About Me
Howdy, friends! I'm a Sr. Product Manager from the midwest. I'm forever in beta, constantly growing and evolving. Personally, this drive or pursuit for more translates to a nonstop hunger to learn and pick up new stills. Professionally, this means check your mediocrity at the door. Being hungry for improvement and change, coupled with stragetic and data-backed moves, will earn more customers, increase customer loyalty + brand awareness and generate more sales. Dare to think outside the box.
One of the biggest challenges I face today is that I'm required to rely on someone else to execute my vision, or the one that I'm driving. I want to DO the things, all the things -- be autonomous. In my current role, it'd be silly to not want to learn new tech skills for a better understanding of product development, project timelines, and to futher my career as someday, a top notch developer.


import { SelectModule } from '@sbb-esta/angular-public';
SelectComponent
Selector: sbb-select
Exported as: sbbSelect| Name | Description |
|---|---|
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@Input('attr.aria-label')
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Returns the aria-label of the select component. |
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@Input('attr.aria-labelledby')
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Returns the aria-labelledby of the select component. |
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@Input()
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A function to compare the option values with the selected values. The first argument is a value from an option. The second is a value from the selection. A boolean should be returned. |
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@Input()
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Whether to center the active option over the trigger. |
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@Input()
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Disables a select field |
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@Input()
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Unique id of the element. |
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@Input()
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Whether the user should be allowed to select multiple options. |
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@Input()
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Classes to be passed to the select panel. Supports the same syntax as |
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@Input()
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Placeholder to be shown if no value has been selected. |
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@Input()
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Whether the component is required. |
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@Input()
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Value of the select control. |
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@Output()
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Event emitted when the select has been closed. |
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@Output()
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Event emitted when the select has been opened. |
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@Output()
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Event emitted when the select panel has been toggled. |
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@Output()
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Event emitted when the selected value has been changed by the user. |
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Whether the input is currently in an autofilled state. If property is not present on the control it is assumed to be false. |
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A name for this control that can be used by |
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Css class of select component. |
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Whether the select has a value. |
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Whether the control is in an error state. |
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Whether the select is focused. |
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Determines the |
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Determines the aria-describedby to be set on the host. |
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Determines the aria-owns to be set on the host. |
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Determines if the attribute aria-disabled is disabled |
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Determines if the attribute aria-required is required |
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Controls if a select is disabled |
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Controls if a select value is invalid |
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Manages keyboard events for options in the panel. |
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The y-offset of the overlay panel in relation to the trigger's top start corner. This must be adjusted to align the selected option text over the trigger text. when the panel opens. Will change based on the y-position of the selected option. |
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All of the defined groups of options. |
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The IDs of child options to be passed to the aria-owns attribute. |
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Combined stream of all of the child options' change events. |
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All of the defined select options. |
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Overlay pane containing the options. |
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Panel containing the select options. |
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Whether or not the overlay panel is open. |
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Panel containing the select options. |
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Value of the scrollbar on top |
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This position config ensures that the top "start" corner of the overlay is aligned with with the top "start" of the origin by default (overlapping the trigger completely). If the panel cannot fit below the trigger, it will fall back to a position above the trigger. |
|
|
Role of select field |
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Flag used to rotate the icon on the select trigger |
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Strategy that will be used to handle scrolling while the select panel is open. |
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The currently selected option. |
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Deals with the selection logic. |
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Emits when the state of the option changes and any parents have to be notified. |
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The value of the select panel's transform-origin property. |
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Trigger that opens the select. |
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The cached font-size of the trigger element. |
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The last measured value for the trigger's client bounding rect. |
|
|
The value displayed in the trigger. |
| close | |
|---|---|
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Closes the overlay panel and focuses the host element. |
| focus | |
|---|---|
|
Focuses the select element. |
| handleKeydown | |
|---|---|
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Handles all keydown events on the select. |
|
| Parameters | |
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event KeyboardEvent
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| onAttached | |
|---|---|
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Callback that is invoked when the overlay panel has been attached. |
| onBlur | |
|---|---|
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Calls the touched callback only if the panel is closed. Otherwise, the trigger will "blur" to the panel when it opens, causing a false positive. |
| onFocus |
|---|
| open | |
|---|---|
|
Opens the overlay panel. |
| toggle | |
|---|---|
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Toggles the overlay panel open or closed. |
SbbSelectChange
Change event object that is emitted when the select value has changed.
| Name | Description |
|---|---|
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|
Reference to the select that emitted the change event. |
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Current value of the select that emitted the event. |


FAITH ACADEMY
French Carnival
Please find your name in the following list and click on it to download
PALAK RAI
DUMB CHARADES
WHAT WAS IT?

ZANUL KHAN
DUMB CHARADES


God of War PC port is wanted by game’s director
by Lewis WhiteGod of War PC is a far-away dream for the game’s director Cory Barlog, but it will probably never come.
After the surprising announcement that Hideo Kojima’s Death Stranding would be coming to PC, Barlog expressed his wish for other games to come to the platform.
With Playstation fans upset that their proposed console exclusive will soon be playable on PC, Barlog took to Twitter to explain his thoughts on the subject.
“Does Death Stranding’s non-exclusivity matter?” asked the director. “Not in the least!”
When a fan replied to Barlog to suggest a full PC port of his recent God of War sequel, Barlog seemed enthusiastic about the subject.
“You know, I would love that,” Barlog replied. “It is, sadly, a decision far above my paygrade. As is proven on a daily basis, I am no Kojima.”
Sony fans were split on Barlog’s response. One triggered fan retorted, “Pretty dangerous opinion coming from a first-party developer. Maybe Sony needs a new developer.” Others were more understanding, realising that the director would want his work to be seen by more people.
As of now, God of War PC is technically already a solid reality. As long as you have access to a PlayStation 4 controller and a membership to PlayStation Now, many first-party Sony titles are playable through streaming.


Marsha’s work invites the viewer to ponder the more resonant meaning of value against the cultural beliefs and conventions. Using symbolism evolved from the Victorians, she expresses vulnerability against a ready ability to fly or even swim. The bird a Victorian symbol for freedom is often used a symbol for woman. The egg symbolizes potential, new life and immortal life cycles. The nest is the home. Sometimes Marsha includes a portrait in her work relating to the bird(s) and their environment. This creates a tension between the woman pictured with the woman symbolized. The stark beauty expresses that suppression of a sustainable and meaningful understanding with nature. Kennedy’s paintings reference connections between humanity and the environment that are often destructive aspects of this relationship.
Hope-2008
Crow Soup-2011
Mr.Peanut And The Crows-2011
Lay Me Down To Sleep-2010
Glorious Triumph-2010
Awards And Shows Main Page Marsha's Biology Art


|
CS 374 Compilers
Homework #8 |
Due: Thursday 4/26 at the beginning of classNote: This work is to be done in assigned groups. Each group will submit one assignment. Although
you may divide the work, both team members should be able to present/describe
their partner's work upon request.
Code Generation - Part III
At this point, we can generate code that prints compound arithmetic and
logical expressions, create new objects, make simple method calls (without
dynamic method lookup), and return results. Now we extend our
implementation to reflect the way Java performs dynamic method lookup.
0. Preparation: Read section 14.2 on static versus dynamic method lookup.
In our previous implementation, a new object has a class identifier (e.g. 0, 1,
2, 3). We will be replacing these with the address of a lookup table for
method addresses, much like those shown in Figure 14.3.
1. Symbol Table Addition: You'll want to add to your symbol table
such that it will be easy to:
- For each class, compute the position of each method in the method lookup
table. For top level classes, one needs merely to keep the methods in a
list. For subclasses, overriding methods keep the same position
as the method that is overridden, and new methods are given higher
indices.
2. Allocating Tables:
- In the ".data" section of your assembly, create a special internal
variable for tables by emitting a label (e.g. "_dml_tables") for the address
of the list of dynamic method lookup tables. In the line before, use the
".align" directive to make sure that the variable is on a word boundary. In the next line, use the
".word" directive to set it to 0 initially.
- After setting up the stack frame in your main method, emit code that does
the following:
Dynamically allocate c words of memory (using the sbrk syscall) and
store the address to your tables variable, where c is the number of
classes.
In this table, for each class, dynamically allocate m words of
memory and store the address in the main table, where m is the number of
methods for each class. That is, class i should have an address at the
ith word of the table which points to another table with a size in words
equal to the number of methods in class i.
In each class table, load the addresses of the appropriate address labels
for each method call. Your symbol table additions should help you compute
these labels.
3. NewObject: Change new object such that the class identifier is
replaced by the address of its dynamic method lookup table.
4. Call: Change your call implementation such that it uses the objects
dynamic lookup table to retrieve the correct method address and jump to this
address. That is, it loads the table address from offset 0 of the object.
Then it loads the method address from the appropriate offset in the table.
Finally, it jumps unconditionally to that address, placing the next instruction
in $ra. Note: there seems to be a discrepancy between the documentation
and the simulator. If you have your jump address in $v0, use "jalr $ra,
$v0".
Now retest your method calls/returns with NewObject.java, Factorial.java, and
DynamicMethodLookup.java.
5. While:
- Emit a numbered label (e.g. "whileTest42").
- Generate the test expression code.
- On a result of 0, branch to the numbered ending label
- Generate the statement code.
- Branch unconditionally to the first label.
- Emit a numbered ending label (e.g. "whileDone42").
Now you can test your code with BinaryTree.java and LinkedList.java
6. NewArray: Allocating a new array is similar to
allocating an object. Allocate an extra word at the beginning which holds
the length of the array.
7. ArrayLength: Generate code for the integer array
expression. Then load the length into $v0 from the first word of the array.
8. ArrayLookup:
-
Generate code for the integer array expression.
-
Push the array address from $v0 to the stack.
-
Generate code for the array index expression.
-
Pop the array address to $v1 from the stack.
-
Compute the address of the array integer and load it into $v0
9. ArrayAssign:
-
Generate code for the array identifier.
-
Push the array address from $v0 to the stack.
-
Generate code for the array index expression.
-
Pop the array address to $v1 from the stack.
-
Compute the address of the array + offset and push the address
onto the stack.
-
Generate code for the assignment expression.
-
Pop the offset array address from the stack to $v1.
-
Store the assignment expression result from $v0 to the address
in $v1.
Now you can test your code with the remainder of the test cases:
SimpleArray.java, BinarySearch.java, BubbleSort.java, LinearSearch.java,
QuickSort.java, and TreeVisitor.java.
Congratulations! You have completed your MiniJava compiler!
- Take a moment to look over the generated assembly code and marvel that
it works! A compiler is one of the most complex pieces of software
that an undergraduate implements.
- Review the stages that make up the project to get the big picture.
As you were going through the project, you couldn't have the perspective
you have now. Take advantage of the project being fresh in your mind and
consider the overall process: lexical analysis, parsing, abstract syntax tree
building, symbol table building, type-checking, code generation.
- Consider the valuable skills you've gained and resolve to improve them.
Although few people write compilers...
- ... many more write interpreters. Interpreters are common in
any application that allows user to extend its capabilities, adding value to
the product with little cost to the software company. Rather than
generating code, one can always write an interpreter.
- ... even more write parsers. Consider the amount of
structured information that flows through networks and between processes.
The front end compiler tools you've learned are the best way to write
correct, robust, bulletproof code for the common task of interpreting a stream
of information.
- Reflect on the value of recursion and design patterns in this task.
There are many more design patterns that are useful for different
problem solving tasks. Watch for them!


1 write to Activity
System.Management.Automation (1)
help\HelpSystem.cs (1)
466
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Activity
= StringUtil.Format(HelpDisplayStrings.SearchingForHelpContent, helpRequest.Target);
1 reference to Activity
System.Management.Automation (1)
help\HelpCommands.cs (1)
688
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This 1 starts to get pretty interesting. The team talks about HAXcms, ideates about the new capabilities of the theme layer. This gets into code and great discussion of intentions surrounding it / needs
We start getting into what we can add to the theme layer and the JSON object structure we're using could be extended to effectively bring content types and field APIs to HAXcms while keeping life relative to a small JSON format. It's a bit ridiculous how fast this system is.
I talk through the developer theme, slide player, and kick around ideas for what can come next and then @hey__MP
and @CGLDevel
show their progress on on a custom theme they're developing for HAXcms
which will start to reshape minds about what this is and is possible.
@NikkiMK
closes us out by showing how her new rich-text-editor tag is coming along as well as explaining to Chuck (and anyone watching) why we're working on two text editors simultaneously.
The video closes the way I really feel. I. Love. This. Team.
Keep pushing gang, we're building something amazing that will change the world.


Apache Commons Collections Issue trackingApache Commons Collections uses ASF JIRA for tracking issues. See the Apache Commons Collections JIRA project page. To use JIRA you may need to create an account (if you have previously created/updated Commons issues using Bugzilla an account will have been automatically created and you can use the Forgot Password page to get a new password). If you would like to report a bug, or raise an enhancement request with Apache Commons Collections please do the following:
Please also remember these points:
For more information on subversion and creating patches see the Apache Contributors Guide. You may also find these links useful: |


Current Version 0.19. Written by Joël Ammann.
To install Powershell RIM, download both the Manifest (rim.psd1) and the Module File (rim.psm1). Use: Import-Module rim.psd1
to import the module into Powershell.
Use Set-MdtDeploymentShare -DeploymentShare "\\DEPLOYMENTSERVER\DEPLOYMENTSHARE$"
to define your default Deployment Share.
Powershell RIM integrates into MDT (Microsoft Deployment Toolkit) and uses a common application database. The Get-MdtApplications Cmdlet can be used to list all exising applications inside a Deployment Share. For more information about this Cmdlet, use:Get-Help Get-MdtApplications
For a nice overview of all your applications (as shown in the screenshot below), use:
Get-MdtApplications | Sort-Object ApplicationName | Format-Table
Use the Install-MdtApplications Cmdlet to install one or many applications to one or multiple Windows Clients. For more information about this Cmdlet, use:Get-Help Install-MdtApplications
Use the -Concurrent switch to install applications on multiple devices simultaniously.
If you found a bug or have an idea to improve Powershell RIM, I am happy to hear from you: joel@simplywing.ch


Over 40% of workers surveyed spend at least a quarter of their work week on manual, repetitive tasks, with email, data collection, and data entry occupying the most time.
This app is designed to highlight the value that you're commiting to repetitive tasks in your workplace.
Think of a single process that your team carries out on a regular basis over the course of a week. By inputting the common position of the person carrying out the task and the number of hours this process takes each week, we can qauntify the potential time saving and the value generated by automating this task.
This list of job titles below is a non-exhaustive list. If yours is not there, please choose the closest one.
The salaries used for the calculations are based on UK averages for the job title selected, and for a more specific valuation for your business please get in contact.


In previous posts, I went over all the key components of Stable Diffusion and how to get a prompt to image pipeline working. In this post, I will show how to edit the prompt to image function to add additional functionality to our Stable diffusion pipeline i.e., Negative prompting and Image to Image pipeline. Hopefully, this will provide enough motivation to play around with this function and conduct your research.
1 Variation 1: Negative Prompt
1.1 What is negative prompting?
A negative prompt is an additional capability we can add to our model to tell the stable diffusion model what we don’t want to see in the generated image. This feature is popular to remove anything a user doesn’t want to see from the original generated image.
1.2 Understanding negative prompting through code
Let’s start by importing the required libraries and helper functions. All of this was already used and explained in the previous part 2 and part 3 of the series.
Code
import torch, logging## disable warningslogging.disable(logging.WARNING) ## Imaging libraryfrom PIL import Imagefrom torchvision import transforms as tfms## Basic librariesfrom fastdownload import FastDownloadimport numpy as npfrom tqdm.auto import tqdmimport matplotlib.pyplot as plt%matplotlib inlinefrom IPython.display import displayimport shutilimport os## For video displayfrom IPython.display import HTMLfrom base64 import b64encode## Import the CLIP artifacts from transformers import CLIPTextModel, CLIPTokenizerfrom diffusers import AutoencoderKL, UNet2DConditionModel, LMSDiscreteScheduler## Initiating tokenizer and encoder.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=torch.float16)text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=torch.float16).to("cuda")## Initiating the VAEvae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae", torch_dtype=torch.float16).to("cuda")## Initializing a scheduler and Setting number of sampling stepsscheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)scheduler.set_timesteps(50)## Initializing the U-Net modelunet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet", torch_dtype=torch.float16).to("cuda")## Helper functionsdef load_image(p):''' Function to load images from a defined path '''return Image.open(p).convert('RGB').resize((512,512))def pil_to_latents(image):''' Function to convert image to latents ''' init_image = tfms.ToTensor()(image).unsqueeze(0) *2.0-1.0 init_image = init_image.to(device="cuda", dtype=torch.float16) init_latent_dist = vae.encode(init_image).latent_dist.sample() *0.18215return init_latent_distdef latents_to_pil(latents):''' Function to convert latents to images ''' latents = (1/0.18215) * latentswith torch.no_grad(): image = vae.decode(latents).sample image = (image /2+0.5).clamp(0, 1) image = image.detach().cpu().permute(0, 2, 3, 1).numpy() images = (image *255).round().astype("uint8") pil_images = [Image.fromarray(image) for image in images]return pil_imagesdef text_enc(prompts, maxlen=None):''' A function to take a texual promt and convert it into embeddings '''if maxlen isNone: maxlen = tokenizer.model_max_length inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt") return text_encoder(inp.input_ids.to("cuda"))[0].half()
Now we are going to change the prompt_2_img function from part 3 by passing an additional function neg_prompts. The way negative prompt works is by using user-specified text instead of an empty string for unconditional embedding(uncond) when doing sampling.
So let’s make this change and update our prompt_2_img function.
def prompt_2_img(prompts, neg_prompts=None, g=7.5, seed=100, steps=70, dim=512, save_int=False):""" Diffusion process to convert prompt to image """# Defining batch size bs =len(prompts) # Converting textual prompts to embedding text = text_enc(prompts) # Adding an unconditional prompt , helps in the generation processifnot neg_prompts: uncond = text_enc([""] * bs, text.shape[1])else: uncond = text_enc(neg_prompts, text.shape[1]) emb = torch.cat([uncond, text])# Setting the seedif seed: torch.manual_seed(seed)# Initiating random noise latents = torch.randn((bs, unet.in_channels, dim//8, dim//8))# Setting number of steps in scheduler scheduler.set_timesteps(steps)# Adding noise to the latents latents = latents.to("cuda").half() * scheduler.init_noise_sigma# Iterating through defined stepsfor i,ts inenumerate(tqdm(scheduler.timesteps)):# We need to scale the i/p latents to match the variance inp = scheduler.scale_model_input(torch.cat([latents] *2), ts)# Predicting noise residual using U-Netwith torch.no_grad(): u,t = unet(inp, ts, encoder_hidden_states=emb).sample.chunk(2)# Performing Guidance pred = u + g*(t-u)# Conditioning the latents latents = scheduler.step(pred, ts, latents).prev_sample# Saving intermediate imagesif save_int: ifnot os.path.exists(f'./steps'): os.mkdir(f'./steps') latents_to_pil(latents)[0].save(f'steps/{i:04}.jpeg')# Returning the latent representation to output an image of 3x512x512return latents_to_pil(latents)
Let’s see if the function works as intended.
 Left SD generated with prompt “A dog wearing a white hat” and on right the same caption with negative prompt of “White hat”
As we can see it can be a really handy feature to fine-tune the image to your liking. You can also use it to generate a pretty realistic face by being really descriptive as this Reddit post. Let’s try it -
prompt = ['Close-up photography of the face of a 30 years old man with brown eyes, (by Alyssa Monks:1.1), by Joseph Lorusso, by Lilia Alvarado, beautiful lighting, sharp focus, 8k, high res, (pores:0.1), (sweaty:0.8), Masterpiece, Nikon Z9, Award - winning photograph']neg_prompt = ['lowres, signs, memes, labels, text, food, text, error, mutant, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, made by children, caricature, ugly, boring, sketch, lacklustre, repetitive, cropped, (long neck), facebook, youtube, body horror, out of frame, mutilated, tiled, frame, border, porcelain skin, doll like, doll']images = prompt_2_img(prompts = prompt, neg_prompts=neg_prompt, steps=50, save_int=False)images[0]
Fig. 5: An image generated using negative prompting.
Pretty neat! I hope this gives you some ideas on how to get going with your own variations of stable diffusion. Now let’s look at another variation of Stable diffusion.
2 Variation 2: Image to Image pipeline
2.1 What is an image to image pipeline?
As seen above, prompt_2_img functions start generating an image from random gaussian noise, but what if we feed an initial seed image to guide the diffusion process? This is exactly how the image to image pipeline works. Instead of purely relying on text conditioning for the output image, we can use an initial seed image mix it with some noise (which can be guided by a strength parameter), and then run the diffusion loop.
2.2 Understanding image to image prompting through code
Now we are going to change the prompt_2_img function defined above. We will introduce two more parameters to our prompt_2_img_i2i function - 1. init_img: Which is going to be the Image object containing the seed image 2. strength: This parameter will take a value between 0 and 1. The higher the value less the final image is going to look similar to the seed image.
def prompt_2_img_i2i(prompts, init_img, neg_prompts=None, g=7.5, seed=100, strength =0.8, steps=50, dim=512, save_int=False):""" Diffusion process to convert prompt to image """# Converting textual prompts to embedding text = text_enc(prompt) # Adding an unconditional prompt , helps in the generation processifnot neg_prompts: uncond = text_enc([""], text.shape[1])else: uncond = text_enc(neg_prompt, text.shape[1]) emb = torch.cat([uncond, text])# Setting the seedif seed: torch.manual_seed(seed)# Setting number of steps in scheduler scheduler.set_timesteps(steps)# Convert the seed image to latent init_latents = pil_to_latents(init_img)# Figuring initial time step based on strength init_timestep =int(steps * strength) timesteps = scheduler.timesteps[-init_timestep] timesteps = torch.tensor([timesteps], device="cuda")# Adding noise to the latents noise = torch.randn(init_latents.shape, generator=None, device="cuda", dtype=init_latents.dtype) init_latents = scheduler.add_noise(init_latents, noise, timesteps) latents = init_latents# Computing the timestep to start the diffusion loop t_start =max(steps - init_timestep, 0) timesteps = scheduler.timesteps[t_start:].to("cuda")# Iterating through defined stepsfor i,ts inenumerate(tqdm(timesteps)):# We need to scale the i/p latents to match the variance inp = scheduler.scale_model_input(torch.cat([latents] *2), ts)# Predicting noise residual using U-Netwith torch.no_grad(): u,t = unet(inp, ts, encoder_hidden_states=emb).sample.chunk(2)# Performing Guidance pred = u + g*(t-u)# Conditioning the latents latents = scheduler.step(pred, ts, latents).prev_sample# Saving intermediate imagesif save_int: ifnot os.path.exists(f'./steps'): os.mkdir(f'./steps') latents_to_pil(latents)[0].save(f'steps/{i:04}.jpeg')# Returning the latent representation to output an image of 3x512x512return latents_to_pil(latents)
Instead of using random noise, you will notice we use the strength parameter to figure out how much noise to add and also the number of steps to run the diffusion loop for. The amount of noise is calculated by multiplying strength(default = 0.8) with steps (default = 50) which is the 10th (50 - 50 * 0.8) step and running the diffusion loop for 40(50*0.8) remaining steps. Let’s load an initial image and pass it through the prompt_2_img_i2i function.
p = FastDownload().download('https://s3.amazonaws.com/moonup/production/uploads/1664665907257-noauth.png')image = Image.open(p).convert('RGB').resize((512,512))prompt = ["Wolf howling at the moon, photorealistic 4K"]images = prompt_2_img_i2i(prompts = prompt, init_img = image)
/home/aayush/miniconda3/envs/fastai/lib/python3.9/site-packages/diffusers/schedulers/scheduling_lms_discrete.py:146: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.
If increasing the limit yields no improvement it is advised to analyze
the integrand in order to determine the difficulties. If the position of a
local difficulty can be determined (singularity, discontinuity) one will
probably gain from splitting up the interval and calling the integrator
on the subranges. Perhaps a special-purpose integrator should be used.
integrated_coeff = integrate.quad(lms_derivative, self.sigmas[t], self.sigmas[t + 1], epsrel=1e-4)[0]
Code
## Plotting side by sidefig, axs = plt.subplots(1, 2, figsize=(12, 6))for c, img inenumerate([image, images[0]]): axs[c].imshow(img)if c ==0 : axs[c].set_title(f"Initial image")else: axs[c].set_title(f"Image 2 Image output")
Fig. 7: Visualization of image to image pipeline. Left is initial image passed in img2img pipeline and right is the output of the img2img pipeline.
We can see our prompt_2_img_i2i function creates a pretty epic image from the initial sketch provided.
3 Conclusion
I hope this gives a good overview of how to tweak the prompt_2_img function to add additional capabilities to your stable diffusion loop. The understanding of this lower-level function is useful for trying your own idea to improve stable diffusion or implement new papers which I might cover in my next post.
I hope you enjoyed reading it, and feel free to use my code and try it out for generating your images. Also, if there is any feedback on the code or just the blog post, feel free to reach out on LinkedIn or email me at aayushmnit@gmail.com.


Class TFSession.Runner
Use the runner class to easily configure inputs, outputs and targets to be passed to the session runner.
Inheritance
Namespace: TensorFlow
Assembly: TensorFlowSharp.dll
Syntax
public class TFSession.Runner
Remarks
The runner has a simple API that allows developers to call the AddTarget, AddInput, AddOutput and Fetch to construct the parameters that will be passed to the TFSession.Run method.
Instances of this class are created by calling the GetRunner method on the TFSession.
The various methods in this class return an instance to the Runner itsel, to allow to easily construct chains of execution like this:
var result = session.GetRunner ().AddINput (myInput).Fetch (MyOutput).Run ();
You do not need to chain the operations, this works just the same:
runner = session.GetRunner ();
runner.AddInput(myInput);
runner.Fetch(myOutput);
var results = runner.Run();
Fields
RunMetadata
Protocol buffer encoded block containing the metadata passed to the TensorFlow.TFSession.Run method.
Declaration
public TensorFlow.TFBuffer RunMetadata;
Field Value
| Type | Description |
|---|---|
| TFBuffer | To be added. |
RunOptions
Protocol buffer encoded block containing the run options passed to the TensorFlow.TFSession.Run method.
Declaration
public TensorFlow.TFBuffer RunOptions;
Field Value
| Type | Description |
|---|---|
| TFBuffer | To be added. |
Methods
AddInput(String, TFTensor)
Adds an input to the session specified by name, with an optional index in the operation (separated by a colon).
Declaration
public TensorFlow.TFSession.Runner AddInput (string input, TensorFlow.TFTensor value);
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | input | Incoming port, with an optional index separated by a colon. |
| TFTensor | value | Value to assing to the incoming port. |
Returns
| Type | Description |
|---|---|
An instance to the runner, so you can easily chain the operations together. |
AddInput(TFOutput, TFTensor)
Adds an input to the session
Declaration
public TensorFlow.TFSession.Runner AddInput (TensorFlow.TFOutput input, TensorFlow.TFTensor value);
Parameters
| Type | Name | Description |
|---|---|---|
| TFOutput | input | Incoming port. |
| TFTensor | value | Value to assing to the incoming port. |
Returns
| Type | Description |
|---|---|
An instance to the runner, so you can easily chain the operations together. |
AddTarget(String[])
Adds the specified operation names as the ones to be retrieved.
Declaration
public TensorFlow.TFSession.Runner AddTarget (string[] targetNames);
Parameters
| Type | Name | Description |
|---|---|---|
| System.String[] | targetNames | One or more target names. |
Returns
| Type | Description |
|---|---|
An instance to the runner, so you can easily chain the operations together. |
AddTarget(TFOperation[])
Adds the specified operations as the ones to be retrieved.
Declaration
public TensorFlow.TFSession.Runner AddTarget (TensorFlow.TFOperation[] targets);
Parameters
| Type | Name | Description |
|---|---|---|
| TFOperation[] | targets | One or more targets. |
Returns
| Type | Description |
|---|---|
An instance to the runner, so you can easily chain the operations together. |
Fetch(String)
Makes the Run method return the output of the tensor referenced by operation, the operation string can contain the output index.
Declaration
public TensorFlow.TFSession.Runner Fetch (string operation);
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | operation | The name of the operation in the graph, which might be a simple name, or it might be name:index, where the index is the . |
Returns
| Type | Description |
|---|---|
The instance of runner, to allow chaining operations. |
Fetch(String[])
Makes the Run method return the output of all the tensor referenced by outputs.
Declaration
public TensorFlow.TFSession.Runner Fetch (string[] outputs);
Parameters
| Type | Name | Description |
|---|---|---|
| System.String[] | outputs | The output sreferencing a specified tensor. |
Returns
| Type | Description |
|---|---|
The instance of runner, to allow chaining operations. |
Fetch(TFOutput)
Makes the Run method return the output of the tensor referenced by output
Declaration
public TensorFlow.TFSession.Runner Fetch (TensorFlow.TFOutput output);
Parameters
| Type | Name | Description |
|---|---|---|
| TFOutput | output | The output referencing a specified tensor. |
Returns
| Type | Description |
|---|---|
The instance of runner, to allow chaining operations. |
Fetch(TFOutput[])
Makes the Run method return the output of all the tensor referenced by outputs.
Declaration
public TensorFlow.TFSession.Runner Fetch (TensorFlow.TFOutput[] outputs);
Parameters
| Type | Name | Description |
|---|---|---|
| TFOutput[] | outputs | The outputs referencing a specified tensor. |
Returns
| Type | Description |
|---|---|
The instance of runner, to allow chaining operations. |
Fetch(String, Int32)
Makes the Run method return the index-th output of the tensor referenced by operation.
Declaration
public TensorFlow.TFSession.Runner Fetch (string operation, int index);
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | operation | The name of the operation in the graph. |
| System.Int32 | index | The index of the output in the operation. |
Returns
| Type | Description |
|---|---|
The instance of runner, to allow chaining operations. |
Run(TFStatus)
Execute the graph fragments necessary to compute all requested fetches.
Declaration
public TensorFlow.TFTensor[] Run (TensorFlow.TFStatus status = null);
Parameters
| Type | Name | Description |
|---|---|---|
| TFStatus | status | Status buffer, if specified a status code will be left here, if not specified, a TFException exception is raised if there is an error. |
Returns
| Type | Description |
|---|---|
| TFTensor[] | One TFTensor for each call to Fetch that you made, in the order that you made them. |
Run(TFOutput, TFStatus)
Run the specified operation, by adding it implicity to the output, single return value
Declaration
public TensorFlow.TFTensor Run (TensorFlow.TFOutput operation, TensorFlow.TFStatus status = null);
Parameters
| Type | Name | Description |
|---|---|---|
| TFOutput | operation | The output of the operation. |
| TFStatus | status | Status buffer, if specified a status code will be left here, if not specified, a TFException exception is raised if there is an error. |
Returns
| Type | Description |
|---|---|
| TFTensor | To be added. |
Remarks
This method is a convenience method, and when you call it, it will clear any calls that you might have done to Fetch() and use the specified operation to Fetch instead.


Vietnam opened my eyes
I feel like I did not do justice to our trip in Vietnam. It was the best one month of my life and I spent it with my best friends Jun and Jonas and a friend I met while travelling Cambodia, Lukas!
- We started in Ho Chi Minh city and visited the cu chi tunnels
- Nha Trang for some beach days
- Dalat for canyoning at the waterfalls (scariest thing I have ever done but most rewarding!)
- Hoi An for culture and its tailoring (It is a pretty little town!)
- followed by motorbike ride to Hue for a walk around the perfume river and a trishaw ride through town
- Hanoi for culture and the secret night life (due to military curfew!) and street food was amazing!
- Halong Bay we took the party boat which allowed us to meet new people; we got to explore the caves and kayak through them. Needless to say the party on the boat was fun; we made new friends and two of them eventually visited us in Singapore!
- Lastly, we ended up in Sapa where we did a home-stay with the local people. They took us for a 13 km walk up the mountains where we met their families and ate with them and learnt from them about their culture and what a daily day is like for them. In the day, we explored the market in town to have a look at all the natural works by the people. They made beautiful handicrafts using natural colours and materials.
I think of this trip often. It was the one time in my life I had no plans and just went with whatever I felt the next morning. Yes, I was broke but I was so happy. I always get carried away worrying about the future, and its made worse with my drive to be perfect. Now, whenever I feel myself drifting away, I think of Vietnam. Letting go all worries and just thinking for today. Thus, for me Vietnam will always be in my heart and I am glad I shared that month with my best mates.
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--- title: Tooltip description: Tooltip sample page. ---
The scarabaeus hung quite clear of any branches, and, if allowed to fall, would have fallen at our feet. Legrand immediately took the scythe, and cleared with it a circular space, three or four yards in diameter, just beneath the insect, and, having accomplished this, ordered Jupiter to let go the string and come down from the tree.


Amanda Komp's Webpage!
This site will be used to post homework assignments for OMIS 475
Basic Webpage
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6
Assignment 8
Web Presence Mockup
Assignment 9
Web Presence Project
Assignment 11
Final Web Presence


Doping: WADA to investigate athletes coached by banned Salazar - report
World Anti-Doping Agency (WADA) President Craig Reedie has said the organisation will look into athletes who trained under banned coach Alberto Salazar as part of the Nike Oregon Project (NOP), according to a London Times report on Tuesday.
REUTERS: World Anti-Doping Agency (WADA) President Craig Reedie has said the organisation will look into athletes who trained under banned coach Alberto Salazar as part of the Nike Oregon Project (NOP), according to a London Times report on Tuesday.
Salazar, who counts Britain's Olympic and world champion Mo Farah among the top distance runners he has coached, was last month banned for four years by the U.S. Anti-Doping Agency (USADA) for doping violations.
The USADA investigation found no evidence of any wrongdoing by Salazar's athletes.
"We can look back at what testing these athletes have been subjected to over the years," Reedie told The Times. "But it will be more interesting for us if there are samples we can retest."
According to the report, USADA officials are in the process of sending all of their evidence to WADA's headquarters and The Times understands it would be willing to work alongside the global agency.
USADA chief Travis Tygart told the newspaper in a statement: "We invited WADA to be observers and what they would have seen is that we left no stone unturned. That said, we are happily compiling the evidence and sending it to them."
American Salazar, who was a celebrated distance runner, winning three consecutive New York City marathons starting in 1980, has vowed to appeal his ban.
(Reporting by Frank Pingue in Toronto; Editing by Ken Ferris)


Service Worker Cache Example
A Simple Sample Web Page
By NodeXperts
Demonstrating a few HTML features
HTML is really a very simple language. It consists of ordinary text, with commands that are enclosed by "<" and ">" characters, or bewteen an "&" and a ";".
You don't really need to know much HTML to create a page, because you can copy bits of HTML from other pages that do what you want, then change the text!
This page shows on the left as it appears in your browser, and the corresponding HTML code appears on the right. The HTML commands are linked to explanations of what they do.
Line Breaks
HTML doesn't normally use line breaks for ordinary text. A white space of any size is treated as a single space. This is because the author of the page has no way of knowing the size of the reader's screen, or what size type they will have their browser set for.
If you want to put a line break at a particular place, you can use the "
" command, or, for a paragraph break, the "
" command, which will insert a blank line. The heading command ("<4>4>") puts a blank line above and below the heading text.
Starting and Stopping Commands
Most HTML commands come in pairs: for example, "" marks the beginning of a size 4 heading, and "
" marks the end of it. The closing command is always the same as the opening command, except for the addition of the "/".
Modifiers are sometimes included along with the basic command, inside the opening command's < >. The modifier does not need to be repeated in the closing command.
This is a size "1" heading
This is a size "2" heading
This is a size "3" heading
This is a size "4" heading
This is a size "5" heading
This is a size "6" heading
Copyright © 1997, by
NodeXperts
If you would like to make a link or bookmark to this page, the URL is:
//www.sheldonbrown.com/web_sample1.html


{% extends "base.html" %} {% block title %}
Medieval catalogues > UNIVERSITY AND COLLEGE LIBRARIES OF CAMBRIDGE > Donor documents > Will of Robert Scolys, 6 June 1470; Clare Hall
{% endif %}3 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


EasyTrans is a smart Pocket Transport Companion App. It allows you to book transport online facilating contactless services using Online Payment & QR Scanning as a strategy. It is integrated with realtime temperature monitoring services to facilitate Temperature Control.


@include("/tssc/include/head.html"){}
${company.name}
${company.introduction}
@for(studio in studioList){
${studio.name}
@}
@for(studio in studioList){
${studio.name}
${studio.synopsis}
@}
${company.personnel}
@for(personnel in personnelList){
${personnel.name}
@for(position in personnel.positionList){
${position}
@}
@}
@for(personnel in personnelList){
${personnel.name}
${personnel.engName}
@for(position in personnel.positionList){
${position}
@}
${personnel.introduce}
@}
${company.field}
@for(field in fieldList){
${field.name}
${field.introduce}
@}
${company.brand}
${company.brandContent}
${company.history}
${company.historyContent}
${company.socialResponsibility}
${company.socialResponsibilityContent}
@include("/tssc/include/foot.html"){}


(password must contain at least one digit/lowercase/uppercase letter and be at least eight characters long)


|
DSL-ABM
|
#include "flu_transitions_tests.h"
Functions | |
| bool | test_susceptible_transitions () |
| Series of tests for transitions of a regular susceptible agent. More... | |
| ABM | create_abm (const double dt, int i0) |
| bool | check_isolation (const Agent &agent, const std::vector< School > schools, const std::vector< RetirementHome > retirement_homes, const std::vector< Workplace > workplaces, bool check_in_isolation) |
| Check if agent is checked out from all the public places. More... | |
| bool | check_fractions (int num1, int num2, double fr_expected, std::string msg) |
| Check if num1/num2 is roughly equal to expected. More... | |
| int | main () |
| bool check_fractions | ( | int | num1, |
| int | num2, | ||
| double | fr_expected, | ||
| std::string | msg | ||
| ) |
Check if num1/num2 is roughly equal to expected.
| bool check_isolation | ( | const Agent & | agent, |
| const std::vector< School > | schools, | ||
| const std::vector< RetirementHome > | retirement_homes, | ||
| const std::vector< Workplace > | workplaces, | ||
| bool | check_in_isolation = true |
||
| ) |
Check if agent is checked out from all the public places.
| ABM create_abm | ( | const double | dt, |
| int | i0 | ||
| ) |
| int main | ( | ) |
| bool test_susceptible_transitions | ( | ) |
Series of tests for transitions of a regular susceptible agent.
Series of tests for transitions of a susceptible non-COVID hospital patient.
Series of tests for transitions of a susceptible hospital employee.


ConfirmType (773)
Identifies the type of Confirmation message being sent.
Type: int
| 1 | Status |
| 2 | Confirmation |
| 3 | Confirmation Request Rejected (reason can be stated in Text (58) field) |
Type: int
| 1 | Status |
| 2 | Confirmation |
| 3 | Confirmation Request Rejected (reason can be stated in Text (58) field) |


All values within a record must be separated by commas and there must not be any carriage return and line feed (CRLF). If a text string contains a space, tab or comma , it must be enclosed in double quotes. All fields must always be present. Each record must be terminated by a carriage return and line feed (CRLF), but no comma. The exception is the last record which must not have either a comma or CRLF terminator.
Composite isotopics data files are entirely in text (ASCII).
First record format
Isotopics id, Isotopics source code, Pu mass, 238Pu, 239Pu. 240Pu, 241Pu, 242Pu, Pu date, 241Am, Am date, 238Pu error, 239Pu error, 240Pu error, 241Pu error, 242Pu error, 241Am error
Format of all records except the first:
Pu mass, 238Pu, 239Pu. 240Pu, 241Pu, 242Pu, Pu date, 241Am, Am date, 238Pu error, 239Pu error, 240Pu error, 241Pu error, 242Pu error, 241Am error
Only the first 12 characters of the isotopics id will be used. If the isotopics id contains a space or other white space character, it must be enclosed in double quotes.
The possible two character isotopics source codes are: OD, IA, IM, OA, OC, OE, OS, CO.
Mass is in grams Pu. Composite isotopics are weighted by mass.
All isotopic values are in weight % Pu.
All error values are in weight % Pu.
The date format is: YYYYMMDD
Repeat the mass, isotopic values and error values for each set of isotopics up to a maximum of six.


The Best of Quora – Product Management
I’ve read every Product Management thread on Quora, my favorites…
General
Ian McAllister of Amazon defines what distinguishes the top 1% of product managers from the top 10%.
Ian McAllister of Amazon talks about working backwards from the customer, including writing press releases in Oprah speak.
Nick Barr of Canvas describes his workflow for writing a product requirements document for small, fast-moving companies.
Jeff Eddings, previously of Google and StumbleUpon, cover some of the best metrics for a Product Manager to track. Ian McAllister of Amazon adds some great resources.
Ian McAllister of Amazon and Adam Nash, Ex Ebay, LinkedIn and Greylock, explore the best ways to prioritize a product and feature list.
Engineering
Fareed Mosavat of Zynga explores why Engineers become Product Managers, looking at the 3 directions an engineer can go over the course of their career.
Anup Cowkur of Y Media Labs uses Dilbert cartoons to break down things that Product Managers do that Engineers dislike.
Breaking Into Product Management
Ian McAllister of Amazon talks about transitioning into product management using the role change two-step.
Rakesh Agrawal of reDesign Mobile covers some frequently asked questions in product manager interviews. Also see Kenneth Norton of Google’s masterpost on the same subject.
Sandeep De, ex TD Ameritrade and Geico talks about the first to do if you just joined a company as a product manager, including developing a 30-60-90day plan.
Scott Owens of Salesforce and Venkatesh Rao, author of Tempo, describe the day in the life of a Product Manager.
Lots of people chime in on must read books for Product Managers.


STSStreamingLiveEventConfig
@interface STSStreamingLiveEventConfig : NSObject
A STSStreamingLiveEventConfig object defines the configuration of a live event.
-
The title of the live event.
Declaration
Objective-C
@property (readonly, nonatomic) NSString *_Nonnull title;
Swift
var title: String { get }
-
The synopsis of the live event.
Declaration
Objective-C
@property (assign, readwrite, nonatomic, nullable) NSString *synopsis;
Swift
var synopsis: String? { get set }
-
The id of the category which the live event belongs to.
If you want to set category id of the live event, you should create categories first. You can create categories in StraaS CMS . Once you create categories, you can get category lists by
STSCMSManager
inStraaSPlayerSDK
or by calling StraaS Get Categories API from your server and get category ids from the response data.Declaration
Objective-C
@property (assign, readwrite, nonatomic, nullable) NSNumber *categoryId;
Swift
var categoryId: NSNumber? { get set }
-
The array of tags which the live event is tagged with.
Declaration
Objective-C
@property (assign, readwrite, nonatomic, nullable) NSArray<NSString *> *tags;
Swift
var tags: [String]? { get set }
-
A boolean value indicates whether the live event should be listed.
Declaration
Objective-C
@property (readonly, nonatomic) BOOL listed;
Swift
var listed: Bool { get }
-
A boolean value indicates whether to list the new VoD of the live event or not. Defaults to
NO
.Declaration
Objective-C
@property (assign, readwrite, nonatomic) BOOL vodListed;
Swift
var vodListed: Bool { get set }
-
A boolean value indicates Whether the new VoD of the live event is available by users or not. Defaults to
NO
.Declaration
Objective-C
@property (assign, readwrite, nonatomic) BOOL vodAvailable;
Swift
var vodAvailable: Bool { get set }
-
The max resolution of the live event. Defaults to
STSStreamingResolution720p
. This property is deprecated. Useprofile
instead.Note: This property only works if
profile
is equal toSTSStreamingProfileNone
.Declaration
Objective-C
@property (assign, readwrite, nonatomic) STSStreamingResolution maxResolution;
Swift
var maxResolution: STSStreamingResolution { get set }
-
The profile of the live event. Defaults to
STSStreamingProfileNone
.Note that
STSStreamingProfile1080pAndSource
can only be used when your account is enabled highest resolution 1080p authority.Declaration
Objective-C
@property (assign, readwrite, nonatomic) STSStreamingProfile profile;
Swift
var profile: STSStreamingProfile { get set }
-
A boolean value indicates whether to merge all VoDs of the live event together. Defaults to
NO
.Note: * If this property is set to
YES
, only one VoD will be generated, and no temporary VoD will be shown until the live event ends. * This configuration can’t be changed after the live event is created.Declaration
Objective-C
@property (assign, readwrite, nonatomic) BOOL vodMerge;
Swift
var vodMerge: Bool { get set }
-
A boolean value indicates whether to enable live DVR feature for a live event. Default value:
NO
.Note: * This configuration can’t be changed after the live event is created. * This property can be set to
YES
only when your account is enabled live-DVR authority.Declaration
Objective-C
@property (assign, readwrite, nonatomic) BOOL dvrEnabled;
Swift
var dvrEnabled: Bool { get set }
-
Creates a STSStreamingLiveEventConfig with given parameters.
Declaration
Objective-C
+ (nonnull instancetype)liveEventConfigWithTitle:(nonnull NSString *)title listed:(BOOL)listed;
Swift
convenience init(title: String, listed: Bool)
Parameters
title
The title of the live event.
listed
Indicates whether the live event should be listed.
Return Value
A newly STSStreamingLiveEventConfig object.


Zinedine Zidane calls for Gareth Bale boos to stop
The Welsh star was jeered by Real Madrid fans against Real Sociedad on Saturday.
by The42 TeamREAL MADRID HEAD coach Zinedine Zidane called on fans to stop booing Gareth Bale following the Wales star’s return to LaLiga action on Saturday.
Bale came on in the second half of the 3-1 win over Real Sociedad at the Santiago Bernabeu and was greeted by an unhappy chorus from sections of the crowd.
Although some fans applauded the player, Bale was then subjected to jeers with every touch of the ball as Madrid saw out a win that moved them level on points with LaLiga leaders Barcelona.
It was Bale’s first appearance for Madrid since October and came at the end of a week in which he risked the wrath of Madrid fans and media by celebrating Wales’ Euro 2020 qualification near a banner that read: “WALES. GOLF. MADRID…IN THAT ORDER”.
The slogan originates from a chant by Wales supporters that pokes fun at perceptions of Bale among the Spanish press, who have accused the former Tottenham player of pushing Madrid down his list of priorities.
Zidane, who was pleased with Bale’s performance, accepts he cannot control fan reactions but called for the player to be supported for the rest of 2019-20.
“I hope this isn’t constant this season. We want the fans to be with us from beginning to end, but we can’t control that,” he told reporters.
The fans have the right to do what they want, but I ask that they applaud everyone. I focus on the game and the victory, and Bale came into the game very well.
“I haven’t spoken with him; I’ve only congratulated him.
“I can’t say if it’s unfair or not. Everyone can say what they want. We need our fans. After that, we can’t control what happens.
There is too much noise with Bale. He wants to be with us and he wants to do well. That’s why we don’t want to talk too much. He is part of the group and wants to play like everyone else.”
Madrid fell behind when Willian Jose scored after just two minutes following an error from captain Sergio Ramos, but they fought back through goals from Karim Benzema, Federico Valverde and Luka Modric.
“I’m happy. We didn’t start well with the ball — we were very slow. Then, the end of the first half was very good and the start of the second, too,” said Zidane.
“We’re happy because there was a very good team facing us. We’re happy for the three points.”


In-Class Notes
Class notes 10/7/2016
- Finite State Machine
- An abstract machine which can be in one of a finite number of states
- Any given finite state machine described by:
- A finite set of states
- Alphabet - a finite set of symbols
- Transition function
- A set of rules (can be given in a table)
- If in state [A] and looking at symbol [*], next state is [B]
- Starting state
- Set of accepting states
- Language: set of symbols and rules to distinguish legal from illegal (describes a machine)
- Machine: set of rules which recognize a language
- Lexx
- Unix program
- Given a description of a regular language, produces a C program (finite state machine) that recognizes the language
- Regular Expressions
- Search for more complicated patterns than exact matches
- 4 Types of Languages
- 4 kinds of codes
- Convenience: unicode/ASCII
- Data compression: ZIP, MP3, JPEG
- Error detection and correction
- Encryption
Class Notes 10/10/2016
- Different forms of identifier are used in different circumstances
- Hostname
- variable-length alphanumeric string identifier
- provide little information about the site's location
- difficult for computers to parse
- IPv4 Address
- 4-byte computer-readable numeric identifier
- describes its location
- Format: byte.byte.byte.byte (<IP_ADDRESS>)
- Mnemonic: device to help you memorize something
- Domain Name System
- Two part definition
- Distributed database
- API for making queries on said database
- Popular example: Berkeley Internet Name Domain
- DNS runs on port 53 over UDP
- Application-level protocol used by other application-level protocols
- Process of using the DNS to fetch an IP address adds delay (from milliseconds to seconds
- To reduce delay, nearby DNS server caches frequently requested IPs
- Cache: local memory that stores what was looked up last
- Specified in RFC 1034 and RFC 1035, updated in several more
- Users do not interact directly with the DNS, only applications do
- DNS Services
- Host Aliasing
- Host with a complicated hostname can have one or more alias names
- Complicated base url is the canonical hostname
- Alias names are usually more mnemonic
- Mail Server Aliasing
- Translate mnemonic e-mail address endings (@hotmail.com, @gmail.com, etc.)
- Invoked by mail application
- MX record permits company's mail server and web server to have identical alias names
- Load Distribution
- Busy sites have IPs to alleviate strain on the system
- DNS alternates between which IP it distributes first to prevent all traffic from going to the same IP
- Simple centralized DNS has many problems
- Single point of failure: if the server fails, the entire internet crashes
- Traffic volume would be immense, creating a long queue delay
- Anyone far from the database would have high delays
- High maintenance requirements
- Distributed DNS
- No single machine contains all of the mappings
- Machines are distributed across the world
- Hierarchy of servers
- Root server
- TLD server
- Authoritative server
- DNS Message Format
- Identification/Flags (4 bytes each)
- Identification: 16-bit number identifying the query
- Flags: Yes/no indicators defining the kind of query
- Number of questions/Number of answer RRs (4 bytes each)
- Number of Authority RRs/Number of additional RRs (4 bytes each)
- Questions (variable number) (8 bytes)
- Name field with the name being queried
- Type field with the kind of question
- Answers (variable number)
- Resource Records which were queried
- Authority (variable number)
- Records of other authoritative servers
- Additional Information (variable number)
- Other helpful records (canonical hostnames and IPs, etc
- DNS Vulnerabilities
- DDoS attack
- Flood server with messages
- Many high-level servers have packet filters, preventing them from being directly attacked
- Man-in-the-middle attack
- Intercepts queries from hosts and returns bogus replies
- May then redirect client to the real server so they don't know anything is wrong
Class notes 10/11/2016
- Apache
- Represented by a feather icon
- Named because it is "a patchy program"
- Runs on port 80 by default
- Good for HTML and PHP pages
- Glassfish
- Good for HTML and Java pages?
- Runs on port 8080 by default
- Cake (PHP Framework)
- Jango (Java Framework)
- Java Faces Server
- Can use ordinary html tags, but also has instructions to the server to generate html
- Generated html allows for use of special characters without manually escaping them
- Allows for neater stylesheet referencing
- Allows long or frequent output strings to be stored in a 'properties' file, displayed by calling a simple variable
- Allows for multiple such properties files to support other languages
- (name the property file the same thing but with the two-letter country tag at the end)
- Standard Generalized Markup Language
- HyperText Markup Language
- eXtensible Markup Language
- HTML and XML derived from SGML, which was a lot more complicated
- Lets you create your own tags as long as you follow the specifications
- Frequently used in configuration files
- Java Annotations
- Marked with an @
- Tells the server to insert a pre-determined block of code
- Session Scope
- Introduce some state to HTML
- Store a cookie until it is no longer necessary
- Application Scope
- Introduce some state to HTML
- Store variables from all users who visit the application
\
There are no hugs in computer science
Home


JavaScript (`accepts any class name`)
var rellax = new Rellax('.rellax');
Basic Markup (Default Speed: -2)

I’m slow and smooth

Optional Speed (-10 to +10)

I’m slow and smooth

I’m super fast!!

I’m extra slow and smooth

Speed
Use the data-rellax-speed attribute to set the speed of a .rellax element to something other than the default value (which is -2). A negative value will make it move slower than regular scrolling, and a positive value will make it move faster. We recommend keeping the speed between -10 and +10.
Responsive Speed
Use responsive speed attributes for breakpoint levels that require a different speed. Defaults to the data-rellax-speed setting in unspecified breakpoints.

I parallax at all different speeds depending on your screen width.

Pass an array of breakpoints. Each breakpoint value represents the resolution for mobile, tablet, desktop respectively. Checkout the responsiveness of the demo.

Centering
After some fantastic work from @p-realinho, we just released the ability to center parallax elements in your viewport! We'll be building a nice demo website, but for now check out the tests folder for several examples of how it works via our tests folder on GitHub.
There's two ways to implement centering, either on specific elements or as a global option.

I’m that default chill speed of "-2" and "centered"

I’m super fast!! And super centered!!

I’m extra slow and smooth, and hella centered.

Z-Index
If you want to sort your elements properly in the Z space,
you can use the
data-rellax-zindex property.

I’m that default chill speed of "-2" and default z-index of 0

I’m super fast!! And on top of the previous element, I'm z-index 5!!

Horizontal Parallax
Horizontal parallax is disabled by default. You can enable it by passing
horizontal: true in the settings
block. This feature is intended for panoramic style websites, where users
scroll horizontally instead of vertically. Note that this can work together
at the same time with the default vertical parallax. If you do not want this,
pass vertical: false.

Custom Wrapper
By default, the position of parallax elements is determined via the scroll position of the body. Passing in the wrapper property in the settings block will tell Rellax to watch that element instead.

Target Node
Instead of using a className you can use a node, handy when using React and you want to use refs instead of className

 { this.rellaxRef = ref}}>
I’m that default chill speed of "-2"

Refresh

Destroy


2 references to RaisePipelineStateEvents
System.Management.Automation (2)
engine\remoting\client\remotepipeline.cs (2)
622
RaisePipelineStateEvents
();
719
RaisePipelineStateEvents
();


Introduction
Three paragraph statement.
First paragraph: General. Motivational. Majors specific finding.
Second paragraph: More about what you did and found.
Third paragraph: Outline of the paper. Future work. Weaknesses. Introduce the scenario and data objective. Type of data variable.
# install.packages("devtools")
# devtools::install_github("hodgettsp/cesR")
cesR::get_decon()
## TO CITE THIS SURVEY FILE: Stephenson, Laura B; Harell, Allison; Rubenson, Daniel; Loewen, Peter John, 2020, '2019 Canadian Election Study - Online Survey',
## https://doi.org/10.7910/DVN/DUS88V, Harvard Dataverse, V1
## LINK: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DUS88V
Data
The CES dataset is….
head(decon)
## # A tibble: 6 x 22
## ces_code citizenship yob gender province_territ… education lr lr_bef
## <chr> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
## 1 ces2019… Canadian c… 1989 A wom… Quebec Master's… 2 <NA>
## 2 ces2019… Canadian c… 1998 A wom… Quebec Master's… 2 <NA>
## 3 ces2019… Canadian c… 2000 A wom… Ontario Some uni… 4 4
## 4 ces2019… Canadian c… 1998 A man Ontario Some uni… 7 7
## 5 ces2019… Canadian c… 2000 A wom… Ontario Complete… 4 4
## 6 ces2019… Canadian c… 1999 A wom… Ontario Some uni… 4 4
## # … with 14 more variables: lr_aft <chr>, religion <fct>,
## # sexuality_selected <fct>, sexuality_text <chr>, language_eng <fct>,
## # language_fr <fct>, language_abgl <fct>, employment <fct>, income <dbl>,
## # income_cat <fct>, marital <fct>, econ_retro <fct>, econ_fed <fct>,
## # econ_self <fct>
Some initial explorations that would be used to guide this section.
#visdat::vis_dat(decon)
#skimr::skim(decon)
#class(decon$ces_code)
decon <-
decon %>%
filter(income < 1000000)#select observations
decon <-
decon %>%
select(religion, income)#select variables
decon %>% summarise(mean_income = mean(income), sd_income = sd(income), min_income = min(income),
max_income = max(income), median_income = median(income))
## # A tibble: 1 x 5
## mean_income sd_income min_income max_income median_income
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 84318. 68724. 0<PHONE_NUMBER>0
The variables we focus on are….
Summary statistics for these variables…
You must include graphs.
decon %>%
ggplot(aes(x = religion)) +
geom_bar() + coord_flip()#categorical data
decon %>%
ggplot(aes(x = income)) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
decon %>%
ggplot(aes(x = income)) +
geom_histogram() +
labs(x = "Income",
y = "Number",
title = "Distribution of income in 2019 CES",
caption = "Source: Stephenson, Harell, Rubenson, Loewen, 2020.") +
theme_bw()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Discussion of the graphs
My discussion… main findings of your graph.
Features of your survey…. Non-response… etc. And how these could affect the results….
Discussion of the Paper
Weaknesses and next steps
My weaknesses…
Reference
-Hadley Wickham, Jim Hester and Winston Chang (2020). devtools: Tools to Make Developing R Packages Easier. https://devtools.r-lib.org/, https://github.com/r-lib/devtools.


{% extends "instructions/summary_base.html" %} {% load staticfiles %} {% block help_content %}
You will be applying labels to 45 tweets about a rumor from the "Sydney Siege" hostage situation. In this case, rumor simply means unsubstantiated information, and it may be true or false.
This rumor claims someone was a suicide bomber or that they had an explosive device in backpacks, vests, or belts.
Each of these tweets will be embedded. Some will have pictures, and some will be part of a discussion. You should use all available information for coding, but focus on the bottom tweet for applying your codes.
You can navigate between tweets using the keyboard arrow keys
↑ - Previous Tweet
↓ - Next Tweet
You will be applying one set of labels. This set consists of 3 labels and only one should be applied to each tweet.
This set of labels describes whether the tweet affirms or denies the rumor. You should label any tweet that supports or just passes along this information as Affirm label. Any tweet that denies or questions the presence of a bomb or the ability of the person to be a suicide bomber should be labeled Deny label. In rare cases, the tweet will not affirm or deny the rumor. You should label these Neutral.
To apply / remove labels, you can use the keyboard keys 1 through 3. Press the button once to add the code and a second time if you want remove it.
| Label | Keyboard | Definition | Example |
|---|---|---|---|
| Affirm | 1 | This tweet affirms, supports, and functions to pass along the story. | @KevinZZ The hostage-taker has a bomb in his backpack. #Sydney #SydneySiege |
| Neutral | 2 | The tweet is exactly neutral (use sparingly). Be careful not to conflate with an implicit affirmation. | RT @JillSmith: CNN has a picture of the hostage taker. |
| Deny | 3 | This tweet denies or questions all or part of the story. | I don't see any sign of a bomb or backpack in that picture. #sydneysiege |


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ReFilament is a PLA filament recycler and spooling system designed for the Olin College prototyping community. It provides a sustainable solution to the problem of waste generated in the rapid prototyping process, without sacrificing the spirit of creation so prevalent at Olin. ReFilament allows students to create new ideas without worrying about recurring filament costs or high environmental impact, enabling the evolution of 3D printing as a sustainable process.


| MATLAB 文件帮助: cv.AlignMTB | 查看 cv.AlignMTB 的代码 | Index |
AlignMTB Aligns images of the same scene with different exposures
This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations.
It is invariant to exposure, so exposure values and camera response are not necessary.
In this implementation new image regions are filled with zeros.
For more information see [GW03].
[GW03]:
Greg Ward. "Fast, robust image registration for compositing high dynamic range photographs from hand-held exposures". Journal of graphics tools, 8(2):17-30, 2003.
| Superclasses | handle |
| Sealed | false |
| Construct on load | false |
| AlignMTB | Creates AlignMTB object |
| Cut | if true cuts images, otherwise fills the new regions with zeros. |
| ExcludeRange | range for exclusion bitmap that is constructed to suppress noise |
| MaxBits | logarithm to the base 2 of maximal shift in each dimension. |
| id | Object ID |
| addlistener | Add listener for event. | |
| calculateShift | Calculates shift between two images | |
| computeBitmaps | Computes median threshold and exclude bitmaps of given image | |
| delete | Destructor | |
| eq | == (EQ) Test handle equality. | |
| findobj | Find objects matching specified conditions. | |
| findprop | Find property of MATLAB handle object. | |
| ge | >= (GE) Greater than or equal relation for handles. | |
| gt | > (GT) Greater than relation for handles. | |
| Sealed | isvalid | Test handle validity. |
| le | <= (LE) Less than or equal relation for handles. | |
| lt | < (LT) Less than relation for handles. | |
| ne | ~= (NE) Not equal relation for handles. | |
| notify | Notify listeners of event. | |
| process | Aligns images | |
| shiftMat | Helper function, that shift Mat filling new regions with zeros |
| ObjectBeingDestroyed | Notifies listeners that a particular object has been destroyed. |


Class Layout
A Layout contains a tree of widgets with a single root
widget.
Layouts will resize to fit the window unless a top
or left
property is found in the root widget.
Layouts are drawn in the order that they were shown, so the most recently shown layout shown will always appear on top.
Other events are sent to layouts in the opposite direction, and are trapped by the first layout that can handle the event (for example, the topmost layer that is focused or hovered).
Functions
| Luigi.Layout (data) | Layout constructor. |
Methods
| Layout:setMaster (layout) | Set the master layout for this layout. |
| Layout:setStyle (rules) | Set the style from a definition table or function. |
| Layout:setTheme (rules) | Set the theme from a definition table or function. |
| Layout:getStyle () | Get the style from master layout or this layout. |
| Layout:getTheme () | Get the theme from master layout or this layout. |
| Layout:show () | Show the layout. |
| Layout:hide () | Hide the layout. |
| Layout:focusNextWidget () | Focus next focusable widget. |
| Layout:focusPreviousWidget () | Focus previous focusable widget. |
| Layout:getWidgetAt (x, y[, root]) | Get the innermost widget at given coordinates. |
Functions
Methods- Luigi.Layout (data)
-
Layout constructor.
Parameters:
- data table A tree of widget data.
Returns:
-
Layout
A Layout instance.
Methods
- Layout:setMaster (layout)
-
Set the master layout for this layout.
This layout's theme and style will be set the same as the master layout, and widgets added to this layout will be indexed and keyboard-accelerated by the master layout instead of this layout.
Parameters:
- layout Layout Master layout
Returns:
-
Layout
Self
- Layout:setStyle (rules)
-
Set the style from a definition table or function.
Parameters:
- rules table or function Style definition.
Returns:
-
Layout
Self
- Layout:setTheme (rules)
-
Set the theme from a definition table or function.
Parameters:
- rules table or function Theme definition.
- Layout:getStyle ()
-
Get the style from master layout or this layout.
Returns:
-
table
Style table.
- Layout:getTheme ()
-
Get the theme from master layout or this layout.
Returns:
-
table
Theme table.
- Layout:show ()
-
Show the layout.
Hooks all appropriate Love events and callbacks.
- Layout:hide ()
-
Hide the layout.
Unhooks Love events and callbacks.
- Layout:focusNextWidget ()
-
Focus next focusable widget.
Traverses widgets using Widget:getNextNeighbor until a focusable widget is found, and focuses that widget.
Returns:
-
Widget
The widget that was focused, or nil
- Layout:focusPreviousWidget ()
-
Focus previous focusable widget.
Traverses widgets using Widget:getPreviousNeighbor until a focusable widget is found, and focuses that widget.
Returns:
-
Widget
The widget that was focused, or nil
- Layout:getWidgetAt (x, y[, root])
-
Get the innermost widget at given coordinates.
Parameters:
- x number Number of pixels from window's left edge.
- y number Number of pixels from window's top edge.
- root Widget Widget to search within, defaults to layout root. (optional)