NATIONAL LAMPOON'S LOADED WEAPON 1 A film review by James Berardinelli Copyright 1993 James Berardinelli
Running Length: 1:23 Rated: PG-13 (Language, sex)
Starring: Emilio Estevez, Samuel L. Jackson, Jon Lovitz, William Shatner, Kathy Ireland Director: Gene Quintano Producers: Suzanne Todd and David Willis Screenplay: Don Holley and Gene Quintano Music: Robert Folk Released by New Line Cinema
Jack Colt (Emilio Estevez) is a burnt-out, alcoholic cop pining for his lost dog. Wes Luger (Samuel L. Jackson) is a straight-laced, by-the-book officer days away from retirement. When Luger's ex-partner is killed, the two are brought together to investigate. They soon discover that the murder is only the tip of the iceberg. The evil General Mortars (William Shatner) is hatching a plot to begin massive shipments of drugs via Wilderness Girl cookies.
As expected, LOADED WEAPON 1 is pure spoof. While its obvious target is the LETHAL WEAPON series (with Estevez playing a Riggs-type, Jackson playing a Murtaugh-type, and Lovitz doing a bad impression of Joe Pesci), it sets a few other recent films in its sites as well, including SILENCE OF THE LAMBS, BASIC INSTINCT, and WAYNE'S WORLD. There are cameos galore (by such notables as Whoopi Goldberg, Charlie Sheen, Bruce Willis, James Doohan, Dr. Joyce Brothers, and others), and a seemingly endless supply of puns and sight gags. Yet somehow the movie never quite clicks.
LOADED WEAPON 1 aspires to be a NAKED GUN, but Emilio Estevez has neither the screen presence nor the deadpan delivery of Leslie Nielsen. Also, with the recent release of so many other films of this sort, the luster is beginning to fade. They're not as clever or quirky as they once were.
LOADED WEAPON 1 has its funny moments. There isn't much subtlety in the humor--it's up front and obvious. There is a whole barrage of jokes--several per scene. Predictably, some work and others don't, and while the film isn't a credit-to-credit barrel of laughs, you would have to be made of stone not to at least chuckle once or twice.
The cast is mostly unremarkable. The most memorable performances are turned in by the various cameos. Bruce Willis' somewhat unexpected appearance is perhaps the shining moment of the movie, although James Doohan's brief scene is almost as good. Of the "regulars", William Shatner is especially disappointing Given his propensity for over-the-top acting, I expected a scenery-chewing romp. Instead, what he delivers is bland and of the "hand me the check so I can get out of here" variety. He's flat, boring, and completely wrong for the role.
Basically, the film is unremarkable. There's nothing new or original in it, so there isn't any compelling reason to see it. With much better fare of this sort available on video (AIRPLANE, THE NAKED GUN, etc.), renting a tape will be more satisfying, not to mention cost-effective. LOADED WEAPON 1 is good for a few laughs, but it's a rather feeble feature-length film.
Rating: 6.0 (C, **)
- James Berardinelli<EMAIL_ADDRESS>.
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Two years ago during the early months of 2019 I was working as a marketing executive for an unnamed company, it was during this time the then COO engaged in what can only be described as sexual misconduct with a much younger/junior member of staff. Keeping the details short, news of the event was taken to our in house lawyers who took it straight to the CEO, he tried to first brush it under the rug & hide it from the board and other members of SMT, when that didn't work and the victims lawyers proved the COO was lying about the events of that the CEO and by extension the company decided to try pay the victim off. Eventually the truth becomes known to the wider company & you couldn't keep up with how fast the resignations were coming across HR's desk. While everyone with options had already left I still remained and the only management left were the small group of people that let this happen in the first place. It was the first time my version of what is right and wrong were being challenged in a professional setting, mostly due to the privilege I have. I knew leaving would mean giving up the "good job/pay" & with the limited experience I had at that time it would be difficult to leverage that into another position of similar quality. In saying that I left quick and fast, material or monetary value in a job means nothing when you know you can't be supporting certain people, behaviours and ideas. In terms of consequences, while I knew I might make the right decision I was back working a entry level job again, not that there's anything wrong with these jobs I just knew I had more to offer and to come full circle during this time I decided to start my dev journey which has led me here.
Questions of culture have been a difficult one for me to answer. Coming from a NZ European background & at least in my experience is not one rich with culture. From my experience with people I have met and built relationships with from different cultures & backgrounds it's easy to see how their cultures have shaped them & help build their identity. This isn't a feeling or experience I've ever had & even after reflecting on it over the years as I get older still feel as my culture is one of the least impactful things on my values and identity. I’m always open to expanding that and look forward to seeing how that evolves over time & with new experiences.
Starting with limitations, imposter syndrome and not enough self belief have hurt me in the past, not letting me see through a roadblock I might be stuck at giving up earlier than my potential can reach. While my strength is persistence and self awareness in those moments to address the feelings during the above mentioned limitations & how they are affecting myself, my work and others. While its sounds counteractive, these strengths and limitations are always being worked on and over time you become better at noticing them & mitigating the effects.
Persistence is what came to mind first, from talking to people who are already in the industry, who have gone through the course before and from my experience of starting large projects in the past consistency is key. Being able to acknowledge how far there might be to go & how confused or stuck you might feel at any given time but continuing anyway has been the key to the success I have had in the past. Before you know it you will be doing things a past version of yourself couldn't even understand, that's an amazing feeling and its surprising how fast you can get there if you just keep pushing.
In the past I worked in a contact centre, the system we used was getting the job but had long way to go in terms of improvement and optimising. The general feeling in the team was things could be better but the manger wasn't going to change anything. A few others and myself found after a few conversations with our manger about such changes it appeared to be case of they were to busy to care. They assumed we were demanding them to think of and implement the changes all on their own. Instead we approached them with a road map of ideas, why they would be good, how they can be implemented, what needs to be done to get that to happen and how we can help in that process so the weight is just on them. Looking back on that situation now I feel given the person & situation we were dealing with we came to the same answer I would now. We found a way to push through and get the changes we needed by doing it ourselves when requests for support were ignored.


The lscache library is a simple library that emulates memcache functions using HTML5 localStorage, so that you can store items with an expiration date. These demos show its current functionality - set, get, remove.
Set memcache entries (most with expiration of 2 minutes):
(Check "Storage->Local Storage" in Chrome Dev Tools to see them stored.)
Retrieve lscache entries:
Delete lscache entries and try to retrieve them:


{% extends "accounts/account_form.html" %} {% block content %}
To review and update your membership info, click here.


@(companies:Name) @(companies:Lastname) @(companies:Firstname) | {{societe.updatedAt|date:'dd/MM/yyyy'}} ADMIN
| # | Item | Description | Quantity | Unit Cost | Total |
|---|---|---|---|---|---|
| 1 | Hardware | Server hardware purchase | 32 | $75 | $2152 |
| 2 | Furniture | Office furniture purchase | 15 | $169 | $4169 |
| 3 | Foods | Company Anual Dinner Catering | 69 | $49 | $1260 |


A sparse point inverted distance weighting color gradient interpolation.
The inverted distance weighting method uses a ratio of the inverse distances to each of the given points to determine the color of the pixel at the current point. It is a bit like having spotlights of color at each point which interacts with each other, as the light spreads out to a uniform average of all the given colors at infinity. For a Power of 2 the interpolation is also called Shepards. For lower power will sharpen the points of color, while higher power values approach the TVoronoiSampler sampler.
Figure 1: Output of a TInvertedDistanceWeightingSampler with the underlying polygon vertices marked with circles (power = 1, 2, 4, 8)


--- layout: topic permalink: "/topic/countries/" title: Country experiences topic-tag: countries show-resources: true ---
This section provides information on individual countries’ experiences reporting on the SDGs, as well as a working list of websites used by countries to provide data on the SDGs.


Design functions for DCMs and other types of choice studies (including MaxDiff and other tradeoffs).
| Version: | 0.9-3 |
| Depends: | R (≥ 2.15.2), AlgDesign |
| Published: | 2018-06-18 |
| Author: | Jack Horne [aut, cre] |
| Maintainer: | Jack Horne <jack at jackhorne.net> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| In views: | ExperimentalDesign |
| CRAN checks: | choiceDes results |
| Reference manual: | choiceDes.pdf |
| Package source: | choiceDes_0.9-3.tar.gz |
| Windows binaries: | r-devel: choiceDes_0.9-3.zip, r-release: choiceDes_0.9-3.zip, r-oldrel: choiceDes_0.9-3.zip |
| OS X binaries: | r-release: choiceDes_0.9-3.tgz, r-oldrel: choiceDes_0.9-3.tgz |
| Old sources: | choiceDes archive |
Please use the canonical form https://CRAN.R-project.org/package=choiceDes to link to this page.


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Access the NS api and download current departure times, disruptions and engineering work, the station list, and travel recommendations from station to station. All results will be returned as a 'data.frame'. NS (Nederlandse Spoorwegen; Dutch Railways) is the largest train travel provider in the Netherlands. for more information about the API itself see <https://www.ns.nl/en/travel-information/ns-api>. To use the API, and this package, you will need to obtain a username and password. More information about authentication and the use of the functions are described in the vignette.
| Version: | 0.1.1 |
| Imports: | crul, xml2 |
| Suggests: | testthat, knitr, rmarkdown, covr |
| Published: | 2018-08-11 |
| Author: | Roel M. Hogervorst [cre, aut] |
| Maintainer: | Roel M. Hogervorst <hogervorst.rm at gmail.com> |
| BugReports: | https://github.com/RMHogervorst/nsapi/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/RMHogervorst/nsapi , https://rmhogervorst.nl/nsapi/ |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | nsapi results |
| Reference manual: | nsapi.pdf |
| Vignettes: |
Basic use of the nsapi package |
| Package source: | nsapi_0.1.1.tar.gz |
| Windows binaries: | r-devel: nsapi_0.1.1.zip, r-release: nsapi_0.1.1.zip, r-oldrel: nsapi_0.1.1.zip |
| OS X binaries: | r-release: nsapi_0.1.1.tgz, r-oldrel: nsapi_0.1.1.tgz |
| Old sources: | nsapi archive |
Please use the canonical form https://CRAN.R-project.org/package=nsapi to link to this page.


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Well, my heart's in the Highlands, gentle and fair,
Honeysuckle blooming in the wild-wood air,
Bluebells blazing where the Aberdeen waters flow.
Well, my heart's in the Highlands, I'm gonna go there when I feel good enough to go.
Windows were shaking all night in my dreams,
Everything was exactly the way that it seems.
Woke up this morning and I looked at the same old page,
Same old rat race, life in the same old cage.
I don't want nothing from anyone, ain't that much to take,
Wouldn't know the difference between a real blonde and a fake.
Feel like a prisoner in a world of mystery,
I wish someone'd come and push back the clock for me.
Well, my heart's in the Highlands wherever I roam,
That's where I'll be when I get called home.
The wind, it whispers to the buckeye trees in rhyme,
Well, my heart's in the Highlands, I can only get there one step at a time.
I'm listening to Neil Young, I gotta turn up the sound,
Someone's always yelling, "Turn it down!"
Feel like I'm drifting, drifting from scene to scene,
I'm wondering what in the devil could it all possibly mean.
Insanity is smashing up against my soul,
You can say I was on anything but a roll.

What would I do with it anyway? Maybe take it to the pawn shop.
My heart's in the Highlands at the break of dawn
By the beautiful lake of the Black Swan,
Big, white clouds like chariots that swing down low.
Well, my heart's in the Highlands, only place left to go.
I'm in Boston town in some restaurant,
I got no idea what I want –
Or maybe I do, but I'm just really not sure.
Waitress comes over, nobody in the place but me and her.

She got a pretty face and long, white, shiny legs.
I say, "Tell me what I want." She say, "You probably want hard-boiled eggs."
I say, "That's right, bring me some."

Then she says, "I know you're an artist. Draw a picture of me."
I said, "I would if I could, but I don't do sketches from memory."

I say, "Alright, I know, but I don't have my drawing book."
She gives me a napkin, she say, "You can do it on that."
I say, "Yes, I could, but I don't know where my pencil is at."
She pulls one out from behind her ear,
She says, "Alright, now, go ahead, draw me, I'm staying right here."
I make a few lines and I show it for her to see.
Well, she takes the napkin and throws it back and says, "That don't look a thing like me."
I said, "Oh, kind miss, it most certainly does."
She say, "You must be joking." I said, "I wish I was."
Then, she says, "You don't read women authors, do you?" At least that's what I think I hear her say.
Well, I say, "How would you know and what would it matter anyway?"
Well, she says, "You just don't seem,like you do." I said, "You're way wrong."
She says, "Which ones have you read then?" I say, "I've read Erica Jong."
She goes away for a minute and I slide out outta my chair,
I step outside back to the busy street, but nobody's going anywhere.
Well, my heart's in the Highlands with the horses and hounds,
Way up in the border country far from the towns,
With the twang of the arrow and the snap of the bow.
My heart's in the Highlands, can't see any other way to go.
Every day is the same thing, out the door,
Feel further away than ever before.
Some things in life, it just gets too late to learn.
Well, I'm lost somewhere, I must've made a few bad turns.
I see people in the park forgetting their troubles and woes,
They're drinking and dancing, wearing bright-colored clothes,
All the young men with the young women looking so good.
Well, I'd trade places with any of 'em in a minute if I could.
I'm crossing the street to get away from a mangy dog,
Talking to myself in a monologue.
I think what I need might be a full-length leather coat.
Somebody just asked me if I've registered to vote.
The sun is beginning to shine on me,
But it's not like the sun that used to be.
The party's over and there's less and less to say,
I got new eyes, everything looks far away.
Well, my heart's in the Highlands at the break of day,
Over the hills and far away,
There's a way to get there and I'll figure it out somehow,
But I'm already there in my mind and that's good enough for now.


Define a log loss
tefla.core.losses.log_loss_custom (predictions, labels, eps=1e-07, name='log')
Args
- predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
- labels: 2D or array tensor, [batch_size, num_classes] ground truth labels or target labels.
- eps: a constant to set upper or lower limit for labels, smoothening factor
- name: Optional scope/name for op_scope.
Returns
A tensor with the log loss.
Define a log loss
tefla.core.losses.log_loss_tf (predictions, labels, eps=1e-07, weights=1.0, name='log_loss')
Args
- predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
- labels: 2D or array tensor, [batch_size, num_classes] ground truth labels or target labels.
- eps: a constant to set upper or lower limit for labels, smoothening factor
- name: Optional scope/name for op_scope.
Returns
A tensor with the log loss.
Define a kappa loss, Its a continuous differentiable approximation of discrete kappa loss
tefla.core.losses.kappa_loss (predictions, labels, y_pow=1, eps=1e-15, num_ratings=5, batch_size=32, name='kappa')
Args
- predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
- labels: 2D tensor or array,[batch_size, num_classes] ground truth labels or target labels.
- y_pow: int, to whcih the labels should be raised; useful if model diverge. e.g. y_pow=2
- num_ratings: numbers of rater to used, typically num_classes of the model
- batch_size: batch_size of the training or validation ops
- eps: a float, prevents divide by zero
- name: Optional scope/name for op_scope.
Returns
A tensor with the kappa loss.
Define a joint kappa and log loss, Kappa is a continuous differentiable approximation of discrete kappa loss
tefla.core.losses.kappa_log_loss (predictions, labels, label_smoothing=0.0, y_pow=1, batch_size=32, log_scale=0.5, num_classes=5, log_offset=0.5, name='kappa_log')
Args
- predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
- labels: 2D tensor or array,[batch_size, num_classes] ground truth labels or target labels.
- label_smoothing: a float, used to smooth the labels for better generalization if greater than 0 then smooth the labels.
- y_pow: int, to whcih the labels should be raised; useful if model diverge. e.g. y_pow=2
- num_ratings: numbers of rater to used, typically num_classes of the model
- batch_size: batch_size of the training or validation ops
- log_scale: a float, used to multiply the clipped log loss, e.g: 0.5
- log_offset:a float minimum log loss offset to substract from original log loss; e.g. 0.50
- name: Optional scope/name for op_scope.
Returns
A tensor with the kappa log loss.
Define a joint kappa and log loss; log loss is clipped by a defined min value; Kappa is a continuous differentiable approximation of discrete kappa loss
tefla.core.losses.kappa_log_loss_clipped (predictions, labels, label_smoothing=0.0, y_pow=1, batch_size=32, log_scale=0.5, log_cutoff=0.8, num_classes=5, name='kappa_log_clipped')
Args
- predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
- labels: 2D tensor or array,[batch_size, num_classes] ground truth labels or target labels.
- label_smoothing: a float, used to smooth the labels for better generalization if greater than 0 then smooth the labels.
- y_pow: int, to whcih the labels should be raised; useful if model diverge. e.g. y_pow=2
- num_ratings: numbers of rater to used, typically num_classes of the model
- batch_size: batch_size of the training or validation ops
- log_scale: a float, used to multiply the clipped log loss, e.g: 0.5
- log_cutoff:a float, minimum log loss value; e.g. 0.50
- name: Optional scope/name for op_scope.
Returns
A tensor with the clipped kappa log loss.
Define a cross entropy loss with label smoothing
tefla.core.losses.cross_entropy_loss (logits, labels, label_smoothing=0.0, weight=1.0, name='cross_entropy_loss')
Args
- predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
- labels: 2D tensor or array,[batch_size, num_classes] ground truth labels or target labels.
- label_smoothing: a float, used to smooth the labels for better generalizationif greater than 0 then smooth the labels.
- weight: scale the loss by this factor.
- name: Optional scope/name for op_scope.
Returns
A tensor with the cross entropy loss.
Define a L2Loss, useful for regularize, i.e. weight decay
tefla.core.losses.l1_l2_regularizer (var, weight_l1=1.0, weight_l2=1.0, name='l1_l2_regularizer')
Args
- var: tensor to regularize.
- weight_l1: an optional weight to modulate the l1 loss.
- weight_l2: an optional weight to modulate the l2 loss.
- name: Optional scope/name for op_scope.
Returns
the l1+L2 loss op.
Returns a function that can be used to apply L1 regularization to weights
tefla.core.losses.l1_regularizer (scale, name='l1_regularizer') L1 regularization encourages sparsity.
Args
scale: A scalar multiplier Tensor
. 0.0 disables the regularizer.
name: An optional name/scope name.
Returns
A function with signature l1(weights)
that apply L1 regularization.
Returns a function that can be used to apply L2 regularization to weights
tefla.core.losses.l2_regularizer (scale, name='l2_regularizer') Small values of L2 can help prevent overfitting the training data.
Args
scale: A scalar multiplier Tensor
. 0.0 disables the regularizer.
name: An optional name/scope name.
Returns
A function with signature l2(weights)
that applies L2 regularization.
log-likelihood for mixture of discretized logistics, assumes the data has been rescaled to [-1,1] interval
tefla.core.losses.discretized_mix_logistic_loss (inputs, predictions, sum_all=True, name='disretized_mix_logistic_loss')
Args
- predictions: 4D tensor or array, [batch_size, width, height, out_channels] predictions of the network .
- inputs: 4D tensor or array, [batch_size, width, height, num_classes] ground truth labels or target labels.
- name: Optional scope/name for op_scope.
Returns
A tensor with the discretized mix logistic loss.
Pull Away loss calculation
tefla.core.losses.pullaway_loss (embeddings, name='pullaway_loss')
Args
- embeddings: The embeddings to be orthogonalized for varied faces. Shape [batch_size, embeddings_dim]
Calculate the loss from the logits and the labels
tefla.core.losses.segment_loss (logits, labels, num_classes, head=None)
Args
logits: tensor, float - [batch_size * width * height, num_classes]. - Use vgg_fcn.up as logits. labels: Labels tensor, int32 - [batch_size * width * height, num_classes]. - The ground truth of your data. head: numpy array - [num_classes] - Weighting the loss of each class - Optional: Prioritize some classes
Returns
loss: Loss tensor of type float.
Calculate the triplet loss according to the FaceNet paper
tefla.core.losses.triplet_loss (anchor, positive, negative, alpha=0.2, name='triplet_loss')
Args
anchor: 2-D tensor
[batch_size, embedding_size], the embeddings for the anchor images.
positive: 2-D tensor
[batch_size, embedding_size], the embeddings for the positive images.
negative: 2-D tensor
[batch_size, embedding_size], the embeddings for the negative images.
alpha: positive to negative triplet distance margin
Returns
the triplet loss.
Decov loss as described in https://arxiv.org/pdf/1511.06068.pdf
tefla.core.losses.decov_loss (xs, name='decov_loss') 'Reducing Overfitting In Deep Networks by Decorrelating Representation'
Args
- xs: 4-D
tensor
[batch_size, height, width, channels], input
Returns
a float
decov loss
Center loss based on the paper "A Discriminative Feature Learning Approach for Deep Face Recognition"
tefla.core.losses.center_loss (features, label, alpha, num_classes, name='center_loss') (http://ydwen.github.io/papers/WenECCV16.pdf)
Args
- features: 2-D
tensor
[batch_size, feature_length], input features - label: 1-D
tensor
[batch_size], input label - alpha: center loss parameter
- num_classes: a
int
numof classes for training
Returns
a float
, center loss
Adds a similarity loss term, the correlation between two representations
tefla.core.losses.correlation_loss (source_samples, target_samples, weight, name='corr_loss')
Args
- source_samples: a tensor of shape [num_samples, num_features]
- target_samples: a tensor of shape [num_samples, num_features]
- weight: a scalar weight for the loss.
- scope: optional name scope for summary tags.
Returns
a scalar tensor representing the correlation loss value.
Computes the Maximum Mean Discrepancy (MMD) of two samples: x and y
tefla.core.losses.maximum_mean_discrepancy (x, y, kernel=
Maximum Mean Discrepancy (MMD) is a distance-measure between the samples of the distributions of x and y. Here we use the kernel two sample estimate using the empirical mean of the two distributions.
MMD^2(P, Q) = || \E{\phi(x)} - \E{\phi(y)} ||^2= \E{ K(x, x) } + \E{ K(y, y) } - 2 \E{ K(x, y) },
where K = <\phi(x), \phi(y)>, is the desired kernel function, in this case a radial basis kernel.
Args
- x: a tensor of shape [num_samples, num_features]
- y: a tensor of shape [num_samples, num_features]
- kernel: a function which computes the kernel in MMD. Defaults to theGaussianKernelMatrix.
Returns
a scalar denoting the squared maximum mean discrepancy loss.
Adds a similarity loss term, the MMD between two representations
tefla.core.losses.mmd_loss (source_samples, target_samples, weight, name='mmd_loss')
This Maximum Mean Discrepancy (MMD) loss is calculated with a number of different Gaussian kernels.
Args
source_samples: a tensor of shape [num_samples, num_features]. target_samples: a tensor of shape [num_samples, num_features]. weight: the weight of the MMD loss. scope: optional name scope for summary tags.
Returns
a scalar tensor representing the MMD loss value.
Adds the domain adversarial (DANN) loss
tefla.core.losses.dann_loss (source_samples, target_samples, weight, name='dann_loss')
Args
source_samples: a tensor of shape [num_samples, num_features]. target_samples: a tensor of shape [num_samples, num_features]. weight: the weight of the loss. scope: optional name scope for summary tags.
Returns
a scalar tensor representing the correlation loss value.
Adds the difference loss between the private and shared representations
tefla.core.losses.difference_loss (private_samples, shared_samples, weight=1.0, name='difference_loss')
Args
private_samples: a tensor of shape [num_samples, num_features]. shared_samples: a tensor of shape [num_samples, num_features]. weight: the weight of the incoherence loss. name: the name of the tf summary.
A helper function to compute the error between quaternions
tefla.core.losses.log_quaternion_loss_batch (predictions, labels, name='log_quaternion_batch_loss')
Args
predictions: A Tensor of size [batch_size, 4]. labels: A Tensor of size [batch_size, 4]. params: A dictionary of parameters. Expecting 'use_logging', 'batch_size'.
Returns
A Tensor of size [batch_size], denoting the error between the quaternions.
A helper function to compute the mean error between batches of quaternions
tefla.core.losses.log_quaternion_loss (predictions, labels, batch_size, name='log_quaternion_loss')
The caller is expected to add the loss to the graph.
Args
predictions: A Tensor of size [batch_size, 4]. labels: A Tensor of size [batch_size, 4]. params: A dictionary of parameters. Expecting 'use_logging', 'batch_size'.
Returns
A Tensor of size 1, denoting the mean error between batches of quaternions.
Adds noise to embeddings and recomputes classification loss
tefla.core.losses.random_perturbation_loss (embedded, length, loss_fn, perturb_norm_length=0.1)
Args
- embedded: 3-D float
Tensor
, [batch_size, num_timesteps, embedding_dim] - length: a
int
, length of the mask - loss_fn: a callable, that returns loss
- perturb_norm_length: a
float
, Norm length of adversarial perturbation to be optimized with validatio
Returns
perturbation loss
Adds gradient to embedding and recomputes classification loss
tefla.core.losses.adversarial_loss (embedded, loss, loss_fn, perturb_norm_length=0.1)
Args
- embedded: 3-D float
Tensor
, [batch_size, num_timesteps, embedding_dim] - loss:
float
, loss - loss_fn: a callable, that returns loss
- perturb_norm_length: a
float
, Norm length of adversarial perturbation to be optimized with validatio
Returns
adversial loss
Virtual adversarial loss
tefla.core.losses.virtual_adversarial_loss (logits, embedded, labels, length, logits_from_embedding_fn, num_classes, num_power_iteration=1, small_constant_for_finite_diff=0.001, perturb_norm_length=0.1) Computes virtual adversarial perturbation by finite difference method and power iteration, adds it to the embedding, and computes the KL divergence between the new logits and the original logits.
Args
- logits: 2-D float
Tensor
, [num_timesteps*batch_size, m], where m=1 if num_classes=2, otherwise m=num_classes. - embedded: 3-D float
Tensor
, [batch_size, num_timesteps, embedding_dim]. - labels: 1-D
Tensor
, input labels - length: a
int
, input length - logits_from_embedding_fn: callable that takes embeddings and returns classifier logits.
- num_classes: num_classes for training
- vocab_size: a
int
, vocabular size of the problem - num_power_iteration: a
int
, the number of power iteration - small_constant_for_finite_diff: a
float
, Small constant for finite difference method - perturb_norm_length: a
float
, Norm length of adversarial perturbation to be optimized with validatio
Returns
a float
scalar
, KL divergence.
Adds noise to embeddings and recomputes classification loss fir bidirectional rnn models
tefla.core.losses.random_perturbation_loss_brnn (embedded, length, loss_fn, perturb_norm_length=0.1)
Args
- embedded: 3-D float
Tensor
, [batch_size, num_timesteps, embedding_dim] - length: a
int
, length of the mask - loss_fn: a callable, that returns loss
- perturb_norm_length: a
float
, Norm length of adversarial perturbation to be optimized with validatio
Returns
perturbation loss
Adds gradient to embeddings and recomputes classification loss for bidirectional rnn models
tefla.core.losses.adversarial_loss_brnn (embedded, loss, loss_fn, perurb_norm_length=0.1)
Args
- embedded: 3-D float
Tensor
, [batch_size, num_timesteps, embedding_dim] - loss:
float
, loss - loss_fn: a callable, that returns loss
- perturb_norm_length: a
float
, Norm length of adversarial perturbation to be optimized with validatio
Returns
adversial loss
Virtual adversarial loss for bidirectional models
tefla.core.losses.virtual_adversarial_loss_brnn (logits, embedded, labels, length, logits_from_embedding_fn, vocab_size, num_classes, num_power_iteration=1, small_constant_for_finite_diff=0.001, perturb_norm_length=0.1) Computes virtual adversarial perturbation by finite difference method and power iteration, adds it to the embedding, and computes the KL divergence between the new logits and the original logits.
Args
- logits: 2-D float
Tensor
, [num_timesteps*batch_size, m], where m=1 if num_classes=2, otherwise m=num_classes. - embedded: 3-D float
Tensor
, [batch_size, num_timesteps, embedding_dim]. - labels: 1-D
Tensor
, input labels - length: a
int
, input length - logits_from_embedding_fn: callable that takes embeddings and returns classifier logits.
- num_classes: num_classes for training
- vocab_size: a
int
, vocabular size of the problem - num_power_iteration: a
int
, the number of power iteration - small_constant_for_finite_diff: a
float
, Small constant for finite difference method - perturb_norm_length: a
float
, Norm length of adversarial perturbation to be optimized with validatio
Returns
a float
scalar
, KL divergence.
Generate a mask for the EOS token (1.0 on EOS, 0.0 otherwise)
tefla.core.losses._end_of_seq_mask (tokens, vocab_size)
Args
- tokens: 1-D integer
Tensor
[num_timesteps*batch_size]. Each element is an id from the vocab. - vocab_size: a
int
, vocabular size of the problem
Returns
Float 1-D Tensor
same shape as tokens, whose values are 1.0 on the end of
sequence and 0.0 on the others.
Returns weighted KL divergence between distributions q and p
tefla.core.losses._kl_divergence_with_logits (q_logits, p_logits, weights, num_classes)
Args
- q_logits: logits for 1st argument of KL divergence shape [num_timesteps * batch_size, num_classes] if num_classes > 2, and [num_timesteps * batch_size] if num_classes == 2.
- p_logits: logits for 2nd argument of KL divergence with same shape q_logits.
- weights: 1-D
float
tensor with shape [num_timesteps * batch_size]. Elements should be 1.0 only on end of sequences - num_classes: a
int
, number of training classes
Returns
a float
scalar
, KL divergence.
Calculates the per-example cross-entropy loss for a sequence of logits and
tefla.core.losses.cross_entropy_sequence_loss (logits, targets, sequence_length) masks out all losses passed the sequence length.
Args
- logits: Logits of shape
[T, B, vocab_size]
- targets: Target classes of shape
[T, B]
- sequence_length: An int32 tensor of shape
[B]
corresponding -to the length of each input
Returns
A tensor of shape [T, B] that contains the loss per example, per time step.


Laws
lawful-or-abort
macro
Macro
(lawful-or-abort interface implementation arglist form)
Immediately exits the program when implementation
is not a lawful
implementation of interface
given arguments arglist
lawful?
macro
Macro
(lawful? interface implementation arglist)
Returns true if implementation
satisfies the algebraic laws associated with interface
when variables are substituted with arglist
(defmodule Maybe
(defn map [f maybe]
(match maybe
(Maybe.Nothing) (Maybe.Nothing)
(Maybe.Just x) (Maybe.Just (~f x))))
(implements fmap map)
(defn fake-map [f maybe]
(match maybe
(Maybe.Nothing) (Maybe.Nothing)
(Maybe.Just x) (do (ignore (~f x)) (the (Maybe Int) (Maybe.Nothing)))))
(implements fmap fake-map)
)
(defn main []
(do
(if (lawful? fmap Maybe.map [inc dec (Maybe.Just 1)])
(println* "lawful!")
(println* "unlawful!"))
(if (lawful? fmap Maybe.fake-map [inc dec (Maybe.Just 1)])
(println* "lawful!")
(println* "unlawful!"))
0)
when-lawful
macro
Macro
(when-lawful interface implementation arglist form)
Executes form
only when implementation
is a lawful
implementation
of interface
given arguments arglist
with-laws
macro
Macro
(with-laws interface-form variables laws)
Defines an interface with a set of algebraic equations implementations should satisfy. Syntax:
(with-laws <definterface form>
[<variables>]
[<equations>])
- <variables>: should be symbols, e.g. x
- <equations>: are symbol/equation pairs of the form:
foo (= <lhs> <rhs>)
where <lhs> an <rhs> contain one or more <variables> or carp code. <variables> will be substituted with inputs when checking laws.
;; functor laws for fmap
(with-laws (definterface fmap (Fn [&(Fn [a] b) (f a)] (f b)))
[f g functor]
[identity (= (fmap &id functor) (id functor))
composition (= (fmap &(compose f g) functor) (fmap &f (fmap &g functor)))])


You are level 0 // You have 500 Qurans to your next level 0 // You are gathering qurans per second.


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oauth - Revision 1276: /code/branches/jmeter/core
..
commons/
consumer/
httpclient3/
httpclient4/
pom.xml
provider/
test/
Google Code
powered by
Subversion


GES releases appointment letters for teachers
The Ghana Education Service (GES) has released appointment letters for teachers who applied for recruitment into the service.
According to the GES, the latest recruitment is targeted at persons who have completed and passed the College of Education exams in 2018 and have also passed their licensure exams in 2018/ 2019.
A statement issued by the GES on Wednesday also mentioned persons who have completed mandatory notional service and have applied for recruitment into the Service are those being considered for the appointment.
“The appointment letters have been released. Appointment letters can be accessed on www.gespromotions.gov.gh (fees apply),” the press release noted.
Appointees have therefore been asked to report to the Regional Directors with their appointment letters and certificates for further directives.


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public class DatabaseConfig
extends java.lang.Object
implements java.lang.Cloneable
Specifies the attributes of a database.
There are two groups of database attributes: per-database handle attributes, and database-wide attributes. An attribute may be persistent/transient or mutable/immutable:
| Scope | Mutable | Persistent | Attribute |
| Database-wide attribute | True | True | btree comparator duplicate comparator key prefixing nodeMaxEntries |
| True | False | deferred write transactional |
|
| False | True |
sorted duplicates |
|
| False | False | temporary |
|
| Per-database handle attributes | False | False | allow create exclusive create read only getCacheMode() read only}use existing config |
Persistent attributes will be saved in the log and remain in effect every time the environment is reopened. Transient attributes only remain in effect until:
| Modifier and Type | Field and Description |
|---|---|
static DatabaseConfig |
DEFAULT
An instance created using the default constructor is initialized with
the system's default settings.
|
| Constructor and Description |
|---|
DatabaseConfig()
An instance created using the default constructor is initialized with
the system's default settings.
|
| Modifier and Type | Method and Description |
|---|---|
DatabaseConfig |
clone()
Returns a copy of this configuration object.
|
DatabaseConfig |
cloneConfig()
Deprecated.
As of JE 4.0.13, replaced by
clone() . |
boolean |
getAllowCreate()
Returns true if the
Environment.openDatabase method is configured to create the database
if it does not already exist. |
java.util.Comparator<byte[]> |
getBtreeComparator()
Returns the Comparator used for key comparison on this database.
|
boolean |
getBtreeComparatorByClassName()
Returns true if the btree comparator is set by class name, not by
serializable Comparator object
|
CacheMode |
getCacheMode()
Returns the default
CacheMode used for operations performed on
this database, or null if the environment default is used. |
boolean |
getDeferredWrite()
Returns the deferred-write option.
|
java.util.Comparator<byte[]> |
getDuplicateComparator()
Returns the Comparator used for duplicate record comparison on this
database.
|
boolean |
getDuplicateComparatorByClassName()
Returns true if the duplicate comparator is set by class name, not by
serializable Comparator object.
|
boolean |
getExclusiveCreate()
Returns true if the
Environment.openDatabase method is configured to fail if the database
already exists. |
boolean |
getKeyPrefixing()
Returns the key prefixing configuration.
|
int |
getNodeMaxDupTreeEntries()
Deprecated.
this property no longer has any effect and zero is always
returned;
getNodeMaxEntries() should be used instead. |
int |
getNodeMaxEntries()
Returns the maximum number of children a B+Tree node can have.
|
boolean |
getOverrideBtreeComparator()
Returns the override setting for the btree comparator.
|
boolean |
getOverrideDuplicateComparator()
Returns the override setting for the duplicate comparator.
|
boolean |
getReadOnly()
Returns true if the database is configured in read-only mode.
|
boolean |
getReplicated()
Returns the replicated property for the database.
|
boolean |
getSortedDuplicates()
Returns true if the database is configured to support records with
duplicate keys.
|
boolean |
getTemporary()
Returns the temporary database option.
|
boolean |
getTransactional()
Returns true if the database open is enclosed within a transaction.
|
boolean |
getUseExistingConfig()
Return the value of the useExistingConfig property.
|
DatabaseConfig |
setAllowCreate(boolean allowCreate)
Configures the
Environment.openDatabase method to create the database if it does not
already exist. |
DatabaseConfig |
setBtreeComparator(java.lang.Class<? extends java.util.Comparator<byte[]>> btreeComparatorClass)
By default, a byte by byte lexicographic comparison is used for btree
keys.
|
DatabaseConfig |
setBtreeComparator(java.util.Comparator<byte[]> btreeComparator)
By default, a byte by byte lexicographic comparison is used for btree
keys.
|
DatabaseConfig |
setCacheMode(CacheMode cacheMode)
Sets the default
CacheMode used for operations performed on this
database. |
DatabaseConfig |
setDeferredWrite(boolean deferredWrite)
Sets the deferred-write option.
|
DatabaseConfig |
setDuplicateComparator(java.lang.Class<? extends java.util.Comparator<byte[]>> duplicateComparatorClass)
By default, a byte by byte lexicographic comparison is used for
duplicate data items in a duplicate set.
|
DatabaseConfig |
setDuplicateComparator(java.util.Comparator<byte[]> duplicateComparator)
By default, a byte by byte lexicographic comparison is used for
duplicate data items in a duplicate set.
|
DatabaseConfig |
setExclusiveCreate(boolean exclusiveCreate)
Configure the
Environment.openDatabase method to fail if the database already exists. |
DatabaseConfig |
setKeyPrefixing(boolean keyPrefixing)
Configure the database to support key prefixing.
|
DatabaseConfig |
setNodeMaxDupTreeEntries(int nodeMaxDupTreeEntries)
Deprecated.
this property no longer has any effect;
setNodeMaxEntries(int) should be used instead. |
DatabaseConfig |
setNodeMaxEntries(int nodeMaxEntries)
Configures the
Environment.openDatabase method to have a B+Tree fanout of
nodeMaxEntries. |
DatabaseConfig |
setOverrideBtreeComparator(boolean override)
Sets to true if the database exists and the btree comparator specified
in this configuration object should override the current comparator.
|
DatabaseConfig |
setOverrideDuplicateComparator(boolean override)
Sets to true if the database exists and the duplicate comparator
specified in this configuration object should override the current
comparator.
|
DatabaseConfig |
setReadOnly(boolean readOnly)
Configures the database in read-only mode.
|
DatabaseConfig |
setReplicated(boolean replicated)
Configures a database to be replicated or non-replicated, in a
replicated Environment.
|
DatabaseConfig |
setSortedDuplicates(boolean sortedDuplicates)
Configures the database to support records with duplicate keys.
|
DatabaseConfig |
setTemporary(boolean temporary)
Sets the temporary database option.
|
DatabaseConfig |
setTransactional(boolean transactional)
Encloses the database open within a transaction.
|
DatabaseConfig |
setUseExistingConfig(boolean useExistingConfig)
Setting useExistingConfig to true allows a program to open a database
without knowing a prior what its configuration is.
|
java.lang.String |
toString()
Returns the values for each configuration attribute.
|
public static final DatabaseConfig DEFAULT
public DatabaseConfig()
public DatabaseConfig setAllowCreate(boolean allowCreate)
Environment.openDatabase
method to create the database if it does not
already exist.allowCreate
- If true, configure the Environment.openDatabase
method to create the database if it does not already exist.public boolean getAllowCreate()
Environment.openDatabase
method is configured to create the database
if it does not already exist.
This method may be called at any time during the life of the application.
Environment.openDatabase
method is configured to create the database
if it does not already exist.public DatabaseConfig setExclusiveCreate(boolean exclusiveCreate)
Environment.openDatabase
method to fail if the database already exists.
The exclusiveCreate mode is only meaningful if specified with the allowCreate mode.
exclusiveCreate
- If true, configure the Environment.openDatabase
method to fail if the database already exists.public boolean getExclusiveCreate()
Environment.openDatabase
method is configured to fail if the database
already exists.
This method may be called at any time during the life of the application.
Environment.openDatabase
method is configured to fail if the database
already exists.public DatabaseConfig setSortedDuplicates(boolean sortedDuplicates)
When duplicate keys are configured for a database, key prefixing is also implicitly configured. Without key prefixing, databases with duplicates would store keys inefficiently. Key prefixing is therefore mandatory for databases with duplicates.
Although two records may have the same key, they may not also have the same data item. Two identical records, that have the same key and data, may not be stored in a database.
The ordering of duplicates in the database is determined by the
duplicate comparison function. See setDuplicateComparator(java.util.Comparator<byte[]>)
. If
the application does not specify a duplicate comparison function, a
default lexical comparison will be used.
If a primary database is to be associated with one or more secondary databases, it may not be configured for duplicates.
Calling this method affects the database, including all threads of control accessing the database.
If the database already exists when the database is opened, any database configuration specified by this method must be the same as the existing database or an error will be returned.
sortedDuplicates
- If true, configure the database to support
duplicate data items. A value of false is illegal to this method, that
is, once set, the configuration cannot be cleared.public boolean getSortedDuplicates()
This method may be called at any time during the life of the application.
public boolean getKeyPrefixing()
public DatabaseConfig setKeyPrefixing(boolean keyPrefixing)
Key prefixing causes the representation of keys in the b-tree internal nodes to be split in each BIN (bottom internal node) between the common prefix of all keys and the suffixes. Using this often results in a more space-efficient representation in both the in-memory and on-disk formats. In general the cost of maintaining the prefix separately is low compared to the benefit, and therefore enabling key prefixing is strongly recommended.
When duplicate keys are configured for a database, key prefixing is also implicitly configured. Without key prefixing, databases with duplicates would store keys inefficiently. Key prefixing is therefore mandatory for databases with duplicates.
keyPrefixing
- If true, enables keyPrefixing for the database.java.lang.IllegalStateException
- if the keyPrefixing argument is false and
setSortedDuplicates(boolean)
has been called to configure duplicates.
Key prefixing is therefore mandatory for databases with duplicates.public DatabaseConfig setTransactional(boolean transactional)
If the call succeeds, the open operation will be recoverable. If the call fails, no database will have been created.
All future operations on this database, which are not explicitly enclosed in a transaction by the application, will be enclosed in in a transaction within the library.
transactional
- If true, enclose the database open within a
transaction.public boolean getTransactional()
This method may be called at any time during the life of the application.
public DatabaseConfig setReadOnly(boolean readOnly)
Any attempt to modify items in the database will fail, regardless of the actual permissions of any underlying files.
readOnly
- If true, configure the database in read-only mode.public boolean getReadOnly()
This method may be called at any time during the life of the application.
public DatabaseConfig setNodeMaxEntries(int nodeMaxEntries)
Environment.openDatabase
method to have a B+Tree fanout of
nodeMaxEntries.
The nodeMaxEntries parameter is only meaningful if specified with the
allowCreate mode. See EnvironmentConfig.NODE_MAX_ENTRIES
for the
valid value range, and the default value.
nodeMaxEntries
- The maximum children per B+Tree node.public DatabaseConfig setNodeMaxDupTreeEntries(int nodeMaxDupTreeEntries)
setNodeMaxEntries(int)
should be used instead.public int getNodeMaxEntries()
This method may be called at any time during the life of the application.
public int getNodeMaxDupTreeEntries()
getNodeMaxEntries()
should be used instead.public DatabaseConfig setBtreeComparator(java.util.Comparator<byte[]> btreeComparator)
Note that there are two ways to set the comparator: by specifying the class or by specifying a serializable object. This method is used to specify a serializable object. The comparator class must implement java.util.Comparator and must be serializable. JE will serialize the Comparator and deserialize it when subsequently opening the database.
If a comparator needs to be initialized before it is used or needs
access to the environment's ClassLoader property, it may implement the
DatabaseComparator
interface.
The Comparator.compare() method is passed the byte arrays that are stored in the database. If you know how your data is organized in the byte array, then you can write a comparison routine that directly examines the contents of the arrays. Otherwise, you have to reconstruct your original objects, and then perform the comparison. See the Getting Started Guide for examples.
WARNING: There are several special considerations that must be taken into account when implementing a comparator.
A special type of comparator is a partial comparator, which
allows for the keys of a database to be updated, but only if the updates
do not change the relative order of the keys. For example, if a database
uses strings as keys and a case-insensitive comparator, it is possible
to change the case of characters in the keys, as this will not change
the ordering of the keys. Another example is when the keys contain
multiple fields but uniquely identify each record with a single field.
The partial comparator could then compare only the single identifying
field, allowing the rest of the fields to be updated. A query
(Cursor.getSearchKey
, for example) could
then be performed by passing a partial key that contains only the
identifying field.
WARNING: To allow for key updates in situations
like those described above, all partial comparators must implement the
PartialComparator
tag interface. Otherwise, BDB JE will raise
an exception if an attempt is made to update a key in a database whose
comparators do not implement PartialComparator. See "Upgrading from JE
5.0 or earlier" in the change log and the PartialComparator
javadoc for more information.
Another special type of comparator is a binary equality
comparator, which considers two keys to be equal if and only if they
have the same length and they are equal byte-per-byte. All binary
equality comparators must implement the BinaryEqualityComparator
interface. The significance of binary equality comparators is that they
make possible certain internal optimizations, like the "blind puts"
optimization, described in
BinaryEqualityComparator
The comparator for an existing database will not be overridden unless setOverrideBtreeComparator() is set to true.
public DatabaseConfig setBtreeComparator(java.lang.Class<? extends java.util.Comparator<byte[]>> btreeComparatorClass)
Note that there are two ways to set the comparator: by specifying the
class or by specifying a serializable object. This method is used to
specify a Comparator class. The comparator class must implement
java.util.Comparator and must have a public zero-parameter constructor.
JE will store the class name and instantiate the Comparator by class
name (using Class.forName
and newInstance
)
when subsequently opening the database. Because the Comparator is
instantiated using its default constructor, it should not be dependent
on other constructor parameters.
The Comparator.compare() method is passed the byte arrays that are stored in the database. If you know how your data is organized in the byte array, then you can write a comparison routine that directly examines the contents of the arrays. Otherwise, you have to reconstruct your original objects, and then perform the comparison. See the Getting Started Guide for examples.
If a comparator needs to be initialized before it is used or needs
access to the environment's ClassLoader property, it may implement the
DatabaseComparator
interface.
WARNING: There are several special considerations that must be taken into account when implementing a comparator.
A special type of comparator is a partial comparator, which
allows for the keys of a database to be updated, but only if the updates
do not change the relative order of the keys. For example, if a database
uses strings as keys and a case-insensitive comparator, it is possible
to change the case of characters in the keys, as this will not change the
ordering of the keys. Another example is when the keys contain multiple
fields but uniquely identify each record with a single field. The
partial comparator could then compare only the single identifying field,
allowing the rest of the fields to be updated. A query
(Cursor.getSearchKey
, for example) could
then be performed by passing a partial key that contains only the
identifying field.
WARNING: To allow for key updates in situations
like those described above, all partial comparators must implement the
PartialComparator
tag interface. See "Upgrading from JE 5.0
or earlier" in the change log and the PartialComparator
javadoc
for more information.
BinaryEqualityComparator
interface. The significance of binary equality comparators is that they
make possible certain internal optimizations, like the "blind puts"
optimization, described in
BinaryEqualityComparator
The comparator for an existing database will not be overridden unless setOverrideBtreeComparator() is set to true.
public java.util.Comparator<byte[]> getBtreeComparator()
public boolean getBtreeComparatorByClassName()
public DatabaseConfig setOverrideBtreeComparator(boolean override)
override
- Set to true to override the existing comparator.public boolean getOverrideBtreeComparator()
public DatabaseConfig setDuplicateComparator(java.util.Comparator<byte[]> duplicateComparator)
Note that there are two ways to set the comparator: by specifying the class or by specifying a serializable object. This method is used to specify a serializable object. The comparator class must implement java.util.Comparator and must be serializable. JE will serialize the Comparator and deserialize it when subsequently opening the database.
The Comparator.compare() method is passed the byte arrays that are stored in the database. If you know how your data is organized in the byte array, then you can write a comparison routine that directly examines the contents of the arrays. Otherwise, you have to reconstruct your original objects, and then perform the comparison. See the Getting Started Guide for examples.
If a comparator needs to be initialized before it is used or needs
access to the environment's ClassLoader property, it may implement the
DatabaseComparator
interface.
WARNING: There are several special considerations that must be taken into account when implementing a comparator.
A special type of comparator is a partial comparator, which
allows for the keys of a database to be updated, but only if the updates
do not change the relative order of the keys. For example, if a database
uses strings as keys and a case-insensitive comparator, it is possible to
change the case of characters in the keys, as this will not change the
ordering of the keys. Another example is when the keys contain multiple
fields but uniquely identify each record with a single field. The
partial comparator could then compare only the single identifying field,
allowing the rest of the fields to be updated. A query
(Cursor.getSearchKey
, for example) could
then be performed by passing a partial key that contains only the
identifying field.
When using a partial duplicates comparator, it is possible to update
the data for a duplicate record, as long as only the non-identifying
fields in the data are changed. See
Cursor.putCurrent
for more information.
WARNING: To allow for key updates in situations
like those described above, all partial comparators must implement the
PartialComparator
tag interface. See "Upgrading from JE 5.0
or earlier" in the change log and the PartialComparator
javadoc
for more information.
Another special type of comparator is a binary equality
comparator, which considers two keys to be equal if and only if they
have the same length and they are equal byte-per-byte. All binary
equality comparators must implement the BinaryEqualityComparator
interface. The significance of binary equality comparators is that they
make possible certain internal optimizations, like the "blind puts"
optimization, described in
BinaryEqualityComparator
The comparator for an existing database will not be overridden unless setOverrideDuplicateComparator() is set to true.
public DatabaseConfig setDuplicateComparator(java.lang.Class<? extends java.util.Comparator<byte[]>> duplicateComparatorClass)
Note that there are two ways to set the comparator: by specifying the
class or by specifying a serializable object. This method is used to
specify a Comparator class. The comparator class must implement
java.util.Comparator and must have a public zero-parameter constructor.
JE will store the class name and instantiate the Comparator by class
name (using Class.forName
and newInstance
)
when subsequently opening the database. Because the Comparator is
instantiated using its default constructor, it should not be dependent
on other constructor parameters.
The Comparator.compare() method is passed the byte arrays that are stored in the database. If you know how your data is organized in the byte array, then you can write a comparison routine that directly examines the contents of the arrays. Otherwise, you have to reconstruct your original objects, and then perform the comparison. See the Getting Started Guide for examples.
If a comparator needs to be initialized before it is used or needs
access to the environment's ClassLoader property, it may implement the
DatabaseComparator
interface.
WARNING: There are several special considerations that must be taken into account when implementing a comparator.
A special type of comparator is a partial comparator, which
allows for the keys of a database to be updated, but only if the updates
do not change the relative order of the keys. For example, if a database
uses strings as keys and a case-insensitive comparator, it is possible to
change the case of characters in the keys, as this will not change the
ordering of the keys. Another example is when the keys contain multiple
fields but uniquely identify each record with a single field. The
partial comparator could then compare only the single identifying field,
allowing the rest of the fields to be updated. A query
(Cursor.getSearchKey
, for example) could
then be performed by passing a partial key that contains only the
identifying field.
When using a partial duplicates comparator, it is possible to update
the data for a duplicate record, as long as only the non-identifying
fields in the data are changed. See
Cursor.putCurrent
for more information.
WARNING: To allow for key updates in situations
like those described above, all partial comparators must implement the
PartialComparator
tag interface. See "Upgrading from JE 5.0
or earlier" in the change log and the PartialComparator
javadoc
for more information.
Another special type of comparator is a binary equality
comparator, which considers two keys to be equal if and only if they
have the same length and they are equal byte-per-byte. All binary
equality comparators must implement the BinaryEqualityComparator
interface. The significance of binary equality comparators is that they
make possible certain internal optimizations, like the "blind puts"
optimization, described in
BinaryEqualityComparator
The comparator for an existing database will not be overridden unless setOverrideDuplicateComparator() is set to true.
public java.util.Comparator<byte[]> getDuplicateComparator()
public boolean getDuplicateComparatorByClassName()
public DatabaseConfig setOverrideDuplicateComparator(boolean override)
override
- Set to true to override the existing comparator.public boolean getOverrideDuplicateComparator()
public DatabaseConfig setTemporary(boolean temporary)
Temporary databases operate internally in deferred-write mode to provide reduced disk I/O and increased concurrency. But unlike an ordinary deferred-write database, the information in a temporary database is not durable or persistent.
A temporary database is not flushed to disk when the database is closed or when a checkpoint is performed, and the Database.sync method may not be called. When all handles for a temporary database are closed, the database is automatically removed. If a crash occurs before closing a temporary database, the database will be automatically removed when the environment is re-opened.
Note that although temporary databases can page to disk if the cache is not large enough to hold the databases, they are much more efficient if the database remains in memory. See the JE FAQ on the Oracle Technology Network site for information on how to estimate the cache size needed by a given database.
See the Getting Started Guide, Database chapter for a full description of temporary databases.
temporary
- if true, the database will be opened as a temporary
database.public boolean getTemporary()
public DatabaseConfig setDeferredWrite(boolean deferredWrite)
Deferred-write databases have reduced disk I/O and improved
concurrency. Disk I/O is reduced when data records are frequently
modified or deleted. The information in a deferred-write database is
not guaranteed to be durable or persistent until Database.close()
or Database.sync()
is called, or a checkpoint is performed. Since
the usual write ahead logging system is relaxed in order to improve
performance, if the environment crashes before a Database.sync()
or Database.close()
, none, all, or a unpredictable set of the
operations previously done may be persistent.
After a deferred-write database is closed it may be re-opened as an ordinary transactional or non-transactional database. For example, this can be used to initially load a large data set in deferred-write mode and then switch to transactional mode for subsequent operations.
Note that although deferred-write databases can page to disk if the cache is not large enough to hold the databases, they are much more efficient if the database remains in memory. See the JE FAQ on the Oracle Technology Network site for information on how to estimate the cache size needed by a given database.
See the Getting Started Guide, Database chapter for a full description of deferred-write databases.
deferredWrite
- if true, the database will be opened as a
deferred-write database.public boolean getDeferredWrite()
public DatabaseConfig setUseExistingConfig(boolean useExistingConfig)
useExistingConfig
- true if this Database should be opened using
the existing configuration.public boolean getUseExistingConfig()
public DatabaseConfig setCacheMode(CacheMode cacheMode)
CacheMode
used for operations performed on this
database. If this property is non-null, it overrides the default
specified using EnvironmentMutableConfig.setCacheMode(com.sleepycat.je.CacheMode)
for operations on
this database. The default cache mode may be overridden on a per-record
or per-operation basis using Cursor.setCacheMode(com.sleepycat.je.CacheMode)
, ReadOptions.setCacheMode(CacheMode)
or WriteOptions.setCacheMode(CacheMode)
.cacheMode
- is the default CacheMode
used for operations
performed on this database. If null
is specified, the
environment default will be used.for further details.
public CacheMode getCacheMode()
CacheMode
used for operations performed on
this database, or null if the environment default is used.CacheMode
used for operations performed on
this database, or null if the environment default is used.setCacheMode(com.sleepycat.je.CacheMode)
public DatabaseConfig setReplicated(boolean replicated)
In a non-replicated Environment, this property is ignored. All databases are non-replicated in a non-replicated Environment.
public boolean getReplicated()
This method returns true by default. However, in a non-replicated Environment, this property is ignored. All databases are non-replicated in a non-replicated Environment.
setReplicated(boolean)
public DatabaseConfig cloneConfig()
clone()
.public DatabaseConfig clone()
clone
in class java.lang.Object
public java.lang.String toString()
toString
in class java.lang.Object
Copyright (c) 2002, 2017 Oracle and/or its affiliates. All rights reserved.


Please enable JavaScript to run this calculator
Body Fat calculator
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Weight
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Height
?
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mm
Wrist
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Wrist size.
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mm
Hips
?
Hips size.
Tape measure
mm
Calf
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Calf size.
Tape measure
mm
Calf
?
Vertical fold, inside of leg on largest part of calf
Caliper required
mm
Forearm
?
Forearm size.
Tape measure
mm
Neck size
?
Relaxed neck circumference.
Tape Measure
mm
Abdominal size
?
Vertical fold, one inch to the right of navel. Caliper required
mm
Abdomen circumference
?
Circumference just above the navel. Tape measure
mm
Chest size
?
Diagonal fold, midway between upper armpit and nipple.
Caliper required
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Thigh size
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Thigh size.
Tape measure
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Vertical fold, midway between knee cap and top of thigh.
Caliper required
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Vertical fold, halfway between shoulder and elbow, directly on bicep.
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Vertical fold, midway between elbow and shoulder.
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Diagonal fold, directly below shoulder blade.
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Horizontal fold, directly over the kidneys, and 2 inches to the right of spine.
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Horizontal fold, directly below armpit.
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Calculation formula
?

Body Fat % based on input data
5.77
Body Fat (
kg
)
?
Body Fat.
Body Fat % * Weight
57.23
Lean Body Weight (
kg
)
?
Lean Body Weight.
Weight - Body Fat
Athletes (6-13%)
Classification (
kg
)
?
Classification based on body fat %


How does understanding the principles of Neuroplasticity benefit People
Neuroplasticity allows your brain to adapt and change, which helps promote: The ability to learn new things. The ability to enhance your existing cognitive capabilities. Recovery from strokes and traumatic brain injuries and the significance of neuroplasticity Neuroplasticity is incredibly important because it is at the root of essential human experiences. Learning and memory, key parts of what it means to be human, would not be possible without this process
How does engaging in the principles of neuroplasticity benefit you
The benefits of neuroplasticity are the ability to learn new things.The ability to enhance your existing cognitive capabilities. Recovery from strokes and traumatic brain injuries. Strengthening areas if some functions are lost or decline. Improvements that can promote brain fitness. Basically Neuroplasticity helps you in everyday life and whenever you need to do anything to better yourself
What can you do to increase your Neuroplasticity
One way is video games which can give better benefits such as motor skills, visual recognition, memory and reaction times, reasoning and problem-solving skills, resilience, cooperation and team participation.  These effects can improve your gameplay, but they also carry over to the rest of your life so if you enjoy playing video games they are really helpful and different games lead to different effects. Learning a new language can help with language, attention, motor skills, memory and emotions and other things. Music can also be a huge help. It can improve your mood, ability to learn, concentration and focus. Other things that can help are Exercise, travel, drawing/Art and these can all be used to increase your Neuroplasticity.
Affects my Learning Plan
What I’ve learned from this and how it affects my Learning plan is interesting as I actually already use a few of these methods when studying. I listen to music to help with my mood and focus asI have a playlist dedicated to studying and play video games in my down time when I’m not reading and I draw when watching long youtube videos to help me focus so finding out these actually help with my learning is really nice and when looking into a growth mindset I found that it was interesting as it encouraged a more positive approach to learning. Something that anyone can do as long as they put the effort in and I found that interesting as long as they think along the lines of not yet instead of you can’t or you failed anyone should be able to succeed in what they are focused on which is honestly something I do believe in.
Resources
Articles
Youtube


---
layout: default
title: Electrical Year 3 Power
---
Electrical Year 3
Electrical Power and Machines Division
2019-2020
{% capture currentmonth %}{{'now' | date: '%m'}}{% endcapture %}{% capture february %}{{'February' | date: '%m'}}{% endcapture %}{% capture july %}{{'July' | date: '%m'}}{% endcapture %}
{% if currentmonth >= february and currentmonth <= july %}
{% assign source = site.data.electrical.year3power.Data_2019-2020_Term2.files %}
{% include query-week.html %}
{% include query-subject.html subject='Term 2 - Electrical Tests 2' %}
{% include query-subject.html subject='Term 2 - Electrical Machines 2' %}
{% include query-subject.html subject='Term 2 - Power Systems Protection' %}
{% include query-subject.html subject='Term 2 - High Voltage Engineering' %}
{% include query-subject.html subject='Term 2 - Electrical Power Systems Analysis 1' %}
{% include query-subject.html subject='Term 2 - Data Communication Systems' %}
{% else %}
{% assign source = site.data.electrical.year3power.Data_2019-2020_Term1.files %}
{% include query-week.html %}
{% include query-subject.html subject='Term 1 - Electrical Machines 1' %}
{% include query-subject.html subject='Term 1 - Transmission and Distribution' %}
{% include query-subject.html subject='Term 1 - Project Management and Marketing' %}
{% include query-subject.html subject='Term 1 - Engineering Mathematics 4' %}
{% include query-subject.html subject='Term 1 - Electrical Tests 2' %}
{% include query-subject.html subject='Term 1 - Automatic Control' %}
{% include query-subject.html subject='Term 1 - Power Electronics' %}
{% endif %}


 However, in every project this is something very real that every Lead Developer has to deal with almost in a daily basis.
Here I’ll try to describe on how to avoid it it, or at least identify it as soon as possible so you can plan ahead.
First of all, if we’re talking about technical debt, there’s something that you can be sure, you’ll have to pay it in the future, in one way or another. However I’m not here to say that this is something that should be avoided at all costs. It’s not! There’s some really good reasons to accept some form of technical debt. Maybe there’s a deadline, or the dreadfull marketing team made some promisses that you’ll have to deal with.  These are just some examples. There’s many more, but I think these everyone can relate to.
With this in mind you should always accept some form of technical debt with a deadline in mind, but if you do so, make sure you have a backup plan to fix it in the future. This is how you balance business and your codebase. Never ever, accept technical debt without a backup plan on how to solve it in the future.
I’m going to quote Martin Fowler here:
If you have this kind of exponential relationship, then if you do it more frequently, you can drastically reduce the pain. And this is what happens with Continuous Integration - by integrating every day, the pain of integration almost vanishes. It did hurt, so you did it more often, and now it no longer hurts. - In Frequency Reduces Difficulty
What I mean is, managing technical debt is something that you’ll learn to do once you start acknowledge is that this is something that you do often. What normally tends to happen is that us, Lead Developers are quite good at identifying these technical debts, however we tend to slack on solve them. Sure this might be ok on smaller projects, where you just have to be sure to avoid them on the next project. But what about big projects? That is just something that you can’t avoid, that will probably mean that somewhere in the future you’ll have a very dificult conversation with management saying that your app needs a complet re-write. I know! I’ve been there and it’s not pleasent.
How to better understand your codebase so you can identify your technical debt easly?
First you need to check your version control often. Try and see how your developers interact with your code. See what part of your code is being used most of the time. With this you can identify these key areas:
-
Hotspots = where most developers are working
-
Refactoring targets = prioritize change
-
Change coupling = implicit change in patters
-
Code biomarkers = aim to indicate specific properties at very low code base
Then you should ask yourself the following questions:
-
Where should we focus improvements?
-
Where are the risk areas?
-
Is team productivity a problem?
When you answer those questions, then there’s time for the walk of shame and start paying your debt.
But how?
You have many options here and you should pick the one that aplies to your case.
You can allocate more time into the development of said feature and that might solve it because this were rushed. This can be done by making a that a team priority and thats what everyone will be doing until things are solved.
Another way of paying it is actually making a rulle of having a technical debt day per week or month, were you pick days where everyone in the team will do it.
You can just choose to educate your team about it, this can be an option with just small debts in small projects, otherwise education can only go so far.
Should definetly have a talk with management and marketing about it so in the future deadlines wont afect the project as much.
There’s only one thing that you might be tempted to do, but maybe it’s not really an answer. Hire more developers. This seems like a great idea at first, but sometimes you want to solve these issues with your team before bringing more people into the mix.


Note
Click here to download the full example code
1.1.1. Using fmas as a black-box application¶
This examples shows how to use py-fmas as a black-box application, that only requires a minimal amount of scripting.
We start by simply importing the required fmas into the current namespace.
import fmas
If an adequate input file is located within the current working directory, fmas can be used as shown below. It features a particular function called run, which reads-in the propagation setting stored in the input file input_file.h5 and runs the simulaton
res = fmas.run('input_file.h5', model_type='FMAS_S_R', solver_type='IFM_RK4IP')
An example that shows how an adequate input file can be generated via python is shown under the link below:
After the proapgation algorithm (specified in input_file.h5) terminates, a simple dictionary data structure with the following keys is available
print(res.keys())
Out:
dict_keys(['t', 'z', 'w', 'u', 'Cp'])
A simple plot that shows the result of the simulation run can be produced using function plot_evolution implemented in module tools
from fmas.tools import plot_evolution
plot_evolution( res['z'], res['t'], res['u'], t_lim=(-500,2200), w_lim=(1.,4.))
The results can be stored for later postprocessing using the function save_h5 implemented in module data_io. It will generate a file out_file.h5 with HDF5 format in the current working directory
from fmas.data_io import save_h5
save_h5('out_file.h5', **res)
Total running time of the script: ( 0 minutes 34.211 seconds)


--- layout: post title: 'More Hosted Hadoop: SunGard Offers Big Data in the Cloud' date: '2012-04-04T15:12:31-07:00' tags: tumblr_url: http://alexhwilliams.tumblr.com/post/20488248873/more-hosted-hadoop-sungard-offers-big-data-in-the ---
http://bit.ly/H9wzkc


|
|
| CalculateEnergy (std::shared_ptr< Model > model, double **f, double **df) |
| |
|
void | calculateData (int n, int i, int j=0, int k=0) override |
| | Pure virtual function to be implemented in the sub class.
|
| |
|
void | write (int loop) |
| |
|
| CalculateBase (double **f, double **df) |
| |
|
void | update () |
| |
|
double | average (int n) |
| |
|
|
double | gradientEnergy (const double *f, int i, int j=0, int k=0) const |
| |
|
|
std::shared_ptr< Model > | _model |
| |
|
|
void | calculate () |
| |
|
double ** | _f |
| |
|
double ** | _df |
| |
|
double ** | _data |
| | The physical quantity you want to calculate is stored.
|
| |
|
double * | _data_tot |
| | Total of _data at each grid point.
|
| |
The documentation for this class was generated from the following files:


Create a parser object to handle parsing RiveScript code.
Read and parse a RiveScript document. Returns a data structure that represents all of the useful contents of the document, in this format:
{
"begin": { // "begin" data
"global": {}, // ! global vars
"var": {}, // ! bot vars
"sub": {}, // ! sub substitutions
"person": {}, // ! person substitutions
"array": {}, // ! array lists
},
"topics": { // main reply data
"random": { // (topic name)
"includes": {}, // included topics
"inherits": {}, // inherited topics
"triggers": [ // array of triggers
{
"trigger": "hello bot",
"reply": [], // array of replies
"condition": [], // array of conditions
"redirect": "", // @ redirect command
"previous": null, // % previous command
},
...
]
}
},
"objects": [ // parsed object macros
{
"name": "", // object name
"language": "", // programming language
"code": [], // object source code (in lines)
}
]
}
onError function receives: (err string[, filename str, line_no int])
Translate deparsed data into the source code of a RiveScript document.
See the stringify()
method on the parent RiveScript class; this is its
implementation.
Check the syntax of a RiveScript command. command
is the single character
command symbol, and line
is the rest of the line after the command.
Returns an empty string on success, or a description of the error on error.
Initialize the topic tree for the parsing phase. Sets up the topic under ast.topics with all its relevant keys and sub-keys, etc.


First, glue seven pieces of wood board
Second, put it in the vacum bag with a mode
Third, wait for few days and put a sticker on the board


public final class Notification
extends java.lang.Object
If some browser instance has been disposed or reloaded than all notifications that were created in the scope of this browser instance context would be marked as closed and all notification event listeners associated with this notification would be informed about it.
| Modifier and Type | Method and Description |
|---|---|
void |
addNotificationListener(NotificationListener listener)
Adds new notification listener to listen events related to this notification instance.
|
void |
click()
Informs browser that this notification was clicked by user.
|
void |
close()
Informs browser that this notification was closed by user and marks it as
closed . |
java.lang.String |
getMessage()
Returns notification message or empty string if notification doesn't have message.
|
java.util.List<NotificationListener> |
getNotificationListeners()
Returns collection of notification listeners associated with this notification.
|
java.lang.String |
getTitle()
Returns notification title or empty string if notification doesn't have title.
|
NotificationType |
getType()
Returns notification type.
|
java.lang.String |
getURL()
Returns notification URL.
|
boolean |
isClosed()
Informs about the state of the notification.
|
void |
removeNotificationListener(NotificationListener listener)
Removes notification listener from the list of registered notification listeners.
|
public NotificationType getType()
NotificationType
value describing type of the notification.public java.lang.String getTitle()
public java.lang.String getMessage()
public java.lang.String getURL()
String
containing URL of the web page that displays this notification. Will not return null
or empty string.public boolean isClosed()
close()
method or via JavaScript code Notification.close()
.true
if this notification is closed.public java.util.List<NotificationListener> getNotificationListeners()
MULTIPLE
of NotificationListener
instances. Will not return null
.
If no listeners were registered returns empty MULTIPLE
public void addNotificationListener(NotificationListener listener)
listener
- NotificationListener
instance. null
value will be ignored.public void removeNotificationListener(NotificationListener listener)
listener
- NotificationListener
instance that will not further receive events related to this
notification instance. null
value will be ignored.public void close()
closed
. See isClosed()
Does nothing if this notification has been already closed or if browser instance in which context this notification
was created has been disposed or reloaded.public void click()
java.lang.IllegalStateException
- if this notification has been already closed or if
browser instance in which context this notification
was created has been disposed or reloaded.


{% extends "layout.html" %} {% block main %} {% from "flask_user/_macros.html" import render_field %}
{{ _("Registrations are collected from the User.registration timestamps of all Users without the Test Role") }}
{{ _("Logins are collected from the start_time timestamps of Encounters whose auth_method='password_authenticated'") }}
{{ _("Intervention Logins are filtered based on the login Encounter's User's User.interventions") }}
| {{ _("Today") }} | {{ _("This Month") }} | {{ _("This Year") }} | {{ _("All Time") }} | |
|---|---|---|---|---|
| {{ _("Registrations") }} | {{ counts['registrations'] | selectattr("day", "equalto", now.day) | list | length }} | {{ counts['registrations'] | selectattr("month", "equalto", now.month) | list | length }} | {{ counts['registrations'] | selectattr("year", "equalto", now.year) | list | length }} | {{ counts['registrations'] | length }} |
| {{ _("Logins") }} | {{ counts['encounters']['all'] | selectattr("day", "equalto", now.day) | list | length }} | {{ counts['encounters']['all'] | selectattr("month", "equalto", now.month) | list | length }} | {{ counts['encounters']['all'] | selectattr("year", "equalto", now.year) | list | length }} | {{ counts['encounters']['all'] | length }} | {{ k }} |
{{ v | selectattr("day", "equalto", now.day) | list | length }} | {{ v | selectattr("month", "equalto", now.month) | list | length }} | {{ v | selectattr("year", "equalto", now.year) | list | length }} | {{ v | length }} | {% endfor %}
{{ _("User stats are collected from all Users without the Test Role") }}
{{ _("Role counts are tallied from User.roles (e.g. a User with both the Patient and Staff Roles, would add 1 to both Roles' counts)") }}
{{ _("No Diagnosis") }}: {{ _("Users whose User.observations does not contain any Observations where the Observation.codeable_concept=CC.BIOPSY and the Observation.value_quantity.value=True") }}
{{ _("Diagnosis, No Treatment") }}: {{ _("Users where known_treatment_not_started(User)=True") }}
{{ _("Diagnosis and Treatment") }}: {{ _("Users where known_treatment_started(User)=True") }}
{{ _("Metastasis") }}: {{ _("Users whose User.observations contains any Observations where the Observation.codeable_concept=CC.PCaLocalized and the Observation.value_quantity.value!=True") }}
| {{ _("Role") }} | {{ _("User Count") }} |
|---|---|
| {{ _("Patients") }} | {{ counts['roles']['patient'] }} |
{{ _("No Diagnosis") }} |
{{ counts['patients']['pre-dx'] }} |
{{ _("Diagnosis, No Treatment") }} |
{{ counts['patients']['dx-nt'] }} |
{{ _("Diagnosis and Treatment") }} |
{{ counts['patients']['dx-t'] }} |
{{ _("Metastasis") }} |
{{ counts['patients']['meta'] }} |
| {{ _("Partners") }} | {{ counts['roles']['partner'] }} |
| {{ _("Clinicians") }} | {{ counts['roles']['staff'] }} |
{{ _("Intervention counts only apply to those interventions that control their subject list manually (eg Sexual Recovery, Care Plan, Community of Wellness). They are tallied from User.interventions (e.g. a User with both the 'Care Plan' and 'Community of Wellness' interventions, would add 1 to both Interventions' counts)") }}
{{ _("Completed Reports are tallied for each User that has any number of PatientReports for that Intervention (e.g. whether a User has 1 or 100 PatientReports for 'Symptom Tracker', that User only adds 1 to that Intervention's Completed Reports tally)") }}
{{ _("Completed Reports are only shown for an Intervention if the report count is above 0") }}
| {{ _("Intervention") }} | {{ _("User Count") }} |
|---|---|
| {{ k }} | {{ v }} |
{{ _("Completed Reports") }} |
{{ counts['intervention_reports'][k] }} |
{{ _("Organization counts are collected from the User.organizations of all Users without the Test Role") }}
{{ _("'None of the above' refers to Users who specifically selected the 'None of the above' organization option") }}
{{ _("'Unspecified' refers to Users who have not yet been assigned to any Organization option (including 'None of the above')") }}
| {{ _("Organization Name") }} | {{ _("User Count") }} | {{ k }} | {{ v }} | {% endfor %}
|---|


Xiaomi Mi Watch is a blocky Apple Watch lookalike running a custom version of Wear OS
by Manuel VonauXiaomi today announced the Mi Note 10 with a 108MP camera, but it also took the stage to introduce its first smartwatch based on its own custom Wear OS interface, the Mi Watch. Its software and exterior do look quite similar to the Apple Watch, although the device's edges are much blockier. Other than that, it's pretty much standard Wear OS hardware sporting a Snapdragon Wear 3100 processor and LTE connectivity.
The Mi Watch has a square OLED display with a resolution of 368x448 pixels. Its screen glass curves into the aluminum alloy chassis. The strip is made from rubber, but an exclusive model brings stainless steel to the table for both the wristband and the housing. A 570mAh battery is supposed to make the regular version last up to 36 hours while the higher-end variant is equipped with a 590mAh.
Specs
| Display | 1.78-inch AMOLED 368x448 |
| Software | MIUI for Watch (based on Wear OS) |
| CPU | Snapdragon Wear 3100 |
| RAM | 1GB |
| Storage | 8GB |
| Battery | 570mAh (standard version), 590 mAh (exclusive version) |
| Connectivity | eSIM, GPS, NFC, Wi-Fi 802.11b/g/n 2.4 GHz, Bluetooth 4.2 |
| Miscellaneous | Swimming-proof, heart rate monitor |
| Measurements | 44.69mm x 36.92mm x 12.28mm, 44g (without strap) |
| Colors | Black, silver (standard version only) |
| Price | Standard version: CNY 1,300 (~$185) Exclusive version: CNY 2,000 (~$285) |
Both versions come with Bluetooth 4.2 and 2.4GHz 802.11/b/g/n Wi-Fi. The inclusion of LTE and GPS make the watches a viable fitness tracker that allows you to leave your phone at home while you work out. Thanks to an integrated NFC chip, you could even make a quick stop at a shop and pay without a wallet.
Xiaomi says its watch is capable of monitoring your stress level and your nightly sleep thanks to 24-hour heart rate monitoring, and offers a selection of sports modes that you can use to track your activities. The company even touts its wearable can detect irregularities in your heartbeat and urge you to see a doctor, just like the Apple Watch.
The analogies to Apple's product don't stop at the exterior and the health features, unfortunately. Thanks to the option for extensive Wear OS customizations only available in China, Xiaomi took the opportunity to make the Mi Watch look as similar to Apple's watchOS as possible. The custom UI's notifications, buttons, and even whole applications like the stock, weather, and fitness tracking apps look way too similar to its competitor's interface. It's a bummer — I would've loved to see an original alternative to the currently available platforms.
The Mi Watch will be sold in China only officially and will cost CNY 1,300 (~$185). The exclusive version made from Sapphire glass and stainless steel will go for CNY 2,000 (~$285). If an international launch is planned, we might hear more about the device tomorrow when the company will introduce the Mi Note 10 internationally. The custom Wear OS version could be a roadblock though since Google only offers it in China.


mvnormtest: Normality test for multivariate variables
Generalization of shapiro-wilk test for multivariate
variables.
| Version: |
0.1-9 |
| Depends: |
stats |
| Published: |
2012-04-12 |
| Author: |
Slawomir Jarek |
| Maintainer: |
Slawomir Jarek <slawomir.jarek at gallus.edu.pl> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: |
no |
| Materials: |
ChangeLog |
| In views: |
Multivariate |
| CRAN checks: |
mvnormtest results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=mvnormtest
to link to this page.


ice40blinkingled_syn (ice40blinkingled_Implmnt)
Synthesis -
Compiler Report
Compiler Constraint Applicator
Pre-mapping Report
Clock Summary
Mapper Report
Clock Conversion
Timing Report
Performance Summary
Clock Relationships
Interface Information
Detailed Report for Clocks
Clock: CLK
Starting Points with Worst Slack
Ending Points with Worst Slack
Worst Path Information
Resource Utilization
Session Log (09:40 18-Jul)


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Public transportation is essential in people's daily life and it is crucial to understand how people move around the city. Some prior works have exploited GPS, Wi-Fi or bluetooth to collect data, in which extra sensors or devices were needed. Other works utilized data from smart card systems. However, some public transportation systems have their own smart card system and the smart card data cannot include all kinds of transportation modes, which makes it unsuitable for our study.
Nowadays, each user has his own mobile phones and from the cellular data of mobile phone service providers, it is possible to know the users' transportation mode and fine-grained crowd flows. As such, given a set of cellular data, we propose a system for public transportation mode detection, crowd density estimation, and crowd flow estimation.
Note that we only have cellular data, no extra sensor data collected from users' mobile phones. In this paper, we refer to some external data sources(e.g., the bus routing networks) to identify transportation modes. Users' cellular data sometimes have uncertainty about user location information. Thus, we propose two approaches for different transportation mode detection considering the cell tower properties, spatial and temporal factors. We demonstrate our system using the data from Chunghwa Telecom, which is the largest telecommunication company in Taiwan,to show the usefulness of our system.
If you want to give us any suggestion, please contact us by email You're always welcome to talk with us!


Passion for Classics offers all of their customers cost effective solutions, repairs and restorations for any classic car requirements.
Are you unable to finish your project? Is it taking up valuable space? Are your neighbours complaining about the noise? Do you feel like there's not enough hours in the day?
Call or visit Passion for Classics in Malaga for great options at affordable prices.
Check out our gallery or read more more about Tony.
Tony has a real passion for classic cars and takes pride in offering customers the very best.
He uses the most up to date equipment and tools to ensure the best service.
With over 35 years experience as a fully qualified mechanic, including servicing and repairs, fabrication work, welding, panel repairs and restoration services, you are in safe hands!
"I engaged Tony from Passion For Classics to restore my 1967 Humber Super Snipe, which had been broken down for about 25 years. I couldn't have wished for a better experience. Tony has done everything 'right' He's kept me informed of progress (including by sending photographs) and contacted me to discuss any issues. He explains everything clearly, and has always welcomed me coming to the workshop to see the progress on my car. I'm impressed by the quality of the work and Tony's determination to get things spot on. It's comforting to know that underneath the shiny paint there's quality metal repairs and anti-rust treatments. In my case I didn't want to just hand my car to him and get it back restored, I was keen to research, choose and track down specific items and parts eg particular carpet, certain tyres. Tony was willing to accept my input and work with me. Knowing that budget was an issue for me, on several matters he recommended the best way a job should be done, but also presented a cheaper option. As well as his professionalism he's also friendly. I particularly value Tony's honesty and integrity. These can make the difference between a quality restoration and losing your precious car - as I know from a previous experience taking another car to another restorer before I met Tony. I got my car back as a mostly painted shell with some parts missing (a better result than some other customers). I'm now hoping to take this car to Tony too."
"I was first introduced to Tony Passion for Classics through a good friend. The introduction is as important as a blind date, you really need to trust the person you’re going to hand over your prized possession to. Mine is a 1970 Chevelle 454 Big Block. On the surface she looked ok. To the trained eye, Tony knew exactly what was needed to restore her to a reliable drivable car. Tony put together a meticulous quote, having gone over everything in detail and time has proven him right. Along the journey there were only a very few unexpected surprises, none too serious, and none to falter Tony on his mission. The quality of workmanship is beyond peer and I have no hesitation at all to recommend you to Tony at Passion for Classics if you have a project too."
"I found Passion for Classics a couple of years ago just after they opened and I am so glad I did. Tony’s passion for his work and looking after his customers is not difficult to see. It’s in his positive, ‘can do’ attitude, his attention to detail, experience and the obvious care and pride he takes in his work. Tony did an A1 restore on my 1963 EH Holden and worked within my budget. The results were amazing. Since then Tony has also taken on the regular servicing and mechanical work of my fleet of company staff vehicles. Again reliable, honest, thorough, well priced and friendly service you just can’t beat.
We are Open: Monday - Friday
8:00 am to 5:00 pm
0427 163 220
<EMAIL_ADDRESS>Passion for Classics
Unit 1 / 23 Irvine Drive
Malaga WA 6090.


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