. If not specified uses value of --ingress. One of: [route|loadbalancer|nodeport|nginx-ingress-v1|contour-http-proxy|ingress|none]. (string)
- ingress-annotations
-
Annotations to add to skupper ingress (strings)
- annotations
-
Annotations to add to skupper pods (strings)
- router-service-annotations
-
Annotations to add to skupper router service (strings)
- controller-service-annotation
-
Annotations to add to skupper controller service (strings)
- enable-service-sync
-
Participate in cross-site service synchronization (default true) (bool)
- router-cpu
-
CPU request for router pods (string)
- router-memory
-
Memory request for router pods (string)
- router-cpu-limit
-
CPU limit for router pods (string)
- router-memory-limit
-
Memory limit for router pods (string)
- router-node-selector
-
Node selector to control placement of router pods (string)
- router-pod-affinity
-
Pod affinity label matches to control placement of router pods (string)
- router-pod-antiaffinity
-
Pod antiaffinity label matches to control placement of router pods (string)
- router-ingress-host
-
Host through which node is accessible when using nodeport as ingress. (string)
- router-load-balancer-ip
-
Load balancer ip that will be used for router service, if supported by cloud provider (string)
- controller-cpu
-
CPU request for controller pods (string)
- controller-memory
-
Memory request for controller pods (string)
- controller-cpu-limit
-
CPU limit for controller pods (string)
- controller-memory-limit
-
Memory limit for controller pods (string)
- controller-node-selector
-
Node selector to control placement of controller pods (string)
- controller-pod-affinity
-
Pod affinity label matches to control placement of controller pods (string)
- controller-pod-antiaffinity
-
Pod antiaffinity label matches to control placement of controller pods (string)
- controller-ingress-host
-
Host through which node is accessible when using nodeport as ingress. (string)
- controller-load-balancer-ip
-
Load balancer ip that will be used for controller service, if supported by cloud provider (string)
- config-sync-cpu
-
CPU request for config-sync pods (string)
- config-sync-memory
-
Memory request for config-sync pods (string)
- config-sync-cpu-limit
-
CPU limit for config-sync pods (string)
- config-sync-memory-limit
-
Memory limit for config-sync pods (string)
- timeout
-
Configurable timeout for the ingress loadbalancer option. (default 2m0s) (duration)
-
skupper -


Hi Alan, This is you!
All your details in one place!
Document Breakdown
My Answers
<% _.each(questions, function(question){ %>
-
- Title:
- <%= question.get("title") %>
- Answered by:
- <%= question.get("answered_by") %>
- Date:
- <%= question.get("date") %>
- Your Answer:
- <%= question.get("answer") %>
<% }); %>


Test the Neural Network!
- Click the "New Image" button to load an image.
- Click on the "Cancer" or "Not Cancer" button to display the true diagnosis and the prediction from the convolutional neural network
- Determining whether or not an image indicates cancer can be very difficult, hence the need for medical professionals


How many bottles of beer are on YOUR wall? Enter a number to sing/drink along.
The number you entered:


This is a collection of the four Forms of the SAFE Notes, which rely on the Sections of the Notes. There are are four demo Notes, one for each of the forms of Notes, at F/Demo/Note_YC/.


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BatchNorm1d
|
Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D inputs with optional additional channel dimension)
|
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BatchNorm2d
|
Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with optional additional channel dimension)
|
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BatchNorm3d
|
Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with optional additional channel dimension)
|
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Conv1d
|
A model that applies a 1D convolution over an input signal composed of several input planes
|
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Conv2d
|
A model that applies a 2D convolution over an input signal composed of several input planes
|
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Conv3d
|
A model that applies a 3D convolution over an input signal composed of several input planes
|
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ConvTranspose1d
|
A model that applies a 1D transposed convolution operator over an input image composed of several input planes.
|
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ConvTranspose2d
|
A model that applies a 2D transposed convolution operator over an input image composed of several input planes.
|
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ConvTranspose3d
|
A model that applies a 3D transposed convolution operator over an input image composed of several input planes.
|
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Dropout
|
A model which during training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.
|
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Dropout2d
|
A model which during training, randomly zero out entire channels. Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.
|
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Dropout3d
|
A model which during training, randomly zero out entire channels. Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.
|
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Linear
|
A model that applies a linear transformation to the incoming data: \(y = xA^T + b\)
|
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LSTM
|
Long short-term memory (LSTM) recurrent neural network.
|
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LSTMCell
|
Unit cell of a long short-term memory (LSTM) recurrent neural network. Prefer using the RNN class instead, which can combine RNNCells in multiple layers.
|
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Mode
|
Indicates the training or evaluation mode for a model.
|
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Model
|
|
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Model<'In, 'Out>
|
Represents a model, primarily a collection of named parameters and sub-models and a function governed by them.
|
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ModelBase
|
Represents the base class of all models.
|
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Parameter
|
Represents a parameter.
|
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ParameterDict
|
Represents a collection of named parameters.
|
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RecurrentShape
|
|
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RNN
|
Recurrent neural network.
|
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RNNCell
|
Unit cell of a recurrent neural network. Prefer using the RNN class instead, which can combine RNNCells in multiple layers.
|
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Sequential
|
|
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VAE
|
Variational auto-encoder
|
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VAEBase
|
Variational auto-encoder base
|
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VAEMLP
|
Variational auto-encoder with multilayer perceptron (MLP) encoder and decoder.
|
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Weight
|
Contains functionality related to generating initial parameter weights for models.
|


Popular Areas in setting burn rate on dvd drive
Drive Cannot Read Discs (Windows 7) | HP® Support
If the drive cannot read discs that were burned on another computer, read . computer burning the disc and the computer playing the disc (CD-R, DVD+R, etc. . Even though the disc type may be compatible with a 16x burn rate, the playback may be much less compatible in other drives. . Step 2: Make sure AutoPlay is set .
http://h10025.www1.hp.com/ewfrf/wc/document?docname=c01897812&lc=en
Slow Burning Discs - Burning Issues - Roxio Community
When I burn DVD's, my DVD has a max burn rate of 8x, why are the . I did read #5 last night but I never looked at my hard drive settings and I .
http://forums.support.roxio.com/topic/69093-slow-burning-discs/
CD / DVD Burning Tips
May 11, 2006 . DVD Burning Tips - Advice and Info on how to burn good DVD video . down the transfer rate of data to and from the hard drive, and it could .
http://www.dvdburning.biz/dvd-burning-tips.htm
Enable DMA mode in Windows XP
How do I enable DMA mode on CD and DVD burner drives? . In order to burn at high speeds you should set the burner drive to operate at DMA mode. This will allow the highest burning rates to work in the background of other applications, .
http://www.onthegosoft.com/dma_setting_nt.htm
controlling idvd and burn speed at DVinfo.net
Dec 16, 2006 . PC or Mac, how to take your video to DVD or the Internet. . on disks burned out of idvd could be disk quality, bit rate, burn speed or dvd player quality. . (with ' best quality' set in prefs) and burn.....where can i get control of burn speed? . Burn speed is only adjustable using your burner's driver software .
http://www.dvinfo.net/forum/distribution-center/81868-controlling-idvd-burn-speed.html
Slow Burning DVD Drive, How To Fix In 4 Easy Steps | PCLicious
Been burning DVDS for many months with the same setup then suddenly I'm getting 1x burn speeds and the drive light flashes slowly rather than staying on .
http://www.pclicious.com/slow-burning-dvd-drive-how-to-fix-in-4-easy-steps/


PayPal uses data encryption and anti-fraud technology to keep your information secure, reducing the risk of online fraud.
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The tailed_advector module generates AVS5 style advectors with dynamic tails.
Input Ports
Name Type Description in_field Field Input data. in_probe Mesh Start positions of advectors. in_glyph Field Geometry to use for glyph output. Parameters
Please see the AVS/Express advector documentation for information on inherited parameters.
Name Type Description UI Control tracer_len int Length of generated tracer line. Slider tracer_style int Style of tracer to generate. Radiobox. time_step_interval float Interval between time step increments. UIslider Output Ports
Name Type Description out_fld Field Generated glyph visualization data. out_stream Field Generated stream visualization data. out_tracer Field Generated advector tail visualization data. out_obj GDobject Renderable glyph visualization. stream_obj GDobject Renderable stream visualization. tracer_obj GDobject Renderable advector tail visualization. time_step_number int Current time step number.
Generating advectors is a well known method of visualisation and both AVS5 and AVS/Express are supplied with modules that perform this sort of visualization. However the AVS/Express version has significant limitations with compared to the AVS5 version. This project aims to correct these limitations by presenting an improved version of the AVS/Express advector module.
The advector technique is intended to help visualize flow through a set of data. It does this by releasing a sample of zero mass particles and showing how they move with time. The actual simulation of this process is done by generating a series of streamlines and moving the particles along these lines. As well as seeing where the particles are at an instance in time it can also be useful to show where they have been. The AVS5 module allowed this to be done by showing a trace behind each particle. Unfortunately the AVS/Express version of the module is not able to do this. The tailed_advector module in this project adapts the standard module so that it is able to generate advector traces.
As the advector technique models the movement of particles over time it would also be useful if the module could support time dependent data. The standard AVS/Express module does not support this. The tailed_advector module adds the functionality necessary to support time dependent data. More information on how this functionality can be used is given in the descriptions of the TimeDependentAdvector and AltTimeDependentAdvector user macros.
The tailed_advector module has a number of limitations that are not present in the standard AVS/Express advector module. Most of these limitations are practical problems caused by the added complexity introduced into the module.
Movement of particles is not interpolated
As a particle moves along the calculated streamline it is unlikely that it will coincide exactly with a streamline node. Hence the normal advector module interpolates between streamline nodes to calculate the position of a particle. To ensure that the particle matches up with the advector tail the interpolated nodes would have to be added into the streamline mesh. Currently the tailed_advector module does not do this. Instead it does not interpolate the position of particles. Hence particle movement will be noticeably less smooth if there are few nodes.
Only one set of particles can be released per run.
The standard advector module allows particles to be released repeatedly during the course of a single run. This significantly complicates support for time dependent data. Therefore multiple release of particles and the release_time parameter are not currently supported.
Tracer length refers to number of streamline nodes.
The length of each advector trace is specified as a number of streamline nodes. It is not specified as a spatial length. Hence the actual length of the advector trace will depend upon the number of nodes present on the streamline.
Time must be reset before a new probe is specified.
The tailed_advector module internally stores an altered version of the input probe mesh. This is necessary so that the module can cope with time dependent data. If the time is not reset before specifying a new probe then this adapted probe will still be in use.
in_field
Input field structure containing the data that is to be analysed. The field should contain a mesh with associated node data. The first node data component should contain the velocity vector data. The velocity vector should have either 2 or 3 components. All other node data components are ignored.
in_probe
Mesh structure containing the initial positions of each particle that is to be released during the advection process. Normally a FPlane geometry can be used as the input to this port.
in_glyph
Geometry that should be used to represent the particles. Any mesh can be used, however for convenience the Geometries library provides many suitable objects.
Only the parameters added to the standard advector module are described here. For a full description of the parameters of the advector module please see the AVS/Express documentation.
tracer_len
Integer parameter that specifies the maximum number of streamline nodes that should be included in the generated advector tracer. This parameter is ignored if a tracer_style of ADD is specified. To simplify the implementation of this module no attempt is made to ensure that the tracer is of a constant spatial length.
tracer_style
Integer parameter that specifies the how the advector tracer should be generated. Currently there are three possible options.
CAP
Generates a tracer that begins at the initial particle position and continues until either tracer_len steps have been completed or the current particle position has been reached.CYCLE
Generates a tracer that begins at the current particle position and continues until either tracer_len steps have been completed or the initial particle position has been reached.ADD
Generates a tracer that begins at the initial particle position and continues until the current particle position is reached.
time_step_interval
This float parameter specifies the time that should elapse between each increment of the time_step_number output. For the time_step_number output to be incremented regularly the time_step_interval should be several times longer than the standard advector step period. If this parameter has a value of 0 the time_step_number output will not be incremented.
out_fld
Output field containing the current particle positions. Each particle position is represented by a glyph geometry. The glyphs are orientated along the current velocity vector. They are scaled and coloured by the magnitude of the velocity vector.
out_stream
Output field containing the streamlines that were used to perform the advection. The field contains a Polyline mesh with the velocity vector stored as a node data component.
out_tracer
Output field containing the generated advector tracers. The field contains a Polyline mesh with the velocity vector stored as a node data component.
out_obj
The GDobject version of the out_fld output, suitable for direct connection to a viewer.
stream_obj
The GDobject version of the out_stream output, suitable for direct connection to a viewer.
tracer_obj
The GDobject version of the out_tracer output, suitable for direct connection to a viewer.
time_step_number
Integer output that is incremented whenever the time specified by the time_step_interval parameter has passed. This output is used by the TimeDependentAdvector module to choose which data set should be used as the input to the tailed_advector module.
The functional macro TailedAdvectorFunc uses the low-level module TailedAdvectorCore. The user macro tailed_advector uses this functional macro and the user interface macro TailedAdvectorUI. The TailedAdvectorParams parameter block is used to connect these components together.
The Synchronize utility module is provided so that two tailed_advector module can be started and reset simultaneously.
Two example applications are provided. The SingleTailedAdvectorEg application reads the 'bluntfin.inp' dataset and allows the user to investigate the air flow by using a single tailed_advector module. The DualTailedAdvectorEg application similarly reads the 'bluntfin.inp' dataset. However it allows the user to investigate the air flow by using two tailed_advector modules simultaneously. The Synchronize module can be used to start both modules.
xp_comm_proj/t_advect/tadvmods.v contains the V definitions of the TailedAdvectorCore module, the Synchronize utility module and the TailedAdvectorParams parameter block.
xp_comm_proj/t_advect/tadvmacs.v contains the V definitions of the TailedAdvectorUI UI macro, the TailedAdvectorFunc functional macro and the tailed_advector, TimeDependentAdvector and AltTimeDependentAdvector user macros.
xp_comm_proj/t_advect/tadvapps.v contains the V definitions of the SingleTailedAdvectorEg, DualTailedAdvectorEg and TimeDependentAdvectorEg example applications.
This project relies on the successful installation of the following components. Without them it will not function correctly.
The low-level TailedAdvectorMods library containing the TailedAdvectorCore module does not specify a process. By default the express process will be used.
Fernand Alcatrao, Advanced Visual Systems, Inc.
Andrew Dodd, International AVS Centre


You have chosen to look at the tutorials, great! They are ordered by level of complexity (in terms of line of code). The nice property of Mithril is that it is fairly small which means nothing gets too complicated!
For those who are completely new, try the hello world example. For the adventurous jump straight to the todo stack!


Issue #2
Welcome to the second edition of the PyTorch newsletter! In this issue, read about how we celebrated the PyTorch community at the first-ever PyTorch Ecosystem Day (PTED), discover a new podcast for PyTorch developers, and learn about important updates to the PyTorch frontend.
PyTorch Ecosystem Day
Piotr Bialecki (Sr. Software Engineer, NVIDIA) spoke about his journey of using PyTorch and what he sees in the future for PyTorch. Miquel Farré (Sr. Technology Manager, Disney) spoke about the Creative Genome project that uses the PyTorch ecosystem to annotate all Disney content. Ritchie Ng (CEO, Hessian Matrix) spoke about the growth of AI in the Asia Pacific region, and how to get started with PyTorch for production AI use cases. Members of the community showcased how they were using PyTorch via 71 posters and pop-up breakout sessions. See all of the posters and listen to the opening keynote talks here!
PyTorch Developer Podcast
Edward Yang (Research Engineer, Facebook AI) talks about internal development concepts like binding C++ in Python, the dispatcher, PyTorch’s library structure and more. Check out this new series; each episode is around 15 minutes long. Listen to it wherever you get your podcasts.
Forward Mode AD
The core logic for Forward Mode AD (based on “dual tensors”) is now in PyTorch. All the APIs to manipulate such Tensors, codegen and view handling are in master (1.9.0a0)
already. Gradcheck and a first set of formulas will be added in the following month; full support for all PyTorch functions, custom Autograd functions and higher order gradients will happen later this year. Read more about this or share your feedback with @albanD on the corresponding RFC.
Make complex conjugation lazy
PR #54987 makes the conjugate operation on complex tensors return a view that has a special is_conj()
bit flipped. Aside from saving memory by not creating a full tensor, this grants a potential speedup if the following operation can handle conjugated inputs directly. For such operations (like gemm
), a flag is passed to the low-level API; for others the conjugate is materialized before passing to the operation.
torch.use_deterministic_algorithms is stable
torch.use_deterministic_algorithms()
(docs) is stable in master (1.9.0a0)
. If True, the flag switches non-deterministic operations to their deterministic implementation if available, and throws a RuntimeError
if not.
torch.linalg and torch.special
torch.linalg
is now stable; the module maintains fidelity with NumPy’s np.linalg linear algebra functions.
torch.special
(beta) contains functions in scipy.special. Here’s the tracking issue if you’d like to contribute functions to torch.special. If you want a function not already on the list, let us know on the tracking issue about your use case and why it should be added.
Generalizing AMP to work on CPU
@ezyang: Intel is interested in bringing automatic mixed precision to CPU in [RFC] Extend Autocast to CPU/CUDA with BF16 data type · Issue #55374 · pytorch/pytorch · One big question is what the API for autocasting should be for CPU; should we provide a single, generalized API torch.autocast (keep in mind that CPU autocasting would be through bfloat16, while the existing GPU autocasting is via float16), or provide separate APIs for CPU/CUDA? If you have any thoughts or opinions on the subject, please chime in on the issue.
Are you enjoying reading this newsletter? What would you like to know more about? All feedback is welcome and appreciated! To share your suggestions, use this form or simply reply to this email.


Sausage Rolls
- ⏲ Prep time: 10 min
- 🍳 Cook time: 30-60 min
Ingredients
- Eggs
- Puff pastry
- Pork meat / sausage meat
Directions
- Place baking paper onto a tray
- Whisk eggs
- Lay out pastry, brush or pour egg whisk so it lightly covers the pastry
- Prepare sausage meat onto pastry
- Fold pastry around the meat, apply extra egg whisk to the ‘seal’ where the pastry is folded together
- Bake until golden brown.
Contribution
Jacob Siggins
Recipe tags:
pork
pastry


Moonsoo Kim
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{% for title in random_titles %}
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The First ‘I’m A Celebrity’ Clue Is Here And If It’s Not Lindsay Lohan, We Riot
by By Louis HansonFrom the beaches of Mykonos to the swamps of the jungle, Lindsay Lohan is rumoured to be the first contestant on the sixth season of I’m a Celebrity…Get Me Out of Here! I literally just wee’d a little. Ms Lohan has that effect on me.
I’m a Celebrity just released their first contestant teaser video and, to be honest, it genuinely looks like it’s going to be Former Cady™.
Let’s unpack the clues and you’ll see what I mean.
Contestant numéro uno is described as a “television megastar” (tick), an “international party animal” (tick, shot, tick) and apparently likes to prove their critics wrong (a valiant tick). Oh, and it’s a her (HI, HOW ARE YA, GENDER?!)
Also, given that she’s just finished her stint on the Masked Singer – another Channel 10 production – it wouldn’t be a stretch to assume that she’d continue on with the network.

Check out the official teaser below, and start sharpening your pitch forks in case it turns out not to be Ms LiLo. In the meantime, stream ‘Xanax’ for clear skin.
To be honest, any day I get to write about Ms Lindsay Lohan is a stunning day… so I’m in a great mood.
Image: not photoshop... really real


Master Selsia:
You have done very well! Let me see that...
(Hand over Giants' Research Documents.)
These can now be safeguarded from any other threats.
You have done magnificent work and have passed all our tests...
Here, take this Badge of Inquisitor, which certifies that you have passed our test...
Show it to a Kamael Grand Master and you many change your class to Arbalester...
Congratulations!


HEART AND SOULS A film review by Lewis C. E. Butler Copyright 1993 Lewis C. E. Butler
Review in Short: A well played and well structured old fashioned "feel-good" movie, owing much to the tradition of Frank Capra. Downey is wonderful to watch, and the entire cast is perfect. Rating: +2
I can't begin to tell you how much I hate the phrase "feel-good" when used as an adjective, but I am at a loss to find another phrase that would be better suited for this movie. Here we have a film that is much like IT'S A WONDERFUL LIFE or MR. SMITH GOES TO WASHINGTON. It's an old fashioned movie with a fairly simple story to tell and a childish innocence in the way it tells it.
I suspect the movie is a remake of an older film, though I don't know this for a fact. Many elements of the story seem rather outdated in this day and age. For example, the small-time hood who's really a pretty nice guy and wants to give back something he stole to the kid he stole it from.
Actually, there are many things about this movie that sound like they would be quite annoying. It is easy to hear a plot summary and think, "Oh PUL-EASE!" but in point of fact, the movie carries itself quite well.
Robert Downey, Jr., plays a man whose imaginary childhood friends come back to him when he's about thirty years old. It seems that they are actually the ghosts of four people killed at the time of Downey's birth who were "attached" to him in some way.
The four are played by Charles Grodin, Alfre Woodard, Kyra Sedgwick, and Tom Sizemore. Each is very good, with Sizemore being the only one who is slightly weak. Elizabeth Shue (ADVENTURES IN BABYSITTING) playes Downey's girlfriend.
Downey gets to be "possessed" by each of the four, and it is just hilarious watching him. He has a true comic gift.
I do have some questions about the movie though. It takes place in 1989, is this a movie that was made several years ago and only now released? Elisabeth Shue does look a little older than in ADVENTURES IN BABYSITTING, but I wouldn't swear to it.
The movie opens nationwide this Friday, I believe.
--<EMAIL_ADDRESS>[303/722-2009] Vox
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


==
At Essential Customer Outreach, we have assembled a talented team of digital strategists, designers, developers, marketers, communications specialists, project managers and business developers to work in a collaborative manner on our client projects. We build on each other's expertise, working together in teams to generate great outcomes for projects.
What makes our company great? That’s simple. It’s the people and the culture. The team members at Essential Customer Outreach are some of the most creative, interesting and dedicated people you'll ever meet, exactly the kind of people you'd love to work with.


{% extends "players/player_base.html" %} {% load i18n %} {% block title %}Players{% endblock %} {% block content %}
The "score" column may indicate how good players are, according to the lap times they entered. Lower values are better. But do not take this value really serious - it's just a dubious number calculated in a weird way that may change at any time.
People with a score of 1.000 may not have entered lap times yet.
| {% trans "Name" %} | {% trans "Score" %} | {% for p in object_list %}
|---|---|
| {{ p.username }} | {{ p.defaultspeedmultiplier|floatformat:3 }} |


|
tclap
1.2.4
|
Files | |
| file | Arg.h [code] |
| file | ArgException.h [code] |
| file | ArgTraits.h [code] |
| file | CmdLine.h [code] |
| file | CmdLineInterface.h [code] |
| file | CmdLineOutput.h [code] |
| file | Constraint.h [code] |
| file | DocBookOutput.h [code] |
| file | HelpVisitor.h [code] |
| file | IgnoreRestVisitor.h [code] |
| file | MultiArg.h [code] |
| file | MultiSwitchArg.h [code] |
| file | OptionalUnlabeledTracker.h [code] |
| file | sstream.h [code] |
| file | StandardTraits.h [code] |
| file | StdOutput.h [code] |
| file | SwitchArg.h [code] |
| file | UnlabeledMultiArg.h [code] |
| file | UnlabeledValueArg.h [code] |
| file | ValueArg.h [code] |
| file | ValuesConstraint.h [code] |
| file | VersionVisitor.h [code] |
| file | Visitor.h [code] |
| file | XorHandler.h [code] |
| file | ZshCompletionOutput.h [code] |


Bases: object
Base class for a proactor implemented with posix-style polling.
Handle completion for a request.
Calls the scheduler to run or schedule the associated coroutine.
Perform additional handling (like register the socket file descriptor in the poll, epoll, kqueue, iocp etc) when a token is added in the proactor.
Overriden in a subclass.
Requests an socket operation (in the form of a callable perform that does the actual socket system call) for coro corutine with parameters and completion passed via act.
The socket operation request parameters are passed in act. When request is completed the results will be set in act.
Note: act is usualy a SocketOperation instance and the request_foo calls are usually made from a Foo subclass.
Perform additional handling (like cleanup) when a token is removed from the proactor.
Overriden in a subclass.


Class: Qpid::Proton::Transport Deprecated
- Inherits:
-
Object
- Object
- Qpid::Proton::Transport
- Defined in:
- lib/core/transport.rb
Overview
all important features are available from #Connection
Constant Summary collapse
- TRACE_OFF =
Turn logging off entirely.
Cproton::PN_TRACE_OFF
- TRACE_RAW =
Log raw binary data into/out of the transport.
Cproton::PN_TRACE_RAW
- TRACE_FRM =
Log frames into/out of the transport.
Cproton::PN_TRACE_FRM
- TRACE_DRV =
Log driver related events; i.e., initialization, end of stream, etc.
Cproton::PN_TRACE_DRV
Instance Method Summary collapse
-
#bind(connection)
Binds to the given connection.
-
#close_head
Indicate that the output has closed.
-
#close_tail
Indicate that the input has reached EOS (end of stream).
-
#condition ⇒ Condition?
Transport error condition or nil if there is no error.
-
#condition=(c)
Set the error condition for the transport.
-
#connection ⇒ Connection?
Return the AMQP connection associated with the transport.
-
#initialize(impl = Cproton.pn_transport) ⇒ Transport
constructor
Creates a new transport instance.
-
#log(message)
Log a message to the transport's logging mechanism.
-
#peek(size) ⇒ String
Returns the specified number of bytes from the transport's buffers.
-
#pop(size)
Removes the specified number of bytes from the pending output queue following the transport's head pointer.
-
#process(size)
Process input data following the tail pointer.
-
#push(data) ⇒ Integer
Pushes the supplied bytes into the tail of the transport.
-
#quiesced? ⇒ Boolean
Returns whether the transport has any buffered data.
-
#sasl ⇒ SASL
Create, or return existing, SSL object for the transport.
-
#set_server
Set server mode for this tranport - enables protocol detection and server-side authentication for incoming connections.
-
#ssl(domain = nil, session_details = nil) ⇒ SSL
Creates, or returns an existing, SSL object for the transport.
-
#tick(now) ⇒ Integer
Process any pending transport timer events.
-
#trace(level)
Updates the transports trace flags.
-
#unbind
Unbinds from the previous connection.
Constructor Details
#initialize(impl = Cproton.pn_transport) ⇒ Transport
Creates a new transport instance.
171 172 173 174 175 |
# File 'lib/core/transport.rb', line 171 def initialize(impl = Cproton.pn_transport) @impl = impl @ssl = nil self.class.store_instance(self, :pn_transport_attachments) end |
Instance Method Details
#bind(connection)
Binds to the given connection.
204 205 206 |
# File 'lib/core/transport.rb', line 204 def bind(connection) Cproton.pn_transport_bind(@impl, connection.impl) end |
#close_head
Indicate that the output has closed.
Tells the transport that no more output will be popped.
311 312 313 |
# File 'lib/core/transport.rb', line 311 def close_head Cproton.pn_transport_close_head(@impl) end |
#close_tail
Indicate that the input has reached EOS (end of stream).
This tells the transport that no more input will be forthcoming.
277 278 279 |
# File 'lib/core/transport.rb', line 277 def close_tail Cproton.pn_transport_close_tail(@impl) end |
#condition ⇒ Condition?
Returns transport error condition or nil if there is no error.
190 191 192 |
# File 'lib/core/transport.rb', line 190 def condition Condition.convert(Cproton.pn_transport_condition(@impl)) end |
#condition=(c)
Set the error condition for the transport.
196 197 198 |
# File 'lib/core/transport.rb', line 196 def condition=(c) Condition.assign(Cproton.pn_transport_condition(@impl), c) end |
#connection ⇒ Connection?
Return the AMQP connection associated with the transport.
231 232 233 |
# File 'lib/core/transport.rb', line 231 def connection Connection.wrap(Cproton.pn_transport_connection(@impl)) end |
#log(message)
Log a message to the transport's logging mechanism.
This can be using in a debugging scenario as the message will be prepended with the transport's identifier.
242 243 244 |
# File 'lib/core/transport.rb', line 242 def log() Cproton.pn_transport_log(@impl, ) end |
#peek(size) ⇒ String
Returns the specified number of bytes from the transport's buffers.
289 290 291 292 293 294 |
# File 'lib/core/transport.rb', line 289 def peek(size) cd, out = Cproton.pn_transport_peek(@impl, size) return nil if cd == Qpid::Proton::Error::EOS raise TransportError.new if cd < -1 out end |
#pop(size)
Removes the specified number of bytes from the pending output queue following the transport's head pointer.
301 302 303 |
# File 'lib/core/transport.rb', line 301 def pop(size) Cproton.pn_transport_pop(@impl, size) end |
#process(size)
Process input data following the tail pointer.
Calling this function will cause the transport to consume the specified number of bytes of input occupying the free space following the tail pointer. It may also change the value for #tail, as well as the amount of free space reported by #capacity.
267 268 269 |
# File 'lib/core/transport.rb', line 267 def process(size) Cproton.pn_transport_process(@impl, size) end |
#push(data) ⇒ Integer
Pushes the supplied bytes into the tail of the transport.
252 253 254 |
# File 'lib/core/transport.rb', line 252 def push(data) Cproton.pn_transport_push(@impl, data, data.length) end |
#quiesced? ⇒ Boolean
Returns whether the transport has any buffered data.
185 186 187 |
# File 'lib/core/transport.rb', line 185 def quiesced? Cproton.pn_transport_quiesced(@impl) end |
#sasl ⇒ SASL
Create, or return existing, SSL object for the transport.
336 337 338 |
# File 'lib/core/transport.rb', line 336 def sasl SASL.new(self) end |
#set_server
Set server mode for this tranport - enables protocol detection and server-side authentication for incoming connections
179 |
# File 'lib/core/transport.rb', line 179 def set_server() Cproton.pn_transport_set_server(@impl); end |
#ssl(domain = nil, session_details = nil) ⇒ SSL
Creates, or returns an existing, SSL object for the transport.
347 348 349 |
# File 'lib/core/transport.rb', line 347 def ssl(domain = nil, session_details = nil) @ssl ||= SSL.create(self, domain, session_details) end |
#tick(now) ⇒ Integer
Process any pending transport timer events.
This method should be called after all pending input has been processed by the transport (see #input), and before generating output (see #output).
It returns the deadline for the next pending timer event, if any art present.
330 331 332 |
# File 'lib/core/transport.rb', line 330 def tick(now) Cproton.pn_transport_tick(@impl, now) end |
#trace(level)
Updates the transports trace flags.
223 224 225 |
# File 'lib/core/transport.rb', line 223 def trace(level) Cproton.pn_transport_trace(@impl, level) end |
#unbind
Unbinds from the previous connection.
210 211 212 |
# File 'lib/core/transport.rb', line 210 def unbind Cproton.pn_transport_unbind(@impl) end |


Select fire location by clicking on the map or filling out the boxes below
The simulation will start at least 30 mins before the ignition time. The maximum forecast length is 48 hrs.
Select a simulation profile from the dropdown box. The simulation profile will specify parameters of your job not set in this form.
University of Utah and CU Denver Wildland Fire Group


God Said Ha! 3 1/2 Stars (Out of 4) Reviewed by Mac VerStandig<EMAIL_ADDRESS>http://www.moviereviews.org September 12, 1999 Available for Rental - September 14, 1999
Sharp, witty and charming, God Said Ha! is a pleasure to watch. The film strikes a particularly refreshing cord with its down-to-earth, realistic subject matter: A woman who finds herself in the prime of her life, then encounters a change so awful, so upsetting, and so unfair; that she discovers humor beneath all the misery that surrounds her.
Julia Sweeney is 30-something years old, recently divorced, and ready to leave New York City. She saves up some cash, buys herself the ultimate bachelor-pad outside of Los Angeles, and prepares for the first day of the rest of her life. Just when she thinks that nothing could get in her way, everything does.
Julia's brother becomes ill with cancer and soon requires constant attention. Not only does he move in with her, but both her parents do as well. She finds herself sneaking around like a teenager, returning to the ways of her childhood. (Like having to say "Noodles with the red topping" since "Pasta with marinara sauce" is too formal.)
The film is based on a play written by Sweeney, which had a successful run on the west coast. She is the only person on stage, except for a brief appearance by Quentin Tarantino at the end. There is no real set other than a few scattered pieces of furniture.
It would be a misconception to assume that since Sweeney is a Saturday Night Live alumni, and did appear in two rather unfortunate SNL films (It's Pat and Stuart Saves his Family), that this movie is of a like nature. She seems to have fully shed all the mediocre aspects of Lorne Michael's films, and found her own style. Not only do all the jokes work well, but the cheesy and often embarrassing aspects of the aforementioned titles are absent.
God Said Ha! is a wonderful movie. Whether it be the jokes about disappearing ovaries, or the touching stories of a terminally ill man; this film is well worth a look.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


The orientation of planes or linears are measured as azimuth
of dip and angle
of dip. Input is the orientation of the fabric elements which are
used for calculation. The calculation starts after clicking after
filling the input field and pressing the return key. Initial data and
the calculated fabric element
appear in a list.
After pressing the button 'Terminate input' initial data and results
can be saved and/or printed.
The maximum
number of data is the amount of data for which the program is set.
This number can be changed on the first form of every program. Changes
are saved in 'Fabric.ini'


Egypt’s petroleum exports record $2.42B in Q1 2019/20
by Egypt Today staffCAIRO - 25: November 2019: The Egyptian petroleum exports recorded $2.42 billion during the first quarter of 2019/2020, compared to $2.87 billion during the same period of 2018/2019, according to the Ministry of Petroleum.
The ministry attributed the decline in the value of the sector's exports from July to Sept. (2019-2020) to the global decline in prices, despite the 2.1-percent increase in quantities exported.
It pointed out that the value of imports of crude and petroleum products during this period amounted to about $2.7 billion, marking a decrease of about 13 percent, despite the 0.4 increase in imported quantities after the decline in the world price.
Egypt's non-petroleum exports hiked 3 percent during the first nine months of 2019, recording $19.201 billion compared to about $18.708 billion in the same period of 2018


text2speech¶
text2speech¶
-
class
data.text2speech.text2speech.
Text2SpeechDataLayer
(params, model, num_workers=None, worker_id=None)[source]¶ Bases:
open_seq2seq.data.data_layer.DataLayer
Text-to-speech data layer class
-
__init__
(params, model, num_workers=None, worker_id=None)[source]¶ Text-to-speech data layer constructor.
See parent class for arguments description.
Config parameters:
- num_audio_features (int) — number of audio features to extract.
- output_type (str) — could be either “magnitude”, or “mel”.
- vocab_file (str) — path to vocabulary file.
- dataset_files (list) — list with paths to all dataset .csv files. File is assumed to be separated by “|”.
- dataset_location (string) — string with path to directory where wavs are stored.
- feature_normalize (bool) — whether to normlize the data with a preset mean and std
- feature_normalize_mean (bool) — used for feature normalize. Defaults to 0.
- feature_normalize_std (bool) — used for feature normalize. Defaults to 1.
- mag_power (int) — the power to which the magnitude spectrogram is scaled to: 1 for energy spectrogram 2 for power spectrogram Defaults to 2.
- pad_EOS (bool) — whether to apply EOS tokens to both the text and the speech signal. Will pad at least 1 token regardless of pad_to value. Defaults to True.
- pad_to (int) — we pad such that the resulting datapoint is a multiple of pad_to. Defaults to 8.
-
_parse_audio_transcript_element
(element)[source]¶ Parses tf.data element from TextLineDataset into audio and text.
Parameters: element – tf.data element from TextLineDataset. Returns: text_input text as np.array of ids, text_input length, target audio features as np.array, stop token targets as np.array, length of target sequence, . Return type: tuple
-
_parse_transcript_element
(transcript)[source]¶ Parses text from file and returns array of text features.
Parameters: transcript – the string to parse. Returns: target text as np.array of ids, target text length. Return type: tuple
-
get_magnitude_spec
(spectrogram)[source]¶ Returns an energy magnitude spectrogram. The processing depends on the data leyer params.
Parameters: spectrogram – output spec from model Returns: mag spec Return type: mag_spec
-
static
get_optional_params
()[source]¶ Static method with description of optional parameters.
Returns: Dictionary containing all the parameters that can be included into the params
parameter of the class__init__()
method.Return type: dict
-
static
get_required_params
()[source]¶ Static method with description of required parameters.
Returns: Dictionary containing all the parameters that have to be included into the params
parameter of the class__init__()
method.Return type: dict
-
input_tensors
¶ Dictionary containing input tensors. This dictionary has to define the following keys: source_tensors, which should contain all tensors describing the input object (i.e. tensors that are passed to the encoder, e.g. input sequence and input length). And when
self.params['mode'] != "infer"
data layer should also define target_tensors which is the list of all tensors related to the corresponding target object (i.e. tensors taht are passed to the decoder and loss, e.g. target sequence and target length). Note that all tensors have to be created insideself.build_graph()
method.
-
iterator
¶ tf.data.Dataset
iterator. Should be created byself.build_graph()
.
-
speech_utils¶
-
data.text2speech.speech_utils.
denormalize
(features, mean, std)[source]¶ Normalizes features with the specificed mean and std
-
data.text2speech.speech_utils.
get_mel
(log_mag_spec, fs=22050, n_fft=1024, n_mels=80, power=2.0, feature_normalize=False, mean=0, std=1, mel_basis=None)[source]¶ Method to get mel spectrograms from magnitude spectrograms
Parameters: - log_mag_spec (np.array) – log of the magnitude spec
- fs (int) – sampling frequency in Hz
- n_fft (int) – size of fft window in samples
- n_mels (int) – number of mel features
- power (float) – power of the mag spectrogram
- feature_normalize (bool) – whether the mag spec was normalized
- mean (float) – normalization param of mag spec
- std (float) – normalization param of mag spec
- mel_basis (np.array) – optional pre-computed mel basis to save computational time if passed. If not passed, it will call librosa to construct one
Returns: mel_spec with shape [time, n_mels]
Return type: np.array
-
data.text2speech.speech_utils.
get_speech_features
(signal, fs, num_features, features_type='magnitude', n_window_size=1024, n_window_stride=256, mag_power=2, feature_normalize=False, mean=0.0, std=1.0)[source]¶ Helper function to retrieve spectrograms from loaded wav
Parameters: - signal – signal loaded with librosa.
- fs (int) – sampling frequency in Hz.
- num_features (int) – number of speech features in frequency domain.
- features_type (string) – ‘magnitude’ or ‘mel’.
- window_size (int) – size of analysis window in samples.
- window_stride (int) – stride of analysis window in samples.
- mag_power (int) – power to raise magnitude spectrograms (prior to dot product with mel basis) 1 for energy spectrograms 2 fot power spectrograms
- feature_normalize (bool) – whether to normalize the data with mean and std
- mean (float) – if normalize is enabled, the mean to normalize to
- std (float) – if normalize is enabled, the deviation to normalize to
Returns: - np.array of audio features with shape=[num_time_steps,
num_features].
Return type: np.array
-
data.text2speech.speech_utils.
get_speech_features_from_file
(filename, num_features, features_type='magnitude', window_size=1024, window_stride=256, mag_power=2, feature_normalize=False, mean=0.0, std=1.0)[source]¶ Helper function to retrieve spectrograms from wav files
Parameters: - filename (string) – WAVE filename.
- num_features (int) – number of speech features in frequency domain.
- features_type (string) – ‘magnitude’ or ‘mel’.
- window_size (int) – size of analysis window in samples.
- window_stride (int) – stride of analysis window in samples.
- mag_power (int) – power to raise magnitude spectrograms (prior to dot product with mel basis) 1 for energy spectrograms 2 fot power spectrograms
- feature_normalize (bool) – whether to normalize the data with mean and std
- mean (float) – if normalize is enabled, the mean to normalize to
- std (float) – if normalize is enabled, the deviation to normalize to
Returns: - np.array of audio features with shape=[num_time_steps,
num_features].
Return type: np.array
-
data.text2speech.speech_utils.
inverse_mel
(log_mel_spec, fs=22050, n_fft=1024, n_mels=80, power=2.0, feature_normalize=False, mean=0, std=1, mel_basis=None)[source]¶ Very hacky method to reconstruct mag spec from mel
Parameters: - log_mel_spec (np.array) – log of the mel spec
- fs (int) – sampling frequency in Hz
- n_fft (int) – size of fft window in samples
- n_mels (int) – number of mel features
- power (float) – power of the mag spectrogram that was used to generate the mel spec
- feature_normalize (bool) – whether the mel spec was normalized
- mean (float) – normalization param of mel spec
- std (float) – normalization param of mel spec
- mel_basis (np.array) – optional pre-computed mel basis to save computational time if passed. If not passed, it will call librosa to construct one
Returns: mag_spec with shape [time, n_fft/2 + 1]
Return type: np.array


Located at 130 King St is the Covent Garden Market. At a seriosly close walking distance from my school I was surprised that I had not been there before. I'm happy to say this review is extremely authentic. I was surprised at how huge the entire market was. It's large and all the stores are packed closely together but...continue reading
This lovely book store is located on 240 Dundas St. It has a wide range of genres. But Attic Books is slighly different from your everyday book store which would most likely carry best sellers, stationery and high fashion magazines. Just like the name suggests, it is a book store but with a more "attic like" prescence. Just like an attic they carry old, historic, used and donated pieces of literature! I for one was surprised at the variety! As you walk in.. continue reading
Right up the street from Attic Books at 186 Dundas St is Heroes Comics which is probably my new favourite place on earth. Heroes Comics is a store that carries every collectors dreams. They have old and new toys and action figures, posters and almost every graphic novel and manga under the sun! As you walk in...continue reading


| AppService (N/A) |
| AppServiceImpl (0%) |
| AppUtil (0%) |
| BrowseData (0%) |
| CrudFormVm (0%) |
| GenericService (N/A) |
| GenericServiceImpl (0%) |
| GenericVm (0%) |
| HttpSessionPool (0%) |
| Index (0%) |
| MsqDialog (0%) |
| PopulateInitData (0%) |
| SpringConfig (0%) |
| StringListConverter (0%) |
| TbExperience (0%) |
| TbPerson (0%) |
| TestData (0%) |


Psalms 140:2
Which imagine mischiefs in their heart; continually are they gathered together for war.


The ins element represent inserted (added) text.
My favorite color is red.
My favorite color is blue red.


Two partners and I raised $1M to buy fourteen abandoned single-family auction homes from the Houston Housing Authority in 2012.
Drawing on my eye for design, I chose every fixture, finishing, and paint color that went into this project- then, as purchasing manager, I sourced and bid on the materials. Working against the approaching home sales season deadline, we were able to sell every house providing a 17% annualized return to our investors.
This project was special in a lot of ways. First, I thrive in fast-paced environments and really enjoy something that requires me to use both my creative and analytical skills. Second, I was so incredibly proud to show my parents the products we delivered in an industry they had already mastered (they were proud too!).


We visualize platforms!
- drive B2B initiatives
- whiteboard leading-edge niches
- recontextualize strategic eyeballs
- expedite strategic users
- integrate real-time markets
- scale frictionless e-tailers
- synergize virtual schemas
- streamline holistic solutions
- reintermediate mission-critical synergies
- utilize vertical methodologies


AWS Billing and Cost Management
AWS Billing and Cost Management includes the AWS Cost Explorer API, the AWS Budgets API, and the AWS Price List API.
AWS Cost Explorer
| Region Name | Region | Endpoint | Protocol |
|---|---|---|---|
| US East (N. Virginia) | us-east-1 | https://ce.us-east-1.amazonaws.com | HTTPS |
AWS Budgets
| Region Name | Region | Endpoint | Protocol |
|---|---|---|---|
| US East (N. Virginia) | us-east-1 | https://budgets.us-east-1.amazonaws.com | HTTPS |
AWS Price List Service
| Region Name | Region | Endpoint | Protocol |
|---|---|---|---|
| US East (N. Virginia) | us-east-1 | api.pricing.us-east-1.amazonaws.com | HTTPS |
| Asia Pacific (Mumbai) | ap-south-1 | api.pricing.ap-south-1.amazonaws.com | HTTPS |
Amazon CloudFront
Amazon CloudFront distributions have a single endpoint: cloudfront.amazonaws.com and only supports HTTPS requests. When you submit requests to CloudFront programmatically, specify us-east-1 for the US East (N. Virginia) Region.
The CloudFront hosted zone ID value is Z2FDTNDATAQYW2
.
Amazon Elastic Compute Cloud (Amazon EC2)
| Region Name | Region | Endpoint | Protocol |
|---|---|---|---|
| US East (Ohio) | us-east-2 | ec2.us-east-2.amazonaws.com | HTTP and HTTPS |
| US East (N. Virginia) | us-east-1 | ec2.us-east-1.amazonaws.com | HTTP and HTTPS |
| US West (N. California) | us-west-1 | ec2.us-west-1.amazonaws.com | HTTP and HTTPS |
| US West (Oregon) | us-west-2 | ec2.us-west-2.amazonaws.com | HTTP and HTTPS |
| Asia Pacific (Tokyo) | ap-northeast-1 | ec2.ap-northeast-1.amazonaws.com | HTTP and HTTPS |
| Asia Pacific (Seoul) | ap-northeast-2 | ec2.ap-northeast-2.amazonaws.com | HTTP and HTTPS |
| Asia Pacific (Osaka-Local) | ap-northeast-3 | ec2.ap-northeast-3.amazonaws.com | HTTP and HTTPS |
| Asia Pacific (Mumbai) | ap-south-1 | ec2.ap-south-1.amazonaws.com | HTTP and HTTPS |
| Asia Pacific (Singapore) | ap-southeast-1 | ec2.ap-southeast-1.amazonaws.com | HTTP and HTTPS |
| Asia Pacific (Sydney) | ap-southeast-2 | ec2.ap-southeast-2.amazonaws.com | HTTP and HTTPS |
| Canada (Central) | ca-central-1 | ec2.ca-central-1.amazonaws.com | HTTP and HTTPS |
| China (Beijing) | cn-north-1 | ec2.cn-north-1.amazonaws.com.cn | HTTP and HTTPS |
| China (Ningxia) | cn-northwest-1 | ec2.cn-northwest-1.amazonaws.com.cn | HTTP and HTTPS |
| EU (Frankfurt) | eu-central-1 | ec2.eu-central-1.amazonaws.com | HTTP and HTTPS |
| EU (Ireland) | eu-west-1 | ec2.eu-west-1.amazonaws.com | HTTP and HTTPS |
| EU (London) | eu-west-2 | ec2.eu-west-2.amazonaws.com | HTTP and HTTPS |
| EU (Paris) | eu-west-3 | ec2.eu-west-3.amazonaws.com | HTTP and HTTPS |
| South America (São Paulo) | sa-east-1 | ec2.sa-east-1.amazonaws.com | HTTP and HTTPS |
| AWS GovCloud (US) | us-gov-west-1 | ec2.us-gov-west-1.amazonaws.com | HTTP and HTTPS |
For information about using Amazon EC2 in the AWS GovCloud (US) Region, see AWS GovCloud (US) Endpoints.
For information about using Amazon EC2 in the China (Beijing) Region, see China (Beijing) Region Endpoints.
Amazon Pinpoint
| Region Name | Region | Endpoint | Protocol |
|---|---|---|---|
|
US East (N. Virginia) |
us-east-1 |
pinpoint.us-east-1.amazonaws.com |
HTTPS |
Amazon Simple Storage Service (Amazon S3)
When sending requests to these endpoints using the REST API, you can use the virtual-hosted style and path-style methods. For more information, see Virtual Hosting of Buckets.
| Region Name | Region | Endpoint | Location Constraint | Protocol |
Signature Version(s) Support |
|---|---|---|---|---|---|
| US East (Ohio) | us-east-2 |
Valid endpoint names for this region:
|
us-east-2 | HTTP and HTTPS | Versions 4 only |
| US East (N. Virginia) | us-east-1 |
Valid endpoint names for this region:
|
(none required) | HTTP and HTTPS | Versions 2 and 4 |
| US West (N. California) | us-west-1 |
Valid endpoint names for this region:
|
us-west-1 | HTTP and HTTPS | Versions 2 and 4 |
| US West (Oregon) | us-west-2 |
Valid endpoint names for this region:
|
us-west-2 | HTTP and HTTPS | Versions 2 and 4 |
| Canada (Central) | ca-central-1 |
Valid endpoint names for this region:
|
ca-central-1 | HTTP and HTTPS | Version 4 only |
| Asia Pacific (Mumbai) | ap-south-1 |
Valid endpoint names for this region:
|
ap-south-1 | HTTP and HTTPS | Version 4 only |
| Asia Pacific (Seoul) | ap-northeast-2 |
Valid endpoint names for this region:
|
ap-northeast-2 | HTTP and HTTPS | Version 4 only |
| Asia Pacific (Osaka-Local) *** | ap-northeast-3 |
Valid endpoint names for this region:
|
ap-northeast-3 | HTTP and HTTPS | Version 4 only |
| Asia Pacific (Singapore) | ap-southeast-1 |
Valid endpoint names for this region:
|
ap-southeast-1 | HTTP and HTTPS | Versions 2 and 4 |
| Asia Pacific (Sydney) | ap-southeast-2 |
Valid endpoint names for this region:
|
ap-southeast-2 | HTTP and HTTPS | Versions 2 and 4 |
| Asia Pacific (Tokyo) | ap-northeast-1 |
Valid endpoint names for this region:
|
ap-northeast-1 | HTTP and HTTPS | Versions 2 and 4 |
| China (Beijing) | cn-north-1 |
Valid endpoint name for this region:
|
cn-north-1 | HTTP and HTTPS | Version 4 only |
| China (Ningxia) | cn-northwest-1 |
Valid endpoint name for this region:
|
cn-northwest-1 | HTTP and HTTPS | Version 4 only |
| EU (Frankfurt) | eu-central-1 |
Valid endpoint names for this region:
|
eu-central-1 | HTTP and HTTPS | Version 4 only |
| EU (Ireland) | eu-west-1 |
Valid endpoint names for this region:
|
EU or eu-west-1 |
HTTP and HTTPS | Versions 2 and 4 |
| EU (London) | eu-west-2 |
Valid endpoint names for this region:
|
eu-west-2 |
HTTP and HTTPS | Version 4 only |
| EU (Paris) | eu-west-3 |
Valid endpoint names for this region:
|
eu-west-3 |
HTTP and HTTPS | Version 4 only |
| South America (São Paulo) | sa-east-1 |
Valid endpoint names for this region:
|
sa-east-1 | HTTP and HTTPS | Versions 2 and 4 |
**Amazon S3 dual-stack endpoints support requests to S3 buckets over IPv6 and IPv4. For more information, see Using Dual-Stack Endpoints.
***You can use the Asia Pacific (Osaka-Local) Region only in conjunction with the Asia Pacific (Tokyo) Region. To request access to the Asia Pacific (Osaka-Local) Region, contact your sales representative.
When using the preceding endpoints the following additional considerations apply:
-
Amazon S3 renamed the US Standard Region to the US East (N. Virginia) Region to be consistent with AWS regional naming conventions. There is no change to the endpoint and you do not need to make any changes to your application.
-
If you use a region other than the US East (N. Virginia) endpoint to create a bucket, you must set the LocationConstraint bucket parameter to the same region. Both the AWS SDK for Java and AWS SDK for .NET use an enumeration for setting location constraints (Region for Java, S3Region for .NET). For more information, see PUT Bucket in the Amazon Simple Storage Service API Reference.
Amazon Simple Storage Service Website Endpoints
When you configure your bucket as a website, the website is available using the following region-specific website endpoints. Note that the website endpoints are different than the REST API endpoints listed in the preceding table. For more information about hosting websites on Amazon S3, see Hosting Websites on Amazon S3 in the Amazon Simple Storage Service Developer Guide. You need the hosted zone IDs when using the Amazon Route 53 API to add an alias record to your hosted zone.
Note
The website endpoints do not support https.
| Region Name | Website Endpoint | Route 53 Hosted Zone ID |
|---|---|---|
| US East (Ohio) | s3-website.us-east-2.amazonaws.com | Z2O1EMRO9K5GLX |
| US East (N. Virginia) | s3-website-us-east-1.amazonaws.com | Z3AQBSTGFYJSTF |
| US West (N. California) |
s3-website-us-west-1.amazonaws.com |
Z2F56UZL2M1ACD |
| US West (Oregon) |
s3-website-us-west-2.amazonaws.com |
Z3BJ6K6RIION7M |
| Canada (Central) |
s3-website.ca-central-1.amazonaws.com |
Z1QDHH18159H29 |
| Asia Pacific (Mumbai) |
s3-website.ap-south-1.amazonaws.com |
Z11RGJOFQNVJUP |
| Asia Pacific (Seoul) |
s3-website.ap-northeast-2.amazonaws.com |
Z3W03O7B5YMIYP |
| Asia Pacific (Osaka-Local) |
s3-website.ap-northeast-3.amazonaws.com |
Z2YQB5RD63NC85 |
| Asia Pacific (Singapore) |
s3-website-ap-southeast-1.amazonaws.com |
Z3O0J2DXBE1FTB |
| Asia Pacific (Sydney) |
s3-website-ap-southeast-2.amazonaws.com |
Z1WCIGYICN2BYD |
| Asia Pacific (Tokyo) |
s3-website-ap-northeast-1.amazonaws.com |
Z2M4EHUR26P7ZW |
| China (Ningxia) |
s3-website.cn-northwest-1.amazonaws.com.cn |
Not supported |
| EU (Frankfurt) |
s3-website.eu-central-1.amazonaws.com |
Z21DNDUVLTQW6Q |
| EU (Ireland) |
s3-website-eu-west-1.amazonaws.com |
Z1BKCTXD74EZPE |
| EU (London) |
s3-website.eu-west-2.amazonaws.com |
Z3GKZC51ZF0DB4 |
| EU (Paris) |
s3-website.eu-west-3.amazonaws.com |
Z3R1K369G5AVDG |
| South America (São Paulo) |
s3-website-sa-east-1.amazonaws.com |
Z7KQH4QJS55SO |
For information about using Amazon Simple Storage Service in the AWS GovCloud (US) Region, see AWS GovCloud (US) Endpoints.
For information about using Amazon Simple Storage Service in the China (Beijing) Region, see China (Beijing) Region Endpoints.


Bilal Ahmed
DIY Oxygen Concentrator
Introduction
An oxygen concentrator is a type of medical device used for delivering oxygen to individuals with breathing-related disorders.:Individuals whose oxygen concentration in their blood is lower than normal often require an oxygen concentrator to replace that oxygen. Oxygen concentrators filter surrounding air, compressing it to the required density and then delivering purified medical grade oxygen into a pulse-dose delivery system or continuous stream system to the patient. There are several technologies available for generating air enriched in oxygen, including air separation by fractional distillation, electrolysis and pressure swing adsorption. From these methods pressure swing adsorption has the lowest infrastructure cost and complexity and was selected for the generation of oxygen in a patient care scenario (10 – 60 l/min).
Project Overview
The aim of this project is to construct oxygen concentrators with minimal technical requirements. The purpose of this project to model the prototype of oxygen concentrator in relatively simple environments using common materials. Basically PSA (pressure Swing Adsorption) technique, is used to extract O2 from surrounding air.O2 concentrator basically of two type: 1) 1-column setup:In general, it consists of one column (one Zeolite Bed) 2) 2-coulumn setup:In general, it consists of two column (two Zeolite Bed) We have worked on 2-coulumn setup, because of optimal capacity of concentrated Oxygen and to get closer to commercial device in term of capacity (production rate) and size. In addition, the prosed production rate of this project is to get above 90% oxygen in outlet stream (1-3 l/min) using pressuring air (2-3 bar) in input.Schematic Diagram Explanation
Project Strategy
Basically we have divided the whole project into three stages.In first stage we have completed the the electronics circuit, including fabrication of PCB Board and implementation of arduino coding to check the safe operation of electronics components.In second stage to design small porous type membrane for air filtration purpose inside each zeoilte containers. In third stage to assembled all the components.Components in Details
1. Power Supply: We have used two separate power supply to provide 12V for air compressor and 24V for pneumatic valve220V/24V(DC) Power supply
2.Oil free air compressor: We have worked with a small,oil-free and cheap air-compressor(usually used in tyre shop).Having pressure of range 2-3 bar. The main advantage of oil-free compressor is that, the oxygen enriched air leaving the unit will have the same purity (oil/toxins) as the air feed to the concentrator.
4.Pressure Gauge: We haved used Pressure Gauge to measures the compressed air pressure at the input side, so to loook out the operated pressure and to avoid any damage to Zeolite Container.
pressure gauge
5. Relay Module: We haved worked with 4-Channel Relay Module for the safe operation of air-compressor and valves switching purpose
4-Channels Relay Module
6.Two 3-way pneumatic valves We have used two pneumatic valves, to feed compressed air into one of the zeolite container and at the time to refuel the other nitrogen contaiminated zeolite container, as mentioned in schematic digram. We have worked with " VT3O7V-5G1-01" Model.The link of data sheet is mentioned below: Click here
7.Two Zeolite bed/container: The proposed dimension of each zeolite container for this prototype would have(Lenght=6" and Dimension=2.5") So to filled with zeolite.
Zeolite Bed
8.Zeolite Materials: The proposed zeolite material for this prototype would be (each column. 300-500g LiX). To adsorb nitrogen from the air in pressure swing adsorption (PSA) processes while allowing the oxygen to pass through the system. The columns filled with a material called Lithium X-zeolite. The material has very small pores and a special chemical structure, so that the oxygen molecules in the air pass through, but the nitrogen molecules remain trapped because the molecular size of Nitrogen is larger as compared to Oxygen size to pass the zeolite materials.The membrane enables them to separate molecules of different sizes. It is, therefore, sufficient to force air through the column in pulses to enrich the oxygen in the gas mixture. The links below lead to information on some of standard zeolite products: eurecat.com tosoh.com/molecular-sieves intechopen.com/zeolites
Molecular Sieves(LiX)
9.PCB Board: We haved worked with Attiny44 microcontroller, so to avoid manually operation of the the oxygen concentrator.
Attiny44 Pin Configiuration
PCB Schematic Diagram (Eagle Software): pin description: VCC - Supply voltage. GND – For grounding pins. PORTB(PB3:PB0) – Port b is a 4-bit bi-directional input and out put port.in that PB3 which has RESET capacity also it is used as an input or output pin. PORTA(PA7:PA0) - Port a is 8-bit bi-directional input or output buffers have symetrical direction. connection reuirment of a attainy programmig: six wires isp programming SCK(serial clock):programming clock,generated by the In-system programmer(Master) MOSI(Master Out-Slave IN):Communication line from In-system Programmer to target AVR being programmed MISO(Master in-Slave Out):Communication line target Avr to in system programmer GND(common ground):it connect to the ground RESET:to enable in-system programming VCC:to allow simple programming of targets operating at any voltage,the in-system programmer can draw power from the target . LED circuit diagram: For the resistor calculation ,I used resistor calculator and I get the result is 470 Ohm then I use two 499 ohm resistor because I am adding two LED in this circuit.
PCB Final Routing Design (Eagle Software)
PCB traces
PCB Components (Eagle Sotware)
10.Oxygen storage container: To store the oxygen rich air.
Electronic Circuit
In an electronic circuit, the 220/12V(DC) power supply is connected with an oil-free air compressor, the 220/24(DC) supplied 24V(DC) to the pneumatic valves through 4-Realay Module. The PCB is connected with with PC, so you don't need to supply 5V(DC) separately to the the PCB Board.Then succesfully uploaded Arduino code on PCB Board, as per requirement.I have used 3 channels of Relay Module. At channel-1 the valve-A is connected, with channel-2 valve-B is connected and with channel-3 air-compressor is connected. During cycle-1(for 5 seconds) the valve-A fuel compressed air into the container-A and at the same same valve-B refuel the container-B and vice versa.We set delay of 3 seconds to control the operation of air-compressor (just to check the function of relay-Module and for safe operation of the circuit). But in general, the process is going simultanouslly.
Challenges
We experienced many challenges during the construction of Oxygen Concentrator.As like to understand the functionality, safe operation and assembling of the components. The main problem was that, we have not any idea about this Biomedical device. We can't take any risk during the whole process because of the components high price, due to unavailibility of the components in local market. To overcome these challenges we have adopted following procedures: First of all,we have gone through the Literature Review,watched many videos and visited many websites i.e GitHub.com,Rreddit.com and ethz.ch/en website Secondly,we have second use cpmponents so we were confused alot about the functionality of the pneumatics valves, but when searched alot and found the datasheet of relevant components and after studied the the data-sheet we learned that the openning and closing of the valves is an internal function of the valve, so we got that we have to just to perform the switching of valves rather than to control their functionality. In addition of that, we watched related videos to know about the assembling and safe operation of an electronics circuit which hepled us a lot. We helped from DVM(Digital Voltmeter) to enssure healthy and equipmentment safety, rather than to Swich-ON directly.Prototype Discussion
Basically we have construct an Electronics Cicuit of Oxygen Concentrator.It includes the Power Supply, Relay Module, Pneumatic Valves,PCB Attiny44(Microcontroller) and Logic Making.Unfortunatelly, due to the situation of Second-Wave of COVID-19 we can't received the Zeolite Materials and closing of all educational Instituitions in Pakistan. So we were unable to finish the project, so far we have done 60-70% work on the project. We would like to mention that we have complete understanding of this project and would like to work on this project in near future. The youtube link of our project is mentioned below:Commercial Importance
The cost of this project is much lower as compared to commercial Portable Oxygen Concentrator. This prototype can yields enough capacity to serve patients with concentrated oxygen. However, this prototype may not be able to cover the current demand, but DIY solutions may help to treat patients suffering from COVID-19 induced pneumonia.Cost Estimation
| Parts | Cost per Unit in Rs. | Sources |
|---|---|---|
| Arduino Unit | 800 | Buy Here |
| 6 Channel 5V Relay Module | 500 | Buy Here |
| Wiring | 100 | |
| 12V oil-free Air Compressor | 1200 | Buy Here |
| 24V Soliend Valves | 10000 | Buy Here |
| Zeolite Material | 8000 | Buy Here |
| 2x Zeolite Casing | 11000 | Buy Here |
| T & In-Line Connector | 700 | Buy Here |
| Oxygen Storage Tank | 700 | Buy Here |
| Pneumatic Air Tubing | 1000 | Buy Here |
| Total Amount | 34000 | |
Conclusion Summary
This new 2-column prototype uses a small oilfree compressor to compressed surronding air. We have used limited number of required 3-way solenoid valves to two although we are using two filled zeolite columns. The valves are switched with a 4 channel-relay module, through Attiny44 microcontroller which can be programmed easily without any further technical knowledge. In short, this project is incomplete as mentioned above.But the details of oxygen concentrator are discussed in details. We hope this would be great motivation for students and other also.References
Below are some citations: github.com/oxycon/ProjectApollo www.reddit.comreprap.org eurecat.com pakoxygen.com www.ou.edu/class/che-design/a-design/projects www.youtube.com/watch merezha.ukrintei.ua/wp www.cmu.edu ethz.ch/en/news-and-events


#include <nn_ops.h>
Says whether the targets are in the top K
predictions.
This outputs a batch_size
bool array, an entry out[i]
is true
if the prediction for the target class is among the top k
predictions among all predictions for example i
. Note that the behavior of InTopK
differs from the TopK
op in its handling of ties; if multiple classes have the same prediction value and straddle the top-k
boundary, all of those classes are considered to be in the top k
.
More formally, let
\(predictions_i\) be the predictions for all classes for example i
, \(targets_i\) be the target class for example i
, \(out_i\) be the output for example i
,
$$out_i = predictions_{i, targets_i} TopKIncludingTies(predictions_i)$$
Arguments:
batch_size
x classes
tensor.batch_size
vector of class ids.Returns:
Constructors and Destructors |
|
|---|---|
InTopKV2(const ::tensorflow::Scope & scope, ::tensorflow::Input predictions, ::tensorflow::Input targets, ::tensorflow::Input k)
|
Public attributes |
|
|---|---|
operation
|
|
precision
|
Public functions |
|
|---|---|
node() const
|
::tensorflow::Node *
|
operator::tensorflow::Input() const
|
|
operator::tensorflow::Output() const
|
|
Operation operation
::tensorflow::Output precision
InTopKV2( const ::tensorflow::Scope & scope, ::tensorflow::Input predictions, ::tensorflow::Input targets, ::tensorflow::Input k )
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output() const


This is a collection for model compression and acceleartion for DNNs, with a bias towards to the CV domain.
List:
| Year | Author | Title | Venue | Pruning | Distillation | Architecture | Quantization, low-rank, etc. | Other |
|---|---|---|---|---|---|---|---|---|
| 1990 | Yann Le Cun, John S. Denker, Sara A. Solla | Optimal Brain Damage | NIPS | x | ||||
| 2017 | Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | arxiv | x | ||||
| 1993 | Babak Hassibi, David.G. Stork, Gregory J. Wolff | Optimal Brain Surgeon and General Network Pruning | IEEE international conference on neural networks | x | ||||
| 2014 | Geoffrey Hinton, Oriol Vinyals, Jeff Dean | Distilling the knowledge in a neural network | NIPS (workshop) | x | ||||
| 2014 | Jimmy Ba, Rich Caruana | Do deep nets really need to be deep? | NIPS | x | x | |||
| 2015 | Song Han, Jeff Pool, John Tran, William Dally | Learning both Weights and Connections for Efficient Neural Networks | NIPS | x | ||||
| 2015 | Suraj Srinivas, R. Venkatesh Babu | Data-free Parameter Pruning for Deep Neural Networks | BMVC | x | ||||
| 2015 | Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio | FitNets: Hints for Thin Deep Nets | ICLR | x | x | |||
| 2016 | Vadim Lebedev, Victor Lempitsky | Fast ConvNets Using Group-Wise Brain Damage | CVPR | x | ||||
| 2016 | Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li | Learning Structured Sparsity in Deep Neural Networks | NIPS | x | ||||
| 2016 | Yiwen Guo, Anbang Yao, Yurong Chen | Dynamic Network Surgery for Efficient DNNs | NIPS | x | ||||
| 2016 | Hao Zhou, Jose M. Alvarez, Fatih Porikli | Less is More: Towards Compact CNNs | ECCV | x | ||||
| 2016 | Hengyuan Hu, Rui Peng, Yu-Wing Tai, Chi-Keung Tang | Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures | arxiv | x | ||||
| 2016 | Yoon Kim, Alexander M. Rush | Sequence-Level Knowledge Distillation | EMNLP | x | ||||
| 2016 | Ping Luo, Zhenyao Zhu, Ziwei Liu, Xiaogang Wang, Xiaoou Tang | Face Model Compression by Distilling Knowledge from Neurons | AAAI | x | ||||
| 2016 | Patrick McClure, Nikolaus Kriegeskorte | Representational Distance Learning for Deep Neural Networks | Frontiers in Computational Neuroscience | x | ||||
| 2016 | Saurabh Gupta, Judy Hoffman, Jitendra Malik | Cross Modal Distillation for Supervision Transfer | CVPR | x | ||||
| 2017 | Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf | Pruning Filters for Efficient ConvNets | ICLR | x | ||||
| 2017 | Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz | Pruning Convolutional Neural Networks for Resource Efficient Inference | ICLR | x | ||||
| 2017 | Jian-Hao Luo, Jianxin Wu, Weiyao Lin | ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression | ICCV | x | x | |||
| 2017 | Jian-Hao Luo, Jianxin Wu | An Entropy-based Pruning Method for CNN Compression | arxiv | x | ||||
| 2017 | Sajid Anwar, Kyuyeon Hwang, Wonyong Sung | Structured Pruning of Deep Convolutional Neural Networks | JETC | x | ||||
| 2017 | Yihui He, Xiangyu Zhang, Jian Sun | Channel Pruning for Accelerating Very Deep Neural Networks | ICCV | x | ||||
| 2017 | Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, Changshui Zhang | Learning Efficient Convolutional Networks Through Network Slimming | ICCV | x | ||||
| 2017 | Frederick Tung, Srikanth Muralidharan, Greg Mori | Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization | BMVC | x | ||||
| 2017 | Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker | Learning Efficient Object Detection Models with Knowledge Distillation | NIPS | x | ||||
| 2017 | Raphael Gontijo Lopes, Stefano Fenu, Thad Starner | Data-Free Knowledge Distillation for Deep Neural Networks | NIPS (workshop) | x | ||||
| 2017 | Sergey Zagoruyko, Nikos Komodakis | Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer | ICLR | x | ||||
| 2017 | Junho Yim, Donggyu Joo, Jihoon Bae, Junmo Kim | A Gift From Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning | CVPR | x | ||||
| 2017 | Shan You, Chang Xu, Chao Xu, Dacheng Tao | Learning from Multiple Teacher Networks | KDD | x | ||||
| 2017 | Jose M. Alvarez, Mathieu Salzmann | Compression-aware Training of Deep Networks | NIPS | x | ||||
| 2018 | Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger | CondenseNet: An Efficient DenseNet Using Learned Group Convolutions | CVPR | x | x | |||
| 2018 | Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, Yi Yang | Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks | IJCAI | x | ||||
| 2018 | Zehao Huang, Naiyan Wang | Data-Driven Sparse Structure Selection for Deep Neural Networks | ECCV | x | ||||
| 2018 | Michael H. Zhu, Suyog Gupta | To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression | ICLR (workshop) | x | x | |||
| 2018 | Qiangui Huang, Kevin Zhou, Suya You, Ulrich Neumann | Learning to Prune Filters in Convolutional Neural Networks | WACV | x | ||||
| 2018 | Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han | AMC: AutoML for Model Compression and Acceleration on Mobile Devices | ECCV | x | ||||
| 2018 | Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, Jinhui Zhu | Discrimination-aware Channel Pruning for Deep Neural Networks | NIPS | x | ||||
| 2018 | Elliot J. Crowley, Gavin Gray, Amos J. Storkey | Moonshine: Distilling with Cheap Convolutions | NIPS | x | x | |||
| 2018 | Zheng Xu, Yen-Chang Hsu, Jiawei Huang | Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks | ICLR (workshop) | x | ||||
| 2018 | Guorui Zhou, Ying Fan, Runpeng Cui, Weijie Bian, Xiaoqiang Zhu, Kun Gai | Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net | AAAI | x | ||||
| 2018 | Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang | DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer | AAAI | x | ||||
| 2018 | Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi | KDGAN: Knowledge Distillation with Generative Adversarial Networks | NIPS | x | ||||
| 2018 | Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani | N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning | ICLR | x | x | |||
| 2019 | Han Cai, Ligeng Zhu, Song Han | ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware | ICLR | x | x | |||
| 2019 | Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, Trevor Darrell | Rethinking the Value of Network Pruning | ICLR | x | x | |||
| 2019 | Jonathan Frankle, Michael Carbin | The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks | ICLR | x | x | |||
| 2019 | Shengcao Cao, Xiaofang Wang, Kris M. Kitani | Learnable Embedding Space for Efficient Neural Architecture Compression | ICLR | x | x | |||
| 2016 | Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi | XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks | ECCV | |||||
| 2019 | Haichuan Yang, Yuhao Zhu, Ji Liu | Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking | ICLR | |||||
| 2019 | Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus | Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds | ICLR | |||||
| 2019 | Daehyun Ahn, Dongsoo Lee, Taesu Kim, Jae-Joon Kim | Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network | ICLR | |||||
| 2019 | Ivan Chelombiev, Conor Houghton, Cian O’Donnell | Adaptive Estimators Show Information Compression in Deep Neural Networks | ICLR | |||||
| 2019 | Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack | Context-adaptive Entropy Model for End-to-end Optimized Image Compression | ICLR | |||||
| 2019 | Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, Cheng-zhong Xu | Dynamic Channel Pruning: Feature Boosting and Suppression | ICLR | |||||
| 2019 | Namhoon Lee, Thalaiyasingam Ajanthan, Philip Torr | SNIP: Single-shot Network Pruning based on Connection Sensitivity | ICLR | |||||
| 2019 | Chun-Fu (Richard) Chen, Quanfu Fan, Neil Mallinar, Tom Sercu, Rogerio Feris | Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition | ICLR |


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| Rating | ||
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| Spread | ||
| Spread multiplicator | x | / x |
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| Avg. income norm. | / / | |
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| LT4EMTLhXT94TH4u27KryA3rf96bv38SZo | |
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| XRP | rnSye1bNuC2aP1GqzLYaAmpPyRDkmQr6Un |
| Binance | BitMEX | Coinmate | Deribit | Simplefx |
Thank you!


Asamah:
I cannot speak about those skills unless you are one of us or have proven yourself a worthy friend of the Elroki tribe!


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| Package | Description |
|---|---|
| com.sucy.skill | |
| com.sucy.skill.api.event | |
| com.sucy.skill.api.player | |
| com.sucy.skill.data |
| Modifier and Type | Method and Description |
|---|---|
static boolean |
SkillAPI.isClassRegistered(PlayerClass playerClass)
Checks whether or not a class is registered.
|
| Modifier and Type | Method and Description |
|---|---|
PlayerClass |
PlayerClassChangeEvent.getPlayerClass() |
PlayerClass |
PlayerExperienceGainEvent.getPlayerClass() |
PlayerClass |
PlayerExperienceLostEvent.getPlayerClass() |
PlayerClass |
PlayerGainSkillPointsEvent.getPlayerClass() |
PlayerClass |
PlayerLevelUpEvent.getPlayerClass() |
PlayerClass |
PlayerPreClassChangeEvent.getPlayerClass() |
| Constructor and Description |
|---|
PlayerClassChangeEvent(PlayerClass playerClass,
RPGClass previousClass,
RPGClass newClass)
Constructor
|
PlayerExperienceGainEvent(PlayerClass playerClass,
double amount,
ExpSource source)
Constructor
|
PlayerExperienceLostEvent(PlayerClass playerClass,
double amount)
Constructor
|
PlayerGainSkillPointsEvent(PlayerClass playerClass,
double amount,
PointSource source)
Constructor
|
PlayerLevelUpEvent(PlayerClass playerClass,
int amount)
Constructor
|
PlayerPreClassChangeEvent(PlayerData playerData,
PlayerClass playerClass,
RPGClass previousClass,
RPGClass newClass)
Constructor
|
| Modifier and Type | Method and Description |
|---|---|
PlayerClass |
PlayerData.getClass(String group)
Retrieves the data of a class the player professed as by group.
|
PlayerClass |
PlayerData.getMainClass()
Retrieves the data of the professed class under the main class group.
|
PlayerClass |
PlayerSkill.getPlayerClass()
Retrieves the owning player class.
|
PlayerClass |
PlayerData.setClass(RPGClass rpgClass)
Sets the professed class for the player for the corresponding group.
|
| Modifier and Type | Method and Description |
|---|---|
Collection<PlayerClass> |
PlayerData.getClasses()
Retrieves the collection of the data for classes the player has professed as.
|
| Modifier and Type | Method and Description |
|---|---|
void |
PlayerData.giveSkill(Skill skill,
PlayerClass parent)
Gives the player a skill using the class data as a parent.
|
boolean |
PlayerData.showSkills(org.bukkit.entity.Player player,
PlayerClass playerClass)
Shows the skill tree to the player for the given class
|
| Constructor and Description |
|---|
PlayerSkill(PlayerData player,
Skill skill,
PlayerClass parent)
Constructs a new PlayerSkill.
|
| Modifier and Type | Method and Description |
|---|---|
double |
Settings.getBreakYield(PlayerClass playerClass,
org.bukkit.Material mat) |
double |
Settings.getCraftYield(PlayerClass playerClass,
org.bukkit.Material mat) |
double |
Settings.getPlaceYield(PlayerClass playerClass,
org.bukkit.Material mat) |
| Constructor and Description |
|---|
PlayerStats(PlayerClass player)
Constructor
|
Copyright © 2021. All rights reserved.


Ecma formal publications
Two types of formal publications are prepared by Ecma:
The Ecma historical archive which contains the scanned copies of all withdrawn Ecma Standards is available here.
A Standard or a Technical Report is a formal document prepared
by an Ecma Technical Committee and approved by the Ecma General
Assembly. A majority of at least two-thirds of all the ordinary
members is required for approval.
Ecma International often submits its Standards to ISO/IEC JTC 1's fast track approval procedure which adheres to the WTO TBT guidelines.
Ecma Standards and Technical Reports are made available to all
interested persons or organizations, free of charge and copyright,
in printed form and, as files in Acrobat ® PDF format.
How to obtain Ecma publications
WARNINGS
The liability and responsibility for the implementation
of an Ecma Standard rests with the implementor, and not with Ecma.
However, implementors might want to consult the
Ecma "Code of conduct in
patent matters".
Only the printed version or the PDF file can
be considered an official version of the formal document.
Back


| American Eskimo Dog Picture |
| Breed Information |
| Popularity |
2017: #1182016: #1222015: #118 |
| Name |
American Eskimo Dog (Toy, Miniature, Standard) |
| Other names |
Eskimo Spitz, American Spitz, German Spitz, Eskie |
| Origin |
United States Germany |
| Breed Group |
Non Sporting (AKC:1994)Northern Breeds (UKC) |
| Size |
Small to Medium |
| Type |
Purebred |
| Life span |
12-15 years |
| Temperament |
AlertEnergeticFriendlyIndependentIntelligentProtective |
| Height |
Toy: 9-12 inches (22-30 cm)Miniature: 12-15 inches (30-40 cm)Standard: 15-20 inches (49-50 cm) |
| Weight |
Toy: 6-10 pounds (3-5 kg)Miniature: 10-17 pounds (5-8 kg)Standard: 18-35 pounds (8-16 kg) |
| Colors |
WhiteWhite & Biscuit |
| Litter Size |
4-6 puppies |
| Puppy Price |
Average $600 - $800 USD |
|
|
| Breed Characteristics |
| Adaptability |
5 stars |
| Apartment Friendly |
3 starsThe American Eskimo will do okay in an apartment if it is sufficiently exercised. It is very active indoors and a small yard will be sufficient. |
| Barking Tendencies |
5 starsFrequent |
| Cat Friendly |
3 stars |
| Child Friendly |
4 starsGood with Kids: This is a suitable breed for kids and is known to be playful, energetic, and affectionate around them. |
| Dog Friendly |
4 stars |
| Exercise Needs |
4 starsThe American Eskimo Dog (Toy) requires less exercise than the standard breed, but should be given the occasional workout or walk. Provide plenty of water and shade during exercise to avoid overheating from the heavy coat.The American Eskimo Dog (Miniature) requires less exercise than the standard breed, but should be given the occasional workout or walk. Provide plenty of water and shade during exercise to avoid overheating from the heavy coat.The American Eskimo Dog (Standard) needs a good workout every day and should be taken for a long jog or walk. Provide plenty of water and shade during exercise to avoid overheating from the heavy coat. Eskies enjoy dog sports. Without interaction they may become bored and destructive. |
| Grooming |
3 starsModerate Maintenance: With a dense undercoat and a longer outer coat of guard hairs they are heavy shedders. Weekly brushing is recommended to control shedding and keep the Eksie’s coat neat and free of mats. Trim nails on a regular basis to keep their feet in good condition. Check their prick ears regularly for any accumulated dirt, wax or debris that make get stuck in their thick hair that covers the ears. Teeth should be brushed regularly. |
| Health Issues |
3 starsProne to hip dysplasia and progressive retinal atrophy. Pay close attention to the eyes and tear ducts. Some are allergic to fleas. This breed can gain weight easily if it does not get enough exercise and/or is overfed.Hypoallergenic: No |
| Intelligence |
2 starsRanking: (#). (See All Rankings) |
| Playfulness |
4 stars |
| Shedding Level |
5 starsConstant Shedding: Expect this dog to shed frequently. Be prepared to vacuum often. Brushing will reduce shedding as well as make the coat softer and cleaner. |
| Stranger Friendly |
2 stars |
| Trainability |
3 starsModerately Easy Training: American Eskimo Dogs learn quickly and are eager to please. Harsh training techniques are not appropriate. American Eskimo Dogs should be trained early in life. Obedience training is recommended. |
| Watchdog Ability |
4 stars |
|
|


set x|y|z|t val1 <val2>
This sets one dimension of the dimension environment using grid
coordinates. You may use whatever coordinates are convenient to you.
Issuing set lon
is equivalent to issuing set x
,
both set the x
dimension. The difference is only the units
you wish to enter the command in.
When you enter just one value, that dimension is said to be "fixed". When you enter two values, that dimension is said to be "varying". The combination of fixed and varying dimensions defines the dimension environment.
An important note: When you enter dimensions in grid coordinates, they are always converted to world coordinates. This conversion requires some knowledge of what scaling is in use for grid to world conversions. The scaling that is used in all cases (except one) is the scaling of the DEFAULT FILE. The exception is when you supply a dimension expression within a variable specification, which will be covered later.
set t 1
(sets time to the first time in the data set -- using
grid coordinates in this case. Time is now a fixed
dimension).


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Reports the calls to java.sql.Connection.prepareStatement(),
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of their variants which take a dynamically-constructed string as the statement to prepare.
Constructed SQL statements are a common source of security breaches.
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