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What ocean is to the east of Florida?
Is the sky blue?
Is the country of Canada directly north of the United States?
Is Mexico directly south of the United States?
Quiz will end in 1:00.


defaultUnits, defaultUnit, onStart, onTween, onComplete, attributes, duration, method, useSeconds, currentFrame, totalFrames
Class YAHOO.util.Scroll
Object | +--YAHOO.util.Anim | +--YAHOO.util.Scroll
class
YAHOO.util.Scroll
Fields inherited from class YAHOO.util.Anim [top]
Constructor Summary [top]
YAHOO.util.Scroll
Anim subclass for scrolling elements to a position defined by the "scroll" member of "attributes".
Method Summary [top]
| Number |
doMethod
(<String> attribute, <Number> start, <Number> end)
Returns the value computed by the animation's "method".
|
| Number |
getAttribute
(<String> attribute)
Returns current value of the attribute.
|
| void |
setAttribute
(<String> attribute, <Number> val, <String> unit)
Applies a value to an attribute
|
Methods inherited from class YAHOO.util.Anim
Constructor Detail [top]
YAHOO.util.Scroll
YAHOO.util.Scroll
()
Anim subclass for scrolling elements to a position defined by the "scroll" member of "attributes". All "scroll" members are arrays with x, y scroll positions.
Usage: var myAnim = new YAHOO.util.Scroll(el, { scroll: { to: [0, 800] } }, 1, YAHOO.util.Easing.easeOut);
- Parameters:
-
HTMLElement
- | String} el Reference to the element that will be animated -
attributes
- The attribute(s) to be animated. Each attribute is an object with at minimum a "to" or "by" member defined. Additional optional members are "from" (defaults to current value), "units" (defaults to "px"). All attribute names use camelCase. -
duration
- (optional, defaults to 1 second) Length of animation (frames or seconds), defaults to time-based -
method
- (optional, defaults to YAHOO.util.Easing.easeNone) Computes the values that are applied to the attributes per frame (generally a YAHOO.util.Easing method)
Method Detail [top]
doMethod
Number
doMethod
(<String> attribute, <Number> start, <Number> end)
Returns the value computed by the animation's "method".
- Parameters:
-
attribute
- The name of the attribute. -
start
- The value this attribute should start from for this animation. -
end
- The value this attribute should end at for this animation.
- Returns:
- The Value to be applied to the attribute.
getAttribute
Number
getAttribute
(<String> attribute)
Returns current value of the attribute.
- Parameters:
-
attribute
- The name of the attribute.
- Returns:
- val The current value of the attribute.
setAttribute
void
setAttribute
(<String> attribute, <Number> val, <String> unit)
Applies a value to an attribute
- Parameters:
-
attribute
- The name of the attribute. -
val
- The value to be applied to the attribute. -
unit
- The unit ('px', '%', etc.) of the value.


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Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sapiente, minus, provident, possimus, aperiam ipsum fugit eos natus est ducimus quasi in libero repudiandae et mollitia perspiciatis adipisci ex. Perferendis, odit!
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sapiente, minus, provident, possimus, aperiam ipsum fugit eos natus est ducimus quasi in libero repudiandae et mollitia perspiciatis adipisci ex. Perferendis, odit!
Single line blockquote:
Stay hungry. Stay foolish.
Multi line blockquote with a cite reference:
People think focus means saying yes to the thing you've got to focus on. But that's not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully. I'm actually as proud of the things we haven't done as the things I have done. Innovation is saying no to 1,000 things.
Steve Jobs - Apple Worldwide Developers' Conference, 1997
| Employee | Salary | |
|---|---|---|
| Jane | $1 | Because that's all Steve Job' needed for a salary. |
| John | $100K | For all the blogging he does. |
| Jane | $100M | Pictures are worth a thousand words, right? So Tom x 1,000. |
| Jane | $100B | With hair like that?! Enough said... |
- Definition List Title:
- Definition list division.
- Startup:
- A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
- #dowork:
- Coined by Rob Dyrdek and his personal body guard Christopher "Big Black" Boykins, "Do Work" works as a self motivator, to motivating your friends.
- Do It Live:
- I'll let Bill O'Reilly willexplain this one.
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1 Infinite Loop Cupertino, CA 95014 United States example text Anchor Tag (aka. Link)
This is an example of alink.
Cite Tag"Code is poetry." -- Automattic
Code TagYou will learn later on in these tests thatword-wrap: break-word;
will be your best friend.
This tag will let you
strikeout text, but this tag is no longer supported in HTML5 (use the <strike> instead).
The emphasize tag shoulditalicize text.
Keyboard TagThis scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.
Preformatted TagThis tag styles large blocks of code.
.post-title { tmargin: 0 0 5px; tfont-weight: bold; tfont-size: 38px; tline-height: 1.2; }Quote Tag
Developers, developers, developers...
--Steve Ballmer
This tag showsbold text.
Subscript TagGetting our science styling on with H2O, which should push the "2" down.
Superscript TagStill sticking with science and Isaac Newton's E = MC2, which should lift the 2 up.
Definition Lists
Unordered Lists (Nested)
Ordered List (Nested)
HTML Tags
These supported tags come from the WordPress.com codeFAQ.
Address Tag
1 Infinite LoopCupertino, CA 95014
United States
Anchor Tag (aka. Link)
This is an example of alink.
Abbreviation Tag
The abbreviationsrsly stands for “seriously”.
Acronym Tag (deprecated in HTML5)
The acronymftw stands for “for the win”.
Big Tag (deprecated in HTML5)
These tests are a big deal, but this tag is no longer supported in HTML5.
Cite Tag
“Code is poetry.” — Automattic
Code Tag
This tag styles blocks of code.
You will learn later on in these tests that word-wrap: break-word;will be your best friend.
Delete Tag
This tag will let you
strike out text, but this tag is recommended supported in HTML5 (use the<s>
instead).Emphasize Tag
The emphasize tag should italicize text.
Horizontal Rule Tag
This sentence is following a
Insert Tag
This tag should denoteinserted text.
Keyboard Tag
This scarcely known tag emulateskeyboard text, which is usually styled like the
Preformatted Tag
This tag is for preserving whitespace as typed, such as in poetry or ASCII art.
Quote Tag for short, inline quotes
--Steve BallmerStrike Tag (deprecated in HTML5) andS Tag
This tag shows
strike-throughtext.Small Tag
This tag showssmaller text.
Strong Tag
This tag showsbold text.
Subscript Tag
Getting our science styling on with H2O, which should push the "2" down.
Superscript Tag
Still sticking with science and Isaac Newton's E = MC2, which should lift the 2 up.
Teletype Tag(obsolete in HTML5)
This rarely used tag emulates teletype text, which is usually styled like the
Underline Tagdeprecated in HTML 4, re-introduced in HTML5 with other semantics
This tag shows underlined text.
Variable Tag
This allows you to denote variables.
This user it trying to be anonymous.
They used a fake email, so there should be noGravatar associated with it.
They did not speify a website, so there should be no link to it in the comment.
Comments? I love comments!
These tests are amazing!
Author Comment.
Comment Depth 01
Comment Depth 02
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Also an author comment.
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Also an author comment.
Image comment.
We are totally going to blog about these tests!
We use these tests all the time! Killer stuff!
Thanks for all the comments, everyone!


--- title: "mentoring" layout: landing permalink: "/mentoring.html" ---
Unlock your full potential in functional programming with personalised mentoring from an expert in TypeScript and fp-ts.
Register for a session nowUnlock your full potential in functional programming with personalised mentoring from an expert in TypeScript and fp-ts.
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class: center, middle, inverse, title-slide .title[ # Prediction intervals for polygenic scores ] .author[ ### Fred Boehm ] .date[ ### September 23, 2022 ] --- ## Outline 1. Polygenic scores 1. DBSLMM 1. Prediction intervals for PGS 1. Jackknife+ for prediction intervals 1. Crossvalidation+ for prediction intervals --- class: center middle # Polygenic scores --- ## Polygenic scores - Polygenic scores aim to summarize genetic contributions to complex traits: `$$\sum_{SNPs}(\text{SNP genotype})*(\text{SNP effect})$$` - Our goal is to develop a strategy for constructing prediction intervals for PGS (quantitative or binary traits) --- ## Popular PGS methods with GWAS summary statistics 1. DBSLMM [YZ20] 1. ldpred2 1. lassosum2 1. SBLUP 1. C + T --- class: center middle # Prediction intervals for PGS --- ## Existing approaches to prediction intervals in PRS 1. Mondrian cross-conformal prediction intervals for PRS [Sun+21] - One approach to conformal prediction 1. Bayesian credible intervals for PRS [Din+21] - ldpred2 used to obtain posterior samples - Observe large variances in PRS --- ## Importance of prediction intervals for PGS - Clinical utility of an interval estimate in addition to point estimate - Large variability in PRS with ldpred2 - Method applies only to ldpred2 --- class: center middle # DBSLMM Model & Methods --- ## DBSLMM - All SNPs have nonzero effects on the trait - Each SNP effect arises from one of two normal distributions - Large variance or small variance - Treat the large variance SNP effects as fixed effects & small variance SNP effects as random effects (omnigenic hypothesis) --- ## DBSLMM Model `$$y = X\beta + \epsilon$$` - trait `\(y\)`: n vector - `\(X\)`: n by m matrix of standardized SNP genotypes - `\(\beta\)`: m vector of SNP effects - `\(\epsilon\)`: n vector of random errors with precision `\(\tau\)` --- ## DBSLMM Model `$$y = X\beta + \epsilon$$` `$$\beta_j \sim \pi N(0, \sigma^2_l\tau^{-1}) + (1 - \pi) N(0, \sigma^2_s \tau^{-1})$$` - `\(\pi\)` proportion of SNPs in the large variance component --- ## DBSLMM Model Fitting `$$y = X\beta + \epsilon$$` - BSLMM: MCMC for model fitting is slow with large memory requirements - Large effect SNPs should be easy to identify from GWAS analysis - Small effect SNPs can't be inferred accurately - But polygenic effects may be inferred with accuracy --- ## DBSLMM Model `$$y = X_l\beta_l + X_s \beta_s + \epsilon$$` - `\(X_l\)` : n by `\(m_l\)` SNP genotypes matrix for large effect SNPs - `\(\beta_l\)` : `\(m_l\)` effects vector for large effect SNPs - `\(X_s\)` : n by `\(m_s\)` SNP genotypes matrix for small effect SNPs - `\(\beta_s\)` : `\(m_s\)` effects vector for small effect SNPs `$$\beta_{lj} \sim N(0, \sigma_l^2\tau^{-1})$$` `$$\beta_{sj} \sim N(0, \sigma_s^2\tau^{-1})$$` - Set `\(\sigma^2_l \to \infty\)` & treat `\(\beta_l\)` as fixed effects --- ## DBSLMM Model: Parameter estimation - Clumping and Thresholding (C + T) procedure in PLINK to select large effect SNPs - One chromosome at a time - p-value threshold: `\(10^{-6}\)` - region size: 1 MB - LD threshold: `\(r^2 = 0.1\)` - Combine large effect SNPs across genome to get `\(m_l\)` SNPs --- ## DBSLMM Model: Parameter estimation `$$\hat \beta_l = (X_l^TH^{-1}X_l)^{-1}X_l^TH^{-1}y$$` `$$\hat \beta_s = \hat\sigma^2_s X_s^TH^{-1}( y - X_l\hat\beta_l)$$` `$$Var(y) = H = \hat\sigma^2_s X_sX_s^T + I_n$$` --- ## DBSLMM Model: Parameter estimation - Set `\(\hat\sigma^2_s\)` to predetermined value instead of estimating it - LD score regression to get SNP heritability, `\(\hat h^2\)` - Set `\(\hat \sigma^2_s = \frac{\hat h^2}{m}\)` --- ## DBSLMM Model: Parameter estimation - Use Woodbury matrix identity to calculate `\(H^{-1}\)` `$$H^{-1} = I_n - X_s(\sigma_s^{-2}I_{m_s} + X_s^TX_s)^{-1}X_s^T$$` --- class: center middle # Prediction intervals for PGS --- ## Jackknife-plus for prediction intervals [Bar+20] - Uses ideas and results from conformal prediction theory - Comes with probabilistic coverage guarantees - Assumes exchangeability of observations - Uses leave-one-out residuals --- ## Jackknife-plus for prediction intervals [Bar+20] - JK+ constructs prediction interval for `\(Y_{n+1}\)` as a function of `\(n\)` training points `\((X_i, Y_i)\)` & `\(X_{n+1}\)` - Naively, we might want to use residuals from the training data to construct the interval: `$$(X_{n+1}) \pm (\text{the }(1- \alpha)\text{ quantile of the }n\text{ absolute residuals})$$` - Residuals are `\(|Y_1 - \hat\mu(X_1)|, …, |Y_n - \hat\mu(X_n)|\)` - Due to overfitting, `\(n\)` training residuals tend to be smaller than that of the `\((n+1)^{th}\)` point --- ## Jackknife-plus for prediction intervals [Bar+20] - JK computes leave-one-out residuals: - `\(R_i = |Y_i - \hat\mu_{-i}(X_i)|\)` - And computes the regression function `\(\hat\mu\)` with all `\(n\)` training points - And outputs the interval: - `\(\hat\mu(X_{n + 1}) \pm (\text{the } (1 - \alpha) \text{ quantile of } R_1, \ldots, R_n)\)` - [Bar+20] point out that JK has no universal theoretical guarantees & may lose predictive coverage in some settings --- ## Jackknife-plus for prediction intervals [Bar+20] - JK+ is a modification of JK - Replace `\(\hat\mu\)` with `\(\hat\mu_{-i}\)` --- ## Jackknife-plus for prediction intervals [Bar+20] - Notation - `\(\hat q_{n, \alpha}^+\lbrace v_i\rbrace = \text{the }\lceil(1-\alpha)(n + 1)\rceil\text{-th smallest value of }v_1, \ldots v_n\)` - `\(\hat q_{n, \alpha}^-\lbrace v_i\rbrace = \text{the }\lfloor\alpha(n + 1)\rfloor\text{-th smallest value of }v_1, \ldots v_n\)` --- ## Jackknife-plus for prediction intervals [Bar+20] - `\(\hat C_{n, \alpha}^{\text{naive}}(X_{n + 1}) = \hat\mu(X_{n + 1}) \pm \hat q_{n, \alpha}^+\lbrace |Y_1 - \hat\mu(X_1)|, \ldots, |Y_n - \hat\mu(X_n)|\rbrace\)` - `\(\hat C_{n, \alpha}^{\text{JK}}(X_{n + 1}) = \left(\hat q_{n, \alpha}^-\lbrace \hat\mu(X_{n + 1}) - R_i^{LOO}\rbrace, \hat q_{n, \alpha}^+\lbrace \hat\mu(X_{n + 1}) + R_i^{LOO}\rbrace\right)\)` - `\(\hat C_{n, \alpha}^{\text{JK+}}(X_{n + 1}) = \left(\hat q_{n, \alpha}^-\lbrace \hat\mu_{- i}(X_{n + 1}) - R_i^{LOO}\rbrace, \hat q_{n, \alpha}^+\lbrace \hat\mu_{-i}(X_{n + 1}) + R_i^{LOO}\rbrace\right)\)` --- ## Jackknife-plus for prediction intervals [Bar+20] ![](F1.png) --- ## Cross-validation+ for `\(K\)`-fold crossvalidation - Split training set into `\(K\)` disjoint sets of equal size, `\(m = \frac{n}{K}\)` - `\(\hat \mu_{-S_k} = \mathcal{A}\left((X_i, Y_i): i \in \lbrace 1, \ldots, n\rbrace \setminus S_k \right)\)` - `\(R_i^{CV} = |Y_i - \hat \mu_{- S_{k(i)}}(X_i)|\)` with `\(i \in S_{k(i)}\)` - `\(\hat C_{n, K, \alpha}^{CV+}(X_{n + 1}) = \left(\hat q^-_{n, \alpha}\lbrace \hat \mu_{- S_{k(i)}}(X_{n + 1}) - R_i^{CV}\rbrace, \hat q^+_{n, \alpha}\lbrace \hat \mu_{- S_{k(i)}}(X_{n + 1}) + R_i^{CV}\rbrace\right)\)` - CV+ requires `\(K\)` model fits instead of `\(n\)` for JK+ - CV+ intervals may be wider due to smaller sample size --- class: center middle # Simulations --- ## Simulations study design - 14,500 subjects randomly chosen from 337129 UKB subjects - 12500 randomly assigned to training set - 1000 randomly assigned to validation set - 500 validation subjects also randomly assigned to reference set - 1000 remaining subjects assigned to "verification" set - 5-fold cross-validation used with the 12,500 training set - Chose all ~95,000 Chr1 SNPs for simulations --- ## Simulations study design - Quantitative traits simulated - with GCTA - according to four scenarios: - Scenario I: Polygenic (all SNPs are causal) - Scenario II: Sparse (0.1% of SNPs are causal) - Scenario III: Hybrid (all SNPs are causal, and 0.1% of SNPs have large effects, PGE = 0.2) - Scenario IV: Hybrid (all SNPs are causal, and 0.1% of SNPs have large effects, PGE = 0.5) - 3 distributions: Laplace, normal, scaled t - 3 heritabilities: 0.1, 0.2, 0.5 - 10 replicates per setting --- ## Simulations results  --- ## Simulations results  --- ## Simulations results  --- ## Simulations results  --- ## UKB quantitative traits - Sheng Yang processed & analyzed 25 quantitative traits from UKB - I've constructed CV+ prediction intervals for the 25 traits - Coverage ranges from 0.943 to 0.965 for nominally ~95% intervals --- ## Next Steps - Troubleshoot simulations - Is there a bug in my CV+ R code? - Analyze binary traits from UKB - Sheng Yang already created the needed summary statistics files (gemma outputs) --- class: center middle # Thank you! --- ## References Barber, R. F., E. J. Candes, A. Ramdas, et al. (2020). "Predictive inference with the jackknife+". In: _arXiv:1905.02928 [stat]_. arXiv: [1905.02928](https://arxiv.org/abs/1905.02928). URL: [http://arxiv.org/abs/1905.02928](http://arxiv.org/abs/1905.02928) (visited on Sep. 12, 2021). Ding, Y., K. Hou, K. S. Burch, et al. (2021). "Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification". In: _Nature Genetics_. ISSN: 1061-4036, 1546-1718. DOI: [10.1038/s41588-021-00961-5](https://doi.org/10.1038%2Fs41588-021-00961-5). URL: [https://www.nature.com/articles/s41588-021-00961-5](https://www.nature.com/articles/s41588-021-00961-5) (visited on Dec. 25, 2021). Sun, J., Y. Wang, L. Folkersen, et al. (2021). "Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction". In: _Nature Communications_ 12.1, p. 5276. ISSN: 2041-1723. DOI: [10.1038/s41467-021-25014-7](https://doi.org/10.1038%2Fs41467-021-25014-7). URL: [https://www.nature.com/articles/s41467-021-25014-7](https://www.nature.com/articles/s41467-021-25014-7) (visited on Oct. 10, 2021). Yang, S. and X. Zhou (2020). "Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets". In: _The American Journal of Human Genetics_ 106.5, pp. 679-693. ISSN: 00029297. DOI: [10.1016/j.ajhg.2020.03.013](https://doi.org/10.1016%2Fj.ajhg.2020.03.013). URL: [https://linkinghub.elsevier.com/retrieve/pii/S0002929720301099](https://linkinghub.elsevier.com/retrieve/pii/S0002929720301099) (visited on Jun. 01, 2021).


A decentralized chat written in JavaScript + Rust (WebAssembly).
Powered by Wavelet. Click here to learn how it works, and here for the source code. Join our Discord to get PERLs.
[secret]
Set Default Private Key
[node]
Connect
[contract]
Load Contract
[node id]
???
[your balance]
0 PERL(s)
[contract gas balance]
0 PERL(s)
Send Message [2 PERLs]


Love the ability to add more seats
“Love the ability to add more seats. Love the size and maneuverability. Love the bassinet. Bought the stand and have used it in our bedroom for three kids now. Onto our third child and stroller still looks and acts brand new—it’s incredible! Well worth the money.”


--- layout: about title: About ---
Nice to meet you.
Hey, you found my portfolio. The name is Thu Minh Hoang Nguyen. I am a Linguistic major at Ho Chi Minh Univerity of Social Sciences and Humanities. Here you can find my sample writings including editorial ones.
Have a nice day!


I2P In Private Browsing
I2P in Private Browsing is a webextension to secure and enhance your I2P use in the browser.
This is an experimental product.
New to I2P? Learn more here.
I2P Browser allows you to surf the internet using the private and secure I2P network. When using it, you are protected against tracking, surveillance, and censorship as a first-class participant in the I2P network. I2P Browser isolates cookies and deletes your browser history after your session. These modifications ensure your privacy and security are protected in the browser.
We also provide you with additional settings for bumping up your browser security. Our Security Settings allow you to block elements that could be used to attack your computer. Click below to see what the different options do. Note: By default, NoScript and HTTPS Everywhere are not included on the toolbar, but you can customize your toolbar to add them. With all the security and privacy features provided by I2P, your experience while browsing the internet may be a little different. Things may be a bit slower, and depending on your security level, some elements may not work or load. You may also be asked to prove you are a human and not a robot.
I2P is capable of using peer-to-peer applications like BitTorrent, protecting your identity when you share files. Our anonymous bittorrent client is available in the browser.
There is also an anonymous e-mail service available inside of I2P, which is accessible from our browser via the menu directly below.
With all the security and privacy features provided by I2P, your experience while browsing the internet may be a little different. Things may be a bit slower, and depending on your security level, some elements may not work or load. You may also be asked to prove you are a human and not a robot.
Applications
These applications use I2P to provide them with security and privacy.
- Hidden Services Manager I2P has a web-based interface for configuring .i2p services like web sites, to set up your own web sites, go here:
- E-Mail I2P also bundles a webmail client which can be used to access in-I2P e-mail. To use it, go here:
- BitTorrent I2P is capable of anonymous Peer-to-Peer file sharing, to use the built-in bittorrent client go here:
If you want to get more information about I2P, you can visit these links.
- I2P Browser Source Code: Go here to browse the source code of our Firefox fork.
- I2P Button Source Code: Much of the functionality of the I2P Browser is implemented in the I2PButton plugin.
- I2P Browser Build Scripts: These scripts are used to build the browser.
- I2P Browser Design Document: This is the I2P Browser Design Document.
- Trac Wiki: To report a bug, visit the Trac Wiki.
- Support Us: I2P is funded by donations. In order to make a donation visit the project web site.


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From data collection to reporting, we work with data from a wide range of domains, including biological science, geo-spatial analysis, and businesses. Services include data wrangling, data mining and visualization.
We specialise in developing interactive (web or desktop) visual analytics tools. We design bespoke visualization solutions tailored to target users and analysis needs, addressing both scientific and engineering challenges.
Maximise the impact of your research by preparing well-designed figures to communicate effectively and succinctly. We produce publication-ready graphs, charts, anatomical illustrations, book cover design, and logos.
We provide a flat-rate consultancy service to evaluate your exisiting visualization systems. Get in touch with us to find out more.
We provide workshops of various length ( 2hr - 1 day) on visualization principles, visualization in R, creative programming (Processing and p5.js), and visualization for biologists.
We are always excited to work with academics and practitioners with an enthusiasm for data visualization.
After completing a BSc in Biological Chemistry at Bates College, Lewiston Maine, USA, Ryo started his first MSc in Biomedical Communications at the University of Toronto, Canada. During this program, he acquired graphical and visual communication skills as a medical illustrator.
Ryo started his second MSc in Computing and Information Systems at Queen Mary, University of London, the UK to build a solid foundation in programming skills to tackle on data visualization projects and to build interactive visualization tools.
Ryo pursued a position as a designer trainee in a post-Master professional engineering program at Eindhoven University of Technology in the Netherlands. During this period, he learned and practiced UX design principles and interned at Phillips Research to develop a mobile application for communication and collaboration among radiologists and radiographers.
Then, Ryo joined the Visual Data Analysis group (led by Prof. Jan Aerts) at KU Leuven STADIUS/ESAT and started his doctoral study in Biological Data Visualization: Design and Analysis. During his studies, he has published several papers, received awards in visualization contests, and established collaborations with scientists and engineers all over the world.
Ryo worked as a freelance data visualization specialist: a designer, training provider and researcher.
Ryo is currently working as a Data Visualization Specialist for PharmiWeb Solutions, providing a wide range of service from general consultancy involving data analytics to implementation of data visualization tools for healthcare professionals and pharmaceutical companies.


---
title: Size options
category: modal
order: 10
description: There are small and large options for modals, depending on how much room your content needs.
---
You should read this
After you're done reading this important message, it's time to make a decision. Use the buttons below to make your decision. Choose wisely...
Welcome to the future
The future is now, which makes this the present and that's not as exciting as the future. Weren't we supposed to have hoverboards and flying cars by now?


{% extends "base.html" %} {% import "camperlist.html" as chart %} {% import "tokenbox.html" as tokenbox %} {% block head %}


Before
1 of 10 articles inside a Feed. Use PageUp and PageDown to navigate on items. Use tab to interact with inner elements. Use ctr + Home, ctr + End move exit from Feed.
Tito's Tacos
5 stars
Mexican, Tacos
Bookmark
Tito's Tacos
5 stars
Mexican, Tacos
Bookmark
Tito's Tacos
5 stars
Mexican, Tacos
Bookmark
After


美国自然科学基金对数字化标本平台的支持 Supporting tagline
The US National Science Foundation has awarded a Research Coordination Grant entitled: Building a National Community of Natural History Collections.
The purpose of the RCN is to build a strong scientific community to better serve researchers in systematics, biodiversity, and other fields that use natural history collections.
Three professional organizations, the American Institute of Biological Sciences, the Natural Science Collections Alliance, and the Society for the Preservation of Natural History Collections will provide strong support as partners in the RCN, and many other societies have already built ties with the RCN by appointing Core Participants.
The RCN has the following objectives:
Identify the institutions and people that will define our community and facilitate dialogue among them about how to better serve the needs of researchers,
identify major opportunities and challenges in the current environment and foreseeable future and develop a strategic plan for the future of collections research,
determine how to strengthen and modernize the role of collections in education and outreach, and
identify the primary needs of collections regarding care, curation, storage, and accessibility.
The objectives will be met with a series of workshops, symposia, internships, and a website, www.CollectionsWeb.org, which will provide a forum for interaction, host workshop reports, provide information on ways to become involved in the RCN, and provide links to resources for NHC.
Several major community resources will be developed, including a catalogue of NHC, a survey of the status of NHC, a register of curatorial expertise, and an inventory of innovative and successful educational programs.
Broadening minority participation in collections activities is a goal of the RCN.
The initial steering committee members are Henry L. Bart (Tulane University, vertebrate systematics), Meredith Blackwell (Louisiana State University, fungal systematics), L. Alan Prather (Michigan State University, plant systematics) and James B. Woolley (Texas A & M University, invertebrate systematics).
Announcements of upcoming events, opportunities to get involved, and reports will be posted to the website, so please check back often. Your input is essential! Contact us at http://www.collectionsweb.org/about/steering_committee.html.
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--- layout: default Title: Keynsham and District Dial-a-Ride Breadcrumb1_url: section-parking.html Breadcrumb1_title: Parking & Travel Breadcrumb2_url: /parking-travel/community-transport.html Breadcrumb2_title: Community Transport Breadcrumb3_url: /parking-travel/community-transport-directory.html Breadcrumb3_title: Community transport directory ---
Dial-a-Ride is a door to door fully accessible minibus service for those who cannot use conventional public transport, You can use this service to go shopping, attend appointments and play an active part in community life.
If you live within the areas served by Dial-a-Ride and the Hopper Service and can't use conventional public transport then you should be eligible to register for this service. If you are unsure whether you are eligible or have any further questions please contact the co-ordinator on 01225 335019.
Please note: We do our best to meet all requests, however, we do operate on a first come first served basis. We may ask you to alter your desired journey times slightly. We reserve the right to refuse or defer an application for registration, to ask for additional information in support of an application or to cancel any existing registration. All wheelchairs must be risk assessed by us before we can transport you.
You must register before you can book any journeys.
To register please print and complete the registration form and return it to the address on the form
or contact the co-ordinator on 01225 335019
This service covers the area within the Bath city boundaries, there is a map showing the full extent of the service within the information leaflet.
There are two buses working constantly within the service hours. The service operates between 9.30 am and 2.30 pm from Monday to Friday excluding Bank Holidays.
Return Journey - £6.00
Single Journey - £3.00
Diamond Travel Card holders are entitled to a 50% discount.
Call the co-ordinator on 01225 335019
Journeys can be booked a maximum of 7 days in advance.
All bookings must be made by 12 noon on the day before the journey.


About me :)
my favourite book
my favourite movie
my favourite place
my name is rana
i wanna be web developer and an artist
my education :
bachulor's degree in english litrature
tawjehe
my experiences :
english teacher for primary and secondry students
custemer servies
my top movies
the shutter island
the invisible man
blue valantine
bride and pregedice
the little women
changiling
evem sesin
bird box
me before you
sweet november


Duration
15 minutes
Goals
The goal of this lab is to combine gestural and tab navigation to give the user both options for navigating the
fragments within an Activity.
Your implementation will use both a ViewPager
and a TabLayout
.
The ViewPager
will handle swipe gestures and the TabLayout
will display the tabs.
Required assets
This is a continuation of the previous exercise. You can use your existing solution or open the solution in the Exercise 3/Completed folder. The Exercise 4/Completed folder contains a solution you can use to check your work. Please make sure you have these folders before you begin.
Steps
Below are the step-by-step instructions to implement the exercise.
Add tab titles to your adapter
Here you will modify your adapter to provide tab titles that will be displayed by the TabLayout
.
The ViewPager
requires that the tab titles be of type ICharSequence
so that's the
type your adapter will use.
- Open ClockAdapter.cs.
-
Add a field to store the collection of titles as shown below.
ICharSequence[] titles;
-
Modify the constructor so it takes an addition argument for the collection of titles.
Assign the collection of titles to your field.
The code is provided below.
public ClockAdapter(Android.Support.V4.App.FragmentManager fm, Android.Support.V4.App.Fragment[] fragments, ICharSequence[] titles) : base(fm) { this.fragments = fragments; this.titles = titles; }
-
Override the
GetPageTitleFormatted
method. It receives an integer which identifies the title that is being requested by theTabLayout
. Use that integer as an index into your titles collection to return the appropriate one.
Create the tab UI
In this part, you will add a TabLayout
above the existing ViewPager
in the UI XML.
- Open Resources > layout > Main.axml in source-code view.
-
Add a
TabLayout
to the top of the existingLinearLayout
. The code is provided below to save you some typing.<android.support.design.widget.TabLayout android:id ="@+id/tabLayout" android:layout_width ="match_parent" android:layout_height="wrap_content" />
-
Change the
layout_height
of the existingViewPager
towrap_content
. It used to occupy the entire UI but now needs to share the space with the tabs.
Connect the adapter, the TabLayout
, and the ViewPager
You now have an implementation of a FragmentPagerAdapter
that includes titles.
The code you write here will populate the adapter with titles and then connect the
TabLayout
and the ViewPager
so they work together.
-
Open MainActivity.cs.
All the code you write here will be inside the
OnCreate
method. -
Create an array of
ICharSequence
and populate it with strings appropriate for your tab labels. This requires the use of a conversion method so the code is provided below.var titles = CharSequence.ArrayFromStringArray(new [] { "Time", "Stopwatch", "About" });
-
Add the array of tab titles to the constructor call where you create the
ClockAdapter
. -
Use
FindViewById
to get a reference to theTabLayout
. CallTabLayout
'sSetupWithViewPager
method and pass it theViewPager
reference. -
The tab titles will be automatically populated from your adapter but the tab icons
will not. Add the following code to manually set the icon for each tab.
tabLayout.GetTabAt(0).SetIcon(Resource.Drawable.ic_access_time_white_24dp); tabLayout.GetTabAt(1).SetIcon(Resource.Drawable.ic_timer_white_24dp); tabLayout.GetTabAt(2).SetIcon(Resource.Drawable.ic_info_outline_white_24dp);
- Run the app to test your work.
Summary
In this exercise, you used the Design Support Library's TabLayout
class to implement tab navigation
and combined it with the Support Library's ViewPager
class to add gestural navigation.
The two classes are designed to work together to support this exact scenario.


Amazon Prime Video Reveals Characters From Its Upcoming Original Series Hostel Daze
After Mirzapur and Made In Heaven's success, Amazon Prime Video is coming up with new series titled as 'Hostel Daze'.
by Koimoi.com TeamAfter Mirzapur and Made In Heaven‘s success, Amazon Prime Video is coming up with new series titled as ‘Hostel Daze‘. They have just released the trailer and it’s garnering praises from all around. The trailer will definitely make you feel nostalgic and you would want to go back to your college life.
Here’s the character synopsis of Hostel Daze:
1) Ankit
A quintessential boy next door, Ankit Pandey has normal looks, average body and ordinary grades. The only thing extra-ordinary about him are his love for the college-sweetheart and his dedication to porn.
2) Jaat
Patriarchy, bro-code and brutal honesty are the mantras this Haryanavi swears by. His stellar athletic capabilities more than compensate for an otherwise dire lack of sophistication. He owes his admission in college to management quota and hectares of farm land.
3) Chirag
Soft, chubby, cuddly and warm, Chirag Bansal is literally a human teddy bear. Being systematic, smart and caring makes him ‘Mummy’ of the wing. His innocence and uprightness is but a huge liability in this mad-house of a hostel.
Recipe to make a Jhantoo
1. Pour equal proportions of body odour, mess dues and backlogs in a hostel.
2. Add a tinge of arrogance and manipulation.
3. Boil hard under sexually-frustrating conditions.
You get your Jhantoo! Best served with cigarettes.
Trailer video link :
Link to Character promo video here:
Link for Kota Factory


This presentation, co-authoed with Amanda Greenwood, overviewed the expansion and later retrenchment of UAlbany’s web archives program due to a lack of permanently funded staff. We found that web archives can be really wasteful and require increasing maintenance and scoping labor over time. While there is a rational case for increased staffing in web archives, we tried to connect our local case study with the broader resource environment in American colleges & universities, showing that this is a long term crisis for research libraries.
Hi everyone. I’m Greg Wiedeman. I’m the University Archivist at Albany, and I should give bit of a heads up that I’m probably going to kill the vibes at this conference with this talk. Hopefully you hear some more optimistic talks about web archives this week, but I think it’s useful to have an honest understanding of what’s happening in web archives collecting right now.
This presentation is about program sustainability in American University web archives programs. I also want to thank my co-author, Amanda Greenwood, who was unable to make it here for IIPC this year but is just as responsible for this work as I am.
[slide]
First a bit of backstory about our web archives program at UAlbany. In 2012 we first started our Archive-It subscription with the Internet Archive, and the goal was to capture as much of our university website, albany.edu, as possible. This is because we are a public university, and there’s public records on our website that we have to collect and preserve permanently according to State records laws.
There was at first, no one really assigned to manage the web archive. There was an inital monthly crawl set up and it did its work for better and worse and no one really touched it.
In 2015, when I was hired it was worked into the University Archivst position as a very minor bullet at the bottom of the job responsibilities, but I was able to at least better scope and schedule the crawls, which is when we first started actually getting all of albany.edu.
In 2016 we expanded to capture our outside collecting areas. The university archives is also part of our special collections department which collects outside manuscript collections and organizational records, mostly on New York State politics and death penalty activism. Much of that work now happens online, so when we get transfers of organizational records, we would start collecting their websites as well.
We also tired to do some topical collecting for the 2016 and 2018 State elections, but we realized we didn’t really have the resources to do that.
And we also noticed that our progress was creating a big maintenance backlog. It was feasible to crawl new collections one-by-one, but crawls would later time out and need rescoping periodically over time. So, while it was kinda easy to expand one-by-one, each one increased the long term mantenance work that we really didn’t allocate for.
[slide]
So I think we are pretty typical. There are many college & university archives collecting the web in the United States. The NDSA web archiving survey is a couple of years old now, but the majority of respondents in 2017 were college and university archives.
Archive-It has also reported at least as late as 2018 that about 60% of their subscribers were college university archives, and their next category was public libraries, which now might be a bit bigger, but is subsidized by their really cool community web program. Archive-It’s efforts to diversify their partner orgs really underscores how dominant colleges and universities are there.
So really, college and university archives are really dominant in American web archives, particularly through Archive-It. The Internet Archive in practice uses us as kind of curators, as all of our collections get added to the Internet Archive, too. So, hopefully, we can do some more specialized collection that they’re not able to do, and all of this gets collected and preserved.
Many of these organizations are public universities, and they’re collecting because of simmilar State records laws that require the preservation of public records on University websites.
[slide]
It’s well understood that web archives are pathetically underfunded. This is a quote from David Rosenthal, and according to those NDSA surveys over two thirds of respondents reported about quarter FTE dedicated to web archives, and this was actually an increase to from earlier surveys.
[slide]
And if we look at the graph here, you can see that there’s very few web archives run by deditcated staff and I agree with David that this looks pretty pathetic.
[slide]
So, there’s a one-year graduate assistantship that we can apply for our library. It kind of goes from department department and the student gets tuition credit and a stipend. We applied for and recieved that position for 2020 in the hopes of making this program more effective.
We hired my co-author who was later hired away by Union College. The goal was to better management over what at that point had grown to 30 web archives collections and try to better document New York State politics and the death penalty. We knew there was stuff we were losing.
In February of 2020 Archive-It 7.0 came out, which incorporated youtube-dl, which was a major advancement that helped us better crawl video content. The downside was, it helped us better crawl video content.
We quickly saw really substantial data increases, and we were soon right at our data budget for the year.
I was scheduled to go on leave for March and April, so I had about two days at the end of February to kind of roll back our cralls and try to get them to capture as minimal as possible, and we turned off many crawls for “the time being.”
Then a pandemic happened.
So, while some other web archives programs were able to shifted more resources to web archives in a remote world, Our web archive was kind of frozen. I was on leave, the person that was managing the web archive program, and this timing and not having dedicated staff coverage resulted in significant loss of documentation of university policies and practices in the rapidly changing early period of the pandemic. So that wasn’t a great situation to be in.
[slide]
So we refocused the GA position around sort of resetting the program. We really had to rescope all active collections, be more thoughtful about collecting priorities, and try to allocate for and limit long term maintinance.
[slide]
We figured out that appraisal for web archives is pretty weird compared to traditional archives. When you’re working with paper, or even born-digital records, appraisal saves us time and labor. It reduces the time to needed to acquire and process collections.
Appraisals for the web archives is different. There’s very little upfront time costs. It’s very easy to crawl a lot without a lot of time dedicated to scoping. But it takes a lot more labor to crawl efficiently.
Lack of staff time doesn’t prevent you from crawling but it creates a lot of waste that is really hard to account for.
[slide]
This makes web archives a really wasteful collecting method. There are so many crawler traps now. I’m not sure if its React or misconfigured Wordpress sites or what, but sites will often just append random strings to URLs infinitely. Browser-based crawling helps some of this, but there still so many sites that need really labor-intensive scoping.
And just mistakes happen. During our 2018 state election crawls we I was working with a grad student at the time, but since he was focused on a bunch of other work, he wasn’t really trained on web archives. He just spent some time going through and collecting all of the state candidates websites URLs for me to crawl.
We had done some test crawls on an initial batch but a couple of days before the election, he came with another batch, and we didn’t have time to do a full test crawl. I unfortunately did not review his seed list that thoroughly, and there was a Youtube link, because one of the candidates used Youtube as their main on candidate website, which means our crawler for the next week started downloading as much of YouTube as possible.
So I think this happens actually, very often. And you know how I know this?
Because most institutions dedicate about .25 FTE to web archives.
Also Archive-it now has automated scoping rules that get applied for these situations to try and avoid this. So when you put a Youtube link it’ll automatically apply some scoping rules to try and reduce the waste.
So web archives have some really significant costs that we don’t see. The compute and storage necessary to run web crawlers are relatively cheap for us, but we need to allocate for the cost of servers responding to the crawler.
And this is all of this is physical infrastructure so when a crawler is stuck in a trap forever and getting a lot of useless data its generating emissions. Even if it’s very efficient, even if you buy carbon offset credits, it’s still generating emissions.
[slide]
There is also access waste that is even harder to measure. The less efficient web archives are, bigger our collections are, the harder they are to use.
So we have some some great progress with the Archives Unleashed project which has made it feasible to use big data tools to work with these web archives, but they would just be a lot easier to use and it would lower the barrier for use if collections were more precise.
The lack of research use for web archives is a widely known problem. This is a direct reflection of the lack of staff we devote to web crawling and scoping.
I know this because some of the faculty I’ve tried to convince to use web archives say if I scrape it myself, I have like a lot more granular control about what I’m getting. I only get what I want and I don’t have to do a lot of filtering and data cleanup afterwards. It’s actually more work for me to use web archives, both either through us or collections that exist.
So this waste, the lack of opportunity for use is really hard to measure. And so we really need to further study this and try to measure and account for this waste.
[slide]
We also tried to be really thoughtful about the experience of web archives, as it turns out web crawling can be a really time consuming job despite the powers of automated crawlers.
Multiple test crawls are now usually needed and sometimes these crawls could last up to 4 weeks.
And one of the hard things is that we have no idea what the crawls are actually gonna finish. Sometimes they’ll finish in a day, sometimes they’ll finish in 5 days.
So that’s really hard to work around other duties, because you don’t really know when the crawl is going to finish.
So it’s actually really hard to like manage web archives and other duties this way.
And you also have really no control of what will cause the maintenance that we need cause we don’t know when websites are going to change.
So all of a sudden you’ll have a crawl that will be working well over time but they make a major update and changed what they’re doing and all of a sudden now our calls breaking, and we have to dedicate a lot of time for rescoping.
So our the time committment is really decided by website changes and Archive-It technology updates that we don’t have a lot of control over and can’t really plan for.
[slide]
As a new web archivist, it was harder for my co-author to get up to speed than when I started crawling as the web keeps geting more complex.
This required a lot of work with myself analyzing host reports that I struggled to explain.
So there’s a lot of technology you need to learn, like what HTTP codes and URL parameters are.
But also even despite the technology learning curve, it’s just hard to read a host report. Looking at lists of URLs and trying to figure out what is happening is really hard.
There’s some really good documentaion about how to learn to use the Archive-It tool and crawl the web. But it’s not going to help the problem of what is this particular scoping issue?
[slide]
Another thing we found out, which is kind of the biggest takeaway is that the maintenance for our web archives is increasing over time quite significantly.
The web is getting more complex and dynamic day by day. I think this is something we all kind of know.
There’s really kind of an arms race between web technology and archiving technology. So now we have browser-based crawling, but even though we have more tools to collect, more tools means we have more options, which takes more time.
So sometimes you do a traditional crawl, and then you do a browser based crawl on top of it to see if that solves the scoping issue you don’t understand.
We found out that the same websites take significantly more time to scope on average, even 3 to 5 years later.
[slide]
So this is some of the results from our 2020 rescoping.
And you can see that the average was about 1.5 test calls over the course of almost 30 days. So about a month here among other duties. This was the time until we saved a final test crawl.
So only like 3 to 5 years later we found a significant increase in both the number of test calls they required, which went to almost 5, and the amount of time to sucessfully crawl which reached over a hundred days.
One of the worst collections was CSEA, which is New York’s public workers union.
[slide]
In February of 2016, it took 5 days to scope and capture, 2 test crawls and we ended up with 0 scoping rules.
In 2020, in the Fall and over the winter it took 6 months from start to finish juggling this among a bunch of different other web collections and other duties.
It required 12 different test calls, each about 3 and a half days each, and we ended up with 10 scoping rules.
[This is actually shorter, as we did an initial test crawl in August and put it aside when we figured out that this wasn’t a good collecion to learn scoping on.]
[slide]
So there’s definitely a rational increase for increasing staff dedicated to web archives, right?
As our maintenance load is growing significantly over time, even to keep capturing what we’re collecting without expanding.
There’s also another weird quirk being in a university special collections, as usually collections come in years or decades after they’re created.
If you talk to our department head, who does a lot of collecting about New York politics, he’s getting great collections from the 1960s, 70s, 80s.
 Maybe they’re more protected from harm in some cases. So they’re more comfortable donating these and managing these collections.
So there’s always been kind of a lag time, but much of State politics and death penalty activism now happens on the web or in born digital archives are hidden on Google drive somewhere or other tools.
So the weird thing is that we’re in this period of double collecting.
We need to collect both the 1970 and 1980 and current records at the same time, which is a kind of peculiar period.
Because that delay for the new stuff is gonna really result on unacceptable loss. Much of that stuff, is going to be gone. But its hard to do twice the work without twice the number of people collecting.
[slide]
But while there’s rational to increase staff dedicated to web archives, there’s been major staff loss in North American research libraries over the past 20 years.
Specifically since 2008 you can see there is a really significant drops in the median FTE in ARL libraries. Over this time it’s dropped from about 210 to a hundred and sixty.
And this has created what Eira Tansey has described as a “poverty cycle” where these losses compound, as libraries are unable to take strategic risks and focused only on short term needs instead of things with long term value.
This is why Libraries are still more focused on collecting decades in the past, because they get more immediate value for them, as researchers are using those collections now.
[slide]
And again, here UAlbany, is pretty typical.
We had a hundred and fifty FTE in the year 2000 at our library, and now this year we’re reporting to ARL 75 FTE for a 50% decrease.
This makes it hard to impossible to maintain current programs and services that are currently producing value to our university community, much devote more staff to web archives or double our collecting staff.
[slide]
So why is this happening? Why? Why are research libraries hemorrhaging staff?
Here again, UAlbany can be a really useful case study.
And the real truth is that administrators are reallocating staff from academic libraries to units that better demonstrate a direct impact on student recruitment and retention.
Our Provost office had made the last 10 years of staffing data open. These numbers are a bit different as they don’t include temporary or adjunct lines.
In the last 10 years we’ve lost 26.7 positions for an over 27% drop but meanwhile, university as a whole has grown in staff and academic affairs has grown in staff.
And, what’s most glaring is that in our university’s functional budgeting we are budgeted as Academic Affairs Support.
Over this period, Academic Affairs Support has actually grown by over 10%, which includes the Libraries’ losses, gaining over 47 positions, most of those in writing and critical inquiry and academic advising.
University administrators are pulling staff away from research libraries towards efforts that can get more students in the door and make sure they graduate.
And this problem might get worse. There’s the potential for enrollment drops starting around 2026 just from demographic changes. This can be really noisy and really dependant on attendence rates. But the resource environment is more likely to get worse than better.
I support our university’s mission of providing an accessible education as broad a set of New Yorkers as possible, but archives and research libraries have a different mission.
[slide]
So this often gets attributed to austerity, and it is austerity and a crisis in how American colleges and universities are resourced.
But its also kinda worse than that. It a lack of value for the mission of research libraries, both by universities and the public.
An example of this is that last year New York State, like many states had a historically pretty unique budget surplus and the state did invest in our university.
The “Albany AI Supercomputing Initiative” includes $75 million in permanent state funding and 49 new tenure track positions in every college & school in the university, most dedicated to AI.
Yet this resulted in 0 hires for the University Libraries.
Even when there’s funding for universities, there is not funding for our work.
Unlike AI apparently, the politicians behind this funding cannot run in their districts on support for research libraries.
[slide]
So the really unsettling conclusions to this are that lack of staff makes web archives really wasteful in ways that we need to start trying to measure and account for.
Even when just maintaining current collections we should budget for increases in mantenance over time.
Faced with this reality, we have slowly stopped collecting the web in our major subject areas. As crawls need rescoping we just turn them off.
We are now just crawling to meet our legal requirements.
I think our pressures and experience are typical. This is a really a long term existential crisis for research libraries.
Improvements in practice or technology won’t address these structural forces.
And it’s really hard in this current environment to see how this gets better.
For this community at IIPC, this means we really cannot rely on most college and university libraries to preserve the web.


Hello! I am a young coder who takes classes at West-Mec coding after my regularly scheduled day at University High school. Coding takes place at the start campus at 6997 N Glen Harbor Blvd, Glendale, AZ 85307. Among many other things, I enjoy gaming, Twitch, coding, mathematics, and football; my favorite football team is the Arizona Cardinals, my favorite Twitch streamers to watch is "loltyler1" and "Greekgodx". Finally, I attend University High school in Tolleson and West-MEC Coding.


---
layout: null
section-type: about
title: About
---
## About
Linux lover, DevOps enthusiast, Kubernetes adept and {{insert_another_catchphrase}}. He/him.
Currently working at:
Helping and getting help on Stack Exchange:
Stack Overflow top tags:
Training and learning cyber security on Hack the Box:


Documentation
How to use?
- Make sure that you included his Plugin depends on it. Then, include in the Plugin this project. After buy, plugin in the folder plugins.
<!-- jQuery (necessary for StrongPass.js JavaScript plugins) -->
<script type="text/javascript" src="jquery.min.js"></script>
<!-- Include all compiled plugins (below), files as needed -->
<script type="text/javascript" src="StrongPass.min.js"></script> - Place the text in which the password will be also a button that will change the password in the event of the pressure in the
div
is to be the parent for them and give any name by propertyclass
orid
. - Call the function responsible for the operation of the Plugin using the property that you giving them for the
div
.$('#test').generatePassword({
passCharSet: "a-z,A-Z,0-9,#", // if you use all character
passSize: "15"
}); - There is some of her Option
For example, to determine the length of the password or to select letters and signs and figures that wants to be in the password.passCharSet
These are used to determine the specific type of special characters, for example, you are a character, or a figure, this property accepts letters of 0-9 and also accepts a-z, A-Z as well as special characters.passSize
It is a property that is used in determining the length of the password.
- Create the HTML
<div class="input-group" id="test1">
<input type="text">
<button type="button">Refresh</button>
</div>


HOME
Discounts
This is the second project from the Practical JavaScript Course
Obtain the price after applying a discount
Type the product price
-10%
-15%
-20%
Price after Discount


BLV leukemia bovine virus infection isolates cattle region regions {border-style px;} #ffffff;} PCR analysis samples genotypes new °C DNA dairy (nt China HLJ env nested used identified indicated sites DQ, []. glycosylation rate sequences study viral QQHR alignment clustered genotype major phylogenetic Fig. Gag However, Virol./s--- domain known px; showed site {font-family CoCoMo-qPCR- HLJ, [], font-size gene protein proviral region, residues using variation China. HLJ. HRB Holstein N-linked Table amino characterization circulating currently double; epidemics epitope gag high human including indicate leukaemia molecular neutralizing performed results s, (Fig. -bp Bovine China, Dairy HRB, Harbin Heilongjiang MDJ Province Science acid blood border-color border-width breast collected conserved control current distributed epidemiology existed genotype, immune motif prevalent program reported sequencing shown solid; substitution {font-style {font-weight min, (Table (–), Chinese Foundation Notably, SH, T-cell Therefore, Veterinary aligned analysis. assay bold cattleArch courier; critical derived detected em; envelope fragment genotypes. investigated leukosis milk nonsynonymous northeastern nucleotide obtained primers provirus pt;} reference region. relatively status substitutions unique vivo.


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S3 Buckets
100
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# -*- coding: utf-8 -*-
from contextlib import contextmanager
from neo4j.v1 import GraphDatabase, basic_auth, TRUST_DEFAULT
[docs]class SessionManager(object):
"""this class is an encapsulation of raw neo4j-driver object, which provides certain level of control on the driver"""
def __init__(self, uri, username=None, password=None, encrypted=True):
self.uri = uri
self.driver = self._get_db_driver(uri, username, password, encrypted)
@staticmethod
def _get_db_driver(uri, username=None, password=None, encrypted=True, max_pool_size=50, trust=TRUST_DEFAULT):
'''
:param uri: Bolt uri
:type uri: str
:param username: Neo4j username
:type username: str
:param password: Neo4j password
:type password: str
:param encrypted: Use TLS
:type encrypted: Boolean
:param max_pool_size: Maximum number of idle sessions
:type max_pool_size: Integer
:param trust: Trust cert on first use (0) or do not accept unknown cert (1)
:type trust: Integer
:return: Neo4j driver
:rtype: neo4j.v1.session.Driver
'''
return GraphDatabase.driver(uri, auth=basic_auth(username, password), encrypted=encrypted,\
max_pool_size=max_pool_size, trust=trust)
@contextmanager
def _session(self):
session = self.driver.session()
try:
yield session
except Exception as ex:
raise ex
session = property(_session)


{% extends 'bill_base.html' %}
{% load staticfiles %} {% block title %}| Bill Amount | {{cost.totalplusdelivery}}.00 |
| {{ value.surcharge__tax_name }} @ {{ value.surcharge__value }}% | {{ value.tax }}.00 |
| Total Amount | {{ cost.grand_total}}.00 |
| TDS | {{tds.tds}}.00 |
| Balance | {{cost.amount_received}}.00 |


➤ Data Scientist for the Environmental Protection Agency, contracted through General Dynamics Information Technology, High Performance Computing Division.
➤ 12 years of professional analytics experience.
➤ Volunteer Scuba Diver at Great Lakes Aquarium.
➤ Masters Student, at Johns Hopkins University.
Current focus:
• Natural Language Processing (NLP): Transformers (Huggingface/BERT), Text Embeddings, Transfer Learning: Text/Document Classification, Optical Character Recognition, Cosine Similarity
• RAPIDS - Nvidia's GPU accelerated end-to-end data science life cycle.
• Nordic Skiing/Mountain Biking (while code is running of course):
Recent ML-OPS coursework:
• (Completed) Deployment of Machine Learning Models.
• (Currently taking) Deployment of NLP Models with AWS.
Far-future focus:
• Advanced Dev Ops
• Kubernets
I use the scientific method in every project I tackle.
Proficient programming skills enabling me to write a hello-world.py script in less than 30min.
Experience in multi-year projects and international collaborations.
Focused and driven by an inherent curiosity to ask why.


Documenting lesser-known features in Ruby
Also known as:
interpreter instructions or compiler directives
Setting a source file's encoding:
# encOding: bInary
p "".encoding # => #<Encoding:ASCII-8BIT>
How many different "magic" comments (a.k.a interpreter instructions) do exist in Ruby 3.0?
1
2
3
4
5
6
How many different "magic" comments (a.k.a interpreter instructions) do exist in Ruby 3.0?
4
# encoding: …
# frozen_string_literal: …
# warn_indent: …
# shareable_constant_value: … ← NEW
# warn_past_scope: … ← NOT COUNTED
What happens when you specify# shareable_constant_value: literal
as your magic comment?
It deep-freezes every literal assigned to a constant
or
It deactivates the warning when you are re-assigning a constant
or
It calls Object#share
on every literal assigned to a constant
or
It literally allows you to share the constant's source code on github.com
What happens when you specify# shareable_constant_value: literal
as your magic comment?
It deep-freezes every literal assigned to a constant
X = [{foo: []}]
# => same as [{foo: [].freeze}.freeze].freeze
Other modes of operation:
experimental_everything
experimental_copy
Where can you place your# shareable_constant_value
magic comment?
Only in the first line of the file
or
In the first line of the file, but when its taken by a Unix shebang (#!/bin/ruby
), or another magic comment, it is possible to put in a subsequent line
or
Anywhere in the file
Where can you place your# shareable_constant_value
magic comment?
Anywhere in the file
Changes the behavior of constants below the magic comment within the current module-scope
Grammar Definition:
github.com/ruby/ruby/blob/v3_0_0/parse.y#L8085
Official Documentation:
ruby-doc.org/core-3.0.0/doc/syntax/comments_rdoc.html
More Examples:
idiosyncratic-ruby.com/58-magic-instructions.html
What does ...
(three dots) do in Ruby 3.0?
Calls a method without any arguments three times or
Forwards a method's arguments to another method or
It is a begin- and endless range (-Infinity to Infinity) or
Separates the RBS type signature from the actual method body
What does ...
(three dots) do in Ruby 3.0?
Forwards a method's arguments
to another method
Originally introduced in 2.7,
it has become useful in 3.0:
def repeat(method_to_call, count, ...)
puts "Will call #{method_to_call} #{count} times"
count.times {
public_send(method_to_call, ...)
}
end
How many of Ruby's 90 default gems are officially unmaintained?
None (all have a maintainer)
1-5
6-10
11-20
21-30
How many of Ruby's 90 default gems are officially unmaintained?
17
benchmark date dbm debug delegate english fileutils getoptlong net-pop net-protocol net-smtp observer open3 pstore tempfile tmpdir weakref
Which global variable was removed from Ruby 3.0?
$-0
$-F
$-I
$-K
Which global variable was removed from Ruby 3.0?
$-K
Removed together with $KCODE
Used to change the behavior of default encodings
Mirrored the -K
CLI option (which is still present)
Which one of the following statements is not valid Ruby 3.0?
def * = 0
def Time.now = 1970
def !(o) = !!(o)
def a x = x ** 2
Which one of the following statements is not valid Ruby 3.0?
def a x = x ** 2
Endless method definitions with arguments require parenthesis:
def a(x) = x ** 2
What is the output of the following Ruby 3.0 code:
a = 1
2 => a
def a = 3
p a
1
2
3
What is the output of the following Ruby 3.0 code:
a = 1
2 => a
def a = 3
p a
2
Local variable a
has precedence over method a
Things I did not cover:
Keyword argument changes, typing with rbs and typeprof, Ractor for concurrency, ...
Checkout Ruby Changes 3.0
at the Ruby Reference by @zverok
Warning[:experimental] = false
# Check if a is true and b is false
if [a, b] in [true, false]
# Separate an array into head an tail
array => [first, *rest]
Which one of the following statements returns false?
2 in 1...3
2 in [1,2,3]
BasicObject in Object
Object in BasicObject
Which one of the following statements returns false?
2 in [1,2,3] # => false
2 => [1,2,3] # NoMatchingPatternError (2)
Which one of the following one-liners does not work?
1 => Numeric => x
2 => a => b => c => d
3 => Integer => Z
4 => -> o { p o } => o
Which one of the following one-liners does not work?
3 => Integer => Z
syntax error, unexpected constant,
expecting local variable or method
Which one of the following statements will raise an error?
0 => 0
0 => 0 => o
0 => 0.0 => o
0 => 0...0 => o
Which one of the following statements will raise an error?
0 => 0...0 => o
# NoMatchingPatternError (0)
0 => 0..0 => o
# => 0
Which of the following new syntaxes is just super neat and awesome?
{ key: 'value' } in { key: }
Returns true if all specified keys were found
Object destructuring: It assigns the specified keys' values to local variables with the keys' names
Ruby got better!
So let's make 2021 better, too ;)
@JanLelis - idiosyncratic-ruby.com/quiz


Wendy:
All I know is that Dr. Chaos operated some kind of device.
According to my memory, the device was meant to control the weather around Pavel.
When the sleeping system awoke, its first act was to activate this device.
I think perhaps the last mode in which it operated was N-45: Arrival of Eternal Spring.
Ask about the Arrival of Eternal Spring.


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

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='input_real')
inputs_z = tf.placeholder(tf.float32, shape=(None, z_dim), name='input_z')
lr = tf.placeholder(tf.float32, shape=(), name='learning_rate')
return inputs_real, inputs_z, lr
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
alpha = 0.2
with tf.variable_scope('discriminator', reuse=reuse):
# 28x28x3 - starting image dimensions
# Using Xavier_Initializer as per project reviewer's feedback to break symmetry and make model converge faster
d_layer1 = tf.layers.conv2d(images, 64,5, strides=2, padding='SAME',
kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True,
seed=None,
dtype=tf.float32
))
relu1 = tf.maximum(alpha * d_layer1, d_layer1)
# 14x14x64
d_layer2 = tf.layers.conv2d(d_layer1, 128, 5, strides=2, padding='SAME')
bn2 = tf.layers.batch_normalization(d_layer2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
# 7x7x128
d_layer3 = tf.layers.conv2d(d_layer2, 256, 5, strides=2, padding='SAME')
bn3 = tf.layers.batch_normalization(d_layer3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
# Dropout
relu3_drop = tf.nn.dropout(relu3, 0.8)
# 4x4x256
# Flatten it
flat = tf.reshape(relu3_drop, (-1, 4*4*256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
alpha = 0.2
with tf.variable_scope('generator', reuse=not is_train):
# First fully connected layer
g_layer1 = tf.layers.dense(z, 7*7*256)
# Reshape it to start the convolutional stack
g_layer1 = tf.reshape(g_layer1, (-1, 7,7,256))
g_layer1 = tf.layers.batch_normalization(g_layer1, training=is_train)
g_layer1 = tf.maximum(alpha * g_layer1, g_layer1)
# 7x7x256
g_layer2 = tf.layers.conv2d_transpose(g_layer1, 128, 5, strides=2, padding='SAME',
kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True,
seed=None,
dtype=tf.float32
))
g_layer2 = tf.layers.batch_normalization(g_layer2, training=is_train)
g_layer2 = tf.maximum(alpha * g_layer2, g_layer2)
# 14x14x128
g_layer3 = tf.layers.conv2d_transpose(g_layer2, 64, 5, strides=2, padding='SAME')
g_layer3 = tf.layers.batch_normalization(g_layer3, training=is_train)
g_layer3 = tf.maximum(alpha * g_layer3, g_layer3)
# Dropout
g_layer3_drop = tf.nn.dropout(g_layer3, 0.8)
# 28x28x64
# Output layer
logits = tf.layers.conv2d_transpose(g_layer3_drop, out_channel_dim, 5, strides=1, padding='SAME')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
smooth_factor = 0.9 # Factor to smooth out real input labels to discriminator to improve performance of discriminator
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*smooth_factor))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# Get weights and bias to update
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
data_image_mode="RGB"
image_width = data_shape[1]
image_height = data_shape[2]
image_depth = data_shape[3]
input_real, input_z, lr = model_inputs(image_width, image_height, image_depth, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, image_depth)
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
saver = tf.train.Saver()
samples, losses = [], []
steps = 0
print_every = 10
show_every = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
batch_images *= 2
steps += 1
# Sample random noise for Generator
batch_z = np.random.uniform(-1,1,size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
_ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
if steps % print_every == 0:
# Print losses at end of each epoch
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps % show_every == 0:
if image_depth == 1:
data_image_mode= "L"
show_generator_output(sess, 10, input_z, image_depth, data_image_mode)
saver.save(sess, './checkpoints/generator.ckpt')
return losses
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 32 #128
z_dim = 100
learning_rate = 0.0005
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 16 #128
z_dim = 100
learning_rate = 0.0005
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Biophilic Design
SDE4 uses biophilic design principles to establish and nurture a relationship with its sorroundings through visual and physical connections with natural elements.
Nature In Place
SDE4 adresses the 'presence of nature within the building' through its landscaping elements at level six, open views to the landscape and water features that are activate when it rains.
Nature of Place
SDE4 incorporates this virtue thorugh its open design, offering visual connections with sorroudings while providing physical shelter from natural elements.
Materiality
SDE4’s use of raw concrete, timber fixtures and fitting, and floor patterns create an esoteric yet comprehensible analogue to nature and biophilia.
Biophilic Profiling
The analytical tool allows designers to assess the biophilic profile of a building using a three dimensional biophilic green map.


Note: The 0 values and the null values have been removed
Route: /api/cyclones
Cyclone Data Since 1990
Route: /api/mldata
Machine Learning Data


Technical Blog June 28, 2015: Modifing My Website June 21, 2015: What is "ActiveRecord"? June 14, 2015: Ruby vs JavaScript Differences June 4, 2015: Ruby - Variable Scoping May 31, 2015: Ruby - When, Why, and How to Use Classes May 24, 2015: Using the 'group_by' Method in Ruby May 17, 2015: Using Array's and Hashes May 10, 2015: Margins, Borders, Padding are you confused? May 3, 2015: Why You Should Consider Using GitHub
Cultrual Blog June 21, 2015: How do I ask a Good Question? June 21, 2015: Conflicts - My Personal Experience June 14, 2015: Values June 4, 2015: Stereotype Threat May 31, 2015: Pairing and Giving Feedback May 24, 2015: Why Women are Leaving the Tech Industry May 17, 2015: What Kind of Thinker am I? May 3, 2015: Kitchen vs. Table...Who am I?


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Glucocorticoid-mediated inhibition of RANTES expression in human T lymphocytes.
Abstract
The chemokine RANTES has been implicated in the pathogenesis of allergic inflammatory diseases including asthma and rhinitis which are frequently treated with glucocorticoids. We observed that dexamethasone dramatically inhibited RANTES mRNA expression dose dependently in anti-CD3 activated Hut-78 T cells and human PBMCs. Inhibition of RANTES expression did not appear to be secondary to IL-2 inhibition and required binding to the intracellular glucocorticoid receptor. The down-regulation of RANTES expression by glucocorticoids in T cells may directly contribute to the efficacy of these agents in suppressing cellular infiltration and to their anti-inflammatory properties.


Cutting-Edge Conservation
For all that we do, healthy oceans are our highest prioity. Starting with lionfish in the Gulf of Mexico, we are dedicated to combating invasive species in scalable, efficient systems that extend conservation capabilities far beyond their limits today. Technology gives us the opportunity to revolutionize marine conservation, and we’re taking it.
High-Precision Responsibility
Leveraging advanced technologies, we deliver high precision with 0% bycatch and and zero marine waste. We’re here to protect our reefs, and that means that we’ll go the extra mile to recruiting the best pilots and engineers to our zero collateral damage policy. When it comes to doing the job right, we go the extra mile.
The Community of Pensacola
We work hard because who we are and where we’re from–not in spite of it. We promise to develop the community Pensacola that has embraced us since our arrival to recruit the best talent to underappreciated problem spaces and cities, create high-leverage jobs, all while working with our neighbors to accelerate STEM education and practicum.


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The angle ABD in the figure is an obtuse angle. It is greater than a right angle, but less than two right angles. Recall that Euclid required that any angle be less than two right angles. The angle CBD is an acute angle. It is less than a right angle. Note that there is no requirement that the angle be rectilinear, indeed, the horn angles mentioned before are not rectilinear, but they are less than right angles, and so are acute (notwithstanding Proclus’ remarks to the contrary). |
With these definitions, we see another aspect of magnitudes, namely, two magnitudes of the same kind, such as two angles, can be compared for size. Euclid frequently uses what is known as the law of trichotomy: given two magnitudes F and G of the same kind, exactly one of the following three situations hold, F is less than G, F equals G, or F is greater than G. See the comments after the Common Notions for more discussion on magnitudes and the law of trichotomy.


Such bullet must be located outside of and prior to each of the 3 thick horizontal black bars.


Honolulu’s 9 city satellite halls now accept credit, debit cards | Honolulu Star-Advertiser
All nine satellite city hall locations are now accepting credit and debit cards, City and County of Honolulu officials announced today.
The satellite city halls join the five driver licensing centers that already accept Visa, MasterCard and Discover as payment for city services, they said.
The satellite city halls will accept credit card payments for motor vehicle transactions, bicycle and moped registration, bills, bus pass sales and dog licenses. A 2.35% service charge will apply, they said.
“We continue to look for ways to help improve and modernize the experience of our customers,” Sheri Kajiwara, director of the city’s Department of Customer Services, said in a news release.
Credit and debit cards are also accepted at the city’s self-service kiosks that are available to residents 24/7 for renewing motor vehicle registrations. The six kiosks are available at select Safeway stores across Oahu. Locations and hours are available at honolulu.gov/csd.