Fundamentals of
Data Analytics and
Predictions
Non-Linear Models
Xi (Rossi) LUO
Department of Biostatistics and Data Science
School of Public Health
The University of Texas Health Science Center at
Houston
Moving Beyond Linearity
- The truth is never linear! Or almost never!
- But often the linearity assumption is good enough
- When its not . . .
- Polynomials
- Step functions
- Splines
- Generalized additive models
- Offer a lot of flexibility, without losing the ease and interpretability of linear models
Example Wage Dataset
- Mid-Atlantic Wage Data: Wage and other data for a group of 3000 male workers in the
Mid-Atlantic region
- plot(age, wage, main="Wage-data", xlab="age", ylab="wage", col="darkgrey")
Polynomial Regression —1
- fit=lm(wage~poly(age,4), data=Wage), or
- fit=lm(wage~age+I(age^2)+I(age^3)+I(age^4), data=Wage)
- In the figure, we have computed the fit and pointwise standard errors on a grid of values
for Age
$$y_i=\beta_0+\beta_1x_i+\beta_2x_i^2+\beta_3x_i^3+\dots+\beta_dx_i^d+\epsilon$$
Polynomial Regression —2
- Create new variables $X_1 = X, X_2 = X^2$, etc. and then treat as multiple linear regression
- Not really interested in the coefficients; more interested in the fitted function values at any
value $x_0$: $\hat
f(x_0)=\hat\beta_0+\hat\beta_1x_0+\hat\beta_2x_0^2+\hat\beta_3x_0^3+\hat\beta_4x_0^4$
- How to choose polynomial degree d ?
- Use cross-validation to choose d
Polynomial Logistic Regression
- Logistic regression follows naturally, for example
$P(y_i \gt 250\mid
x_i)=\frac{\exp(\beta_0+\beta_1x_i+\beta_2x_i^2+\beta_3x_i^3+\dots+\beta_dx_i^d)}{1+\exp(\beta_0+\beta_1x_i+\beta_2x_i^2+\beta_3x_i^3+\dots+\beta_dx_i^d)}$
Polynomial Regression Tail Behaviors
- Using polynomial functions of the features as predictors in a linear model imposes a
global structure on the non-linear function of X
- Polynomials have notorious tail behavior $-$ very bad for extrapolation
Step Function -1
- In order to avoid imposing such a global structure, we can instead use step functions
- Here we break the range of X into bins, and fit a different constant in each bin
- A way of creating transformations of a variable — cut the variable into distinct regions
$C_1(X)=I(X\lt 25)$,
$C_2(X)=I(25 \le X\lt 40)$,
$\dots$
$C_4(X)=I(X\ge 65)$
Step Functions —2
- Easy to work with,
- Create a series of dummy variables representing each group
- Useful for creating summary reports
- Useful for creating interactions,
- For example, interaction effect of Year and Age: $I(Year \lt 2005)*Age, I(Year \ge
2005)*Age$
would allow for different linear functions in each age category
- In R: $I(year\lt 2005)$. You can also use cut(age, c(18, 25, 40, 65, 90))
- Choice of cutpoints or knots can be problematic
Piecewise Polynomials —1
- Instead of a single polynomial in X over its whole domain, we can rather use different
polynomials in regions defined by knots
$y_i= \begin{cases}
\beta_{01}+\beta_{11}x_i+\beta_{21}x_i^2+\beta_{31}x_i^3+\dots+\beta_{d1}x_i^d+\epsilon_i, if
x_i \lt c \\
\beta_{02}+\beta_{12}x_i+\beta_{22}x_i^2+\beta_{32}x_i^3+\dots+\beta_{d2}x_i^d+\epsilon_i, if
x_i \gt c\end{cases} $
Piecewise Polynomials —2
- Better to add constraints to the polynomials, e.g. continuity & smoothness
- Splines: continuity with smoothness
Splines
- A cubic spline with knots at $\epsilon_k, k = 1, . . . , K$ is a piecewise cubic polynomial
with continuous derivatives up to order 2 at each knot.
- Linear Spline
- fit=lm(wage~bs(age,knots=c(50), degree=1), data=Wage)
- Cubic Spline
- fit=lm(wage~bs(age,knots=c(25,40,65)), data=Wage)
Natural Splines
- A natural cubic spline extrapolates linearly beyond the boundary knots.
- Fitting splines in R is easy: bs(x, ...) for any degree splines, and ns(x, ...) for natural
cubic splines, in package splines.
Knot Placement —1
- One strategy is to decide K, the number of knots, and then place them at appropriate quantiles
of the observed X
- It is common to place knots in a uniform fashion
- Have R automatically place the corresponding number of knots at uniform quantiles of the
data
- Another strategy; place more knots in places where you feel the function might vary most
rapidly, and place fewer knots where it seems more stable
- degrees of freedom = coefs + knots
- A cubic spline with K knots has K + 4 parameters (DoF)
- A natural spline with K knots has K DoF.
Knot Placement -2
- How many knots should we use, or equivalently how many degrees of freedom should our spline
contain?
- Try out different numbers of knots and see which produces the best looking curve (use
cross-validation)
Splines vs. Polynomials
- Regression splines often give superior results to polynomial regression
- Splines introduce flexibility by increasing the number of knots but keeping the degree fixed
Smoothing Splines —1
- A natural spline, with a knot at every unique value of $x_i$
- avoid the knot-selection issue, leaving a single $\lambda$ to be chosen
($\lambda$: tuning parameter controls smoothness)
- In R, the function smooth.spline() will fit a smoothing spline
- We can also specify df rather than $\lambda$
- smooth.spline(age, wage, df=16)
- The leave-one-out (LOO) cross-validated error
- smooth.spline(age, wage, cv=TRUE)
Smoothing Splines —2
- smooth.spline(age, wage, df=16)
- smooth.spline(age, wage, cv=TRUE)
Generalized Additive Models
Generalized Additive Models —1
- Allows for flexible nonlinearities in several variables but retains the additive structure of
linear models
- $y_i=\beta_0+f_1(x_{i1})+f_2(x_{i2})+\dots+f_p(x{ip})+\epsilon_i$
Generalized Additive Models —2
- Fit a GAM, for example using natural splines,
- lm(wage ~ ns(year, df = 5) + ns(age, df = 5)) or
- gam(wage ~ ns(year, df = 5) + ns(age, df = 5))
- Can mix terms - some linear, some nonlinear
- Can use smoothing splines as well
- gam(wage ~ s(year, df = 5) + s(age, span = .5))
Pros and Cons of GAMs
- Pros:
- Can fit a non-linear $f_j$ to each $x_j$ so haven't go manually try many different
transformations on each variable individually
- Can get more accurate predictions of Y if the relationship is nonlinear
- Can examine the effect of each $x_j$ on Y individually while holding other variables fixed.
This is useful for inference
- Cons:
- Restricted to be additive
- Interactions may be missed (but can add interactions)
- May be too many parameters to estimate given size of data


--- project-id: 3 title: Interpreting Chat Actors type: social network analysis, data visualization, front-end development tag: datadesign date: 1000-01-06 img: network.png alt: a social network description: We studied the community dynamics of Ubuntu IRC forums over one month. Through this study, we tracked the development of the chat network over time and examined dynamic participation patterns. We also observed factors that affect a user's embeddedness in the network and their 'importance' as discussion initiators or responders. These findings informed a web application that predicts the best times for discussion on a given topic for practical collaboration. ---
{% include head.html %} {% include js.html %} {% include stylesheets.html %} {% include nav.html %}messages
conversations
users
We studied the community dynamics of Ubuntu IRC forums over one month. Through this study, we tracked the development of the chat network over time and examined dynamic participation patterns. We also observed factors that affect a user's embeddedness in the network and their 'importance' as discussion initiators or responders. These findings informed a web application that predicts the best times for discussion on a given topic for practical collaboration.
Project Type: {{ page.type }}
Role: graph visualization, data mining, UX design, prototyping, front-end development
Tools: Python (BeautifulSoup, scikit-learn) for data mining, Gephi for graph visualization, Python (matplotlib, seaborn) for visualization, Figma for design and prototyping, Python (Tkinter) for app development
Date: February - April 2019
Code: GitHub
Open source software holds advantages over proprietary software in terms of security, cost, and flexibility. Because these products are widely used, they are also rapidly deployed for performance assessment. Developers often create specialized support forums called Internet Relay Chats (IRCs) to help users navigate these changes. IRCs provide opportunities for leveraging computing methods to increase collaboration across a large community.
The primary objective of this study was to decipher connections and restructure communication strategies among users in the channel to facilitate information flow. These strategies could guide a variety of users towards achieving their desired objectives via online communication. Our analysis aimed to:
The biggest advantage of the Ubuntu IRC over other IRCs is that it follows a peer-to-peer approach towards primarily software-related discussions, which allows for research that applies to other technical domains like business, collaborative learning, and military control. As more and more people join the IRC, they provide diverse approaches to problem-solving, thereby increasing the quality of solutions. This community thus evolves as a goal-oriented community, where the primary objective is to facilitate discussions.
Our dataset contained 601,705 messages from 9,731 users. To build the chat corpus, we scraped Ubuntu technical support channel logs. Each message had four relevant properties: channel, timestamp, username, and data.
Participants were divided into 'experts' and 'non-experts' – users with high network authority scores (high input and output content nodes) were labelled experts and the rest were labelled non-experts. This notion of role-influenced behaviour taken from centralized systems helped us informally discover the nature of these social interactions if such roles were mapped to conversations.
We used reply structure and word context techniques to extract a network from the data. Because IRC etiquette states that users address each other directly ('@___'), we were able to establish ties between participants. The network consisted of outward links from message senders to recipients. Multiple references built stronger ties between the corresponding nodes (users). Thus, our directed weighted network consisted of IRC users as nodes, with the edge weights corresponding to their communication frequency. Nodes were sized according to the betweenness centrality of the participant. Finally, sub-networks were coloured according to their modularity classes.
Often, participants on an open forum such as IRC are subjected to long wait times until their queries are resolved. If an expert is unavailable for a long time, the question can get buried under others and eventually remain unsolved, leading to a lossy information exchange. We identified the times of day when experts discussed a topic to help new users predict when their query would most likely be resolved.
The machine learning approaches we used for studying linguistic behaviour and topic models[1] are based on the assumption that the corpus is dynamic. Therefore we developed an indicative term-based categorization approach using chat sessionization and keyword mining. We restricted the report to hour-long bins to ensure that the analysis is neither too narrow nor too broad.
I incorporated this approach into a user-facing and administrator-operated message analysis system.
It was interesting to examine IRCs because "people who are located in geographically distant locales, who are of different national and linguistic backgrounds, and who might otherwise never come into contact, can engage in real-time interactions that resemble the immediacy of in-person face-to-face encounters"[2]. We provided:
[1] Guille, Adrien, Hakim Hacid, Cécile Favre, and Djamel A. Zighed. “Information diffusion in online social networks: A survey.” ACM SIGMOD Record 42, no. 1 (2013): 17-28.
[2] Paolillo, John. “The virtual speech community: Social network and language variation on IRC.” Journal of Computer-Mediated Communication 4.4 (1999): 0-0.


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jQuery Mobile provides a number of icons that can be used by applying a data-icon
attribute or a ui-icon-
class to a suitable widget.
There is an SVG and PNG image of each icon. By default the SVG icons, that look great on both SD and HD screens, are used. On platforms that don't support SVG the framework falls back to PNG icons. The icon name is self-describing. For example, the following will display a button with a home icon:
Home
The following is a full list of the icons provided:
The semi-transparent black circle behind the white icon ensures good contrast on any background color so it works well with the jQuery Mobile theming system. Here are examples of the same icons sitting on top of a range of different color swatches with themed buttons.


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Lucain Miller is a 22 year old living in Vancouver, WA. They've played D&D concistantly for 3 years and enjoy every second of it. They've played 4 characters: Chad, the lizardfolk wild magic sorcerer. Balsam, the aasimar divine soul sorcerer. Tacita, the changeling circle of the shepherd druid. And Pokie Avis, a tegu bard that was used one their first try of pathfinder. They have played Balsam for the longest amount of time, which has been almost their entiredy of their D&D playing career, and Chad the shortest who has only been used for a total of 6 hours during 2 ones-shots.
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Teletubbies star Pui Fan Lee makes a guest appearance in EastEnders
She played Dr Fremantle
by Charlotte RodriguesChildren's TV favourite Pui Fan Lee, who starred in Teletubbies, made a guest appearance in last night's EastEnders (November 12 2019).
The actress was in the BBC soap as Dr Fremantle, the doctor conducting assessments on Ollie Carter.
Although fans may not recognise her face, Pui played Po in Teletubbies from 1997 until 2001.
In 2015 the series was revived after 14 years with actress and stunt performer Rachelle Beinart now playing Po.
As well as her role in Teletubbies, Pui is also well known for being a CBeebies presenter.

For months, Mick and Linda have been concerned that their youngest has autism.
After Ollie had his assessment, Dr Fremantle told the Carters that he demonstrated good attention to detail and carried out most of the tasks she had set him with a high degree of accuracy.
Read More: EastEnders SPOILERS: Max proposes to Ruby
However, she also said taking into account the outcome of the assessment, the information Mick and Linda have given, and the findings from the school's observations, they have concluded Ollie has Autism Spectrum Disorder but is at the milder end.
Mick and Linda were stunned by the news.
Fans already know Linda is going to turn to alcohol to help her cope with the diagnosis.
Their eldest son, Lee Carter, is set to return as part of the storyline.
EastEnders airs Monday and Friday at 8pm and Tuesday and Thursday at 7.30pm on BBC One.
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--- title: Users and Organizations (User Card) section: Visual Tests --- Users and Organizations Action Another action Something else here Separated link Users Users Organizations Filter and Order Filter Action 1 Filter Action 2 Filter Action 3 Cards Lists Table
Principal Resonance Orchestrator
Product Designer
Human Response Architect
Internal Interactions Consultant
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Describe what is making you feel anxious.
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Writing document providers
Document providers are classes which implement the interface IReadsAndWritesDocument
.
You are encouraged to read the API docs for this interface.
As well as implementing this interface, a document provider is almost certain to require its own implementations of the following:
The abstractions package contains minimal abstract base classes for each of these interfaces, which cater for some of the boilerplate.
These are DocumentBase
, NodeBase
& AttributeBase
respectively.
You may use these base classes if desired, but their use is optional.
Registering/activating a document provider
In order to activate a document provider it must be added to dependency injection during start-up and it must be registered with the list of available document provider types.
Typically both of these are performed by an extension method for the IBuildsHostingEnvironment
interface.
The hosting environment builder includes an IServiceCollection
which is used to add relevant services to DI.
Here is the typical syntax for registering a document provider type:
builder.ServiceRegistry.DocumentProviderTypes
.Add(typeof(MyDocumentProvider));
Where:
builder
is theIBuildsHostingEnvironment
MyDocumentProvider
is the concrete class which implementsIReadsAndWritesDocument


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About
Adrenaline Engineering specializes in creating custom electro-mechanical racing systems for all forms of motorsports, as well as offering trackside support, consulting, development, manufacturing, service, and tuning for all things racing.
Products
Android/IOS App calibratable AutoBlippers/Quick-Shifters, Launch Control, Cruise Control, and Traction Control modules for most models of motorcycles.
Vehicle Control Modules
Data Acquisition linear stroke sensors from 10mm to 300mm.
Data Acquisition Equipment
Adrenaline Engineering will build one-off, race ready prototype systems to meet your specifications and needs.
Custom Builds
Services
We can up-fit your bike with a quick install data acquisition system and review data after each session as well as listen to your feedback. We will tune your bike to your individual riding style, make you feel more comfortable on your bike, and drop your lap times.
Track Side Data Acquisition/Suspension Tuning
Hire an AE race engineer to individually work with you for a day or even an entire race weekend to assist with ECU/electronics tuning, chassis/suspension/bike setup using data acquisition, rider coaching, maintenance, and much more to get the most out of you and your bike.
Hire AE Race Engineer
Do you need a custom electrical system for your race machine or want a system built for your bike/car that currently doesn't exist or is unavailable?? Let the experienced engineers at AE bring that idea to life and even help you get your device/product into productions. (Wire harness, circuit boards, App development, website creation, advanced algorithm development, 3D printing, cast prototypes, engine controllers, piggy-back modules, TC, autoblipper/quckshifter, Launch control, temperature monitoring, GPS turn by turn dynamic system, data collection, wireless systems, etc) Contact AE to find or create a solution that meets your needs.
Custom Electro-Mechanical System
Did you just buy new suspension or build a new race bike and need help calibrating/tuning the electronics, chassis geometry, suspension, gearing, etc? Let AE help you get the most out of your new racing machine and have the bike be setup for YOU. Just buying new suspension or electronics often times will not make you faster. They need to be tuned and calibrated for each rider and the experts at AE are here to help you and teach you so you can get the true potential from your machine/upgraded components.
Complete Bike Tuning
Make sure your lines, brake markers, and body positioning are correct along with full bike setup. Video footage and logged data will be reviewed with detailed explanations of how to improve your riding.
One-on-one on Track Rider Coaching
Let AE build your race bike for you. The AE team has been building championship winning race bikes for over 10 years and have the connections, resources, and partnerships to build you the best bike possible while staying within your budget. Drop off a stock bike and pick up a turn key ready to race bike, built to your specifications to meet your organizations class/tech inspection rules. Engine builds, suspension packages, exhausts, bodywork, hand/foot electronics, etc, will all be hand picked and installed.
Complete Bike Builds
For more information and orders please Contact Us


TeamWorship
is an online tool intended to help worship ministries schedule, communicate and
collaborate.
Worship leaders can build complete services online and have team members reference the
website for updates.
Members can update their availability, view service details, request service changes and
communicate with other team members.
This Online Help Is Currently Under Construction
Please be patient as we work towards providing you with the information needed.


Next: Characters With Case, Previous: Graphic Characters, Up: Character Categories
The alphabetic_1 characters are a subset of the graphic characters.  For each implementation-defined graphic character that has no case, it is implementation-defined whether that character is alphabetic_1.


HEAD ? ======= <<<<<<< HEAD ======= >>>>>>> origin/master >>>>>>> origin/master
I love the direction that Selena Gomez has gone with this song. The message behind this song is portrayed in an effective way. She’s trying to say that instead of treating someone who mistreated you poorly, you need to take the high road and be kind to them instead, something they wouldn’t expect. The beat during the chorus is also something that makes the song fun and nice to dance to.
The song is very catchy and makes me want to move around. The song starts off calm and then during the chorus, the song does a 360 and makes you want to get up and dance. I love these types of songs because of the variety within them.
I typically don’t listen to EDM, but a friend suggested it to me so I decided to give it a listen. Zedd is known for remixing songs and adding sick beats to them. A good example is this song. It made me literally get out of my chair and start moving. It’s various beats kept me entertained throughout the whole 5 minutes and 52 seconds of the song.
Usually when we listen to songs for the first time, it takes us two or three listens to actually know if we like the song or not. For this song however, by the end, I loved the beat, the instrumental, and the mood the song created.
Whenever I hear the introduction to this song, I smile because of how it starts off so loud and bubbly. The song is so catchy and I have not been able to get it out of my head since I first listened to it. Although I feel like I’ve heard this melody or a similar melody from a different song, I feel like it goes well with Ariana’s style and her whole album itself.
EXO released their album EX'ACT this past June with their title song being Monster which is a bass heavy song with a combination of singing and rapping. This song is one of their "darker" songs bringing the group back to their earlier days. It balances melodic verses with a thundering chorus that pulses with unlikely hooks. This song goes well with their fast complicated choreography and is definitely worth listening to.
I remember hearing this song growing up because of my older siblings. I’ve grown to love every part of it. The vocals, the instrumentals, the beats--- all are so unique and soulful compared to what is popular today. The leader singer puts so much emotion into the lyrics, setting a precedent basically. The piano, the drums, and the electric guitars go in sync with the lyrics to convey the emotions even further.
This song is such an old song and was probably liked mostly by teenage girls back in the 90's. However, I like this song because it sounds like something that wouod be popular today. I love their vocals and the lyrics.
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Solid background in psychology, with strong emphasis in non-profit management. Consistently exceeding goals of supervisors and customer service expectations. Excels at collaborating with staff to build out database for multiple non profit agencies. Experienced in handling multiple contracts with various insurance companies nationwide and clinicians and community staff within a company.
Skilled in developing and implementing standardized policies and procedures for support staff, clinical staff, and community staff..
Currently attending full-time classes at the University of Minnesota to gain skills necessary to become a Fullstack Developer.


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The Twins introduced new throwback alternate uniforms, and they are just delightful
by Joe RiveraLet the offseason of uniform reveals continue.
The Twins wanted to get in on the fun that the Padres and Brewers were having, so they decided to reintroduce aclassic uniform, and they are so, so pretty.
MORE: Padres' new unis are super-sexy | Brewers reimagine old-school look
The powder blues, which were first introduced in 1973, were used until the 1986 season. The Twins are bringing them back to celebrate the 60th year of the franchise's existence. Interestingly enough, those alternates were used during some of the leaner years in franchise history.
The baby blues will pair nicely with the other unis in the Twins' closet, including arguably the best alternate uniforms in the league, the navy blue with red lettering and gold trim outline.*Fanning himself.* Hot.
All in all, what better time to do introduce new throwbacks than now? Fresh off a101-win season and a Manager of the Year award for Rocco Baldelli, the Twins are primed for a stretch run of good baseball inthe coming years. They have a lot of work to do this offseason, but hopefully these sexy new-old uniforms bring some free agents — and goodwill from fans — their way.


YOU'VE GOT MAIL by DeWyNGaLe * * *
Meg Ryan is Kathleen Kelly, the owner of the cute children's book store, Shop Around The Corner. It is a business that has been going on for years, and was previously owned by her mother. Kathleen is a sweet, funny, nice type of person who loves kids. One of her favorite things to do is log on to American Online. Her screen name is ShopGirl. She chats with someone named NY152 every day, and they are the greatest of friends. They don't go into specifics, but they just have a simple friendship. But now, The Shop Around The Corner is having a problem. A store called Fox Books has moved in right across the street! It is owned by Joe Fox. (Tom Hanks) This store is huge, with many floors of books and magazines. Their goal is to put every bookstore in New York City out of business.
Joe Fox also loves American Online. There, he chats with a girl named ShopGirl. They are the best of friends but they don't take their friendship into specifics. He knows nothing about her. His screen name is NY152. He meets Kathleen Kelly at The Shop Around The Corner when he is trying to buy out their inventory of books that Fox Books doesn't have. He simply states his name is Joe. They continue to meet around the city and she discovers that he is Joe Fox, the man that is going to put her out of business. The two of them still don't know that they are in love on America Online. They fight and fight and fight whenever they see each other. These arguments are quite humourous.
NY152 asks ShopGirl if they should meet. They plan to meet at the Coffee Shop. ShopGirl is waiting and waiting at a table with a present for NY152. When NY152 discovers that ShopGirl is Kathleen Kelly, he freaks out, for he is Joe Fox. He enters the coffee shop not telling her who he is. He simply sits down and annoys her to death. He doesn't mean to, but she takes it that way. Now that he knows that they are actually perfect for each other, he is extra nice to her. The next day he brings her flowers and stays in her apartment and chats with her for awhile. Eventually, he asks ShopGirl to meet again over America Online. They do, and they discover that they were in love the entire time. Kathleen simply states "I wanted it to be you."
You've Got Mail is a nice, cute movie with some good laughs. Tom Hanks and Meg Ryan are so wonderful together. Both did great jobs of acting. You've Got Mail shows that sometimes if you look beyond the business in people, they may be just right for you. Although at some points in the movie, it seemed a little slow, the overall product was great. Warner Brothers is out of the slump and definitely has a hit. America Online is supporting this movie a great deal. They even have a feature where you can E-Mail Tom Hanks and Meg Ryan under their screen names in the movie. Parker Posey, Dave Chapelle, and Greg Kinnear also have smaller supporting rolls. Parker Posey plays Joe's girlfriend, Dave Chapelle plays his friend, and Greg Kinnear plays Kathleen's boyfriend. The bottom line: Not Oscar Material, but a wonderful movie for all of you AOL lovers out there.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


Wedding photos of man who married three wives on the same day
- A Ghanian man identified as Osman Hafiz has married three women on the same day
- The man and his wives tied the knot on Saturday, November 23
- Photos from Hafiz's wedding to his wives surfaced on social media
A man identified as Osman Hafiz, a native of Walewale in the North-east region of Ghana, and three ladies have done something strange in the name of love. The man tied the knot with the three women on the same day.
The three wives, Yidana Sikena, Ali Kamara and Yakubu Huzaima married their husband on Saturday, November 23.
According to Ghana's Ghsplash.com, the husband and his three wives released flyers some weeks ago to announce their forthcoming marriage.
PAY ATTENTION: Read best news on Nigeria's #1 news app
The wedding was said to have taken place at Walewale. Photos taken at the historic wedding ceremony were shared on social media. The photos showed the three brides smiling brightly beside their husband.
This is why INEC must cancel Bayelsa, Kogi elections - Former PDP governor explains
Hafiz and his wives were dressed in beautiful traditional wedding attire for the ceremony.
PAY ATTENTION: Do you have news to share? Contact Legit.ng instantly
A Twitter user identified as Saddick Adams shared the photos on the micro-blogging platform. Adams, who congratulated Hafiz, stated that the wedding lasted for an hour thirty minutes.
READ ALSO: NAIJ.com upgrades to Legit.ng: a letter from our Editor-in-Chief Bayo Olupohunda
Meanwhile, Legit.ng had earlier reported that a Nigerian man had also done a strange thing on his wedding day when he walked down the aisle with two women at the same time. Photos from the white wedding ceremony were shared on the internet.
NAIJ.com (naija.ng) -> Legit.ng We have upgraded to serve you better
Would you invite your ex to your wedding? Why? on Legit TV


I am finding it hard to overstress the importance of types in the WSDL framework. This is especially important to understand for Tcl programmers who rarely have to deal with types until some bug shows up and forces the issue. But the same can be said of programmers in general. Many many bugs and corresponding developer time can be traced to feeding the wrong data to a function. A Web Service essentially allows a remote user to invoke functions on a system with data of the user's choice. Even with the best of intentions, it is unwise to accept un-checked data from users.
Fortunately WSDL demands the use of XML Schema to define types. At first, XML Schema appears to be difficult and complicated. In actual fact, it is rather easy to explain and use. In explaining it here, I will introduce the expected API for defining types.
proc ::wsdl::types::newPrimitiveType {typeName code description} { namespace eval ::wsdl::tcl::$typeName [list variable description "$description"] proc ::wsdl::tcl::${typeName}::validate { value } $code }
The top level namespace is ::wsdl. This may change, for instance maybe ::twsdl would be better. Below that is the ::wsdl::tcl namespace. I am temporarily using this namespace as an indication that the type checking are based upon tcl types, and are not yet exactly the same as the definition given by XML Schema. Once we have a complete list of primitive types that match XML Schema types, we can move the primitive namespace to something else. However, it will still be possible to create derived types based upon the Tcl types, they will still be primitive in tWSDL, but will require the client to be the tWSDL client.
proc ::wsdl::types::addSimpleType {tns typeName {base "tcl::anySimpleType"} } { namespace eval ::wsdl::${tns}::${typeName} [list variable base "$base"] proc ::wsdl::${tns}::${typeName}::validate { value } " variable base if {\[::wsdl::\${base}::validate \$value]} { return 1} else { return 0}" }
This API also demonstrates that types have namespaces. (Note that this is different than a Tcl namespace, although we will use the type namespace name as the name of the Tcl namespace for the type.) The example API puts the type namespace as a child of the ::wsdl namespace. It might be necessary to create a separate toplevel for all types.
New simpleTypes can also be derived from other simpleTypes or primitive types. You derive a new simpleType by several means: one is by enumeration and the other is by pattern.
Here are example calls of both API:
::wsdl::types::restrictionByEnumeration::new "CreditCardTypeCode" "s:string" { "Visa" "Mastercard" "AmericanExpress" "Discover" "DinersClub" } ::wsdl::types::restrictionByPattern::new "guid" "s:string" \ {[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}}


Introduction
-
- Big Idea:
- Express a numeric computation as a graph.
-
- Main Components:
-
- Graph Nodes:
are Operations which have any number of Inputs and Outputs - Graph Edges:
are Tensors which flow between nodes
- Graph Nodes:
-
- Example:
- \(h = \text{ReLU}(Wx+b) \\
\rightarrow\)
-
- Components of the Graph:
-
- Variables: are stateful nodes which output their current value.
- State is retained across multiple executions of a graph.
- It is easy to restore saved values to variables
- They can be saved to the disk, during and after training
- Gradient updates, by default, will apply over all the variables in the graph
- Variables are, still, by “definition” operations
- They constitute mostly, Parameters
- Placeholders: are nodes whose value is fed in at execution time.
- They do not have initial values
- They are assigned a:
- data-type
- shape of a tensor
- They constitute mostly, Inputs and labels
- Mathematical Operations:
- Variables: are stateful nodes which output their current value.
-
- Sample Code:
-
import tensorflow as tf b = tf.Variable(tf.zeros((100,))) W = tf.Variable(tf.random_uniform((784, 100) -1, 1)) x = tf.placeholder(tf.float32, (100, 784)) h = tf.nn.relu(tf.matmul(x, W) + b)
-
- Running the Graph:
- After defining a graph, we can deploy the graph with a
Session: a binding to a particular execution contexti.e. the Execution Environment
-
- CPU
or - GPU
- CPU
-
- Getting the Output:
-
- Create the session object
- Run the session object on:
- Fetches: List of graph nodes.
Returns the outputs of these nodes. - Feeds: Dictionary mapping from graph nodes to concrete values.
Specifies the value of each graph node given in the dictionary.
- Fetches: List of graph nodes.
-
- CODE:
sess = tf.Session() sess.run(tf.initialize_all_variables()) sess.run(h, {x: np.random.random(100, 784)})
- CODE:
-
- Defining the Loss:
-
- Use placeholder for labels
- Build loss node using labels and prediction
-
- CODE:
prediction = tf.nn.softmax(...) # output of neural-net label = tf.placeholder(tf.float32, [100, 10]) cross_entropy = -tf.reduce_sum(label * tf.log(prediction), axis=1)
- CODE:
-
- Computing the Gradients:
-
- We have an Optimizer Object:
tf.train.GradientDescentOptimizaer
- We, then, add Optimization Operation to computation graph:
tf.train.GradientDescentOptimizer(lr).minimize(cross_entropy)
- We have an Optimizer Object:
-
- CODE:
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
- CODE:
-
- Training the Model:
-
sess = tf.Session() sess.run(tf.initialize_all_variables()) for i in range(1000): batch_x, batch_label = data.next_batch() sess.run(train_step, feed_dict={x: batch_x, label: batch_label})
-
- Variable Scope:
THIRD
-
- Introduction and Definitions:
-
- Hierarchy:
- Hierarchy:
-
- Input Function:
An input function is a function that returns a tf.data.Dataset object which outputs the following two-element tuple:- features - A python dict in which:
- Each key is the name of a feature
- Each value is an array containing all of that features values
- label - An array containing the values of the label for every example.
def train_input_fn(features, labels, batch_size): """An input function for training""" # Convert the inputs to a Dataset. dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels)) # Shuffle, repeat, and batch the examples. return dataset.shuffle(1000).repeat().batch(batch_size)
- features - A python dict in which:
- Input Function:
-
- Import and Parse the data sets:
- e.g. Iris DataSet with KERAS
-
TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv" TEST_URL = "http://download.tensorflow.org/data/iris_test.csv" # Train train_path = tf.keras.utils.get_file(fname=TRAIN_URL.split('/')[-1], origin=TRAIN_URL) train = pd.read_csv(filepath_or_buffer=train_path, names=CSV_COLUMN_NAMES) train_features, train_label = train, train.pop(label_name) # Test test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL) test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0) test_features, test_label = test, test.pop(label_name)
-
- Describe the data:
- Creating features.
E.g. Numeric -
my_feature_columns = [] for key in train_x.keys(): my_feature_columns.append(tf.feature_column.numeric_column(key=key))
-
- Select the type of model:
- To specify a model type, instantiate an Estimator class:
- Pre-made
- Custom
- E.g. DNNClassifier
-
classifier = tf.estimator.DNNClassifier(feature_columns=my_feature_columns, hidden_units=[10, 10], n_classes=3, optimizer='SGD')
-
- Train the model:
- Instantiating a tf.Estimator.DNNClassifier creates a framework for learning the model. Basically, we’ve wired a network but haven’t yet let data flow through it.
- To train the neural network, call the Estimator object’s train method:
-
classifier.train( input_fn=lambda:train_input_fn(train_feature, train_label, args.batch_size), steps=args.train_steps)
-
- The steps argument tells train to stop training after the specified number of iterations.
- The input_fn parameter identifies the function that supplies the training data. The call to the train method indicates that the train_input_fn function will supply the training data with signature:
def train_input_fn(features, labels, batch_size):
The following call converts the input features and labels into a tf.data.Dataset object, which is the base class of the Dataset API:dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels)) # Modifying the data dataset = dataset.shuffle(buffer_size=1000).repeat(count=None).batch(batch_size)
Setting the buffer_size to a value larger than the number of examples (120) ensures that the data will be well shuffled
-
- Evaluate the model:
- To evaluate a model’s effectiveness, each Estimator provides an evaluate method:
-
# Evaluate the model. eval_result = classifier.evaluate( input_fn=lambda:eval_input_fn(test_x, test_y, args.batch_size)) print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
- The call to classifier.evaluate is similar to the call to classifier.train. The biggest difference is that classifier.evaluate must get its examples from the test set rather than the training set
-
- Predicting:
- Every Estimator provides a predict method, which premade_estimator.py calls as follows:
-
predictions = classifier.predict( input_fn=lambda:eval_input_fn(predict_x,labels=None, batch_size=args.batch_size))
-
- JIRA:
- Project:
- A JIRA Project is a collection of issues
- Every issue belongs to a Project
- Each project is identified by a Name and Key
- Project Key is appended to each issue associated with the project
- Example:
- Name of Project: Social Media Site
- Project Key: SM
- Issue: SM 24 Add a new friend
- Issue:
- Issue is the building block of the project
- Depending on how your organization is using JIRA, an issue could represent:
- a software bug
- a project task
- a helpdesk ticket
- a product improvement
- a leave request from client
- Components:
- Components are sub-sections of a project
- Components are used to group issues within a project to smaller parts
- Workflow:
- A JIRA workflow is the set of statuses and transitions that an issue goes through during its lifecycle.
- Workflows typically represent business processes.
- JIRA comes with default workflow and it can be customized to fit your organization
- Workflow Status and Transitions:
FOURTH
- To write a TF program based on pre-made Estimator:
- Create one or more input function
- Define the models feature columns
- Instantiate an Estimator, specifying the feature columns and various hyperparameters.
- Call one or more methods on the Estimator object, passing the appropriate input function as the source of the data.
-
- Checkpoint:
- are saved automatically and are restored automatically after training.
- Save by specifying which
model_dir
to save into: -
classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns, hidden_units=[10, 10], n_classes=3, model_dir='models/iris')
-
- Feature Column:
- check this page for the feature column functions
-
- Custom Estimator:
- Basically, you need to write a model function (
model_fn
) which implements the ML algo. -
- Write an Input Function (same)
- Create feature columns (same)
- Write a Model Function:
def my_model_fn( features, # This is batch_features from input_fn labels, # This is batch_labels from input_fn mode, # An instance of tf.estimator.ModeKeys params): # Additional configuration
The mode argument indicates whether the caller is requesting training, predicting, or evaluation.
- Create the estimator:
classifier = tf.estimator.Estimator( model_fn=my_model, params={ 'feature_columns': my_feature_columns, # Two hidden layers of 10 nodes each. 'hidden_units': [10, 10], # The model must choose between 3 classes. 'n_classes': 3, })
-
- Writing Model Function:
- You need to do:
- Define the Model
- Specify additional calculations:
- Predict
- Evaluate
-
- Train
- Define the Model:
- The Input Layer:
convert the feature dictionary and feature_columns into input for your model# Use `input_layer` to apply the feature columns. net = tf.feature_column.input_layer(features, params['feature_columns'])
- Hidden Layer:
The Layers API provides a rich set of functions to define all types of hidden layers.# Build the hidden layers, sized according to the 'hidden_units' param. for units in params['hidden_units']: net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
The variable net here signifies the current top layer of the network. During the first iteration, net signifies the input layer.
On each loop iteration tf.layers.dense creates a new layer, which takes the previous layer’s output as its input, using the variable net. - Output Layer:
Define the output layer by callingtf.layer.dense
# Compute logits (1 per class). logits = tf.layers.dense(net, params['n_classes'], activation=None)
- When defining an output layer, the units parameter specifies the number of outputs. So, by setting units to params[‘n_classes’], the model produces one output value per class.
- Predict:
# Compute predictions. predicted_classes = tf.argmax(logits, 1) if mode == tf.estimator.ModeKeys.PREDICT: predictions = { 'class_ids': predicted_classes[:, tf.newaxis], 'probabilities': tf.nn.softmax(logits), 'logits': logits, } return tf.estimator.EstimatorSpec(mode, predictions=predictions)
-
- Embeddings:
- An embedding is a mapping from discrete objects, such as words, to vectors of real numbers.
-
- Saving Params:
- ```python
saver = tf.train.Saver() # before ‘with’
saver.save(sess, ‘./checkpoints/generator.ckpt’) # After (within) first for-loop
To restore
saver.restore(sess, tf.train.latest_checkpoint(‘checkpoints’)) # after ‘with’
Complete Training Example (Low-Level)
-
- Computational Graph:
-
- Operations: the nodes of the graph.
They take in tensors and produce tensors. - Tensors: the edges in the graph.
They are the values flowing through the graph.
- Operations: the nodes of the graph.
-
- Tensorboard:
-
- First, save the computation graph to a tensorboard summary file:
writer = tf.summary.FileWriter('.') writer.add_graph(tf.get_default_graph())
This will produce an event file in the current directory.- Launch Tensorboard:
tensorboard --logdir
-
- Session:
- To evaluate tensors, instantiate a tf.Session object, informally known as a session.
A session encapsulates the state of the TensorFlow runtime, and runs TensorFlow operations. -
- First, create a session:
sess = tf.Session()
- Run the session:
sess.run()
It takes a dict of any tuples or any tensor.
It evaluates the tensor.
- First, create a session:
- Some TensorFlow functions return tf.Operations instead of tf.Tensors. The result of calling run on an Operation is None. You run an operation to cause a side-effect, not to retrieve a value. Examples of this include the initialization, and training ops demonstrated later.
-
- Feeding:
-
x = tf.placeholder(tf.float32) y = tf.placeholder(tf.float32) z = x + y
-
print(sess.run(z, feed_dict={x: 3, y: 4.5})) print(sess.run(z, feed_dict={x: [1, 3], y: [2, 4]}))
-
- Datasets:
- The preferred method of streaming data into a model instead of placeholders.
- To get a runnable tf.Tensor from a Dataset you must first convert it to a tf.data.Iterator, and then call the Iterator’s get_next method.
- Create the Iterator:
slices = tf.data.Dataset.from_tensor_slices(my_data) next_item = slices.make_one_shot_iterator().get_next() # Then pass as follows while True: try: print(sess.run(next_item)) except tf.errors.OutOfRangeError: break
- If the Dataset depends on stateful operations (e.g. random value) you may need to initialize the iterator before using it, as shown below:
iterator = dataset.make_initializable_iterator() next_row = iterator.get_next()
-
- Layers:
- A trainable model must modify the values in the graph to get new outputs with the same input. Layers are the preferred way to add trainable parameters to a graph.
- Layers package together both the variables and the operations that act on them.
The connection weights and biases are managed by the layer object.E.g. a densely-connected layer performs a weighted sum across all inputs for each output and applies an optional activation function.
- Creating Layers:
x = tf.placeholder(tf.float32, shape=[None, 3]) linear_model = tf.layers.Dense(units=1) y = linear_model(x)
- Initializing Layers:
The layer contains variables that must be initialized before they can be used.init = tf.global_variables_initializer() sess.run(init)
- Executing Layers:
print(sess.run(y, {x: [[1, 2, 3],[4, 5, 6]]}))
- Layer Function shortcuts:
For each layer class (like tf.layers.Dense) TensorFlow also supplies a shortcut function (like tf.layers.dense).
The only difference is that the shortcut function versions create and run the layer in a single call. -
x = tf.placeholder(tf.float32, shape=[None, 3]) y = tf.layers.dense(x, units=1) # Is Equivalent to: x = tf.placeholder(tf.float32, shape=[None, 3]) linear_model = tf.layers.Dense(units=1) y = linear_model(x)
While convenient, this approach allows no access to the tf.layers.Layer object. This makes introspection and debugging more difficult, and layer reuse impossible.
-
- Example:
-
x = tf.constant([[1], [2], [3], [4]], dtype=tf.float32) y_true = tf.constant([[0], [-1], [-2], [-3]], dtype=tf.float32) linear_model = tf.layers.Dense(units=1) y_pred = linear_model(x) loss = tf.losses.mean_squared_error(labels=y_true, predictions=y_pred) optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for i in range(5000): _, loss_value = sess.run((train, loss)) print(loss_value) print(sess.run(y_pred))


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[CHAPTER 21E]
JOINT LEGISLATIVE MANAGEMENT COMMITTEE
Section
21E-1 Definitions
21E-2 Establishment of the joint legislative management
committee; members; terms; vacancies
21E-3 Meetings and attendance of quorum
21E-4 Powers and duties


Angular Material tabs organize content into separate views where only one view can be visible at a time. Each tab's label is shown in the tab header and the active tab's label is designated with the animated ink bar. When the list of tab labels exceeds the width of the header, pagination controls appear to let the user scroll left and right across the labels.
The active tab may be set using the selectedIndex
input or when the user selects one of the
tab labels in the header.
The selectedTabChange
output event is emitted when the active tab changes.
The focusChange
output event is emitted when the user puts focus on any of the tab labels in
the header, usually through keyboard navigation.
If a tab's label is only text then the simple tab-group API can be used.
For more complex labels, add a template with the mat-tab-label
directive inside the mat-tab
.
By default, the tab group will not change its height to the height of the currently active tab. To
change this, set the dynamicHeight
input to true. The tab body will animate its height according
to the height of the active tab.
While <mat-tab-group>
is used to switch between views within a single route, <nav mat-tab-nav-bar>
provides a tab-like UI for navigating between routes.
The mat-tab-nav-bar
is not tied to any particular router; it works with normal <a>
elements and
uses the active
property to determine which tab is currently active. The corresponding
<router-outlet>
must be wrapped in an <mat-tab-nav-panel>
component and should typically be
placed relatively close to the mat-tab-nav-bar
(see Accessibility).
By default, the tab contents are eagerly loaded. Eagerly loaded tabs will initialize the child components but not inject them into the DOM until the tab is activated.
If the tab contains several complex child components or the tab's contents rely on DOM calculations during initialization, it is advised to lazy load the tab's content.
Tab contents can be lazy loaded by declaring the body in a ng-template
with the matTabContent
attribute.
If you want to align the tab labels in the center or towards the end of the container, you can
do so using the [mat-align-tabs]
attribute.
You can control the duration of the tabs' animation using the animationDuration
input. If you
want to disable the animation completely, you can do so by setting the properties to 0ms
. The
duration can be configured globally using the MAT_TABS_CONFIG
injection token.
By default the <mat-tab-group>
will remove the content of off-screen tabs from the DOM until they
come into the view. This is optimal for most cases since it keeps the DOM size smaller, but it
isn't great for others like when a tab has an <audio>
or <video>
element, because the content
will be re-initialized whenever the user navigates to the tab. If you want to keep the content of
off-screen tabs in the DOM, you can set the preserveContent
input to true
.
MatTabGroup
and MatTabNavBar
both implement the
ARIA Tabs design pattern. Both components
compose tablist
, tab
, and tabpanel
elements with handling for keyboard inputs and focus
management.
When using MatTabNavBar
, you should place the <mat-tab-nav-panel>
component relatively close to
if not immediately adjacent to the <nav mat-tab-nav-bar>
component so that it's easy for screen
reader users to identify the association.
Always provide an accessible label via aria-label
or aria-describedby
for tabs without
descriptive text content.
When using MatTabNavGroup
, always specify a label for the <nav>
element.
MatTabGroup
and MatTabNavBar
both implement the following keyboard interactions:
| Shortcut | Action |
|---|---|
LEFT_ARROW |
Move focus to previous tab |
RIGHT_ARROW |
Move focus to next tab |
HOME |
Move focus to first tab |
END |
Move focus to last tab |
SPACE or ENTER |
Switch to focused tab |


@(board: Board, hasEditRights: Boolean)
@sortable() = @{
if(hasEditRights) "sortable" else ""
}
@for(bList <- board.bLists) {
-
@bList.name
@if(hasEditRights) {
x
}
@if(hasEditRights) {
}
@cards(bList, hasEditRights)
}
@if(hasEditRights) {
}
@history(board)


This structure defines the attributes of A15 MMU descriptor. More...
#include <mmu_a15.h>
Data Fields | |
| uint32_t | descriptorType |
| uint32_t | nsTable |
| uint32_t | apTable |
| uint32_t | xnTable |
| uint32_t | pxnTable |
| uint32_t | noExecute |
| uint32_t | privNoExecute |
| uint32_t | contiguous |
| uint32_t | notGlobal |
| uint32_t | accessFlag |
| uint32_t | shareable |
| uint32_t | accPerm |
| uint32_t | nonSecure |
| uint32_t | attrIndx |
This structure defines the attributes of A15 MMU descriptor.
Details Table Descriptor: Bits[58:52] are Ignored Bits[51:40] are UNK(Unknown on fault)/SBZP (Should be zero or preserved) Bits [39:12] Contain Next table address, Bits[11:0] of table address are 0 Bits [11:2] Ignored Block Descriptor: Bits[63:52] Upper Block Attributes Bits[51:40] are UNK(Unknown on fault)/SBZP (Should be zero or preserved) Bits[39:12] For first level descriptor Bits[39:30] are output address of 1 GB block, Bits[29:12] are UNK/SBZP For second level descriptor Bits[39:21] are output address of 2 MB block, Bits[29:12] are UNK/SBZP For third level descriptor Bits[39:12] are output address of 4 KB page. Bits[11:2] are Lower Block Attributes
| uint32_t mmuA15DescriptorAttrs::accessFlag |
Bit[10] Access Flag, Access Fault is generated if this flag is 0 and entry is read into TLB
| uint32_t mmuA15DescriptorAttrs::accPerm |
Bits[7:6] AP[2:1] AP[2]: Disable write access AP[1]: Enable Unprivileged access 00: Read/write, only at PL1 01: Read/write, at any privilege level 10: Read-only, only at PL1 11: Read-only, at any privilege level
| uint32_t mmuA15DescriptorAttrs::apTable |
Bits[62:61] Next Level table attribute, access permissions for lookup 0: No effect on permissions in subsequent lookup 1: Access at PL0 is not permitted, regardless of subsequent permissions 2: Write access is not permitted at any privilege level regardless of subsequent permissions 3: Write access is not permitted at any privilege level and read access is not permitted at PL0 regardless of subsequent permissions
| uint32_t mmuA15DescriptorAttrs::attrIndx |
Bits[4:2] Memory Attributes field, for Memory Attriute Indirection Register MAIR Bit[4]:0 Use MAIR0, 1:Use MAIR1 AttrIndx[2:0] gives the value of m in Attrm Attrm[7:4] 0100 Normal memory, outer non-cacheable 1011 Normal memory, outer write through cacheable 1111 Normal memory, outer write back cacheable Attrm[3:0] 0100 Normal memory, inner non-cacheable 1011 Normal memory, inner write through cacheable 1111 Normal memory, inner write back cacheable
| uint32_t mmuA15DescriptorAttrs::contiguous |
Bit[52] Contiguous hint bit, Setting this bit to 1 indicates that 16 adjacent translation table entries point to a contiguous output address range
| uint32_t mmuA15DescriptorAttrs::descriptorType |
Bits[1:0] Descriptor type. Refer enum mmuA15DescriptorType_t for values.
| uint32_t mmuA15DescriptorAttrs::noExecute |
Bit [54], Execute Never 1:Enable 0:Disable
| uint32_t mmuA15DescriptorAttrs::nonSecure |
Bit[5] Non-secure bit. For memory access from secure state, specifies whether output is in secure or non-secure memory 1:Non-secure 0:Secure
| uint32_t mmuA15DescriptorAttrs::notGlobal |
Bit[11] Not Global bit, Setting this bit to 1 indicates that translation is process specific.
| uint32_t mmuA15DescriptorAttrs::nsTable |
Bit[63] Next Level table attribute, Table is in secure or non-secure memory 0: Defined table is in secure physical address space 1: Defined table is in non-secure physical address space
| uint32_t mmuA15DescriptorAttrs::privNoExecute |
Bit[53], Privilege execute never 1:Enable 0:Disable
| uint32_t mmuA15DescriptorAttrs::pxnTable |
Bit[59] Next Level table attribute, privilege execute never 0: No effect 1: Privilege Execute never bit is 1 in all subsequent levels
| uint32_t mmuA15DescriptorAttrs::shareable |
Bits[9:8] Shareablitity field 00: Non-shareable 01:Unpredictable 10:Outer Shareable 11:Inner Shareable
| uint32_t mmuA15DescriptorAttrs::xnTable |
Bit[60] Next Level table attribute, execute never 0: No effect 1: Execute never bit is 1 in all subsequent levels


...one of the most highly
regarded and expertly designed C++ library projects in the
world.
— Herb Sutter and Andrei
Alexandrescu, C++
Coding Standards
Root Pointer was designed to be easy to use and to be versatile in terms of object variants it can refer to. Its only requirement is limited to the usage of a special type needed to instantiate objects referred to. For example:
root_ptr<int> p = make_root<int>(11);
Will instantiate a special object node<int> having an integer as one of its member. The pointer to the object is then passed to the root_ptr<int> that will manage its existence and later destroy and deallocate it when it is found to be no longer referenced.
The root_ptr<int> guarantees all associated allocations, cyclic or not, will be freed upon its destruction. Once the root is defined, we can derive a node_ptr<int> from it:
node_ptr<int> q = make_node<int>(p, 12);
A node_ptr<int> is an internal pointer in a set of objects that is compact which uses the information of the associated root_ptr<int> to define its length of existence. As its name suggests, this can be used as node pointers inside a container for a given root for example.
See the following example for different cases of its usage.
Firstly a root_ptr derives from node_proxy and node_ptr. You can propagate the information of the node_proxy by passing it as a reference:
struct A { node_ptr<A> p; A(node_proxy const & x) : p(x) {} }; root_ptr<A> x; x = make_root<A>(x);
Secondly in the case where a cyclic set is being destroyed, in order to prevent root_ptrs member pointers from accessing an object that has already been destroyed the function cyclic() is provided to explicitly check the state of the pointee object:
struct A { node_ptr<A> p; A(node_proxy const & x) : p(x) {} ~A() { if (! p.cyclic()) p->foo(); } }; root_ptr<A> x; x = make_root<A>(x); x->p = x;
| Note: This function should only be used in a destructor. |
Thirdly creating pointee objects in a faster way is possible by calling make_fastroot:
root_ptr<int> p = make_fastroot<int>(10);
Fourthly you can actually use the allocator of your choice using the following function calls:
root_ptr<int> p = allocate_node<int>(make_node_allocator<fast_pool_allocator, int>(), 12);
Hierarchies with multiple inheritance without virtual tables will cause undefined behavior if a pointer to a derived class is assigned to a pointer of one of its base class. For example:
struct M { int i; }; struct N { int i; }; struct O : N, M { int i; }; root_ptr<O> po = make_root<O>(); root_ptr<N> pn = po; // incorrect root_ptr<M> pm = po; // incorrect
A way to bypass any problem that might be created by the example above is to add virtual destructors to the classes:
struct M { int i; virtual ~M() {} }; struct N { int i; virtual ~N() {} }; struct O : N, M { int i; }; root_ptr<O> po = make_root<O>(); root_ptr<N> pn = po; // correct root_ptr<M> pm = po; // correct
Revised 22 March, 2016
Copyright © 2011-2016 Phil Bouchard
Distributed under the Boost Software License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)


Basics
React is a declarative, efficient, and flexible JavaScript library for building user interfaces. It lets you compose complex UIs from small and isolated pieces of code called “components”.
class ShoppingList extends React.Component {
render() {
return (
Shopping List for {this.props.name}
- Instagram
- WhatsApp
- Oculus
);
}
}
JSX is a JavaScript Extension Syntax used in React to easily write HTML and JavaScript together.
const jsx = This is JSX
Passing data from one component to another.
Uni-directional flow. (one way from parent to child).
Props data is read-only.
class ParentComponent extends Component {
render() {
return (
);
}
}
const ChildComponent = (props) => {
return {props.name}
;
};
React has another special built-in object called state, which allows components to create and manage their own data. So unlike props, components cannot pass data with state, but they can create and manage it internally.
class Test extends React.Component {
constructor() {
this.state = {
id: 1,
name: "test"
};
}
render() {
return (
{this.state.id}
{this.state.name}
);
}
}
State should not be modified directly, but it can be modified with a special method called setState( ).
this.state.id = “2020”; // wrong
this.setState({ // correct
id: "2020"
});
The literal difference is that one has state, and the other doesn’t. That means the stateful components are keeping track of changing data, while stateless components print out what is given to them via props, or they always render the same thing.
class Main extends Component {
constructor() {
super()
this.state = {
books: []
}
}
render() {
}
}
const BooksList = ({books}) => {
return (
{books.map(book => {
return - book
})}
)
}


 Directed by Randal Kleiser. Written by Thom Eberhardt, Peter Elbling, Garry Goodrow. Cinematography, John Hora. Production design, Leslie Dilley. Cast: Daniel and Joshua Shalikar, Rick Moranis, Marcia Strassman, Robert Oliveri, Amy O'Neill, Lloyd Bridges et al. A Walt Disney release. 89 minutes. Rated PG.
A honey of a movie? Not. Its predecessor, "Honey, I Shrunk the Kids," was far richer in possibilities, suspense, plot, characters and humor.
Had the filmmakers kept faith with the original's grammar, the sequel would have been titled "Honey, I Blown Up the Kid." But nobody cares about tradition these days.
It's all about Adam, played by the Shalikar twins who are variously reported as 2 or 2 1/2 years old. It depends, I suppose, from whether you count the age at the start or the end of the filming.
Accidentally zapped by a special ray, Adam becomes larger each time he is exposed to an electromagnetic force field. In stages, he reaches 112 feet--at least that's what the press release says--a world record for a human. The previous champion I could locate was "The Amazing Colossal Man," only 60 feet tall.
The ray's inventor is Wayne Szalinski, Adam's dad. He is once again played by Rick Moranis as a hyper type and a super high-tech Rube Goldberg. He looks dim and is inept with simple daily chores. Welcome back to the old cliche about the absent-minded professor, the spacey scientist or the intellectual discombobulated by "real" life. (A lot of strange people are named Wayne these days, in movies).
"Honey 2" is a single-joker, whereas "Honey1" had variety. The Spanish saying "if you lie down with babies, you wake up wet" applies here. If your entire movie is about a colossal kid, there's little you can do except follow him with the camera, hope for the best and try to be flexible, fitting whatever baby does to your framework.
As Adam's size increases so does the movie's yawn quotient. Two feeble mini-subplots--Wayne's nasty superior, Adam's older brother and a babysitter--are of small help. The film does very little with people, context or dialogue. It's all about special effects and Adam's "ooh, aah" cuteness. Big deal. Adam doesn't say much either. A little Eve would have been more interesting, since girls seem to develop speech faster.
I retained just one nice, throwaway conceit and two good lines. The Szalinskis' suburb in desert-surrounded Las Vegas is called "Vista del Mar" ("Sea View"). The sentences, funny in context, are: "There's no way I'm changing those diapers" and "Look at that mother!"
The special effects bring modern techniques to the venerable screen genres about gigantic people, animals or prehistoric creatures, whose size was caused by arrested evolution, encounters with aliens, nuclear radiation and such. Some of those pictures became classics, like "The Lost World" (1924) or, better yet, "King Kong" with all its mythological, Freudian or sociological implications. Sometimes, as in "The Attack of the 50 Ft.Woman," the movies dreadful enough to become howls.
"Honey 2" is neither campy, droll, or suspenseful. You know that no harm will come to the child, that Adam will do only material damage, and that he will get back to regulation size at the end. I heard just one big laugh from the audience, and this from an adult. The many children in the theater were quiet throughout. Confused by the sights? By the technobabble? Overawed? Plain bored?
Nowadays even the best special effects seldom impress the public. We take movie magic for granted. It is no longer like earlier periods when giant squids, invisible men or Lilliputians took the spectators' breath away and had them asking each other: "How did they do it?"
In the small part of a laboratory watchman is redhead Ken Tobey, that likable second-shelf actor, who, as Kenneth Tobey, had his hour of glory when he played the lead in the classic sci-fi horror "The Thing" of 1951. Nice to see him again. It's a small bonus for aficionados clutching at straws.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Subscribing for and reacting to context change events
The ICtxBroker interface provides methods for registering CtxChangeEventListeners in order to listen for context change events. There are two ways to subscriber for context change event notification:
// 1a. Register listener by specifying the context attribute identifier
this.internalCtxBroker.registerForChanges(new MyCtxChangeEventListener(), this.ctxAttributeStringIdentifier);
// 1b. Register listener by specifying the context attribute scope and type
this.internalCtxBroker.registerForChanges(new MyCtxChangeEventListener(), this.ctxEntityIdentifier, "DeviceID");
The subscriber's implementation of the CtxChangeEventListener interface allows reacting to the following event notifications:
private class MyCtxChangeEventListener implements CtxChangeEventListener {
public void onCreation(CtxChangeEvent event) {
LOG.info(event.getId() + ": *** CREATED event ***");
}
public void onModification(CtxChangeEvent event) {
LOG.info(event.getId() + ": *** MODIFIED event ***");
}
public void onRemoval(CtxChangeEvent event) {
LOG.info(event.getId() + ": *** REMOVED event ***");
}
public void onUpdate(CtxChangeEvent event) {
LOG.info(event.getId() + ": *** UPDATED event ***");
}
}


Der Blaue Engel (1930)
Grade: 85
"Der Blaue Engel" is a German film better known by its English translation title, "The Blue Angel". Two versions were filmed simultaneously, with the same cast, one in English and one in German. The version discussed here is in English. I have heard that the German language version is better, but that may only be because it is easier to read the English subtitles than to decipher the thick German accents of the actors. Having been made very early in the talkie era does not help matters, as the sound quality is only adequate.
The story takes place in Germany during the 1920s. A High School professor (Emil Jannings) has some rowdy pupils in his class. These students frequently visit a disreputable nightclub (The Blue Angel). When Jannings learns of this, he visits the nightclub to catch them in the act. But instead of catching them, he falls in love with a showgirl (Marlene Dietrich), resulting in his ruin.
At the time, Jannings was a much bigger star than Dietrich. He had made dozens of German films, and had recently won the 1929 Academy Award for Best Actor (for both "The Way of All Flesh" and "The Last Command"). While Dietrich's role is much smaller, it catapulted her to Hollywood stardom. She would never make another German film. Curiously, it is the only German title credited to the director, Austrian-born Josef von Sternberg, who had a lengthy but stormy career in Hollywood.
In the U.S., a high school teacher is not a high status position, and the wages are much lower than that person could make in the business world. In Germany during the 1920s, a high school professor was apparently very well respected. The students stand at attention when he enters the room, and fear his wrath. At the nightclub, the entertainers refer to him by title, and even bow to him. Perhaps this help makes the romance between Jannings and Dietrich more believable, as what the glamorous showgirl sees in the unkempt, stodgy, middle-aged man is beyond my understanding.
The film's strength does not come from Dietrich but from Janning's character. From the film's beginning, he is shown to be somewhat foolish, but as his fortunes change, he becomes bitter, and later humiliated and frightened. Perhaps the lesson is that we all have a certain place in society and life, and changing that position can only lead to trouble. This pessimistic view is unusual for the cinema, which even in themes of tragic love have both partners remain in good faith, and more often promotes the notion that true love conquers all.
<EMAIL_ADDRESS>http://members.tripod.com/~Brian_Koller/movies.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


|
Binding of Isaac - Afterbirth+ Lua Reference/Guide
|
Public Member Functions | |
| SetRoomTypes (RoomType CurrentRoomType, RoomType TargetRoomType) | |
| Open () | |
| Close (boolean Force) | |
| Bar () | |
| SpawnDust () | |
| boolean | CanBlowOpen () |
| boolean | TryBlowOpen (boolean FromExplossion) |
| try to open the door by explosive force, true for success More... | |
| boolean | TryUnlock (boolean Force) |
| try to unlock the door using a key, true for success More... | |
| boolean | IsOpen () |
| boolean | IsKeyFamiliarTarget () |
| SetLocked (boolean Locked) | |
| boolean | IsLocked () |
| boolean | IsBusted () |
| boolean | IsRoomType (RoomType Type) |
| boolean | IsTargetRoomArcade () |
| const Vector & | GetSpriteOffset () |
| Public Member Functions inherited from GridEntity | |
| SetType (GridEntityType Type) | |
| SetVariant (integer Variant) | |
| Init (integer Seed) | |
| PostInit () | |
| Update () | |
| Render (Vector Offset) | |
| boolean | Hurt (integer Damage) |
| boolean | Destroy (boolean Immediate) |
| GridEntityType | GetType () |
| integer | GetVariant () |
| integer | GetGridIndex () |
| Desc & | GetSaveState () |
| GridEntityDoor | ToDoor () |
| GridEntityPit | ToPit () |
| GridEntityPoop | ToPoop () |
| GridEntityRock | ToRock () |
| GridEntityPressurePlate | ToPressurePlate () |
| GridEntitySpikes | ToSpikes () |
| GridEntityTNT | ToTNT () |
Public Attributes | |
| Direction | Direction |
| integer | TargetRoomIndex |
| DoorSlot | Slot |
| RoomType | CurrentRoomType |
| RoomType | TargetRoomType |
| Sprite | ExtraSprite |
| boolean | ExtraVisible |
| boolean | Busted |
| State | PreviousState |
| Variant | PreviousVariant |
| string | OpenAnimation |
| string | CloseAnimation |
| string | LockedAnimation |
| string | OpenLockedAnimation |
| Public Attributes inherited from GridEntity | |
| const Vector | Position |
| returns the position of the grid cell's center point More... | |
| integer | State |
| integer | VarData |
| Desc | Desc |
| RNG | RNG |
| CollisionClass | CollisionClass |
| Sprite | Sprite |
| GridEntityDoor::Bar | ( | ) |
| boolean GridEntityDoor::CanBlowOpen | ( | ) |
| GridEntityDoor::Close | ( | boolean | Force | ) |
| const Vector& GridEntityDoor::GetSpriteOffset | ( | ) |
| boolean GridEntityDoor::IsBusted | ( | ) |
| boolean GridEntityDoor::IsKeyFamiliarTarget | ( | ) |
| boolean GridEntityDoor::IsLocked | ( | ) |
| boolean GridEntityDoor::IsOpen | ( | ) |
| boolean GridEntityDoor::IsRoomType | ( | RoomType | Type | ) |
| boolean GridEntityDoor::IsTargetRoomArcade | ( | ) |
| GridEntityDoor::Open | ( | ) |
| GridEntityDoor::SetLocked | ( | boolean | Locked | ) |
| GridEntityDoor::SpawnDust | ( | ) |
| boolean GridEntityDoor::TryBlowOpen | ( | boolean | FromExplossion | ) |
try to open the door by explosive force, true for success
| boolean GridEntityDoor::TryUnlock | ( | boolean | Force | ) |
try to unlock the door using a key, true for success
| boolean GridEntityDoor::Busted |
| string GridEntityDoor::CloseAnimation |
| RoomType GridEntityDoor::CurrentRoomType |
| Direction GridEntityDoor::Direction |
| Sprite GridEntityDoor::ExtraSprite |
| boolean GridEntityDoor::ExtraVisible |
| string GridEntityDoor::LockedAnimation |
| string GridEntityDoor::OpenAnimation |
| string GridEntityDoor::OpenLockedAnimation |
| State GridEntityDoor::PreviousState |
| Variant GridEntityDoor::PreviousVariant |
| DoorSlot GridEntityDoor::Slot |
| integer GridEntityDoor::TargetRoomIndex |
| RoomType GridEntityDoor::TargetRoomType |


Below are a set of computer generated questions and corresponding answers related to a table on Wikipedia.
Please paraphrase, in natural English, the generated question by rewriting it using your own words such that the answer does not change. Try to be original so as to not copy the generated paraphrase shown. Below, you will find some examples of good paraphrases and bad paraphrases. For your convenience, we have highlighted words that correspond to table column names in blue. These words have a tendency to sound awkward, in which case please try to rephrase them in particular.
Once finished, please click the "Submit" button (scroll down) to submit your work. Your paraphrase will be examined by a human to make sure that it is original and correct. After this, you will receive your payment. If the question is very confusing or the table is very difficult to interpret, just try your best and leave a comment in the feedback section.
Your work will greatly contribute to research in automatic information extraction from tables. Thank you for your help!
Title: The Dalek Invasion of Earth: Serial details by episode
| Question to rephrase | what are all the viewers (in millions) where archive is 16mm t/r and episode is " world's end " |
| Good | how many millions of people watched the episodes that were taped on 16mm archive and titled "world's end? |
| Bad | how many viewers (in millions) watched episdoes where archive is 16mm t/r and episode is " world's end " |
| Why this is bad | it is too close to the original |
Title: The Dalek Invasion of Earth: Serial details by episode
| Question to rephrase | what is the total number of season no. where original airdate is 6november2011 |
| Good | how many seasons were originally aired on november 6th, 2011? |
| Bad | how many seasons were aired in november? |
| Why this is bad | it has a different meaning because november can be in any year and have any date, not just november 6th, 2011 |
Title: Zina Garrison: Doubles: 46 (20–26)
| Question to rephrase | what are all the date where surface is carpet (i) and outcome is winner |
| Good | list the dates when Zina Garrison played on carpet curface and won. |
| Bad | select dates, surface=carpet & output=winner |
| Why this is bad | it is not a natural sentence |
Title: 2006 AFL season: Round 7
| Question to rephrase | what are all the home team score where date is 14 may and away team is essendon |
| Good | how many points did the home team score on may 14 against essendon? |
| Bad | score vs. essendon |
| Why this is bad | this does not have the same meaning in that it does not have a date and it is not clear whose score it is asking for. |
Title: List of Oregon ballot measures: 1976 General Election
| Question to rephrase | what are all the passed where meas. num. is smaller than 3.1827829895505158 and % yes is 29.61% |
| Good | did the measure with a number less than 3.18 and 29.61% yes vote pass? |
| Bad | which measure passed with a number less than 3.18 and where 29.61% of the people voted yes? |
| Why this is bad | the original question asks for the pass status of the measure, but the bad example asks for the measures that passed. |
| Settlement | Cyrillic Name Other Names | Type | Population (2011) | Largest ethnic group (2002) | Dominant religion (2002) |
|---|---|---|---|---|---|
| Settlement | Cyrillic Name Other Names | Type | Population (2011) | Largest ethnic group (2002) | Dominant religion (2002) |
| Odžaci | Оџаци | town | 8810 | Serbs | Orthodox Christianity |
| Bački Brestovac | Бачки Брестовац | village | 2819 | Serbs | Orthodox Christianity |
| Bački Gračac | Бачки Грачац | village | 2286 | Serbs | Orthodox Christianity |
| Bogojevo | Богојево (Hungarian: Gombos) | village | 1744 | Hungarians | Catholic Christianity |
| Deronje | Дероње | village | 2487 | Serbs | Orthodox Christianity |
| Karavukovo | Каравуково | village | 4215 | Serbs | Orthodox Christianity |
| Lalić | Лалић (Slovak: Laliť) | village | 1343 | Slovaks | Protestantism |
| Ratkovo | Ратково | village | 3411 | Serbs | Orthodox Christianity |


--- ---
I've seen the tip to make a TEdit accept only numbers but there is an easier way to do it. This is the function I use in my applications:
procedure SetEditNumeric(Handle: THandle); begin SetWindowLong(Handle, GWL_STYLE, GetWindowLong(Handle, GWL_STYLE) or ES_NUMBER); end;


This page runs Javascript code and measures execution time. The scripts can use the function log(data)
to print results in the textarea on the right
Its purpose is to compare the execution speed of native Javascript to the equivalent with Brython
| Javascript | ||


Let this tool help the team get through standup on time.
Specify who is present with the buttons below and then press the 'Randomize and Start' button to get going!


Analysis
| Scouting
Scouting Form 2015 Developed by Team 3184 Blaze Robotics
Autonomous:
Tote Set
Tote Stack
Can Set
Robot Set
Who is making this stack?
Team
Team
Team
Commit
Current stack score:
0
Litter:
0
In Landfill
Not In Landfill
Edits
Stack Logs
Fouls & adjustments
Current Match:
All Regionals
00s
Currently Scouting:
Red Alliance
Blue Alliance
Start Match
Team


Sarah's Blogs
My journey through EDA
Reflections
Time and Habits
HTML, CSS and the DOM
Emotional Intelligence
Technical Blog 2
Technical Blog-Design to Web
Meditation Process
Home


--- layout: nosidebar title: Post Archives sitemap: true nav: true post-date: 2014-01-18 13:00:00 ---


|
LIST OF STUDENTS QUALIFIED IN GATE 2008 |
||
|
Sl. No. |
Name |
All |
|
1 |
YADU
VASUDEV |
47 |
|
2 |
AISWARYA
CYRIAC |
61 |
|
3 |
ARUN
A N |
98 |
|
4 |
MAZIN
SAYID ALI |
98 |
|
5 |
GIRISH
R VARMA |
137 |
|
6 |
DEV
BABURAJ |
160 |
|
7 |
DILEEP
P G |
207 |


Miley Cyrus postpones recording following vocal surgery
The singer was unable to comment on the recent news
Miley Cyrus will postpone all performance and recording while she recovers from recent vocal surgery.
While receiving treatment for tonsillitis last month, Cyrus was diagnosed with a separate issue with her vocal cords. The ‘Don’t Call Me Angel’ singer underwent surgery, and will now be resting her voice for the next several weeks under advice from her doctor, according to People.
The singer and actress released her most recent single ‘Slide Away’ in August, addressing her break-up with Liam Hemsworth. Her next studio album, ‘She Is Miley Cyrus’, was initially expected to be released before the end of the year. Watch the video for ‘Slide Away’ below.
Cyrus recently duetted with Jeff Goldblum on a surprise new track, ‘The Thrill is Gone’, taken from his second jazz album which came out earlier this month (November 1), ‘I Shouldn’t Be Telling You This’. The collaboration came about after Goldblum asked Cyrus, who is a close friend, to record the song while planning the new record.
While the Jurassic Park actor has been playing the song for years with his long-time band The Mildred Snitzer Orchestra, he has been “waiting for the perfect soloist with whom to record the song,” according to a press release.
Speaking about the collaboration, Goldblum said: “Hearing the one and only Miley Cyrus sing one of our arrangements is both surreal and mesmerizing. I can’t stop listening. It’s perfect.”


My applicaation is about github lets us search a github repository ,followers and without entering in github.if you need more information you can contact me for more info.


Rusty Steele
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