My Life
God gifted me three precious things in my life.First country, second family and third life itself.My life has always been centered around the only thought of serving my country, making my parents proud and working selflessly for my countrymen.I am a staunch believer and supporter of my idol the legend,the supreme commander of INA,Netaji Subhas Chandra Bose.I have learnt a lot from him in my life. I completed my schooling from Don Bosco School Liluah wherein 13 years I enjoyed the warmth of a family.The virtues of selflessness,kindness, compassion,discipline are the ones which i learnt from my school.
My hobbies include reading books,solving puzzles,playing chess. Adding to this, i have a great appetite for travelling. in my school life i have received certificates for merit in the field of academics followed by debating.I had been associated with the Quiz club of my school.Some of the virtues for which i have always been praised in my school are punctuality,time management and leadership qualities.That's all for now but in a nutshell I am the kind of person who is a man for all seasons.


This page is being served by dynserver, a dynamic catalogue server for the UK TSB IoT Demonstrator project.
This server provides a single catalogue on /cat. The catalogue is a list of resources items, each with a URI and a set of metadata. The metadata itself is a list of URIs and values, which describe the catalogue item.
The catalogue may be filtered to show just items matching href, rel or val by using query parameters. One, two or three parameters may be given.
An item may be inserted into the catalogue using HTTP POST. The data submitted is an object to go into the items array. Submitted data must have content type "application/json". With this server, POST must be accompanied by Basic Auth credentials of username:"MAGICKEY", password:"". For example:
curl -v http://localhost:8001/cat --user MAGICKEY: -XPOST \ --header "Content-Type: application/json" \ --data '{"href":"http://www.example.org/mytest", "metadata":[{"rel":"urn:X-tsbiot:hasDescription:en","val":"My test"}]}'
An existing catalogue item may be modified using HTTP PUT. The data submitted is an object to go into the items array. A query parameter of href must be given to identify the item to modify. Submitted data must have content type "application/json". With this server, PUT must be accompanied by Basic Auth credentials of username:"MAGICKEY", password:"". For example:
curl -v http://localhost:8001/cat?href=http://www.example.org/mytest \ --user MAGICKEY: -XPUT \ --header "Content-Type: application/json" \ --data '{"href":"http://www.example.org/mytest", "metadata":[{"rel":"urn:X-tsbiot:hasDescription:en","val":"Updated description"}]}'
An existing catalogue item may be removed using HTTP DELETE. With this server, DELETE must be accompanied by Basic Auth credentials of username:"MAGICKEY", password:"". For example:
curl -v http://localhost:8001/cat?href=http://www.example.org/mytest \ --user MAGICKEY: -XDELETE
The following catalogue shows a catalogue containing two items, "thing1" and "thing2". The items are identified by unique URIs. The metadata "rel" and "val" pairs make statements about the items.
{ "metadata":[ { "rel":"urn:X-tsbiot:rels:isContentType", "val":"application/vnd.tsbiot.catalogue+json" }, { "rel":"urn:X-tsbiot:rels:hasDescription:en", "val":"my home" } ], "items":[ { "href":"http://www.example.org/thing1", "metadata":[ { "rel":"urn:X-tsbiot:rels:hasDescription:en", "val":"Thing 1" }, { "rel":"http://www.example.org/ontologies/fiction#character", "val":"urn:isbn:9780394800011" }, { "rel":"http://xmlns.com/foaf/0.1/knows", "val":"http://www.example.org/thing2" } ] }, { "href":"http://www.example.org/thing2", "metadata":[ { "rel":"urn:X-tsbiot:rels:hasDescription:en", "val":"Thing 2" }, { "rel":"http://xmlns.com/foaf/0.1/knows", "val":"http://www.example.org/thing1" } ] } ] }


First Post! November 25, 2019
It's late, but here's your First Post!
by Asif KhanHey Shacknews, it's time for First Post! Sorry for being so late, I was on a Shacknews.com dev call. Please understand.  Please take a look.
Sakurai was confused by Smash Ultimate GOTY 2019 nomination
The tweet translated says something like, "Why is the Smash Bros. SP last year's game nominated? When I thought, The Game Awards last year was one day before the release date. This is where Joker stole the invitation. I was also impressed by the performance of the orchestra." Will we see another Smash DLC character reveal at this year's The Game Awards? Only Geoff Keighley knows.
Cybertruck memes continue to spread across the Internet
So many dank memes are popping off of the Cybertruck design. Share your favorites with me!
Disney is taking down dank Baby Yoda gifs and memes
Love seeing Star Wars surprised by Disney acting like the Empire. Dank memes will prevail!
Browns owner Dee Haslam shows silent support for suspended DE Myles Garrett
This is the first time that I have actually liked either of the Haslams. Oh yeah, the Browns beat the Dolphins too.
In case you missed it at Shacknews:
- 9to5 Elon - Episode 07: Tesla Cybertruck broken window fail memes & hot takes
- Google responds to criticism over Stadia performance issues
- Red Bull's Anakin on Red Bull Conquest, the Tekken World Tour, Arslan Ash, and more
- How to get all starter Pokemon in Pokemon Sword and Shield
There you have it, Shacknews. Your First Post! for November 25, 2019. Please consider subscribing to Shacknews Mercury to support our site for as little as $1/month. Here is a photo of Lola to brighten your day.
What are you up to this week? Let us know in the Shacknews Chatty comment thread below.


If you have an idea and need help developing it, get in touch with us. Together we can achieve the goal that we propose
Make a pull of some of our projects and work together with more professionals and share your knowledge
Our only goal is to share knowledge, meet people and learn something new every day


PEGASUS
User
New User
List of users
Category
New category
List of category
Findings
New finding
List of findings
Protected areas
New protected area
List of protected areas
Configuration
Figures
Public map
Log
Configuration
Finding to update
Fill in all the data
Basic Data
Number
Name of conflict
Country
Date
State or Province
Type of affected population
Source of Conflict
Activity affected or activity used for/related to the drug trafficking (Type of conflict 1st level)
Specific Commodity*
Conflict details and actors
Relevant government Actors
Relevant private sector actors
Relevant EJOs/actors
Supporters
The Conflict and Mobilization
Intensity of the conflict
Start of the conflict
Groups affected
Forms of mobilization
Environmental impacts
Health impacts
Socio-economic impacts
Drug Trade Link with the Conflict
Status of Confirmation
Mode of Connection
Trafficking groups
Type of route
HotSpot related
Sources
Quotes or another comments to reinforce
Loss of value in ecosystem services by country
State in the system
Select
Inactive
Active
Keywords
Update data


com.anji.neat
contains classes implementing
NEAT
framework, including genes, mutation operators, and fitness function used for XOR. Also includes subtractive mutations added to NEAT by James and Tucker for GECCO 2004.


Go, Node and PHP hacker, E46 fanatic, badass and vagabond. Resides in Brisbane, Australia.
Follow @{{ site.author.twitter }}


Contact form :
Contact info:
First Name:
Last Name:
Phone Number:
e-mail:
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Message:
Submit button


My name is Ilya Ivanchikov
LODASH is the library with a huge number of functions for working with various data structures.
LODASH is the JavaScript utility library that helps programmers write more compact and easy-to-maintain JavaScript code.
“Work smart, not hard.”
On the LODASH website you can select the SDN link in the download section and then direct links to the JS file will appear.
Need to download the file lodash.js and connect it next to the html page. The connection path will be as follows
There is an index .html file in which the script is connected:
The following lines are written in the entry.js file
Natively “forEach” is commonly uses like this.
Now the same with “each” LODASH”.


| Number of Classes | Line Coverage | Mutation Coverage | Test Strength |
|---|---|---|---|
| 6 | 56% | 28% | 100% |
| Name | Line Coverage | Mutation Coverage | Test Strength |
|---|---|---|---|
| AbstractFreeMarkerTemplateLoaderOptionsAdapter.java | 75% |
0% |
0% |
| ClassPathResolverAdapter.java | 92% |
89% |
100% |
| FileResolverAdapter.java | 21% |
0% |
0% |
| FirstSupportingResolverAdapter.java | 96% |
33% |
100% |
| StringContentTemplateLoader.java | 0% |
0% |
0% |
| StringResolverAdapter.java | 25% |
0% |
0% |


---
layout: talk_reveal3
title: SARS-CoV-2 variants of concern
venue: Fred Hutch
author: Trevor Bedford
widescreen: true
---
Trevor Bedford (@trvrb)
{{ page.title }}
Associate Professor
{{ page.venue }}, Seattle, WA
{{ page.date | date: "%-d %b %Y" }}
Slides at: bedford.io/talks
1. Evolutionary origins of variant viruses
2. Implications for unfolding pandemic
Evolutionary origins
Over 300k SARS-CoV-2 genomes shared and evolution tracked in real-time at nextstrain.org
Spike protein is critical for cell invasion by the virus and is the primary target for human immune response
484K and 501Y repeatedly emerging across the world
Emergence of 501Y.V1 in the UK
Emergence of 501Y.V2 in the South Africa
Emergence of 501Y.V3 in the Brazil
Three occurences makes a pattern.
Working hypothesis of rare events occurring during chronic infection,
driven by natural selection for immune escape.
Pandemic implications
Three different phenotypic traits may be affected


GeoMesa Lambda Quick Start¶
This tutorial can get you started with the GeoMesa Lambda data store. Note that the Lambda data store is for advanced use-cases - see Overview of the Lambda Data Store for details on when to use a Lambda store.
About this Tutorial¶
In the spirit of keeping things simple, the code in this tutorial only does a few small things:
- Establishes a new (static) SimpleFeatureType
- Prepares the Accumulo table and Kafka topic to write this type of data
- Creates a few thousand example SimpleFeatures
- Repeatedly updates these SimpleFeatures in the Lambda store through Kafka
- Visualize the changing data in GeoServer
- Persists the final SimpleFeatures to Accumulo
- Uses GeoServer to visualize the data (optional)
Background¶
Apache Kafka is “publish-subscribe messaging rethought as a distributed commit log.”
In the context of GeoMesa, Kafka is a useful tool for working with streams of geospatial data. The Lambda data store leverages a transient in-memory cache of recent updates, powered by Kafka, combined with long-term persistence to Accumulo. This allows for rapid data updates, alleviating the burden on Accumulo from constant deletes and writes.
Prerequisites¶
Before you begin, you must have the following installed and configured:
- Java JDK 1.8
- Apache Maven 3.5.2 or later
- a GitHub client
- a Kafka instance version 0.9.x or later
- an Accumulo 1.9.1 or later instance
- an Accumulo user that has both create-table and write permissions
- the GeoMesa distributed runtime installed for your instance (see below)
Ensure your Kafka and Zookeeper instances are running. You can use Kafka’s quickstart to get Kafka/Zookeeper instances up and running quickly.
Installing the GeoMesa Distributed Runtime¶
Follow the instructions under Installing the Accumulo Distributed Runtime Library to install GeoMesa in your Accumulo instance.
Configure GeoServer (optional)¶
You can use GeoServer to access and visualize the data stored in GeoMesa. In order to use GeoServer, download and install version 2.12.x. Then follow the instructions in Installing GeoMesa Lambda in GeoServer to enable GeoMesa.
Download and Build the Tutorial¶
Pick a reasonable directory on your machine, and run:
$ git clone https://github.com/geomesa/geomesa-tutorials.git
$ cd geomesa-tutorials
Warning
Make sure that you download or checkout the version of the tutorials project that corresponds to your GeoMesa version. See About Tutorial Versions for more details.
To ensure that the quick start works with your environment, modify the pom.xml
to set the appropriate versions for Accumulo, Hadoop, Kafka, Zookeeper, etc.
For ease of use, the project builds a bundled artifact that contains all the required dependencies in a single JAR. To build, run:
$ mvn clean install -pl geomesa-tutorials-accumulo/geomesa-tutorials-accumulo-lambda-quickstart -am
About this Tutorial¶
The quick start operates by writing several thousand feature updates. The same feature identifier is used for each update, so there will only be a single “live” feature at any one time. After approximately 30 seconds, the updates stop and the feature is persisted to Accumulo.
The data used is from New York City taxi activity data published by the University of Illinois. More information about the dataset is available here.
For this demo, only a single taxi is being tracked.
Running the Tutorial¶
On the command line, run:
$ java -cp geomesa-tutorials-accumulo/geomesa-tutorials-accumulo-lambda-quickstart/target/geomesa-tutorials-accumulo-lambda-quickstart-${geomesa.version}.jar \
com.example.geomesa.lambda.LambdaQuickStart \
--lambda.accumulo.instance.id <instance> \
--lambda.accumulo.zookeepers <accumulo.zookeepers> \
--lambda.accumulo.user <user> \
--lambda.accumulo.password <password> \
--lambda.accumulo.catalog
 \
--lambda.kafka.brokers <brokers> \
--lambda.kafka.zookeepers <kafka.zookeepers> \
--lambda.expiry 2s
where you provide the following arguments:
<instance>
the name of your Accumulo instance<accumulo.zookeepers>
your Accumulo Zookeeper nodes, separated by commas<user>
the name of an Accumulo user that has permissions to create, read and write tables<password>
the password for the previously-mentioned Accumulo user

the name of the destination table that will accept these test records. This table should either not exist or should be empty<brokers>
your Kafka broker instances, comma separated. For a local install, this would belocalhost:9092
<kafka.zookeepers>
your Kafka Zookeeper nodes, comma separated. For a local install, this would belocalhost:2181
Warning
If you have set up the GeoMesa Accumulo distributed
runtime to be isolated within a namespace (see
Namespace Install) the value of

should include the namespace (e.g. myNamespace.geomesa
).
Optionally, you can also specify that the quick start should delete its data upon completion. Use the
--cleanup
flag when you run to enable this behavior.
Once run, the quick start will create the Kafka topic, then pause and prompt you to register the layer in GeoServer. If you do not want to use GeoServer, you can skip this step. Otherwise, follow the instructions in the next section before returning here.
Once you continue, the tutorial should run for approximately thirty seconds. You should see the following output:
Loading datastore
Creating schema: taxiId:String,dtg:Date,geom:Point
Feature type created - register the layer 'tdrive-quickstart' in geoserver then hit <enter> to continue
Generating test data
Writing features to Kafka... refresh GeoServer layer preview to see changes
Wrote 2202 features
Waiting for expiry and persistence...
Total features: 1, features persisted to Accumulo: 0
Total features: 0, features persisted to Accumulo: 0
Total features: 1, features persisted to Accumulo: 1
Done
Visualize Data With GeoServer (optional)¶
You can use GeoServer to access and visualize the data stored in GeoMesa. In order to use GeoServer, download and install version 2.12.x. Then follow the instructions in Installing GeoMesa Lambda in GeoServer to enable GeoMesa.
Register the GeoMesa Store with GeoServer¶
Log into GeoServer using your user and password credentials. Click
“Stores” and “Add new Store”. Select the Kafka/Accumulo Lambda (GeoMesa)
vector data
source, and fill in the required parameters.
Basic store info:
workspace
this is dependent upon your GeoServer installationdata source name
pick a sensible name, such asgeomesa_quick_start
description
this is strictly decorative;GeoMesa quick start
Connection parameters:
- these are the same parameter values that you supplied on the command line when you ran the tutorial; they describe how to connect to the Kafka and Accumulo instances where your data reside
Click “Save”, and GeoServer will search Zookeeper for any GeoMesa-managed feature types.
Publish the Layer¶
If you have already run the command to start the tutorial, then GeoServer should recognize the
tdrive-quickstart
feature type, and should present that as a layer that can be published. Click on the
“Publish” link. If not, then run the tutorial as described above in Running the Tutorial. When
the tutorial pauses, go to “Layers” and “Add new Layer”. Select the GeoMesa Lambda store you just
created, and then click “publish” on the tdrive-quickstart
layer.
You will be taken to the Edit Layer screen. You will need to enter values for the data bounding boxes. For this demo, use the values MinX: 116.22366, MinY: 39.72925, MaxX: 116.58804, MaxY: 40.09298.
Click on the “Save” button when you are done.
Take a Look¶
Click on the “Layer Preview” link in the left-hand gutter. If you don’t
see the quick-start layer on the first page of results, enter the name
of the layer you just created into the search box, and press
<Enter>
.
At first, there will be no data displayed. Once you have reached this point, return to the quick start console and hit “<enter>” to continue the tutorial. As the data is updated in Kafka, you can refresh the layer preview page to see the feature moving around.
Transient vs Persistent Features¶
The layer preview will merge the results of features from Kafka with features from Accumulo. You may disable
results from one of the source by using the viewparams
parameter:
...&viewparams=LAMBDA_QUERY_TRANSIENT:false
...&viewparams=LAMBDA_QUERY_PERSISTENT:false
While the quick start is running, all the features should be returned from the transient store (Kafka). After the quick
start finishes, all the feature should be returned from the persistent store (Accumulo). You can play with the
viewparams
to see the difference.
Looking at the Code¶
The source code is meant to be accessible for this tutorial. The logic is contained in
the generic org.geomesa.example.quickstart.GeoMesaQuickStart
in the geomesa-tutorials-common
module,
and the Kafka/Accumulo-specific org.geomesa.example.lambda.LambdaQuickStart
in the
geomesa-tutorials-accumulo-lambda-quickstart
module. Some relevant methods are:
createDataStore
get a datastore instance from the input configurationcreateSchema
create the schema in the datastore, as a pre-requisite to writing datawriteFeatures
overridden in theKafkaQuickStart
to simultaneously write and read features from KafkaqueryFeatures
not used in this tutorialcleanup
delete the sample data and dispose of the datastore instance
Looking at the source code, you can see that normal GeoTools FeatureWriters
are used; feature persistence
is managed transparently for you.
The quickstart uses a small subset of taxi data. Code for parsing the data into GeoTools SimpleFeatures is
contained in org.geomesa.example.data.TDriveData
:
getSimpleFeatureType
creates theSimpleFeatureType
representing the datagetTestData
parses an embedded CSV file to createSimpleFeature
objectsgetTestQueries
not used in this tutorial
Re-Running the Quick Start¶
The quick start relies on not having any existing state when it runs. This can cause issues with older versions
of Kafka, which by default do not delete topics when requested. To re-run the quick start, first ensure that your Kafka
instance will delete topics by setting the configuration delete.topic.enable=true
in your server properties.
Then use the Lamdba command-line tools (see Setting up the Lambda Command Line Tools) to remove the quick start schema:
$ geomesa-lambda remove-schema -f tdrive-quickstart ...