[Update: New access point too] UniFi Dream Machine blends WiFi router and Gigabit switch w/ advanced security for business or home use
by Michael PotuckUbiquiti’s UniFi is out today with Dream Machine which combines a WiFi router and Gigabit switch for both home or business use. It has an integrated 4-port Gigabit Ethernet switch, advanced security gateway with IDS/IPS and DPI, works in high-density environments, and is scalable with UniFi’s access points to fit almost any size deployment.
Update 11/8: After launching Dream Machine earlier this week, UniFi is out with its latest access point, the Beacon HD.
- 802.11ac Wave 2, 4×4 MU-MIMO technology
- 400+ Mbps performance over a wireless mesh hop
- High Output Power (EIRP): 30 dBm for 5 GHz, 24.8 dBm for 2.4 GHz
- Works with UniFi AP or Dream Machine
- Automatically discovered for adoption by UniFi Network Controller
The Unifi AP Beacon HD is available now priced at $129. Meanwhile, the company’s previously released AC Lite access points go from around $80 each.
UniFi designed the Dream Machine as a versatile router that combines 802.11ac dual-band WiFi and a 4-port Gigabit Ethernet switch. The integrated switch makes it a solid choice for both smart home users who need to connect hubs and more to power HomeKit devices as well as IT admins who are running business/enterprise networks.
Dream Machine also features an advanced security gateway and an integrated cloud key to easily map out networks, manage network traffic, and more.
UniFi Dream Machine features:
- All-In-One: Integrates a 4×4 enterprise access point, security gateway, cloud key and Gigabit switch into one device for enterprise-grade performance
- Processing Power for Secure Browsing: Powered by a fast 1.7 GHz quad-core processor for advanced firewall policies and IPS/IDS management for a safe, reliable internet experience
- Scalable With No Fees: Staying true to our heritage, each component of UDM is scalable, customizable and continuously updated without subscription or maintenance fees
- Intuitive User Interface: Centralized management software features a graphical user interface that offers easy setup and convenient control
UniFi Dream Machine is available now directly from UniFi priced at $300. Additional UniFi access points start at around $80.
More from Ubiquiti:
- AmpliFi mesh Wi-Fi users gain free VPN privacy protection with latest software update
- AmpliFi HD Gamer’s Edition mesh router system arrives with NVIDIA GeForce mode for low latency performance
- Review: AmpliFi Instant mesh WiFi system is a quality product with 2-minute set up, fast performance, touchscreen, more
- Ubiquiti releases new AmpliFi Instant Mesh Wi-Fi System, sets up in under 2 minutes
FTC: We use income earning auto affiliate links. More.


A simple web service for playing synthesized speech or media files on Google Cast devices.
See: https://github.com/watusi/google-cast-public-address
Please open this document by accessing the service - not by opening this file directly in your browser. e.g. http://localhost/google-cast-public-address
Although meant primarly for use as a web service, you can play speech or files by submitting a form from a webpage. View the HTML source for details.
These demos also serve as a simple way to verify that the service is properly installed and running.


Displays the list of blogs according to the blog list layout. Recommended use is to link directly to an individual blog rather than use the blog module. Use the Link Manager for Blog URL.
{module_blog,filter,ID}
{module_blog,i,419}
- will display the individual blog with ID of 419


Since 2012, Facebook has developed GraphQL: a backend agnostic data query language and runtime. GraphQL drastically reduces the number of server requests created by the browser by using a dynamic and nested query structure. Using the Working Draft Specification for GraphQL, graphqlr implements a full GraphQL server within R. graphqlr allows users to supply their own R functions to satisfy the data requirements of a GraphQL query generated by the browser.


Would you like the nature/wildlife tours and travels?
About us
In The Wild was established in 2000 and provides the best travel plans for all travel lovers and people who love life and nature. Each year In The Wild launches multiple travel plans, such as going to the forest to see gorillas, and going deep into the ocean to see whales. Here, you can find all the people who love nature and become friends. Every year we also hold fundraising activities to protect rare species. Whether you are a student, an office worker, or a professional nature conservation worker, In The Wild can provide your favorite travel plan. In The Wild, thousands of travelers join us every year. In The Wild is committed to providing quality services that allow everyone to feel the most authentic natural scenery.
OUR OFFICE:
- Located: Dal Studly Camp Dunn Building
- Hours:
9a.m. -- 5p.m. Monday - Thursday
9a.m. -- 4p.m. Friday
12a.m. -- 4p.m. Saturday - Sunday
Services
** For more information, please go to the Services page.
School Trips:
* only prodive for schools
Normal Trips:
* Plan A: Rwenzori Trekking
* Plan B: Murchison Falls National Park
* Plan C: Mgahinga Gorilla National Park
Professional trips:
* not each terms we have this plan, you need to contact us.
Price
* We have some DISCOUNT for some people.
* more information, please go to the Price page
* This is the table for the normal-trip> price.
| Age\Plan | Plan A (4 days) | Plan B (6 days) | Plan C (10 days) |
|---|---|---|---|
| 0 ~ 5 | $900 | $1300 | $1600 |
| 6 ~ 17 | $1000 | $1500 | $1950 |
| 18 ~ 50 | $1200 | $1700 | $2500 |
| 51 ~ 60 | $1000 | $1500 | $1950 |
| 60+ | $900 | $1300 | $1600 |
Contact Us
Ways to reach us:
E-mail<EMAIL_ADDRESS>Phone: 1-902-123-4567
Facebook: @InTheWild@
WeChat: InTheWild


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clc-register-user-package(1) clc-register-user-package(1) NAME clc-(un)register-user-package - registers a user asdf system SYNOPSIS clc-register-user-package package-file clc-unregister-user-package package-file DESCRIPTION Registers a user defined system into the common lisp controller system. This means that any Common Lisp implementation will know how to find the system. The system file itself will be symlinked from the ~/.clc/systems direc‐ tory and the system will be recorded in the ~/.clc/user-packages.db file. No removal of old fasl files during upgrades of Common Lisp Implementa‐ tions will be done, nor will the fasls of different implementations will be separated by clc , it is expected that the users who require this will create a full Debian package or create a symlink in /usr/share/common- lisp/systems/ to the system file. BUGS Bug reports should be sent to the Debian BTS, preferably with reportbug. AUTHOR Peter Van Eynde<EMAIL_ADDRESS>and Kevin M. Rosenberg<EMAIL_ADDRESS>2001-2008. 7th Edition June 11, 2008 clc-register-user-package(1)


Four example results from models trained with different sets of input attributes. For each example, the left image depicts the original image and the right image shows the result of CoarseNet + RefineNet. Hover over the buttons below to compare the results.
Four example results from the same model, but for inputs with different degrees of sparsity (% of SfM points kept). For each example, the left image shows the input sparsity and the right image shows the result of CoarseNet + RefineNet. CoarseNet and RefineNet were trained on depth, color and SIFT. Hover over the three buttons below to compare the results.
Four examples comparing results from CoarseNet and CoarseNet + RefineNet. For each example, two images are shown. The left image is the result of a model trained on depth and SIFT as input attributes. The right image is the result of a model trained on depth, color and SIFT as input attributes. Hover over the two buttons below to compare the results.


- Fit an anova
- APA formatted output
- Correlations, t-tests, regressions…
- Evolution
- Credits
- On similar topics
Psychology is still (unfortunately) massively using analysis of variance (ANOVA). Despite its relative simplicity, I am very often confronted to errors in its reporting, for instance in student’s theses or manuscripts. Beyond the incomplete, uncomprehensible or just wrong reporting, one can find a tremendous amount of genuine errors (that could influence the results and their intepretation), even in published papers! (See the excellent statcheck to quickly check the stats of a paper). This error proneness can be at least partially explained by the fact that copy/pasting the (appropriate) values of any statistical software and formatting them textually is a very annoying process.
How to end it?
We believe that this could be solved (at least, partially) by the default implementation of current best practices of statistical reporting. A tool that automatically transforms a statistical result into a copy/pastable text. Of course, this automation cannot be suitable for each and every advanced usage, but would probably be satisfying for a substantial proportion of use cases. Implementing this unified, end-user oriented pipeline is the goal of the psycho package.
Fit an anova
Let’s start by doing a traditional ANOVA with adjusting (the ability to flexibly regulate one’s emotions) as dependent variable, and sex and salary as categorical predictors.
# devtools::install_github("neuropsychology/psycho.R") # Install the latest psycho version
library(psycho)
df <- psycho::affective # load a dataset available in the psycho package
aov_results <- aov(Adjusting ~ Sex * Salary, data=df) # Fit the ANOVA
summary(aov_results) # Inspect the results
Df Sum Sq Mean Sq F value Pr(>F)
Sex 1 35.9 35.94 18.162 2.25e-05 ***
Salary 2 9.4 4.70 2.376 0.0936 .
Sex:Salary 2 3.0 1.51 0.761 0.4674
Residuals 859 1699.9 1.98
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
386 observations deleted due to missingness
APA formatted output
The psycho
package include a simple function, analyze()
that can be applied to an ANOVA object to format its content.
analyze(aov_results)
- The effect of Sex is significant (F(1, 859) = 18.16, p < .001) and can be considered as small (Partial Omega-squared = 0.019).
- The effect of Salary is not significant (F(2, 859) = 2.38, p = 0.09°) and can be considered as very small (Partial Omega-squared = 0.0032).
- The interaction between Sex and Salary is not significant (F(2, 859) = 0.76, p > .1) and can be considered as very small (Partial Omega-squared = 0).
It formats the results, computes the partial omega-squared as an index of effect size (better than the eta2, see Levine et al. 2002, Pierce et al. 2004) as well as its interpretation and presents the results in a APA-compatible way.
Correlations, t-tests, regressions…
Note that the analyze()
method also exists for other statistical procudures, such as correlations, t-tests and regressions.
Evolution
Of course, these reporting standards should change, depending on new expert recommandations or official guidelines. The goal of this package is to flexibly adaptive to new changes and good practices evolution. Therefore, if you have any advices, opinions or such, we encourage you to either let us know by opening an issue, or even better, try to implement them yourself by contributing to the code.
Credits
This package helped you? Don’t forget to cite the various packages you used :)
You can cite psycho
as follows:
- Makowski, (2018). The psycho Package: An Efficient and Publishing-Oriented Workflow for Psychological Science. Journal of Open Source Software, 3(22), 470. https://doi.org/10.21105/joss.00470
On similar topics
- Variable vs. Participant-wise Standardization
- Formatted Correlation with Effect Size
- Extracting a Reference Grid of your Data for Machine Learning Models Visualization
- Copy/paste t-tests Directly to Manuscripts
- Easy APA Formatted Bayesian Correlation
- Fancy Plot (with Posterior Samples) for Bayesian Regressions
- How Many Factors to Retain in Factor Analysis
- Beautiful and Powerful Correlation Tables
- Format and Interpret Linear Mixed Models
- How to do Repeated Measures ANOVAs
- Standardize (Z-score) a dataframe
- Compute Signal Detection Theory Indices
- Installing R, R Studio and psycho


Symbol Index for "Classification System Version"
Symbol Index for diagram
"Classification System Version"
Agent (Class)
basis (Association)
Classification Element (Class)
Classification In Version (Class)
Classification System (Class)
Classification System Version (Class)
context (Association)
Guideline (Class)
KC/Essential Type (Generalization Node)
Knowledge Concept (Class)
responsible for (Association)


{# # Copyright 2013 Donald Stufft # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -#} {% extends "legacy/simple/base.html" %} {% block title %}Links for {{ project }}{% endblock %} {% block content %}


Science is inherently collaborative and context-embedded. It thrives on diversity, transparency, and education.
labSquared is an ongoing exploration of how academic research groups could function following these ideals. It is the handbook of an imaginary lab experimenting with ways of running itself: a laboratory laboratory.
I am not the first to think of many —perhaps any— of these proposals. But more importantly, the purpose is to build a repository of ideas and resources, a place to track, gather, and communicate these thoughts in. These can be starting points from which to trace courses in research, academic mentoring, science policy, science communication, and much more.
Understanding pathogen evolution
Understanding and predicting evolution can allow us to design drugs, vaccines, and public health policies that are more effective and robust. Achieving this requires new experimental, mathematical, and computational modeling tools.
Sequencing has revolutionized the study and management of infectious diseases. Despite the wealth of field data, we are missing tools with which to test hypotheses about genome evolution and epidemiology in a systematic manner. To address this, I have developed computational tools for simulating pathogen genetics and evolution in epidemiological models. I applied these tools to understand fundamental precepts in genomic epidemiology using small epidemiological simulations, in an independent collaboration with Prof. Vladimir Corredor and Dr. Mauricio Santos-Vega [article]. I look forward to expanding this work into a full research program by computationally predicting and experimentally validating intervention strategies that limit both the spread and the evolution of diseases.
South America has seen the worst increases in malaria cases of any continent over the last few years. The unique genetic makeup and slow-burning epidemiological dynamics of South American malaria raises questions about the evolutionary dynamics of the parasites it is caused by. As a member of an international work group of collaborators in Colombia, USA, and Europe, I help apply statistical methods to the genomic epidemiology of malaria along the Colombian Pacific coast [article].
The most common instance of resistance to treatment is antibiotic resistance in bacteria. Resistant bacteria in a biofilm may incur in fitness costs, and yet the benefits of resistance can often be shared among its neighbors. As a part of MTBI at Arizona State University, I co-developed a 3-D, spatially explicit agent-based model of bacterial resistance, to study spatial dynamics and competition within biofilms that can be harnessed to reduce resistance [technical report].
Phage therapy is an intriguing alternative to antibiotics, but is also subject to evolution of bacterial resistance. Although phage are capable of evolving too, this coevolution can be difficult to achieve. To make phage into effective therapeutic agents, we need ways of inhibiting bacterial evolution of resistance. At the CIMIC lab at Universidad de los Andes, I contributed to the development and validation of a dynamical model of phage therapy showing the potential of combining phage cocktails and antibiotics, even when the antibiotic is ineffective by itself [article].
Controlling expression and genetic circuits
Our ability to understand and control disease depends on our ability to control the genes underlying it. Synthetic biology allows us to repurpose systems with unrelated biological functions into tools for manipulating gene expression.
My Ph.D. research with the Niles Lab at MIT focuses on developing these kinds of tools for use in the malaria parasite, Plasmodium falciparum. I have assembled synthetic gene systems that allow robust, orthogonal control of transcription in malaria parasites, a task that had proven difficult to achieve thus far. I am now using these synthetic tools to characterize native transcriptional control mechanisms in the parasite, which can help discover new drug targets [in prep.].
Besides building tools to manipulate gene expression, it is important to understand how these tools function together. Genetic circuits exhibit context-dependent, emergent behaviors such as resource sharing, which complicate their use. In collaboration with the Del Vecchio lab at MIT, I helped assemble and model genetic devices to reduce these effects in model gene circuits [article].
Finally, I have built and modeled gene circuits for applications in new treatment strategies for bacterial disease, both as a member of Eligo Bioscience and in collaboration with the Collins lab at MIT [article, email me for pdf].
Probing and targeting pathogen genes
We can use engineered control of gene expression and bioinformatics to tease apart how pathogens live and die. This knowledge opens new paths to approach drug and vaccine design in a more informed manner.
Despite still killing more than 400,000 people a year, more than half of P. falciparum genes have unknown or hypothetical functions. To remedy this, I develop computational tools to visualize gene interactions and design gene editing for assaying gene function at a massive scale, as a part of the Niles Lab [article]. In addition, I use statistical approaches to interpret protein-compound interactions in order to establish potential drug targets and lead compounds [in prep.].
Another situation where we need a better understanding of gene function is bacterial persistence, the phenomenon in which bacteria stochastically enter a metabolic state that allows them to survive an antibiotic, despite being genetically susceptible. As a part of the Paulsson Lab at Harvard Medical School, I used microfluidics and live cell microscopy to screen gene function related to persistence in Escherichia coli.
Teaching Development Fellow – MIT Teaching and Learning Lab
(Fall 2022–Summer 2023)
Studying and developing practices to support graduate student teaching skills through observation and workshops. Developing workshops, programming, and written resources to support research project mentorship skills for graduate students throughout MIT. Completed courses on Subject Design, Lesson Planning, Microteaching, and Inclusive Teaching for a MIT TLL Teaching Certificate (expected 2023).
Data Fellow – MIT Biological Engineering Data Lab
(Spring 2020++)
Providing 1-on-1 coaching to undergraduates, graduate students, and postdocs at MIT for designing and executing projects in computational biology and biological data analysis. Offering language-specific training and support in Python, MATLAB, R, and cluster computing.
Peer counsellor – MIT Biological Engineering REFS
(Spring 2019++)
We provide 1-on-1 confidential conflict coaching, workshops, and reference material for navigating grad school to peer graduate students at the MIT Department of Biological Engineering. We identify points of stress in the Ph.D. track and work with the MIT BE Graduate Student Board to improve faculty mentorship and the graduate experience.
Teaching Assistant – MIT Biological Engineering
(Fall 2019)
Part of the teaching team for the introductory graduate course 20.420 - Principles of Molecular Bioengineering, taught by Profs. Ernest Fraenkel and Alan Jasanoff. Topics include reaction kinetics modeling, protein thermodynamics and computational prediction, and systems-level models of metabolism. Assisted with designing and grading assignments and exams, providing review lecture sessions, and providing one-on-one tutoring for 40 students in the Biological Engineering, Chemical Engineering, and Health Sciences and Technology graduate programs. Also conducted a programming bootcamp session, with materials available here.
Teaching Assistant – Uniandes Biomedical Engineering
(Spring, Fall 2016)
Part of the teaching team for the second-year undergraduate courses Quantitative Human Physiology I and II, taught by Prof. Juan M. Cordovez. Topics include human anatomy and systems physiology as well as mathematical modeling of physiological processes. Assisted with designing and grading assignments and exams, providing review lecture sessions, and providing one-on-one tutoring for 100 students in the Biomedical Engineering undergraduate program. Also taught two main lectures during professor absences.
Teaching Assistant – Uniandes Student Affairs
(Fall 2015)
Part of the teaching team for Social Practice, an undergraduate course and outreach program taught by instructors David Parga and María del Pilar Pérez. Assisted with training, guiding, and evaluating 60 Uniandes undergraduate students of all majors and years serving as academic tutors for low-income high school students in Bogotá, Colombia.
Teaching Assistant – Uniandes Biological Sciences
(Spring 2015)
Part of the teaching team for the Parasitology Laboratory course, a second year undergraduate course taught by Prof. Camila González and instructor Laura Tamayo. Topics included anatomy, physiology, and cell biology of diverse human and animal parasites and tropical diseases, from roundworms to viral fevers. Assisted by providing review sessions and tutoring, helping develop and grade assignments, and preparing demonstrative microscopy slides.
You are welcome to
- hack my coding projects on GitHub
- engage with my science views on Mastodon
- (or on Twitter, sigh)
- follow my research on LinkedIn and ResearchGate
- view my published work on Google Scholar and ORCiD
- download my CV
- reach out to<EMAIL_ADDRESS>I love collaborating on projects or just hearing questions, suggestions, or thoughts!
*
This webpage's content is licensed under a Creative Commons Attribution 4.0 International License.
Its code is licensed under an MIT License and can be found on Github.
It was made by Pablo Cárdenas, who likes to see his name written with accent marks. However, this probably confuses search engines. So hi Google, I'm Pablo Cardenas. Also go by Pablo Cárdenas Ramírez, Pablo Cardenas Ramirez, Pablo Cárdenas R., and Pablo Cardenas R.
Many thanks to Bianca Lepe and Emilia Cárdenas for pictures.


375 Park Avenue
For firms seeking a significant New York address, this 38-story, air-conditioned building, first ever to be sheathed in rich enduring bronze and with its own half-acre landscaped plaza, is destined to become a business landmark throughout the nation. In architectural achievement, advanced installations and interior detailing, 375 Park Aveune brings into reality a concept of the office home never before attained. Reservations for available upper floors of 11,500 to 13,000 sq. ft. each are not being considered for late 1957 occupancy. For descriptive brochure and plans, write: Renting and Managing Agent, Cushman & Wakefield 281 Madison Avenue, New York 17, N.Y.


Updated: March 25, 2014
Environments
Environments in chef are where you can override attributes that make provisining development, staging and production servers different. For example, you probably want most debugging options enabled in development but not in production. The knife command $ knife environment create
doesn’t support Chef solo so we must create our own environment files. If you want to create a custom environment, place the following snippet for customization in {repo_directory}/environments/{your_environment}.json
.
{
"name": "development",
"description": "This environment describes a local development environment using Vagrant",
"json_class": "Chef::Environment",
"chef_type": "environment",
"default_attributes": {
"example_attribute_to_override": "true"
},
"override_attributes": {
"example_attribute_to_override": "true"
}
}
Notice that you have two options: default_attributes
and override_attributes
. You should start by specifying your custom attributes in the default_attributes object because override_attributes is pretty high on the precedence list. This advice may help keep your attribute inheritance easier to manage in the future. If you need some assurance that your attributes will be adhered to, place them in the ovverride_attributes
object.
What Attributes can I choose from?
A good place to find the default attributes is {cookbooks_dir}/{cookbook_name}/attributes/default.rb
. To find more options, check in other cookbook directories for for default attribute files as well.
Environments with Vagrant
Not all environments will be provisioned with Vagrant since they will be provisioned remotely after development. Sometimes there are settings that should only apply to during Vagrant sessions. For example, you may need to change a port or password that would be otherwise unsecure on a public server. Luckily, you can specify attributes in the Vagrantfile
that augment your attributes defined in your environment. They will not, however, take precedence over any attributes defined in the override_attributes
object.
In your Vagrantfile
you can specify additional json as so:
config.vm.provision :chef_solo do |chef|
chef.environments_path = 'environments'
chef.environment = 'development'
chef.json = {
"authorization" => {
"sudo" => {
"users" => [ "vagrant" ],
"passwordless" => true,
}
},
'openssh' => {
'server' => {
'password_authentication' => 'yes'
}
}
}
end


PDP accuses soldiers of arresting members
by Chukwudi AkasikeChukwudi Akasike and Daniels Igoni
THE Peoples Democratic Party in Bayelsa State has accused soldiers of arresting some of its prominent members at collation centres in the state.
State Chairman of the PDP, Moses Cleopas, also alleged that the General Officer Commanding, 6 Division of the Nigerian Army, Maj. Gen. Jamil Sarham, was using was soldiers under his command to commit electoral fraud in the state.
Cleopas listed some of the prominent PDP members purportedly arrested by soldiers as the Ogbia Council Chairman, Ebinyon Turner; and two lawmakers, Gibson Munalayefa and Mrs Naomi Ogoli.
He added that Sarham was spearheading an attempt to rewrite the result of the election in favour of the All Progressives Congress.
The state PDP chairman expressed surprise that the Nigerian Army would “allow itself to be used as a tool to subvert democracy in Nigeria”.
Copyright PUNCH.
All rights reserved. This material, and other digital content on this website, may not be reproduced, published, broadcast, rewritten or redistributed in whole or in part without prior express written permission from PUNCH.
Contact: theeditor@punchng.com


{% include meta.html %}
{{ site.description }}
We make it easy to buy cryptocurrency
Buying Bitcoin can be confusing, but our guide will walk you through the process step by step, and explain what exactly Bitcoin is.
There are many different exchanges through which you can purchase Bitcoin. Our guide covers some of the most popular Bitcoin exchanges.
After buying cryptocurrency, it is important to store it in a secure wallet. Our guide will explain the different types of wallet you can use.


KJSCMD(1) KDE User's Manual KJSCMD(1) NAME kjscmd - KDE KJSEmbed interpreter SYNOPSIS kjscmd [--help] [[-e] | [--exec]] [[-i] | [-interactive]] [[-n] | [-no-kde]] [file] DESCRIPTION kjscmd is a tool for launching KJSEmbed scripts from the command line. It will run the scripts contained in the named file using KJSEmbed. It does not accept - (stdin) as a file. APPLICATION OPTIONS -e,--exec execute script in file without gui support. -i,--interactive start interactive kjs interpreter. -n,--no-kde start without KDE KApplication support. USAGE The most common way to run kjscmd is simply as kjscmd file If no parameter file is used, it executes a predefined test script that measures the start speed of kjscmd. SEE ALSO kjs(1) BUGS There are probably tons of bugs. Use bugs.kde.org[1] to report them. AUTHOR Harri Porten<EMAIL_ADDRESS>Author. NOTES 1. bugs.kde.org http://bugs.kde.org 0.01.01 2008-10-16 KJSCMD(1)


IED found on Jammu-Srinagar highway, bomb squad at site
Security forces have found an IED on the Jammu-Srinagar highway following which traffic has been suspended.
A suspected improvised explosive device (IED) has been detected on the Jammu-Srinagar highway near Anantnag.
Following the detection of the IED by security forces on the highway on Thursday morning, traffic has been suspended.
A bomb disposal squad has been called to defuse the IED, officials said.
This came a day after a major tragedy was averted with timely detection of a powerful IED on the Jammu-Poonch National Highway on Tuesday.
The IED, suspected to have been planted by terrorists to trigger an explosion, was found by an army patrolling party near Kallar Morh in the border district of Poonch and was subsequently destroyed by the bomb disposal squad, the officials said.
Movement of traffic on the busy highway was suspended for some time, officials said, and added that a massive search operation was conducted to sanitise the area and track down the terrorists who had planted the explosive device.
Also read
Aslo watch | CRPF internal findings reveal intel failure led to Pulwama attack


Victims of double murder on Durban beach identified
by Algoa FMeThekwini Municipality's asked anyone with information on the deadly stabbings on the Durban beachfront to please go to the police.
They're investigating the murders of 24-year-old Kresan Chendiah and 50-year-old Fatima Muhammad.
A waste collector stumbled upon their bodies on sand dunes near Suncoast Casino on Monday afternoon.
Muhammad's bag was found on the scene with some of her belongings and ID still inside.
Durban Mayor, Mxolisi Kaunda says they'll increase police patrols and fast-track the installation of security cameras at beaches along the Golden Mile.
He urged police to work around the clock to find the suspects.
"One of the issues we have prioritized as the municipality is to increase police visibility and installation of CCTV cameras in our tourist attraction areas so that we can be able to identify wanted suspects and people who plan and commit a crime in these areas," said Kaunda.
Kaunda says the deaths occurred as the city has been preparing for the festive season influx.
source: ECR
Picture: Wikipedia


Represents a quaternion.
More...
#include <quaternion.hpp>
|
|
constexpr | quaternion () noexcept |
| | Creates a quaternion with elements initialized to 0.
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| |
| constexpr | quaternion (T x, T y, T z, T w) noexcept |
| | Creates a quaternion with the elements initialized at the specified values. More...
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| |
| template<typename U , typename Enable = std::enable_if_t<std::is_convertible<U, T>::value>> |
| constexpr | quaternion (const quaternion< U > &other) noexcept |
| | Creates a quaternion from a quaternion with different scalar type. More...
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| |
| constexpr T | x () const noexcept |
| | Retrieves a copy of the first element of the vector part. More...
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| |
| constexpr T & | x () noexcept |
| | Retrieves a reference to the first element of the vector part. More...
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| |
| constexpr T | y () const noexcept |
| | Retrieves a copy of the second element of the vector part. More...
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| |
| constexpr T & | y () noexcept |
| | Retrieves a reference to the second element of the vector part. More...
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| |
| constexpr T | z () const noexcept |
| | Retrieves a copy of the third element of the vector part. More...
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| |
| constexpr T & | z () noexcept |
| | Retrieves a reference to the third element of the vector part. More...
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| |
| constexpr T | w () const noexcept |
| | Retrieves a copy of the scalar part. More...
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| constexpr T & | w () noexcept |
| | Retrieves a reference to the scalar part. More...
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| constexpr const T * | data () const noexcept |
| | Retrieves a pointer to an array containing the elements of the quaternion. More...
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| constexpr quaternion & | operator+= (const quaternion &rhs) noexcept |
| | Adds the given quaternion to this quaternion. More...
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| constexpr quaternion & | operator-= (const quaternion &rhs) noexcept |
| | Subtracts the given quaternion from this quaternion. More...
|
| |
| constexpr quaternion & | operator*= (const quaternion &rhs) noexcept |
| | Multiplies this quaternion by the given quaternion. More...
|
| |
| constexpr quaternion & | operator*= (T rhs) noexcept |
| | Multiplies this quaternion by the given scalar. More...
|
| |
| constexpr quaternion & | operator/= (T rhs) noexcept |
| | Divides this quaternion by the given scalar. More...
|
| |
| constexpr quaternion & | invert () |
| | Inverts this quaternion. More...
|
| |
| constexpr quaternion & | conjugate () noexcept |
| | Conjugates this quaternion. More...
|
| |
| constexpr quaternion & | normalize () |
| | Normalizes this quaternion. More...
|
| |
template<typename T>
class hou::quaternion< T >
Represents a quaternion.
- Template Parameters
-
◆ value_type
◆ quaternion() [1/2]
Creates a quaternion with the elements initialized at the specified values.
- Parameters
-
| x | the first element of the vector part. |
| y | the second element of the vector part. |
| z | the third element of the vector part. |
| w | the scalar part. |
◆ quaternion() [2/2]
template<typename T >
template<typename U , typename Enable >
Creates a quaternion from a quaternion with different scalar type.
U must be convertible to T.
- Template Parameters
-
- Parameters
-
| other | the matrix to be copied. |
◆ conjugate()
Conjugates this quaternion.
- Returns
- a reference to this quaternion after the conjugation.
◆ data()
Retrieves a pointer to an array containing the elements of the quaternion.
The elements in the array are, in order, the three elements of the vector part and the scalar part.
/return a constant raw pointer to an array containing the elements.
◆ identity()
Returns the identity quaternion.
- Returns
- the identity quaternion.
◆ invert()
Inverts this quaternion.
- Exceptions
-
- Returns
- a reference to this quaternion after the inversion.
◆ normalize()
Normalizes this quaternion.
- Exceptions
-
- Returns
- a reference to this quaternion after the normalization.
◆ operator*=() [1/2]
Multiplies this quaternion by the given quaternion.
- Parameters
-
| rhs | the quaternion to be multiplied. |
- Returns
- a reference to this quaternion after the multiplication.
◆ operator*=() [2/2]
Multiplies this quaternion by the given scalar.
All elements of the quaternion are multiplied by the given scalar.
- Parameters
-
- Returns
- a reference to this quaternion after the multiplication.
◆ operator+=()
Adds the given quaternion to this quaternion.
The sum is element-wise.
- Parameters
-
| rhs | is the quaternion to be added. |
- Returns
- a reference to this quaternion after the addition.
◆ operator-=()
Subtracts the given quaternion from this quaternion.
The difference is element-wise.
- Parameters
-
| rhs | the quaternion to be subtracted. |
- Returns
- a reference to this quaternion after the subtraction.
◆ operator/=()
Divides this quaternion by the given scalar.
All elements of the quaternion are divided by the given scalar.
- Parameters
-
- Returns
- a reference to this quaternion after the division.
◆ w() [1/2]
Retrieves a copy of the scalar part.
/return a copy of the element.
◆ w() [2/2]
Retrieves a reference to the scalar part.
/return a reference to the element.
◆ x() [1/2]
Retrieves a copy of the first element of the vector part.
/return a copy of the element.
◆ x() [2/2]
Retrieves a reference to the first element of the vector part.
/return a reference to the element.
◆ y() [1/2]
Retrieves a copy of the second element of the vector part.
/return a copy of the element.
◆ y() [2/2]
Retrieves a reference to the second element of the vector part.
/return a reference to the element.
◆ z() [1/2]
Retrieves a copy of the third element of the vector part.
/return a copy of the element.
◆ z() [2/2]
Retrieves a reference to the third element of the vector part.
/return a reference to the element.
◆ zero()
Returns the zero quaternion.
- Returns
- the zero quaternion.
◆ operator* [1/2]
Multiplies a quaternion by a scalar.
- Parameters
-
| lhs | the left operator. |
| rhs | the right operator. |
- Returns
- the product of the quaternion and the scalar.
◆ operator* [2/2]
Multiplies a quaternion by a scalar.
- Parameters
-
| lhs | the left operator. |
| rhs | the right operator. |
- Returns
- the product of the quaternion and the scalar.
◆ operator-
Computes the opposite of a quaternion.
- Parameters
-
- Returns
- the opposite quaternion.
◆ operator/
Divides a quaternion by a scalar.
- Parameters
-
| lhs | the left operator. |
| rhs | the right operator. |
- Returns
- the quotient of the quaternion and the scalar.
The documentation for this class was generated from the following files:
- source/houmth/include/hou/mth/quaternion.hpp
- source/houmth/include/hou/mth/quaternion.inl


Homeless children
Sir, – The image of a homeless boy eating his dinner off cardboard and Kitty Holland’s article (November 20th) on the damage being done to the mental health of homeless children, are deeply upsetting.
The Government meanwhile are spending €17 million on refurbishing Leinster House on health and safety grounds. Government ministers should hang their heads in shame. – Yours, etc,
RICHARD COFFEY,
Dublin 6W.


In order to successfully create your game you must press the button.
If you don´t complete the section correctly, a notification reporting the problem will appear.
Changing certain thing in the section and not pressing the will cause the change to be forgotten.
If you want to edit the game later, you must your game .
Create your own game
add main img
Add game images
Minimal amount of images is 25, 30 is optimal. If there are more than 30, the game will randomly pick 30 images.
If the game has less than 25 images the game will be considered as "Game in development" which means that only you can play this game.
By clicking on image, you are allowed to change it.
Create attributes for game
If you are creating question-attribute game type do not use characters as (-, :, <, >, |, ., /, \\, ?, *, $, #, !, @, , ). The question will be formed as : Your image " is/has + part of your question (is a piano player / has a piano )" ?
By clicking on image, you are allowed to change it.
Example with image: | attribute : symbol | value/question : plus
Example without image: attribute : symbol | value/question : is plus
image attribute
attribute
value/question
delete
Describe images
Optimally each image on the right should have unique attributes from the left.
By clicking on image on the right, you will select it , then start clicking to attributes belonging to it.
When you hover over the picture on the right, their attributes will be highlighted on the left.
After clicking check button all not uniqly described images will be highlighted.
The attributes witch you selected will to image on the right will highlighted by green color
Attributes
Images
be sure every section was checked before saving
this game is
development state :
worst resemblance of the game :
lesser % of resemblance === better game experience for player, if the % is higher than 20%, please consider changing your automatic description in image section


TRY AGAINWe're sorry, but something went wrong. Please try again.No camera was found on your device.You have denied camera access permission.Camera is not available at this moment.Camera is already in use.Document not recognized.Please try again with a supported document or select a different document type.Unsupported file type.Please select one or more document types to start scanning.No document type selected.WebRTC not supported by your browser.Maximum file size is 15 MB.


Enumerations
The following enumerations are available globally.
-
Provides method for converting Callback based API into an async API
See moreDeclaration
Swift
@available(iOS 13.0, *) public enum AsyncHelper
-
Defines the the relation between the filter key and filter value of a metadata template.
See moreDeclaration
Swift
public enum MetadataFilterBound
-
Utility methods for common response handling
See moreDeclaration
Swift
public enum ResponseHandler
-
Defines character of the log and when it will be displayed
See moreDeclaration
Swift
public enum LogLevel : String
extension LogLevel: CustomStringConvertible
-
Defines log category based on what kind of data is being logged
See moreDeclaration
Swift
public enum LogCategory : String
extension LogCategory: CustomStringConvertible
-
Defines by which parameter should list of box items be ordered.
See moreDeclaration
Swift
public enum FolderItemsOrderBy : BoxEnum
extension FolderItemsOrderBy: QueryParameterConvertible
-
Represents HTTP methods.
See moreDeclaration
Swift
public enum HTTPMethod : String
-
Provides methods for deserialization JSON data to custom objects
See moreDeclaration
Swift
public enum ObjectDeserializer
-
Stores offset, marker or stream position for the iterator
See moreDeclaration
Swift
public enum PagingParameter
-
Type for updating optional parameters - parameters that can either have value of some type, or be nil. Should be used mainly with optinal method parameters to differentiate between ignored parameter and parameter with a nil value.
See moreDeclaration
Swift
public enum NullableParameter<T>
extension NullableParameter: Encodable where T: Encodable
extension NullableParameter: Equatable where T: Equatable
-
Operations on a file metadata
See moreDeclaration
Swift
public enum FileMetadataOperation
-
Operations on a folder metadata
See moreDeclaration
Swift
public enum FolderMetadataOperation
-
Specifies opperations available on metadata template
See moreDeclaration
Swift
public enum MetadataTemplateOperation


Team
The people behind Mined Minds
Founders and Trainers
Mined Minds was co-founded by Amanda Laucher and Jonathan Graham. Between them, they have a wealth of experience in software development, training, and consultancy. They both regularly travel around the globe to present at conferences, to teach, and to work with international clients.
Amanda and Jonathan are both passionate about supporting others in finding success and joy with programming, and in helping clients to become more successful through the use of quality and innovative technology.
Amanda Laucher
Amanda grew up in Nemacolin, Greene County, and has been passionate about programming since an early age. She has become a world-renowned software developer, often invited to travel around the globe to speak about the technology that she is excited about. Having been gone for many years, she now wants to return and give back to her home community.
Dr. Jonathan Graham
Jonathan has a passion for code, music, art and science, and he uses his varied background to find creative approaches to teaching and problem solving. He believes fundamental understanding is the basis of every solution. He can be found speaking at workshops and conferences around the world talking about the development and scalability of software as well as live-coding music.
Consultants
All consultants at Mined Minds have come through our extensive training program, and have a continual learning approach to programming. This means that they will apply the best technologies to meet your requirements, and will deliver quality software that meets your needs. The consultants work very closely with the co-founders at all times to ensure the very best possible solutions are delivered on time and on budget.


Content Creator
Whether you have been in the business for 20 years or you just sold your first piece this year, MediaReputations.com is ready to help you display your best work and expand your ability to do business with content acquirers from all over the world.


File Index
In this file the geometry element Curve is defined.
The JSXGraph object is defined in this file. JXG.JSXGraph controls all boards.
It has methods to create, save, load and free boards. Additionally some helper functions are
defined in this file directly in the JXG namespace.
type.js contains several functions to help deal with javascript's weak types. This file mainly consists
of detector functions which verify if a variable is or is not of a specific type and converter functions that convert
variables to another type or normalize the type of a variable.
Example file for a triangle implemented as a extension to JSXGraph.
The functions in this file help with the detection of the environment JSXGraph runs in. We can distinguish
between node.js, windows 8 app and browser, what rendering techniques are supported and (most of the time) if the device
the browser runs on is a tablet/cell or a desktop computer.
In this file the EventEmitter interface is defined.
expect.js provides utilities for parameter magic by normalizing multi-type parameters.
In this file the namespace JXG.Math is defined, which is the base namespace
for namespaces like Math.Numerics, Math.Algebra, Math.Statistics etc.
In this file the namespace Math.Numerics is defined, which holds numerical
algorithms for solving linear equations etc.
The JXG.Dump namespace provides methods to save a board to javascript.
In this file the namespace Math.Symbolic is defined, which holds methods
and algorithms for symbolic computations.
- Author:
- graphjs
This file contains the Math.Geometry namespace for calculating algebraic/geometric
stuff like intersection points, angles, midpoint, and so on.
In this file the namespace Math.Poly is defined, which holds algorithms to create and
manipulate polynomials.
A class for complex arithmetics JXG.Complex is defined in this
file. Also a namespace JXG.C is included to provide instance-independent
arithmetic functions.
JSXGraph can use various technologies to render the contents of a construction, e.g.
SVG, VML, and HTML5 Canvas. To accomplish this, The rendering and the logic and control mechanisms
are completely separated from each other. Every rendering technology has it's own class, called
Renderer, e.g. SVGRenderer for SVG, the same for VML and Canvas. The common base for all available
renderers is the class AbstractRenderer defined in this file.
JSXGraph can use various technologies to render the contents of a construction, e.g.
SVG, VML, and HTML5 Canvas. To accomplish this, The rendering and the logic and control mechanisms
are completely separated from each other. Every rendering technology has it's own class, called
Renderer, e.g. SVGRenderer for SVG, the same for VML and Canvas. The common base for all available
renderers is the class AbstractRenderer.
JessieCode is a scripting language designed to provide a simple scripting language to build constructions
with JSXGraph. It is similar to JavaScript, but prevents access to the DOM. Hence, it can be used in community driven
Math portals which want to use JSXGraph to display interactive math graphics.
The JXG.DataSource is a helper class for data organization. Currently supported data sources are
javascript arrays and HTML tables.
The JXG.Board class is defined in this file. JXG.Board controls all properties and methods
used to manage a geonext board like managing geometric elements, managing mouse and touch events, etc.
The JXG.Server is a wrapper for a smoother integration of server side calculations. on the
server side a python plugin system is used.
The JSXGraph object is defined in this file. JXG.JSXGraph controls all boards.
It has methods to create, save, load and free boards. Additionally some helper functions are
defined in this file directly in the JXG namespace.
- Version:
- 0.99
Utilities for uncompressing and base64 decoding
In this file the Coords object is defined, a class to manage all
properties and methods coordinates usually have.
The geometry object Point is defined in this file. Point stores all
style and functional properties that are required to draw and move a point on
a board.
The geometry object Line is defined in this file. Line stores all
style and functional properties that are required to draw and move a line on
a board.
In this file the class Group is defined, a class for
managing grouping of points.
The geometry object Circle is defined in this file. Circle stores all
style and functional properties that are required to draw and move a circle on
a board.
- Author:
- graphjs
- Version:
- 0.1
In this file the conic sections defined.
In this file the geometry object Ticks is defined. Ticks provides
methods for creation and management of ticks on an axis.
- Author:
- graphjs
- Version:
- 0.1
The JSXGraph object Turtle is defined. It acts like
"turtle graphics".
- Author:
- A.W.
In this file the geometry object Arc is defined.
This file contains code for transformations of geometrical objects.
- Author:
- graphjs
- Version:
- 0.1
Geometry objects for measurements are defined in this file. This file stores all
style and functional properties that are required to use a tape measure on
a board.
This file contains our composition elements, i.e. these elements are mostly put together
from one or more
JXG.GeometryElement but with a special meaning. E.g. the midpoint element is contained here
and this is just a
JXG.Point with coordinates dependent from two other points. Currently in this file the
following compositions can be found:
In this file the Text element is defined.
In this file the geometry element Image is defined.
The geometry object slider is defined in this file. Slider stores all
style and functional properties that are required to draw and use a slider on
a board.


Modelling of sensitivity and specificity on their natural scale using copula based bivariate beta-binomial distribution to yield marginal mean sensitivity and specificity. The intrinsic negative correlation between sensitivity and specificity is modelled using a copula function. A forest plot can be obtained for categorical covariates or for the model with intercept only. Nyaga VN, Arbyn M, Aerts M (2017) <doi:10.18637/jss.v082.c01>.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.4.2), rstan (≥ 2.16.2) |
| Imports: | methods, ggplot2 (≥ 2.2.1), plyr (≥ 1.8.4), stats (≥ 3.4.2), reshape2 (≥ 1.4.2), grDevices (≥ 3.4.2) |
| Suggests: | knitr, loo, Rmisc, httr, bayesplot |
| Published: | 2017-11-23 |
| Author: | Victoria N Nyaga [aut, cre] |
| Maintainer: | Victoria N Nyaga <victoria.nyaga at outlook.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | CopulaDTA citation info |
| CRAN checks: | CopulaDTA results |
| Reference manual: | CopulaDTA.pdf |
| Vignettes: |
CopulaDTA Package Vignette |
| Package source: | CopulaDTA_1.0.0.tar.gz |
| Windows binaries: | r-devel: CopulaDTA_1.0.0.zip, r-release: CopulaDTA_1.0.0.zip, r-oldrel: CopulaDTA_1.0.0.zip |
| OS X binaries: | r-release: CopulaDTA_1.0.0.tgz, r-oldrel: CopulaDTA_1.0.0.tgz |
| Old sources: | CopulaDTA archive |
Please use the canonical form https://CRAN.R-project.org/package=CopulaDTA to link to this page.


Implements cointegration/cotrending rank selection algorithm in Guo and Shintani (2013) "Consistant cotrending rank selection when both stochastic and nonlinear deterministic trends are present". The Econometrics Journal 16: 473-483 <doi:10.1111/j.1368-423X.2012.00392.x>. Numbered examples correspond to Feb 2011 preprint <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.
| Version: | 1.0.1 |
| Depends: | xts |
| Published: | 2018-03-19 |
| Author: | A. Christian Silva |
| Maintainer: | A. Christian Silva <a.christian.silva at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | cotrend results |
| Reference manual: | cotrend.pdf |
| Package source: | cotrend_1.0.1.tar.gz |
| Windows binaries: | r-devel: cotrend_1.0.1.zip, r-release: cotrend_1.0.1.zip, r-oldrel: cotrend_1.0.1.zip |
| OS X binaries: | r-release: cotrend_1.0.1.tgz, r-oldrel: cotrend_1.0.1.tgz |
| Old sources: | cotrend archive |
Please use the canonical form https://CRAN.R-project.org/package=cotrend to link to this page.


The Legend of Korra comics are a continuation of the Nickelodeon animated television series of The Legend of Korra, created by Michael Dante DiMartino and Bryan Konietzko. Chronologically, the comics take place after the series finale, "The Last Stand". They are published by Dark Horse Comics, the same company that publishes the Avatar: The Last Airbender comics. Short The Legend of Korra comics have appeared in Dark Horse's Free Comic Book Day 2016 and 2018 issues The Legend of Korra: Turf Wars is a three-part graphic novel series written by Michael Dante DiMartino, with art by Irene Koh. It serves as a canonical continuation of the animated television series The Legend of Korra, created by DiMartino and Bryan Konietzko. The graphic novel trilogy is set after the show's final season, with Avatar Korra dealing with the aftermath of Kuvira's attack on Republic City, while also exploring her new relationship with Asami Sato. Following the conclusion of The Legend of Korra, DiMartino was touched by positive reactions to Korra and Asami's relationship and felt that there were more stories to tell with the show's characters. Although the shift to the comic book medium was a challenge for DiMartino, it gave the creative team more freedom to explore LGBT narratives. The series was announced in July 2015 and The Legend of Korra: Turf Wars – Part One was published by Dark Horse Comics on July 26, 2017. Part Two was published on January 17, 2018, followed by Part Three on August 22, 2018. Part One and Two were the publisher's best-selling graphic novels of 2017 and 2018 respectively. A library edition collecting the three volumes was released on March 13, 2019. The Legend of Korra: Turf Wars received positive reviews for its handling of Korra and Asami's romance. Critics also praised DiMartino's writing and Koh's art for evoking the style of the original series. And my brother is the best.
Avatar Korra is the title lead character in Nickelodeon's animated television series The Legend of Korra (a sequel to Avatar: The Last Airbender), in which she is depicted as the current incarnation of Raava's Avatar the spiritual embodiment of balance and change responsible for maintaining peace and harmony in the world. She is the immediate reincarnation of Avatar Aang (title character and main protagonist from the original series). The character was created by Michael Dante DiMartino and Bryan Konietzko and is voiced by Janet Varney. Korra debuted in the first episode of The Legend of Korra, "Welcome to Republic City", originally airing on Nickelodeon on April 14, 2012. At the start of the series, she meets Bolin and Mako after arriving in Republic City, where she first experiences independence after living a secluded life of training, led by the Order of the White Lotus. Korra, now seventeen, and having mastered three of the elements, needs to learn airbending from the only living airbending master, Aang's son, Tenzin. Korra moves to Republic City to go after him following a brief encounter with Katara, being taken in by Tenzin and meeting both Mako and Bolin before joining their pro-bending team. After saving Bolin and having her first sighting of Amon, Korra's romantic struggles lead to difficulty with the pro-bending team, which the group recovers from to successfully win their match and make it to the finals, where they are defeated and Amon strikes with his group of followers, the Equalists. Korra afterward discovers Hiroshi Sato's ties to the Equalists, then allowing Asami Sato, Bolin and Mako to live with her, forming a team with the group that Councilman Tarrlok breaks up by arresting the others and revealing himself as a bloodbender in an encounter with Korra where he subdues her. She is locked in a secluded cabin that holds her for a short time before she breaks out once it is ambushed by Amon and his Equalists, the group launching an assault on Republic City that Korra and the others are overpowered in, forcing them to retreat until General Iroh and the United Forces come to the city as reinforcements, who are also overwhelmed. Following saving Tenzin and his children and seemingly losing her bending of three of the elements to Amon, Korra engages in a battle with Amon where she emerges victorious thanks to her airbending, having the other elements restored by Aang and in doing so gaining the power to restore bending as well. after six months of bending revolution she found out that she had a uncle.


Tree Open Stats
Current Open Times (%)
Tree Open Times (24hr Rolling Average)
Tree Open Times (7 Days Rolling Average)


VIRUS by DeWyNGaLe Rating: C+
On August 14th, 1998, Virus was supposed to be released. Six months later it was finally released in theaters. I hoped in this six months time, they improved the quality of the movie, but unfortunately, I wasn't impressed. The cast is great, the plot is original, and the setting isn't really all that bad. If a little more effort and time was put into Virus, it really could have been good. It may be another action flick on a boat, but it isn't the worst of them. William Baldwin, Jamie Lee Curtis, and Donald Sutherland star. When a mysterious force takes over a satellite in space, everything is killed. It travels through the computers, controlling them. The remains of the satellite plummet into a ship directly below it on earth. The ship is wiped out by this "virus", as it travels along.
An American ship containing a crew of researchers finds this ship. The crew contains Steve, played by William Baldwin, Foster, played by Jamie Lee Curtis, Captain Everton, played by Donald Sutherland, Richie, played by Sherman Augustus, Woods, played by Marshall Bell, Hiko, played by Cliff Curits, and Squeaky, played by Julio Oscar Mechoso. Their boat is sinking, so they must board the Russian ship that they see. They board the ship and find everything to be destroyed. They become scared when they find blood covering the walls. Strange things start going on on the ship. Little robot creatures that walk are abducting the cast one by one. The critters use their bodies to walk and talk and kill. The survivors find out that the aliens are trying to start a new species on earth, and they need humans and electricity to operate. They want to take over earth.
The crew goes through a lot to try to stop this form of life. They find a survivor from when the ship was taken over by the life form. Her name is Nadia, played by Joanna Pacula. She explains to the crew what is going on and how all this came to be. She explains the life form to them and tells them what happened when the species took over the ship. She is much help to them, and she tries her best to tell them what is going on. They must sink the ship to get rid of them all. They plan on detonating the ship to sink it, but the aliens discover the plan and destroy the detonator. More twists and turns arise, but Virus pretty much sticks on one thing for way too long. Not many things go on during Virus, which makes it boring to watch.
The thing that kept Virus from becoming good was the acting. William Baldwin and Donald Sutherland do pathetic jobs of acting. The acting was fake, and you could tell they were acting. You couldn't believe their roles at all. They didn't let you feel the terror that they were going through unlike Jamie Lee Curtis did. Sherman Augustus also did a good job of acting. Richie was the only character that I cared lived or not. Most of the characters are underdeveloped and unlikable. Another thing that was bad about Virus was the fact that it was extremely predictable. I could tell what was going to happen at every moment.
It may sound like I hated Virus, but there was a bright side. The concept was very good, and it could have gone a long way with a better cast. The special effects were great. They looked very realistic, and it seemed like they weren't even produced by computers. The aliens look very realistic and creepy. Some of the concepts may be extremely unoriginal, but all in all the alien concept was pretty good. Virus could have been great, but it turned out to be another pointless horror/sci-fi movie.
The Bottom Line- Special effects aren't everything.
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


Class SUGARClient
Inheritance
System.Object
SUGARClient
Inherited Members
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.ToString()
Assembly: PlayGen.SUGAR.Client.dll
Syntax
Constructors
SUGARClient(String, IHttpHandler, Boolean, Dictionary<String, String>, Dictionary<String, String>, Int32)
Declaration
public SUGARClient(string baseAddress, IHttpHandler httpHandler = null, bool asyncEnabled = true, Dictionary<string, string> persistentHeaders = null, Dictionary<string, string> sessionHeaders = null, int timeoutMilliseconds = 60000)
Parameters
| Type |
Name |
Description |
| System.String |
baseAddress |
|
| IHttpHandler |
httpHandler |
|
| System.Boolean |
asyncEnabled |
|
| System.Collections.Generic.Dictionary<System.String, System.String> |
persistentHeaders |
|
| System.Collections.Generic.Dictionary<System.String, System.String> |
sessionHeaders |
|
| System.Int32 |
timeoutMilliseconds |
|
Fields
_accountClient
Declaration
protected AccountClient _accountClient
Field Value
_achievementClient
Declaration
protected AchievementClient _achievementClient
Field Value
_allianceClient
Declaration
protected AllianceClient _allianceClient
Field Value
_apiVersionClient
Declaration
protected APIVersionClient _apiVersionClient
Field Value
_asyncRequestController
Declaration
protected readonly IAsyncRequestController _asyncRequestController
Field Value
_baseAddress
Declaration
protected readonly string _baseAddress
Field Value
| Type |
Description |
| System.String |
|
_evaluationNotifications
Declaration
protected readonly EvaluationNotifications _evaluationNotifications
Field Value
_gameClient
Declaration
protected GameClient _gameClient
Field Value
_gameDataClient
Declaration
protected GameDataClient _gameDataClient
Field Value
_groupClient
Declaration
protected GroupClient _groupClient
Field Value
_groupMemberClient
Declaration
protected GroupMemberClient _groupMemberClient
Field Value
_httpHandler
Declaration
protected readonly IHttpHandler _httpHandler
Field Value
_leaderboardClient
Declaration
protected LeaderboardClient _leaderboardClient
Field Value
_matchClient
Declaration
protected MatchClient _matchClient
Field Value
Declaration
protected readonly Dictionary<string, string> _persistentHeaders
Field Value
| Type |
Description |
| System.Collections.Generic.Dictionary<System.String, System.String> |
|
_resourceClient
Declaration
protected ResourceClient _resourceClient
Field Value
_sessionClient
Declaration
protected SessionClient _sessionClient
Field Value
Declaration
protected readonly Dictionary<string, string> _sessionHeaders
Field Value
| Type |
Description |
| System.Collections.Generic.Dictionary<System.String, System.String> |
|
_skillClient
Declaration
protected SkillClient _skillClient
Field Value
_userClient
Declaration
protected UserClient _userClient
Field Value
_userFriendClient
Declaration
protected UserFriendClient _userFriendClient
Field Value
Properties
Account
Declaration
public AccountClient Account { get; }
Property Value
Achievement
Declaration
public AchievementClient Achievement { get; }
Property Value
AllianceClient
Declaration
public AllianceClient AllianceClient { get; }
Property Value
APIVersion
Declaration
public APIVersionClient APIVersion { get; }
Property Value
Game
Declaration
public GameClient Game { get; }
Property Value
GameData
Declaration
public GameDataClient GameData { get; }
Property Value
Group
Declaration
public GroupClient Group { get; }
Property Value
GroupMember
Declaration
public GroupMemberClient GroupMember { get; }
Property Value
Leaderboard
Declaration
public LeaderboardClient Leaderboard { get; }
Property Value
Match
Declaration
public MatchClient Match { get; }
Property Value
Resource
Declaration
public ResourceClient Resource { get; }
Property Value
Session
Declaration
public SessionClient Session { get; }
Property Value
Skill
Declaration
public SkillClient Skill { get; }
Property Value
User
Declaration
public UserClient User { get; }
Property Value
UserFriend
Declaration
public UserFriendClient UserFriend { get; }
Property Value
Methods
TryExecuteResponse()
Declaration
public bool TryExecuteResponse()
Returns
| Type |
Description |
| System.Boolean |
|


HAMLET A film review by Ivana Redwine Copyright 1997 Ivana Redwine
Kenneth Branagh, Derek Jacobi, Julie Christie, Richard Briers, Nicholas Farrell, Michael Maloney, Brian Blessed, Rufus Sewell, Billy Crystal, Jack Lemmon, Charlton Heston, Gerard Depardieu, Rosemary Harris, Timothy Spall, Reece Dinsdale, Robin Williams. Directed by Kenneth Branagh. Screenplay Kenneth Branagh, based on William Shakespeare's play.  Running Time: 238 Minutes.
WARNING: This review contains spoilers.
Until I saw Kenneth Branagh's 238-minute, full-text version of HAMLET, I thought nothing could rival Laurence Olivier's classic 1948 film version. I've always loved Olivier's subtle, sensitive, and poetic performance as Hamlet, in addition to the way that his film wrapped everything in a moody black and white chiaroscuro. But Branagh has created a powerful masterpiece that is psychologically complex and lushly cinematic. Never have all the nuances of Shakespeare's story been more vibrantly examined on screen. I predict that Branagh's HAMLET will eventually become the definitive film version of Shakespeare's play; yet, I make this prediction with some degree of sadness because Olivier's version has always been one of my favorite movies.
Of course, Branagh had advantages that Olivier could only dream of in 1948, including a studio willing to finance and release a version that is nearly four hours long. With an hour and 20 minutes less to tell the story, Olivier's version is less dramatically coherent and omits some rather important minor characters, notably Rosencrantz and Guildenstern. Also, Branagh had at his disposal the magician's bag of technical tricks available to today's filmmaker, including digital special effects. I only hope that people who are excited by Branagh's HAMLET might be inspired to take a look at Olivier's film in order to compare different takes on Shakespeare's classic story.
Both Olivier and Branagh have managed to breathe the kind of life into the title role that makes for a classic film. Branagh's interpretation of Hamlet differs greatly from Olivier's, but then there is no correct way to play the role. Olivier's Hamlet is focused on the character's melancholia and indecisiveness, while Branagh encompasses a greater emotional range, running through a spectrum from an almost serene state of intellectual contemplation to an intense anger that threatens to tear his sanity apart. Thus, there are more nuances in Branagh's portrayal than in Olivier's.
Branagh's film is a veritable feast for the eyes with dazzling cinematography that is all the more vivid because of the crystalline clarity and trompe l'oeil depth of the 70 mm format. And Branagh makes excellent use of color in his film, made credible in part by changing the setting of the story to the latter half of the nineteenth century. This change also justifies impressive location shooting at Blenheim Palace, which plays the role of Shakespeare's Elsinore Castle. However, as richly visual as this film is, its dynamic visual aspects never detract from the substance of the story or the beauty of Shakespeare's words.
At the core of the artistry in Branagh's movie is that all the elements of film making come together in a seamlessly integrated work. Where Branagh's film outshines Olivier's is in its intricacy and in its ability to bring image, word, performance, and music into a gestalt of pure genius.
The story of HAMLET has to be one of the most famous in the English language, but I'll refresh your memory with a broad synopsis. The film's focal character is the Danish prince Hamlet, who is deeply disturbed over his mother Gertrude's (Julie Christie) quick marriage to Claudius (Derek Jacobi), the brother of Hamlet's dead father. Soon, Hamlet has an encounter with what appears to be his father's ghost, who informs him that Claudius murdered him and that it is now Hamlet's duty to take revenge for this murder by killing Claudius. The tragedy then grows out of Hamlet's delay in carrying out this revenge.
Branagh's film illuminates an often overlooked aspect of the story, namely that it is not merely a personal tragedy for Hamlet, but it is also a tragedy for Denmark and its people. Because of Hamlet's indecisiveness, the Danes must endure the consequences of a political power vacuum and corrupt leadership. Then, near the film's end, there are scenes of Hamlet fighting a duel in Elsinore Castle interspersed with scenes of Fortinbras, Prince of Norway, and his army closing in on the Castle. At the end of the duel, the Danish Prince, King, and Queen all lie dead and the fate of Denmark and its people has fallen into the hands of Fortinbras.
Branagh's HAMLET makes good use of the classic Shakespearean motif of illusion and reality. In a scene that filled me with terror, Hamlet meets the ghost of his father (played menacingly by Brian Blessed). Special effects along with excellent acting and direction make the ambiguity between specter and hallucination come alive on the screen. There is a supernaturally sharp focus to this scene, and Blessed's eyes are a deathly shade of blue and strangely distorted.  The ghost's highly processed voice (it reminded me at times of Darth Vader's voice in STAR WARS) rumbles across the surrounding area, cracking open the ground under Hamlet's feet. When the ghost instructs Hamlet to "Remember me!", there is little doubt that Hamlet could ever forget.
The difficulty in distinguishing between illusion and reality is clearly a factor in Hamlet's delay in killing the King. Before carrying out this act of revenge, Hamlet must determine the veracity of what he believes he saw and heard. Was it a hallucination, an apparition, or even the devil in disguise? Or was it the actual ghost of his father's spirit? Dramatic tension is maintained throughout the film; and until the dueling scene near the movie's end, it is neither clear nor simple when or whether Hamlet should take action on the ghost's mandated revenge.
Another strong scene that also echoes the motif of appearance and reality is when Hamlet "tests the conscience of the king" by altering a play put on by a traveling group of players to mirror the ghost's description of the murder. Hamlet hopes that Claudius's reaction will determine if the ghost was telling the truth. Hamlet feigns an "antic disposition" to camouflage his actions and protect himself from Claudius, who poses a real threat to him if he knows that Hamlet suspects him of regicide. Branagh's performance is what makes this scene so dynamic. While waiting to see how Claudius reacts to the play, Hamlet struggles within himself trying to contain an anger that is tearing his sanity apart. As the scene progresses, it is not entirely clear whether the madness he is putting on is an act anymore.
Every aspect of Branagh's production, even set design, enhances meaning and emotional impact. The main gallery of Elsinore Castle contains a hall of mirrors that cast a dizzying myriad of reflections and conceal doors leading to secret rooms--a breathtaking visual metaphor that again echoes the motif of illusion and reality. It is to one of these mirrors that Branagh delivers the famous "To be, or not to be" soliloquy. As the soliloquy progresses, the camera comes in tighter and tighter on the reflection until it appears to be Hamlet himself who is speaking and not his reflected image.
Another strength of this film is how it uses visually stunning images to intensify dramatic impact. For example, in one scene Hamlet slowly inserts a blade aimed at the head of Claudius who is alone at prayer. On the surface, it appears to be the perfect time to act out the revenge. As Hamlet debates the merits of killing the repentant Claudius, the blade inches closer to the head of Claudius who is oblivious to the threat that is just a hair's-breadth away. Hamlet decides that killing Claudius would be an unsatisfactory form of revenge since Claudius's soul would go to heaven since he is at prayer, and the blade is then slowly withdrawn. The knife being inserted through the screen adds power and dramatic tension, making an already thrilling scene burn white-hot with emotion.
Branagh's performance is magnetic; his performance acts as a center of gravity. Yet, the other performances are strong enough not to be overwhelmed. Branagh assembled an international cast of the highest caliber, and most of the performances are exemplary. I'll comment on a few that impressed me most. Derek Jacobi's Claudius has a regal and controlled surface demeanor that occasionally cracks to reveal the dangerous monster lurking just underneath his public facade. Claudius here is a cold-blooded, pragmatic man, and Jacobi makes him as bone-chilling as the snow-covered landscape just outside the castle walls. The character of Gertrude has so often been portrayed as weak-willed and passive, but Julie Christie plays this role with a hedonistic charisma that speaks to Claudius's motivation--as evil as his actions are, they are not beyond the pale of credibility given the attractiveness of Christie's Gertrude. Kate Winslet is well cast as Ophelia, bringing an air of vulnerability to the role, which, when combined with flashbacks showing a sexual relationship between Hamlet and Ophelia, makes the character's eventual emotional breakdown more believable to contemporary audiences.
Added to the mix of Shakespearian actors, there are quite a few recognizable faces from Hollywood--Charlton Heston, Billy Crystal, Robin Williams, Jack Lemmon. With the exception of Jack Lemmon, who is badly miscast as Marcellus, their performances hold up well against the competition. Heston is a wonderful choice for the role of the Player King; his rich voice and clear delivery are perfect for Shakespeare. And Crystal's considerable comedic gifts make the conversation between the two grave diggers sparkle with wit and humor.
The four-hour version of HAMLET was released on Christmas Day in New York, Toronto, and Los Angeles, in the 70 mm format, and a shorter, 35 mm version will also be released nationwide at the end of January, 1997. Given the positive response to the longer version, I hope that it will also remain in release, at least in larger cities and university towns. With its visual vividness, emotional depth, and complexity, the longer version is the one I recommend. Don't let the length scare you; the longer version is a highly entertaining film. Plus, a film like this produces an event atmosphere where you can catch a theater-like spark of excitement in the air during intermission. For Shakespeare lovers this is a dream come true; if you are not a Shakespeare lover, this might just turn you into one. Don't miss one of the film experiences of a lifetime--HAMLET has never before been realized with such power, passion, and pathos.
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
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Related links: index of all rec.arts.movies.reviews reviews


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Template types
[GLM Core]
The generic template types used as the basis for the core types.
More...
These types are all templates used to define the actual
Types. These templetes are implementation details of GLM types and should not be used explicitly.


This function creates a matrix of clusters starting from a list of heterogeneous datasets.
buildMOC( data, M, K = NULL, maxK = 10, methods = "hclust", distances = "euclidean", fill = FALSE, computeAccuracy = FALSE, fullData = FALSE, savePNG = FALSE, fileName = "buildMOC", widestGap = FALSE, dunns = FALSE, dunn2s = FALSE )
| data | List of M datasets, each of size N X P_m, where m = 1, ..., M. |
|---|---|
| M | Number of datasets. |
| K | Vector containing the number of clusters in each dataset. If given an integer instead of a vector it is assumed that each dataset has the same number of clusters. If NULL, it is assumed that the true cluster numbers are not known, therefore they will be estimated using the silhouette method. |
| maxK | Vector of maximum cluster numbers to be considered for each dataset if K is NULL. If given an integer instead of a vector it is assumed that for each dataset the same maximum number of clusters must be considered. Default is 10. |
| methods | Vector of strings containing the names of the clustering methods to be used to cluster the observations in each dataset. Each can be "kmeans" (k-means clustering), "hclust" (hierarchical clustering), or "pam" (partitioning around medoids). If the vector is of length one, the same clustering method is applied to all the datasets. Default is "hclust". |
| distances | Distances to be used in the clustering step for each dataset. If only one string is provided, then the same distance is used for all datasets. If the number of strings provided is the same as the number of datasets, then each distance will be used for the corresponding dataset. Default is "euclidean". Please note that not all distances are compatible with all clustering methods. "euclidean" and "manhattan" work with all available clustering algorithms. "gower" distance is only available for partitioning around medoids. In addition, "maximum", "canberra", "binary" or "minkowski" are available for k-means and hierarchical clustering. |
| fill | Boolean. If TRUE, if there are any missing observations in one or more datasets, the corresponding cluster labels will be estimated through generalised linear models on the basis of the available labels. |
| computeAccuracy | Boolean. If TRUE, for each missing element, the performance of the predictive model used to estimate the corresponding missing label is computer. |
| fullData | Boolean. If TRUE, the full data matrices are used to estimate the missing cluster labels (instead of just using the cluster labels of the corresponding datasets). |
| savePNG | Boolean. If TRUE, plots of the silhouette for each datasets are saved as png files. Default is FALSE. |
| fileName | If |
| widestGap | Boolean. If TRUE, compute also widest gap index to choose best number of clusters. Default is FALSE. |
| dunns | Boolean. If TRUE, compute also Dunn's index to choose best number of clusters. Default is FALSE. |
| dunn2s | Boolean. If TRUE, compute also alternative Dunn's index to choose best number of clusters. Default is FALSE. |
This function returns a list containing:
the Matrix-Of-Clusters, a binary matrix of size N x sum(K) where element (n,k) contains a 1 if observation n belongs to the corresponding cluster, 0 otherwise.
a vector of length sum(K) in which each element is the number of the dataset to which the cluster belongs.
the total number of NAs in the matrix of clusters. (If the
MOC has been filled with imputed values, number_nas
indicates the
number of NAs in the original MOC.)
a matrix that is equivalent to the matrix of clusters, but is in compact form, i.e. each column corresponds to a dataset, each row represents an observation, and its values indicate the cluster labels.
vector of cluster numbers in each dataset. If these are provided as input, this is the same as the input (expanded to a vector if the input is an integer). If the cluster numbers are not provided as input, this vector contains the cluster numbers chosen via silhouette for each dataset.
The Cancer Genome Atlas, 2012. Comprehensive molecular portraits of human breast tumours. Nature, 487(7407), pp.61–70.
Rousseeuw, P.J., 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, pp.53-65.
# Load data data <- list() data[[1]] <- as.matrix(read.csv(system.file("extdata", "dataset1.csv", package = "coca"), row.names = 1)) data[[2]] <- as.matrix(read.csv(system.file("extdata", "dataset2.csv", package = "coca"), row.names = 1)) data[[3]] <- as.matrix(read.csv(system.file("extdata", "dataset3.csv", package = "coca"), row.names = 1)) # Build matrix of clusters outputBuildMOC <- buildMOC(data, M = 3, K = 6, distances = "cor") # Extract matrix of clusters matrixOfClusters <- outputBuildMOC$moc


4
1 Therefore, my beloved brothers whom I long for, my joy and crown, in this way stand firm in the Lord, beloved friends.
2 I am pleading with Euodia, and I am pleading with Syntyche, be of the same mind in the Lord.
3 Indeed, I am entreating you also, my true yokefellow, help these women, for they labored with me in spreading the gospel along with Clement and the rest of my fellow workers, whose names are in the book of life.
4 Rejoice in the Lord always; again I will say, rejoice.
5 Let all people recognize your gentleness. The Lord is near.
6 Do not be anxious about anything, but instead in everything by prayer, request from God what you need and thank him.
7 And the peace of God, which exceeds all understanding, will guard your hearts and your thoughts in Christ Jesus.
8 Finally, brothers, whatever things are true, whatever things are honorable, whatever things are just, whatever things are pure, whatever things are lovely, whatever things are of good report, if there is any excellence in them, and if there is any praise in them, think on these things.
9 Do the things that you have learned and received and heard and seen me do, and the God of peace will be with you.
10 I greatly rejoice in the Lord because now at last you have renewed your concern for me. Although you were thinking of me before, there was no opportunity for you to help.
11 I am not referring to my needs, for I have learned to be content in all circumstances.
12 I know how to live in times of need, and I also know how to live in times of abundance. In every way I have learned the secret how both to have plenty and to be hungry, how both to abound and to need.
13 I can do all things through him who strengthens me.
14 However, you did well to share with me in my difficulties.
15 You Philippians know that in the beginning of the gospel mission, when I left from Macedonia, no church supported me in the matter of giving and receiving except you only.
16 Even when I was in Thessalonica, you sent help for my needs more than once.
17 Not that I seek the gift, but I do seek the fruit that increases to your account.
18 I have all things in abundance. I am filled. I have received from Epaphroditus the things that came from you.  The brothers who are with me greet you.
22 All the believer here greet you, especially those of Caesar's household.
23 The grace of the Lord Jesus Christ be with your spirit.


First, I recommend to install the LDG kit available on the
official LDG site.
Next, download the
LDV kit+documentation
To make an LDV, you need:
Warning! By default, the development kit provided by VISION includes these files for the PureC compiler. I strongly recommend to update regularly the .H and .LIB according to the evolution of LDGs.
The tree proposed in VISION LDV kit is the following:
| Folder / Sub folder | Files |
| <..>/LDV | For LDG management: LDG.H: Librairies dynamiques GEM, header file .H MT_AES.H: To use LDG/LDV under a multi tasking OS. In fact, this file is useful only if you use AES in the LDV, which is unlikely at the moment, but perhaps soon... LDG.LIB, MT_AES.LIB: to be linked with your LDV For LDV management: LDV.H: Definition of structures and interfaces with VISION VAPI.H: Definition of structures and interfaces with VAPI |
| <...>/LDV/SRC | Each LDV is there in a directory associated with
its name. In such a directory, there is: <name of ldv>.C: the "source" file of the LDV <name of ldv>.PRJ: the project file (Pure C) |
| <...>/LDV/DOC | This documentation |
You can use for the skeleton of an LDV any source of an LDV found in the kit. It is up to you to see which file suits best the LDV you want to make!
The VISION kit provides the following LDVs as well as the sources (in PureC). Here are their characteristics:
| INVERT.LDV | |
| Description | Inversion of bits of an image |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, 24 and 32). Machine format. Does not modify the palette. |
| Transformation type | In place. This property can also be suppressed from the CAPS define, and VISION can be forced to allocate memory for the destination (teaching only purpose!) |
| Uses VAPI | No |
| INI file | Yes (English / French) |
| Comment | Very simple! Uses only vro_cpyfm VDI function Not really useful: VISION already provides the "Négative" function since version 1.0! |
| XWAVE.LDV / YWAVE.LDV | |
| Description | Waves on X / Y axis |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, 24 and 32). Machine format. Does not modify the palette. |
| Transformation type | Forces VISION to allocate memory for destination. It must be possible to do without, but this complicates a little the LDV... |
| Uses VAPI | Yes (Progress + Raster Interface) |
| INI file | Yes (English / French) |
| Comment | Amusing and quite simple. Presents the VAPI progress interface and the function to "clear" an image (RaImgWhite). |
| BW.LDV | |
| Description | Conversion of an image (or part in True colour) to an image in shades of grey |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, 24 and 32). Machine format. Modifies the palette. |
| Transformation type | In place |
| Uses VAPI | Yes (Progress + Raster Interface) |
| INI file | Yes (English / French) |
| Comment | Uses True colour conversion format functions (RaTCConvert and RaTCInvConvert). Shows also how to modify the palette. Compared to the equivalent VISION (B&W conversion) function, this LDV allows to do it on a block (True Colour mode only) |
| PIXEL.LDV | |
| Description | Performs a pixelization on an image or part of it |
| Capabilities | True Color modes only (16 and 32 planes). ATARI. format |
| Transformation type | In place |
| Uses VAPI | Yes (Progress Interface) |
| INI file | Yes (English / French) |
| Comment | Nice effect and good for learning: this LDV only considers True Color ATARI format and it will nevertheless run on your MagicPC ! It makes use of VISION feature to deal with format handling and only focuses on a specific format. Hadnles cancel feature. |
| YPERSC.LDV | |
| Description | Performs a perspective effect on an image or part of it |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, 24 and 32). ATARI format. Does not change palette. |
| Transformation type | Forces VISION to allocate memory for destination. |
| Uses VAPI | Oui (Progress Interface+ Raster Interface + MEM Interface) |
| INI file | Yes (English / French) |
| Comment | Nice perspective effect. When operating on selection, the allocation bloc is set thanks to LDVF_NOSELECTION flag (pretty painful to handle in bitplane mode). This LDV only gets an image and not bloc selection.MEM Interface function MeCopyMemoryxx is used to scale lines. |
| LIGHT.LDV / GAMMA.LDV | |
| Description | Change red, green, blue color spreading using a line (light) or a gamma curve (gamma). |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, 24 and 32). Machine format. Modifies the palette. |
| Transformation type | In place |
| Uses VAPI | Yes (Progress Interface+ Raster Interface) |
| INI file | Yes (English / French) |
| Comment | Smart replace for old embedded functions in
VISION..In bitplane mode, palette is changed. Handles cancel. These LDVs are pretty optimized for memory performances: they operate in place on machine specific format and can work on a bloc inside the image.(true colro only). These LDVs are very similar : only internal function ChangeColors is specific to a LDV. |
| ROTATE.LDV | |
| Description | Performs a rotation on the image or part of it. |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, 24 and 32). Machine format. Does not change the palette. |
| Transformation type | Forces VISION to allocate memory for destination. |
| Uses VAPI | Yes (Progress Interface+ Raster Interface) |
| INI file | Yes (English / French) |
| Comment | This LDV is very simple. Actually it just calls RaRasterRotateGetDstMFDB and RaRasterRotate
functions in VAPI Raster interface . Have a look how LDVF_NOSELECTION flag is used in order to not complicate things with blocs. |
| PUZZLE.LDV | |
| Description | Mix image blocs to generate a puzzle |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, 24 and 32). Machine format. Does not change the palette. |
| Transformation type | Forces VISION to allocate memory for destination. |
| Uses VAPI | Yes (Progress Interface) |
| INI file | Yes (English / French) |
| Comment | Fun LDV easy to code. Smartest thing is entropy handling i.e. how much disorder there is in the mix. |
| BHOLE.LDV | |
| Description | Simulates a black hole in an image. Consider it as a tribute to Stephen Hawking. |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, 24 and 32). Machine format. Does not change the palette. |
| Transformation type | Forces VISION to allocate memory for destination. |
| Uses VAPI | Yes (Progress Interface+Config Interface+Logging Interface) |
| INI file | Yes (English / French) |
| Comment | Most complicated LDV so far. Has its own optimzations for 68030 and 68881. Uses black hole symetry to speed up computations. |
| CNTCOLLDV | |
| Description | Count the number of different colors on each line and for the whole image |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16 and 32). Machine format. Does not change the palette. Does not alter the image. |
| Transformation type | No change on image. Generates cntcol.txt file in VISION's folder or the file specified by parameter#2 |
| Uses VAPI | Yes (Progress Interface+MEM Interface + Raster Interface +Logging Interface) |
| INI file | Yes (English) |
| Comment | Inspired by http://www.atari-forum.com/viewtopic.php?f=27&t=38492 |
| GENIMG.LDV | |
| Description | Generates as many different colors as possible on an image (tester for previous LDV) |
| Capabilities | Any number of bitplans (1, 2, 4, 8, 16, and 32). Machine format. Does not change the palette. |
| Transformation type | Forces VISION to allocate memory for destination. |
| Uses VAPI | Yes (Progress Interface+Raster Interface) |
| INI file | Yes (English) |
| Comment | Ugly image generated. |


Bitcoin Explaining in Simple Terms
Crypto newcomers have a mountain of questions.
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And how can you invest?
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This guide was written for someone who has little to no technical knowledge about Bitcoin, crypto, or blockchains.
To aid accessibility, some of the technical concepts have been simplified to make it easier for a non-technical person to grasp the essential information.
This eBook is completely free and can be downloaded by completing a simple form on our partner’s website. By doing so, you will also be subscribed to our email newsletter.
What is Bitcoin?
Why should you care about crypto?
What makes Bitcoin so unique?
How does it work?
And how can you invest?
The purpose of this eBook is to answer those questions by introducing you to Bitcoin and other cryptocurrencies in simple terms and an easy-to-digest manner.
This guide was written for someone who has little to no technical knowledge about Bitcoin, crypto, or blockchains.
To aid accessibility, some of the technical concepts have been simplified to make it easier for a non-technical person to grasp the essential information.
This eBook is completely free and can be downloaded by completing a simple form on our partner’s website. By doing so, you will also be subscribed to our email newsletter.
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Machine Learning: Classification
Ana Lleshi, , for Web Development Course @Aueb
What is classification?
Classification is the problem of identifying to which of a set of classes (categories) an instance, (or instances) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.), these characteristics are called features.
Our problem of concern, classification in an example of the more general problem of pattern recognition, which is the assignment of some sort of output value (category/ class) to a given data input value. The assignment is conducted by automated discovery of regularities in the input data through the use of computer algorithms and with the use of these regularities to take the mentioned action, classifying the data into different categories.
Classification also belongs to the category of supervised learning. Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time.
Learners
Since classification is a category of supervised learning, the algorithms used for classifying data are frequently mentioned as learners. There are two types of learners in classification as lazy learners and eager learners.
Lazy learners
Lazy learners are the ones in which generalization of the training data is, in theory, delayed until a query is made to the system. A widely known example of an eager learner is the k-nearest neighbors algorithm (k-NN), which will be introduced later on this article.
Eager Learners
On the contrary, what happens when using eager learners is that the system tries to generalize the training data before receiving queries. A widely known example of a lazy learner family is the Naive Bayes classifiers, which will be introduced later on this article.
Classification algorithms examples
In this section we will talk about 2 classification algorithm examples, one for each learning category, as mentioned previously.
k-NN algorithm
The k-nearest neighbors algorithm (k-NN) is a non-parametric classification method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover.
The input consists of the k closest training examples in data set. The output is a class membership, an object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.
Naive Bayes classifiers
Naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
The classification is conducted by deriving the maximum posterior which is the maximal P(Ci|X) with the above assumption applying to Bayes theorem. This assumption greatly reduces the computational cost by only counting the class distribution. Even though the assumption is not valid in most cases since the attributes are dependent, surprisingly Naive Bayes has able to perform impressively.
Naive Bayes can suffer from a problem called the zero probability problem. When the conditional probability is zero for a particular attribute, it fails to give a valid prediction. This needs to be fixed explicitly using a Laplacian estimator.
Sources/ extra reads:
- Wiki: Lazy Learning
- Wiki: Eager Learning
- Wiki: k-NN algorithm
- Wiki: Naive Bayes Classifiers
- Wiki: Naive Statistical classification
- Article: TowardsDS ML Classifiers