In this topic, we will discuss Unsupervised Learning, or as we talked about last time, the situation where you are looking for groups in your data when your data don’t come with a group variable. I.e. sometimes you want to find groups of similar observations, and you need a statistical tool for doing this.
In statistics, this is called Cluster analysis, another case of the machine learning people inventing a new word for something and taking credit for a type of analysis that’s been around for fifty years.
Cluster analysis
- Attempts to find sub-groups within a data set
- Observations within a particular sub-gruop are statistically more similar to other members of their sub-group than to members of another sub-group
- Many ways in which to do this:
- K-means/K-medioids
- Hierarchical clustering
- Model based clustering
- Latent class analysis
- All of these methods use observed data to measure the dissimilarity between observations, then create groups, or clusters (buckets) from these observations.
Metrics of similiarity
- Distance based
- Euclidean distances between two observations, i and j is
\[d(x_i,x_j) = \sqrt{(x_i-x_j)'(x_i-x_j)}\]
Where the x’s are the variables measured on the two observations, for instance, if we have 3 x variables for two observations, then the distance between them is:
## x1
## x2 5.744563
If the two observations are more similar, the distance is smaller:
## x1 x2
## x2 3.162278
## x3 11.575837 11.747340
and vice versa.
library(readr)
prb<-read_csv(file = "https://raw.githubusercontent.com/coreysparks/data/master/PRB2008_All.csv", col_types = read_rds(url("https://raw.githubusercontent.com/coreysparks/r_courses/master/prbspec.rds")))
names(prb)<-tolower(names(prb))
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
prb<-prb%>%
# mutate(africa=ifelse(continent=="Africa", 1, 0))%>%
filter(complete.cases(imr, tfr, percpoplt15, e0total, percurban, percmarwomcontramodern))%>%
select(imr, tfr, percpoplt15, e0total, percurban, percmarwomcontramodern)
| imr | tfr | percpoplt15 | e0total | percurban | percmarwomcontramodern |
|---|---|---|---|---|---|
| 163.0 | 6.8 | 45 | 43 | 20 | 9 |
| 8.0 | 1.6 | 27 | 75 | 45 | 8 |
| 27.0 | 2.3 | 30 | 72 | 63 | 52 |
| 132.0 | 6.8 | 46 | 43 | 57 | 5 |
| 26.0 | 1.7 | 21 | 71 | 64 | 20 |
| 4.7 | 1.9 | 19 | 81 | 87 | 75 |
Create data partition
Here we use 80% of the data to train our simple model
## Loading required package: lattice
## Loading required package: ggplot2
## Warning: The `i` argument of ``[`()` can't be a matrix as of tibble 3.0.0.
## Convert to a vector.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
Hierarchical clustering
First we form our matrix of distances between all the countries on our observed variables:
Then we run a hierarhical clustering algorithm on the matrix. There are lots of different ways to do this, we will just use the simplest method, the single-linkage, or nearest neighbor approach. This works by first sorting the distances from smallest to largest, then making clusters from the smallest distance pair.
Once this is done, this pair is merged into a cluster, their distance is then compared to the remaining observations, so on and so on, until you have a set of clusters for every observation.
The original way to plot these analyses is by a dendrogram, or tree plot.
hc1<-hclust(d= dmat, method="single")
plot(hc1, hang=-1, main="Single linkage cluster analysis of PRB data")
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(class)
library(RColorBrewer)
fviz_dend(hc1, k=5, k_colors =brewer.pal(n=5, name="Accent"),
color_labels_by_k = TRUE, ggtheme = theme_minimal())
## groups
## 1 2 3 4 5
## 2 130 1 1 1
So this is silly becuase the method round 3 cluster that only had one observation. This is a weakness of the single linkage method, instead we use another method. Ward’s method is typically seen as a better alternative because it tends to find clusters of similar size.
hc2<-hclust(d= dmat, method="ward.D")
plot(hc2, hang=-1, main="Ward's cluster analysis of PRB data")
fviz_dend(hc2, k=5, k_colors = brewer.pal(n=5, name="Accent"),
color_labels_by_k = TRUE, ggtheme = theme_minimal())
## groups
## 1 2 3 4 5
## 13 38 43 12 29
K-means
- Another type of cluster finder
- Will always find a given number of k clusters.
- Ideally we can minimize a within cluster variance measure to find the optimal number
## K-means clustering with 3 clusters of sizes 41, 56, 38
##
## Cluster means:
## imr tfr percpoplt15 e0total percurban percmarwomcontramodern
## 1 88.31707 5.263415 42.70732 54.39024 35.39024 15.12195
## 2 11.75536 2.035714 22.50000 75.23214 75.03571 53.71429
## 3 31.74737 2.915789 32.34211 66.92105 38.28947 41.86842
##
## Clustering vector:
## [1] 1 3 1 2 2 3 3 2 2 3 1 3 3 3 2 1 1 1 2 1 2 2 1 1 2 2 2 1 2 3 2 1 2 1 3 3 2
## [38] 1 3 1 3 1 1 3 3 3 3 2 1 2 2 2 3 1 3 2 2 2 3 2 2 1 2 2 1 3 1 2 1 3 2 3 3 1
## [75] 3 2 3 2 2 2 1 1 2 1 2 1 3 2 2 2 2 2 2 2 1 3 1 2 1 2 1 2 2 2 3 1 2 3 1 2 1
## [112] 3 1 1 1 1 3 3 2 2 1 3 1 2 2 2 2 2 3 3 3 1 3 3 2
##
## Within cluster sum of squares by cluster:
## [1] 44040.58 38082.11 30145.52
## (between_SS / total_SS = 68.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
## Loading required package: gtools
km2<-KMeans_rcpp(data=prbtrain, cluster=3, num_init = 10)
prbtrain$cluster<-as.factor(km2$cluster)
prbtrain%>%
ggplot(aes(x=imr, y=tfr, group=cluster, color=cluster, cex=3))+geom_point()
Finding optimal number of clusters
Here I loop over 1 to 10 clusters and store the between group variances, then plot the relative differences. You are looking for the number of clusters where you see a shoulder in the plot.
ss<-NULL
for(i in 1:10){
km<-kmeans(x=prbtrain, nstart = 10, centers = i)
ss[i]<-km$betweenss / km$totss
}
plot(x=2:10, y=diff(ss))
Looks like the difference in variances stops declining dramatically at k=3 clusters.
Here are the test cases plotted as X’s
Gaussian mixtures
- These are a type of clustering method that is based on finding a finite mixture of Gaussian distributions within data.
- Only useful for continuous variables in data
prbtrain<-prb[train,]
prbtest<-prb[-train,]
prbtrain<-center_scale(prb[train,])
prbtest<-center_scale(prb[-train,])
plot(Optimal_Clusters_GMM(data = prbtrain, max_clusters = 10,seed_mode = "random_subset", km_iter = 20, em_iter = 20))
gmprb<-GMM(data = prbtrain, gaussian_comps = 3,seed_mode = "random_subset", km_iter = 10, em_iter = 10)
#gmprb
prbtrain<-data.frame(prbtrain)
names(prbtrain)<-names(prb)
prbtrain$cls<-as.factor(apply(gmprb$Log_likelihood, 1, which.max))
pred<-predict_GMM(data=prbtest, CENTROIDS = gmprb$centroids, COVARIANCE = gmprb$covariance_matrices, WEIGHTS = gmprb$weights)
prbtest<-data.frame(prbtest)
names(prbtest)<-names(prb)
prbtest$cls<-as.factor(pred$cluster_labels+1)
prbtest%>%
ggplot(aes(x=imr, y=tfr, group=cls, color=cls))+geom_point(pch="x", aes(cex=2.5) )+geom_point(data=prbtrain, aes(x=imr, y=tfr, group=cls, color=cls))


R.N.Shah High School was established in the year 1990. The school is a non-denominational and co-educational institution affiliated to Maharashtra State Board of Secondary and Higher Secondary Education.
The Students of Vth CSE Drishti Gugal (1380015) and Hamsika Shah (1380047) have presented a technical paper on ‘Securing Vehicle to Vehicle Communication in VANET’ under the domain Cyber Forensics and won the First Prize among 24 papers presented
State Level Technical Quiz Competition, “TECHTRIONICS-2015”,for final year students of Diploma in Electronics Engineering and allied branches was organized on Friday,28th August’2015 by Electronics Department of SBM Polytechnic. The Quiz was organized under ISTE-SBM Polytechnic chapter. It was inaugurated by Prof.V.M Joshi, Adjunct Faculty, Electronics Dept., Vivekananda Engg. College, Mumbai.The polytechnics in and around Mumbai were invited to participate in the Technical Quiz. In all sixty students from eleven polytechnics took part in the competition.
This year the Annual Social Gathering was noteworthy as it was graced by the presence of Shri Pravinbhai Doshi- mentor of Shri Bhagubhai Mafatlal Polytechnic and the guests for the day were Shri Chetan Raikar – Chairman and Managing Director of Structwel Designers and Consultants Pvt. Ltd. and his wife Mrs. Sujata Raikar. Principal Shri Umesh Kantute introduced the guests and felicitated them. Both Shri Pravinbhai Doshi and Shri Chetan Raikar addressed the students. .
CCE (Continuous and Comprehensive Evaluation) a new method of assessment has been introduced from this academic year that covers all the aspects of a student’s
In our journey towards excellence, the Secondary Section of CNM School emphasizes on the all round development of each child. Every student is trained to
We at CNMS aim at providing an all round and holistic development to our students. The focus is on activity based education and widening the children’s horizons with enriching experiences. CCE (Continuous and Comprehensive Evaluation) a new method of assessment has been introduced from this academic year that covers all the aspects of a student’s development. CCE helps in improving student’s performance by identifying his/her learning difficulties at regular time intervals right from the beginning of the academic session and employing suitable remedial measures for enhancing their learning performance. It also helps in reducing stress of students by identifying learning progress of students at regular time intervals on small portions of content and encouraging learning through employment of a variety of teaching aids and techniques.
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Copyright © 2015- All Rights Reserved - R.N Shah High School


who am i?
I'm a computer engineer. I do mostly software development. Nowadays I'm calling myself as a "Back-End Developer". Simplicity is always on my mind when I'm coding.
I'm a believer of the free and open source software philosophy. My favorite part of my profession is that it will never became monotonous. Because this profession requires following and teaching new technologies or approaches, and I'm in love with learning new things!
Emacs is my daily driver. Major technologies I use are: PHP, JavaScript, Bash, Ruby, Git and GNU/Linux. Some of my completed software projects are listed in the projects section below. Please check my GitHub or GitLab profile for more.
One of my greatest passions is: Writing. On my blog, you can find my articles on programming, book reviews and many other topics, mostly written in Turkish.
projects
# semantic-video-search
web, academic | source code: github
I worked on Image Processing and Semantic Web for my undergrad thesis. I developed a web interface that provides detailed video search for users. I also conducted user experiments to show the difference between keyword-based search methods and semantic video search. Major technologies I used in this project are: JavaScript, VueJS, Apache Jena Fuseki, RDF & SPARQL.
Hatirnaz, E., Sah, M. & Direkoglu, C. A novel framework and concept-based semantic search Interface for abnormal crowd behaviour analysis in surveillance videos. Multimed Tools Appl 79, 17579–17617 (2020). https://doi.org/10.1007/s11042-020-08659-2
# mgm-radar
cli | source code: github
A command-line application that downloads and shows meteorological radar images from mgm.gov.tr (Turkey's national meteorology agency). I wrote this tool due to my personal interest in meteorology, I'm using it almost every day. This tool written in Bash and works well on GNU/Linux distros.
# w3c-sql-editor-with-turkish-database
web | source code: github
I cloned W3C's Tryit Editor to provide Turkish language support. I developed this project when I was at my first university. As far as I know, my lecturer still uses this project in its Database Design class. I used SQLite as the relational database in this project.
# anger.co-startup-parser
cli, academic | source code: github
I developed this project for my lecturer from my first university. A Ruby script that takes the marketplaces and locations of all startup companies working in the IoT industry from angel.co and generates a CSV file with that information. This CSV file was then converted into a graph. My lecturer used this graph in his presentation at the International Conference on Information Technology and Science in Japan in 2016.
latest 5 blog posts
- Ön İnceleme: Sürgündeki Şili, Kardelenler, Marksist Estetik
- ImageMagick Türkçe Dil Sorunu ve Geçici Çözümü
- Kodladım: mgm-radar - Meteorolojik Radar Görüntüsü İndirme Aracı
- Okudum: Hayatın Kökleri - İlk Canlılar Nasıl Oluştu? (Mahlon B. Hoagland)
- PHP'de Composer ile Bağımlılık Yönetimi - 2
contact
| e-mail: |<EMAIL_ADDRESS>|
| gpg key: | in plain text; on Ubuntu keyserver |


MEMENTO Reviewed by Jerry Saravia May 25th, 2001
"I can't make new memories. Everything fades, nothing sticks. By the time we finish this conversation I won't remember how it started, and the next time I see you I won't know that I've ever met you before." - Leonard.
Thus, the above quotation says it all for "Memento," one of the most electrifying of all postmodern noir thrillers in years. Whereas "Run Lola Run" and "Pulp Fiction" revelled in irony, hipness and fractured timelines to tell their stories, "Memento" is a more eager return to what makes noir what it is - the purely existential. Martin Scorsese dared to go through such existential leaps minus the irony in "Bringing Out the Dead" as did Sean Penn in the brilliant "The Pledge." "Memento," though, will be remembered as the most disorienting and fatalistic of the new century, linking to the most frightening ordeal man could ever face, the inability to make new memories.
Such is the case with Leonard (Guy Pearce), a former insurance claims investi gator who suffers from a rare disorder defined as "anterior grade memory loss" (also known as Korsokoff's syndrome). You see Leonard can't make new memories and the only way he can remember anything is by writing it down or by tatooing his body with written messages. After a few minutes, if he fails to write down important information or clues (usually on a handy Polaroid he shoots of any person he meets), he will forget as if the incident had never occured.
At the beginning of the film, Leonard kills an unarmed man named Teddy (Joe Pantoliano). Teddy's murder seems cold-blooded and, like most noir films that deal with a central narrator (that being Leonard), we expect flashbacks to see what led up to this event. A strange thing happens, though, in "Memento." Rather than seeing typical flashbacks in a convential, linear fashion, the story is told backwards thus the ending of the film is really the beginning and the beginning of the film is really the ending. Now the audience is faced with the daunting task of keeping all the facts straight in their minds as the events unfold on screen. I did not find it daunting as much as challenging and this Harold Pinter approach serves the purpose of the film.
It would not be fair to reveal much of "Memento" because the thrill of the film is in its surprise factor. This much can be said about the plot: Leonard is investigating the death of his wife by some character named "John G." His investigation leads to a sour, sweetly morose barmaid, Natalie (Carrie-Anne Moss), who has gone through a similar loss and can help him out of pity. Naturally, as the film regresses, we learn Natalie is not quite the sour, kind woman we thought she was - your typical femme fatale. Then there's Teddy, the smiling cop or drug dealer (depending on your interpretation) who may be trying to help Leonard or may be trying to kill him. Add to the mix a local drug dealer, Leonard's memory of Sammy Jankis who supposedly suffered similar memory loss (and was part of Leonard's insurance claims), and co nstant betrayals and backstabbing ploys. There is also a motel clerk who is nonplussed to learn that Leonard never remembers any conversation they have had, and thus tries to jokingly cheat Leonard out of a better motel room. No one is ever what they seem in this morally ambiguous universe and by structuring the film backwards, we learn the evolution of each character through Leonard's point-of-view. This also serves a more finite attraction: when we learn new information about a character as the film regresses, we realize the character's prior behavior and begin to see that Leonard may have the wrong idea or wrong motive for his later actions. This nonlinear breakdown of the story will no doubt lead to further viewings just to keep the characters straight.
"Memento" has a great, meaty story but if it were not for the beautifully restrained performances from the cast, the film would not work the same way. Guy Pearce is fierce and alive in every scene he is in. He is vulnerable and yet impulsive, no doubt due to his constant memory loss. He is not quite your typical hero, more of a disillusioned antihero. In a sense, Pearce undergoes a rather strange dilemma - if he does not remember what a person had done to him, he can just easily hurt the person who may have tried to help him. The Polaroids and the tattoos are messages that could lead to dangerous avenues if he is not quick enough to jot down finite details.  But as the film comes to a surprisingly fatalistic finish, we also sense that Leonard may have mixed up certain facts or perhaps he is in denial about his own mysterious past. Pearce brings Leonard to life in such an intoxicating manner that he becomes something of a human mine - don't step on him or you will explode.
Carrie-Anne Moss gives her first truly great performance here as the mysterious, duplicitous Natalie, exuding both toughness and sincerity with equal aplomb. She has a memorably frightening moment where she takes advantage of Leonard's condition and taunts him. Joe Pantoliano (who co-starred with Moss in "The Matrix") gives one of his funniest, quixotic performances in years as Teddy, and his character remains as much an enigma as Leonard does. Teddy may seem trustworthy but could he be using Leonard as well?
Harold Pinter's "Betrayal" was a tale of divorce told backwards and probably the first to use such a complicated device. There was also the hilarious "Seinfeld" episode that was told backwards and succeeded in delivering its punchlines with more gusto than usual. But "Memento" uses a gimmicky device to its advantage. We, the audience, only know as much information as Leonard knows thus his short-term memory loss is accentuated by telling the story backwards, especially since he can't make new memories. When Leonard is unsure of something, so are we. When he is disoriented, so are we. It is rare for a noir tale like this to make us feel the internal anxieties and fears of its main character. Watching "Memento" is like enduring an endless nightmare where our memory is a constant illusion in search of the truth. In the case of Leonard, his search for the truth may be fruitless and all we are left with are mementos to remind us of his own journey.
For more reviews, check out JERRY AT THE MOVIES at http://moviething.com/members/movies/faust/JATMindex.shtml
E-mail me with any questions, comments or complaints at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>The review above was posted to the
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Timeseriesanalysis Overview
less than 1 minute read
Joining #Masks4All against Coronavirus
3 minute read
I am a avid viewer of Lex Fridman’s podcasts and other misc. content, particularly his Youtube videos. I recently watched one of his videos on “Face Masks fo...
2019 Lookback and the #2020Vision
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It’s been a while since I’ve produced some quality personal content. Moreover, I’ve noticed that it’s been 1 year since my time as a Data Analyst at Autodesk...


The emergence of condensed systems
While being entirely distributed, Condensation can store and retrieve data like a database or a file system, send data to other users or devices like a messaging system, and share and synchronize data like cloud services. Thereby, Condensation follows a distributed actor-message-passing approach and encrypts all data end-to-end.
Condensation builds the bridge between the simplicity of immutable data and the easiness of implementation of mutable documents. On the client, documents are split efficiently into smaller immutable units that can be transferred freely across the network. Then, they can be condensed back when they arrive on the receiver's device. Condensed systems are free from trusting a third-party server and many new features come by design to create trust between clients such as data certification.
This shift from merging data on the server to doing it on the client side, opens many new possibilities for application design. Furthermore, it is significantly more efficient if compared to existing solutions which need to build several application layers on top of existing systems to achieve the same functionalities. Before to start explaining the mechanics, this section explains the history of databases and what makes Condensation the next step in the evolution of databases.
Bridging the gap between mutable and immutable data structures
The structure of today's file and database systems dates back to the 1970s, when storage space was extremely scarce and computers were few. These systems were designed to run on a single machine, and mostly on a single disk.
While both databases and file systems have greatly evolved over time, their main structure has hardly changed. Database systems are based on tables with mutable records (rows), while file systems use a hierarchy of folders with mutable files inside. In both systems, data can be modified with little effort, and at any time. It also has the advantage of being very efficient with regards to storage space needs. Data synchronization, however, is notoriously difficult and error prone.
In todays' connected world, data is used on different devices, or is shared with other people. And, for most applications, storage space is not a limiting factor any more. Hence, efficient data synchronization is key.
Aside of file and database systems, revision control systems have been developed and used since the 1980s. Some of them, such as git or hq, are fully distributed and do not require any central server whatsoever. Each user has their own version of the data and can merge changes from other users. Such systems allow for efficient and provably correct data synchronization.
While they are great for source code management, current version control systems are not suited as general purpose data systems. In order to benefit from such systems, the user needs to have a certain understanding of branches, merging, and conflict resolution, which is far beyond the knowledge of an average computer user. In addition, occasional merge conflicts are inevitable, and prevent such systems from being used in a transparent way.
Condensation has been designed from the ground up to address this. The result is a general-purpose data system with lightweight transactions and efficient data synchronization in a completely distributed setting. Merge conflicts are impossible by design, hence no user intervention is required during the synchronization process. The data itself is end-to-end encrypted and may be spread across multiple storage systems.
The evolution of architectures from online to offline and serverless systems.
The structure of the data has a direct influence on the dependency from and the role of a server. With SQL and noSQL databases, the centralized server is needed to synchronize data. Accordingly, it is the place where the application logic occurs. As a result, the system only works when being online. Also, as the data is read and processes by the server, it is vulnerable to data breaches.
Working offline became possible later, by storing documents in the application and defining schemas for synchronization when the application is turned back to online mode. However, this process is complex and requires a handling logic on both the client and the server side. Moreover, scaling a central database was a major issue. Many systems developed distributed systems for horizontal scaling but in a controlled data center setup where all servers are entrusted and available.
As described previously, Condensation only transfer immutable data on the network, which allows to build fully distributed but yet very simple systems. The following scheme summarizes the comparison between existing systems types.
|
Online-only 2000-2015 |
Offline-first 2015-2020 |
Condensated 2021 |
|
|---|---|---|---|
| Code base | Small | Large | Small |
| Architecture | Simple architecture, easy to understand | Relatively complex architecture | Simple architecture but requires "distributed mindset" |
| Structure | Centralized with full trust in cloud | Centralized with full trust in cloud | Distributed/Federated |
| Synchronization | No synchronization necessary | Correct data synchronization (two-way) is hard. Potentially different database schemas on client and server. |
Based on synchronization Direct device-to-device sync possible |
| Security | Transport encryption only | Transport encryption and SPOF engineered mitigation | End-to-end encryption |
| Authentication | Login required | Login required | Login not necessary (but sometimes desired) |
Condensation leverage the advantages of immutable objects. You never have to lock them, which extremely improves concurrency also it improves simplicity as persistance to certify the data isn't compromised and exactly the same as the source. Furthermore, it allows to reduce the memory usage as objects can be reused to create new trees.
Developers can benefit from features available by design such as: data certification with user signature, versionning with transaction history, and conflict free merge based on CRDTs.
So, Condensation has a hybrid data structure, it merge data into mutable documents stored locally and transfers immutable objects through stores. The stores can be managed in a single server or in a purely distributed manner without introducing complexities.
In the following sections, first the data structure is technically described to explain how Condensation transform a document into an immutable merkle tree. Next, to better understand how Condensation manage securely data on the network, the flow of the data through a decentralized network will be described.


We welcome community and developer feedback on all aspects of WebAssembly, including the high-level design, binary format, JS API, developer experience, and browser implementations.
We're especially interested in hearing from developers during the Browser Preview period, while WebAssembly is still implemented behind a flag.
Please contribute your feedback or issues in the following forums:
High level design, binary format, and JS API feedback: WebAssembly/design


Working Papers
- The President and the Cardinals: Interbranch Bargaining and Subcommittee
Influence in Federal Appropriations (with Jon C. Rogowski)
Works in Progress
- Ambiguity in Leadership: Race, Public Goods Provisions and Ethnic Conflict
- A Model of Minority Candidate Emergence
- Drawing Blanks: Redistricting and Turnout Effects (with Angelo Dagonel and Kiara Hernandez)
- The Direct Primary Election (with James M. Snyder, Jr.)
Data
- The Candidates in American General Elections dataset
is a comprehensive Presidential, Congressional, and gubernatorial
candidate-level dataset for elections between 2006 and 2020, which includes variables for standardized names,
incumbency status, party affiliation, and election results. Our standardized naming convention
allows researchers to track candidates across years and offices.
Policy and Data Reports
- Desphande, Pia and Jeremiah Cha. 2022.
The 2020 CES: Duplicate Respondents and Handling Asian and Hispanic Subsamples.
Cooperative Election Study.
- Devlin, Kat, Regina Widjaya and Jeremiah Cha. 2020.
For Global Legislators on Twitter, an Engaged Minority Creates Outsize Share of Content.
Pew Research Center.
- Wong, Tom K., Jeremiah Cha and Erika Villareal-Garcia. 2019.
The Impact of Changes to the Public Charge Rule on Undocumented Immigrants Living in the United States.
U.S. Immigration Policy Center (USIPC) at UC San Diego.
- Silver, Laura, Emily A. Vogels, Mara Mordecai, Jeremiah Cha, Raea Rasmussen and Lee Rainie. 2019.
Mobile Divides in Emerging Economies.
Pew Research Center.
Blog Posts
- Cha, Jeremiah. 2020.
Fast facts about South Koreans’ views of democracy as legislative election nears.
Pew Research Center.
- Cha, Jeremiah. 2020.
People in Asia-Pacific regard the U.S. more favorably than China, but Trump gets negative marks.
Pew Research Center.
- Huang, Christine and Jeremiah Cha. 2020.
Russia and Putin receive low ratings globally.
Pew Research Center.
- Fetterolf, Janell and Jeremiah Cha. 2020.
Few in other countries approve of Trump’s major foreign policies, but Israelis are an exception.
Pew Research Center.
- Cha, Jeremiah. 2017.
Dreaming of DACA.
Claremont Journal of Law and Policy.
Note: This website is made using the
minimalist theme
created by Steve Smith. Content inspired by Shiro Kuriwaki,
Emma Remy, and Sono Shah.
Design elements taken from Brad Bolman.


|
ViennaCL - The Vienna Computing Library
1.5.2
|
The main class for representing a statement such as x = inner_prod(y,z); at runtime. More...
#include <forwards.h>
Public Types | |
| typedef statement_node | value_type |
| typedef viennacl::vcl_size_t | size_type |
| typedef std::vector< value_type > | container_type |
Public Member Functions | |
| statement (container_type const &custom_array) | |
| template<typename LHS , typename OP , typename RHS > | |
| statement (LHS &lhs, OP const &, RHS const &rhs) | |
| Generate the runtime statement from an expression template. | |
| container_type const & | array () const |
| size_type | root () const |
The main class for representing a statement such as x = inner_prod(y,z); at runtime.
This is the equivalent to an expression template tree, but entirely built at runtime in order to perform really cool stuff such as kernel fusion.
| typedef std::vector<value_type> container_type |
| typedef viennacl::vcl_size_t size_type |
| typedef statement_node value_type |
| statement | ( | container_type const & | custom_array | ) | [inline] |
| statement | ( | LHS & | lhs, |
| OP const & | , | ||
| RHS const & | rhs | ||
| ) | [inline] |
Generate the runtime statement from an expression template.
Constructing a runtime statement from expression templates makes perfect sense, because this way only a single allocation is needed when creating the statement.
| container_type const& array | ( | ) | const [inline] |


Description:
This course is ONLY offered at Carnegie Mellon in Qatar. This course is meant to offer Computer Science undergraduate students in their junior or senior year a broad overview of the field of computer security. Students will learn the basic concepts in computer security including software vulnerability analysis and defense, networking and wireless security, applied cryptography, as well as ethical, legal, social and economic facets of security. Students will also learn the fundamental methodology for how to design and analyze security critical systems.


Match Play. Double Match Point. How should Red play 54?
pips Red=110 White=117
(CorrectAnswer= 0/0, ErrorScore= 0)
The 16-pt must be kept as 16/7 is too dangerous. That leaves 13/4 or 13/9, 13/8.
In such situations which point would you rather your opponent had to give up to hit you? The answer is his anchor on your 5-pt – he must give up the security of that anchor if he wants to hit. In addition, 13/4 takes a checker beyond the field of play and leaves 17 shots instead of just 12.
13/9, 13/8 is very clear here.
In such situations which point would you rather your opponent had to give up to hit you? The answer is his anchor on your 5-pt – he must give up the security of that anchor if he wants to hit. In addition, 13/4 takes a checker beyond the field of play and leaves 17 shots instead of just 12.
13/9, 13/8 is very clear here.
1. Cubeful 2-ply 13/9 13/8 Eq.: +0.061 0.531 0.073 0.002 - 0.469 0.086 0.001 2. Cubeful 2-ply 8/4 8/3 Eq.: +0.025 (-0.036) 0.513 0.059 0.000 - 0.487 0.101 0.001 3. Cubeful 2-ply 13/4 Eq.: -0.000 (-0.062) 0.500 0.079 0.002 - 0.500 0.114 0.001 4. Cubeful 2-ply 13/8 6/2 Eq.: -0.034 (-0.096) 0.483 0.072 0.002 - 0.517 0.126 0.001 5. Cubeful 2-ply 8/4 6/1 Eq.: -0.079 (-0.140) 0.461 0.085 0.001 - 0.539 0.172 0.003


Money Talks (1997)
A movie review by Michael J. Legeros Copyright 1997 by Michael J. Legeros
(New Line) Directed by Brett Ratner Written by Joel Coen and Alec Sokolow Cast Chris Tucker, Charlie Sheen, Paul Sorvino, Heather Locklear, Gerard Ismael, Elise Neal, David Warner MPAA Rating "R" (presumably for profanity) Running Time 95 minutes Reviewed at The Carmike, Raleigh, NC (22AUG97)
==
Oh, what they've tried in the fifteen years since 48 HRS: young cop/old cop (STAKEOUT), young cop/really old cop (THE ROOKIE), young cop/old cop/district attorney (ANOTHER STAKEOUT), good cop/ crazy cop (LETHAL WEAPON), good cop/crazy cop/witness (LETHAL WEAPON 2), good cop/crazy cop/she cop (LETHAL WEAPON 3), American cop/Russian cop (RED HEAT), American cop/Japanese cop (COLLISON COURSE), hungover cop/hapless citizen (DIE HARD WITH A VENGEANCE), smart cop/slob cop (TANGO AND CASH), real cop/fake cop (THE HARD WAY), real cop/alien cop (ALIEN NATION), tall cop/short cop (BAD BOYS), silly cop/sillier cop (NATIONAL LAMPOON'S LOADED WEAPON I), young cop/old convict (THE ROCK), old cop/ young convict again (ANOTHER 48 HRS.), young cop/young convict (BULLET- PROOF), bail bondsman/convict (MIDNIGHT RUN), convict/convict (FLED), and, now, convict/reporter, with Chris Tucker and Charlie Sheen as the respective abrasive personalities thrown together for the duration of a weekend. Or maybe longer, but I can't be sure, because I walked out after about a half-hour. Mr. Tucker, so good in THE FIFTH ELEMENT, has enough energy for *four* films, but his limitless enthusiasm is really no match for a cliched plot, witless profanity, and a co-star who won't stop scowling. And what's David Warner doing here?
Grade: W/O
-- Mike Legeros - Movie Hell http://www.nonvirtual.com/hell/
-- Mike Legeros Corporate Training SAS Institute Inc, Cary, NC, USA, Earth<EMAIL_ADDRESS>(w<EMAIL_ADDRESS>(h) Everyone who ate apples in England in 1850 later died.
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.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Lambda Expressions in Java 8
30 Jan 2017Lambda expressions are the most popular feature of Java 8. They introduce functional programming concepts to Java, which is a completely object-oriented and imperative programming language.
How functional programming languages work is beyond the scope of this article, but we will extract a feature that will make the difference obvious for us who work with OOP. How functional programming languages work is beyond the scope of this article, but we will extract a feature that will make the difference obvious to us who work with OOP.
In this post, we will learn what, exactly, lambda expressions are and how they fit into the whole Java ecosystem. We will also look at example code that works without using lambda expressions and then refactor this code to use lambdas.
Understanding a Lambda Expression
A Lambda expression is a block of code that we can pass around to execute. Passing a block of code to a function is something that we as Java programmers are not used to.
All our behavior defining code is encapsulated inside method bodies and executed through object references as it is with this code:
This is classic OOP style of hiding method implementations from the caller. The caller simply passes a variable to the method which then does something with the value of the variable and returns another value or produces a side effect as it is in our case.
We are now going to see an equivalent implementation that uses behavior passing other than variable passing. To achieve this, we have to create a functional interface that defines that abstracts the behavior instead of a method.
A functional interface is an interface that has only one method:
In the above implementation, the Printer
interface is responsible for all printing. The method printSomething
no longer defines the behavior but rather executes one defined by Printer
:
We may have noticed we have not done something new here. This is true because we have not applied a lambda expression yet. We simply created a concrete implementation of the Printer interface and passed it to the printSomething method.
The above demo was meant to bring us to a key objective of introducing Lambda expressions in Java:
Lambda expressions are used primarily to define inline implementation of a functional interface. Before we refactor the above example using lambda expressions, let’s learn the necessary syntax:
A lambda constitutes a comma separated list of formal parameters enclosed in parenthesis as we would define in a method declaration, followed by an arrow token pointing to the code to execute. Now let us refactor the above code to use a lambda:
I find the above snippet very compact and beautiful to look at. Since the functional interface declares only a single method, the parameters passed in the first part of the lambda are automatically mapped to the method’s parameter list and the code on the right of the arrow is treated as the method’s concrete implementation
Why Use Lambda Expressions?
As pertains the demo in the previous section, lambda expressions enable us to have more compact code, easier to read and follow. There are other benefits in line with performance and multi-core processing, but they can only make sense after understanding the Streams API, therefore beyond the scope of this article.
Comparing the main
method implementations with and without lambdas sure shows us the power of lambda expressions when it comes to shortening code:
We can make our code more concise than this. It so happens that even without specifying the type of the parameter on the left part of the arrow, its type will be inferred by the compiler from the formal params of the interface method:
We can still do better. Another characteristic of lambdas is that: if there is only one parameter, we can eliminate the parenthesis altogether. Similarly, if there is only one statement on the right of the arrow, we can also eliminate the braces:
Now our code is really starting to look cute. We are just getting started. If our interface method did not take any parameters, we could replace the declaration with empty parenthesis:
How about we just inline the lambda without first creating an object and then passing it to the saySomething method:
Now we are really starting to talk functional programming. Our initial main body of nine lines is now down to only 3 lines. This compactness of code is what makes lambda expressions very appealing to Java programmers.
Conclusion
In this post, we have gone through a gentle introduction to Lambda expressions in Java and learned how they can be used to improve the code quality of functional interface implementations.
Watch out for more coverage of Lambdas on this site as I will be touching the Stream API and discussing how it gives us more advantages of Lambdas when used together with the Collections framework.


Endless Plains [aj68], plain, in Imperia, peasants: 117
Last updated Turn #95
Endless Plains [aj68], plain, in Imperia, peasants: 117
------------------------------------------------------------------------
4: Stack of nazgul [bm4c], nazgul, number: 4, arrived from the west.
6: Stack of nazgul [bm4c], nazgul, number: 4, went east.
15: It has begun to rain.
29: Stack of nazgul [he2f], nazgul, number: 2, arrived from the north.
30: The population grows by one peasant.
Routes leaving Endless Plains:
North, plain, to Endless Plains [ai68], 7 days
East, plain, to Endless Plains [aj69], 7 days
South, plain, to Endless Plains [ak68], 7 days
West, forest, to New Forest [aj67], 8 days
Inner locations:
Grassy field [ad2l], pasture, 1 day, owner:
Harpie [jg2k], harpies, number: 14
It is raining.
Seen here:
Stack of nazgul [ih7t], nazgul, number: 5
Stack of nazgul [he2f], nazgul, number: 2


{% extends 'base.html' %} {% block body %} {% block content %}
FAIR EVA for DIGITAL.CSIC is a new service to provide feedback to CSIC researchers about the current level of FAIRness of digital objects hosted by CSIC institutional repository. It is a pilot implementation of the FAIR Eva tool which allows multiple use cases through plug-ins.
FAIR EVA tool has been designed to start working by a generic technical implementation (DIGITAL.CSIC) using a set specific metrics (RDA Data Maturity Indicators) as reference. This default configuration matches the RDA levels of importance (namely, essential, important and useful) with x3, x2 and x1 in value to give more weight to most important indicators.
The goal of FAIR EVA for DIGITAL.CSIC is not only to provide a percentage of digital object FAIRness *at time of running a test*, but also to guide CSIC researchers and repository's administrators to improve FAIRness compliance.
By entering the handle identifier or the DOI of a specific dataset available in DIGITAL.CSIC, the tool calculates automatically level of compliance with respect to every single RDA indicator. In addition, the tool provides with information on what automatic tests have been implemented for the assessment as well as feedback to understand the scoring within the context of DIGITAL.CSIC policies and technical infrastructure and recommendations for data creators to improve scoring. Generally speaking, it could be argued that while the set of F (Findable) and A (Accessibility) indicators mostly fall under the responsability of repository administrators, data creators can contribute to increasing FAIR compliance as far as I (Interoperability) and R (Reusability) indicators are concerned in a significant way.
Please note that this is a beta release of the tool and future work will refine calculations as well as support information.
The tool should be used as a guidance to improve FAIRness on DIGITAL.CSIC infrastructure. It is important to bear in mind that any given digital object under assessment can improve scoring as more metadata are provided and more technological improvements are enabled on the repository software.
{% include "snippets/footer.html" %} {% endblock %} {% endblock %}


Your information: For {{location}}, your latitude and longitude are {{lat}}, {{long}} degrees. The Sun's altitude was {{sun_alt}} degrees at time {{c2time}} UTC.
Your results: The estimated distance to the moon is {{d_est}} km, which differs from the actual distance at that time ({{d_real}} km) by {{percent_diff}}%. This was estimated from your perceived umbra diameter of {{su_est}} km, though the actual umbra diameter should have been {{su_real}} km.
Return to form


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object framenet.corpus.ObjectFactory
public class ObjectFactory
This object contains factory methods for each Java content interface and Java element interface generated in the framenet.corpus package.
An ObjectFactory allows you to programatically construct new instances of the Java representation for XML content. The Java representation of XML content can consist of schema derived interfaces and classes representing the binding of schema type definitions, element declarations and model groups. Factory methods for each of these are provided in this class.
| Constructor Summary | |
|---|---|
ObjectFactory()
Create a new ObjectFactory that can be used to create new instances of schema derived classes for package: framenet.corpus |
| Method Summary | |
|---|---|
AnnotationSet |
createAnnotationSet()
Create an instance of AnnotationSet |
AnnotationSets |
createAnnotationSets()
Create an instance of AnnotationSets |
Corpus |
createCorpus()
Create an instance of Corpus |
Document |
createDocument()
Create an instance of Document |
Documents |
createDocuments()
Create an instance of Documents |
Label |
createLabel()
Create an instance of Label |
Labels |
createLabels()
Create an instance of Labels |
Layer |
createLayer()
Create an instance of Layer |
Layers |
createLayers()
Create an instance of Layers |
Paragraph |
createParagraph()
Create an instance of Paragraph |
Paragraphs |
createParagraphs()
Create an instance of Paragraphs |
Sentence |
createSentence()
Create an instance of Sentence |
Sentences |
createSentences()
Create an instance of Sentences |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public ObjectFactory()
| Method Detail |
|---|
public Labels createLabels()
Labels
public Label createLabel()
Label
public Layer createLayer()
Layer
public Paragraphs createParagraphs()
Paragraphs
public AnnotationSet createAnnotationSet()
AnnotationSet
public Sentences createSentences()
Sentences
public AnnotationSets createAnnotationSets()
AnnotationSets
public Documents createDocuments()
Documents
public Corpus createCorpus()
Corpus
public Sentence createSentence()
Sentence
public Layers createLayers()
Layers
public Paragraph createParagraph()
Paragraph
public Document createDocument()
Document
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


MICKEY BLUE EYES A film review by Christopher Null Copyright 1999 Christopher Null filmcritic.com
Hugh Grant hit paydirt once this summer, in NOTTING HILL. Can he
do it again if you take away Julia Roberts? And throw in an unlikely
comedy about murderous gangsters, an auction house, and a botched
wedding?
Yes he can! MICKEY BLUE EYES, against all odds, is nothing short of fall-down funny – on par with NOTTING HILL, SOUTH PARK, and AUSTIN POWERS 2 as one of the best comedies of the summer.
The plot involves Grant's milquetoast auctioneer, who, after three months of dating, proposes to his girlfriend (Tripplehorn), whom he doesn't realize is the daughter of a major New York Mafioso (Caan). Hilarity ensues when the mob family tries to rein Grant in for various money laundering schemes, dubbing him `Mickey Blue Eyes.' Enter the FBI, and accidental shooting, nocturnal shallow-grave burials, and a payback/revenge scheme, and you've got some pretty weighty elements here – but BLUE EYES usually makes the most of them all – in a comic sense, of course.
Don't be fooled: Grant carries this picture all by his lonesome. Tripplehorn is fine, but uninspiring. The mob guys are broadly drawn stereotypes. Caan is playing his usual tough guy and doesn't have a whole lot of screen time. With the exception of a few bizarre supporting characters (see also NOTTING HILL, again), Hugh Grant owns this show.
And therein we see what a comic talent he really is. Essentially, MICKEY BLUE EYES is a fish out of water tale, only the water finds the fish this time around. And watching this fish squirm is one hell of a good time.
RATING: ****
|------------------------------| \ ***** Perfection \ \ **** Good, memorable film \ \ *** Average, hits and misses \ \ ** Sub-par on many levels \ \ * Unquestionably awful \ |------------------------------|
MPAA Rating: PG-13
Director: Kelly Makin Producer: Elizabeth Hurley, Charles Mulvehill Writer: Adam Scheinman, Robert Kuhn Starring: James Caan, Hugh Grant, Jeanne Tripplehorn, Scott Thompson,
http://www.mickeyblueeyes.com/
Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com Author - Network Administrator's Reference, 1999, ISBN 0-07-882588-1 http://www.amazon.com/exec/obidos/ASIN/0078825881/filmcriticcom
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


| History of Arches | ||
| Virtual Tour | Location Map/More History | General Info |
Prehistory Rocks have attracted visitors to Arches National Park for thousands of years. However, sightseeing has not been the main activity for very long. Hunter-gatherers migrated into the area about 10,000 years ago at the end of the Ice Age. As they explored Courthouse Wash and other areas in what is now Arches, they found pockets of chert and chalcedony, microcrystalline quartz perfect for making stone tools. Chipping or knapping these rocks into dart points, knives, and scrapers, they created debris piles that are still visible to the trained eye. Then, roughly two thousand years ago, the nomadic hunters and gatherers began cultivating certain plants and settled into the Four Corners region. These early agriculturalists, known as the ancestral Puebloan and Fremont people, raised domesticated maize, beans, and squash, and lived in villages like those preserved at Mesa Verde National Park. While no dwellings have been found in Arches, the northern edge of ancestral Puebloan territory, there are rock inscription panels. Like earlier people, the ancestral Puebloans left lithic scatters, often overlooking waterholes where someone may have shaped tools while watching for game. People living in modern-day pueblos like Acoma, Cochiti, Santa Clara, Taos, and the Hopi Mesas are descendants of the ancestral Puebloans. The Fremont were contemporaries of the ancestral Puebloans and lived in the same general area, so distinctions between the two cultures are blurry. However, Fremont rock inscriptions, pottery and other artifacts clearly demonstrate the existence of different technologies and traditions. Both the Fremont and the ancestral Puebloans left the region about 700 years ago. As the ancestral Puebloan and Fremont peoples were leaving, nomadic Shoshonean peoples such as the Ute and Paiute entered the area and were here to meet the first Europeans in 1776. The petroglyph panel near Wolfe Ranch is believed to have some Ute images since it shows people on horseback, and horses were adopted by the Utes only after they were introduced by the Spanish. National Park Service Content/www.nps.gov |


Simple Wheels
Thanks for your order #2357654, your package will be on it's way shortly.
Chapelli Vintage Single Speed
$317
CastelliArenberg Gel Gloves
$39
Total
$356


Machine Learning Models:
In this project we are currently using 3 machine learning models as plugins in the Data Science pipeline.
Autoregressive integrated moving average (ARIMA)
ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. It is a class of model that captures a
suite of different standard temporal structures in time series data.
In statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average
model is a generalization of an autoregressive moving average model. Both of these models are fitted to time series data
either to better understand the data or to predict future points in the series.
We used the python library statsmodels
to create the ARIMA class used in the visualizations
Vector autoregression (VAR)
Vector autoregression (VAR) is a stochastic process model used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate autoregressive model (AR model) by allowing for more than one evolving variable. We used the python library statsmodels to create the VAR class used in the visualizations and in the controller
Random Forest
Random Forest is a supervised learning algorithm. ... The general idea of the bagging method is that a combination of learning models increases the overall result. To say it in simple words: Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction


We are organizing an event in which anyone can submit the entry of their birth details and we will pick any random birth details. Then we will do live analysis of that particular kundli in depth on our youtube channel for free. So be ready to submit the entry and get your kundli analyzed for free. For submitting entry download our app and subscribe to our youtube channel.
We will pick your details and will analyze you kundli live. After that we will publish the video on our Youtube channel.
Learn More


The key to breaking the cycle is to prepare the next generation. This can be done by instilling excitement in them through providing a glimpse into their future. We will give them the opportunity to broaden their housing horizons.
Some or no highschool
Finish highschool, but no college
Some college
Finish college


Our group health insurance pays for eligible expenses not normally covered by
your Government Health Insurance Plan and includes retirement travel health coverage. More than
76,000 retired teachers and other education retirees and workers in Canada are covered by our
plan.
Coverage and Benefits
Prescription drug coverage
The prescription drugs benefit covers drugs, sera, injectables, and compounds/mixtures
with a Drug Identification Number (DIN), that require a prescription from a physician,
dentist or practitioner and are dispensed by a licensed pharmacist.
$0 annual deductible
85% reimbursement of ingredient costs
$3,400 annual maximum per person – we set the amount based on what members
have needed
Reimbursement of Government Health Insurance Plan deductibles for prescription
medications (e.g. ODB, RAMQ, and Fair Pharmacare)
Sexual dysfunction treatments and diabetic supplies for glucometers and insulin
pumps are included
Option to arrange for a second 100-day drug supply if you’re taking an extended
vacation
Paramedical practitioner coverage
Paramedical coverage includes non-surgical and surgical services. You don’t need a
physician referral for services to be covered.
80% reimbursement
$1,300 annual maximum per person for all practitioners combined
Includes acupuncture and foot care services when provided by a registered nurse
Reflexology services are eligible when performed by one of the covered paramedical
practitioners within their scope of practice
Vision coverage
The plan includes prescription glasses and contact lenses, laser eye surgery and corneal
incision. Plus, support is available for vision training or remedial exercises not
covered by government plans.
80% reimbursement
$400 per person for prescription eyewear and laser surgery in any two consecutive
years
$150 per person for eye exams in any two consecutive years, and per calendar year
for dependent children
An additional lifetime limit of $400 per person for new lenses required within six
months of eye surgery
Lifetime maximum of $300 per eye for cataract surgery costs that aren’t covered
elsewhere in the Extended Medical Plan
Aids and appliances coverage
The plan covers the costs of aids and appliances prescribed by a physician. Government
plans can help offset costs. Our claims team will review your quote to help you
determine what will be covered so you understand expenses before making a purchase.
80% reimbursement
Includes trusses, splints, braces, casts, crutches, canes, walkers, wheelchairs and
hospital beds
Covers artificial limbs or eyes and breast prosthesis, including three mastectomy
bras per year
$640 per year for wigs
Medical supplies including $400 per person, per year for surgical stockings, $750
per person, per year for incontinence supplies and up to $200 per person every two
consecutive calendar years for post-surgical comfort and convenience items
$500 per person for custom-made orthopedic shoes every two consecutive calendar
years
$1,100 per person for purchase or repair of hearing aids every three successive
calendar years
Retirement travel health coverage
The travel plan covers emergency medical expenses during out of province and out of
country trips, and trip cancellation and interruption costs. You’re covered for up to 93
days and can add a Supplemental Travel Plan for your extended trips.
100% reimbursement
Up to $10,000,000 for eligible medical emergency expenses per person, per trip
Unlimited number of trips
Up to $6000 for cancellation or interruption per person, per trip
Costs related to a pre-existing condition may be covered, subject to a 90-day
medical stability clause
Additional Coverage and Benefits
Private duty nursing coverage
Private duty nursing coverage
The plan covers medically necessary out-of-hospital nursing services. Additional related
coverage is available through our Hospital and Convalescent Care Plan.
80% reimbursement
$2000 per person every two consecutive calendar years
Transportation/ambulance coverage
Transportation/ambulance coverage
The plan covers medically necessary ground and air transportation to a hospital.
80% reimbursement
Includes one round trip per person per year for air transportation from your
hospital to the nearest hospital able to provide treatment
Education program coverage
Education program coverage
80% reimbursement
Includes medically-related program(s) recommended by a physician to an annual
maximum of $200 per person
Accidental dental coverage
Accidental dental coverage
The plan covers dental treatment required as a result of an accidental blow to natural or
artificial teeth. For additional dental coverage, see our Dental Plan.
80% reimbursement
Up to $1000 per person per calendar year
Diagnostic services coverage
Diagnostic services coverage
The plan covers diagnostic lab tests, including screening tests. The coverage helps
offset the costs of tests not covered by government plans.
80% reimbursement
$250 annual maximum per person
More Value-Added Benefits
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{% extends 'base.html' %} {% load static %} {% block content %} My Blog My Thinking over the internet
Posted by {{ post.author }} on {{ post.timestamp }}


Nguyễn Thành Long
Web developer
My name is Nguyen Thanh Long. Currently, I am a web developer. I am a careful and hardworking person. I want to study and enjoy working with people. I adapt easily to work and the environment is proactive at work.
I strongly believe that a great software engineer is a hybrid combination of technical skills as well as soft skills, which are important in communicating with customers and colleagues to reach the best result for the business.I have learned how important is to maintain the functionality while boosting the performance, reliability, flexibility, and manageability.
I like to work in groups rather than work individually, because when working by In groups, I can support people, or vice versa, if I have a problem, I can. Thanks for everyone's help. That can improve the progress of the work up significantly.


Aishell is an open-source Chinese Mandarin speech corpus published by Beijing Shell Shell Technology Co.,Ltd.
400 people from different accent areas in China are invited to participate in the recording, which is conducted in a quiet indoor environment using high fidelity microphone and downsampled to 16kHz. The manual transcription accuracy is above 95%, through professional speech annotation and strict quality inspection. The data is free for academic use. We hope to provide moderate amount of data for new researchers in the field of speech recognition.
You can cite the data using the following BibTeX entry:
@inproceedings{aishell_2017, title={AIShell-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline}, author={Hui Bu, Jiayu Du, Xingyu Na, Bengu Wu, Hao Zheng}, booktitle={Oriental COCOSDA 2017}, pages={Submitted}, year={2017} }


{% load static %} {# weirdly, everything, including all the bootstrap stuff, seems to be included here#} {# #} {# further styling in the extender html #} {% block links %} {% endblock %}
{% block body %} {% endblock %} {% block scripts %} {% endblock scripts %}


{% extends "layout.html" %} {% block title %}
| Name | Designation | Phone extension | E-mail ID |
|---|---|---|---|
| Amit Kumar | Assistant Professor | 8124 | amitkt |
| Debabrata Seth | Associate Professor | 8028 | debabrata |
| Md. L. H. Choudhary | Associate Professor | 8038 | lokman |
| Neeladri Das | Associate Professor | 8023 | neeladri |
| Prolay Das | Associate Professor | 8057 | prolay |
| Ranganathan Subramanian | Associate Professor | 8094 | ranga |
| Sahid Hussain | Associate Professor | 8022 | sahid |
| T. Rajagopala Rao | Assistant Professor | 8238 | rajgopal |
| Snehasis Daschakraborty | Assistant Professor | 8675 | snehasis |
| Debajit Sarma | Assistant Professor | 8677 | debajit |
| Subrata Chattopadhyay | Assistant Professor | 8676 | sch |
| Name | Designation | Phone extension | E-mail ID |
|---|---|---|---|
| Antara Garai | Scientific Officer | 8172 | antara |
| Aziz Ahmad | Junior Technical Superintendent | 8216,8218 |
azizahmed |
| Gudipati Srinivasarao | Junior Technical Superintendent |<PHONE_NUMBER> | gudipati |
| Laboratory & Offices | Phone extension |
|---|---|
| HOD Office |
8225 |
| Conference Room | 8227 |
| 1st Year B. Tech Lab | 8070 |
| Department Office | 8073 |
| Chemistry Instrument Room | 8119 |
| CST Lab 1 | 8215 |
| CST Lab 2 | 8216 |
| PG Lab 1 | 8217 |
| PG Lab 2 | 8218 |
| Project Lab | 8219 |
| NMR Room -1 | 8220 |
| NMR Room - 2 | 8221 |
| Chemistry Instrument Room 1 | 8222 |
| Store Room | 8223 |
| Glyco Chemistry Lab | 8228 |
| Green Synthesis Lab | 8229 |
| Theoretical Chemistry Lab | 8230 |
| Gas Phase and Computational Lab | 8231 |
| Organometallics Lab | 8232 |
| Supramolecular Synthesis Lab | 8233 |
| Catalysis Lab | 8234 |
| Biochemistry and Molecular Biology Lab | 8235 |
| Ultrafast Spectroscopy Lab | 8236 |
| Departmental Library | 8237 |
| Chemistry Instrument Room - 2 | 8238 |


Implementation of the mid-n algorithms presented in Wellek S (2015) <doi:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact sample size calculation for superiority trials with binary outcome.
| Version: | 1.0 |
| Depends: | BiasedUrn |
| Published: | 2016-10-28 |
| Author: | Stefan Wellek, Peter Ziegler |
| Maintainer: | Peter Ziegler <peter.ziegler at zi-mannheim.de> |
| License: | CC0 |
| NeedsCompilation: | no |
| CRAN checks: | MIDN results |
| Reference manual: | MIDN.pdf |
| Package source: | MIDN_1.0.tar.gz |
| Windows binaries: | r-devel: MIDN_1.0.zip, r-release: MIDN_1.0.zip, r-oldrel: MIDN_1.0.zip |
| OS X binaries: | r-release: MIDN_1.0.tgz, r-oldrel: MIDN_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=MIDN to link to this page.


Daniel Phan
Hai Dang Daniel Phan
(April 23, 1998) is a student and
Web designer at Logos International School. Phan in the
12th grade, and is suffering from senioritis.
Interests
Worldview
Classes
Guestbook


What?
An interactive Python console.
Learn the most elegant programming language.
Learn by practice. Athlete of God
Why?

Learn at your own pace.
For the true Python lovers!
Python is the way to go. Python Rocks!
How?
Google App Engine Goodness.
Github.
Guido Van Rossum He The Man
Gracias.
Thanks for opting to make programming better.
Do get in touch. I make nice coffee. Hi!
An interactive Python console.
Learn the most elegant programming language.
Learn by practice. Athlete of God
Why?

Learn at your own pace.
For the true Python lovers!
Python is the way to go. Python Rocks!
How?
Google App Engine Goodness.
Github.
Guido Van Rossum He The Man
Gracias.
Thanks for opting to make programming better.
Do get in touch. I make nice coffee. Hi!


Data Scientist with over 5 years of multinational experience in data analysis, quality assurance and project engineering management
Find Out More
Being an engineering major and possessing a master’s degree in management with five years of work experience has given me the necessary insight and background essential to help organizations make better business decisions.
I have Prove Expertise in Providing engineering and technical support, preparing project reports and technical documents, project presentations, seminars and business development initiatives.
Being a visual person and a firm believer that data can create meaningful stories that can help companies improve their business intelligence and processes has driven me into the world of data analytics.
I wish to utilize my keen analytical and problem-solving skills to identify and solve day-to-day business problems through a multidisciplinary approach.
Data Analytics Program - 2018
Master of Science- Environmental Management - 2012
Bachelor of Engineering - Environmental - 2008
Python 3, Numpy, Pandas, Matplotlib, API Interactions, Social Media Mining
MySQL, MongoDB, Sqlite3, NoSQL
HTML, CSS, Bootstrap, Dashboarding, JavaScript Charting, D3.js, Geomapping with leaflet.js, Plotly
Tableau, Machine Learning, Big Data with Hadoop


--- layout: post title: CNN meets Onion date: 2005-01-21 00:00:00 category: hilarity ---
"Poll: Nation split on Bush as uniter or divider"
As Mr Gooby said, "CNN, meet the Onion"...


Provides a pure Java Globus GASS server for
transferring files via HTTPS.
The server is multi-threaded and accepts HTTPS connection from GASS
clients to copy from, copy to, and append to files that are local to the
server.
This version has not been fully tested against the full GASS protocol,
but works for all cases tested with globus-url-copy.
Package Specification
(none)
Related Documentation
The following classes are used by the org.globus.gass.server class:
For overviews, tutorials, examples, guides, and tool documentation,
please see:


---
layout: page
title: (CG) Fractals (HTML5)
---
Fractals
Goal: Learn how fractals work and recreate the fractal given in the picture
Time: 2 weeks
Requirements:
Find the four transformations of which this fractal was created
Create a step/level system to interactively change the fractalization
The four quarters of the fractal must be colorized differently
Animate all four transformations
Solution:
Implemented buttons to change the level of fractalization
To colirize quarters differently I applied the fillstyle to only the previous to last level of fractal
To animate transformations for each iteration I added increments to shape's position, scale and rotation
You can see the code here.
End Result:
Level
0
Transformation
0


Clipper 📋
remove
clear
Storage Limit:
0 / 10
CLEAR
delete_forever
settings
© 2020 • Clipper
With 💖 Girkov Arpa
Settings
Enable Audio
Capture Clipboard
Note:
You can save text of maximum 500 characters only. This application uses
Sciter Engine
, © Terra Informatica Software, Inc. and is based on the original
Clipper
© Akash Rajpurohit.
CLOSE


|
YDLIDAR SDK
V1.3.4
|
SDK test application for YDLIDAR
Visit EAI Website for more details about YDLIDAR .
$ git clone https://github.com/yangfuyuan/sdk $ cd sdk $ git checkout master $ cd .. $ mkdir build $ cd build $ cmake ../sdk $ make ###linux $ vs open Project.sln ###windows
$ cd samples
linux:
$ ./ydlidar_test $Please enter the lidar port:/dev/ttyUSB0 $Please enter the lidar baud rate:230400
windows:
$ ydlidar_test.exe $Please enter the lidar port:COM3 $Please enter the lidar baud rate:230400
=====================================================================
You should see YDLIDAR's scan result in the console:
Yd Lidar running correctly ! The health status: good [YDLIDAR] Connection established in [/dev/ttyUSB0]: Firmware version: 2.0.9 Hardware version: 2 Model: G4 Serial:<PHONE_NUMBER>000003 [YDLIDAR INFO] Current Sampling Rate : 9K [YDLIDAR INFO] Current Scan Frequency : 7.400000Hz [YDLIDAR INFO] Now YDLIDAR is scanning ...... Scan received: 43 ranges Scan received: 1361 ranges Scan received: 1412 ranges
data structure:
struct node_info { uint8_t sync_quality;//!intensity uint16_t angle_q6_checkbit; //!angle uint16_t distance_q2; //! distance uint64_t stamp; //! time stamp uint8_t scan_frequence;//! current_frequence = scan_frequence/10.0, If the current value equals zero, it is an invalid value } __attribute__((packed)) ;
example:
if(data[i].scan_frequence != 0) { current_frequence = data[i].scan_frequence/10.0; } current_time_stamp = data[i].stamp; current_distance = data[i].distance_q2; current_angle = ((data[i].angle_q6_checkbit>>LIDAR_RESP_MEASUREMENT_ANGLE_SHIFT)/64.0f); current_intensity = (float)(data[i].sync_quality >> 2); ###note:current_frequence = data[0].scan_frequence/10.0. ###if the current_frequence value equals zero, it is an invalid value.
code:
void ParseScan(node_info* data, const size_t& size) { double current_frequence, current_distance, current_angle, current_intensity; uint64_t current_time_stamp; for (size_t i = 0; i < size; i++ ) { if( data[i].scan_frequence != 0) { current_frequence = data[i].scan_frequence;//or current_frequence = data[0].scan_frequence } current_time_stamp = data[i].stamp; current_angle = ((data[i].angle_q6_checkbit>>LIDAR_RESP_MEASUREMENT_ANGLE_SHIFT)/64.0f);//LIDAR_RESP_MEASUREMENT_ANGLE_SHIFT equals 8 current_distance = data[i].distance_q2; current_intensity = (float)(data[i].sync_quality >> 2); } if (current_frequence != 0 ) { printf("current lidar scan frequency: %f\n", current_frequence); } else { printf("Current lidar does not support return scan frequency\n"); } }
2018-05-23 version:1.3.4
1.add automatic reconnection if there is an exception
2.add serial file lock.
2018-05-14 version:1.3.3
1.add the heart function constraint.
2.add packet type with scan frequency support.
2018-04-16 version:1.3.2
1.add multithreading support.
2018-04-16 version:1.3.1
1.Compensate for each laser point timestamp.


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Each recognition result has the following fields:
| Property Name | Type | Description |
|---|---|---|
| recognitionLevel | Number | The recognition level. 1.0 for classes in known Java packages, 0.5 otherwise. |
| packageName | String | The package name of the Java class |
| simpleClassName | String | The simple name of the Java class (without the package) |
| fullyQualifiedClassName | String | The fully qualified name of the Java class (including the package) |
{ "com.solveforall.recognition.programming.java.JavaClassName": [ { "recognitionLevel": 1.0, "packageName": "java.util", "simpleClassName": "Map", "fullyQualifiedClassName": "java.util.Map" } ] }


Make a money claim
You can make a money claim if someone owes you money and won’t agree to pay you back.
A money claim might not be your only or best option. Find out about other ways to get your money.
Your claim, including your name and address, will be sent to the person you say owes you money. They will get a chance to respond to your claim.
 It can take up to 7 months to get a hearing date.
What you'll need
To make a claim you must have:
- the current address of the person or company you’re claiming against
- a credit or debit card to pay the claim fee
Claim fee
You must pay a fee to make a claim. The claim fee is based on the amount you’re claiming, but will be at least £25.
The fee will be calculated for you when you make your claim. The fee is added to your claim. If your claim is successful, the defendant must pay your fee.
You can only use this service for claims of less than £10,000.
Find out how claim fees are calculated
| Claim amount | Claim fee | Hearing fee |
|---|---|---|
|
£25 | £25 |
|
£35 | £55 |
|
£60 | £80 |
|
£70 | £115 |
|
£105 | £170 |
|
£185 | £335 |
|
£410 | £335 |
|
4.5% of the claim | £545 |
|
4.5% of the claim | £1090 |
Other ways to claim
You can print and fill in a paper claim form. Send it and a cheque for your claim fee to the address on the form.
Help to use this service
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