Enjoy the videos, and see you next year...
You may Follow us on Twitter or Join our Group
You can get a good sense of the topics we're interested in from last year's website Reversim Summit 2013
Apart from that, here's a good grocery list of topics just to give you ideas. We're interested in everything including software development, software product development, UX, startups mobile, web, devops, data processing, scaling and more. You should know - this is not a closed list, if your topic doesn't seem to fit here and is of general interest, we want it!
Unlike last year, we do not set out a closed list of tracks. This time we'd like to leave the topics (tracks) open and only after accepting the submissions we might devide up to tracks, but we will not rule out a single good talk just b/c it's not a natural fit to any predefined list, so don't worry so much about categorizing your submissions.
Full feature are frontal presentations b/w 30 - 50 minutes. They will be held in either the large room (200 ppl) or the small room (50 ppl) in two parallel sessions
Lightning are speedy 5 min talks. They will be presented in a series in which each presenter has exactly 20 slides, 15 sec per slide, slides are auto advanced and in total 5 min. No break b/w the sessions. It's fun, it's speedy, it's consice and it's breathtaking :-)
Open space is a free form discussion in which folks gather around a tables or just in circles and discuss a topic of common interest. There's no need to submit anything before the conference, just think of topics that interest you and suggest them for voting at the conference itself. We will hold a few parallel tracks of open spaces in which each space will revolve around one chosen topic.
We are especially interested in open source projects made in israel or created by Israelis. We will have a special stage for that.


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--- title: Core keywords: fastai sidebar: home_sidebar summary: "Team-strength models in Python" description: "Team-strength models in Python" nb_path: "nbs/core.ipynb" ---
import json
import datetime as dt
import numpy as np
import pprint
Let's demo
with open('../data/premier-league-1516.json', 'r') as f:
pl_1516 = json.load(f)
# Let's parse the dates, too
for match in pl_1516:
match['date'] = dt.datetime.strptime(match['date'], '%Y-%m-%d')
pl_1516[0:3]
A model in mezzala
is composed of 2 parts:
mezzala.blocks
)mezzala.adapters
)The model blocks determine which terms your model estimates. In general, you will want
to estimate offensive and defensive strength for each team (TeamStrength
) and
as well as home advantage HomeAdvantage
.
The selected model blocks can be supplied to the model as a list:
blocks = [TeamStrength(), HomeAdvantage()]
An adapter connects your model to the data source. In other words, it tells the model how find the information needed to fit.
The information needed is determined by which model blocks are used. In our case,
home_goals
and away_goals
TeamStrength
- requires home_team
and away_team
HomeAdvantage
doesn't require any information, since it assumes all matches have equal home-field advantage by default.
adapter = KeyAdapter( # `KeyAdapter` = data['...']
home_team='team1',
away_team='team2',
home_goals=['score', 'ft', 0], # Get nested fields with lists of fields
away_goals=['score', 'ft', 1], # i.e. data['score']['ft'][1]
)
Pulling this together, we can construct a model from an adapter and blocks
model = DixonColes(adapter=adapter, blocks=blocks)
model.fit(pl_1516)
# All estimates should be valid numbers
assert all(not np.isnan(x) for x in model.params.values())
# Home advantage should be positive
assert 1.0 < np.exp(model.params[HFA_KEY]) < 2.0
Let's inspect the parameters a bit. First, let's look at the boring (non-team) ones:
param_keys = model.params.keys()
param_key_len = max(len(str(k)) for k in param_keys)
for k in param_keys:
if not isinstance(k, TeamParameterKey):
key_str = str(k).ljust(param_key_len + 1)
print(f'{key_str}: {np.exp(model.params[k]):0.2f}')
And the team ones. Let's look at each team's attacking quality:
teams = {k.label for k in param_keys if isinstance(k, TeamParameterKey)}
team_offence = [(t, np.exp(model.params[OffenceParameterKey(t)])) for t in teams]
for team, estimate in sorted(team_offence, key=lambda x: -x[1]):
print(f'{team}: {estimate:0.2f}')
team_defence = [(t, np.exp(model.params[DefenceParameterKey(t)])) for t in teams]
for team, estimate in sorted(team_defence, key=lambda x: x[1]):
print(f'{team}: {estimate:0.2f}')
Making predictions for a single match
scorelines = model.predict_one({
'team1': 'Manchester City FC',
'team2': 'Swansea City FC',
})
# Probabilities should sum to 1
assert np.isclose(
sum(p.probability for p in scorelines),
1.0
)
scorelines[0:5]
outcomes = scorelines_to_outcomes(scorelines)
# MCFC should have a better chance of beating Swansea
# at home than Swansea do of winning away
assert outcomes[Outcomes('Home win')].probability > outcomes[Outcomes('Away win')].probability
list(outcomes.values())
Or for multiple matches
many_scorelines = model.predict([
{'team1': 'Manchester City FC',
'team2': 'Swansea City FC'},
{'team1': 'Manchester City FC',
'team2': 'West Ham United FC'}
])
What about a model with a different weighting method?
By default, the DixonColes
model weights all matches equally. However, it's more realistic to give matches
closer to the current date a bigger weight than those a long time ago.
The original Dixon-Coles paper suggests using an exponential weight, and we can use the same:
season_end_date = max(match['date'] for match in pl_1516)
weight = ExponentialWeight(
# Value of `epsilon` is taken from the original paper
epsilon=-0.0065,
key=lambda x: (season_end_date - x['date']).days
)
model_exp = DixonColes(
adapter=adapter,
blocks=blocks,
weight=weight
)
model_exp.fit(pl_1516)
How much does that change the ratings at season-end?
for k in sorted(param_keys, key=lambda x: x.label):
key_str = str(k).ljust(param_key_len + 1)
model_param = np.exp(model.params[k])
model_exp_param = np.exp(model_exp.params[k])
print(f'{key_str}: {model_param:0.2f} -> {model_exp_param:0.2f} ({model_exp_param/model_param:0.2f})')


It just made me be more at peace with the fact that I am learning a lot and really cementing my knowledge with the process even if the product isn't ideal.
I was already an occasional practitioner of meditation before reading this chapter but my focus had been guided meditations. I had never really considered doing completely silent and relatively unstructured meditation before reading this chapter.
I already knew it was hugely beneficial in many areas of life but I had never thought of it as a way to practice delaying your responses to stimuli. This is a really helpful insight for me as it gives me a new thing to focus on while I meditate.
I found the unstructured "focus on your breath" type of meditation very different to what I usually do. I actually found it helpful to not be distracted by music or guiding words and just to listen to myself and my body.
Not really, been meditating more this week and have found it really useful for managing stress.


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This webapp is a part of the course DM2518 at KTH.
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Relaxation practices!

Experience the contact with the ground.
Feel into the soles of the feet.
Feel the knees.
The hips.
The spine.
Shoulders.
Neck.
Head.
Feel the connection between the top of the head and the feet.
Shoulders
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Let your arms hang loose.
Become aware of the shoulders.
Slightly lift one shoulder and release it.
Let the shoulder come all the way down.
Lift the other shoulder and release it.
Alternate three more times on each side.
Experience the breath flowing in and out of the body.

Feel the forehead.
The eyebrows.
Eyes.
Nose.
Nostrils.
Air flowing through the nostrils.
Cheeks.
Jaw.
Lips.
Tongue.
Whole of the head.
The space surrounding the head.


It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).
For demo purpose you can use anything in username and password field. You will still need to register but you can provide a spammy or not a valid email.
Some information about privacy policy, terms etc.
For demo purpose you can use anything in username and password field.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).
For demo purpose you can use anything in username and password field.


To get started select "create bot." You will create a Developer Application to connect the bot to Glimesh. This also allows the bot to have a custom name.
When you are finished enter the client ID and secret key. Save them to GlimBoi. Then press authorize to connect the bot with your application. After the initial auth completion Glimboi will automatically authenticate itself on launch. If any problems arise you can simple authenticate again.
GlimBoi is an open source project started by Mytho. It is written in Electron which is a framework for building desktop applications. Server side is NodeJS. Data storage is NEDB. The front end is HTML and Bootstrap CSS. DataTables are used for showing the database and Apex charts for that chart =>
It was inspired by Scottybot on Mixer. Special thanks to Daddy Robot and Codestics for the help and inspiration. The goal of this bot is to provide an easy to use interface while using minimal resources.
Contributions are always welcome :)
1: Create Bot
2: Create an application on Glimesh. Redirect URI must be http://localhost:3000/success
3: Enter the client ID and secret ID from the application into GlimBoi. Save them.
4: Select Authorize. It will open a new tab in your browser. Authorize your application.
GlimBoi can join the chat!


Luxury five-star hotel founded in 1875 part of Preferred Hotels & Resorts, Le Richemond is Geneva legend. Ideally set on the shores of Lake Geneva, Le Richemond offers a breath-taking view on the emblematic Jet d’Eau and the highest Europe mountain Mont Blanc.
Experience five-star travel at its finest when you stay at one of the 109 rooms and suites at Le Richemond.
The knowledge, expertise and attention to detail of the entire team makes for outstanding service.
Welcome to the only exclusive Spa by Sisley in Switzerland.
Located in the centre of Geneva in a luxurious oasis, indulge in pampering spa treatments from acclaimed French skincare expert Sisley. For complete wellbeing, our state-of-the-art fitness studio features TechnoGym equipment for a full body workout.
          Spa treatments available daily from 10am to 8pm.
Fitness studio open daily from 7am to 10pm.
Fitness memberships
"Had three nights at the Adventure-Haze and two meals in the restaurant.  Great service, beautiful rooms, fantastic food. Adventure-Haze is a wonderful addition to the City's accommodation and hospitality options."
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Plugin Information |
|---|
No information for the plugin 'container-slaves-plugin' is available. It may have been removed from distribution. |
Use Containers to setup build executors, without any constraint on images you can use.
This plugin is currently under development, as a proposal for Docker Global Hack Day.
Read more on https://github.com/ndeloof/container-slaves-plugin


|
Netxtendable
5.1.0
A .NET library with extension functions for some standard types and usefull data structures
|
Class with extension methods for Tuple More...
Static Public Member Functions | |
| static T[] | ToArray< T > (this ValueTuple< T, T > t) |
| Creates an array using items from t More... | |
| static T[] | ToArray< T > (this ValueTuple< T, T, T > t) |
| Creates an array using items from t More... | |
| static T[] | ToArray< T > (this ValueTuple< T, T, T, T > t) |
| Creates an array using items from t More... | |
| static T[] | ToArray< T > (this ValueTuple< T, T, T, T, T > t) |
| Creates an array using items from t More... | |
| static T[] | ToArray< T > (this ValueTuple< T, T, T, T, T, T > t) |
| Creates an array using items from t More... | |
| static T[] | ToArray< T > (this ValueTuple< T, T, T, T, T, T, T > t) |
| Creates an array using items from t More... | |
| static List< T > | ToList< T > (this ValueTuple< T, T > t) |
| Creates a list using items from t More... | |
| static List< T > | ToList< T > (this ValueTuple< T, T, T > t) |
| Creates a list using items from t More... | |
| static List< T > | ToList< T > (this ValueTuple< T, T, T, T > t) |
| Creates a list using items from t More... | |
| static List< T > | ToList< T > (this ValueTuple< T, T, T, T, T > t) |
| Creates a list using items from t More... | |
| static List< T > | ToList< T > (this ValueTuple< T, T, T, T, T, T > t) |
| Creates a list using items from t More... | |
| static List< T > | ToList< T > (this ValueTuple< T, T, T, T, T, T, T > t) |
| Creates a list using items from t More... | |
| static HashSet< T > | ToSet< T > (this ValueTuple< T, T > t) |
| Creates a set using items from t More... | |
| static HashSet< T > | ToSet< T > (this ValueTuple< T, T, T > t) |
| Creates a set using items from t More... | |
| static HashSet< T > | ToSet< T > (this ValueTuple< T, T, T, T > t) |
| Creates a set using items from t More... | |
| static HashSet< T > | ToSet< T > (this ValueTuple< T, T, T, T, T > t) |
| Creates a set using items from t More... | |
| static HashSet< T > | ToSet< T > (this ValueTuple< T, T, T, T, T, T > t) |
| Creates a set using items from t More... | |
| static HashSet< T > | ToSet< T > (this ValueTuple< T, T, T, T, T, T, T > t) |
| Creates a set using items from t More... | |
| static Dictionary< K, V > | ToDictionary< K, V > (this ValueTuple<(K, V),(K, V)> t) |
| Creates a dictionary using items from t More... | |
| static Dictionary< K, V > | ToDictionary< K, V > (this ValueTuple<(K, V),(K, V),(K, V)> t) |
| Creates a dictionary using items from t More... | |
| static Dictionary< K, V > | ToDictionary< K, V > (this ValueTuple<(K, V),(K, V),(K, V),(K, V)> t) |
| Creates a dictionary using items from t More... | |
| static Dictionary< K, V > | ToDictionary< K, V > (this ValueTuple<(K, V),(K, V),(K, V),(K, V),(K, V)> t) |
| Creates a dictionary using items from t More... | |
| static Dictionary< K, V > | ToDictionary< K, V > (this ValueTuple<(K, V),(K, V),(K, V),(K, V),(K, V),(K, V)> t) |
| Creates a dictionary using items from t More... | |
| static Dictionary< K, V > | ToDictionary< K, V > (this ValueTuple<(K, V),(K, V),(K, V),(K, V),(K, V),(K, V),(K, V)> t) |
| Creates a dictionary using items from t More... | |
| static IEnumerable< T > | Enumerate< T > (this ValueTuple< T, T > t) |
| Enumerates items in t More... | |
| static IEnumerable< T > | Enumerate< T > (this ValueTuple< T, T, T > t) |
| Enumerates items in t More... | |
| static IEnumerable< T > | Enumerate< T > (this ValueTuple< T, T, T, T > t) |
| Enumerates items in t More... | |
| static IEnumerable< T > | Enumerate< T > (this ValueTuple< T, T, T, T, T > t) |
| Enumerates items in t More... | |
| static IEnumerable< T > | Enumerate< T > (this ValueTuple< T, T, T, T, T, T > t) |
| Enumerates items in t More... | |
| static IEnumerable< T > | Enumerate< T > (this ValueTuple< T, T, T, T, T, T, T > t) |
| Enumerates items in t More... | |
Class with extension methods for Tuple
|
static |
Enumerates items in t
| T | Type of items in t |
| t | Tuple whose items will be enumerated |
|
static |
Enumerates items in t
| T | Type of items in t |
| t | Tuple whose items will be enumerated |
|
static |
Enumerates items in t
| T | Type of items in t |
| t | Tuple whose items will be enumerated |
|
static |
Enumerates items in t
| T | Type of items in t |
| t | Tuple whose items will be enumerated |
|
static |
Enumerates items in t
| T | Type of items in t |
| t | Tuple whose items will be enumerated |
|
static |
Enumerates items in t
| T | Type of items in t |
| t | Tuple whose items will be enumerated |
|
static |
Creates an array using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the array |
|
static |
Creates an array using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the array |
|
static |
Creates an array using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the array |
|
static |
Creates an array using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the array |
|
static |
Creates an array using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the array |
|
static |
Creates an array using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the array |
|
static |
Creates a dictionary using items from t
| K | Type of keys in key-value pairs in t |
| V | Type of values in key-value pairs in t |
| t | Tuple with key-value pairs that will be inserted into the dictionary |
| K | : | notnull |
|
static |
Creates a dictionary using items from t
| K | Type of keys in key-value pairs in t |
| V | Type of values in key-value pairs in t |
| t | Tuple with key-value pairs that will be inserted into the dictionary |
| K | : | notnull |
|
static |
Creates a dictionary using items from t
| K | Type of keys in key-value pairs in t |
| V | Type of values in key-value pairs in t |
| t | Tuple with key-value pairs that will be inserted into the dictionary |
| K | : | notnull |
|
static |
Creates a dictionary using items from t
| K | Type of keys in key-value pairs in t |
| V | Type of values in key-value pairs in t |
| t | Tuple with key-value pairs that will be inserted into the dictionary |
| K | : | notnull |
|
static |
Creates a dictionary using items from t
| K | Type of keys in key-value pairs in t |
| V | Type of values in key-value pairs in t |
| t | Tuple with key-value pairs that will be inserted into the dictionary |
| K | : | notnull |
|
static |
Creates a dictionary using items from t
| K | Type of keys in key-value pairs in t |
| V | Type of values in key-value pairs in t |
| t | Tuple with key-value pairs that will be inserted into the dictionary |
| K | : | notnull |
|
static |
Creates a list using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the list |
|
static |
Creates a list using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the list |
|
static |
Creates a list using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the list |
|
static |
Creates a list using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the list |
|
static |
Creates a list using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the list |
|
static |
Creates a list using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the list |
|
static |
Creates a set using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the set |
|
static |
Creates a set using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the set |
|
static |
Creates a set using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the set |
|
static |
Creates a set using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the set |
|
static |
Creates a set using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the set |
|
static |
Creates a set using items from t
| T | Type of items in t |
| t | Tuple whose items will be inserted into the set |


BTCFLASH(8) firmware flash utility BTCFLASH(8) NAME BTCFLASH - firmware flash utility for BTC DRW1008 DVD+/-RW recorder. SYNOPSIS btcflash DEVICE FILE DESCRIPTION BTCFLASH may also work with other BTC DVD recorders, such as the DRW1004 and DRW1108, but they have not been tested. USE AT YOUR OWN RISK! Firmware files may be obtained by running BTC's Windows flash utility, then searching in the WINDOWSTEMP or WINNTTEMP directory for a *.HEX file. It will probably be in a subdirectory named PAC*.tmp.DIR, and the HEX file will be named Vnnnn.HEX, where nnnn is the firmware version num‐ ber. You'll also find IDEFLASH.EXE or BTCFLASH.EXE in the same directory. OPTIONS DEVICE Write the firmware to the given device. FILE Read the firmware from the given file. This utility will also accept firmware files in ".BIN" format. AUTHOR BTCFLASH was written by David Huang<EMAIL_ADDRESS>This manual page was written by Daniel Baumann<EMAIL_ADDRESS>for the Debian project (but may be used by others). 6.0 2006-01-17 BTCFLASH(8)


New version available Update
Welcome, {{me?.username}}! Select a User or a Conversation to start or continue a chat


Unblock your thinking with the help of our expert virtual consultants.
Virtual training is a complex web of moving parts. Any one of these parts can hit a snag at any time. When this happens, taking the right actions to get things moving again is crucial.
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Before Change
// prevent conflict with preprocess in epi_reset
state = self.preprocess_state(state, append=False)
next_state = self.preprocess_state(next_state, append=False)
self.add_experience(state, action, reward, next_state, done)
def add_experience(self, state, action, reward, next_state, done):
"""Implementation for update() to add experience to memory, expanding the memory size if necessary"""
// TODO downcast to dtype
After Change
// prevent conflict with preprocess in epi_reset
state = self.preprocess_state(state, append=False)
next_state = self.preprocess_state(next_state, append=False)
if self.body.env.is_venv:
for sarsd in zip(state, action, reward, next_state, done):
self.add_experience(*sarsd)
else:
self.add_experience(state, action, reward, next_state, done)
def add_experience(self, state, action, reward, next_state, done):
"""Implementation for update() to add experience to memory, expanding the memory size if necessary"""
// Move head pointer. Wrap around if necessary
self.head = (self.head + 1) % self.max_size


Class: EmojiChar
| Defined in: | src/emoji_char.coffee |
Overview
EmojiChar represents a single Emoji character and its associated metadata.
Properties
name
- The standardized name used in the Unicode specification to represent this emoji character.unified
- The primary unified codepoint ID for the emoji.variations
- A list of all variant codepoints that may also represent this emoji.short_name
- The canonical "short name" or keyword used in many systems to refer to this emoji. Often surrounded by:colons:
in systems like GitHub & Campfire.short_names
- A full list of possible keywords for the emoji.text
- An alternate textual representation of the emoji, for example a smiley face emoji may be represented with an ASCII alternative. Most emoji do not have a text alternative. This is typically used when building an automatic translation from typed emoticons.
It also contains a few helper functions to deal with this data type.
Class Method Summary
- . (String) _unified_to_char(uid) Private Convert a unified codepoint ID to the UCS-2 string representation.
Instance Method Summary
-
#
(Boolean)
is_doublebyte()
Is the
EmojiChar
represented by a doublebyte codepoint in Unicode? -
#
(Boolean)
has_variants()
Does the
EmojiChar
have an alternate Unicode variant encoding? -
#
(String, null)
variant()
Returns the most likely variant-encoding codepoint ID for an
EmojiChar
. - # (String) render({variant_encoding} = { variant_encoding: true }) Renders a UCS-2 string representation of the glyph for writing to screen.
-
#
(Array<String>)
chars()
Returns a list of all possible UTF-8 string renderings of an
EmojiChar
. - # (String) toString()
Class Method Details
.
(String)
_unified_to_char(uid)
Private
Convert a unified codepoint ID to the UCS-2 string representation.
Constructor Details
#
(EmojiChar)
constructor(blob)
Instance Method Details
#
(Boolean)
is_doublebyte()
Is the EmojiChar
represented by a doublebyte codepoint in Unicode?
#
(Boolean)
has_variants()
Does the EmojiChar
have an alternate Unicode variant encoding?
#
(String, null)
variant()
Returns the most likely variant-encoding codepoint ID for an EmojiChar
.
For now we only know of one possible variant encoding for certain characters, but there could be others in the future.
This is typically used to force Emoji rendering for characters that could be represented in standard font glyphs on certain operating systems.
The resulting encoded string will be two codepoints, or three codepoints for doublebyte Emoji characters.
#
(String)
render({variant_encoding} = {
variant_encoding: true
})
Renders a UCS-2 string representation of the glyph for writing to screen.
If you want to specify whether or not to use variant encoding, pass an options hash such as:
foo.char({variant_encoding: true})
By default this will use the variant encoding if it exists.
variant encoding.
#
(Array<String>)
chars()
Returns a list of all possible UTF-8 string renderings of an EmojiChar
.
E.g., normal, with variant selectors, etc. This is useful if you want to have all possible values to match against when searching for the emoji in a string representation.
#
(String)
toString()


Connect with us:
Image recognition using traditional computer vision techniques. I refer to techniques that are not Deep Learning based as traditional computer vision techniques because they are being quickly replaced by Deep Learning based techniques. That said, traditional computer vision approaches still power many applications. Many of these algorithms are also available in computer vision libraries like OpenCV and work very well out of the box.
MyAI lets you find content by visual similarity, keyword tag, or a combination of both so you can serve powerful image-based recommendations and related content in real-time.


WarningThis demo is not completed. Please do not run unless you are me. Additionally, when you run the demo, the results of running PMD will be available for the whole world to see, as well as linked off of the PMD homepage for a period of time. If you don't want this, don't submit to the demo. |


Last month I decided that it was time for Bitcoin and I to spend more quality time together. So I went to MegaMine and opened an account. Why Megamine and not some others? well, truth is that they are local. It’s a Startup from London, you can go and visit them. Depending on the time of the day they will buy you a coffee or a beer and have a nice chat with you… and yes, their prices are super competitive!
I bumped into them because I was looking for a cheap datacenter to host my machines to build my OpenStack Empire, but that’s another story. Let’s focus!
If you want to get into Bitcoins there are two ways:
- You can mine Bitcoins, aka Bitcoin mining
- You can buy Bitcoins. In the same way that you would buy any other foreign currency.
The main difference is the price. Buying Bitcoins is like buying US Dollars, or British Pounds. (Buying and selling prices are different.) When mining you pay for the usage of a mining machine (or a pool), regardless of the price of the Bitcoin (well, that’s the theory). This means that under certain circumstances, buying Bitcoin is more convenient that mining.
buying Bitcoin is not as straightforward as many people think. Yes, there are places where you can go and buy Bitcoins. for example, this website. Now, if you notice, the website does not sell Bitcoins, the website will allow you to buy Bitcoins (from someone else), and because of that, every transaction has different conditions (looks dodgy, doesn’t it?)
At the beginning of the month SatoshiPoint presented their machines and I rushed to go and compare what I could get from them compared to what I could get from mining.
The difference was that the machines will give you your bitcoin straightaway (in reality it the transaction took around 13 minutes to go through) compared to the 5~ish days that I had to spend mining to get the same amount of Bitcoin.
As I see it, if you want your Bitcoins now, go and buy them out of some place. Make sure the website is the one selling the Bitcoins and not-yet-another-middle-man-in-the-middle.

I’m not sure if Megamine will sell Bitcoins in the future or not, but I will be asking them next time I see them for a coffee or a beer.
Happy Mining!
Disclaimer: I’m not related to Megamine in any way nor they give me any satoshis to write about them (although any offered will be most welcome!).


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The Merchant of Venice
by William Shakespeare
Merchant of Venice: Act 2, Scene 3
Additional Information
- Year Published: 1597
- Language: English
- Country of Origin: England
- Source: Shakespeare, W. (1597). The Merchant of Venice.New York: Sully and Kleinteich.
-
Readability:
- Flesch–Kincaid Level: 11.0
- Word Count: 185
- Genre: Tragedy
- Keywords: 16th century literature, british literature, comedy, drama, merchant of venice, william shakespeare
- ✎ Cite This
Downloads
SCENE. The same. A room in SHYLOCK's house.
(Enter JESSICA and LAUNCELOT.)
JESSICA.
I am sorry thou wilt leave my father so:
Our house is hell, and thou, a merry devil,
Didst rob it of some taste of tediousness.
But fare thee well; there is a ducat for thee;
And, Launcelot, soon at supper shalt thou see
Lorenzo, who is thy new master's guest:
Give him this letter; do it secretly.
And so farewell. I would not have my father
See me in talk with thee.
LAUNCELOT.
Adieu! tears exhibit my tongue. Most beautiful pagan,
most sweet Jew! If a Christian do not play the knave and get
thee, I am much deceived. But, adieu! these foolish drops do
something drown my manly spirit; adieu!
JESSICA.
Farewell, good Launcelot.
(Exit LAUNCELOT)
Alack, what heinous sin is it in me
To be asham'd to be my father's child!
But though I am a daughter to his blood,
I am not to his manners. O Lorenzo!
If thou keep promise, I shall end this strife,
Become a Christian and thy loving wife.
(Exit)


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @import play.twirl.api.HtmlFormat @import uk.gov.hmrc.govukfrontend.views.html.components.implicits._ @import views.ViewUtils.titleBuilder @this(newMain: newMain, formHelper: FormWithCSRF, input: GovukInput, button: GovukButton, govukBackLink: GovukBackLink, govukErrorSummary: GovukErrorSummary) @(editContactDetailsForm: Form[models.EditContactDetails], serviceInfoContent: Html = HtmlFormat.empty, backLink: Option[String])(implicit authContext: StandardAuthRetrievals, messages: Messages, request: Request[AnyContent], appConfig: ApplicationConfig) @backLinkHtml = { @if(backLink.isDefined) { @govukBackLink(BackLink( href = backLink.get, content = Text("Back") )) } } @newMain(title = titleBuilder(messages("ated.contact-details.title"), Some(editContactDetailsForm)), serviceInfoContent = serviceInfoContent) { @backLinkHtml @if(editContactDetailsForm.hasErrors) { @govukErrorSummary(ErrorSummary().withFormErrorsAsText(editContactDetailsForm)) }
@messages("ated.contact-details.subheader")
@formHelper(action = controllers.subscriptionData.routes.EditContactDetailsController.submit) {


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Programando un CMS con Laravel en duilio.me


Kafka Security
Apache Kafka™ 2.4.0
@jaceklaskowski
/
StackOverflow
/
GitHub
/
LinkedIn
The "Internals" Books:
Apache Kafka
•
Kafka Streams
## Agenda
(1 of 2)
1. [Secure Communication Using SSL](#/secure-communication-using-ssl) * [Keys and Certificates](#/keys-and-certificates) * [Key and Trust Stores](#/key-and-trust-stores) * [security.protocol Client Property](#/security-protocol-client-property) * [Demo: Secure Communication Using SSL](#/demo-secure-communication-using-ssl) 1. [Secure Inter-Broker Communication](#/secure-inter-broker-communication) * [security.inter.broker.protocol Broker Property](#/security-inter-broker-protocol-broker-property) * [listeners Broker Property](#/listeners-broker-property) * [Demo: Secure Inter-Broker Communication](#/demo-secure-inter-broker-communication)
## Agenda
(2 of 2)
1. [SSL Authentication](#/ssl-authentication) * [ssl.client.auth Broker Property](#/ssl-client-auth-broker-property) * [Demo: SSL Authentication](#/demo-ssl-authentication) 1. [ACL Authorization](#/acl-authorization) * [authorizer.class.name Broker Property](#/authorizer-class-name-broker-property) * [Other Broker Properties](#/other-broker-properties) * [kafka-acls Utility](#/kafka-acls-utility) * [Demo: ACL Authorization](#/demo-acl-authorization)
## Secure Communication Using SSL * **Secure Sockets Layer (SSL)** - a cryptographic protocol for communications security over a computer network * Used for **secure communication** (data encryption) and **authentication** * Deprecated by **Transport Layer Security (TLS)** * Often referred to as **SSL/TLS** * [Transport Layer Security](https://en.wikipedia.org/wiki/Transport_Layer_Security) in Wikipedia
## Keys and Certificates * **Private And Public Keys** * Identity of an entity (such as a computer or a website) * **Public Key Certificate** (Digital Identity Certificate) * Identity of the owner (the subject) * Digital signature of an entity that has verified the certificate's contents (the issuer) * The certificate issuer is a **Certificate Authority (CA)** * **Certificate Signing Request (CSR)** * Request to Certificate Authority to sign a certificate
## Key and Trust Stores * **Key Store** (or Keystore) is a database of keys and certificates * By default the Java keystore is implemented as a file * Contains the private key and any certificates to complete a chain of trust of the primary certificate * Initially only with the private key * Eventually the certificate and any root certificates after a CSR (Certificate Signing Request) * **Trust Store** is a file with trusted certificates (public keys) * Each certificate is associated with a unique alias * Java **keytool** to manage keys and certificates
## security.protocol Client Property * Consult [The Internals Of Apache Kafka](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-clients-CommonClientConfigs.html#security.protocol)
## Demo 👉 [Securing Communication Between Clients and Brokers Using SSL](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-demo-securing-communication-between-clients-and-brokers.html)
## Secure Inter-Broker Communication * ...
## security.inter.broker.protocol Broker Property * Consult [The Internals Of Apache Kafka](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-properties.html#security.inter.broker.protocol)
## listeners Broker Property * Consult [The Internals Of Apache Kafka](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-properties.html#listeners)
## Demo 👉 [Secure Inter-Broker Communication](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-demo-secure-inter-broker-communication.html)
## SSL Authentication * In SSL authentication, Kafka brokers authenticate clients (using "2-way authentication") * Requires SSL encryption
discussed earlier
* Client Truststore Only * Broker and clients use keystores (for identity) and truststores with the Certificate Authority (CA)
## ssl.client.auth Broker Property * Consult [The Internals Of Apache Kafka](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-properties.html#ssl.client.auth)
## Demo 👉 [SSL Authentication](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-demo-ssl-authentication.html)
## ACL Authorization * FIXME
## authorizer.class.name Broker Property * Consult [The Internals Of Apache Kafka](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-properties.html#authorizer.class.name)
## Other Broker Properties * allow.everyone.if.no.acl.found * super.users
## kafka-acls Utility * FIXME
## Demo 👉 [ACL Authorization](https://jaceklaskowski.gitbooks.io/apache-kafka/kafka-demo-acl-authorization.html)
## Recap
(1 of 2)
1. [Secure Communication Using SSL](#/secure-communication-using-ssl) * [Keys and Certificates](#/keys-and-certificates) * [Key and Trust Stores](#/key-and-trust-stores) * [security.protocol Client Property](#/security-protocol-client-property) * [Demo: Secure Communication Using SSL](#/demo-secure-communication-using-ssl) 1. [Secure Inter-Broker Communication](#/secure-inter-broker-communication) * [security.inter.broker.protocol Broker Property](#/security-inter-broker-protocol-broker-property) * [listeners Broker Property](#/listeners-broker-property) * [Demo: Secure Inter-Broker Communication](#/demo-secure-inter-broker-communication)
## Recap
(2 of 2)
1. [SSL Authentication](#/ssl-authentication) * [ssl.client.auth Broker Property](#/ssl-client-auth-broker-property) * [Demo: SSL Authentication](#/demo-ssl-authentication) 1. [ACL Authorization](#/acl-authorization) * [authorizer.class.name Broker Property](#/authorizer-class-name-broker-property) * [Other Broker Properties](#/other-broker-properties) * [kafka-acls Utility](#/kafka-acls-utility) * [Demo: ACL Authorization](#/demo-acl-authorization)
# Questions? * Read [The Internals of Apache Kafka](https://bit.ly/apache-kafka-internals) * Read [The Internals of Kafka Streams](https://bit.ly/kafka-streams-internals) * Follow [@jaceklaskowski](https://twitter.com/jaceklaskowski) on twitter (DMs open) * Upvote [my questions and answers on StackOverflow](http://stackoverflow.com/users/1305344/jacek-laskowski) * Contact me at **jacek@japila.pl**


5.000.000 Downloads - Better Battlelog Forums #123858
Sitemap
25. July 2014 08:22
Post edited 2 x times, last by
BrainFooLong
Administrator
25. July 2014 08:23
Topicstarter
BrainFooLong
Administrator
5mill.jpg
The title say it all.
Just amazing.
Thx to the community for your support.
Up to the next million :)
25. July 2014 08:35
DaniluK4000
Community Manager
wow
I congratulate you, that's great! And already downloaded 5.000.536 :)
30. July 2014 20:46
ORFK
Global Moderator
BrainFooLong wrote:
Up to the next million :)
next big step is 10'000'000 downloads ;-)
25. August 2014 19:57
ReicitoEscobar
Nice


--- title: "David Attenborough – Wildlife Specials | Quicklook TV+" titlemain: "Quicklook TV+" bio: "Quicklook TV+" description: "Learn about David Attenborough on Quicklook TV. Browse shows and movies that feature David Attenborough including Planet Earth, Nature, and more." keywords: "" photo: "/assets/images/fit/32/1_m-R_BkNf1Qjr1YbyOIJY2w.png" avatar: "/assets/images/fit/32/1_m-R_BkNf1Qjr1YbyOIJY2w.png" favicon: "/assets/images/fit/32/1_m-R_BkNf1Qjr1YbyOIJY2w.png" layout: personMedia permalink: "/media/watch/person/david-attenborough/wildlife-specials" linkmain: "/media/watch/person" color: "#e50914" colorhover: "#e50914" ---
David Attenborough Wildlife Specials


Networked microsensors technology is a key technology for the future.
Smart disposable microsensors can be deployed almost anywhere.
Ubiquitous wireless networks of microsensors probably offer the most potential in changing the world of sensing.
Paper structure:
History of research in sensor networks
Technology trends
New applications
Research issues and hard problems
Some examples
The devbelopment of sensor networks requires technologies from three different research areas:
Sensing
Communication
Computing (hardware, software, and algorithms)
Can be traced back to Cold War.
The sensor networks generally adopt a hierarchical processing structure where processing occurs at consecutive levels until the information about events of interest reaches the user.
Modern research on sensor networks started around 1980 with the DSN program at the DARPA.
R. Kahn, who was coinventor of the TCP/IP protocols and played a key role in developing the Internet, wanted to know whether the Arpanet (predecessor of the Internet) approach for communication could be extended to sensor networks.
The network was assumed to havbe many spatially distributed low-cost sensing nodes that collaborate with each other but operate autonomously, with information being routed to whichever node can best use the information.
Researchers:
CMU focused on providing a network operating system that allows flexible, transparent access to distributed resources needed for a fault-tolerant DSN. They developed a communication-oriented operating system called Accent (evolved into the Mach OS), whose primitives support transparent networking, system reconfiguration, and rebinding.
MIT focused on knowledge-based signal processing techniques for tracking helicopters using a distributed array of acoustic microphones by means of signal abstractions and matching techniques.
ADS1 developed a multiple-hypothesis tracking algorithm to deal with difficult situations involving high target density, missing detections, and false alarms, and decomposed the algorithm for distributed implementation.
In platform-centric warfare: platforms own specific weapons,k which in turn own sensors in a fairly rigid architecture.
In network-centric warfare: sensors donot necessarily belong to weapons or platforms. Instead, they collaborate with each other over a communication network, and information is sent to the appropriate shooters.
Latest technological advances boosted sensor networks development
Sensor Information Technology (SensIT) program pursued two key research and development thrusts:
New networking techniques: sensor devices or nodes should e ready for rapid deployment, in an ad hoc fasion, and in highly dynamic environments.
NEtworked information processing.
Sensors, processors, and communication devices are all getting much smaller and cheaper.
WIreless netowrkds based upon IEEE 802.11 standards can now provide bandwidth approaching those of wired networks.
IEEE 802.15 standard for personal area networks (PANs) have a radius of 5 to 10 meters.
In the future, the advances in MEMS technology will produce sensors that are even more capable and versatile.
Examples:
Infrastructure security
Environment and Habitat Monitoring
Industrial Sensing
Traffic Control
Knowledge of the network is essential for a sensor in the network to operate properly.
In planned networks, the topology of the network is usually known a priori.
For ad hoc networks, the network topology has to be constructed in real time, and updated periodically as sensors fail or new sensors are deployed.
In addition to knowledge of the topology, each sensor also needs to know its own location.
The network muse deal with resources that are dynamically changing, and the system should operate autonomously, changing its configuration as required.
Without requiring IP addresses at each node is that one can deploy network devices in very large numbers.
Survivability and adaptation to the environment are ensured through deploying an adequate number of nodes to provide redundancy in paths, and algorithms to find the right paths.
Collaborative signal and information processing over a network is a new area of research and is related to distributed information fusion.
Issue1: degree of information sharing between nodes and how nodes fuse the information from other nodes.
Issue2: tradeoff between performance and robustness.
Issue3: optimal data association is computationally expensive and requires significant bandwidth for communication.
meet mission latency and reliability requirements.
how to maximize sensor network operational life.
A sensor field is like a database with many unique features, and it is challenging for querying because of its instability.
It is important that users have a simple interface to interactively task and query the sensor network.
Mobile platforms can carry sensors and query devices. As a result, seamless internetwork between mobile and fixed devbices in the absence of any infrastructure is a critical and unique requirement for sensor networks.
Sensor network should be protected against intrusion and spoofing.
Centralized algorithms:
collect data from multiple sensor nodes
potentially provide the best performance
undesireable because of high communication cost and lack of robustness and reliability.
Localized algorithms:
the sensor nodes only communicate with sensors within a neighborhood.
robust to network changes and node failures.
scale will with increasing network size.
Difficult to design because of the potentially complicated relationship between local behavior and global behavior.
Estrin et al. developed directed diffusion routing algorithms that belong to the class of localized algorithms.
Diffusion is a form of broadcast routing that does not specify a destination node address.
Packets are forwarded to neighboring nodes, and a direction or gradient is overlaid to control the broadcast or forwarding of the packet, which eventually reaches the destination.
The gradient could be based on geographic information or other attributes such as power, congestion and other resources available in the network nodes.
It requires considerably less energy than standard routing mechanisms such as flooding and omniscient multicast.
Tracking in ad hoc networks with microsensors poses different challenges due to communication, processing and energy constraints.
Zhao et al. addressed the dynamic sensor collaboration problem in distributed tracking to determine dynamically which sensor is most appropriate to perform the sensing, what needs to be sensed, and to whom to communicate the information.
Distributed data association algorithms are available for networks with large nodes but are computationally too expensive to implement on ad hoc networks. An approximate approach for cheap data association is called identity management.
The bandwidth of a wireless sensor network is typically lower than that of a wired network, a sensor network’s communications requirements may exceed their capacities.
Mobile agents have been proposed as a solution to this dilemma (above sentence).
In a mobile-agent-based DSN, data stay at each local site or sensor, while the integration or fusion code is moved to the data. Communication bandwidth requrieemnt may be reduced if the agent is smaller in size than the data.
Advanced Decision Systems, Mountain View, CA↩


Peripartum cardiomyopathy
[cards
physiology
]
19 Jul 2018
Context
During pregnancy, the body goes through significant hemodynamic changes. RAAS activation maintains blood pressure during the first trimester. Cardiac remodeling begins to occur to increase left-ventricular mass, along with enhanced angiogenesis. Delivery itself leads to a state of maximum cardiac output with increased catecholamines and auto transfusion of 300-500 mL of blood from the uterus into maternal circulation.
This figure shows the concept that pregnancy leads to a period of dilation and hypertrophy that will be required to generate higher cardiac outputs to assist with delivery1. This may lead to a period of vulnerability.
Definition
The working definition, most recently described by the European Society of Cardiology in 2010:
- Idiopathic cardiomyopathy, diagnosis of exclusion
- Reduced ejection fraction < 45% (ventricle does not need to be dilated)
- Presenting at the end of pregnancy or in the months following delivery
What raises suspicion?
The classic phenotype to be at high risk for PPCM is an African descent, multiparty, pregnancy complications (preeclampsia, eclampsia, gestational hypertension), greater than 30 years of age, cocaine abuse, and beta agonist use (tocolytic therapy), and family history.
Genetic etiology
The incidence of this is quoted from 1:1000-4000 in the USA, with rising numbers. Its less frequent in Northern Europe and even less so in Asia. In Africa however there is a much higher risk, with 1:300 in Haiti, and up to 1:100 in a city in Nigeria (this particular area has a tradition of eating a ritual lake salt 40 days postpartum(missing reference). This suggests a cultural pattern versus an underlying genomic influence. In addition, family history increases risk of developing PPCM, suggesting a genetic etiology.
Inflammation and autoimmune response
Myocarditis and higher levels of inflammatory markers have been found during acute PPCM. This has led to potential viral etiologies, but biopsies results have been varied in results in different populations.
Cardiac demand and adrenergic toxicity
Risk factors that include cocaine, hypertension, like eclampsia, and beta agonism have all shown higher trends of PPCM.
Angiogenesis and oxidative stress
Prolactin is cleaved by hormones of pregnancy (e.g. cathepsin D) into angiostatin and pro-apoptotic fragments. Acute PPCM shows increased oxidative stress and increased serum enzyme levels. Both apoptosis and angiogenesis failure seem to be indicated (at least in mouse models)
Recovery
Theoretically, after 5 months postpartum there is a chance at recovery of ejection fraction. The markers that suggest lack of recovery include LVEF < 30% and LVEDD > 6 cm.
Standard heart failure management, after delivery, is the recommended course. Early beta-blockage, even at low dosages, may be protective even in those with severely depressed ejection fraction, along with ivabradine. However, in terms of treatment to help with actual recovery, novel studies suggest bromocriptine, with results showing ~90% with improvement, and ~50% with full recovery2.
Take away points
- PPCM has rising incidence in the US
- Its an idiopathic cardiomyopathy with lV dysfunction at the end of pregnancy or months after pregnancy
- TTE is the mainstay for diagnosis, but cardiac MRI is being investigated
- Low selenium levels, viruses, inflammation, and hemodynamic stressors are important to developing PPCM
- Important biomarkers: N-terminal pro-BNP, prolactin, interferon gamma, ADMA, cathepsin D, microRNA-146a
- Treat like other heart failure guidelines, however bromocriptine can help in the acute setting in recovering function
- If they get pregnant again, there’s a high risk of worsening of cardiac function
References
- 1. Ruys TPE, Cornette J, Roos-Hesselink JW. Pregnancy and delivery in cardiac disease. Journal of Cardiology. 2013;61(2):107–112. doi:10.1016/j.jjcc.2012.11.001.
- 2. Haghikia A, Podewski E, Libhaber E, et al. Phenotyping and outcome on contemporary management in a German cohort of patients with peripartum cardiomyopathy. Basic Research in Cardiology. 2013;108(4):366. doi:10.1007/s00395-013-0366-9.


?PLANTS
OF
BAHAMA
ISLANDS
Ludwigia
repens
J.
R.
Forst.
Grand
Bahama:
in
wet
soil
of
depression
along
coastal
road.
Eight
Mile
Rock.
August
20,
1974
NEW
YORK
BOTAMCALGARDEN
01447687
d.s.correli
¤
Robert
Krai
43084
Herbarium
FAIRCHILD
TROPICAL
GARDEN
01447687
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Tesla ATV: Elon Musk’s ‘One More Thing’ Is an ATV That Fits In Cybertruck
by John KoetsierTesla founder Elon Musk pulled a trick out of Steve Jobs’ playbook today.At the Tesla Cybertruck event tonight, Elon Musk unveiled the futuristic Blade Runner-like pickup truck.
It has autopilot included standard, Musk said, a 17” touchscreen, 3,500 pounds of payload capacity, goes from zero to 60 MPH in just 2.9 seconds, and has an adjustable air suspension. In addition, the triple-motor version has “a towing capability of over 14,000 pounds,” Tesla says.
But at the end of the show, Musk hesitated, then said he had “one more thing.” That was Apple co-founder Steve Jobs’ signature move: unveil something amazing, then drop one more gem on the audience.
That gem turned out to be an all-electric ATV.
“We also made an ATV,” Elon Musk said.
The ATV, which Musk gave few details about, looks almost as futuristic as the truck, and not only fits in Cybertruck’s cargo area, it also charges from Cybertruck’s big batteries.
In addition, Cybertruck has a built-in ramp that unfolds and extends from the truck’s tailgate, allowing owners to simply ride right up into the cargo area without any other hardware.
That’s impressive.
Road vehicles have been designed and built with electric propulsion for almost a decade now, but off-road vehicles and recreational vehicles for traditional motorsports have been slow to follow.
There are available models from manufacturers such as Polaris, which makes a 30 horsepower electric utility vehicle, as well as options from relatively new companies such as the New York-based EVS and Oregon’s Barefoot Motors.

And, like it has done with electric cars, Tesla clearly intends to accelerate the adoption of electric motors for vehicles we play in (and on) as well as commute in.
One problem: Cybertruck’s “armored glass” shattered in on-stage demo.


History of Oregon
Humans have inhabited the area that is now Oregon for at least 15,000 years. In recorded history, mentions of the land date to as early as the 16th century. During the 18th and 19th centuries, European powers – and later the United States –
quarreled over possession of the region until 1846, when the U.S. and Great Britain finalized division of the region. Oregon became a state on February 14, 1859, and as of 2015 is home to over 4 million residents. - Wikipedia


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Steven Schultz, Ph.D., is president of Writing at Work.
Since starting Writing at Work in 1985, he has trained business and industry professionals to
produce their own effective and clear documentation.
He has also written, edited, and managed large-scale communications projects. These projects include web sites, marketing materials, self-study and training guides, standard operating procedures,
technical reports, computer user manuals, educational textbooks, and articles for professional publication. He has designed and taught electronic distance-learning, CD-based, web-based, and on-line college courses.
Steven has taught at several universities and colleges in their graduate schools of business, corporate training divisions, and English departments.
Steven graduated Phi Beta Kappa and Department's Distinction from the University of Illinois, with a Class Merit Scholar Award for his
year's study at the University of Edinburgh, Scotland. His doctorate in English is from the State
University of New York at Buffalo.
Robert Steffeck, senior writer, has worked for 20 years as a research chemist in new product development and pharmaceutical R & D. Well immersed in the language of science and engineering, he is an active copywriter and editor and a valuable resource for scientific marketing projects.
Robert has written marketing literature for Alltech Associates (Grace Davison), Resolution Systems, and SIELC Technologies. These projects included converting technical information to marketing literature, including website content, brochures, catalogs, and posters.
Robert has authored publications in chiral and achiral separations, ion-exclusion chromatography, and gas chromatography. While at Alltech, he published the first extensive comparison of C18 and C8 columns. At Pfizer, he published a new mode of chromatography, enantioselective ion-exclusion, and published the first separation of a secondary amine on a chiral crown ether in the then 35-year history of crown ethers.
Jim Costello, Ph.D., senior writer, brings strong writing talents, programming skills, and educational expertise to
product development and service delivery. He specializes in creative and effective uses of the Internet for
training, marketing, documentation, and support. For the past 20 years, Jim has worked in both the private and
public sectors as an editor, writer, trainer, instructional designer, computer-based training developer, and web designer.
He is an Oracle Certified Application Developer and a member of ASP Guild International and the American Society for
Training & Development. He was a presenter at Multimedia 96 Conference and Trade Show and a three-time presenter at
SALT's Annual Conference on Interactive Systems for Training, Education, and Job Performance.
Jim has edited and written a variety of publications from advanced engineering reference books to primary school textbooks
to computer user manuals to annual reports. He has written and published several articles of his own as well as a
full-length book on baseball, In the Shadows of the Diamond. Jim's Ph.D. in English and American Literature is from the
State University of New York at Buffalo.
Mark Malcolm, M.A., manages large project engagements for Writing at Work. He has authored and edited several management
and organizational manuals, such as employee policy manuals, newsletters, and procedures guides. His technical writing projects
include product and installation guides.
Mark also manages document production and quality control, making sure projects meet scheduling and budget expectations.
Mark received his B. A. from the University of Chicago and his M. A. in Industrial Relations from the University of Minnesota.
|


{% extends "layout.html" %} {% block body %} Admin Panel You can edit the site data here
We will add file uploader


Mushrooms - P. cubensis, Citalopram (Celexa) & Aripiprazole (Abilify)
I am a seasoned tripper who has eaten shrooms several times a year for quite a few years now. Recently, I was perscribed Celexa (citalopram) for depression. I also take Abilify (a mood stabilizer). While taking only abilify, I would dose my usual 5 grams of shrooms (dry) with no diminished effects. When taking celexa however, I noticed a significant decrease in the mushroom's effects.
Last time I dosed I had been taking celexa daily for about 6 weeks and noticed an overall decrease in the trip. Nearly no visuals were noted, and this has been consistent ever since I started taking celexa. I am considering getting off this medication since I really miss my trips which result in a mood lift lasting days after the 'official' trip ended.
Judging by my experience, SSRI's definitely seem to interfere with much of the shroom's potency. Every 'test batch' of shrooms was freshly grown and expertly dried, so it must be the SSRI's influence.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


class: center, middle, inverse, title-slide # STA 235 - Causal Inference: Introduction to Observational Studies ## Spring 2021 ### McCombs School of Business, UT Austin ---  # Last class .pull-left[ - **.darkorange[Randomized controlled trials]** - Why is it considered the gold standard? - How to analyze an RCT in practice? - Assumptions and limitations. - **.darkorange[Statistical power]**: - How do sample sizes play a role? - Randomized inference. ] .pull-right[ .center[ ![:scale 80%](https://media.giphy.com/media/jsN192JGdyWvS1gqTb/giphy.gif)] ] --- # Today .pull-left[ .center[ ![](https://github.com/maibennett/sta235/raw/main/exampleSite/content/Classes/Week5/1_Matching/images/data.jpg)] ] .pull-right[ - **.darkorange[Introduction to Observational Studies]**: - Can we identify causal effects without RCTs? - Assumptions - Matching vs OLS ] --- background-position: 50% 50% class: left, bottom, inverse .big[ No more chance[s] ] --- # Introduction to observational studies - Most times, we will not be able to randomize, and we need to work with **.darkorange[existing data]** --

 .box-5LA[Observational data] -- - Data for which we can't manipulate the treatment assignment, e.g. data in its "natural state". --

 .box-7[Can we reasonably assume that the ignorability assumption holds?] --- # Introduction to observational studies (cont.) .pull-left[ .center[ .![:scale 80%](https://media.giphy.com/media/fu2rIw18eIDz1YCrCT/giphy.gif) ] ] .pull-right[ - Moving away from the core assumption of RCTs: that **.darkorange["the probability of treatment assignment is a known function"]** (Imbens & Rubin, 2015). ] --- # Introduction to observational studies (cont.) .pull-left[ .center[ .![:scale 80%](https://media.giphy.com/media/fu2rIw18eIDz1YCrCT/giphy.gif) ] ] .pull-right[ - Moving away from the core assumption of RCTs: that **.darkorange["the probability of treatment assignment is a known function"]** (Imbens & Rubin, 2015). - We will maintain the assumption of **.darkorange[unconfoundnedness]** (to a certain extent). .box-4t[What is that?] ] --- # Calling in the CIA - **.darkorange[Unconfoundnedness]** means that the treatment assignment is independent from the potential outcomes. - If you recall, the ignorability assumption assumes that: `$$Y(0), Y(1) \perp\!\!\!\perp Z$$` -- - What if you could assume that this holds **.darkorange[conditional on some covariates]**? -- .box-2LA[Conditional Independence Assumption (CIA)]
 `$$Y(0), Y(1) \perp\!\!\!\perp Z|X$$` --- # The assignment mechanism .pull-left[ - **.darkorange[Key component]** in causal analysis: - In RCTs, **.darkorange[assignment mechanism]** is *known*. - But in **.darkorange[observational studies?]** ] .pull-right[ .center[ ![:scale 70%](https://github.com/maibennett/sta235/raw/main/exampleSite/content/Classes/Week5/1_Matching/images/confusion.png)] ] --- # The assignment mechanism (cont.) - For now, we will consider 3 assumptions: -- .box-2[Individualistic assignment] -- .box-4[Probabilistic assignment] -- .box-7[Unconfounded assignment] --- # Individualistic assignment - Limits the dependence of treatment assignment for unit `\(i\)` on the outcomes and assignments for other units. - For some function `\(q(\cdot) \in [0,1]\)`: `$$p_i(\mathbf{X},\mathbf{Y}(0),\mathbf{Y}(1)) = q(X_i,Y_i(0),Y_i(1))$$` --

 .box-6LA[From last class, can you think of an example where this doesn't hold?] --- # Probabilistic assignment - Nonzero probability for each treatment value, for each unit. `$$0 < p_i(\mathbf{X},\mathbf{Y}(0),\mathbf{Y}(1)) < 1 \ \ \forall \ i = 1,...,N$$` ,for each possible `\(\mathbf{X},\mathbf{Y}(0),\mathbf{Y}(1)\)` --

 .box-4LA[When could this assumption fail?] --- #Unconfounded assignment - The assignment mechanism does not depend on the potential outcomes: `$$Pr(\mathbf{Z}|\mathbf{X},\mathbf{Y}(0),\mathbf{Y}(1)) = Pr(\mathbf{Z}|\mathbf{X},\mathbf{Y'}(0),\mathbf{Y'}(1))$$` ,for all possible `\(\mathbf{Z}, \mathbf{X},\mathbf{Y}(0),\mathbf{Y}(1), \mathbf{Y'}(0),\)` and `\(\mathbf{Y'}(1)\)` --- # Selection on observables - Units select into treatment based on characteristics **.darkorange[I can observe]**. --

 .center[ ![](https://github.com/maibennett/sta235/raw/main/exampleSite/content/Classes/Week5/1_Matching/images/unconfound1.png) ] --- # Selection on observables - Units select into treatment based on characteristics **.darkorange[I can observe]**.

 .center[ ![](https://github.com/maibennett/sta235/raw/main/exampleSite/content/Classes/Week5/1_Matching/images/unconfound2.png) ] --- # How do we adjust for observables? - One way we have seen so far is **.darkorange[regression adjustment]** `$$Y_i = \beta_0 + \beta_1 Z_i + \beta_2 X_i + \varepsilon_i$$` --

 .box-2[Under unconfoundedness, how would we interpret β1?] --- # How do we adjust for observables? - One way we have seen so far is **.darkorange[regression adjustment]** `$$Y_i = \beta_0 + \beta_1 Z_i + \beta_2 X_i + \varepsilon_i$$`

 .box-2[β1 is the effect of Z on Y, holding X constant] --- # How do we adjust for observables? - But what if our data looks like this? Do you see a problem?  --- # How do we adjust for observables? - But what if our data looks like this? Do you see a problem?  --- # How do we adjust for observables? - But what if our data looks like this? Do you see a problem?  --- background-position: 50% 50% class: left, bottom, inverse .big[ Finding your perfect match... ] --- # Two peas in a pod - One other route we could take is to **.darkorange[find similar units]** in our sample and **.darkorange[group them together]**. -- .pull-left[ - There are different ways to do it: - E.g. subclassification, matching. ] .pull-right[ ![](https://media.giphy.com/media/l36kU80xPf0ojG0Erg/giphy.gif) ] --- # Two peas in a pod - One other route we could take is to **.darkorange[find similar units]** in our sample and **.darkorange[group them together]**. .pull-left[ - There are different ways to do it: - E.g. subclassification, matching. .box-2t[What do we gain?] ] .pull-right[ ![](https://media.giphy.com/media/l36kU80xPf0ojG0Erg/giphy.gif) ] --- # Advantages of matching methods .box-2[Reduce model dependence] --
 .box-2t[Imbalance → model dependence → researcher discretion → bias] -- .box-4[Compare like to like] --
 .box-4t[No extrapolation!] -- .box-7[Can adjust closely by covariates] --
 .box-7t[Exact matching, coarsened exact matching, fine balance..] --- $$ \color{white}{\beta_0 \text{E}^2} $$  --- $$ \color{white}{\beta_0 \text{E}^2} \text{Outcome} = \beta_0 + \beta_1 \text{Education} + \beta_2 \text{Treatment} \color{white}{\beta_0 \text{E}^2} $$  --- $$ \text{Outcome} = \beta_0 + \beta_1 \text{Education} + \beta_2 \text{Education}^2 + \beta_3 \text{Treatment} $$  --- $$ \color{white}{\beta_0 \text{E}^2} $$  --- $$ \color{white}{\beta_0 \text{E}^2} \text{Outcome} = \beta_0 + \beta_1 \text{Education} + \beta_2 \text{Treatment} \color{white}{\beta_0 \text{E}^2} $$  --- $$ \text{Outcome} = \beta_0 + \beta_1 \text{Education} + \beta_2 \text{Education}^2 + \beta_3 \text{Treatment} $$  --- .box-4[How do we know we can remove those observations?]  --- # Subclassification .pull-left[ - Very similar to **.darkorange[stratifying]**. ] .pull-right[  ] --- # Subclassification .pull-left[ - Very similar to **.darkorange[stratifying]**. - Build **.darkorange[combination]** of X1 and X2 (strata). ] .pull-right[  ] --- # Subclassification .pull-left[ - Very similar to **.darkorange[stratifying]**. - Build **.darkorange[combination]** of X1 and X2 (strata). - Compare **.darkorange[within stratum]**. ] .pull-right[  ] --- # Subclassification .pull-left[ - Very similar to **.darkorange[stratifying]**. - Build **.darkorange[combination]** of X1 and X2 (strata). - Compare **.darkorange[within stratum]**. ] .pull-right[  ] --- # Subclassification .pull-left[ - Very similar to **.darkorange[stratifying]**. - Build **.darkorange[combination]** of X1 and X2 (strata). - Compare **.darkorange[within stratum]**. ] .pull-right[  ] --- # Subclassification .pull-left[ - Very similar to **.darkorange[stratifying]**. - Build **.darkorange[combination]** of X1 and X2 (strata). - Compare **.darkorange[within stratum]**. ] .pull-right[  ] --- # Subclassification .pull-left[ - Very similar to **.darkorange[stratifying]**. - Build **.darkorange[combination]** of X1 and X2 (strata). - Compare **.darkorange[within stratum]**. ] .pull-right[  ] --- # Subclassification .pull-left[ - Very similar to **.darkorange[stratifying]**. - Build **.darkorange[combination]** of X1 and X2 (strata). - Compare **.darkorange[within stratum]**. ] .pull-right[  ] --- # Subclassification - To estimate the Average Treatment Effect, we take a **.darkorange[weighted average]**: `$$\hat{ATE}=\sum_{s=1}^S\frac{N_s}{N}(\bar{Y}_{1s}-\bar{Y}_{0s})$$` -- .box-3[What happens when we have too many variables to build strata?] --- # The curse of dimentionality .pull-left[ - When we have too many covariates, the number of strata or groups grow **.darkorange[exponentially]**! - E.g. with 4 covariates, each with 5 categories, we have **.darkorange[625 combinations]**! - Very possible that a stratum only has treatment or control units. ] .pull-right[ .center[ ![:scale 80%](https://media.giphy.com/media/eZkKm90RRqfHrL1MUe/giphy.gif) ] ] --- # The curse of dimentionality .pull-left[ - When we have too many covariates, the number of strata or groups grow **.darkorange[exponentially]**! - E.g. with 4 covariates, each with 5 categories, we have **.darkorange[625 combinations]**! - Very possible that a stratum only has treatment or control units. .box-5t[What to do?] ] .pull-right[ .center[ ![:scale 80%](https://media.giphy.com/media/eZkKm90RRqfHrL1MUe/giphy.gif) ] ] --- # Breaking the curse: Balancing scores - Want to **.darkorange[reduce the dimentionality]** of our covariates --

 - A balancing score `\(b(x)\)` is a function of the covariates such that: `$$Z_i \perp\!\!\!\perp X_i|b(X_i)$$` -- - This means that conditioning on the balancing score is **.darkorange[enough to remove bias]** associated to the covariates. - Under unconfoundedness: `$$Z_i \perp\!\!\!\perp Y_i(0),Y_i(1)|b(X_i)$$` -- - There are different balancing scores: - E.g. propensity scores, mahalanobis distance. --- # Estimating balancing scores .box-5LA[Propensity score] `$$\log(\frac{p}{1-p}) = \beta_0 + \beta_1X_1 + \beta_2X_2 + ... + \beta_p X_p + \varepsilon$$` where `\(p = Pr(Z=1)\)` -- ```r e <- predict(glm(z ~ x1 + x2 + x3, data = d, family = binomial(link="logit")), type="response") ``` --- # Estimating balancing scores .box-5LA[Propensity score] - Importance of overlap region --  --- # Estimating balancing scores .box-7LA[Mahalanobis Distance] $$D_M(\mathbf{x}) = \sqrt{(\mathbf{x}-\mathbf{\mu})^TS^{-1}(\mathbf{x}-\mathbf{\mu})} $$ where `\(\mathbf{x}\)` is the covariate vector for observation `\(i\)`, `\(\mathbf{\mu}\)` is the mean vector of covariates for the sample, and `\(S\)` is the covariance matrix. -- ```r Sx <- cov(x) D <- mahalanobis(x, colMeans(x), Sx) # We can also use a rank-based Mahalanobis distance matrix library(designmatch) D <- distmat(z, x) ``` --- # Making groups comparable - Using the previous balancing scores (or covariates directly!) we can **.darkorange[match observations between the treatment and control group]** -- .box-4[Step 1: Preprocessing] .box-4t[Try to model the treatment assignment] -- .box-5[Step 2: Estimation] .box-5t[Use the new trimmed/preprocessed data to build a model, calculate difference in means, etc.] --- # How matchy-matchy - There are different matching methods (and different ways to use them!) -- .box-2[Nearest neighbor (NN)] .box-2t[Use balancing scores; Greedy algorithm] -- .box-4[Optimal matching] .box-4t[Solves an optimization problem; slow on large samples] -- .box-7[Mixed Integer Programming (MIP) matching] .box-7t[Balances covariates directly; can generate smaller samples] --- .center2[ .box-5LA[Let's go to R] ] --- # Propensity score matching .box-1[Super popular method] -- .box-3[There are mathy reasons why it's not great
for matching *for identification purposes*] -- .box-7[Some researchers argue propensity scores are fine!
Using them for matching isn't!] --- .center[ ![:scale 100%](https://github.com/maibennett/sta235/raw/main/exampleSite/content/Classes/Week5/1_Matching/images/gary-king.png) ] --- # Weighting, the cousin of matching - We can also use **.darkorange[weights]** to make two samples look alike. --

 .box-4[Inverse Probability Weighting (IPW)] - Make some observations more important than others. --- # Weighting, the cousin of matching - You can make the treatment and the control group look more like each other: `$$w_{ATE} = \frac{Z_i}{e_i} + \frac{1-Z_i}{1-e_i}$$` - Observations in the control group will have a weight of `\(\frac{1}{1-e_i}\)`, while observations in the treatment group will have weights of `\(\frac{1}{e_i}\)` -- .box-3[What happens with obs that are very likely to be in the treatment group?] --- # Weighting, the cousin of matching - You can make the treatment and the control group look more like each other: `$$w_{ATT} = \frac{e_i\cdot Z_i}{e_i} + \frac{e_i(1-Z_i)}{1-e_i}$$` - Observations in the control group will have a weight of `\(\frac{e_i}{1-e_i}\)`, while observations in the treatment group will have weights of 1 -- .box-5[Why do observations in the treatment group get a weight of 1?] --- # Weighting for approximating a population - If we assume that our sample was selected on observables, and we want it to **.darkorange[look more like a population of interest]**, we can also do that! -- - We can use the same formula for `\(ATT\)`, but now `\(e_i\)` is not the probability of being assigned to treatment, but the probability of **.darkorange[being in the population sample]**. -- - For this type of weighting, we only need population covariates. --- .center2[ .box-3LA[Let's go to R] ] --- # The shortcomings of matching - Many researchers missuse matching and **.darkorange[confuse it with an identification strategy]** -- - In terms of identification, **.darkorange[matching still relies on selection on observables]** -- .box-2[You need other source of exogeneous variation!] -- - Claiming that you can identify a **.darkorange[causal effect]** just by using matching is almost the same as claiming this using a regression approach. .box-7[Not a good idea...] --- # Don't get it twisted .pull-left[ - Matching works great as an **.darkorange[adjustment method]**. - Combined with **.darkorange[other identification strategies]**, it can improve results! ] .pull-right[ .center[ ![](https://media.giphy.com/media/kBNhZkdEPOgoB8p2nT/giphy.gif) ] ] --- # Main takeaways .pull-left[ .center[ ![](https://media.giphy.com/media/xThtalMViID4iGemWI/giphy-downsized-large.gif) ] ] .pull-right[ - Matching and weighting methods can be great tools for your analysis. - Create more similar groups of comparisons. - Reduce model dependence - Even help with external validity (under assumptions) ] --- # Next week - We will look at some **.darkorange[identification strategies]** for observational studies: - Natural experiments and differences-in-differences. - What **.darkorange[assumptions]** need to hold? - How do we **.darkorange[identify a natural experiment]**? - What does **.darkorange[DD]** buy us? --- # References - Angrist, J. and S. Pischke. (2015). "Mastering Metrics". *Chapter 2*. - Heiss, A. (2020). "Program Evaluation for Public Policy". *Class 7: Randomization and Matching, Course at BYU* - Imbens, G. and D. Rubin. (2015). "Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction". *Chapter 3* - Cunningham, S. (2021). "Causal Inference: The Mixtape". *Chapter 5*


Vision:
Our vision is to produce high quality software that enhances the way people view the world around them. We will be able to process an image and return an enhanced image to the user. We will be able to measure our milestones by using Asana as a way to divide up our tasks to each member of the group.Mission:
Our mission is to take commercially available software on desktops, and run it on a mobile device. Without consuming a vast amount of storage space, we will empower people to produce high quality images on their phones.Success Criteria:
On completion of the software, it should be capable of running as a flexible, reliable, and scalable environment. The Software should be able to run on different platforms. It should also easily return an enhanced image within considerable processing time.Requirements:
CPF shall have a MIT License. Clients shall be able to put images on the cloud server Cloud server shall be able to edit the image received by the client Cloud server shall be able the send the image back to the client. The project shall be open source Product shall be downloadable through a Github Product shall also include a user manual Cloud server will use Node.JS to run the server Cloud Server shall use SQL to save image data Cloud Server shall use a Debian based Linux Operating System Cloud server shall have multiple styles of image processing Cloud server shall be portable. Cloud server shall be scalable. Framework shall be modular The website shall use the server that implements the frameworkResults
Completed the project successfully Allow users to log in and edit images Allow users to edit images that were uploaded to the server previously Allow users to download images that were previously edited Successfully made software portable enough to move to other machinesApproach
Javascript to create the server Python for image processing HTML for the web page layout NodeJS as framework for the server MongoDB as library to setup database OpenCV as a library for image processing JIMP as library for image processingFuture Improvements
Android Application Add More Security


---
layout: null
section-type: about
title: About
---
## About
Hi. I am Nuriddin Kamardinov, a Web Developer at Revolution Group and Co-founder at Effort.uz, from Andijan, Uzbekistan. I aim to make a positive difference in the world.
I write IT-blogs in Uzbek to create more qualitative content for Uzbek learners and manage Telegram channel, which announces different events. Additionally, keen on reading books, love travelling, but not good at playing chess :)


Inception Wave has excelled in part owing to the laboriously strong knit network of young innovators from across the country, while at the same imbibing thoroughly uplifting industry exposure in our student community.
IWSCSRM is dedicated to provide a platform for innovative minds to come together and explore creative ideas. We build a workplace to help each individual in improving the knowledge and better their skills. We have collaborated with various clubs to achieve common goals. Iwscsrm is a media partner to these clubs and publicizes their events. Our team consists of ambitious individuals from different domains who strive to achieve perfection and grow in their respective fields. We at iwscsrm aim to accomplish greater heights and provide our members with apt exposure.
IWSC DRC Chapter is aimed at bringing young creative minds together to explore themselves and grow with each other. We are a community of dedicated people who strive to create innovative ideas and thus improving their skills simultaneously. We have organized successful and constructive webinars with very reputed and prestigious institutions and personalities. Since an organization's biggest asset is their resource, we have a team of highly committed people who aims to put their best in their respective fields. We believe in: 'Coming together is a beginning, staying together is progress, and working together is success.
IWSC IGDTUW focuses on the opportunities that are available to students of every stream and every department and notifies the students about the same. Later on we plan to organizing some events and webinars to provide a wholesome experience to every student of our college.
IWSC INDORE is a part of IWSC network led by inception wave, a tech company led by students of SRM Chennai. It was started in June 2020 and have organized several successful events in the interest of youth of the city. It aims to enhance the skills of students of indore and nearby areas. Having bunch of creative minds, the chapter is always pushing it's boundaries with the every event. The most successful event was the Among Us tournament, loved by the youth of Indore and they enjoyed a lot in that 3 hrs tournament.
We, at IWSC LSR, strive to create a work culture that we always envisioned. It’s a platform where a set of like-minded individuals come together to change the dynamics of the corporate and entrepreneurial world. We have a vibrant team of talented individuals from diverse backgrounds who have come together to work towards a common goal: making workspaces more conducive through the medium of social media and virtual platforms. We endeavour to not only limit our learnings and experiences to ourselves but extend it to the access of the entire college and beyond, hopefully. We have organised a number of events including webinars and skill-building workshops in order to achieve the same. Our Instagram handle continues to grow every day, expanding our reach and vision, thus, aligning more and more people with our goals and ideas.
We, the MSI-T chapter of Inception Wave primarily work towards building a close-knit network of college students who come together to exchange ideas, creativity, updates, participate in events and much more. Our chapter is mainly focused on giving a fun-filled experience to the collegiates and making every moment count of the best phase of their life; college! And this is made possible through a team of dedicated young minds in various fields like Social Media Marketing, Content Curators, Web developers, Event managers etc. None of it would've been possible without this team and our supporters.
Inception Wave Student Chapter Shaheed Bhagat Singh College is divided into smaller sub-teams based on domains and areas of interest and all these sub-teams operate synchronously. Namely Event Management, Human Resource Management, Social Media Marketing , Content Writing and Graphic Designing. Currently we have a team of 18 members striving to create a social media presence by hosting webinars, events and online workshops based on the interests and preferences of our audience. Our ultimate aim is to make like minded individuals come together to promote work culture while in college itself .
We bring in students all around the Vellore and industrial professional on a same platform to work and share ideas. We have department leads from all engineering branches expert in their respective domains who indulge in generating a healthy environment in our committee. Our flagship events includes MIP- Mentored Internship program, which was a success in its first edition. We regularly oraganise workshops and webinars with industrial experts who share their ideas. Our in-core sessions include interaction with the committee members to indulge in competitions. We also train our members through Localhost workshops. We are looking forward to expand our collaboration with other organisations in and around our campus.
iwsc_vips in here abide by the same mantra of "All students can learn and Succeed, but not in the same day and definately not in the same way". If you are one of those vipsians who wish to get INDUSTRY LEVEL EXPERIENCE or curious to learn what happens inside a company while being at college, you definately dropped in at the right place! No more waiting for college to get over. Let's get started right away. We'll help you to get closer to you aimed destinations while you being in college itself. We're glad to make you aware that STUDENT CHAPTER-VIPS, Inception wave is up with its first ever Student Community, where the main agenda of ours will be putting up all the day-to-day Economics related Factuals on the board, since Economics is definitely NEED OF THE HOUR and keeping you updated on the same will be ours! Iwsc_vips is a team of 15-20 skilled students who are willing to get industrial level exposure, where our main aim is to work in coordination as a team to live by our Agenda, of creating a student community providing the audience with first hand kick ass economics related stuffs!
IWSC-Vivek Vihar is working on to provide training to the students in financial literacy, corporate exposure and skill development.
IWSC BHARATI is a part of IWSC network led by Inception Wave, a tech based start-up by students of SRM Chennai. We started in June 2020 and have organized several online events amid of this pandemic situation. Currently, we are just 16, but still we try to do something new and innovative each time to promote professional mind-set in the college itself. In future, we aim to organise some more Seminars or Events after reopening of colleges and expand our chapter
IWSC Banasthali aims to adequately enhance the professional environment by building a community of individuals sharing similar skills and interests. We focus on providing significant information associated with Banasthali, a stage to showcase talents and act as a media partner in various events.
"Make it work, make it right, make it fast.” – Kent Beck Inception Wave Student Chapter Bharati Vidyapeeth Deemed University College of Engineering, Pune, or as we call it IWSC BVP focuses on the motto of "learn - understand - go for it🏆🏅". We work on the principle of self-development focusing on competitive coding, we welcome all sources of knowledge and would be very grateful if we could help the students in other genres.
Inception Wave Delhi Technological University Chapter is a student-led and student-driven enterprise of DTU which started with a vision to provide a platform for like-minded individuals to cooperate and improve their skills by collaborating with industries and dealing with real-world problems with their creative and innovative solutions. We aim to create a platform to build and solve real problems working with NGOs, NPOs and Social Organisations to create a tangible social impact by developing solutions in various domains which include:
At IWSC BVCOEW our motive is to create a sense of community bringing together like-minded people to strive towards growth.A group of people who take responsibility at their own free will,hold a sense of leadership,and keep the will to create a welcoming environment full of positivity.We believe in empowering each other with whatever knowledge we hold working towards a common goal as a team.
We,the BRAC chapter of Inception wave focuses to provide theoretical and practical knowledge about financial literacy, corporate exposure and skill development. We encourage young creative minds together to explore themselves and grow with each other.We are looking forward to expand our collaboration with other organisations in and around our campus.
IWSCSRM is dedicated to provide a platform for innovative minds to come together and explore creative ideas. We build a workplace to help each individual in improving the knowledge and better their skills. We have collaborated with various clubs to achieve common goals. Iwscsrm is a media partner to these clubs and publicizes their events. Our team consists of ambitious individuals from different domains who strive to achieve perfection and grow in their respective fields. We at iwscsrm aim to accomplish greater heights and prov
IWSC DRC Chapter is aimed at bringing young creative minds together to explore themselves and grow with each other. We are a community of dedicated people who strive to create innovative ideas and thus improving their skills simultaneously. We have organized successful and constructive webinars with very reputed and prestigious institutions and personalities. Since an organization's biggest asset is their resource, we have a team of highly committed people who aims to put their best in their respective fields. We believe in: 'Coming together is a beginning, staying together is progress, and working together is success.
IWSC IGDTUW focuses on the opportunities that are available to students of every stream and every department and notifies the students about the same. Later on we plan to organizing some events and webinars to provide a wholesome experience to every student of our college.
IWSC INDORE is a part of IWSC network led by inception wave, a tech company led by students of SRM Chennai. It was started in June 2020 and have organized several successful events in the interest of youth of the city. It aims to enhance the skills of students of indore and nearby areas. Having bunch of creative minds, the chapter is always pushing it's boundaries with the every event. The most successful event was the Among Us tournament, loved by the youth of Indore and they enjoyed a lot in that 3 hrs tournament.
We, at IWSC LSR, strive to create a work culture that we always envisioned. It’s a platform where a set of like-minded individuals come together to change the dynamics of the corporate and entrepreneurial world. We have a vibrant team of talented individuals from diverse backgrounds who have come together to work towards a common goal: making workspaces more conducive through the medium of social media and virtual platforms. We endeavour to not only limit our learnings and experiences to ourselves but extend it to the access of the entire college and beyond, hopefully. We have organised a number of events including webinars and skill-building workshops in order to achieve the same. Our Instagram handle continues to grow every day, expanding our reach and vision, thus, aligning more and more people with our goals and ideas.
We, the MSI-T chapter of Inception Wave primarily work towards building a close-knit network of college students who come together to exchange ideas, creativity, updates, participate in events and much more. Our chapter is mainly focused on giving a fun-filled experience to the collegiates and making every moment count of the best phase of their life; college! And this is made possible through a team of dedicated young minds in various fields like Social Media Marketing, Content Curators, Web developers, Event managers etc. None of it would've been possible without this team and our supporters.
Inception Wave Student Chapter Shaheed Bhagat Singh College is divided into smaller sub-teams based on domains and areas of interest and all these sub-teams operate synchronously. Namely Event Management, Human Resource Management, Social Media Marketing , Content Writing and Graphic Designing. Currently we have a team of 18 members striving to create a social media presence by hosting webinars, events and online workshops based on the interests and preferences of our audience. Our ultimate aim is to make like minded individuals come together to promote work culture while in college itself .
We bring in students all around the Vellore and industrial professional on a same platform to work and share ideas. We have department leads from all engineering branches expert in their respective domains who indulge in generating a healthy environment in our committee. Our flagship events includes MIP- Mentored Internship program, which was a success in its first edition. We regularly oraganise workshops and webinars with industrial experts who share their ideas. Our in-core sessions include interaction with the committee members to indulge in competitions. We also train our members through Localhost workshops. We are looking forward to expand our collaboration with other organisations in and around our campus.
iwsc_vips in here abide by the same mantra of "All students can learn and Succeed, but not in the same day and definately not in the same way". If you are one of those vipsians who wish to get INDUSTRY LEVEL EXPERIENCE or curious to learn what happens inside a company while being at college, you definately dropped in at the right place! No more waiting for college to get over. Let's get started right away. We'll help you to get closer to you aimed destinations while you being in college itself. We're glad to make you aware that STUDENT CHAPTER-VIPS, Inception wave is up with its first ever Student Community, where the main agenda of ours will be putting up all the day-to-day Economics related Factuals on the board, since Economics is definitely NEED OF THE HOUR and keeping you updated on the same will be ours! Iwsc_vips is a team of 15-20 skilled students who are willing to get industrial level exposure, where our main aim is to work in coordination as a team to live by our Agenda, of creating a student community providing the audience with first hand kick ass economics related stuffs!
IWSC-Vivek Vihar is working on to provide training to the students in financial literacy, corporate exposure and skill development.
IWSC BHARATI is a part of IWSC network led by Inception Wave, a tech based start-up by students of SRM Chennai. We started in June 2020 and have organized several online events amid of this pandemic situation. Currently, we are just 16, but still we try to do something new and innovative each time to promote professional mind-set in the college itself. In future, we aim to organise some more Seminars or Events after reopening of colleges and expand our chapter
IWSC Banasthali aims to adequately enhance the professional environment by building a community of individuals sharing similar skills and interests. We focus on providing significant information associated with Banasthali, a stage to showcase talents and act as a media partner in various events.
"Make it work, make it right, make it fast.” – Kent Beck Inception Wave Student Chapter Bharati Vidyapeeth Deemed University College of Engineering, Pune, or as we call it IWSC BVP focuses on the motto of "learn - understand - go for it🏆🏅". We work on the principle of self-development focusing on competitive coding, we welcome all sources of knowledge and would be very grateful if we could help the students in other genres.
Inception Wave Delhi Technological University Chapter is a student-led and student-driven enterprise of DTU which started with a vision to provide a platform for like-minded individuals to cooperate and improve their skills by collaborating with industries and dealing with real-world problems with their creative and innovative solutions. We aim to create a platform to build and solve real problems working with NGOs, NPOs and Social Organisations to create a tangible social impact by developing solutions in various domains which include:
At IWSC BVCOEW our motive is to create a sense of community bringing together like-minded people to strive towards growth.A group of people who take responsibility at their own free will,hold a sense of leadership,and keep the will to create a welcoming environment full of positivity.We believe in empowering each other with whatever knowledge we hold working towards a common goal as a team.
We,the BRAC chapter of Inception wave focuses to provide theoretical and practical knowledge about financial literacy, corporate exposure and skill development. We encourage young creative minds together to explore themselves and grow with each other.We are looking forward to expand our collaboration with other organisations in and around our campus.


The concept of providing elecctronic gadgets to rural schools including internet facility whwere we gonna teach the technology to rural students periodically by creating & supporting passion along.
You have to grow from the inside out. None can teach you, none can make you spiritual. There is no other teacher but your own soul.
Swami Vivekananda
Indian monk