>
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The Auto Width command will attempt to guess reasonable widths (more accurately reasonable left and right side bearings) for your font. The command splits the selection into scripts and looks at all possible combinations of selected glyphs within each script.
The Separation text box allows you to say how close you want the glyphs to be. If you look at a vertical bar (or sanserif "I"), and you autowidth it the sum of the left and right side bearings will be equal to the spacing. The min and max values will prevent sidebearings from becoming unrealistically large (these rarely matter).
Adjusting the left and right bearings of a letter will also adjust the left
and right bearings of all accented letters based (by reference) on it. You
should not try to use AutoWidth on accented letters directly (unless those
glyphs do not use references).
There used to be an Auto Kern dialog. There is no longer, this is now done in the kerning subtable dialog and the kerning class dialog.
GlyphSeparationHook
When FontForge does AutoKerning (and in a more complicated fashion, when it does AutoWidthing) it tries to guess the optical separation between two glyphs, and then it applies an appropriate kern value to make the optical separation be the desired spacing.
FontForge assumes that the optical spacing is linear (so if you increase the separation between two glyphs by 3 em-units, then the optical spacing will also increase by 3 em-units).
FF guesses the optical separation between two glyphs by figuring a value for the separation of two glyphs when their bounding boxes are adjacent. For two rectangles (where the bounding box is the same as the glyph itself) the separation will be 0 as the two glyphs will be touching. For a combination like "To" the optical separation will be quite large.
The GlyphSeparationHook is a python routine which will be called with a bunch of information about two glyphs, and is expected to return an integer representing the optical separation between the two. It is a low-level routine; the actual calculation of kerning offsets or setting glyph widths is done elsewhere, but this routine provides the information those higher level routines need. Of course, if you don't provide a routine, FontForge has a built-in routine which will provide a default behavior. Here is a python version of that default routine:
import fontforge; def GlyphSeparation(onLeft,onRight,context) : # the goal is to give a weighted average that expresses the visual # separation between two glyphs when they are placed so their bounding # boxes are adjacent. The separation between two rectangles would be 0 # While the separation between "T" and "o" would be fairly large # The trick is to guess a good weighting function. My guess is that # things that look close are more important than those which look far # So "T" and "O" should be dominated by the crossbar of the "T"... # # Find the area the two glyphs have in common (so when comparing "o" and # "l" or "g", ignore the ascenders and descenders since that's outside # the range of "o" and won't affect its visual proximity. imin_y = max(onRight.iminY,onLeft.iminY); imax_y = min(onRight.imaxY,onLeft.imaxY); # # Some glyph combinations will have no overlap -- the grave accent and the # letter "a" will have none. So they don't interact visually. if imax_y < imin_y : return( 0 ) # # Otherwise compute some sort of weighted average of the separations between # the two glyphs at various heights. tot,cnt = 0,0 j = imin_y while j<=imax_y : if onRight.left[j] < 32767 and onLeft.right[j] > -32767 : # beware of gaps such as those in "i" or "aaccute" # a gap has a left or right value which is huge # so ignore any such, again it doesn't contribute to the # visual separation. sep = onRight.left[j] - onLeft.right[j] weight = 1.0/(sep + context.denom) weight *= weight tot += weight*sep cnt += weight j += 1 if cnt!=0 : tot /= cnt return( int(round( tot )) ); fontforge.registerGlyphSeparationHook(GlyphSeparation)
The hook will be called with three arguments, a structure with information about the glyph on the left, a structure with information about the glyph on the right, and a structure containing general information about the operation as a whole.
The context argument contains the following fields:
| font | The font being worked on |
| emSize | The emsize of the font |
| layer | The active layer for this operation |
| regionHeight | (explained below) by default emSize/100 |
| denom | 1/50th of the emSize. A number I found handy. |
FontForge preprocess all the glyphs, extracting pertinant information from each and storing it in a separate per-glyph structure than the normal python Glyph object. The most important information is a representation of the leftmost and rightmost edges of the glyph. Basically, for every value of y, FontForge finds the smallest x coordinate on any of the glyph's contours at that height and stores that information in an array. There is a similar array containing the largest x coordinates. Of course there are an infinite number of y values, so that isn't quite accurate. FontForge divides the vertical axis into many regions, each regionHeight high (this is usually 1/100th of the emsize). It then finds the smallest x coordinate achieved by any contour within that region.
The value stored in the array representing the leftmost edge of the glyph is not quite the smallest x coordinate. Instead we normalize the numbers so that the x coordinate of the left side bearing of the glyph is 0. None of the numbers in the left array will be negative.
Similarly the value stored in the rightmost array is normalized so that the glyph's right side bearing is 0. None of the numbers in the right array will be positive.
There can be gaps in a glyph -- for example in a lower case "i" there is a hole in the body of the glyph between the dot on top and the rest down below. In this case the left array will hold the special value 32767, and the right array will hold -32767.
So I don't pass a standard glyph object. The underlying glyph object can
be obtained through the glyph
member. The most important things
in this structure are the arrays representing the left and right contours
called left
and right
. The structure also contains
the upper and lower bounds of the arrays (both arrays have the same bounds).
The glyph structures contain the following members:
| glyph | This is the standard python glyph object. This can (rarely) be None. |
| boundingbox | A tuple of 4 values (minx,miny,maxx,maxy) for the glyph |
| iminY | The low bound of the arrays. |
| imaxY | The high bound of the arrays |
| left | an array of integers representing the separation between the left edge of the bounding box and the leftmost contour at that height (never negative) |
| right | an array of integers representing the separation between the right edge of the bounding box and the rightmost contour at that height (never positive) |
iminY
and imaxY
are scaled by
regionHeight
from the y units in the glyph itself. The value
contain at left[0] would represent minimum separation between boundingbox.minx
and any the x location of any contour when the y location was between 0 and
regionHeight. Similarly left[1] would be the minimum separation with the
y location between regionHeight and 2*regionHeight. And so forth.
imin_y = max(onRight.iminY,onLeft.iminY); imax_y = min(onRight.imaxY,onLeft.imaxY); if imax_y < imin_y : return( 0 )
Here we figure out the range along the y axis where the glyphs both exist. If the two glyphs don't share any area along the y axis (as, for example, the glyph "a" and the grave accent might not) then we assume they do not interact and return a visual separation of 0.
tot,cnt = 0,0 j = imin_y while j<=imax_y : if onRight.left[j] < 32767 and onLeft.right[j] > -32767 : sep = onRight.left[j] - onLeft.right[j] weight = 1.0/(sep + context.denom) weight *= weight tot += weight*sep cnt += weight j += 1
Otherwise loop over the y range where both glyphs are active. Checking to make sure there are no holes.
We find the actual separation between the two glyphs at this y value.
Then we calculate some magic weighting function (this is handwaving. your routine will have a better weighting function)
if cnt!=0 : tot /= cnt return( int(round( tot )) );
And finally we take the weighted average of those separations, and return that as the optical separation.
fontforge.registerGlyphSeparationHook(GlyphSeparation)
And this routine will tell FontForge to use the hook you provide.


Class: Aws::Kendra::Types::SlackConfiguration
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb |
Overview
Provides the configuration information to connect to Slack as your data source.
Constant Summary
Instance Attribute Summary
-
#crawl_bot_message ⇒ Boolean
rw
TRUE
to index bot messages from your Slack workspace team. -
#exclude_archived ⇒ Boolean
rw
TRUE
to exclude archived messages to index from your Slack workspace team. -
#exclusion_patterns ⇒ Array<String>
rw
A list of regular expression patterns to exclude certain attached files in your Slack workspace team.
-
#field_mappings ⇒ Array<Types::DataSourceToIndexFieldMapping>
rw
A list of
DataSourceToIndexFieldMapping
objects that map Slack data source attributes or field names to Amazon Kendra index field names. -
#inclusion_patterns ⇒ Array<String>
rw
A list of regular expression patterns to include certain attached files in your Slack workspace team.
-
#look_back_period ⇒ Integer
rw
The number of hours for change log to look back from when you last synchronized your data.
-
#private_channel_filter ⇒ Array<String>
rw
The list of private channel names from your Slack workspace team.
-
#public_channel_filter ⇒ Array<String>
rw
The list of public channel names to index from your Slack workspace team.
-
#secret_arn ⇒ String
rw
The Amazon
::Aws::Kendra::Resource
Name (ARN) of an Secrets Manager secret that contains the key-value pairs required to connect to your Slack workspace team. -
#since_crawl_date ⇒ String
rw
The date to start crawling your data from your Slack workspace team.
-
#slack_entity_list ⇒ Array<String>
rw
Specify whether to index public channels, private channels, group messages, and direct messages.
-
#team_id ⇒ String
rw
The identifier of the team in the Slack workspace.
-
#use_change_log ⇒ Boolean
rw
TRUE
to use the Slack change log to determine which documents require updating in the index. -
#vpc_configuration ⇒ Types::DataSourceVpcConfiguration
rw
Configuration information for an Amazon Virtual Private Cloud to connect to your Slack.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#crawl_bot_message ⇒ Boolean
(rw)
TRUE
to index bot messages from your Slack workspace team.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#exclude_archived ⇒ Boolean
(rw)
TRUE
to exclude archived messages to index from your Slack
workspace team.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#exclusion_patterns ⇒ Array
<String
> (rw)
A list of regular expression patterns to exclude certain attached files in your Slack workspace team. Files that match the patterns are excluded from the index. Files that don’t match the patterns are included in the index. If a file matches both an inclusion and exclusion pattern, the exclusion pattern takes precedence and the file isn't included in the index.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#field_mappings ⇒ Array
<Types::DataSourceToIndexFieldMapping> (rw)
A list of DataSourceToIndexFieldMapping
objects that map Slack
data source attributes or field names to Amazon Kendra index field
names. To create custom fields, use the UpdateIndex
API before you
map to Slack fields. For more information, see Mapping data source
fields. The Slack data source field names must exist in your
Slack custom metadata.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#inclusion_patterns ⇒ Array
<String
> (rw)
A list of regular expression patterns to include certain attached files in your Slack workspace team. Files that match the patterns are included in the index. Files that don't match the patterns are excluded from the index. If a file matches both an inclusion and exclusion pattern, the exclusion pattern takes precedence and the file isn't included in the index.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#look_back_period ⇒ Integer
(rw)
The number of hours for change log to look back from when you last synchronized your data. You can look back up to 7 days or 168 hours.
Change log updates your index only if new content was added since
you last synced your data. Updated or deleted content from before
you last synced does not get updated in your index. To capture
updated or deleted content before you last synced, set the
LookBackPeriod
to the number of hours you want change log to look
back.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#private_channel_filter ⇒ Array
<String
> (rw)
The list of private channel names from your Slack workspace team. You use this if you want to index specific private channels, not all private channels. You can also use regular expression patterns to filter private channels.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#public_channel_filter ⇒ Array
<String
> (rw)
The list of public channel names to index from your Slack workspace team. You use this if you want to index specific public channels, not all public channels. You can also use regular expression patterns to filter public channels.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#secret_arn ⇒ String
(rw)
The Amazon ::Aws::Kendra::Resource
Name (ARN) of an Secrets Manager secret that
contains the key-value pairs required to connect to your Slack
workspace team. The secret must contain a JSON structure with the
following keys:
- slackToken—The user or bot token created in Slack. For more information on creating a token in Slack, see Authentication for a Slack data source.
^
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#since_crawl_date ⇒ String
(rw)
The date to start crawling your data from your Slack workspace team.
The date must follow this format: yyyy-mm-dd
.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#slack_entity_list ⇒ Array
<String
> (rw)
Specify whether to index public channels, private channels, group messages, and direct messages. You can specify one or more of these options.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#team_id ⇒ String
(rw)
The identifier of the team in the Slack workspace. For example, T0123456789.
You can find your team ID in the URL of the main page of your Slack workspace. When you log in to Slack via a browser, you are directed to the URL of the main page. For example, https://app.slack.com/client/T0123456789/....
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#use_change_log ⇒ Boolean
(rw)
TRUE
to use the Slack change log to determine which documents
require updating in the index. Depending on the Slack change log's
size, it may take longer for Amazon Kendra to use the change log
than to scan all of your documents in Slack.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end
#vpc_configuration ⇒ Types::DataSourceVpcConfiguration (rw)
Configuration information for an Amazon Virtual Private Cloud to connect to your Slack. For more information, see Configuring a VPC.
# File 'gems/aws-sdk-kendra/lib/aws-sdk-kendra/types.rb', line 9320
class SlackConfiguration < Struct.new( :team_id, :secret_arn, :vpc_configuration, :slack_entity_list, :use_change_log, :, :exclude_archived, :since_crawl_date, :look_back_period, :private_channel_filter, :public_channel_filter, :inclusion_patterns, :exclusion_patterns, :field_mappings) SENSITIVE = [] include Aws::Structure end


Cross Entropy method in Reinforcement Learning
If you have ever gathered the courage to explore the field of Reinforcement Learning, there are good chances that you found yourself lost in fancy vocabulary. Big words, names of complex algorithms with even more complex math behind them. But what if there were simpler, much more intuitive, and super-efficient algorithms out there that worked well?
Meet the Cross-Entropy Method: An evolutionary algorithm for parameterized policy optimization that John Schulman claims works “embarrassingly well” on complex RL problems².
What is Cross-Entropy Method?
From a biological viewpoint, it is an Evolutionary Algorithm. Some individuals are sampled from a population and only the best ones govern the characteristics of future generations.
Mathematically, it can be seen as a Derivative-Free Optimization (DFO) technique, i.e., it can find optima without the overhead of calculating derivatives (no backpropagation!).
How does this method work?
Assume for a second that you do not know what are agents, environments, and policies. You are just given a “black-box” which takes some numbers as inputs and outputs some other numbers. You can only choose the values for your inputs and observe the outputs.
One simple way of doing this would be to take a bunch of inputs, see the outputs produced, choose the inputs that have led to the best outputs and tune them till you are satisfied with the outputs you see. This is essentially what the cross-entropy method does.
So, how do I use it to solve my RL problem?
Let’s understand the working of CEM step-by-step with an example. I have added some python code snippets with each step for a better understanding of the implementation. The code is heavily borrowed from Udacity’s course on Deep Reinforcement Learning (amazing python RL resources btw, Github link at the end of this article)¹.
Consider your policy network. You want to find the best weights which can take the right “meaningful” actions based on your agent’s state. A CEM-based approach for finding these weights is as follows:
Step 1: Draw a bunch of initial weights from a random distribution. Although this distribution is generally chosen to be Gaussian, you can choose any distribution that you believe the weights are from. Let’s say I drew 10 candidates for weights w1, w2, …, w10 from a Gaussian distribution with mean μ and variance σ².
Consider μ=0, σ=1, n_weights=10 (number of candidates) and weights_dim represents dimensions of the weight vector.
1
2
3
4
mean = 0.0
std = 1.0
n_weights = 10
weights_pop = [mean + std*np.random.randn(weights_dim) for i_weight in range(n_weights)]
Step 2: Now let the agent pick actions from the policy network based on these weights, run the agent through an episode and collect the rewards generated by the environment. For our example, say w1 generates a cumulative reward r1, w2 generates r2 and so on.
The evaluate method for an agent takes a weight candidate as input, plays an episode and outputs the cumulative reward from that episode.
1
rewards = [agent.evaluate(weights) for weights in weights_pop]
Step 3: Find the weights which generated the best rewards. Assume the best 4 weights were w1, w2, w5 and w6 (also called the “elite” weights). Here 4 is a number that we have chosen. In general, you consider best n weights, where n is chosen by you.
1
2
3
n_elite = 4
elite_idxs = np.array(rewards).argsort()[-n_elite:]
elite_weights = [weights_pop[idx] for idx in elite_idxs]
Step 4: Pick the new weights from a distribution defined by the elite weights. Say μ’ is the mean of the best weights (w1, w2, w5 and w6) and σ’² is their variance. We now draw 10 candidates from a gaussian distribution with mean μ’ and variance σ’².
1
2
3
mean = np.array(elite_weights).mean()
std = np.array(elite_weights).std()
weights_pop = [mean + std*np.random.randn(weights_dim) for i_weight in range(n_weights)]
Step 5: Repeat steps 2–4 until you are happy with the rewards you get.
If python code is not your thing and you love to read algorithms with math jargon, here is the pseudocode for you²:
Conclusion
Cross-Entropy Method is a simple algorithm that you can use for training RL agents. This method has outperformed several RL techniques on famous tasks including the game of Tetris⁴. You can use this as a baseline³ before moving to more complex RL algorithms like PPO, A3C, etc. There are several variants of CEM, however, the structure defined in this article is the backbone of all of them.
That concludes this article on the Cross-Entropy Method for Reinforcement Learning. I hope you liked what you just read and thank you for your time.
References
[1] https://github.com/udacity/deep-reinforcement-learning/tree/master/cross-entropy
[2] MLSS 2016 on Deep Reinforcement Learning by John Schulman
[3] http://karpathy.github.io/2016/05/31/rl/
[4] I. Szita and A. Lorincz, Learning Tetris Using the Noisy Cross-Entropy Method (2006), Neural Computation


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Elizabeth Elliott
DevloperABOUT
It is a long established fact that a reader will be distracted by the readable content.
PERSONAL INFORMATION
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{% extends "base.html" %} {% load static %} {% block extracss %} {% endblock %} {% block content %}
{% endblock %}


Lyrics of Lowly Life
by Paul Laurence Dunbar
"Columbian Ode"
Additional Information
- Year Published: 1913
- Language: English
- Country of Origin: United States of America
- Source: Dunbar, P.L. (1913). The Complete Poems of Paul Laurence Dunbar. New York: Dodd, Mead, and Company.
-
Readability:
- Flesch–Kincaid Level: 6.5
- Word Count: 302
- Genre: Poetry
- ✎ Cite This
Downloads
I
Four hundred years ago a tangled waste
Lay sleeping on the west Atlantic’s side;
Their devious ways the Old World’s millions traced
Content, and loved, and labored, dared and died,
While students still believed the charts they conned,
And revelled in their thriftless ignorance,
Nor dreamed of other lands that lay beyond
Old Ocean’s dense, indefinite expanse.
II
But deep within her heart old Nature knew
That she had once arrayed, at Earth’s behest,
Another offspring, fine and fair to view,—
The chosen suckling of the mother’s breast.
The child was wrapped in vestments soft and fine,
Each fold a work of Nature’s matchless art;
The mother looked on it with love divine,
And strained the loved one closely to her heart.
And there it lay, and with the warmth grew strong
And hearty, by the salt sea breezes fanned,
Till Time with mellowing touches passed along,
And changed the infant to a mighty land.
III
But men knew naught of this, till there arose
That mighty mariner, the Genoese,
Who dared to try, in spite of fears and foes,
The unknown fortunes of unsounded seas.
O noblest of Italia’s sons, thy bark
Went not alone into that shrouding night!
O dauntless darer of the rayless dark,
The world sailed with thee to eternal light!
The deer–haunts that with game were crowded then
To–day are tilled and cultivated lands;
The schoolhouse tow’rs where Bruin had his den,
And where the wigwam stood the chapel stands;
The place that nurtured men of savage mien
Now teems with men of Nature’s noblest types;
Where moved the forest–foliage banner green,
Now flutters in the breeze the stars and stripes!


Steeped in history, style and culture, the City of Light has more than enough to draw visitors all year around – and that’s before we even get started on the food and wine. But there are certain times of year when a trip to Paris can be extra-special.
If you’ll be coming to Paris in the fall, you’re in for a treat.The Festival d’Automne à Paris, this multidisciplinary contemporary art festival.It takes place in 47 different venues throughout Paris.It brings a wide variety of performances and exhibitions including visual arts, theatre, dance, music, and film.
All-night-long party Nuit Blanche landmarks the Eiffel Tower, Louvre and Pompidou stay open super-late, while artists from around the world set up temporary exhibits and installations across the city.
Spring in Paris evokes quaint streets bursting with cherry blossoms, parks erupting in lush shades, and Parisians and visitors alike shedding their winter coats to enjoy the milder weather. The City of Light does not disappoint during this season,and the paris carnival compltes the season with influences from endless cultural and religious groups.
During the Summers,the city gives way to two of the most unique events of the Paris.The Bastille Day and the Paris Jazz Festival. The Paris Jazz Festival has hosted some of the most revered names in jazz from around the world, making it a unique opportunity to experience live performances of some of these legendary artists.As a visitor to France, you won’t want to miss this special national holiday.July 14 France marks the storming of the Bastille prison – a turning point in the French Revolution. In the morning crowds gather to watch a military parade along the Champs-Élysées.
If you wish to visit Paris in Winters,you will definetly not regret it.The City of Lights prove its name on the Christmas Eve and the New Year's Eve. Whole of the city is decorated with blushess and glamour covers the city. The fireworks add a sense of pleasure and the theare,music bands at the effiel tower makes the city roar in happiness.
Hundreds of theatres throughout the city add to Paris’ illustrious reputation as a cultural center, offering world-class theatrical, ballet, and opera performances.All this life rotates around the Eiffel Tower.
Know MoreParis offers a wide range of clubbing atmospheres for many different tastes. Internationally acclaimed DJs run the turntables at many posh electronica clubs throughout the city. Dressing to impress is of critical importance in such areas.
Know MoreThe best way to visit the "City of Light" is enjoying a cruise on the Seine, especially at night.You get a panoramic view to the forefront so you can enjoy the beauty of Paris. A dinner on cruise on the Seine aboard a boat.
Know More


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Apple making iPhone XR in India, expanding operations: IT minister
by PTIHighlights
- Ravi Shankar Prasad said that Salcomp, a major supplier of chargers to Apple for iPhone, has reached agreement to take over closed facility of Nokia in a SEZ near Chennai
- Salcomp will revive the unit, which was closed for nearly 10 years, and the facility will become operational from March 2020
NEW DELHI: Tech titan Apple has started production of iPhone XR in India for domestic market and exports, underlining the government's efforts to bolster mobile manufacturing in the country, IT Minister Ravi Shankar Prasad said on Monday.
The minister also said that Salcomp, the world's largest manufacturer and a major supplier of chargers to Apple for iPhone, has reached agreement to take over closed facility of Nokia in a SEZ near Chennai.
Salcomp will revive the unit, which was closed for nearly 10 years, and the facility will become operational from March 2020. The unit will produce charger and other equipment, and will boast of a diversified portfolio. This will entail an investment of Rs 2,000 crore in five years.
"The big SEZ of Nokia which was closed for 10 years will get into life again. As many as 10,000 people will get jobs directly and 50,000 jobs will be generated indirectly," Prasad said.
India's overall mobile as well as components exports are expected to cross $1.6 billion each in 2019-20, he said.


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public interface IRowCreator
Interface returned by DesignGridLayout.row()
in order to specify the
type of row to be actually added to the current layout (grid, centered, left-
or right-aligned).
| Method Summary | |
|---|---|
IBarRow |
bar()
Creates a "command bar" row. |
INonGridRow |
center()
Creates a center-aligned row. |
IRowCreator |
group(RowGroup group)
Adds the current row to a given group . |
INonGridRow |
left()
Creates a left-aligned row. |
INonGridRow |
right()
Creates a right-aligned row. |
| Methods inherited from interface net.java.dev.designgridlayout.ISubGridStarter |
|---|
grid, grid, grid, grid |
| Method Detail |
|---|
IRowCreator group(RowGroup group)
group
. Grouping rows is useful
when you want to show or hide several rows at a time.
It is also possible to add one row to several groups.
group
- the group to which the current row must be added
this
instance of IRowCreator, allowing for chained
calls to other methods (also known as "fluent API")INonGridRow center()
LayoutManager
s
for one single dialog.
The new row is located under the previously created row, which means that
each line of code using this method creates a new row and all lines can
be read as defining the visual UI of the container.
INonGridRow left()
LayoutManager
s for
one single dialog.
The new row is located under the previously created row, which means that
each line of code using this method creates a new row and all lines can
be read as defining the visual UI of the container.
INonGridRow right()
LayoutManager
for one single dialog.
The new row is located under the previously created row, which means that
each line of code using this method creates a new row and all lines can
be read as defining the visual UI of the container.
IBarRow bar()
Tag
) based on
platform preferences. This means that you don't have to care about where you
must locate "OK" and "Cancel" if your application must run on both Windows and
Mac OS.
These rows are split into 3 sub-rows: one on the left, one
in the center and one on the right.
The new row is located under the previously created row, which means that
each line of code using this method creates a new row and all lines can
be read as defining the visual UI of the container.
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