When a landlord agrees to reserve rental property, the landlord will probably ask the tenant for a “deposit.” This transaction obligates the tenant to occupy the property on the agreed date, and obligates the landlord to have the property fit for occupancy on the agreed date. If the tenant fails to occupy the property on the agreed date, then the tenant could lose the deposit and may be liable for damages. If the landlord fails to fulfill his obligation, the landlord will have to refund the deposit and may be liable for damages.
A security or damage deposit may be required by the landlord to satisfy nonpayment of rent or any other default, including physical damage to the property. The landlord may keep the deposit for such defaults. However, normal wear and tear is not deductible. If the deposit is not enough to cover all damages, then the tenant may be held responsible for additional damages.
Before putting down a deposit on the property, the tenant should inspect the property for any visible defects, damage or missing items. When the tenant signs an agreement to lease the property, the landlord and the tenant should agree on what problems are to be corrected before the tenant moves in.


See: Description
| Class | Description |
|---|---|
| SoyFileSet |
Represents a complete set of Soy files for compilation as one bundle.
|
| SoyFileSet.Builder |
Builder for a
SoyFileSet . |
| SoyModule |
Guice module for Soy's programmatic interface.
|
| SoyMsgExtractor |
Executable for extracting messages from a set of Soy files into an output messages file.
|
| SoyMsgPruner |
Executable for pruning messages from extracted msgs files, given a set of Soy files as reference
for which messages to keep.
|
| SoyParseInfoGenerator |
Executable for generating Java classes containing Soy parse info.
|
| SoyToJsSrcCompiler |
Executable for compiling a set of Soy files into corresponding JS source files.
|
| SoyUtils |
Public utilities for Soy users.
|
SoyFileSet
. The Guice module for all of Soy (excluding
any message plugin) is SoyModule
.


{% extends 'base.html' %} {% block content %}
Hey you! My friend, it appears to me that you did something wrong. :(


Trinacoin
Home
Technology
GitHub
Block Explorer
Download
how to create a web page
Trinacoin (TRIC)
Cryptocurrency Project
GitHub
Download Wallet (Windows)
Contacts


Daniel Lisko
A Master's student of Computer Science at Northeastern Univeristy where I hope to become a A Full Stack Web Developer looking to integrate experience in computer science with prior experiences in Microbiome and T-cell Biology .
Microbiome
Interested in understanding how microbes can be used as away of promoting human health.
Immunology
My interest in the microbiome led me to T-cell biology to understand how our immune system interacts with commensal microbes.
Programming
My exposure to R during my biological studies exposed me to a new found interst in computer science. Now as a computer science major I hope to push biological research forward.


Image Coordinates Extraction Project
Image Coordinates Extraction Project is a django project to select elements from a image
Functionalities
- Create a category
- Create a template format
- Upload image document
- Zoom in and zoom out on an image
- Draw and Select (rectangular) elements from the image
- Auto-suggest position of a newly created element based on its name
Installation
It is advised to install all the requirements inside virtualenv, use virtualenvwrapper to manage virtualenvs
pip install -r requirements.txt
python manage.py migrate --noinput
Running the server
Start the python server by running python manage.py runserver
command in the root folder of the project.
Check the server running on http://localhost:8000/
End User
Acronyms
- Category : The umbrella unit. e.g. Cafe, School, Hospital, Grocery Store, etc.
- Template Format/ Format/ Template : Refers to the particular type of format of documents. e.g. for category 'Cafe', formats can be CCD, Barista, Starbucks, etc.
- Document : The image file for which the elements are to be extracted
- Element : The part of the document that is selected
Creating a category
Go to /category/add
and fill up the form
Go to /template/add
and fill up the form
Uploading a document
Go to /upload-document
and fill up the form
Selecting elements from the document
- Image can be zoomed in/ zoomed out by scrolling over image area.
You can zoom in at a particular point by keeping the mouse pointer to that particular point
- To create an element, either you can add it by selecting the element name from the dropdown on top of the right hand side, or you can create an element by clicking on the image
- If an element is selected, its border will turn red and its corresponding entry in the output will have red background
- To change the name of the element, select the element for which you want to change the name. Click on the name of the object, and type the new name in the modal box that opens. To save, click save element button
Saving the elements to the database
- Once you have created/ selected elements on the document, click on Update Selection button
Preview the document/ update the document
- Document can be previewed or updated from
/document/preview/(ID-of-the-document)
link
{%endblock%}


Now integrated in BBLog Dev Version 1.6
All Team Members can now test it.
MooViking
Looks awesome man.
ORFK
Global Moderator
Background is cool, but i don't like the header/footer :-/
Topicstarter
xXJumpyXx
Helping Hand
ORFK wrote:
Background is cool, but i don't like the header/footer :-/
Thanks for the feedback :)
I'll see what I can do in future version.
If you like the background, make sure you drop by k4Orta's deviantART profile, he made the background.
http://k4orta.deviantart.com/
Topicstarter
xXJumpyXx
Helping Hand
Updated the theme to 1.0.4 :P
It includes some nice stuff, look in the changelog at the main post.
Topicstarter
xXJumpyXx
Helping Hand
As we can all see, Battlelog got a pretty sweet new facelift. This new facelift of course broke all the BBLog themes and BBLog itself. I'll be working on fixing the Glow theme these coming days.
DBD-Revo
Is there a way to stretch the background image? because at the moment I just have a 1080p image but it doesn't stretch to fit my 1440p screen.
Topicstarter
xXJumpyXx
Helping Hand
DBD-Revo wrote:
Is there a way to stretch the background image? because at the moment I just have a 1080p image but it doesn't stretch to fit my 1440p screen.
I'm not the image creator so I wont edit the picture in any way. All I can do is to make it repeat but that would look bad.
If I think of something I might try it but as it is now there's nothing I can do.
xXChuckNorri5Xx
xXJumpyXx wrote:
DBD-Revo wrote:
Is there a way to stretch the background image? because at the moment I just have a 1080p image but it doesn't stretch to fit my 1440p screen.
I'm not the image creator so I wont edit the picture in any way. All I can do is to make it repeat but that would look bad.
If I think of something I might try it but as it is now there's nothing I can do.
try this one:
#background {
/* Set rules to fill background */
min-height: 100%;
min-width: 1024px;
/* Set up proportionate scaling */
width: 100%;
height: auto;
/* Set up positioning */
position: fixed;
top: 0;
left: 0;
}
Topicstarter
xXJumpyXx
Helping Hand
xXChuckNorri5Xx wrote:
try this one:
#background {
/* Set rules to fill background */
min-height: 100%;
min-width: 1024px;
/* Set up proportionate scaling */
width: 100%;
height: auto;
/* Set up positioning */
position: fixed;
top: 0;
left: 0;
}
Thanks, I'll try it out.
EDIT: Didn't work, thanks for giving it a shot, though :)


Step 11) (Swapping a sprites Image at any time) You might have a need in some animations to actually swap out one image with another at some point. This can be done by simply scrolling to the point in the animation you'd like an image changed, selecting the sprite by left clicking on it in the canvas and then right-clicking any image in the file palette. When you play the animation or scroll through the time-line you'll see that the sprite now changes from its original images to the new one you'd selected at that exact moment in the animation.
IMPORTANT: When swapping, a new image will be placed in the exact location of the current image based on their respective pivot points, so it's very important to have the default pivot-point of the new image set appropriately. One way to ensure perfect continuity when swapping from one image to the next is to actually insure that each image to be swapped has the same exact pixel-dimensions as each other, and are arranged so that all of their default pivot points would have the same exact pixel coordinate. Look at all these head images as an example... all images are buffered with enough transparent pixels to ensure the actual head images are placed per image in perfect alignment with each other... so the exact same default pivot-point coordinates can be set for all...ensuring there will be no jarring “pop” in position as one image is switched to the next.


About the library
SiaNet is developed with keeping in mind to reduce the burden of learning mathmatical intricacies. You will find the similarity with popular python library "Keras". It is a high-level deep learning library developed in C#. Similar to Keras it will support various backend: CNTK, TensorFlow, MxNet, ArrayFire, TensorSharp etc. It is designed to enable fast experimentation with deep neural networks with focus on user-friendly API, modularity and extensibility.
Few dot point of SiaNet
- Easy to use API, just focus on research
- Multiple backends � ArrayFire (In Progress), TensorSharp (In Progress), CNTK (Not Started), TensorFlow (Not Started), MxNet (Not Started)
- CUDA / OpenCL support for some of the backends
- Lightweight library, built with .NET standard 2.0
- Code well structured, easy to extend if you would like to extend with new layer, loss, metrics, optimizers, constraints, regularizer
The project is open sourced and here is the link to GitHub project: https://github.com/deepakkumar1984/SiaNet/


Kafka Connector Tutorial#
Introduction#
The Kafka Connector for Presto allows access to live topic data from Apache Kafka using Presto. This tutorial shows how to set up topics and how to create the topic description files that back Presto tables.
Installation#
This tutorial assumes familiarity with Presto and a working local Presto installation (see Deploying Presto). It will focus on setting up Apache Kafka and integrating it with Presto.
Step 1: Install Apache Kafka#
Download and extract Apache Kafka.
Note
This tutorial was tested with Apache Kafka 0.8.1. It should work with any 0.8.x version of Apache Kafka.
Start ZooKeeper and the Kafka server:
$ bin/zookeeper-server-start.sh config/zookeeper.properties
[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)
...
$ bin/kafka-server-start.sh config/server.properties
[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)
...
This will start Zookeeper on port 2181
and Kafka on port 9092
.
Step 2: Load data#
Download the tpch-kafka loader from Maven central:
$ curl -o kafka-tpch https://repo1.maven.org/maven2/de/softwareforge/kafka_tpch_0811/1.0/kafka_tpch_0811-1.0.sh
$ chmod 755 kafka-tpch
Now run the kafka-tpch
program to preload a number of topics with tpch data:
$ ./kafka-tpch load --brokers localhost:9092 --prefix tpch. --tpch-type tiny
2014-07-28T17:17:07.594-0700 INFO main com.facebook.airlift.log.Logging Logging to stderr
2014-07-28T17:17:07.623-0700 INFO main de.softwareforge.kafka.LoadCommand Processing tables: [customer, orders, lineitem, part, partsupp, supplier, nation, region]
2014-07-28T17:17:07.981-0700 INFO pool-1-thread-1 de.softwareforge.kafka.LoadCommand Loading table 'customer' into topic 'tpch.customer'...
2014-07-28T17:17:07.981-0700 INFO pool-1-thread-2 de.softwareforge.kafka.LoadCommand Loading table 'orders' into topic 'tpch.orders'...
2014-07-28T17:17:07.981-0700 INFO pool-1-thread-3 de.softwareforge.kafka.LoadCommand Loading table 'lineitem' into topic 'tpch.lineitem'...
2014-07-28T17:17:07.982-0700 INFO pool-1-thread-4 de.softwareforge.kafka.LoadCommand Loading table 'part' into topic 'tpch.part'...
2014-07-28T17:17:07.982-0700 INFO pool-1-thread-5 de.softwareforge.kafka.LoadCommand Loading table 'partsupp' into topic 'tpch.partsupp'...
2014-07-28T17:17:07.982-0700 INFO pool-1-thread-6 de.softwareforge.kafka.LoadCommand Loading table 'supplier' into topic 'tpch.supplier'...
2014-07-28T17:17:07.982-0700 INFO pool-1-thread-7 de.softwareforge.kafka.LoadCommand Loading table 'nation' into topic 'tpch.nation'...
2014-07-28T17:17:07.982-0700 INFO pool-1-thread-8 de.softwareforge.kafka.LoadCommand Loading table 'region' into topic 'tpch.region'...
2014-07-28T17:17:10.612-0700 ERROR pool-1-thread-8 kafka.producer.async.DefaultEventHandler Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.region
2014-07-28T17:17:10.781-0700 INFO pool-1-thread-8 de.softwareforge.kafka.LoadCommand Generated 5 rows for table 'region'.
2014-07-28T17:17:10.797-0700 ERROR pool-1-thread-3 kafka.producer.async.DefaultEventHandler Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.lineitem
2014-07-28T17:17:10.932-0700 ERROR pool-1-thread-1 kafka.producer.async.DefaultEventHandler Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.customer
2014-07-28T17:17:11.068-0700 ERROR pool-1-thread-2 kafka.producer.async.DefaultEventHandler Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.orders
2014-07-28T17:17:11.200-0700 ERROR pool-1-thread-6 kafka.producer.async.DefaultEventHandler Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.supplier
2014-07-28T17:17:11.319-0700 INFO pool-1-thread-6 de.softwareforge.kafka.LoadCommand Generated 100 rows for table 'supplier'.
2014-07-28T17:17:11.333-0700 ERROR pool-1-thread-4 kafka.producer.async.DefaultEventHandler Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.part
2014-07-28T17:17:11.466-0700 ERROR pool-1-thread-5 kafka.producer.async.DefaultEventHandler Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.partsupp
2014-07-28T17:17:11.597-0700 ERROR pool-1-thread-7 kafka.producer.async.DefaultEventHandler Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.nation
2014-07-28T17:17:11.706-0700 INFO pool-1-thread-7 de.softwareforge.kafka.LoadCommand Generated 25 rows for table 'nation'.
2014-07-28T17:17:12.180-0700 INFO pool-1-thread-1 de.softwareforge.kafka.LoadCommand Generated 1500 rows for table 'customer'.
2014-07-28T17:17:12.251-0700 INFO pool-1-thread-4 de.softwareforge.kafka.LoadCommand Generated 2000 rows for table 'part'.
2014-07-28T17:17:12.905-0700 INFO pool-1-thread-2 de.softwareforge.kafka.LoadCommand Generated 15000 rows for table 'orders'.
2014-07-28T17:17:12.919-0700 INFO pool-1-thread-5 de.softwareforge.kafka.LoadCommand Generated 8000 rows for table 'partsupp'.
2014-07-28T17:17:13.877-0700 INFO pool-1-thread-3 de.softwareforge.kafka.LoadCommand Generated 60175 rows for table 'lineitem'.
Kafka now has a number of topics that are preloaded with data to query.
Step 3: Make the Kafka topics known to Presto#
In your Presto installation, add a catalog properties file
etc/catalog/kafka.properties
for the Kafka connector.
This file lists the Kafka nodes and topics:
connector.name=kafka
kafka.nodes=localhost:9092
kafka.table-names=tpch.customer,tpch.orders,tpch.lineitem,tpch.part,tpch.partsupp,tpch.supplier,tpch.nation,tpch.region
kafka.hide-internal-columns=false
Now start Presto:
$ bin/launcher start
Because the Kafka tables all have the tpch.
prefix in the configuration,
the tables are in the tpch
schema. The connector is mounted into the
kafka
catalog because the properties file is named kafka.properties
.
Start the Presto CLI:
$ ./presto --catalog kafka --schema tpch
List the tables to verify that things are working:
presto:tpch> SHOW TABLES;
Table
----------
customer
lineitem
nation
orders
part
partsupp
region
supplier
(8 rows)
Step 4: Basic data querying#
Kafka data is unstructured and it has no metadata to describe the format of the messages. Without further configuration, the Kafka connector can access the data and map it in raw form but there are no actual columns besides the built-in ones:
presto:tpch> DESCRIBE customer;
Column | Type | Extra | Comment
-------------------+---------+-------+---------------------------------------------
_partition_id | bigint | | Partition Id
_partition_offset | bigint | | Offset for the message within the partition
_key | varchar | | Key text
_key_corrupt | boolean | | Key data is corrupt
_key_length | bigint | | Total number of key bytes
_message | varchar | | Message text
_message_corrupt | boolean | | Message data is corrupt
_message_length | bigint | | Total number of message bytes
(11 rows)
presto:tpch> SELECT count(*) FROM customer;
_col0
-------
1500
presto:tpch> SELECT _message FROM customer LIMIT 5;
_message
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
{"rowNumber":1,"customerKey":1,"name":"Customer#000000001","address":"IVhzIApeRb ot,c,E","nationKey":15,"phone":"25-989-741-2988","accountBalance":711.56,"marketSegment":"BUILDING","comment":"to the even, regular platelets. regular, ironic epitaphs nag e"}
{"rowNumber":3,"customerKey":3,"name":"Customer#000000003","address":"MG9kdTD2WBHm","nationKey":1,"phone":"11-719-748-3364","accountBalance":7498.12,"marketSegment":"AUTOMOBILE","comment":" deposits eat slyly ironic, even instructions. express foxes detect slyly. blithel
{"rowNumber":5,"customerKey":5,"name":"Customer#000000005","address":"KvpyuHCplrB84WgAiGV6sYpZq7Tj","nationKey":3,"phone":"13-750-942-6364","accountBalance":794.47,"marketSegment":"HOUSEHOLD","comment":"n accounts will have to unwind. foxes cajole accor"}
{"rowNumber":7,"customerKey":7,"name":"Customer#000000007","address":"TcGe5gaZNgVePxU5kRrvXBfkasDTea","nationKey":18,"phone":"28-190-982-9759","accountBalance":9561.95,"marketSegment":"AUTOMOBILE","comment":"ainst the ironic, express theodolites. express, even pinto bean
{"rowNumber":9,"customerKey":9,"name":"Customer#000000009","address":"xKiAFTjUsCuxfeleNqefumTrjS","nationKey":8,"phone":"18-338-906-3675","accountBalance":8324.07,"marketSegment":"FURNITURE","comment":"r theodolites according to the requests wake thinly excuses: pending
(5 rows)
presto:tpch> SELECT sum(cast(json_extract_scalar(_message, '$.accountBalance') AS double)) FROM customer LIMIT 10;
_col0
------------
6681865.59
(1 row)
The data from Kafka can be queried using Presto but it is not yet in
actual table shape. The raw data is available through the _message
and
_key
columns but it is not decoded into columns. As the sample data is
in JSON format, the JSON Functions and Operators built into Presto can be used
to slice the data.
Step 5: Add a topic description file#
The Kafka connector supports topic description files to turn raw data into
table format. These files are located in the etc/kafka
folder in the
Presto installation and must end with .json
. It is recommended that
the file name matches the table name but this is not necessary.
Add the following file as etc/kafka/tpch.customer.json
and restart Presto:
{
"tableName": "customer",
"schemaName": "tpch",
"topicName": "tpch.customer",
"key": {
"dataFormat": "raw",
"fields": [
{
"name": "kafka_key",
"dataFormat": "LONG",
"type": "BIGINT",
"hidden": "false"
}
]
}
}
The customer table now has an additional column: kafka_key
.
presto:tpch> DESCRIBE customer;
Column | Type | Extra | Comment
-------------------+---------+-------+---------------------------------------------
kafka_key | bigint | |
_partition_id | bigint | | Partition Id
_partition_offset | bigint | | Offset for the message within the partition
_key | varchar | | Key text
_key_corrupt | boolean | | Key data is corrupt
_key_length | bigint | | Total number of key bytes
_message | varchar | | Message text
_message_corrupt | boolean | | Message data is corrupt
_message_length | bigint | | Total number of message bytes
(12 rows)
presto:tpch> SELECT kafka_key FROM customer ORDER BY kafka_key LIMIT 10;
kafka_key
-----------
0
1
2
3
4
5
6
7
8
9
(10 rows)
The topic definition file maps the internal Kafka key (which is a raw long
in eight bytes) onto a Presto BIGINT
column.
Step 6: Map all the values from the topic message onto columns#
Update the etc/kafka/tpch.customer.json
file to add fields for the
message and restart Presto. As the fields in the message are JSON, it uses
the json
data format. This is an example where different data formats
are used for the key and the message.
{
"tableName": "customer",
"schemaName": "tpch",
"topicName": "tpch.customer",
"key": {
"dataFormat": "raw",
"fields": [
{
"name": "kafka_key",
"dataFormat": "LONG",
"type": "BIGINT",
"hidden": "false"
}
]
},
"message": {
"dataFormat": "json",
"fields": [
{
"name": "row_number",
"mapping": "rowNumber",
"type": "BIGINT"
},
{
"name": "customer_key",
"mapping": "customerKey",
"type": "BIGINT"
},
{
"name": "name",
"mapping": "name",
"type": "VARCHAR"
},
{
"name": "address",
"mapping": "address",
"type": "VARCHAR"
},
{
"name": "nation_key",
"mapping": "nationKey",
"type": "BIGINT"
},
{
"name": "phone",
"mapping": "phone",
"type": "VARCHAR"
},
{
"name": "account_balance",
"mapping": "accountBalance",
"type": "DOUBLE"
},
{
"name": "market_segment",
"mapping": "marketSegment",
"type": "VARCHAR"
},
{
"name": "comment",
"mapping": "comment",
"type": "VARCHAR"
}
]
}
}
Now for all the fields in the JSON of the message, columns are defined and
the sum query from earlier can operate on the account_balance
column directly:
presto:tpch> DESCRIBE customer;
Column | Type | Extra | Comment
-------------------+---------+-------+---------------------------------------------
kafka_key | bigint | |
row_number | bigint | |
customer_key | bigint | |
name | varchar | |
address | varchar | |
nation_key | bigint | |
phone | varchar | |
account_balance | double | |
market_segment | varchar | |
comment | varchar | |
_partition_id | bigint | | Partition Id
_partition_offset | bigint | | Offset for the message within the partition
_key | varchar | | Key text
_key_corrupt | boolean | | Key data is corrupt
_key_length | bigint | | Total number of key bytes
_message | varchar | | Message text
_message_corrupt | boolean | | Message data is corrupt
_message_length | bigint | | Total number of message bytes
(21 rows)
presto:tpch> SELECT * FROM customer LIMIT 5;
kafka_key | row_number | customer_key | name | address | nation_key | phone | account_balance | market_segment | comment
-----------+------------+--------------+--------------------+---------------------------------------+------------+-----------------+-----------------+----------------+---------------------------------------------------------------------------------------------------------
1 | 2 | 2 | Customer#000000002 | XSTf4,NCwDVaWNe6tEgvwfmRchLXak | 13 | 23-768-687-3665 | 121.65 | AUTOMOBILE | l accounts. blithely ironic theodolites integrate boldly: caref
3 | 4 | 4 | Customer#000000004 | XxVSJsLAGtn | 4 | 14-128-190-5944 | 2866.83 | MACHINERY | requests. final, regular ideas sleep final accou
5 | 6 | 6 | Customer#000000006 | sKZz0CsnMD7mp4Xd0YrBvx,LREYKUWAh yVn | 20 | 30-114-968-4951 | 7638.57 | AUTOMOBILE | tions. even deposits boost according to the slyly bold packages. final accounts cajole requests. furious
7 | 8 | 8 | Customer#000000008 | I0B10bB0AymmC, 0PrRYBCP1yGJ8xcBPmWhl5 | 17 | 27-147-574-9335 | 6819.74 | BUILDING | among the slyly regular theodolites kindle blithely courts. carefully even theodolites haggle slyly alon
9 | 10 | 10 | Customer#000000010 | 6LrEaV6KR6PLVcgl2ArL Q3rqzLzcT1 v2 | 5 | 15-741-346-9870 | 2753.54 | HOUSEHOLD | es regular deposits haggle. fur
(5 rows)
presto:tpch> SELECT sum(account_balance) FROM customer LIMIT 10;
_col0
------------
6681865.59
(1 row)
Now all the fields from the customer
topic messages are available as
Presto table columns.
Step 7: Use live data#
Presto can query live data in Kafka as it arrives. To simulate a live feed of data, this tutorial sets up a feed of live tweets into Kafka.
Setup a live Twitter feed#
Download the twistr tool
$ curl -o twistr https://repo1.maven.org/maven2/de/softwareforge/twistr_kafka_0811/1.2/twistr_kafka_0811-1.2.sh
$ chmod 755 twistr
Create a developer account at https://dev.twitter.com/ and set up an access and consumer token.
Create a
twistr.properties
file and put the access and consumer key and secrets into it:
twistr.access-token-key=...
twistr.access-token-secret=...
twistr.consumer-key=...
twistr.consumer-secret=...
twistr.kafka.brokers=localhost:9092
Create a tweets table on Presto#
Add the tweets table to the etc/catalog/kafka.properties
file:
connector.name=kafka
kafka.nodes=localhost:9092
kafka.table-names=tpch.customer,tpch.orders,tpch.lineitem,tpch.part,tpch.partsupp,tpch.supplier,tpch.nation,tpch.region,tweets
kafka.hide-internal-columns=false
Add a topic definition file for the Twitter feed as etc/kafka/tweets.json
:
{
"tableName": "tweets",
"topicName": "twitter_feed",
"dataFormat": "json",
"key": {
"dataFormat": "raw",
"fields": [
{
"name": "kafka_key",
"dataFormat": "LONG",
"type": "BIGINT",
"hidden": "false"
}
]
},
"message": {
"dataFormat":"json",
"fields": [
{
"name": "text",
"mapping": "text",
"type": "VARCHAR"
},
{
"name": "user_name",
"mapping": "user/screen_name",
"type": "VARCHAR"
},
{
"name": "lang",
"mapping": "lang",
"type": "VARCHAR"
},
{
"name": "created_at",
"mapping": "created_at",
"type": "TIMESTAMP",
"dataFormat": "rfc2822"
},
{
"name": "favorite_count",
"mapping": "favorite_count",
"type": "BIGINT"
},
{
"name": "retweet_count",
"mapping": "retweet_count",
"type": "BIGINT"
},
{
"name": "favorited",
"mapping": "favorited",
"type": "BOOLEAN"
},
{
"name": "id",
"mapping": "id_str",
"type": "VARCHAR"
},
{
"name": "in_reply_to_screen_name",
"mapping": "in_reply_to_screen_name",
"type": "VARCHAR"
},
{
"name": "place_name",
"mapping": "place/full_name",
"type": "VARCHAR"
}
]
}
}
As this table does not have an explicit schema name, it will be placed
into the default
schema.
Feed live data#
Start the twistr tool:
$ java -Dness.config.location=file:$(pwd) -Dness.config=twistr -jar ./twistr
twistr
connects to the Twitter API and feeds the “sample tweet” feed
into a Kafka topic called twitter_feed
.
Now run queries against live data:
$ ./presto-cli --catalog kafka --schema default
presto:default> SELECT count(*) FROM tweets;
_col0
-------
4467
(1 row)
presto:default> SELECT count(*) FROM tweets;
_col0
-------
4517
(1 row)
presto:default> SELECT count(*) FROM tweets;
_col0
-------
4572
(1 row)
presto:default> SELECT kafka_key, user_name, lang, created_at FROM tweets LIMIT 10;
kafka_key | user_name | lang | created_at
--------------------+-----------------+------+-------------------------
494227746231685121 | burncaniff | en | 2014-07-29 14:07:31.000
494227746214535169 | gu8tn | ja | 2014-07-29 14:07:31.000
494227746219126785 | pequitamedicen | es | 2014-07-29 14:07:31.000
494227746201931777 | josnyS | ht | 2014-07-29 14:07:31.000
494227746219110401 | Cafe510 | en | 2014-07-29 14:07:31.000
494227746210332673 | Da_JuanAnd_Only | en | 2014-07-29 14:07:31.000
494227746193956865 | Smile_Kidrauhl6 | pt | 2014-07-29 14:07:31.000
494227750426017793 | CashforeverCD | en | 2014-07-29 14:07:32.000
494227750396653569 | FilmArsivimiz | tr | 2014-07-29 14:07:32.000
494227750388256769 | jmolas | es | 2014-07-29 14:07:32.000
(10 rows)
There is now a live feed into Kafka which can be queried using Presto.
Epilogue: Time stamps#
The tweets feed that was set up in the last step contains a time stamp in
RFC 2822 format as created_at
attribute in each tweet.
presto:default> SELECT DISTINCT json_extract_scalar(_message, '$.created_at')) AS raw_date
-> FROM tweets LIMIT 5;
raw_date
--------------------------------
Tue Jul 29 21:07:31 +0000 2014
Tue Jul 29 21:07:32 +0000 2014
Tue Jul 29 21:07:33 +0000 2014
Tue Jul 29 21:07:34 +0000 2014
Tue Jul 29 21:07:35 +0000 2014
(5 rows)
The topic definition file for the tweets table contains a mapping onto a
timestamp using the rfc2822
converter:
...
{
"name": "created_at",
"mapping": "created_at",
"type": "TIMESTAMP",
"dataFormat": "rfc2822"
},
...
This allows the raw data to be mapped onto a Presto timestamp column:
presto:default> SELECT created_at, raw_date FROM (
-> SELECT created_at, json_extract_scalar(_message, '$.created_at') AS raw_date
-> FROM tweets)
-> GROUP BY 1, 2 LIMIT 5;
created_at | raw_date
-------------------------+--------------------------------
2014-07-29 14:07:20.000 | Tue Jul 29 21:07:20 +0000 2014
2014-07-29 14:07:21.000 | Tue Jul 29 21:07:21 +0000 2014
2014-07-29 14:07:22.000 | Tue Jul 29 21:07:22 +0000 2014
2014-07-29 14:07:23.000 | Tue Jul 29 21:07:23 +0000 2014
2014-07-29 14:07:24.000 | Tue Jul 29 21:07:24 +0000 2014
(5 rows)
The Kafka connector contains converters for ISO 8601, RFC 2822 text formats and for number-based timestamps using seconds or milliseconds since the epoch. There is also a generic, text-based formatter which uses Joda-Time format strings to parse text columns.


Resistivity vs temperature measurements: Resistivity vs temperature plots for ( a) x = 0.0 and ( b) x = 1.0 compositions. Insets showing the insulating electrical resistivity of both the compositions could be modelled by variable range hopping mechanism in 3-dimension.


sho
Ruby Cheatsheet
Integer
Float
Some tree text here and here is some more.
Array
Strings
Hash
Control Structures
Loops
Classes
Blocks
File I/O


Click here to access the information you are looking for.
Click for Google or click for Hack UC HackUC website.


{% extends "accounts/base.html" %} {% load avatars djblets_deco i18n %} {% block title %}{% trans "Link Account" %}{% endblock %} {% block auth_content %}
{% blocktrans %} It looks like you already have an account on {{PRODUCT_NAME}}. Log in with your existing password to complete SSO setup. {% endblocktrans %}
{% avatar user 175 %}{{user.username}}
{% if form.errors %} {% errorbox %}{{form.non_field_errors}}{% enderrorbox %} {% endif %}{% trans "I have a different account" %}
{% blocktrans %} Don't have an account? Please contact your system administrator about correcting the username in your SSO provider. {% endblocktrans %}


Hello
This is a paragraph, with a
link
to nowhere.
Place order
Item:
Apple
Orange
Grapes
Qty:
Confirmation email:
Place order
Google image search
Search images


Flori remembers to take care of your plants so you don't have to.
“Flori saves me so much time!”
-Flori User
Never worry about over or under-watering your plants again. We'll tell you exactly how much water they need, when they need it.
Learn More →Get expert tips on caring for your plants, stylizing your home, and more.
Learn More →It reminds me on schedule to water my plants and they're always healthy! So little effort required to take care my plants now.
The app is well designed and so easy to use! I was blown away by how simple it is.
There are so many useful tips and tricks that they offer to make gardening even easier, I'm a novice and my garden looks like it's done by a professional!


from .intent_threshold_optimizer import IntentThresholdOptimizer
from .nlu_framework import NLUFramework
# Other imports only for the type hints
from .nlu_data_set import NLUDataSet
from .nlu_intent_rating import NLUIntentRating
[docs]class OptimizableNLUFramework(NLUFramework):
"""
Base class for NLU frameworks that need a threshold-based implementation of the None-intent.
Beware that some of the method names you have to implement differ from
:class:`~nlutestframework.nlu_framework.NLUFramework`, namely :meth:`_prepareDataSet`
(was: :meth:`~nlutestframework.nlu_framework.NLUFramework.prepareDataSet`) and
:meth:`_rateIntents` (was: :meth:`~nlutestframework.nlu_framework.NLUFramework.rateIntents`).
"""
# pylint: disable=arguments-differ
[docs] async def construct( # type: ignore
self,
intent_threshold: float = 0.3,
optimize_intent_threshold: bool = False,
optimizer_iterations: int = 5,
optimizer_grid_search_step_size: float = 0.01
) -> None:
"""
Args:
intent_threshold: Some frameworks don't have the None-intent or a comparable, compatible
concept built-in. For these frameworks, this implementation selects the None-intent
based on the highest confidence of all intents. If the highest confidence is below
intent_threshold, the rated intent is interpreted as the None-intent instead.
Confidence values range from 0 to 1, the default value of intent_threshold is 0.3.
optimize_intent_threshold: When this flag is set, the value of :obj:`intent_threshold`
is ignored. Instead, the threshold is automatically optimized for each data set
during a few additional training iterations.
optimizer_iterations: The number of iterations to repeat and average the threshold
optimization. Defaults to 5.
optimizer_grid_search_step_size: The step size for the grid search over the threshold.
The optimal threshold is searched for in a window from 0 to 1, so a step size of
e.g. 0.01 means that 100 different values are tested.
"""
self.__intent_threshold = intent_threshold
self.__optimize_intent_threshold = optimize_intent_threshold
self.__optimizer_iterations = optimizer_iterations
self.__optimizer_grid_search_step_size = optimizer_grid_search_step_size
async def prepareDataSet(self, data_set: NLUDataSet) -> None:
await self._prepareDataSet(data_set)
if self.__optimize_intent_threshold:
self.__intent_threshold = 0 # Set the threshold to 0 during the optimization
self.__intent_threshold = await IntentThresholdOptimizer.optimize(
self,
data_set,
self.__optimizer_iterations,
self.__optimizer_grid_search_step_size
)
# pylint: disable=attribute-defined-outside-init
[docs] async def _prepareDataSet(self, data_set: NLUDataSet) -> None:
"""
Args:
data_set: The data set that will be benchmarked next.
"""
pass
async def rateIntents(self, sentence: str) -> NLUIntentRating:
rating = await self._rateIntents(sentence)
rating.noneIfBelow(self.__intent_threshold)
return rating
[docs] async def _rateIntents(self, sentence: str) -> NLUIntentRating:
"""
Args:
sentence: A sentence to find intents and entities for.
Returns:
The intent rating information as returned by the framework.
"""
raise NotImplementedError("To be implemented by subclasses.")


{% comment %} * * This include lets you loop through a collection to list * all entries in that collection and limit the output if needed. * gghertyt5545 * By default include.limit is a string. To convert it * into a variable you have to use *assign* and the * filter | plus:0 * * If you set `published: false` in front matter of a collection page * the page gots filtered out via unless * * Example: {% include list-collection.html collection='wordpress' limit='5' %} * {% endcomment %} {% assign limit = include.limit | plus:0 %} {% assign counter = 0 %}


Storage:
sessionStorage(key=value):
localStorage(key=value):
Add Storage
Get Storage
Clear Storage
Message:
N/A


Welcome
The Angular coding exercise is a way for us to learn about your coding style.
Tasks
- Wire up the routing framework of your choice
- Build a screen where you can list out existing users
- Add a screen for adding a new user
- Add a screen for editing a user or reuse the new screen
- Add a function for deleting a user from the users list
- Make the list of users sortable by name or by email address
- Make the list of users filterable by name or by email address or both
Endpoints
- Get all users from /api/users
- Create a new user by posting to /api/users
- Get user by getting /api/users/:userId
- Delete user by deleting /api/users/:userId
- Update user by putting /api/users/:userId


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@() @master_layout { @partials.leftnav() Add Till Business Name Business Name Business Cell_2 Sub-County Type of Business Company Registration No. Select Registration Type Incorporation Certificate Business Permit KRA PIN Number of Directors MSISDN Select Till Type Buy Goods PayBill Certificate of Incorporation/Business Permit Scanned Application Form Submit send } { } { }
Certificate of Incorporation/Business Permit
Scanned Application Form


 Simply stated. This film is so bizarre that not only is it absolutely hysterical to watch, but it's also tense and frightening and one could probably benefit from a small break halfway through the film just to collect yourself. I mean, one can only take so much of Kafka and Freud being presented at the same time.
Written by a film student and directed with darkly comic flair by Martin Scorsese (A True God Among Men), "After Hours" is a kafkaesque nightmare of a movie, starting out as a little unusual date movie and turning into a wild multi-leveled nightmare. Our hero, Paul Hackett (Griffin Dunne, at his best), is a typical computer programmer in New York who hates his job, flicks channels all night, and goes to coffee shops to read George Miller's "Tropic of Cancer" with hopes of meeting a female. This particular night it happens with Marcy (Rosanna Arquette) who sees him reading Miller and makes some wild, sexually fierocious comments on the book. Paul is immeadiately turned on and gets her number.
He calls her that night and discovers she's living with some artist named Kiki Dees (a young Linda Fiorentino, working on her "Last Seduction" role early) in Soho (Let me just say that after watching this movie I will NEVER step foot in Soho). He goes there, experiences some very bizarre situations, gets to know her better and then ditches her because: a) she's weird and more importantly: b) she might just have some burn scars all over her body.
The film, which has started off as a date film, has slowly become bizarre and, well, kafkaesque (a counter, please, for the number of times I say that word in this review alone). But even though it has been weird so far, it's nothing as for what will happen next. Paul's life is turned into a nightmare by associating with other people in an attempt of trying to get back to his apartment (see, the subway tolls have gone up and he only has about 90-some cents on him).
This might just be a stupid everything-bad-happens film but it's so well written and so brilliantly directed by Scorsese that it seems to have several dimensions: on the surface, it's a wicked comedy; beneath, it has Freudian undertones (most of the problems he gets into are sex-related in some way...and the end is, well, incestuous, but I won't divulge) and, well, Kafka undertones as well (no need to explain this). It all projects Scorsese's view of New York as a kind of Dante's Inferno, with everybody seeming to be losers just waiting around for whatever and actually being so bored that it might be fun to form a mob and chase an innocent man through the streets of Soho.
Besides, this has Scorsese written all over it: the music playing slowly and drearily; the amazing slow takes; the dry wit and black humor; etc, etc, etc. But, unlike many of his other films, I believe "After Hours" to stand on the same level as his other masterpieces (Which are? Glad you asked: "Mean Streets," "Taxi Driver," "Raging Bull" and "Goodfellas"), presenting Scorsese's talent with comedy; his non-traditional way of presenting a story with flair and style and making a full dimensional work of art out of it. But if you really don't care about these things, you'll still have a great time with this brilliantly funny film.
MY RATING (out of 5): *****
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


Classes: 1 with failures
Number of nodes affected for each class:
| Class | Failures | Skips | Changes | No-op |
|---|
| X_puppet::Master |
1 | 1 | – | – |
Number of nodes affected for each class:
| Class | Failures | Skips | Changes | No-op |
|---|
| X_puppet::Master |
1 | 1 | – | – |
You are using a browser that is not supported or you have disabled JavaScript. For more information, refer to the Puppet Enterprise Browser Requirements.


One: A Creative Habit on Display
To kick this whole year of creativity off, I've created the HTML and CSS structure of this site. You're looking at it right now! The structure doesn't make many stylistic choices, but it does lay the foundation for what's to come.
Its main page consists of a 52-item list with only one filled-in spot... so far. Each item of my 52-week journey will soon fill this space. Additionally, I have plans for this list to become dynamic and help track my progress. More on that later!
The foundation also includes a template for gallery pages such as this one. The primary goal of these gallery pages is to provide a flexible, branded expereience for housing my creative endeavours throghout 2020.
Here's some technical details: this whole site is hand-coded in HTML, CSS, and Javascript. The CSS has a framework in place called Milligram. Milligram is great because it provides some sane defaults for styling that I will build upon.
All of this code is stored on GitHub at https://github.com/alexmarc-us/Create-52.


Struct Kernel.SYSTEM_INFO
The SYSTEM_INFO structure contains information about the current computer system. This includes the architecture and type of the processor, the number of processors in the system, the page size, and other such information.
Inherited Members
Namespace: CADability
Assembly: CADability.dll
Syntax
public struct SYSTEM_INFO
Fields
| Improve this Doc View SourceActiveProcessorMask
Mask representing the set of processors configured into the system. Bit 0 is processor 0; bit 31 is processor 31.
Declaration
public int ActiveProcessorMask
Field Value
| Type | Description |
|---|---|
| System.Int32 |
AllocationGranularity
Granularity with which virtual memory is allocated. For example, a VirtualAlloc request to allocate 1 byte will reserve an address space of AllocationGranularity bytes. This value was hard coded as 64K in the past, but other hardware architectures may require different values.
Declaration
public int AllocationGranularity
Field Value
| Type | Description |
|---|---|
| System.Int32 |
MaximumApplicationAddress
Pointer to the highest memory address accessible to applications and DLLs.
Declaration
public IntPtr MaximumApplicationAddress
Field Value
| Type | Description |
|---|---|
| System.IntPtr |
MinimumApplicationAddress
Pointer to the lowest memory address accessible to applications and dynamic-link libraries (DLLs).
Declaration
public IntPtr MinimumApplicationAddress
Field Value
| Type | Description |
|---|---|
| System.IntPtr |
NumberOfProcessors
Number of processors in the system.
Declaration
public int NumberOfProcessors
Field Value
| Type | Description |
|---|---|
| System.Int32 |
PageSize
Page size and the granularity of page protection and commitment. This is the page size used by the VirtualAlloc function.
Declaration
public int PageSize
Field Value
| Type | Description |
|---|---|
| System.Int32 |
ProcessorLevel
System's architecture-dependent processor level. It should be used only for display purposes. To determine the feature set of a processor, use the IsProcessorFeaturePresent(Int32) function.
If SystemInfoUnion.ProcessorArchitecture is
If SystemInfoUnion.ProcessorArchitecture is
If SystemInfoUnion.ProcessorArchitecture is
| Value | Description |
|---|---|
| 0004 | MIPS R4000 |
If SystemInfoUnion.ProcessorArchitecture is
| Value | Description |
|---|---|
| 21064 | Alpha 21064 |
| 21066 | Alpha 21066 |
| 21164 | Alpha 21164 |
If SystemInfoUnion.ProcessorArchitecture is
| Value | Description |
|---|---|
| 1 | PPC 601 |
| 3 | PPC 603 |
| 4 | PPC 604 |
| 6 | PPC 603+ |
| 9 | PPC 604+ |
| 20 | PPC 620 |
Declaration
public int ProcessorLevel
Field Value
| Type | Description |
|---|---|
| System.Int32 |
ProcessorRevision
Architecture-dependent processor revision. The following table shows how the revision value is assembled for each type of processor architecture:
| Processor | Description |
|---|---|
| Intel 80386 or 80486 |
A value of the form xxyz. If xx is equal to 0xFF, y - 0xA is the model number, and z is the stepping identifier. For example, an Intel 80486-D0 system returns 0xFFD0. If xx is not equal to 0xFF, xx + 'A' is the stepping letter and yz is the minor stepping. |
| Intel Pentium, Cyrix, or NextGen 586 |
A value of the form xxyy, where xx is the model number and yy is the stepping. Display this value of 0x0201 as follows: Model xx, Stepping yy. |
| MIPS | A value of the form 00xx, where xx is the 8-bit revision number of the processor (the low-order 8 bits of the PRId register). |
| ALPHA |
A value of the form xxyy, where xxyy is the low-order 16 bits of the processor revision number from the firmware. Display this value as follows: Model A+xx, Pass yy. |
| PPC |
A value of the form xxyy, where xxyy is the low-order 16 bits of the processor version register. Display this value as follows: xx.yy. |
Declaration
public int ProcessorRevision
Field Value
| Type | Description |
|---|---|
| System.Int32 |
ProcessorType
An obsolete member that is retained for compatibility with Windows NT 3.5 and earlier. Use the SystemInfoUnion.ProcessorArchitecture, ProcessorLevel, and ProcessorRevision members to determine the type of processor.
Windows Me/98/95: Specifies the type of processor in the system. This member is one of the following values:
Declaration
public int ProcessorType
Field Value
| Type | Description |
|---|---|
| System.Int32 |
SystemInfoUnion
Union for the OemId, ProcessorArchitecture, and Reserved fields of the SYSTEM_INFO structure. See Kernel.SYSTEM_INFO_UNION.
Declaration
public Kernel.SYSTEM_INFO_UNION SystemInfoUnion
Field Value
| Type | Description |
|---|---|
| Kernel.SYSTEM_INFO_UNION |


For this assignment, you will work on a real data science project. The goal of the project is to go through the complete data science process to answer questions you have about some topic of your own choosing. You will acquire the data, design your visualizations, run statistical analysis, and communicate the results.
You may work individually or in a team. If part of a team, you will work closely with other classmates (a maximum of 2 students per team) on this project. You can come up with your own teams or use the comment section of this website to find a teammate. If you can’t find partners we can help you find a team. In general, we do not anticipate that the grades for each group member will be different. However, we reserve the right to assign different grades to each group member based on peer assessments (see below).
There are a few milestones for your final project.
| Date | Assignment |
|---|---|
| April 8 | Project proposal |
| May 13 | Project update |
| May 27 | RMarkdown and HTML |
| May 27 | Peer assessment |
| May 27 | Website |
| May 28 | Project presentation |
Note that:
There are several deliverables for your project that will be graded individually to make up your final project grade.
Fill out this google form to propose a project by April 8th, 2018.
Fill out this google form to update us on your project by May 13th, 2018.
The final report is composed of three parts:
The most important deliverable of your project is the set of RMarkdown and compiled HTML files by May 27th, 2018. They should detail your steps in developing your solution, including how you collected the data, alternative solutions you tried, describing statistical methods you used, and the insights you got. Equally important to your final results is how you got there! Your RMarkdown and HTML files are the place you describe and document the space of possibilities you explored at each step of your project. We strongly advise you to include many visualizations.
Your RMarkdown should include the following topics. Depending on your project type, the amount of discussion you devote to each of them will vary:
Overview and Motivation: Provide an overview of the project goals and the motivation for it. Consider that this will be read by people who did not see your project proposal.
Related Work: Anything that inspired you, such as a paper, a website, or something we discussed in class.
Initial Questions: What questions are you trying to answer? How did these questions evolve over the course of the project? What new questions did you consider in the course of your analysis?
Data: Source, scraping method, cleanup, etc.
Exploratory Analysis: What visualizations did you use to look at your data in different ways? Justify the decisions you made, and show any major changes to your ideas. How did you reach these conclusions?
Modeling: What are the different statistical methods you considered? Why did you choose a given model? How about competing approaches?
Final Analysis: What did you learn about the data? How did you answer the questions? How can you justify your answers?
As this will be your only chance to describe your project in detail, make sure that your RMarkdown file and compiled HTML file are standalone documents that fully describe your process and results. For instructions on how to submit, please see Submission Instructions below.
We expect you to write high-quality and readable R code in your RMarkdown file. You should strive for doing things the right way and think about aspects such as reproducibility, efficiency, cleaning data, etc. We also expect you to document your code.
It is important to provide positive feedback to your teammate and to also make suggestions to those you perceived not to be working as effectively on team tasks. We ask you to provide an honest assessment of the contributions of the members of your team, including yourself. The feedback you provide should reflect your judgment of each team member:
Preparation: were they prepared during team meetings?
Contribution: did they contribute productively to the team discussion and work?
Respect for others’ ideas: did they encourage others to contribute their ideas?
Flexibility: were they flexible when disagreements occurred?
Your teammates’ assessment of your contributions and the accuracy of your self-assessment will be considered as part of your overall project grade. The peer assessment is due May 27t, 2018. For instructions on how to submit, please see Submission Instructions below.
The website should effectively summarize the main results of your project and tell a story. Consider your audience (the site is public) and keep the level of discussion at the appropriate level. Your RMarkdown file, HTML file and data should be linked from your GitHub Repository (see below) to the website as well. Also embed your main visualizations in your website.
We suggest that you use Github Pages, Google Sites to host your website and blogdown to actually build it (see the blogdown book for more details, especially the part about deployment on GitHub Pages). However, you can use Google Sites, or any other web hosting service of your choice, and similarly any tool you like to build the website.
The project website is due May 27t, 2018. For instructions on how to submit, please see Submission Instructions below.
Each team will prepare a seven minutes presentation showing a demo of your project and/or some slides.
We will strictly enforce the time limit, so please make sure you are not running longer. Use principles of good storytelling and presentations to get your key points across. Focus the majority of your presentation on your main contributions rather than on technical details. What do you feel is the best part of your project? What insights did you gain? What is the single most important thing you would like your audience to take away? Make sure it is upfront and center rather than at the end.
While the project presentations will be during class on May 28t, 2018, slides should be submitted by May 27th, 2018. For instructions on how to submit, please see Submission Instructions below.
Fill out this google form to propose a project (one per team).
Fill out this google form to update us on your project (one per team).
Use your team’s GitHub repository to commit the data used for the final project, the RMarkdown file and the compiled HTML file:
Each individual team member needs to fill out this google form for the peer evaluation. Your individual project grade will take into account your self and peer assessment.
Fill out this google form to submit the links to the website. Note that:
The final project is graded in three parts:
Your individual project grade will also be determined by your peer evaluations.
Here are some examples of final projects from the course Introduction to Data Science: BST 260 from Harvard (note that these projects were done when the course was taught using Python, not R).
You can find additional ideas in the blog of the Data Incubator and FiveThirtyEight website.


ims-health
Businessmodel of IMS Health
Customer Segments
IMS Health provides information and technology services to organisations operating within the healthcare sector. Its customer base comprises a broad range of companies within the sector, with around 90% of its sales coming from:
- life science companies;
- pharmaceutical groups;
- biotechnology firms;
- consumer health providers;
- medical device manufacturers;
- diagnostic companies; According to the Company’s annual report, it includes almost all of the top 100 pharmaceutical and biotechnology companies, measured by revenue, among its clients, many of which subscribe to products and services across multiple jurisdictions. IMS Health also serves medical product distributors, government and regulatory agencies, pharmaceutical distributors, financial services firms, and pharmacies. Sales to government agencies represent only around 1% of the Company’s business.
IMS Health’s largest market is its native US, which accounted for around 39% of the Company’s 2015 revenue. The Company, however, also serves customers across more than 100 countries worldwide, with business in emerging markets such as China, India, Brazil and Russia growing significantly in recent years.
Value Propositions
IMS Health provides value to its customers in the following ways:
- Its industry standing and brand identity, being one of the largest companies in its sector and having a number of longstanding client relationships with high-profile customers;
- The quality and efficiency of its products and services, comprising a diverse suite of solutions developed by in-house experts with the support of market-leading technology partners;
- Its flexible and scalable products and solutions, which can be implemented across businesses of all sizes and in various jurisdictions; and
- Its commitment to innovation, with the Company devoting substantial resources to research and development activities each year. ### Channels
IMS Health operates a website at www.imshealth.com, where customers are able to find information on the Company’s services and solutions. The Company, however, does not appear to operate an online sales channel.
The majority of IMS Health’s sales are made directly via its in-house sales teams, which are organised by jurisdiction, managing sales and customer relationships on a local basis. The Company has a sales presence across North America, Latin America, Europe, and Asia, including offices in Buenos Aires, Caracas, Los Ruices, Lisbon, and Bangalore
IMS Health also sells a portion of its products and solutions to distributors, which sell on to end-users, and collaborates with a network of other channel partners.
Customer Relationships
While the Company has an online presence, it does not appear to provide services and solutions on a self-service basis. Instead, the Company implements a predominantly direct sales model, with customers consulting directly with members of the Company’s local sales teams before making purchases. This allows the Company to provide a more personalised service that fully covers the client’s brief.
IMS Health maintains long-standing relationships and high renewal rates with its clients, due in part to the nature of the services and solutions its provides. According to its annual report, the average length of relationships with its top 25 clients, as measured by revenue, is over 25 years and its retention rate for its top 1,000 clients from 2014 to 2015 was 99%.
Customers are able to contact IMS Health directly through its website, as well as over the phone, receiving personal care for any issues and enquiries. The Company also operates Twitter, Facebook, YouTube and LinkedIn accounts, through which customers are able to interact directly with IMS Health.
Key Activities
IMS Health is an information and technology services company that provides solutions to its clients operating across multiple areas of the healthcare sector. The Company is headquartered in the US, its principal market, but has a presence in more than 100 countries worldwide.
IMS Health provides services and solutions to healthcare organisations and life science companies, including pharmaceutical, biotechnology, consumer health and medical device manufacturers, as well as distributors, government agencies, policymakers, researchers and clients within the financial community.
The Company’s principal information offerings include national information offerings, sub-national information offerings and its proprietary OneKey service.
Its technology services offerings include technology solutions, real-world evidence solutions, and workflow analytics and consulting services, as well as data warehousing, information technology outsourcing, development of software applications and other services.
Key Partners
IMS Health partners with a range of companies across its numerous operating jurisdictions companies that help the Company to provide value-added solutions to mutual clients. These partners include:
- Channel Partners, including market partners, authorized resellers, distributors and independent software vendors, which help to extend the Company’s sales and marketing reach;
- Development Partners, which assist the Company in developing its software-based service and solutions and help provide third-party integrations;
- Service Partners, which provide various service that facilitate the Company development and distribution of its products;
- Technology Partners, which share tools and resources with the Company and collaborate on joint projects; and
- Digital agencies, which assist the Company in the deployment of its digital solutions. Among IMS Health’s partners are Ping Identity, Qlik, Hootsuite, Aktana, Concur, Reltio, Scribe, Xamarin, and SAFE Biopharma.
Key Resources
IMS Health’s key resources are its technology and intellectual properties, its IT infrastructure, its network of partnerships, its sales and distribution channels, and its personnel.
The Company owns a range of intellectual property assets, including patents and patent applications related to its products and services, and trademarks and trademark applications related to its brand and products. Searches of records published by the US Patent and Trademark Office identified one patent application filed in IMS Health’s name, entitled ‘System and method for analyzing de-identified health care data’.
Cost Structure
IMS Health incurs costs in relation to the development of its products and services, the maintenance of its IT infrastructure, the operation of its sales and marketing channels, the management of its partnership and the retention of its personnel.
The Company’s general and administrative costs are its most significant. As of 31st December 2015, the Company had 15,000 employees worldwide, which represent costs in the form of salaries and benefits. It also operates an international network of offices, accruing costs in the form of rent and utilities.
Revenue Streams
IMS Health records its revenue across two business lines:
- Information, comprising revenue generated through the provision of small and large-scale service and consulting projects, multi-year outsourcing contracts, and software-as-a-service licenses; and
- Technology, comprising revenue generated through the sale of hosted and cloud-based applications, and the provision of implementation services, subscription software, analytic services and consulting solutions. In 2015 IMS Health generated $2.92 billion in annual revenue, up slightly on the $2.64 billion generated the previous year. Total global revenue from the Company’s information offerings, including national and sub-national information services, was $1.48 billion in 2015, representing approximately 51% of IMS Health’s total annual revenue for the year. Total global revenue generated by the Company’s technology services was approximately $1.43 billion, representing 49% of total annual 2015 revenue. Around 93% of IMS Health’s revenue is generated through its tops 1,000 clients.


The SoftGroup approach
Our approach is to combine our understanding of user behavior with a commitment to meeting the business goals of our clients. We are proactive in our aftercare service and will alert our customers of any malicious behavior that we find. Our staff is diverse and talented in all aspects of website technology and marketing, including content creation. We love what we do and it shows in the solutions we produce.


Meeting Title
Agenda
Write the meeting agenda here.
Screen name
Post contents
Participants
Tasks
Mentions


(For a full list see below
This device successfully characterizes a IgG1 antibody (mAb1) sample and the result is compared with an advanced commercial DSC.This device could potentially lead to a reduction in the cost and time for the drug formulation in the pharmaceutical industry.
Shuyu Wang, Shifeng Yu, Michael Siedler, Peter M Ihnat, Dana I Filoti, Ming Lu, Lei Zuo
Sensors and Actuators B Chemical
In this paper we propose a novel micro heater design to reach a uniform temperature in a large heating area by optimizing the heating power density distribution in the heating area.
Shifeng Yu, Shuyu Wang, Ming Lu, Lei Zuo
Sensors and Actuators A Physical
We developed an ultrasensitive micro-DSC (differential scanning calorimeter) for liquid protein sample characterization.
Shuyu Wang, Shifeng Yu, Michael S Siedler, Peter M Ihnat, Dana I Filoti, Ming Lu, Lei Zuo
Review of Scientific Instruments
The high TCR, good uniformity, and low processing temperature of the material make it a good candidate for thermistor application.
Shuyu Wang, Shifeng Yu, Ming Lu, Mingzhao Liu, Lei Zuo
Journal of Electronic Materials
In this paper, we present an improved method to bond poly(dimethylsiloxane) (PDMS) with polyimide (PI) to develop flexible substrate microfluidic devices. This proposed versatile method could bond the microfluidic device and plastic substrate together and be applied in the fabrication of some biosensors and lab-on-a-chip systems.
Shuyu Wang, Shifeng Yu, Ming Lu, Lei Zuo
Journal of Micromechanics and Microengineering
roper oxidation conditions can significantly reduce the thermal hysteresis. The fabricated VO2 thin films showed the potential to be applied in the development of electrical sensors and other smart devices.
Shifeng Yu, Shuyu Wang, Ming Lu, Lei Zuo
This review provides an up to date overview of the applications of DSC in biomolecular study such as the bovine serum albumin denaturation study, the relationship between the melting point of lysozyme and the scanning rate.
Shifeng Yu, Shuyu Wang, Ming Lu, Lei Zuo
Frontiers of Mechanical Engineering
A power compensated differential scanning calorimeter for protein stability characterization
Shuyu Wang, Shifeng Yu, Michael Siedler, Peter M Ihnat, Dana I Filoti, Ming Lu, Lei Zuo
Sensors and Actuators B Chemical
A novel polyimide based micro heater with high temperature uniformity
Shifeng Yu, Shuyu Wang, Ming Lu, Lei Zuo
Sensors and Actuators A Physical
Micro-differential scanning calorimeter for liquid biological samples
Shuyu Wang, Shifeng Yu, Michael S Siedler, Peter M Ihnat, Dana I Filoti, Ming Lu, Lei Zuo
Review of Scientific Instruments
Atomic layer-deposited titanium-doped vanadium oxide thin films and their thermistor applications
Shuyu Wang, Shifeng Yu, Ming Lu, Mingzhao Liu, Lei Zuo
Journal of Electronic Materials
Microfabrication of plastic-PDMS microfluidic devices using polyimide release layer and selective adhesive bonding
Shuyu Wang, Shifeng Yu, Ming Lu, Lei Zuo
Journal of Micromechanics and Microengineering
A metal-insulator transition study of VO2 thin films grown on sapphire substrates
Shifeng Yu, Shuyu Wang, Ming Lu, Lei Zuo
Journal of Applied Physics
Review of MEMS differential scanning calorimetry for biomolecular study
Shifeng Yu, Shuyu Wang, Ming Lu, Lei Zuo
Frontiers of Mechanical Engineering


With a passion for applying front end engineering best practices and user-centric design principles , I aspire to craft beautifully functional, polished and scalable robust products that bring delight to users!
'Cambridge, MA'
> john.occupation'Senior Software Engineer @ HubSpot'
> john.education'UC San Diego'
> john.degree'B.S. in Computer Engineering'
EXPERIENCE
HubSpot, Inc.
- Working on the Invited Sales User / Sales Manager team
- Working on the Meetings app and growth initatives for Sales Hub.
- ReactJS, Redux, ES6, SASS, DraftJS
HubSpot, Inc.
- Front end engineer on the Sales Engagement product team to build solutions for sales reps to engage with prospects.
- Worked on Sequences, Templates, Documents, Snippets, Playbooks, and Meetings apps.
- ReactJS, Redux, ES6, SASS, DraftJS
Perspectium Corp.
- Designed and developed various UI features of Observer and Dashboard web applications which provide solutions for ServiceNow and Salesforce developers to monitor and analyze instance metrics. Used jQuery, jqWidgets, and JavaScript.
- Implemented new Java EE features of Replicator Agent, a replication and synchronization data replication cloud service which propagates ServiceNow data to other ServiceNow instances or databases, and message broker service which brokers messages between Replicator Agents and ServiceNow instances.
- Built tools for internal database table editor and archive viewer web applications using jQuery which optimized user experience by providing a quicker and more intuitive solution for data lookup and editing.
- Performed quality assurance testing to ensure robustness of features and discover and resolve defects.
Reflexion Health, Inc.
- Designed and developed new features of AngularJS web clinician application that allows clinicians to customize physical therapy exercise plans to be completed by patients via Kinect/Unity patient application.
- Built hybrid application proof-of-concept using AngularJS and Ionic that allows patients to track therapy session exercise progress and to perform/self-report exercises on mobile.
- Visualized patient data and constructed software version watcher of Data Analytics internal application using Ruby on Rails, C3.js and AngularJS.
INVOLVEMENT
SD Hacks
- Responsible for developing technical aspects of hackathon. Integrated and maintained open-source mentor queue that allowed participants to request for mentors during the hackathon.
- Assisted with planning, logistics, and execution of hackathon.
IEEE UCSD Student Branch
- Co-lead team of five in building software aspects of autonomous robot involving writing maze-solving flood-fill algorithm and implementing maze-mapping algorithm using Arduino and C.
UCSD Circle K International
- Worked in a team of five developers and designers to build organization's new site from the ground up.
- Contributed to AngularJS codebase and met with team weekly during the summer to design and develop site.
PORTFOLIO
A robotic bartending robot controlled by a web application. Served as team lead and lead software developer. Consists of three main components: IoTea Device, IoTea API, and IoTea Dashboard. IoTea API built with Node.js, Express, and MongoDB. Implemented API endpoints for drinks and authentication. Uses Mosquitto MQTT message broker for communication to hardware. Can run as Docker service. IoTea Dashboard built with Vue.js and UIkit. Web application for users to create, manage, and dispense drinks. IoTea device build with Raspberry Pi. Runs Python script to dispense drinks.
SaaS customer check-in and employee management web application. Worked as lead developer in team of ten students to refactor existing application code base and build form builder, patient check-in, company customization settings, and appointment scheduling features.
Built with Mocha, Selenium, Nightwatch, Travis, MongoDB, Express, Node.js, and Handlebars.
Web application designed for collecting cards and managing custom deck builds from the card game, Magic: The Gathering.
Implemented CRUD features to add, remove, and edit user-created decks and filter search cards by name.
Worked as lead designer and developer in team of three. Built with Vue.js, Firebase, and Bulma CSS.
Information architecture redesign of playlist creation and management functionality to improve user experience and flow based on popular mobile music applications that support streaming such as Spotify and SoundCloud. Design decisions made based on results
collected from user interviews, comparison of flow, and competitive analysis. Used Photoshop and Invision for high-fidelity prototypes of application.
Web application designed to provide users safe and lit routes calculated based on street light density visualized as a heat map from 2012 San Diego City street light data. Maps live crime data in San Diego and visualizes UC San Diego DELPHI aggregated crime data from San Diego. Live newsfeed allows users to discuss commute conditions and safety. Built with jQuery, Node.js, Express, MongoDB, Bootstrap, D3.js, Socket.io, Google Maps API, PassportJS.
Built responsive website using HTML, CSS, JavaScript and jQuery to showcase skills and projects in development and design. Utilized Google's Material Design Lite and Material Icons, Animate.css, WOW.js, Stellar.js, and Font Awesome. Assets by Leann Gamboa.
Android application built by team of five developers that keeps track of the user's health profile, body mass index, and calorie intake. Implemented back-end solution using Parse, led and coordinated team stand-up meetings, wrote tests to verify basic functionality.
Web application designed to allow users to easily search, join and create course study groups. Led team of two student designers in planning and building application, spearheaded development of front-end and back-end aspects, led and coordinated meetings. Built with AngularJS, Node.js, Express, Parse, Bootstrap.
Implemented front-end aspects of organization’s AngularJS site with team of five designers and developers by building a smooth and appealing interface that allows users to easily register for, create, and view social and service events and look up organization information.


Across: 1. At home, play guitar with Anne and Tito providing musical arrangement (15) 9. Princess Mary quietly returns to tomb (7) 10. Composer I follow—e.g. Rock (7) 11. Deer kept in extremely large kangaroo sanctuary initially (4) 12. Frighten nomadic races (5) 13. Relatives take equipment to hospital (4) 16. Greek character gets tarts back on the level (7) 17. Fast or legal? (7) 18. Integral working committee (7) 21. He fails to catch dispenser (7) 23. Lied about not working (4) 24. Africa’s language contains a word for “closet lion” (5) 25. Mum left one in the country (4) 28. Raffle lamb, too? (7) 29. Catch eggheads holding last character in swinger’s bar (7) 30. Categories in which top Formula One Jaguar gets ten points (15) Down: 1. Inspire stoicism based on personal ideas? (15) 2. Is he out to score? (7) 3. Means of access to artist’s representative (4) 4. In 2000 Detective Inspector and copper followed band for a bit (7) 5. I rage in the country (7) 6. One goes round King’s piece of land (4) 7. Moving slowly to add note to fashionable feature (7) 8. Friendly local feeling or genuine bliss about hospital (15) 14. Wander south with tea carrier, perhaps (5) 15. Force weaver into depression (5) 19. Album I compiled about one’s eating disorder (7) 20. Female spinning assistant? (7) 21. Severe cold picked up by Astrid (7) 22. Locate smell coming from fake medicine (7) 26. Rolling Stones would not get doctor on boat (4) 27. Tease one in club (4)


Apple, the Apple logo, iPad, iPhone logo, Touch ID and Face ID are trademarks of Apple Inc., registered in the U.S. and other countries. App Store, CloudKit, iCloud and iTunes Store are service marks of Apple Inc., registered in the U.S. and other countries. Microsoft, MS Access are trademarks of Microsoft Corporation, registered in the U.S. and other countries.


HaplowebMaker
Unfortunately HaplowebMaker was not published yet. Please cite this software using the URL.
Draw a Haploweb
Calculate and draw an haploweb network.
Draw a phylogenetic network
Calculate and draw a phylogenetic network.
Calculate FFRs
Calculate the field for recombination.
Open project
Load an existing project from hard disc or the internet.
Settings
Change the settings of this program.
New
Open another instance of this program in another tab/window.
?
Documentation
Browse the documentation.
Download
Download, install and run HaplowebMaker on your own computer.
Contact
E-mail the authors.


Interaction Flow for a spanish user
Spanish User Preconditions
Santiago is determined to find a website to read and save articles for his pre-diabetic condition.
Santiago wants a website where the information he is looking for is both in english and spanish.
Spanish User Post-conditions
Santiago found a website with spanish and english articles related to pre-diabetes and lifestyle modification.
Santiago reads the articles in spanish and saves them for later.
Interaction Flow
- Santiago clicks safari on his iphone.
- His phone loads up safari.
- He then types on the search bar for health portal.
- Once he gets to the website homepage, Santiago then clicks the button to translate the website into Spanish.
- The website then translate everything from english to spanish.
- Santiago then finds a button within the website that is able to let him sign in to his account.
- He browses for articles by clicking the searchbar.
- Health portal then loads many useful articles for santiago.
- After finding and reading a few articles Santiago then clicks the like and save button to read the article later.
- Saved articles will show on Santiago's account feed next time he logs in.


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface RubberBandCanvas
An interface for which any JComponent
that
wishes to enable rubber band functionality should implement.
The canvas is the area on which the RubberBand
will be drawn
onto.
| Method Summary | |
|---|---|
JComponent |
getCanvas()
Returns the JComponent which is the canvas for the
RubberBand |
void |
setRubberBand(RubberBand rubberband)
Sets the RubberBand for the canvas |
| Method Detail |
|---|
JComponent getCanvas()
JComponent
which is the canvas for the
RubberBand
JComponent
which is the canvas for the
RubberBand
void setRubberBand(RubberBand rubberband)
RubberBand
for the canvas
rubberband
- the RubberBand
for the canvas
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Reporting bugs and requesting features
Before reporting a bug or requesting a new feature, make sure you’ve done these first:
- Check that someone hasn’t already filed the bug or feature request by searching the “Issues” for the repo in question, or in the “Reef” repository.
- If it’s for Ask, check the features in our Trello board.
- Don’t reopen issues that have been marked “wontfix” by a core developer.
Reporting bugs
We use GitHub Issues to track bugs. Each Coral component app has its own repository, but since it may not be clear precisely which component the bug is originating from, we use the Reef repository to track bugs.
Complete, reproducible, specific bug reports are very helpful. When writing a bug report, be sure to include the following:
- A clear description of the issue.
- A set of instructions for replicating it.
Add as much additional information as you can, such as code snippets, logs, and screenshots. A small test case is ideal.
Reporting user interface bugs and features
If your bug or feature request involves the user interface, include screenshots in your ticket. Try to make the screenshots small enough to only show the relevant area of the screen, but large enough to provide context for that area.
Requesting features
We want your help in deciding what to build! We have a section of our community dedicated to each of our main software products:
When submitting a feature request, try to adhere to these guidelines:
- Describe clearly what the feature is and how you’d like to see it implemented.
- Explain why you’d like the feature.
- If the feature can be developed as a plug-in or extension to Coral, consider developing it yourself as a plug-in and adding it to our code base.


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Converts two packed signed doubleword integers in the source operand (second operand) to two packed double-precision floating-point values in the destination operand (first operand). The source operand can be an MMX technology register or a 64-bit memory location. The destination operand is an XMM register.
This instruction causes a transition from x87 FPU to MMX technology operation (that is, the x87 FPU top-of-stack pointer is set to 0 and the x87 FPU tag word is set to all 0s [valid]). If this instruction is executed while an x87 FPU floating-point exception is pending, the exception is handled before the CVTPI2PD instruction is executed.
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mattrib(1) mattrib(1) Name mattrib - change MSDOS file attribute flags Note of warning This manpage has been automatically generated from mtools's texinfo docu‐ mentation, and may not be entirely accurate or complete. See the end of this man page for details. Description Mattrib is used to change MS-DOS file attribute flags. It has the follow‐ ing syntax: mattrib [-a|+a] [-h|+h] [-r|+r] [-s|+s] [-/] [-p] [-X] msdosfile [ msdosfiles ... ] Mattrib adds attribute flags to an MS-DOS file (with the `+' operator) or remove attribute flags (with the `-' operator). Mattrib supports the following attribute bits: a Archive bit. Used by some backup programs to indicate a new file. r Read-only bit. Used to indicate a read-only file. Files with this bit set cannot be erased by DEL nor modified. s System bit. Used by MS-DOS to indicate a operating system file. h Hidden bit. Used to make files hidden from DIR. Mattrib supports the following command line flags: / Recursive. Recursively list the attributes of the files in the subdirectories. X Concise. Prints the attributes without any whitespace padding. If neither the "/" option is given, nor the msdosfile contains a wildcard, and there is only one MS-DOS file parameter on the com‐ mand line, only the attribute is printed, and not the filename. This option is convenient for scripts p Replay mode. Outputs a series of mformat commands that will reproduce the current situation, starting from a situation as left by untarring the MS-DOS file system. Commands are only output for attribute settings that differ from the default (archive bit set for files, unset for directories). This option is intended to be used in addition to tar. The readonly attribute is not taken into account, as tar can set that one itself. See Also Mtools' texinfo doc Viewing the texi doc This manpage has been automatically generated from mtools's texinfo docu‐ mentation. However, this process is only approximative, and some items, such as crossreferences, footnotes and indices are lost in this transla‐ tion process. Indeed, these items have no appropriate representation in the manpage format. Moreover, not all information has been translated into the manpage version. Thus I strongly advise you to use the original texinfo doc. See the end of this manpage for instructions how to view the texinfo doc. * To generate a printable copy from the texinfo doc, run the follow‐ ing commands: ./configure; make dvi; dvips mtools.dvi * To generate a html copy, run: ./configure; make html A premade html can be found at `http://www.gnu.org/software/mtools/man‐ ual/mtools.html' * To generate an info copy (browsable using emacs' info mode), run: ./configure; make info The texinfo doc looks most pretty when printed or as html. Indeed, in the info version certain examples are difficult to read due to the quot‐ ing conventions used in info. mtools-4.0.17 29Jun11 mattrib(1)


{% extends "dreamsapp/base.html" %} {% load dreamutils %} {% block body %}
Here's a list of your recent measured nights. Select one for which to compose music!
| Date | Amount of sleep | |
|---|---|---|
| {{ night.date|date }} | {% if night.analysis_valid %} {{ night.time_sleeping|hoursminutes|default:"(Not slept)" }} {% else %} - {% endif %} | |
| Hmm, you don't seem have any sleep info :/ |


academics
This section has details about my education, coursework and teaching.
Education
I graduated from the Indian Institute of Technology, Kanpur (IITK) in 2019 with a Bachelor of Science in Mathematics and Scientific Computing.
Currently, I am pursuing a Master’s degree in AI at the University of Amsterdam (UvA).
Courses
Here is a an overview of my relevant coursework:
AI and ML
| Course | Institute |
|---|---|
| Computer Vision 1 & 2 | UvA |
| Machine Learning 1 & 2 | UvA |
| Deep Learning 1 & 2 | UvA |
| NLP 1 | UvA |
| Information Retrieval 1 | UvA |
| Fairness, Accountability, Transparency | UvA |
| Interpretability and Explainability | UvA |
| Knowledge Representation and Reasoning | UvA |
| Visual Recognition | IITK |
| Probabilistic Modelling & Inference | IITK |
| Full Stack Deep Learning | Online |
| Multi-view Geometry | Online (TUM) |
| Game Theory and Mechanism Design | IITK |
Mathematics and Statistics
| Course | Institute |
|---|---|
| Linear algebra | IITK |
| Probability and Statistics | IITK |
| Real and Complex analysis | IITK |
| Abstract algebra | IITK |
| Mathematical logic | IITK |
| Numerical analysis and scientific computing | IITK |
| Several variable calculus | IITK |
| Ordinary and Partial differential equations | IITK |
Computer Science
| Course | Institute |
|---|---|
| Data Structures and Algorithms | IITK |
| Theory of Computation | IITK |
| Number Theory | IITK |
| Fundamentals of Computing (C language) | IITK |
Teaching
During my undergrad at IITK, I liked teaching to my friends and juniors in an informal setting.
During my master’s degree, I am fortunate to be a TA for Computer Vision 1 and Deep Learning 1 courses at UvA.