Education is a must. But today this very very requiring subject is getting stopped because the term "Corona Virus". The good news is everyone can quit but our S.M. Model Govt. High Schools teachers can't quit! They have managed to take classes at this breaking point of education. They have managed to keep our education alive! We show many many respects to them for their fighting from the front. We, the students of S.M. Model Govt. High School, obey to them.
Manoj Hira (sir), one of our most respective teachers, is taking online classes through ZOOM app. It's a video conferencing app. We all are able to connect with zoom very easily. He is talking classes of Physics Chemistry. It is guaranteed that his attractive lesson and teaching style will captivate all of you. We think that his lessons will keep us a step ahead in the long run of S.S.C. We recommend joining his online classes as soon as possible. For further information contact:
Riad: +8801840983212
Bidhan Kumar Pande (sir), one of the most inspirable teachers, has also started taking online classes. His magic words and easy tricks give us strength to beat the fear of English. We have covered a lot of topics in English from him. His lectures are posted on YouTube. His YouTube channel is "Learn English With Bidhan (leb)". Click me! to visit his channel.


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@@include('../analytics-code.html') @@include('../styles.html') @@include('../gtm-code.html')
@@include('../gtm-iframe-code.html')Start your Marketplace with our current BETA version, give us feedback regarding the platform, get rewarded once we go public.
Once you start your free marketplace, you will gain access to BETA version of VQ-Marketplace which means that the marketplace platform is still under development based on the feedback we collect from the BETA users.
You have your own custom marketplace in you mind?
That's not a problem! We would be happy to to talk to you and figure out how we can help you.


We start by separating all tags into single words and assessing the quality of the words. In a later stage there will be the opportunity to reconstruct multi-word tags again.
By default, less than 5,000 words will be kept.
A dataset with 5,000 words takes around 30 seconds to compute. A dataset with 15,000 words takes around 1.5 minutes to compute.
One suggestion is to start with a small dataset to find good parameters, and then run the workflow on the complete dataset.
The default setting removes English stop-words, prepositions, and single characters. This reduces computation time and also helps eliminate awkward but frequent phrases such as “of 10 stars” and “of the” later on in multi-word tag detection.
On the other hand, tags such as “Rock and Roll” will suffer from this step. You will have a chance to inspect and fix these in the final step.
By default a list of the 3000 most used words will be imported.
Here, you can import your own domain specific lists to improve the quality of the spell correction algorithm. For instance, a list of genres for music.
The list can contain both single-word and multi-word tags.
This step is performed by replacing low quality words (below a certain threshold) with high quality ones that are similarly spelled. Two words are deemed similar if their similarity is higher than a threshold.
Both quality and similarity are between 0 and 1. By default, a word quality of 0.05 and a similarity of 0.7 are chosen as thresholds.
Sequences of words that appear frequently are grouped into multi-word tags, such as "hard rock" and "female vocalists".
Group frequency is between 0 and 1, with 0 meaning no occurrence and 1 meaning the most frequent group. By default, a value of 0.35 is used.
Sequences of words that appear together frequently, but rarely by themselves, are grouped into multi-word tags, such as “Jon Bon Jovi” or “bay area”.
Group strength is between 0 and 1, with 0 meaning the words are never together and 1 meaning the words are most often together. By default, a value of 0.35 is used.
Narrow your dataset to a final size depending on your intended use for the tags.
In this step you can manually inspect your final dataset and fix problems. You can remove undesirable tags, such as subjective or non-informative terms. You can also replace tags them with other tags (such as fixing synonyms, translations, etc.).
The salvager goes through all the tags that have been previously removed and looks for informative tags within them.
For instance, if “rock” is in your final dataset and “some weird rock” is not, the salvager will turn the latter into “rock”. By performing this step, the amount of information we have on the items in your library (songs, papers, etc.) can increase significantly.


Comparison Functions and Operators#
Comparison Operators#
Operator |
Description |
|---|---|
|
Less than |
|
Greater than |
|
Less than or equal to |
|
Greater than or equal to |
|
Equal |
|
Not equal |
|
Not equal (non-standard but popular syntax) |
Range Operator: BETWEEN#
The BETWEEN
operator tests if a value is within a specified range.
It uses the syntax value BETWEEN min AND max
:
SELECT 3 BETWEEN 2 AND 6;
The statement shown above is equivalent to the following statement:
SELECT 3 >= 2 AND 3 <= 6;
To test if a value does not fall within the specified range
use NOT BETWEEN
:
SELECT 3 NOT BETWEEN 2 AND 6;
The statement shown above is equivalent to the following statement:
SELECT 3 < 2 OR 3 > 6;
The presence of NULL in a BETWEEN
or NOT BETWEEN
statement
will result in the statement evaluating to NULL:
SELECT NULL BETWEEN 2 AND 4; -- null
SELECT 2 BETWEEN NULL AND 6; -- null
The BETWEEN
and NOT BETWEEN
operators can also be used to
evaluate string arguments:
SELECT 'Paul' BETWEEN 'John' AND 'Ringo'; -- true
Not that the value, min, and max parameters to BETWEEN
and NOT
BETWEEN
must be the same type. For example, Presto will produce an
error if you ask it if John is between 2.3 and 35.2.
IS NULL and IS NOT NULL#
The IS NULL
and IS NOT NULL
operators test whether a value
is null (undefined). Both operators work for all data types.
Using NULL
with IS NULL
evaluates to true:
select NULL IS NULL; -- true
But any other constant does not:
SELECT 3.0 IS NULL; -- false
IS DISTINCT FROM and IS NOT DISTINCT FROM#
In SQL a NULL
value signifies an unknown value, so any comparison
involving a NULL
will produce NULL
. The IS DISTINCT FROM
and IS NOT DISTINCT FROM
operators treat NULL
as a known value
and both operators guarantee either a true or false outcome even in
the presence of NULL
input:
SELECT NULL IS DISTINCT FROM NULL; -- false
SELECT NULL IS NOT DISTINCT FROM NULL; -- true
In the example shown above, a NULL
value is not considered
distinct from NULL
. When you are comparing values which may
include NULL
use these operators to guarantee either a TRUE
or
FALSE
result.
 Like most other functions in Presto, they return null if any argument is null. Note that in some other databases, such as PostgreSQL, they only return null if all arguments are null.
The following types are supported:
DOUBLE
,
BIGINT
,
VARCHAR
,
TIMESTAMP
,
TIMESTAMP WITH TIME ZONE
,
DATE
-
greatest
(value1, value2, ..., valueN) → [same as input]# Returns the largest of the provided values.
-
least
(value1, value2, ..., valueN) → [same as input]# Returns the smallest of the provided values.
Quantified Comparison Predicates: ALL, ANY and SOME#
The ALL
, ANY
and SOME
quantifiers can be used together with comparison operators in the
following way:
expression operator quantifier ( subquery )
For example:
SELECT 'hello' = ANY (VALUES 'hello', 'world'); -- true
SELECT 21 < ALL (VALUES 19, 20, 21); -- false
SELECT 42 >= SOME (SELECT 41 UNION ALL SELECT 42 UNION ALL SELECT 43); -- true
Here are the meanings of some quantifier and comparison operator combinations:
Expression |
Meaning |
|---|---|
|
Evaluates to |
|
Evaluates to |
|
Evaluates to |
|
Evaluates to |
|
Evaluates to |
|
Evaluates to |
ANY
and SOME
have the same meaning and can be used interchangeably.
LIKE#
The LIKE operator is used to match a specified character pattern in a string. Patterns can contain regular characters as well as wildcards. Wildcard characters can be escaped using the single character specified for the ESCAPE parameter. Matching is case sensitive.
Syntax:
expression LIKE pattern [ ESCAPE 'escape_character' ]
if pattern
or escape_character
is null, the expression evaluates to null.
Wildcard |
Representation |
|---|---|
|
The percent sign represents zero, one, or multiple characters |
|
The underscore represents a single character |
Examples:
SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name)
WHERE name LIKE '%b%'
--returns 'abc' and 'bcd'
SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name)
WHERE name LIKE '_b%'
--returns 'abc'
SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name)
WHERE name LIKE 'b%'
--returns 'bcd'
SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name)
WHERE name LIKE 'B%'
--returns nothing
SELECT * FROM (VALUES ('a_c'), ('_cd'), ('cde')) AS t (name)
WHERE name LIKE '%#_%' ESCAPE '#'
--returns 'a_c' and '_cd'
SELECT * FROM (VALUES ('a%c'), ('%cd'), ('cde')) AS t (name)
WHERE name LIKE '%#%%' ESCAPE '#'
--returns 'a%c' and '%cd'


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The xsl:for-each-group
element selects a sequence of nodes and/or atomic
values and organizes them into subsets called groups. There are four possible ways of defining the
grouping:
group-by: This groups together all items having the same value for a grouping key. The grouping key may have multiple values (a sequence of values) in which case the item is added to more than one group.
group-adjacent: This groups together all items having the same value for a grouping key,
provided that they are also adjacent in the input sequence. This is useful when you need to wrap a
new element around a sequence of related elements in the source documents, for example a consecutive
sequence of <bullet>
elements. In this case the grouping key must be single-valued.
group-starting-with: This processes the items in the supplied sequence in turn, starting
a new group whenever one of the items matches a specified pattern. This is useful, for example,
when matching an

element and its following

elements.
group-ending-with: This processes the items in the supplied sequence in turn, closing
the current group whenever one of the items matches a specified pattern. This is useful
when matching a sequence of items in which the last item in the group carries some distinguishing
attribute such as continued="no"
.
Saxon implements the xsl:for-each-group
instruction in full, except that some error
conditions are not detected. For examples of using the instruction, see
the draft XSLT 2.0 specification.


Teens Are Absolutely Dragging Mayor Pete for His 'High Hopes' Dance
by Madison FellerHave you heard the rumor? Mayor Pete Buttigieg, the 37-year-old military vet, language enthusiast, and 2020 presidential candidate is...a millennial. (Chances are, if you've been following the race at all, this will come as no surprise due to the fact that people mention it constantly.) And while such a moniker might give you street cred at a Freaks and Geeks trivia night, turns out the teens are mostly unimpressed—especially after they saw the Buttigieg campaign's campy dance set to Panic! at the Disco's "High Hopes."
Turns out, Pete's people are dancing in Iowa:
Dancing during lunch breaks:
And dancing before the Polk County Steak Fry:
The routine—which consists of a few simple, repetitive movements aimed in different directions—is objectively silly, and people are unapologetically dragging it across all corners of the internet.
As @AdamFriedland on Twitter explains, "All over America, young people are learning a new dance. It’s not “the dab” or “the whip and then shanaenae”, it’s the mayor Pete Buttigeg dance, a hip modern routine set to panic! at the disco smash “high hopes”"
Another predicts a dance-crazed future where Mayor Pete is president, writing: "me every morning during Emperor Pete's 4th term in office to do the mandatory Morning Dance to our new national anthem"
MEL magazine reports that teens have been taking to TikTok to straight up roast Pete for a number of reasons, and many are using the dance to call out parts of Buttigieg’s platform they disagree with. (Some have even taken to calling him “Mayo Pete” because “the candidate is bland and overwhelmingly white,” people told MEL.)
But also, the roasting has no age limit! All were welcome to take a jab.
Obviously Pete's supporters are just trying to jazz up the election and dance away with the candidacy, but cheerleading-esque moves just won't suffice in this cut-throat race. Better luck—and choreography—next time.


Conditional statements
Problem 3. The biggest of Three
Write a script that finds the biggest of three numbers.
Use nested if statements.
a:
b:
c:
Check
Clear


Halsey throws shade at the Grammys during AMAs winner's speech
Halsey might not have been nominated for any awards at the 2020 Grammys, but she did pick up a prize at the American Music Awards on Sunday night.
The 25-year-old won the award for Favourite Song Pop/Rock with her track Without Me and on thanking fans for voting for her, Halsey pretty much threw shade at the Grammys for their snub.
On picking up her gong, she launched into a huge speech about how she used to think winning awards gave artists validation for all the hard work they put in to writing music, but actually she doesn’t think they’re ‘what they seem’ now she’s older.
She explained: ‘The truth is I am older now and I’m also an artist… most of these awards really aren’t what they seem at all. But that’s OK and I’m up here right now and I’m so thankful for the AMAs because they are the world’s largest fan voted award show.’
 And I’m thankful to the AMAs for giving those people a voice. I’m going to keep making music for them and for us.
‘Sometimes you grow up and the stuff that you believed in starts to lose its magic, but the music never does because real fans, real artists and real stories keep that magic alive.’
Fans were quick to tweet about her speech and decided that Halsey was pretty much dragging the Grammys.
The nominations for next year’s Grammy Awards were revealed last week and Halsey was noticeably absent from the list, despite Without Me becoming a Billboard Hot 100 number one.
Halsey’s fans were kicking off about her snub on Twitter, prompting her to respond herself and she admitted not being nominated had left her pretty upset.
She tweeted: ‘my fans please do not waste your anger or frustration. i see a lot of you are upset. of course im sad too. none of it matters. literally none of it.
‘you’re here. im here. + everything is gonna stay exactly the same and without me is still a super tight, record breaking song.
‘congrats to the nominees. so much great music nominated this year. definitely see a lot of my friends and colleagues missing from the nom lists, but the music speaks! this is how it goes every year right?’
Advertisement Advertisement
Those that did make the cut included Lizzo, who joined Halsey at the AMAs and slayed on the red carpet, Billie Eilish and Lil Nas X.


If your browser doesn't automatically go to kiali.io within a few seconds, you may want to go to kiali.io manually.


{% extends 'layouts/main.html' %} {% block title %}Case Report Instructions{% endblock %} {% block content %}
Please send in prime cases of your career and win the best prizes in each category ( 10 categories).
The cases which are not shortlisted for oral presentation will be considered for poster presentation.
Note: All the cases discussed in the conference will be published in an indexed journal-JMSR.
Last date of submission : 31st Oct 2018
Please follow guidelines given below before submitting the case report.


{% extends 'salad/base.html' %} {% block content %}
Salad is a miniature language used in place of English in the Python room. It consists of words which happen to be English names for vegetables, fruit, fungi or seeds.
| Term | Definition | |
|---|---|---|
| {% if item == wod %} {% endif %}{{ item.term }} | {{ item.definition }} | {% if has_group('Dark Council') %} Edit Remove{% endif %} |
The Salad language was created and introduced to the room on April 10, 2013 by Jon Clements and Inbar Rose. The usage of the greeting quickly spread among regulars, and participants added additional words to the lexicon over time.
| Jon Clements | Greetings and salutations or, as I prefer to express it - Cabbage |
| Inbar Rose | :/ Cabbage to you too then. |
| Jon Clements | Yup - I believe the correct reply should probably be Asparagus... (but haven't quite worked out the semantics of my new language fully...) |
| Inbar Rose | Vegetebarian? |
| Jon Clements | Ummm... not a bad name - might consider that ;0 |
| Inbar Rose | or LinguaVega ? Or, going with the theme. Lets call the language: "Salad" And let me just say, "Turnip orange banana, tomato celery onion."  You must think I said "Turnip orange banana, eggpland celery pear." |
| Jon Clements | paw-paw goatsbeard :( |
| pcalcao | Good day people |
| Inbar Rose | Cabbage |
Much like the Smurfs, chat members will occasionally use “cabbage” as a placeholder word in circumstances where it would normally make no sense. For example, the motto of the room is “The productive programming cabbage”. Here, “cabbage” means “language”.


Jane Lee
About Me
We challenged ourselves to create a visual language for our users that synthesizes the classic principles of good design with the innovation and possibility of technology and science.
| Things | Hour |
|---|---|
| Work | 8 AM |
| Shop | 4 PM |
| Hobbies | 6 PM |
| Dinner | 8 PM |
| Movie | 10 PM |
54:26 / 1:15:30
11:57 AM
Latest Uploads


Genesis 26:17
And Isaac departed thence, and pitched his tent in the valley of Gerar, and dwelt there.


This application lists the current number of cases reported across the Globe.
Total cases reported as of today.


Weathermaster:
Research Lab Building Status
North Wing: Project stopped. Natural freezing in progress.
West Wing: Project in progress. Project N45.
: Guardian is operating normally.
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East Wing: Energy flowing in. Unable to confirm signal.
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Return to menu.


Did the candidate participate in group activities?
Candidate did not participate
Candidate barely participated or prevented others from participating.
Candidate participated to a reasonable standard.
Candidate participated well and encouraged others to also participate.
Should this candidate be considered further?


X Factor winner James Arthur will return to celebrity show as guest performer
James, who won the show back in 2012, will take to the X Factor stage once again during Saturday's live show
by Will Twigger, https://www.mirror.co.uk/authors/will-twigger/X Factor champ James Arthur is set to make his return on the celeb edition of the show.
Saturday's live show will see the Impossible singer, 31, take to the stage as the contestants wait to learn if they've been given the boot or will survive to sing another day.
The news was dropped by the X Factor Twitter account: "The #XFactor stage is a second home for @JamesArthur23!
"Rumour has it he's quite missing it...Catch him on Saturday night, we're so excited to have him back! #XFactorCelebrity."
James himself shared with fans that he'll be performing one of his most popular hits.
The star wrote: "Excited to say I'll be making a return to @TheXFactor on Saturday night to perform 'Quite Miss Home.' Tune in at 8:30 on @ITV."
James triumphed in the X Factor's ninth series, back in 2012, beating Jahmene Douglas.
His cover of Impossible, Shontelle's smash hit sensation, soon made James the fastest-selling X Factor winner to that date.

In 2014, he received a major blow when SyCo, his record label, dropped him. After he'd cleaned up his act, the iconic label signed him back up.
2016 saw the release of his emotional ballad Say You Won't Let Go, which won James his fans back and set him on course for an impressive comeback.
He also allegedly had a fling with Spice Girl Mel B after they crossed paths on America's Got Talent.
A source told The Sun : "Mel met James soon after her marriage ended while she was working in LA on America's Got Talent.
"They were brought together by Simon Cowell who secured James an opportunity to perform on the show.
"James was single at the time and they hit it off instantly...They had a wild fling and loved spending time together, but it ended respectfully."
James recently struck out at music "snobs", insisting his music is good enough to be performed at the hugely popular Glastonbury Festival.


Using existing models¶
In this tutorial we will describe everything you can do with OpenSeq2Seq without writing any new code. We will cover the following topics: how to run one of the implemented models (for training, evaluation or inference), what parameters can be specified in the config file/command line and what are the different kinds of output that OpenSeq2Seq generates for you.
How to run models¶
The main script to run all models is run.py
. Since it is a fairly simple
Python script, you can probably understand
how to use it by running run.py --help
which will display all available
command line parameters and their short description. If that does not contain
enough details, continue reading this section. Otherwise, you can safely skip
to the next section, which describes config parameters.
There are 2 main parameters of run.py
that will be
used most often: --config_file
and --mode
. The first one is a required
parameter with path to the python configuration file (described in the
next section). --mode
parameter can be one of the
“train”, “eval”, “train_eval” or “infer”. This will do what it says: run
the model in the corresponding mode (with “train_eval” executing training
with periodic evaluation).
The other parameters of the run.py
script are the following:
--continue_learning
— specify this when you want to continue learning from existing checkpoint. This parameter is only checked when--mode
is “train” or “train_eval”.--infer_output_file
— this specifies the path to output of the inference. This parameter is only checked when--mode
is “infer”.--no_dir_check
— this parameter disables log directory checking. By default,run.py
will be checking that the log directory (specified in the python config) is valid. Specifically, it will check that it exists when--mode
equals “eval” or “infer” (or when--continue_learning
is specified for training). If training is performed, but--continue_learning
is not specified, the script will check that log directory is empty or does not exist, otherwise finishing with exception. Finally, whenever necessary it will check that the log directory contains a valid TensorFlow checkpoint of the saved model.--benchmark
— specifying this parameter will automatically prepare config for time benchmarking: disable all logging and evaluation. This parameter is only useful for training benchmarking, since in other cases no config preparation is needed. Moreover, specifying it will force the model to run in the “train” mode.--bench_steps
— number of steps to run the model for benchmarking. For now this can only be used in conjunction with--benchmark
parameter and thus only works in the training benchmarking.--bench_start
— first step to start counting time for benchmarking. This parameter works in all modes whether or not--benchmark
parameter was specified.--debug_port
— this enables TensorFlow debugging. To use it first run, e.g.tensorboard --logdir=. --debugger_port=6067
and while tensorboard is running executerun.py
with--debug_port=6067
attribute. After that tensorboard should have debugging tab.--enable_logs
— specifying this parameter will enable additional convenient log information to be saved. Namely, the script will save all output (both stdout and stderr), exact configuration file, git information (git commit hash and git diff) and exact command line parameters used to start the script. For all log files it will automatically append current time stamp so that subsequent runs do not overwrite any information. One important thing to note is that specifying this parameter will force the script to save all TensorFlow logs (tensorboard events, checkpoint, etc.) in thelogs
subfolder. Thus, if you want to restore the model that was saved withenable_logs
specified you will need to either specify it again or move the model checkpoints from thelogs
directory into the baselogdir
folder (which is a config parameter).
Config parameters¶
The experiment parameters are completely defined in one Python configuration
file. This file must define base_params
dictionary and base_model
class.
base_model
should be any class derived from
Model
. Currently it can be
Speech2Text
,
Text2Text
or
Image2Label
.
Note that this parameter is not a string, but an actual Python class, so you
will need to add corresponding imports in the configuration file. In addition
to base_params
and base_model
you can define
train_params
, eval_params
and infer_params
dictionaries that will
overwrite corresponding parts of base_params
when the corresponding mode
is used. For examples of configuration files look in the example_configs
directory. The complete list of all possible configuration parameters is
defined in the documentation in various places. A good place to look first is
the Model.__init__()
method
(config parameters section), which defines most of the first level parameters:
-
Model.
__init__
(params, mode='train', hvd=None)[source] Model constructor. The TensorFlow graph should not be created here, but rather in the
self.compile()
method.Parameters: - params (dict) – parameters describing the model.
All supported parameters are listed in
get_required_params()
,get_optional_params()
functions. - mode (string, optional) – “train”, “eval” or “infer”. If mode is “train” all parts of the graph will be built (model, loss, optimizer). If mode is “eval”, only model and loss will be built. If mode is “infer”, only model will be built.
- hvd (optional) – if Horovod is used, this should be
horovod.tensorflow
module. If Horovod is not used, it should be None.
Config parameters:
- random_seed (int) — random seed to use.
- use_horovod (bool) — whether to use Horovod for distributed execution.
- num_gpus (int) — number of GPUs to use. This parameter cannot be
used if
gpu_ids
is specified. Whenuse_horovod
is True this parameter is ignored. - gpu_ids (list of ints) — GPU ids to use. This parameter cannot be
used if
num_gpus
is specified. Whenuse_horovod
is True this parameter is ignored. - batch_size_per_gpu (int) — batch size to use for each GPU.
- num_epochs (int) — number of epochs to run training for.
This parameter cannot be used if
max_steps
is specified. - max_steps (int) — number of steps to run training for.
This parameter cannot be used if
num_epochs
is specified. - save_summaries_steps (int or None) — how often to save summaries. Setting it to None disables summaries saving.
- print_loss_steps (int or None) — how often to print loss during training. Setting it to None disables loss printing.
- print_samples_steps (int or None) — how often to print training samples (input sequences, correct answers and model predictions). Setting it to None disables samples printing.
- print_bench_info_steps (int or None) — how often to print training benchmarking information (average number of objects processed per step). Setting it to None disables intermediate benchmarking printing, but the average information across the whole training will always be printed after the last iteration.
- save_checkpoint_steps (int or None) — how often to save model checkpoints. Setting it to None disables checkpoint saving.
- eval_steps (int) — how often to run evaluation during training.
This parameter is only checked if
--mode
argument ofrun.py
is “train_eval”. If no evaluation is needed you should use “train” mode. - logdir (string) — path to the log directory where all checkpoints and summaries will be saved.
- data_layer (any class derived from
DataLayer
) — data layer class to use. - data_layer_params (dict) — dictionary with data layer configuration. For complete list of possible parameters see the corresponding class docs.
- optimizer (string or TensorFlow optimizer class) — optimizer to use for training. Could be either “Adam”, “Adagrad”, “Ftrl”, “Momentum”, “RMSProp”, “SGD” or any valid TensorFlow optimizer class.
- optimizer_params (dict) — dictionary that will be passed to
optimizer
__init__
method. - initializer — any valid TensorFlow initializer.
- initializer_params (dict) — dictionary that will be passed to
initializer
__init__
method. - regularizer — and valid TensorFlow regularizer.
- regularizer_params (dict) — dictionary that will be passed to
regularizer
__init__
method. - dtype — model dtype. Could be either
tf.float16
,tf.float32
or “mixed”. For details see mixed precision training section in docs. - lr_policy — any valid learning rate policy function. For examples,
see
optimizers.lr_policies
module. - lr_policy_params (dict) — dictionary containing lr_policy parameters.
- max_grad_norm (float) — maximum value of gradient norm. Clipping will be performed if some gradients exceed this value (this is checked for each variable independently).
- loss_scaling — could be float or string. If float, static loss scaling is applied. If string, the corresponding automatic loss scaling algorithm is used. Must be one of ‘Backoff’ of ‘LogMax’ (case insensitive). Only used when dtype=”mixed”. For details see mixed precision training section in docs.
- loss_scaling_params (dict) — dictionary containing loss scaling parameters.
- summaries (list) — which summaries to log. Could contain “learning_rate”, “gradients”, “gradient_norm”, “global_gradient_norm”, “variables”, “variable_norm”, “loss_scale”.
- iter_size (int) — use this parameter to emulate large batches.
The gradients will be accumulated for
iter_size
number of steps before applying update. - larc_params — dictionary with parameters for LARC (or LARS)
optimization algorithms. Can contain the following parameters:
- larc_mode — Could be either “scale” (LARS) or “clip” (LARC). Note that it works in addition to any other optimization algorithm since we treat it as adaptive gradient clipping and learning rate adjustment.
- larc_eta (float) — LARC or LARS scaling parameter.
- min_update (float) — minimal value of the LARC (LARS) update.
- epsilon (float) — small number added to gradient norm in denominator for numerical stability.
- params (dict) – parameters describing the model.
All supported parameters are listed in
Note that some of the parameters are also config dictionaries for corresponding
classes. To see list of their configuration options, you should proceed to the
corresponding class docs. For example, to see all supported data layer parameters,
look into the docs for data.data_layer.DataLayer
. Sometimes, derived classes
might define their additional parameters, in that case you should be looking
into both, parent class and its child. For example, look into
models.encoder_decoder.EncoderDecoderModel
, which defines parameters
specific for models that can be expressed as encoder-decoder-loss blocks.
You can also have a look at
encoders.encoder.Encoder
(which defines some parameters shared across
all encoders) and encoders.ds2_encoder.DeepSpeech2Encoder
(which
additionally defines a set of DeepSpeech-2 specific parameters).
Note
For convenience all string or numerical config parameters can be overwritten
by command line arguments. To overwrite parameters of the nested
dictionaries, separate the dictionary and parameter name with “/”.
For example, try to specify --logdir
argument or
--lr_policy_params/learning_rate
in your run.py
execution.
What is being logged¶
This section is going to be completed soon.


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In Short
All Users
Kerry Subtimn
So this is the story all about how.
So this is the story all about how.
Terry Nguyen
My life got twist turned upside down and I'd like to take a minute, just sit right there.
My life got twist turned upside down and I'd like to take a minute, just sit right there.
Latasha Bonita
I'll tell you how I became the Prince of a town called Bel Air
I'll tell you how I became the Prince of a town called Bel Air
Terry Nguyen
In West Philladelphia, born and raised, well the playground was where I spent most of my days.
In West Philladelphia, born and raised, well the playground was where I spent most of my days.
Terry Nguyen
Chillin' out, maxin' relaxin' all cool, and just shooting some BBall outside of the school. When a couple of guys who were up to no good.
Chillin' out, maxin' relaxin' all cool, and just shooting some BBall outside of the school. When a couple of guys who were up to no good.
Terry Nguyen
Started making trouble in my neighbourhood. I got in one little fight and my mom got scared. She said "You're moving in with your Uncle and Auntie in Bel Air."
Started making trouble in my neighbourhood. I got in one little fight and my mom got scared. She said "You're moving in with your Uncle and Auntie in Bel Air."
Latasha Bonita
Rolled up to the house about 7 or 8, and I yelled to the cabbie, "Yo Homes! Smell you later."
Rolled up to the house about 7 or 8, and I yelled to the cabbie, "Yo Homes! Smell you later."
Terry Nguyen
I looked at my Kingdom, I was finally there, To sit on my throne as the prince of Bel Air.
I looked at my Kingdom, I was finally there, To sit on my throne as the prince of Bel Air.


Compares another object for equality.
Compares another object for equality.
If the passed object is a Span
, this method will return true
only if the other
Span
returns the exact same value as this Span
for totalNanos
.
the object to compare with this one for equality
true if the other object is a Span
with the same totalNanos
value.
Returns a hash code for this Span
.
Returns a hash code for this Span
.
a hash code based only on the totalNanos
field.
This time span converted to milliseconds, computed via totalNanos / 1000000
, which
truncates off any leftover nanoseconds.
This time span converted to milliseconds, computed via totalNanos / 1000000
, which
truncates off any leftover nanoseconds.
The millisPart
and nanosPart
can be used, for example, when invoking
Thread.sleep
. For example, given a Span
named span
, you could
write:
Thread.sleep(span.millisPart, span.nanosPart)
The number of nanoseconds remaining when this time span is converted to milliseconds, computed via
(totalNanos % 1000000).toInt
.
The number of nanoseconds remaining when this time span is converted to milliseconds, computed via
(totalNanos % 1000000).toInt
.
The millisPart
and nanosPart
can be used, for example, when invoking
Thread.sleep
. For example, given a Span
named span
, you could
write:
Thread.sleep(span.millisPart, span.nanosPart)
Returns a localized string suitable for presenting to a user that describes the time span represented
by this Span
.
Returns a localized string suitable for presenting to a user that describes the time span represented
by this Span
.
For example, for Span(1, Millisecond)
, this method would return "1 millisecond"
.
For Span(9.99, Seconds)
, this method would return "9.9 seconds"
.
a localized string describing this Span
Returns a Span
representing this Span
scaled by the passed factor.
Returns a Span
representing this Span
scaled by the passed factor.
The passed factor
can be any positive number or zero, including fractional numbers. A number
greater than one will scale the Span
up to a larger value. A fractional number will scale it
down to a smaller value. A factor of 1.0 will cause the exact same Span
to be returned. A
factor of zero will cause Span.ZeroLength
to be returned.
If overflow occurs, Span.Max
will be returned. If underflow occurs, Span.ZeroLength
will be returned.
Returns a string that looks similar to a factory method call that would have produced this Span
.
Returns a string that looks similar to a factory method call that would have produced this Span
.
For example, for Span(1, Millisecond)
, this method would return "Span(1, Millisecond)"
.
For Span(9.99, Seconds)
, this method would return "Span(9.99, Seconds)"
.
a string that looks like a factory method call that would produce this Span
The total number of nanoseconds in this time span.
The total number of nanoseconds in this time span.
This number will never be negative, but can be zero.
A time span.
A
Span
is used to express time spans in ScalaTest, in constructs such as thefailAfter
method of traitTimeouts
, thetimeLimit
field of traitTimeLimitedTests
, and the timeouts of traitsEventually
, , andAsyncAssertions
. Here's an example:If you prefer you can mix in or import the members of
SpanSugar
and place a units value after the timeout value. Here are some examples:In addition to expression the numeric value with an
Int
or aLong
, you can also express it via aFloat
orDouble
. Here are some examples:Internally, a
Span
is expressed in terms of aLong
number of nanoseconds. Thus, the maximum time span that can be represented isLong.MaxValue
nanoseconds, or approximately 292 years. Hopefully these won't be "famous last words," but 292 years should be sufficient for software testing purposes. Any attempt to create aSpan
longer thanLong.MaxValue
nanoseconds will be met with anIllegalArgumentException
:All of class
Span
's constructors are private. The only way you can create a newSpan
is via one of the twoapply
factory methods inSpan
's companion object. Here is a table showing one example of each numeric type and unit value:Int
Long
Float
Double


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London is the capital city of England.
Paris is the capital of France.
Tokyo is the capital of Japan.


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Pour-over YOLO vice fingerstache quinoa, post-ironic XOXO hella chia beard mumblecore paleo meh. Yuccie cornhole stumptown mustache actually. Gochujang tattooed offal meditation PBR&B bicycle rights tacos, four dollar toast flannel wayfarers echo park kickstarter jianbing shoreditch pitchfork. Marfa stumptown pitchfork, occupy heirloom hammock tumeric poke locavore. Vegan scenester 8-bit tousled umami actually, 90's


--- layout: page title: API redirect_from: ---
The Smartling Translation API is a REST-like API that allows you to access functionality provided by the Smartling Translation Management Platform using defined HTTPS requests and structured JSON responses. You can integrate translated content files into your application by simply uploading source content files, Smartling will handle the translations, and then you can download the translated files whenever it makes sense (most likely during deployment). You can always check the status of a file or get a list of files in translation.
All URLs within this documentation have the following base:
https://api.smartling.com
Smartling Translation API requests must be served over HTTPS. This ensures data privacy which is not provided by HTTP.
Every API request you make must include an authentication token in the header. See Authentication for more information.
All dates will follow the common ISO 8601 date and time standard format, and will be expressed in UTC:
YYYY-MM-DDThh:mm:ssZ
For example, 10:31 PM UTC on Saturday, August 21, 2010 would be:
2010-08-21T22:31:00Z


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R package starsExtra
provides several miscellaneous functions for working with stars
objects, mainly single-band rasters. Currently includes functions for:
CRAN version:
install.packages("starsExtra")
GitHub version:
install.packages("remotes")
remotes::install_github("michaeldorman/starsExtra")
The complete documentation can be found at https://michaeldorman.github.io/starsExtra/.
The following code applied a 15*15 mean focal filter on a 533*627 stars
Digital Elevation Model (DEM):
data(carmel)
carmel_mean15 = focal2(
x = carmel, # Input 'stars' raster
w = matrix(1, 15, 15), # Weights
fun = "mean", # Aggregation function
na.rm = TRUE, # 'NA' in neighborhood are removed
mask = TRUE # Areas that were 'NA' in 'x' are masked from result
)
The calculation takes: 0.5625446 secs.
The original DEM and the filtered DEM can be combined and plotted with the following expressions:
r = c(carmel, carmel_mean15, along = 3)
r = st_set_dimensions(r, 3, values = c("input", "15*15 mean filter"))
plot(r, breaks = "equal", col = terrain.colors(10), key.pos = 4)
The following code section compares the calculation time of focal2
in the above example with raster::focal
(both using C/C++) and the reference method focal2r
(using R code only).
library(microbenchmark)
library(starsExtra)
library(raster)
data(carmel)
carmelr = as(carmel, "Raster")
res = microbenchmark(
focal2 = focal2(carmel, w = matrix(1, 15, 15), fun = "mean", na.rm = FALSE),
focal = focal(carmelr, w = matrix(1, 15, 15), fun = mean, na.rm = FALSE),
focal2r = focal2r(carmel, w = matrix(1, 15, 15), mean),
times = 10
)
res
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> focal2 542.3506 546.0164 587.9224 554.3557 609.8761 793.5663 10
#> focal 114.3561 115.9764 142.2213 119.7932 125.9327 339.7064 10
#> focal2r 17236.5407 17367.5100 17734.2765 17634.1378 17902.5663 19048.0060 10
boxplot(res)


I'm a software developer based in New York with experience in Ruby on Rails, JavaScript, React.js and Flux. I've loved programming since college, but took a few years off to get my Ph.D. in Geometric Topology. In my spare time I'm into cooking, rock climbing and board games.


I am not well versed in drugs and will never claim to be. Before Thursday February 02, 2006 I had only taken mushrooms once and other than that had only smoked marijuana and been drunk. A week or two prior to the second I heard that hash was coming into town. I was highly intrigued because my father who lives in europe had on many occassions smoked hash in my presence and therefore I was tempted to buy some. My regular dealer told me that he would have some and I would be able to buy it by that friday.
I did not receive a phone call from him until the sunday before the second and he told me he would not be able to get me hash. I was very dissapointed until later when he called me back and told me he was investing in a quarter pound of mushrooms. I usually don't act on impulse and for things of this nature like to have time to prepare and think through if I will proceed or not. This time, however, I immediately told him I would buy 3/8 of an ounce and at that time had no clue it would be the best 105 dollars I have spent yet in my young life.
My girlfriend had been telling me she wanted to try mushrooms with me a lot (for the sake of safety but practicality I will call her 'Lisa') and that was my biggest reasoning for acting on buying mushrooms without giving myself more time to think. The one time before that I had taken mushrooms, I took an eighth of an ounce and it did not help me accomplish what I desired to accomplish. It felt very clouded my first time and was not very visual but merely a mental experience. I wanted the mental experience but I also wanted to be transported physically and spiritually. I decided I would make sure my second time taking shrooms would encompass all three desired aspects for me and so I decided my dosage would be 1/4 of an ounce and it being Lisa's first time, she would take 1/8.
I did not have school that Thursday but Lisa did so I had to patiently wait to get on a bus at around 3 pm to go meet up with her and bring her back to my house. I spent the day meditating on the trip to come and understandably it was the longest wait I have ever experienced, as I am sure everyone who has had a similar wait will be willing to proclaim the same.
I eventually met Lisa at her subway stop and we took the bus back to my house. We ate our doses at around 4:15 and the trip continued as follows:
4:45- We walked up towards the woods in my neighborhood finding things to do while we waited for the shrooms to kick in.  We found a clearing in the woods and sat talking until we felt the onset. As soon as the onset hit me I knew that this trip was bound to be far more intense than my somewhat dissapointing first trip. I believe in spiritual connection to others and I think my love for Lisa increased and enhanced the trip and throughout my belief in this grew stronger and stronger.
By time I started feeling utterly strange a lone woman came walking through the woods. My senses were numbed somewhat at this point and she came as an unwanted suprise. Lisa felt uncomfortable in the slowly darkening and foggy woods and asked if we could leave. It didn't matter to me whether we stayed or left but I felt like the conductor so I agreed to leave. As I left the woods intrigued me more as faces appeared in trees and lights that flashed by from cars looked like animals. I did not attempt to stay however. My belief was that the only way to have a good trip was to make sure Lisa had a good trip. This was one of the most important decisions I made because as we went on our trips seemed to merge more and more.
5:15- I decided I wanted to feel a bit of nostalgia so I asked if we could venture to my grade school. She agreed and we slowly made our way. I appreciated everything around me and her and I both talked as we went but I am sure neither of us was really listening to the other at first, I had more of a sense of feeling her words than hearing them. This was the first odd implication that senses would switch roles. As we walked on I noticed many colors and shapes would pop out at me but none as much as the earthy green. I looked at Lisa and she looked at me and we almost simultaneously said 'Everything is so beautiful.'
We walked further and as we got closer to the school she started talking about glowing patterns on the ground.  I looked down momentarily and saw nothing so I looked back up and realised that the only light on in any house on the block we were on was up in an attic painted entirely green. It seemed to call out to me but that may have just been Lisa talking to herself which she ended up doing many times. I pointed it out to Lisa and we stood transfixed looking at it for about 10 minutes. At this point I decided that I would look for my desired religious aspects within everything earthy I could identify with. Basically I was converting my religious beliefs and cherishing anything natural as holy.
5:40- We finally got to my elementary school's yard and I hear my name called out by a group of boys playing football. I did not recognize them but after a few moments recalled them as my old school mates by their voices. Lisa was talking loud and acting all over the place so I did not want to stop to chat with the boys. We walked on and sat in the lower school yard. Lisa laid on a step and told me she was going to try and melt into the earth. At this point I realized mushrooms strongly connects the user to the earth. I also realized Lisa was tripping balls, for lack of a better phrase.
In this moment I noticed the steps she was sitting on had the basic pattern of any set of steps but to me it seemed entirely foreign. I could not comprehend that this pattern would take me into the upper school yard so I looked for a moment as the boys started walking away but decided I would stay near Lisa and not go into the upper school yard. She was lost and I had to stay nearby. I decided to look down and entirely dark alley that was in the lower schoolyard and was going to venture down it but then decided not to because it looked entirely too scary.
As Lisa 'melted' into the steps I sat next to her and stared at my old school. The building warped in the strangest ways and it felt as tho I could pull the windows out and push them back in like giant filing cabinets. This visual distortion was the first absolutely intense and unexpected thing I experienced. I was not prepared for it an immediately after it tried to convince myself it had not happened. This kind of scenario would end up happening again. I couldn't explain these things so eventually I just decided to accept them.
Now I realised I was getting deep into the trip. I recalled my last trip and remembered how comfortable my house was and was about to suggest we go there when Lisa said 'I need to get to your bed.' In any other case I may have taken that in another way but I knew exactly how she felt and wanted to get there as well. We walked very slowly examining walls and how everything was oddly layered. I looked down at the ground and Lisa said 'The patterns...' but I cut her off and finished her sentence 'Are absolutely amazing.' And she started cracking up and said that was exactly what she was going to say. We were off the planet at this point. I came to a point on our trip to my house where I could not stop looking at the moon. Now my religion felt like it was being torn apart by the sky and the planet, each vying for my complete attention and faith. We reached my house without me realizing I had taken us directly to it and I was also without a plan for gettin past my mom.
6:10- A lot had seemed to happen. In fact an eternity must have had gone by but it was still quite early. We sat on my indoor patio for a fair amount of time deciding how to get past my mom and grandmom but eventually I didn't care. I went into the bathroom and everything around me started swirling. Her and I both took our turns and the only way I can describe being seperated from her at this point was a feeling of being in limbo. Watching. Waiting. All the while enjoying the fantastic feelings going through my body and mind. I knew at this point that I needed her during this trip and she came out and we told each other what we felt and we felt identical feelings of need for each other. We want upstairs and I dodged my mom's bullets and I locked myself into my room with Lisa. We sat down and I took my shoes off but she couldn't. Once again I felt like the conductor of the trip and helped her out. This was possibly the longest process of the entire night.
6:30- This was around the peak of the trip. I put on Alice in Wonderland and the screen had a rippling effect that I had experienced on my first trip but this was far more intense. I could not focus on anything for long and this movie was no exception. We put on many more movies throughout the night but none caught me. The first thing that happened to me as I lost focus of the movie was that I stood up and touched my keys. They were cold and felt damp. Suddenly my entire body felt like liquid. If I took a sip of water it felt as if I was running down my own throat. When I touched Lisa she was warm and made the liquid parts of me feel like syrup. My senses were all mixed up but felt enhanced.
She asked me to lay on top of her. It was an odd request but I obliged. We kissed and I felt myself fall into her mouth and then into her entirely. It was erotic yet entirely did not feel sexual to me. It just felt as if we had merged. From then on in we finished each others sentences and felt what the other was feeling. She was beautiful to me beyond belief and she kept grabbing my face to inspect it and she said that it was absolutely captivating to her. We were both beautiful beings to each other but when I looked at myself in the mirror I looked hideous and as though my face were coming off in layers.
As we lay next to each other later on in our pajamas my mind and body felt like an ancient pyramid like puzzle with my body parts and thoughts coming in at odd and unexpected angles. I could reach out in front of Lisa and feel as though I was coming in from behind to touch her back. It was all too much for me so I went back into the bathroom for a moment. I missed her after moments and came back to her. I lay in the corner and she knew I was in the room but not quite where. The funniest moment of the night came when she said 'Where are you?' and the only way I could respond was 'Over here somewhere.' That was how I felt, I was somewhere but not quite sure of where. I was hardly aware of what was happening and yet was awake to the entire world. I had never had so many feelings and emotions.
8:00- At this point the peak was almost over but my senses were highly mixed up. I smelled what I saw, I felt what I heard, I tasted random tastes and all of it was incredible to me. That was the moment I realized I was having an incredible and perhaps one of the best moments in recent times.
The come down afterwards sucked and I tried by any means to hold on to the feeling of absolution and completion that was the trip. It was amazing and has inspired me to do many more hallucinogens. I will also go back and do shrooms, possibly alone to find the true personal spiritual awakining I need as apposed to a shared one. The fact that Lisa came with me was great but may have detracted from my personal experience. Shrooms should be experienced by the world if everyone could enjoy them as much as I did. I have come to love and appreciate them. I could have described so much more but that would truly take an eternity. This is not a drug to hear about or read about, it is an experience to be had that will change the world for you.
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.


Next: char-code-limit, Previous: char-int, Up: Characters Dictionary
code-char
code ⇒ char-p
code—a character code.
char-p—a character or nil.
Returns a character with the code attribute given by code. If no such character exists and one cannot be created, nil is returned.
(code-char 65.) ⇒ #\A ;in an implementation using ASCII codes (code-char (char-code #\Space)) ⇒ #\Space ;in any implementation
The implementation’s character encoding.


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BART Station
WS
Warm Springs Train
DP
Dublin/Pleasanton Train
A
Antioch Train
R
Richmond Train
24
24th St & Mission Train
DC
Daly City Train
M
Millbrae Train
S
SFO Airport Train
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* Note that this map displays estimated train locations based off of
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public class Transform
extends java.lang.Object
The transform parameters are estimated by minimizing the weighted differences between the observations and an idealized model of the system, a "least squares adjustment".
The model equations can generally be
written as A * (l + v) + B * dx = d
where A
and B
are the
model matrixes for the observations and paramaeters respectively (or the matrixes of the
partial derivatives, the jacobi matrixes in case of non-linear model equations). l
is the vector of observations, v
the vector of residuals (the difference between the
observations and least-squares adusted observations), dx
the
corrections to the approximated parameters added to x0
the vector of approximated
parameters (necessary when solving non-linear model equations by iteration) and d
a
vector of constants.
The problem is solved by finding the vector of corrections to parameters that minimizes the weighted sum
squared of the residuals transpose(v) * inverse(Q) * v
, subject to the constraints of the model equations
above, which results in the solution dx = inverse(N) * t
where N =
transpose(B) * We * B
, t = transpose(B) * We * f
, We = inverse(A * Q * transpose(A))
and f = d - A * l
. Q
is the matrix of cofactors, which is equal to the
a priori covariance matrix of the observations, possibly scaled my a constant.
For more information about the mathematics behind the formulas please consult a standard text on least squares adjustment for geodesy and photogrammetry, for example "Observations and least squares", by Edward M: Mikhail, 1976
Created by runaas on 11.05.2017.
| Modifier and Type | Class and Description |
|---|---|
private class |
Transform.ModelEquations
All matrixes of the model equations for a set of observations.
|
static interface |
Transform.Observation
Generic observation
|
class |
Transform.OrientationObservation
Orientation obeservation, typically from a compass
|
private class |
Transform.Parameters
Block of estimated transform parameters
|
class |
Transform.PointInPlaneObservation
observation of a tango point that should lie in a plane described by world coordinates
|
class |
Transform.PointInTerrainObservation
observation of a tango point that should lie on a terrain model surface
|
class |
Transform.PositionObservation2D
A 2D position observation, contains a "tango" and a "world" position describing the same point,
and their respecive a-priori standard deviations
|
class |
Transform.PositionObservation3D
A 3D position observation, contains a "tango" and a "world" position describing the same point,
and their respecive a-priori standard deviations
|
| Modifier and Type | Field and Description |
|---|---|
private org.ejml.data.DMatrixRMaj |
N
Normal equations system matrix
|
private org.ejml.data.DMatrixRMaj |
Ninv
Inverted normal equations system matrix
|
(package private) static int |
num_parameters
Number of estimated parameters, currently 4 (x, y, z and orientation)
|
private Transform.ModelEquations |
orientEquations |
private Transform.Parameters[] |
parameters
Array of three parameter blocks, distinct for each of the three computation steps in
the
adjust(List) method |
private Transform.ModelEquations |
planeEquations |
private Transform.ModelEquations |
posEquations2D
Model equations for observations
|
private Transform.ModelEquations |
posEquations3D |
private org.ejml.data.DMatrixRMaj |
t
Normal equations system vector
|
private org.ejml.data.DMatrixRMaj |
tmp1
Temp matrix for internal use
|
private org.ejml.data.DMatrixRMaj |
tmp2 |
private org.ejml.data.DMatrixRMaj |
tmp3 |
private org.ejml.data.DMatrixRMaj |
vWv
Computed weighted sum squared of residuals
|
private org.ejml.data.DMatrixRMaj |
x
Computed adjustments to parameters (
Ninv = inverse(N); x = Ninv * t; ) |
| Constructor and Description |
|---|
Transform()
Create and initialize a transform object with all relevant temp structures
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
adjust(java.util.List<Transform.Observation> observations)
Recompute the transform from a list of observation data.
|
double |
az()
Rotation around z axis
|
double |
azSigma2() |
private void |
computeQvv(org.ejml.data.DMatrixRMaj Q,
org.ejml.data.DMatrixRMaj A,
org.ejml.data.DMatrixRMaj We,
org.ejml.data.DMatrixRMaj B,
org.ejml.data.DMatrixRMaj Ninv,
org.ejml.data.DMatrixRMaj Qvv)
Compute cofactor matrix for residuals
Qvv = Q * A' * (We - We * B * Ninv * B' * We) * A * Q
|
private void |
computeV(org.ejml.data.DMatrixRMaj Q,
org.ejml.data.DMatrixRMaj A,
org.ejml.data.DMatrixRMaj We,
org.ejml.data.DMatrixRMaj f,
org.ejml.data.DMatrixRMaj B,
org.ejml.data.DMatrixRMaj x,
org.ejml.data.DMatrixRMaj v)
Compute residual vector
v = Q * A' * We * (f - B * x)
|
private static void |
computeVWV(org.ejml.data.DMatrixRMaj x,
org.ejml.data.DMatrixRMaj t,
org.ejml.data.DMatrixRMaj vWv)
Update the weighted sum of residuals with the corrections to the parameters
vWv=vWv-x'*t
|
double |
cosAz()
Precomputed cosine of roatation around z-axis
|
static double |
normalizeAngle(double a)
Normalize an angle to the -pi, pi interval
|
double |
sigma2()
Observation sample variance of the unit weight (translation in meters)
|
double |
sinAz()
Precomputed sine of roatation around z-axis
|
private static boolean |
solveNormal(org.ejml.data.DMatrixRMaj N,
org.ejml.data.DMatrixRMaj t,
org.ejml.data.DMatrixRMaj Ninv,
org.ejml.data.DMatrixRMaj x)
Solve normal equations
Ninv = inv(N)
x = Ninv * t
|
private void |
updateN(org.ejml.data.DMatrixRMaj B,
org.ejml.data.DMatrixRMaj We,
org.ejml.data.DMatrixRMaj N)
Update normal equation matrix N with observation matrixes B and weights We
N = N + B' * We * B
|
private void |
updateT(org.ejml.data.DMatrixRMaj B,
org.ejml.data.DMatrixRMaj We,
org.ejml.data.DMatrixRMaj f,
org.ejml.data.DMatrixRMaj t)
Update normal equation vector t with observation
t = t + B' * We * f
|
double |
x0()
x translation
|
double |
xySigma2()
Estimated variance of the horizontal (xy) translation component
|
double |
y0()
y translation
|
double |
z0()
z translation
|
double |
zSigma2()
Estimated variance of the vertical (z) translation component
|
static final int num_parameters
private final Transform.Parameters[] parameters
adjust(List)
methodprivate final org.ejml.data.DMatrixRMaj N
private final org.ejml.data.DMatrixRMaj Ninv
private final org.ejml.data.DMatrixRMaj t
private final org.ejml.data.DMatrixRMaj x
Ninv = inverse(N); x = Ninv * t;
)private final org.ejml.data.DMatrixRMaj vWv
private final Transform.ModelEquations posEquations2D
private final Transform.ModelEquations posEquations3D
private final Transform.ModelEquations orientEquations
private final Transform.ModelEquations planeEquations
private final org.ejml.data.DMatrixRMaj tmp1
private final org.ejml.data.DMatrixRMaj tmp2
private final org.ejml.data.DMatrixRMaj tmp3
public Transform()
public double x0()
public double y0()
public double z0()
public double az()
public double sinAz()
public double cosAz()
public double sigma2()
public double xySigma2()
public double zSigma2()
public double azSigma2()
public boolean adjust(java.util.List<Transform.Observation> observations)
observations
- the list of observationspublic static double normalizeAngle(double a)
a
- a possibly un-normalized angleprivate void updateN(org.ejml.data.DMatrixRMaj B, org.ejml.data.DMatrixRMaj We, org.ejml.data.DMatrixRMaj N)
B
- We
- N
- private void updateT(org.ejml.data.DMatrixRMaj B, org.ejml.data.DMatrixRMaj We, org.ejml.data.DMatrixRMaj f, org.ejml.data.DMatrixRMaj t)
B
- We
- f
- t
- private static boolean solveNormal(org.ejml.data.DMatrixRMaj N, org.ejml.data.DMatrixRMaj t, org.ejml.data.DMatrixRMaj Ninv, org.ejml.data.DMatrixRMaj x)
N
- t
- Ninv
- x
- private static void computeVWV(org.ejml.data.DMatrixRMaj x, org.ejml.data.DMatrixRMaj t, org.ejml.data.DMatrixRMaj vWv)
x
- t
- vWv
- private void computeV(org.ejml.data.DMatrixRMaj Q, org.ejml.data.DMatrixRMaj A, org.ejml.data.DMatrixRMaj We, org.ejml.data.DMatrixRMaj f, org.ejml.data.DMatrixRMaj B, org.ejml.data.DMatrixRMaj x, org.ejml.data.DMatrixRMaj v)
Q
- A
- We
- f
- B
- x
- v
- private void computeQvv(org.ejml.data.DMatrixRMaj Q, org.ejml.data.DMatrixRMaj A, org.ejml.data.DMatrixRMaj We, org.ejml.data.DMatrixRMaj B, org.ejml.data.DMatrixRMaj Ninv, org.ejml.data.DMatrixRMaj Qvv)
Q
- A
- We
- B
- Ninv
- Qvv
-


array explode ( string $delimiter , string $string [, int $limit ] )
Returns an array of strings, each of which is a substring of string formed by splitting it on boundaries formed by the string delimiter.
Returns an array of strings created by splitting the string parameter on boundaries formed by the delimiter. If delimiter is an empty string (""), explode() will return FALSE. If delimiter contains a value that is not contained in string and a negative limit is used, then an empty array will be returned, otherwise an array containing string will be returned.


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My Name is Binjal Patel, a front end developer from Jersey city, New Jersey. I completed my masters in Computer Science from Stevens Institute of Technlnology with 3.7/4 GPA. I have been designing form 2 years. I have always been into everything involving creativity ever since I was little. I took up a Bachelor of Engineering in Information Technology and soon after earning my degree, I worked professionally as a Web Developer. I took up crash courses in web design and development while attending college, but most of the things I have learned are self-taught through online tutorials off the internet and constant self-study.
Custom Barres, New Jersey
Web Developer/Programmer, 2/2017 – Present
• Developing company website from scratch using latest technology stencil framework.
• Researching on stencil framework and deploy it on website to make it user friendly and responsive.
• Designing website model using Pencil prototyping software.
• Use SCSS, HTML5, XML, Java Script, JQuery etc for development.
• Create database using SQL.
• Use agile methodology to achieve mile stones.
• Perform quality testing on current program.
• Creating the code according to the client’s requirement.
• Maintaining live website which is developed on Bigcommerce platform.
• Working on Bigcommerce to add products in live website.
• Providing customer support according to need.
• Use git for code management.
Shoptaki Inc, New York, NY
Software Engineering, 8/2016 – Present
• Researching and deploying a B2B platform using Python Django framework and JavaScript by following Agile
Software development methodology.
• Collaborate with the Software Development team to build API connectors for an omni-channel sales platform
utilizing the Django REST Framework.
• Developing high-quality code, establish development standards and led by example.
• Performing software quality testing and modifying programs with the help of Artificial Intelligence along with
creating algorithms.
• Use git for code management.
Stevens Institute of Technology, Hoboken, New Jersey
Graduate Assistant, 6/2015 – 12/2015
• Developed and reconciled reports documenting past student financial transactions.
• Organized and coordinated summer career discovery programs serving approximately 700 undergraduate students.
• Worked as a front-end developer with IT team and redesigned the Stevens institute of technology website.
• Used HTML5, CSS3, jQuery, Bootstrap.
• Provided technical support to other web design team members as directed.
Dream Tech Developers, Ankleshwar, India
Web Developer, 7/2013 – 7/2014
• Developed the web application screens based on the Visual Designs using Adobe Dreamweaver CS5, HTML5, CSS3 and JavaScript & jQuery.
• Front End Development using the Bootstrap framework.
• Worked in Adobe Photoshop & Illustrator CS5 and wireframes for designing portal layouts.
• Created user interaction model, workflows, information architecture, schematics, prototypes and interface guidelines.
• Analyzed the Usability Specifications provided by the clients.
• Requirement gathering through direct client interaction.
Bhaskaracharya Institute for Space Application and Geo-Informatics, Gandhinagar, India
Web Developer, 6/2012 – 5/2013
• Designed and developed client and server database applications.
• Maintained and updated HTML/CSS templates on a regular basis and as required.
• Assisted in planning and implementing dynamic and online electronic commerce applications.
• Conducted layout design and construction for a variety of websites as assigned.
• Provided technical support to other web design team members as directed.


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Click to view this email online
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Dear Alumni and Friends,
I have been working at St. Mike’s for a few months now. Like I mentioned in my InsightOut blog post, this is an interesting time to be working in university alumni relations, as so many things have shifted in the last several months due to COVID-19.
Traditionally, St. Mike's alumni would be looking forward to various in-person social events. On the flip side, alumni who may not have been able to access a local event in the past can now tune in, a unique opportunity to connect.
So, while we all wait to return to “normal” when it is safe to do so, it is my job to engage and support the alumni community throughout this time—and I think we can all agree it is more important than ever to stay as connected to one another as we can.
Over the next few months, I look forward to onboarding new Alumni Association Board and Young Alumni Committee members, as these groups provide invaluable perspectives and contributions as the mouthpiece for St. Mike's alumni at large.
We have implemented new virtual programming which you will see below, as well as traditional programming—the Annual Twilight Lenten Retreat and Alumni Reunion—offered in new ways, for now.
Even in the midst of a challenging season, I am constantly reminded that St. Mike’s is a special place. I look forward to continuing to spend time in conversation with alumni in an effort to connect, learn, share ideas, and make plans. I always enjoy hearing from alumni—feel free to reach out if you have not already!
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Maxine Webster, Advancement Officer, Alumni Engagement
University of St. Michael’s College
in the University of Toronto
<EMAIL_ADDRESS>|
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Newest Edition: Alumni Profile
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From her time at St. Mike’s and beyond, alumna Samantha Peters' career has been shaped by her commitment to social justice. Read the profile online.
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New Programming: St. Mike’s Alumni Workshops
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In February and March, this series provided a space for various alumni to meet with a small group of current students one time, focused on particular themes: Business Success, Finding a Job in a Difficult Market, Opportunities in Education, Women in STEM, and Entrepreneurship. Thank you to all participating alumni who offered great insight and encouragement! Read about the series online.
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As Alumni Reunion approaches, we are collecting St. Mike’s stories to share on InsightOut, a blog for students, alumni, faculty, and staff that runs Mondays and Thursdays.
We would love to hear from you! What was your favourite place to study or celebrate? Did you perform at Kelly's Corner or in a St. Mike’s production? Were you involved in sports or in student government?
Send us a piece about how your time at St. Mike’s made a difference in your life.
Share your memories in 750-1000 words, in a conversational tone, as you would share with classmates, accompanied by a brief biography and photo. We will be happy help with editing and spell checking.
For more information, or to talk about a possible submission, please contact Catherine Mulroney, Communications Officer, at<EMAIL_ADDRESS>|
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Survey: St. Mike’s Social Groups & Magazine
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Complete this 5-question survey to help us create relevant social media groups and provide a place for alumni in the same industry or with similar interests to share their experiences and build connections.
To support our commitment to sustainability, we are now going to circulate St. Michael’s magazine electronically as well as by mail. In this survey you can confirm how you wish to receive the magazine.
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Accepting Applications: Alumni Association Board
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Do you have ideas about how to engage, celebrate, and strengthen alumni connections at St. Mike’s? Would you like to learn more about St. Mike’s plans and priorities and offer your perspectives?
The mission of the USMC Alumni Association Board is to create and nurture relationships between the University and its alumni, as well as within the alumni community. The Board also represents alumni perspectives on decisions that affect the future of St. Mike’s.
A strong Alumni Association Board makes for a stronger St. Mike’s, and we are seeking new Board members to help bring your voices to the table, champion your passions, and develop alumni programming geared to alumni like you.
For more information about Board membership, please contact Maxine Webster, Advancement Officer, at<EMAIL_ADDRESS>|
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Alumni and Friends Virtual Twilight Lenten Retreat
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You are warmly invited to St. Mike's Alumni and Friends Virtual Twilight Lenten Retreat, which will be held via Zoom.
Date: Tuesday, March 23, 2021
Time: 5:00 p.m. – 6:45 p.m. EST
This year’s retreat is dedicated to the memory of Gloria Buckley, St. Mike’s class of 1948, who founded this annual gathering with Father Robert Madden, CSB in 1995.
"Finding inspiration and hope in the year of St. Joseph"
Alumni Mass celebrated by Father Morgan Rice, CSB from St. Basil’s Church
Followed by reflections from Sister Sue Mosteller CSJ, OC
Time for conversation and a moment of gratitude
To register, please email<EMAIL_ADDRESS>|
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New Programming: Virtual Lectures
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Please save the date for Alumni Reunion — Home Edition, taking place May 26–30, 2021. Our biggest annual alumni gathering is going virtual, with a wide array of reimagined events and activities that will bring the excitement and camaraderie of reunion into your home.
This year, we are planning special events for the honoured alumni of 2020 and 2021: anyone who graduated in a year ending in 0, 1, 5, or 6. Of course, there will be something for everyone and all alumni are welcome to attend.
There will be events through UofT and USMC, including lectures by the University's top thinkers, virtual get-togethers, and fun activities.
Evites for all UofT events will be sent in March. Evites for all USMC events will be sent in April and May. Be sure to check your email for ongoing updates and details. For more information about Alumni Reunion — Home Edition, please contact Maxine Webster, Advancement Officer, at<EMAIL_ADDRESS>|
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{% extends 'navbar.html' %}
{% block content %} {% macro capitalize(text) %} {{ text[0]|upper}}{{text[1:] }} {% endmacro %}We are a small team of web developers and designers based in San Francisco State University. We are specialized in Python, JavaScript and MySQL.


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{if condition="$collection_info->building"}{$collection_info->building}栋{/if}
{if condition="$collection_info->unit"}{$collection_info->unit}单元{/if}
{if condition="$collection_info->floor"}{$collection_info->floor}楼{/if}
{if condition="$collection_info->number"}{$collection_info->number}号{/if}
({$collection_info->id})
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This was a site we worked on at Betamore Academy. The site was designed & developed by Ben Plum. Apps used were Gridlock & Normalize, Grunt. We created the website, and then we refactored it to produce a responsive design.
Much coffee was imbibed whilst working on this project.
You can find the demo here at: Grinders Coffee Co. Demo