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Anaesthesia1ST
Paper summary: Comparison of direct and Doppler arterial blood pressure measurements in rabbits during isoflurane anaesthesia.
Comparison of direct and Doppler arterial blood pressure measurements in rabbits during isoflurane anaesthesia. Harvey L., Knowles T. & Murison P.J. (2012). Veterinary Anaesthesia and Analgesia, 39: 174–184
The sedation and anaesthesia of the rabbit is becoming increasingly frequent in modern veterinary practice and it is well known that some drugs e.g. inhalation agents and sedatives can cause hypotension, which may be further aggravated by haemorrhage or other fluid loss. Hypotension can compromise the perfusion of vital organs, including the heart, and may be a factor in the 1.39% peri-anaesthetic mortality rate in the rabbit observed by Broadbelt et al, (2008).
Direct arterial blood pressure measurement (BP) is considered the gold-standard and provides a continuous assessment of arterial BP. However, the technique requires experience, skill & specialised equipment and is not without problems: infection; haemorrhage; haematoma formation; thromboembolism; accidental intra-arterial administration of drugs; and expense. Indirect measurement of blood pressure is less technical but is also considered less accurate. This study was performed to compare the accuracy of direct (invasive) auricular systolic BP (SAP) and mean arterial BP (MAP) measurements to indirect values obtained using the Doppler technique in rabbits under isoflurane general anaesthesia.
Twenty four client-owned, mixed breed, mixed age (3-36 months), mixed sex (12 male, 12 female), American Society of Anaesthesiologists (ASA) physical status classification I rabbits admitted for routine neutering were allocated to the study.
Following clinical assessment both ears of each patient were clipped and prepared with EMLA cream at least 30 minutes prior to premedication with intramuscular fentanyl/fluanisone (Hypnorm; VetaPharma, UK). An intravenous cannula was placed in the marginal ear vein of one ear and anaesthesia induced with intravenous propofol followed by endotracheal intubation and maintenance with isoflurane in oxygen. Non-steroidal anti-inflammatory drugs (meloxicam or carprofen) were administered together with buprenorphine as required and some patients also received local anaesthesia at the incision site. Pulse oximetry, capnography and routine clinical monitoring of anaesthesia were performed. All patients were monitored for temperature and placed on an insulated heat mat with a warm ambient temperature to minimise heat loss.
Placement of direct arterial BP cannula
A 26-gauge x 19mm cannula was placed in the central auricular artery of the ear contralateral to that used for intravenous cannulation and connected to the direct blood pressure monitor via a non-compliant tube. Once positioned in dorsal recumbency for surgery the thoracic inlet was used as the zero-level for the transducer. If arterial catheterisation was not successful on the first attempt, the rabbit was excluded from the study but neutered as intended.
Placement of Doppler probe
The hair was clipped over the left radial artery and an ultrasonic Doppler flow detector transducer positioned. A paediatric blood pressure cuff with a width 40-50% of the circumference of the site applied and secured with tape. Headphones were used to listen to the Doppler flow signal.
A single clinician recorded both direct and Doppler ABP measurements every 5–10 minutes during anaesthesia: the direct measurements were recorded immediately after the Doppler readings to produce pairs of recordings that were as simultaneous as possible.
Hypotension was defined as direct MAP <60 mmHg, direct SAP <80 mmHg or Doppler BP <80 mmHg. If hypotension was observed, depth of anaesthesia was assessed, and the volatile levels reduced and/or fluid therapy was administered. Pain scoring was performed during recovery and analgesia provided as necessary.
Of the 24 rabbits recruited to the study 23 were successfully intubated with the unsuccessful case maintained on isoflurane in oxygen delivered by face mask. It was not possible to cannulate the auricular artery in 5 rabbits and these were excluded from the study. A further 2 rabbits were excluded due to difficulties reading the direct ABP waveforms. In total 190 paired direct + Doppler BP measurements were collected from 17 rabbits (11 male, 6 female) weighing 1.3–2.8 kg. There were no complications following auricular artery catheterisation and no rabbit appeared to require, or was given, additional analgesia during the recovery period. All rabbits had rectal temperatures of >37.0 oC at the end of anaesthesia.
The mean difference between direct and Doppler SAP was +1 ± 8 mmHg. This was consistent over the range of pressures monitored (direct systolic range 44–99 mmHg;). During the anaesthetic period the differences between direct SAP and Doppler SAP was ≤15mmHg for 92% of the time, ≤10mmHg for 85% of the time and ≤5mmHg for 59% of the time.
The mean between method difference between direct MAP and Doppler MAP was minus 13 ± 8 mmHg with a range of 33–83mmHg for direct MAP.
79% (15/17) rabbits experienced hypotension on at least one measurement. In 6/17 rabbits (35%) direct MAP <45mmHg occurred at least once.
Arterial blood pressure measurement is standard in human anaesthesia (Association of Anaesthetists of Great Britain and Ireland, 2007) and is now being performed more frequently in veterinary anaesthesia (American College of Veterinary Anesthesiology, 1995).
Some advantages of Doppler BP assessment are: the readily available sites for cuff placement (i.e. areas with a palpable pulse) meaning it can be used in small patients; the simplicity of the technique; wide availability; and relatively low cost.
In this study the agreement between direct and Doppler SAP fell within the recommended minimum standards of the Association for the Advancement of Medical Instrumentation (AAMI) guidelines i.e. a variation of <5 +/- 8mm Hg. However, when MAP measurement were compared the level of agreement suggested an overestimation of Doppler MAP. This conflicts with studies performed in other species where Doppler assessment has been reported to underestimate SAP, although this may be related to cuff location, cuff size, extraneous noise, and location of the direct SAP cannula. One explanation for the apparent overestimation of Doppler BP when compared to direct BP is that central SAP (measured in the aorta or carotid artery) is higher than that measured via the auricular artery. Therefore, the direct measurements using the auricular artery may have underestimated central SAP resulting in an apparent overestimation of Doppler MAP.
The rabbits used in the study were small and the ratio of cuff width:limb circumference was higher than other studies (Grandy et al. 1992; Binns et al. 1995; Caulkett et al. 1998; Haberman et al. 2006). This can significantly affect Doppler and oscillometric BP measurements as a cuff that is too small overestimates BP and vice versa. Therefore, the results presented in this study may not be representative of results obtained should a different cuff width:limb circumference ratio be used.
Doppler BP measurements can be affected by the location of the transducer and it can also change over the measurement range (Binns et al. 1995; Caulkett et al. 1998) therefore it is not recommended to compare results from one probe site to another.
Observer experience may affect the consistency of Doppler BP measurements: a finding of this study was that the difference between paired measurements decreased over time suggesting familiarity improved reliability and that inter-observer differences may occur, particularly if there is a wide disparity in experience.
Although alpha-2 agonists were not included in this study they do result in peripheral vasoconstriction therefore the degree of agreement between direct/central BP and Doppler measurements via the auricular artery may be different to those collected in this study.
In conclusion, this study demonstrated a close agreement between direct BP measured via the auricular artery and Doppler BP measured at the radial artery therefore Doppler BP assessment is useful in the rabbit, although this is in conflict with other studies. Direct SAP measurements taken via the auricular artery may underestimate central SAP. Cuff size can have a significant effect on Doppler BP measurements, as can operator experience.
Dr. Karen Heskin
BVSc CertSAO MRCVS
Veterinary Technical Manager, Jurox UK
Originally published: Thursday, 5th December 2019
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Keep reading... More news items that may interest you.
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In this article Matt Gurney, RCVS/ECVS specialist in Veterinary Anaesthesia and Analgesia, examines whether co-induction techniques are advantageous.
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Paper summary: Clinical efficacy and cardiorespiratory effects of alfaxalone, or diazepam/fentanyl for induction of anaesthesia in dogs that are a poor anaesthetic risk.
In this summary of a paper by Psatha et al (2011) we examine the clinical benefits of alfaxalone in high-risk patients when compared to the induction protocol of diazepam + fentanyl.
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In this article Miranda discusses the challenges of anaesthetising patients with cardiac disease and how protocols can be modified to accommodate these patients.
Paper summary: How does buprenorphine compare to butorphanol for postoperative analgesia in cats?
A prospective multi-centre clinical trial comparing buprenorphine vs butorphanol for postoperative analgesia in cats.
Paper summary: Analgesic effects of maxillary and inferior alveolar nerve blocks in cats undergoing dental extractions.
With dental surgery one of the most commonly performed procedures in small animal practice, what benefits do dental nerve blocks provide in cats?
What should we consider when anaesthetising patients, including geriatrics, for dental procedures?
What should we consider when anaesthetising both adult and senior patients admitted for dental treatment?
Checklist for geriatric dental anaesthesia
This article summarises and combines "Anaesthesia for the geriatric patient" and "What should we consider when anaesthetising patients, including geriatrics, for dental procedures?" into a single checklist for anaesthesia for the geriatric dental patient. A downloadable summary is also available by following the link.
How do we recognise, assess and treat chronic pain in companion animals?
In this article Ian Self, Associate Professor in Veterinary Anaesthesia and Analgesia, School of Veterinary Medicine and Science, Nottingham, reflects on the recognition, assessment and management options for chronic pain in companion animals
The physiology of acute and chronic pain.
In this article, which accompanies " How do we recognise, assess and treat chronic pain in companion animals?", Ian Self describes the physiology of acute and chronic pain
What should we consider when anaesthetising the geriatric patient?
With an increasing number of anaesthetics being performed in older pets is there anything we should be aware of or do differently? In this article Carl Bradbrook examines the management of anaesthesia in geriatrics..
Paper summary: What effect does does rapid, high volume fluid therapy have on cardiovascular function?
In this summary of a paper by Valverde (2012) we examine the effects of high-volume, rapid fluid therapy on cardiovascular function and hematological values during isoflurane-induced hypotension in healthy dogs.
Paper summary: Pre-anaesthetic screening of geriatric dogs
In this summary of a paper by Joubert (2007) we examine the value of pre-anaesthetic screening in geriatric dogs and how the results influence the anaesthetic process.
Anaesthesia for Canine Cushing's disease: What should we assess and what should we monitor?
Cushing's disease (hyperadrenocorticism) is relatively common in the dog and this article discusses the appropriate pre-anaesthetic assessment we should perform and why careful monitoring is essential.
Paper summary: How frequently are intravenous catheters removed as a result of complications due to bacterial contamination?
In this summary of a paper by Ramos (2018) we examine the incidence of bacterial colonisation of intravenous catheters removed as a result of cannula complication
What's the Point? Peripheral Intravenous Cannulation.
Peripheral venous cannulation is a common invasive procedure in small animals, but what are the best-practice insertion techniques and what can we do to avoid complications?
Rabbit Anaesthesia – Understanding Your Patient.
How does the anatomy, physiology, behaviour and response to drugs affect your decision making when anaesthetising the rabbit patient?
Paper summary: Heated intravenous fluids alone fail to prevent hypothermia in cats under general anaesthesia.
In this summary of a paper by Jourdan et al (2017) we examine the common practice of warming intravenous fluids and the effect on patient temperature.
Considerations for anaesthesia of the brachycephalic dog.
In this article Matt Gurney discusses the induction of anaesthesia and intubation of the brachycephalic patient.
Watch the induction and intubation of a brachycephalic.
Induction of anaesthesia and intubation of a brachycephalic dog with Matt Gurney.
Paper summary: The effect of omeprazole on oesophageal pH in dogs during anaesthesia
This summary of a publication by Panti et al., examines the effect of orally administered omeprazole on gastro-oesophageal reflux in the anaesthetised dog.
How does a syringe driver benefit your patients?
Syringe drivers are becoming increasingly commonplace in modern veterinary practice and are a useful tool for multiple applications. This article looks at the science behind constant rate infusions and the basics of syringe driver use.
Paper summary: Pet owner opinions about anaesthesia, pain and surgery in small animals
In this paper we explore perceptions and opinions of Canadian pet owners about anaesthesia, pain and surgery in small animals.
Achieving Safer Anaesthesia with ASA and Joanne Michou MA VetMB DipECVAA MRCVS
How can a Veterinary version of the ASA Physical Status Classification help you achieve safer anaesthesia? To find out how watch our webinar.
Paper summary: ASA classification and risk of anaesthetic related death in dogs and cats.
This scientific paper assessed whether the American Society of Anesthesiologists (ASA) Physical Status Classification correlated with the risk of anaesthetic death in dogs and cats.
New! Methadyne, Our New Methadone Now Available
This is our third product launch this year, and the latest addition to our anaesthesia and analgesia portfolio, Methadyne, contains 10mg/ml methadone as its active ingredient. It can be administered for analgesia of moderate to severe pain in dogs and cats, to provide neuroleptanalgesia, and as part of a patient's premedication protocol prior to general anaesthesia.
A retrospective comparison of two analgesic strategies after uncomplicated tibial plateau levelling osteotomy in dogs.
In this review we summarise a publication by Bini (2018) examining two protocols for the administration of methadone following TPLO surgery in dogs.
Practical Acute Pain Assessment
In this summary of acute pain assessment, Carl Bradbrook examines why we should be monitoring patients for pain and looks at the commonly used scoring systems.
Alfaxan for the maintenance of anaesthesia: Peer reviewed clinical papers.
In this article we have identified the key clinical peer reviewed papers to support the use of Alfaxan for maintenance of Anaesthesia in Cats and Dogs.
TIVA or not? (Total intravenous anaesthesia).
In this article the Jurox UK Technical Team discuss the use of intravenous agents to maintain anaesthesia in the dog and cat.
Benzodiazepines - can they help reduce anaesthesia related side effects?
In part 4 of this series on premedicant agents we examine the pros and cons of benzodiazepines.
Paper summary: Effect of benzodiazepines on the dose of alfaxalone needed for endotracheal intubation in healthy dogs
This paper examined whether a benzodiazepine, administered as a co-induction agent with alfaxalone, improved endotracheal intubation, and reduced the dose of alfaxalone, in the dog
Putting methadone in its place in your pain management.
In this article we examine why methadone could be considered the analgesic of choice for many of our patients and understand its importance in modern veterinary medicine. The article includes a link to a downloadable summary sheet.
Food for Thought: Pre-anaesthetic Fasting
In this article Karen examines why we fast our canine and feline patients prior to anaesthesia and what the current recommendations are. Karen also investigates why rabbits are different and should not be starved before anaesthesia.
Purr-fecting Pain Management
In this article summary we examine which of the two opioids, buprenorphine or butorphanol, provides the most appropriate analgesia following ovariohysterectomy in the cat.
Perspectives on Premeds - Phenothiazines: from Mental Health to Premedication
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Methadone with Acepromazine - when is enough, enough?
This study looks at the effects of three methadone doses combined with acepromazine on sedation and some cardiopulmonary variables in dogs.
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Paper Summary - Effects of alfaxalone, thiopental, propofol and diazepam on laryngeal motion in healthy dogs
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Keeping the Finger on the Pulse - Nuances in CV Monitoring
All patients are exposed to the risks associated with general anaesthesia. Continuously monitoring anaesthetised patients maximises patients safety and wellbeing. In this article, Dan takes us through the common monitoring techniques that provide information about the cardiovascular status of your patient.
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Despite being widely recognized in humans, postoperative nausea and vomiting (PONV), and the role of maropitant in reducing inhalational anaesthetic requirements have been poorly documented in dogs. This recent study evaluates PONV and isoflurane requirements after maropitant administration during routine ovariectomy in bitches.
New! Alfaxan® Multidose Now Available
We are happy to announce we have enhanced our anaesthesia and analgesia portfolio with the introduction of Alfaxan®Multidose for dogs, cats and pet rabbits.
Sevoflurane requirement in dogs premedicated with medetomidine and butorphanol
Little information is available about the effect that different doses of medetomidine and butorphanol may have when using sevoflurane for maintenance of anaesthesia in dogs. This recent study evaluates heart rate and median sevoflurane concentration required at different dose rates.
Capnography II - What happened to the elephants? A summary of abnormal traces
In this second article of the capnography series, James provides a guide to a few of the most common traces that you will encounter during surgery. Scroll to the end of the article to download a printable capnography cheatsheet.
Pain, what a Pain! (Part 2) – Practical Tips On How To Perform Dental Nerve Blocks In Companion Animal Practice
In this second article of the Pain, what a Pain! series, Dan takes us through the LRA techniques associated with dental and oral surgery. In this article, you will find practical tips and pictures on common dental nerve blocks as well as safety concerns to consider.
Peri-anaesthetic mortality and nonfatal gastrointestinal complications in pet rabbits
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Pain, what a Pain! How Locoregional Anaesthesia can Improve the Outcome and Welfare of Veterinary Patients (Part 1)
In this first article out of a series of two, Dan takes us through an introduction and practical tips for appropriate local anaesthesia delivery. Find out why these anaesthesia techniques, that are well recognised in human medicine, have seen an increase in popularity in veterinary medicine over the recent years
Perspectives on Premeds – Opioids
Perspectives on Premeds is a series of articles touching on different pharmacological, physiological and clinical aspects of pre-anaesthetic medication. This second article aims to provide a refresher on opioids.
Effects of Dexmedetomidine with Different Opioid Combinations in Dogs
Read the highlights of a recently published research paper that evaluates cardiorespiratory, sedative and antinociceptive effects of dexmedetomidine alone and in combination with morphine, methadone, meperidine, butorphanol, nalbuphine and tramadol.
Preoxygenation Study Highlights
This study evaluates the effectiveness of two methods of preoxygenation in healthy yet sedated dogs and the impact of these methods on time taken to reach a predetermined haemoglobin desaturation point (haemoglobin saturation (SpO2) of 90%) during an experimentally induced period of apnoea.
Capnography – Not Just a Load of Hot Air
Capnography is the measurement of inhaled and exhaled carbon dioxide (CO2) concentration. The graphical illustration of CO2 within respired gases versus times is known as the capnogram.
Perspectives on Premeds – Alpha-2 Agonists
Perspectives on Premeds is a series of articles touching on different pharmacological, physiological and clinical aspects of pre-anaesthetic medication. This first article aims to provide a refresher on α2 agonists.
We are 'injecting' a bit of fun into BSAVA Congress!
We will be 'injecting' a bit of fun into BSAVA Congress on our stand (stand 702).
Alfaxan - now licensed for use in pet rabbits
Jurox Animal Health is delighted to announce that Alfaxan is now licensed for cats, dogs and pet rabbits. This is an exciting advance and could change the way rabbits are anaesthetised in the U.K.
Best Practice Rabbit Anaesthesia Roadshows
Jurox Announces eight country wide events on Best Practice Rabbit Anaesthesia
Considerations in Rabbit Anaesthesia at the 2017 London Vet Show
Jurox to host talks on Considerations in Rabbit Anaesthesia at the 2017 London Vet Show.
Vets needing more support for anaesthesia
Jurox research reveals that veterinary professionals have questions about their anaesthetic protocols
Repeatable. Reliable. Relax.
About Alfaxan
Using Alfaxan
Patient, Equipment and Risk Assessments
Optimal Recovery
Alfaxan® contains alfaxalone 10mg/ml. Legal category: POM-V. Buprelieve® contains buprenorphine 0.3mg/ml. Legal category POM-V CD Sch 3. Alfaxan® and Buprelieve® are trademarks of Jurox Pty Limited, Rutherford, NSW 2320, Australia.
Further information is available on request from Jurox (UK) Private Limited Company, Richmond House, Second Floor, 105 High Street, Crawley, West Sussex, RH10 1DD. Tel 01293 510681.
Copyright © Jurox Pty Limited 2020


Herb to help with the symptoms of menopause.
Black Cohosh is a traditional herb for women with mild estrogen-like activity. It is especially helpful during menstruation and menopause. Cohosh is a Native American word meaning "rough", which is descriptive of the herb's rugged black root.


text:
Q: Fix the background-image so it fits the rounded corners Are there any way to fix the background image so it doesn't go outside the borders that are rounded?
jsFiddle
Pure CSS solution is preferable.
The code is here:

some text

A: This happens because you're not preventing the inner content from overflowing the boundaries of the divider. To fix this, simply add overflow: hidden to your div:
div {
    ...
    overflow: hidden;
}

A: Apply the background image to your div container, I've modified your jsfiddle:
div {
border-radius: 30px;
background-color: #dddddd;
background-image: url("http://www.moveoneinc.com/blog/wp-content/uploads/2011/12/Dog-2.jpg");
background-position: 0 top;
background-repeat: repeat;
margin:60px;
}

a{
position: relative;
display: block;
height: 90px;
padding: 15px;
}

The other image you had didn't work, so I changed it..
EDIT: Someone got me by 1 sec, I think that the overflow solution is also valid :)

redpajama_set_name: RedPajamaStackExchange


Like never before, The Modi government has started a new scheme exclusively for Indian Army Battle Casualties and Weapon Purchase.
The government has opened bank account where people can donate funds directly to Army Welfare Account which will be used for purchasing weapons to the Indian Army and providing facilities for war casualties.
A country's population of 130 crore of which even 100 crore people (70%) deposit One Rupee Each, the ministry will get 100 crores/day, 3000 crores/month and 36000 crores/year.
36,000 Crore which is greater than Pakistan's total defence expenditure.
We spend hundreds and thousands of rupees on many unnecessary expenditure, but if we can spend ONE RUPEE on army, it can surely make India a SUPER POWER.
The money will directly reach the defence ministry for army benefit and war casualties. This is the most unique idea to help our army Jawans who have lost life in battle fields.
Come together, show solidarity to our DEFENCE FORCES, PARA MILITARY FORCE and CRPF.
JOIN THE MISSION TO MAKE INDIA A SUPER POWER!
Facts Check Analysis: The information related to the name of the donation ie Army Welfare Fund Battle Casualties and the bank account details given in the message is correct.
As the information was published on Army veterans official website, indianarmyveterans, there is such account for donation known as ARMY WELFARE FUND BATTLE CASUALTIES.
Similiary syndicatebank also published this information in its website.
An account titled "Army Welfare Fund Battle Casualties" has been opened at Syndicate Bank, South Block Branch, New Delhi (IFSC Code: SYNB0009055), with account number 90552010165915, which has been approved by the Integrated HQ of Ministry of Defence (Army)and as per their approval the donations received in the fund will be utilized to pay financial assistance/grant to widows of our Battle Casualties, their next of kin and dependents.
So the information and bank details are correct, however the information is partially correct. The donations received in the fund will be utilized to pay financial assistance/grant to widows of our Battle Casualties, their next of kin and dependents.


text:
Q: Android Studio Main Class (Crashlytics, Parse) I am a fairly new to android development, and I don't understant how the main class works on Android Studio.
I'm trying to make my app have the Crashlytics and Parse services but I'm not sure where to put them. Currently I have the code on the OnCreate method in the Login Class:
@Override
protected void onCreate(Bundle savedInstanceState) {

    super.onCreate(savedInstanceState);
    Fabric.with(this, new Crashlytics());

    Parse.initialize(this, "CODE", "CODE");
    ParseInstallation.getCurrentInstallation().saveInBackground();
}

But I heard that this code should go on the Application Class... That's because the Application Class is always started and its basically the main Class for the app... I am not sure about this, please correct me if I'm wrong.
If that's the case, how can I access the Application Class and where should I put the code?

A: Just create a class which should extends the Application , Using this you can initialize the parse installation
   public class DemoClass extends Application {
        @Override
        public void onCreate() {
            super.onCreate();
         Fabric.with(this, new Crashlytics());

          Parse.initialize(this, "CODE", "CODE");
          ParseInstallation.getCurrentInstallation().saveInBackground();
        }
    }

Copy this above code


Excellent item about youth culture in Liverpool.
L/S, across the Mersey, of the Liver building. C/U of a man looking. L/S of the docks. Various shots around the city centre. Low angle shot of the cathedral, tilt down to a gang of teenagers walking past.
M/S of the exterior of a gramophone record shop. A shop assistant takes a single and shows it to two ugly teenage boys with greasy mop tops. Two girls look through a rack of records. Various shots of teenagers grooving to the latest sounds in listening booths.
M/S of a youth entering a barbers. The youth sits in a barber's chair. M/S of the barber combing the youth's hair into a trendy Beatle style. Various shots of boys having mop top cuts. C/U of a black youth having his hair combed.
Various shots of black and white youths hanging out around the city centre - the narrator compares their 'decadence' to that of declining Rome! Point of view shot from inside a car going through the Mersey tunnel.
Very dim panning shot inside the Cavern. Various C/Us of graffiti - mostly names of bands. Long panning shot of long queues of teenagers outside the cavern. Various C/Us of teenagers larking about in the queue. M/Ss of teenagers going into the Cavern. M/S of youths smoking. Various shots of teenagers chatting and jiving.
Various shots of Manchester group, the Ko-Dells performing in the "New Sound of 64" contest finals at the Cavern. Lots of great shots of teenagers listening to the music and dancing. Various shots of The Jinks from Berkhamsted (the eventual winners) performing. Various shots of The Drovers from Sidcup performing. Various shots of The Mersey Beats performing.
Point of view shot inside a car going through the Mersey Tunnel. More general shots of Liverpool streets. Low angle shot of a statue of a man on a horse. L/S of the Liver Building from the back of the Mersey Ferry. The narrator ends with the thought: "But is this the Mersey music that will live on?"


On International Women's Day we look at the proportions of women undertaking international assignments and the benefits of a diverse assignee workforce.
Turkey in turmoil - how are organisations managing their assignees?
We are running a spot survey to find out more about what organisations are currently doing for their staff in Turkey. The survey is very short and will take only a few minutes to complete.


text:
import json
from math import ceil
from typing import Tuple
from unittest.mock import patch, Mock

import mxnet as mx
import numpy as np
import pytest

import sockeye.constants as C
import sockeye.data_io
import sockeye.inference
import sockeye.lexical_constraints
import sockeye.utils

_BOS = 0
_EOS = -1

def mock_translator(batch_size: int = 1,
                    beam_size: int = 5,
                    nbest_size: int = 1,
                    beam_prune: float = 0,
                    num_source_factors: int = 1):
    """
    Creates a fake translator object but with real values for things that we need.
    This lets us avoid a messy call to the constructor.
    """
    with patch.object(sockeye.inference.Translator, '__init__', lambda self, **kwargs: None):
        translator = sockeye.inference.Translator(context=None,
                                                  ensemble_mode=None,
                                                  bucket_source_width=None,
                                                  length_penalty=None,
                                                  beam_prune=None,
                                                  beam_search_stop=None,
                                                  nbest_size=None,
                                                  models=None,
                                                  source_vocabs=None,
                                                  target_vocab=None,
                                                  restrict_lexicon=None,
                                                  store_beam=None,
                                                  strip_unknown_words=None)

        # This is needed for returning the right number of source factors
        def mock_model():
            t_mock = Mock(sockeye.inference.InferenceModel)
            t_mock.num_source_factors = num_source_factors
            return t_mock

        translator.models = [mock_model()]

        translator.batch_size = batch_size
        translator.beam_size = beam_size
        translator.nbest_size = nbest_size
        translator.beam_prune = beam_prune
        translator.zeros_array = mx.nd.zeros((beam_size,), dtype='int32')
        translator.inf_array = mx.nd.full((batch_size * beam_size,), val=np.inf, dtype='float32')
        translator.inf_array = mx.nd.slice(translator.inf_array, begin=(0), end=(beam_size))
        return translator

def test_concat_translations():
    beam_history1 = {"id": [1]}
    beam_history2 = {"id": [2]}
    beam_history3 = {"id": [3]}
    expected_beam_histories = [beam_history1, beam_history2, beam_history3]
    expected_target_ids = [0, 1, 2, 0, 8, 9, 0, 3, 4, 5, -1]
    num_src = 7

    length_penalty = sockeye.inference.LengthPenalty()

    expected_score = (1 + 2 + 3) / length_penalty.get(len(expected_target_ids))

    translations = [sockeye.inference.Translation([0, 1, 2, -1], np.zeros((4, num_src)), 1.0 / length_penalty.get(4),
                                                  [beam_history1]),
                    # Translation without EOS
                    sockeye.inference.Translation([0, 8, 9], np.zeros((3, num_src)), 2.0 / length_penalty.get(3),
                                                  [beam_history2]),
                    sockeye.inference.Translation([0, 3, 4, 5, -1], np.zeros((5, num_src)), 3.0 / length_penalty.get(5),
                                                  [beam_history3])]
    combined = sockeye.inference._concat_translations(translations, stop_ids={_EOS}, length_penalty=length_penalty)

    assert combined.target_ids == expected_target_ids
    assert combined.attention_matrix.shape == (len(expected_target_ids), len(translations) * num_src)
    assert np.isclose(combined.score, expected_score)
    assert combined.beam_histories == expected_beam_histories

def test_length_penalty_default():
    lengths = mx.nd.array([[1], [2], [3]])
    length_penalty = sockeye.inference.LengthPenalty(1.0, 0.0)
    expected_lp = np.array([[1.0], [2.], [3.]])

    assert np.isclose(length_penalty.get(lengths).asnumpy(), expected_lp).all()
    assert np.isclose(length_penalty(lengths).asnumpy(), expected_lp).all()
    length_penalty.hybridize()
    assert np.isclose(length_penalty(lengths).asnumpy(), expected_lp).all()

def test_length_penalty():
    lengths = mx.nd.array([[1], [2], [3]])
    length_penalty = sockeye.inference.LengthPenalty(.2, 5.0)
    expected_lp = np.array([[6 ** 0.2 / 6 ** 0.2], [7 ** 0.2 / 6 ** 0.2], [8 ** 0.2 / 6 ** 0.2]])

    assert np.isclose(length_penalty.get(lengths).asnumpy(), expected_lp).all()
    assert np.isclose(length_penalty(lengths).asnumpy(), expected_lp).all()
    length_penalty.hybridize()
    assert np.isclose(length_penalty(lengths).asnumpy(), expected_lp).all()

def test_length_penalty_int_input():
    length = 1
    length_penalty = sockeye.inference.LengthPenalty(.2, 5.0)
    expected_lp = [6 ** 0.2 / 6 ** 0.2]

    assert np.isclose(np.asarray([length_penalty.get(length)]), np.asarray(expected_lp)).all()

@pytest.mark.parametrize("sentence_id, sentence, factors, chunk_size",
                         [(1, "a test", None, 4),
                          (1, "a test", None, 2),
                          (1, "a test", None, 1),
                          (0, "", None, 1),
                          (1, "a test", [['h', 'l']], 4),
                          (1, "a test", [['h', 'h'], ['x', 'y']], 1)])
def test_translator_input(sentence_id, sentence, factors, chunk_size):
    tokens = sentence.split()
    trans_input = sockeye.inference.TranslatorInput(sentence_id=sentence_id, tokens=tokens, factors=factors)

    assert trans_input.sentence_id == sentence_id
    assert trans_input.tokens == tokens
    assert len(trans_input) == len(tokens)
    assert trans_input.factors == factors
    if factors is not None:
        for factor in trans_input.factors:
            assert len(factor) == len(tokens)

    chunked_inputs = list(trans_input.chunks(chunk_size))
    assert len(chunked_inputs) == ceil(len(tokens) / chunk_size)
    for chunk_id, chunk_input in enumerate(chunked_inputs):
        assert chunk_input.sentence_id == sentence_id
        assert chunk_input.tokens == trans_input.tokens[chunk_id * chunk_size: (chunk_id + 1) * chunk_size]
        if factors:
            assert len(chunk_input.factors) == len(factors)
            for factor, expected_factor in zip(chunk_input.factors, factors):
                assert len(factor) == len(chunk_input.tokens)
                assert factor == expected_factor[chunk_id * chunk_size: (chunk_id + 1) * chunk_size]

@pytest.mark.parametrize("supported_max_seq_len_source, supported_max_seq_len_target, training_max_seq_len_source, "
                         "forced_max_input_len, length_ratio_mean, length_ratio_std, "
                         "expected_max_input_len, expected_max_output_len",
                         [
                             (100, 100, 100, None, 0.9, 0.2, 89, 100),
                             (100, 100, 100, None, 1.1, 0.2, 75, 100),
                             # No source length constraints.
                             (None, 100, 100, None, 0.9, 0.1, 98, 100),
                             # No target length constraints.
                             (80, None, 100, None, 1.1, 0.4, 80, 122),
                             # No source/target length constraints. Source is max observed during training and target
                             # based on length ratios.
                             (None, None, 100, None, 1.0, 0.1, 100, 113),
                             # Force a maximum input length.
                             (100, 100, 100, 50, 1.1, 0.2, 50, 67),
                         ])
def test_get_max_input_output_length(
        supported_max_seq_len_source,
        supported_max_seq_len_target,
        training_max_seq_len_source,
        forced_max_input_len,
        length_ratio_mean,
        length_ratio_std,
        expected_max_input_len,
        expected_max_output_len):
    max_input_len, get_max_output_len = sockeye.inference.get_max_input_output_length(
        supported_max_seq_len_source=supported_max_seq_len_source,
        supported_max_seq_len_target=supported_max_seq_len_target,
        training_max_seq_len_source=training_max_seq_len_source,
        forced_max_input_len=forced_max_input_len,
        length_ratio_mean=length_ratio_mean,
        length_ratio_std=length_ratio_std,
        num_stds=1)
    max_output_len = get_max_output_len(max_input_len)

    if supported_max_seq_len_source is not None:
        assert max_input_len <= supported_max_seq_len_source
    if supported_max_seq_len_target is not None:
        assert max_output_len <= supported_max_seq_len_target
    if expected_max_input_len is not None:
        assert max_input_len == expected_max_input_len
    if expected_max_output_len is not None:
        assert max_output_len == expected_max_output_len

@pytest.mark.parametrize("sentence, num_expected_factors, delimiter, expected_tokens, expected_factors",
                         [
                             # sentence with single factor
                             ("this is a test", 1, "|", ["this", "is", "a", "test"], None),
                             # sentence with additional factor-like tokens, but no additional factors expected
                             ("this|X is| a|X test|", 1, "|", ["this|X", "is|", "a|X", "test|"], None),
                             # multiple spaces between token sequence
                             ("space   space", 1, "|", ["space", "space"], None),
                             # empty token sequence
                             ("", 1, "|", [], None),
                             ("", 2, "|", [], [[]]),
                             # proper factored sequences
                             ("a|l b|l C|u", 2, "|", ["a", "b", "C"], [["l", "l", "u"]]),
                             ("a-X-Y b-Y-X", 3, "-", ["a", "b"], [["X", "Y"], ["Y", "X"]]),
                             ("a-X-Y ", 3, "-", ["a"], [["X"], ["Y"]])
                         ])
def test_make_input_from_factored_string(sentence, num_expected_factors, delimiter,
                                         expected_tokens, expected_factors):
    sentence_id = 1
    translator = mock_translator(num_source_factors=num_expected_factors)

    inp = sockeye.inference.make_input_from_factored_string(sentence_id=sentence_id, factored_string=sentence,
                                                            translator=translator, delimiter=delimiter)
    assert isinstance(inp, sockeye.inference.TranslatorInput)
    assert inp.sentence_id == sentence_id
    assert inp.tokens == expected_tokens
    assert inp.factors == expected_factors
    if num_expected_factors > 1:
        assert len(inp.factors) == num_expected_factors - 1

@pytest.mark.parametrize("sentence, num_expected_factors, delimiter",
                         [
                             ("this is a test", 2, "|"),  # expecting additional factor
                             ("this|X is a test", 2, "|"),  # expecting additional factor
                             ("this|X is|X a|X test", 2, "|"),  # fail on last token without factor
                             ("this| is|X a|X test|", 2, "|"),  # first token with delimiter but no factor
                             ("this|X is|X a|X test|", 2, "|"),  # last token with delimiter but no factor
                             ("w1||w2||f22", 2, "|"),
                             ("this", 2, "|"),  # single token without factor
                             ("this|", 2, "|"),  # single token with delimiter but no factor
                             ("this||", 3, "|"),  # double delimiter
                             ("this|| another", 2, "|"),  # double delimiter followed by token
                             ("this|||", 2, "|"),  # triple delimiter
                             ("|this", 2, "|"),  # empty token with 1 additional factor
                             ("|this|that", 3, "|"),  # empty token with 2 additional factors
                             ("|this|that|", 4, "|")  # empty token with 3 additional factors
                         ])
def test_factor_parsing(sentence, num_expected_factors, delimiter):
    """
    Test to ensure we fail on parses with invalid factors.
    """
    sentence_id = 1
    translator = mock_translator(num_source_factors=num_expected_factors)
    inp = sockeye.inference.make_input_from_factored_string(sentence_id=sentence_id,
                                                            factored_string=sentence,
                                                            translator=translator, delimiter=delimiter)
    assert isinstance(inp, sockeye.inference.BadTranslatorInput)

@pytest.mark.parametrize("delimiter", ["\t", "\t \t", "\t\t", "\n", "\r", "\r\n", "\u0020",
                                       "\n\n", "  ", " \t", "\f", "\v", "\u00a0", "\u1680",
                                       "\u2000", None, "", "\u200a", "\u205f", "\u3000"])
def test_make_input_whitespace_delimiter(delimiter):
    """
    Test to ensure we disallow a variety of whitespace strings as factor delimiters.
    """
    sentence_id = 1
    translator = mock_translator(num_source_factors=2)
    sentence = "foo"
    with pytest.raises(sockeye.utils.SockeyeError) as e:
        sockeye.inference.make_input_from_factored_string(sentence_id=sentence_id,
                                                          factored_string=sentence,
                                                          translator=translator, delimiter=delimiter)
    assert str(e.value) == 'Factor delimiter can not be whitespace or empty.'

@pytest.mark.parametrize("text, factors",
                         [("this is a test without factors", None),
                          ("", None),
                          ("test", ["X", "X"]),
                          ("a b c", ["x y z"]),
                          ("a", [])])
def test_make_input_from_valid_json_string(text, factors):
    sentence_id = 1
    expected_tokens = list(sockeye.data_io.get_tokens(text))
    inp = sockeye.inference.make_input_from_json_string(sentence_id, json.dumps({C.JSON_TEXT_KEY: text,
                                                                                C.JSON_FACTORS_KEY: factors}))
    assert len(inp) == len(expected_tokens)
    assert inp.tokens == expected_tokens
    if factors is not None:
        assert len(inp.factors) == len(factors)
    else:
        assert inp.factors is None

@pytest.mark.parametrize("text, text_key, factors, factors_key", [("a", "blub", None, "")])
def test_failed_make_input_from_valid_json_string(text, text_key, factors, factors_key):
    sentence_id = 1
    inp = sockeye.inference.make_input_from_json_string(sentence_id, json.dumps({text_key: text, factors_key: factors}))
    assert isinstance(inp, sockeye.inference.BadTranslatorInput)

@pytest.mark.parametrize("text, factors",
                         [("this is a test without factors", None),
                          ("", None),
                          ("test", ["X", "X"]),
                          ("a b c", ["x y z"]),
                          ("a", [])])
def test_make_input_from_valid_dict(text, factors):
    sentence_id = 1
    expected_tokens = list(sockeye.data_io.get_tokens(text))
    inp = sockeye.inference.make_input_from_dict(sentence_id, {C.JSON_TEXT_KEY: text,
                                                               C.JSON_FACTORS_KEY: factors})
    assert len(inp) == len(expected_tokens)
    assert inp.tokens == expected_tokens
    if factors is not None:
        assert len(inp.factors) == len(factors)
    else:
        assert inp.factors is None

@pytest.mark.parametrize("text, text_key, factors, factors_key", [("a", "blub", None, "")])
def test_failed_make_input_from_valid_dict(text, text_key, factors, factors_key):
    sentence_id = 1
    inp = sockeye.inference.make_input_from_dict(sentence_id, {text_key: text, factors_key: factors})
    assert isinstance(inp, sockeye.inference.BadTranslatorInput)

@pytest.mark.parametrize("strings",
                         [
                             ["a b c"],
                             ["a b c", "f1 f2 f3", "f3 f3 f3"]
                         ])
def test_make_input_from_multiple_strings(strings):
    inp = sockeye.inference.make_input_from_multiple_strings(1, strings)

    expected_tokens = list(sockeye.data_io.get_tokens(strings[0]))
    expected_factors = [list(sockeye.data_io.get_tokens(f)) for f in strings[1:]]
    assert len(inp) == len(expected_tokens)
    assert inp.tokens == expected_tokens
    assert inp.factors == expected_factors

# batch size, beam size, prune thresh, accumulated scores, finished, expected_inactive
prune_tests = [
    # no pruning because nothing is finished
    (1, 10, 0, list(range(10)), [0] * 10, [0] * 10),
    # top item finished, threshold of 0.5, so one everything except top inactive
    (1, 10, 0.5, list(range(10)), [1] + [0] * 9, [0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
    # same but here the threshold doesn't include the second item
    (1, 10, 1.5, list(range(10)), [1] + [0] * 9, [0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
    # finished item is in the middle
    (1, 5, 1.5, [10, 16, 4, 5, 8], [0, 0, 1, 0, 0], [1, 1, 0, 0, 1]),
    # multiple finished items, lowest in last position
    (1, 5, 1.5, [10, 16, 4, 5, 8], [1, 0, 0, 0, 1], [1, 1, 0, 0, 0]),
    # batch setting, so pruning only applies to the first sentence
    (2, 10, 1.5, list(range(20)), [1] + [0] * 19, [0, 0] + [1] * 8 + [0] * 10),
]

@pytest.mark.parametrize("batch, beam, prune, scores, finished, expected_inactive", prune_tests)
def test_beam_prune(batch, beam, prune, scores, finished, expected_inactive):
    scores = mx.nd.array(scores)
    finished = mx.nd.array(finished, dtype='int32')
    inf_array = mx.nd.full((batch * beam,), val=np.inf)
    zeros_array = mx.nd.zeros((batch * beam,), dtype='int32')
    best_word_indices = mx.nd.zeros((batch * beam,), dtype='int32')

    prune_hyps = sockeye.inference.PruneHypotheses(prune, beam)
    prune_hyps.initialize()
    inactive, _, _ = prune_hyps(best_word_indices, scores, finished, inf_array, zeros_array)
    assert inactive.asnumpy().tolist() == expected_inactive

    prune_hyps.hybridize()
    inactive, _, _ = prune_hyps(best_word_indices, scores, finished, inf_array, zeros_array)
    assert inactive.asnumpy().tolist() == expected_inactive

def test_sort_by_index():
    data = [mx.nd.random.uniform(0, 1, (3, i)) for i in range(1, 5)]
    indices = mx.nd.array([2, 0, 1], dtype='int32')
    expected = [d.asnumpy()[indices.asnumpy()] for d in data]

    sort_by_index = sockeye.inference.SortByIndex()
    sort_by_index.initialize()

    out = sort_by_index(indices, *data)
    assert len(out) == len(data) == len(expected)
    for o, e in zip(out, expected):
        assert (o.asnumpy() == e).all()

    sort_by_index.hybridize()
    out = sort_by_index(indices, *data)
    assert len(out) == len(data) == len(expected)
    for o, e in zip(out, expected):
        assert (o.asnumpy() == e).all()

def numpy_topk(scores: mx.nd.NDArray,
               k: int,
               offset: mx.nd.NDArray) -> Tuple[mx.nd.NDArray, mx.nd.NDArray, mx.nd.NDArray]:
    """
    Get the lowest k elements per sentence from a `scores` matrix using an intermediary Numpy conversion.
    This should be equivalent to sockeye.utils.topk() and is used as a comparative implementation in testing.

    :param scores: Vocabulary scores for the next beam step. (batch_size * beam_size, target_vocabulary_size)
    :param k: The number of smallest scores to return.
    :param offset: Array to add to the hypothesis indices for offsetting in batch decoding.
    :return: The row indices, column indices and values of the k smallest items in matrix.
    """
    # (batch_size, beam_size * target_vocab_size)
    folded_scores = scores.reshape((-1, k * scores.shape[-1]))
    batch_size = folded_scores.shape[0]

    folded_scores = folded_scores.asnumpy()
    # Get the scores
    # Indexes into folded_scores: (batch_size, beam_size)
    flat_idxs = np.argpartition(folded_scores, range(k))[:, :k]
    # Score values: (batch_size, beam_size)
    values = mx.nd.array(folded_scores[np.arange(folded_scores.shape[0])[:, None], flat_idxs], ctx=scores.context)
    best_hyp_indices, best_word_indices = mx.nd.array(np.unravel_index(flat_idxs.ravel(), scores.shape),
                                                      dtype='int32', ctx=scores.context)

    if batch_size > 1:
        # Offsetting the indices to match the shape of the scores matrix
        best_hyp_indices += offset

    values = values.reshape((-1, 1))
    return best_hyp_indices, best_word_indices, values

@pytest.mark.parametrize("batch_size, beam_size, target_vocab_size",
                        [(1, 5, 200),
                         (5, 5, 200),
                         (1, 1, 200),
                         (5, 1, 200),
                         (10, 10, 100)])
def test_topk_func(batch_size, beam_size, target_vocab_size):
    # Random model scores. Shape: (batch_size * beam_size, target_vocab_size)
    scores = mx.nd.random.uniform(0, 1, (batch_size * beam_size, target_vocab_size))
    # offset for batch sizes > 1
    offset = mx.nd.array(np.repeat(np.arange(0, batch_size * beam_size, beam_size), beam_size), dtype='int32')

    np_hyp, np_word, np_values = numpy_topk(scores, k=beam_size, offset=offset)
    np_hyp, np_word, np_values = np_hyp.asnumpy(), np_word.asnumpy(), np_values.asnumpy()

    mx_hyp, mx_word, mx_values = sockeye.utils.topk(scores, k=beam_size, offset=offset)
    mx_hyp, mx_word, mx_values = mx_hyp.asnumpy(), mx_word.asnumpy(), mx_values.asnumpy()
    assert all(mx_hyp == np_hyp)
    assert all(mx_word == np_word)
    assert all(mx_values == np_values)

    topk = sockeye.inference.TopK(k=beam_size, batch_size=batch_size, vocab_size=target_vocab_size)
    topk.initialize()
    assert all(topk.offset.data() == offset)

    mx_hyp, mx_word, mx_values = topk(scores)
    mx_hyp, mx_word, mx_values = mx_hyp.asnumpy(), mx_word.asnumpy(), mx_values.asnumpy()
    assert all(mx_hyp == np_hyp)
    assert all(mx_word == np_word)
    assert all(mx_values == np_values)

    topk.hybridize()
    mx_hyp, mx_word, mx_values = topk(scores)
    mx_hyp, mx_word, mx_values = mx_hyp.asnumpy(), mx_word.asnumpy(), mx_values.asnumpy()
    assert all(mx_hyp == np_hyp)
    assert all(mx_word == np_word)
    assert all(mx_values == np_values)

@pytest.mark.parametrize("batch_size, beam_size, target_vocab_size, top_n",
                        [(1, 5, 200, 0),
                         (5, 5, 200, 0),
                         (1, 100, 200, 5),
                         (5, 100, 200, 5)])
def test_samplek_func(batch_size, beam_size, target_vocab_size, top_n):
    # arrange scores increasing values from left to right, so the best item is always index 0, next-best 1, and so on
    scores = mx.nd.array([list(range(1, target_vocab_size + 1)) for x in range(batch_size * beam_size)])
    # normalize
    target_dists = mx.nd.broadcast_div(scores, scores.sum(axis=1, keepdims=True))

    samplek = sockeye.inference.SampleK(k=beam_size, n=top_n, batch_size=batch_size, context=mx.context.cpu())
    samplek.initialize()

    # 0..(batch_size * beam_size)-1
    expected_hyps = mx.nd.array(range(batch_size * beam_size), dtype='int32')
    finished = mx.nd.cast(mx.nd.random.uniform(0, 1, (batch_size * beam_size)) > 0.5, dtype='int32')

    for i in [1, 2]:
        if i == 2:
            samplek.hybridize()

        hyps, words, values = samplek(scores, scores, finished)
        assert hyps.shape[0] == batch_size * beam_size

        # The indices should always be the integers from 0 to batch*beam-1
        assert sum(hyps == expected_hyps).asscalar() == (batch_size * beam_size)
        if top_n != 0:
            # Scores are increasing left-to-right, so best items are all the lowest word IDs.
            # No word id greater than the cap (top_n) should be selected
            assert mx.nd.sum(words >= top_n)[0].asscalar() == 0

        # word index should be zero for all finished hypotheses
        assert mx.nd.sum(mx.nd.where(finished, words, finished))[0].asscalar() == 0

def test_get_best_word_indices_for_kth_hypotheses():
    # data
    all_hyp_indices = np.array([[0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 2, 0, 0, 4, 3],
                                [0, 2, 2, 0, 1, 0, 0, 2, 1, 1, 3, 1, 1, 0, 1, 4, 0, 4],
                                [0, 1, 0, 1, 2, 1, 4, 3, 2, 3, 0, 4, 3, 1, 2, 1, 1, 0],
                                [0, 1, 0, 0, 3, 2, 2, 1, 3, 4, 4, 2, 2, 3, 3, 2, 2, 1],
                                [0, 2, 4, 1, 4, 2, 3, 4, 4, 2, 0, 3, 4, 4, 4, 3, 3, 2]], dtype='int32')
    ks = [np.array([0]), np.array([1]), np.array([2]), np.array([3]), np.array([4])]
    expected_indices = [np.array([[2, 1, 0, 0, 0, 0, 1, 3, 3, 2, 0, 0, 0, 1, 1, 2, 3]], dtype='int32'),
                        np.array([[1, 2, 1, 2, 2, 3, 4, 4, 4, 3, 1, 1, 1, 2, 2, 3, 4]], dtype='int32'),
                        np.array([[2, 1, 0, 0, 0, 1, 0, 0, 0, 0, 4, 2, 3, 3, 3, 4, 0]], dtype='int32'),
                        np.array([[2, 1, 0, 0, 0, 1, 0, 0, 0, 0, 2, 3, 2, 0, 0, 0, 1]], dtype='int32'),
                        np.array([[2, 1, 0, 1, 1, 2, 3, 2, 2, 4, 3, 4, 4, 4, 4, 1, 2]], dtype='int32')]

    # extract individually
    for k, expected_result in zip(ks, expected_indices):
        result = sockeye.inference.Translator._get_best_word_indices_for_kth_hypotheses(k, all_hyp_indices)
        assert result.shape == expected_result.shape
        assert (result == expected_result).all()

    # extract all at once
    ks = np.concatenate(ks, axis=0)
    expected_indices = np.concatenate(expected_indices, axis=0)
    result = sockeye.inference.Translator._get_best_word_indices_for_kth_hypotheses(ks, all_hyp_indices)
    assert result.shape == expected_indices.shape
    assert (result == expected_indices).all()

@pytest.mark.parametrize("raw_constraints, beam_histories, expected_best_ids, expected_best_indices",
                         [([[], [], [], []], [None, None], np.array([0, 2], dtype='int32'), np.array([[1, 1, 1], [3, 3, 3]], dtype='int32')),
                          ([[[1]], [], [[3]], []], [None, None], np.array([1, 3], dtype='int32'), np.array([[1, 0, 0], [3, 2, 2]], dtype='int32'))
                          ])
def test_get_best_from_beam(raw_constraints, beam_histories, expected_best_ids, expected_best_indices):
    best_hyp_indices = np.array([[0, 1, 0, 1],
                                 [0, 1, 1, 0],
                                 [2, 3, 2, 3],
                                 [2, 3, 3, 2]],
                                dtype='int32')
    best_word_indices = np.array([[3, 3, 0],
                                  [4, 4, 3],
                                  [3, 3, 0],
                                  [4, 5, 3]],
                                 dtype='int32')
    attentions = np.array([[[0.1748407 , 0.17223692, 0.153318  , 0.16618672, 0.15373373,
                             0.1796839 , 0.        , 0.        , 0.        , 0.        ],
                            [0.17484048, 0.17223585, 0.15332589, 0.16618879, 0.15374145,
                             0.17966755, 0.        , 0.        , 0.        , 0.        ],
                            [0.17483611, 0.17222905, 0.15335034, 0.16619477, 0.15375796,
                             0.17963174, 0.        , 0.        , 0.        , 0.        ]],
                           [[0.1748407 , 0.17223692, 0.153318  , 0.16618672, 0.15373373,
                             0.1796839 , 0.        , 0.        , 0.        , 0.        ],
                            [0.17484048, 0.17223585, 0.15332589, 0.16618879, 0.15374145,
                             0.17966755, 0.        , 0.        , 0.        , 0.        ],
                            [0.1748425 , 0.17223647, 0.15333334, 0.16618758, 0.15375413,
                             0.17964599, 0.        , 0.        , 0.        , 0.        ]],
                           [[0.20974289, 0.1808782 , 0.18161033, 0.20220006, 0.22556852,
                             0.        , 0.        , 0.        , 0.        , 0.        ],
                            [0.20973803, 0.18088503, 0.18162282, 0.20220187, 0.22555229,
                             0.        , 0.        , 0.        , 0.        , 0.        ],
                            [0.20973288, 0.18088858, 0.1816678 , 0.20219383, 0.2255169 ,
                             0.        , 0.        , 0.        , 0.        , 0.        ]],
                           [[0.20974289, 0.1808782 , 0.18161033, 0.20220006, 0.22556852,
                             0.        , 0.        , 0.        , 0.        , 0.        ],
                            [0.20973803, 0.18088503, 0.18162282, 0.20220187, 0.22555229,
                             0.        , 0.        , 0.        , 0.        , 0.        ],
                            [0.20972022, 0.1809091 , 0.18161656, 0.20222935, 0.22552474,
                             0.        , 0.        , 0.        , 0.        , 0.        ]]],
                           dtype='float32')
    seq_scores = np.array([[3.8197377],
                           [5.081118 ],
                           [3.8068485],
                           [5.0746527]],
                          dtype='float32')
    lengths = np.array([[3], [2], [3], [2]], dtype='int32')

    translator = mock_translator(beam_size=2, batch_size=2)

    expected_result = [sockeye.inference.Translator._assemble_translation(*x) for x in zip(
                            best_word_indices[expected_best_indices, np.arange(expected_best_indices.shape[1])],
                            lengths[expected_best_ids],
                            attentions[expected_best_ids],
                            seq_scores[expected_best_ids],
                            beam_histories)]

    constraints = [sockeye.lexical_constraints.ConstrainedHypothesis(rc, _EOS) for rc in raw_constraints]

    actual_result = sockeye.inference.Translator._get_best_from_beam(translator,
                                                                     best_hyp_indices,
                                                                     best_word_indices,
                                                                     attentions,
                                                                     seq_scores,
                                                                     lengths,
                                                                     constraints,
                                                                     beam_histories)

    for expected_translation, actual_translation in zip(expected_result, actual_result):
        assert expected_translation.target_ids == actual_translation.target_ids
        assert np.array_equal(expected_translation.attention_matrix,
                              actual_translation.attention_matrix)
        assert expected_translation.score == actual_translation.score
        assert expected_translation.beam_histories == actual_translation.beam_histories

redpajama_set_name: RedPajamaGithub


Navidad Energy Advisors is a team of experienced oil and gas experts with backgrounds in engineering, operations, project management and business leadership.
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Our 30+ Year Professionals also provide support to legal counsel and clients during periods of dispute.


Real Proof Of Ramayana This is a video which clearly shows the places of Lord Rama and his kingdom. .Ashok vatika, .The Ravana Palace, .Sugriva Cave, .
Ram Setu or Rama's Bridge is a causeway that was created across the sea connecting Pamban Island in Tamil Nadu, India to Mannar Island in Sri Lanka.
Ramayanam is an ancient Indian epic poem which narrates the struggle of the divine prince Rama to rescue his wife Sita from the demon king Ravana.
https://mocomi.com/ presents: Ramayana- The Story of Diwali in Hindi Diwali is a festival of joy and prosperity, and a celebration of the victory of good over evil.
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I still don't know the words completely; the music and the mood is so tell-tale, I don't need them anymore!
When The Ramayana Was Written?
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text:
The first story: Altmark
The first story from series of short stories about ships entering the Norwegian waters in 1940-45, and the fate of people who served on them. Original photos from the Oslo Photo Tour collection were are used for illustration.
Altmark was a German oil tanker and supply vessel. It became famous because of the so-called "Altmark Incident". After the loss of the cruiser "Admiral Graf Spee", of which Altmark supported across the Atlantic, the oil tanker headed for Germany, with almost 300 English POW. At certain stage Altmark was trying to use Norwegian territorial neutral waters as a safe pass to Germany, but was pursued by the British destroyers.
Altmark at Jøssingfjord
Memorial sign by Jøssingfjord: " At this place on February 16th 1940 Altmark was attacked
Captain has tried to hide in Jossingfjord, in south-western Norway. Despite the measures taken, British landing team boarded the ship, and managed to free the prisoners. This event, which took place on 16 of February, 1940, went down in history as the "Altmark Incident", and was kind of a spark that ignited a new phase of the WW2. A few days later, Hitler ordered the preparation of an attack on Norway, which happened in less then two months later.
However, the story of Altmark still wasn't finished. The ship returned to Germany, and after repairs, continued it's service as an oil tanker. In November, 1942, in Japan, there was an explosion that together with the ship, killed most of the crew, and the surviving sailors were sent home on a passing ship. Here fate strikes its last blow. On the evening of 3 of March, 1943, in the middle of the Atlantic, the ship was mistakenly sunk by a German submarine.
From 365 people on board, only one made it to land. The others, if managed not to drown, died of thirst, or went insane of those three weeks wandering around the sea.
And in conclusion, a few words about the "Altmark incident", which has serious consequences for Norway. The country' situation became between a rock and a hard place. Germany at the official level considered that the Norwegians violated neutrality, since their ships did not prevent the capture of the German Altmark. The inaction of the Norwegians was unambiguously interpreted as an indulgence of British interests at the expense of the interests of Germany. Hitler gave the order to begin preparations for the invasion of the country, which began on April 9, 1940.
Last photo: Seven sailors from Altmark were killed during the attack at Jøssingfjord.
The second story: Blucher
Americans Try Norwegian Christmas Food
Oslo Riddle

redpajama_set_name: RedPajamaCommonCrawl


text:
AASSA - Teachers Conference | March 27, 2018
Senior Associates Jennifer and Bob Imholt recently attended the 2018 AASSA (Association of American Schools in South America) Conference...
Search Bangkok-Spring 2018 | March 27, 2018
Senior Associates Nick Kendell and Harry Deelman welcomed 23 international schools and 58 registered candidates to the Search Bangkok-Spring...
IB Flourishes Worldwide | March 26, 2018
The introduction of International Baccalaureate programmes continues to spread across the globe. Most international educators will encounter...
Assistive Technology: A Positive Impact for Children with Disabilities | March 25, 2018
According to the National Education Association, the number of U.S. students enrolled in special education programs has risen at least 30% since...
SCHOOL SPOTLIGHTS
Peking University Experimental School, Jiaxing: Where the Time-Honored and Modern Merge | March 23, 2018
Search Associates is pleased to announce its collaboration with Peking University Experimental School (PKUES JX), a new K—12 boarding school...
CANDIDATE STORIES
Francy Johnson's International Journey | March 21, 2018
When Francy Johnson was young and teaching her two step-daughters to read, she had a Eureka moment: This is what I want to do with my life ....
Teaching in Muscat, Oman | March 21, 2018
Senior Associate Bill Turner and his wife Alison visited five Search Associates Schools in Muscat this February. They learned what teaching...
For a Just and Peaceful World: Dar Al Marefa School, Dubai | March 17, 2018
Dar al Marefa School (DMS), a bilingual International Baccalaureate (IB) world school, is now with Search Associates, and we are pleased to help...
For a Better World: International Leadership Academy Beijing | March 9, 2018
Search Associates welcomes the International Leadership Academy Beijing (ILABJ) to its catalog of fine international schools. Founded by Asia...
International School Eastern Seaboard Thailand: A Hidden Gem | March 5, 2018
One hour southeast of Bangkok, the International School Eastern Seaboard (ISE) rests in the fresh air and abundant greenery of the surrounding...
International School of Stavanger: Teach in the Most Livable Country! | March 2, 2018
The International School of Stavanger (ISS) located in Norway—named "Most Livable Country in the World" by the United Nations for 12 of the past...
52nd Annual AAIE Conference | March 2, 2018
The Association for the Advancement of International Education (AAIE) hosted its 52 nd annual Leadership Conference in New York City February...
Dubai Fair 2018 | February 27, 2018
February 15 th -17 th , Senior Associate Bill Turner and his wife Alison Turner hosted the annual Search Dubai Fair. Representatives from...
St. Michael's School in the Caribbean | February 21, 2018
An urban environment on a tropical island: perhaps Santo Domingo has just the mix for you! St. Michael's School (SMS), celebrating 25 great years,...
Busan Foreign School: A Close and Treasured Community | February 20, 2018
For 20 years, Busan Foreign School (BFS) in Korea has been serving international children from Early Childhood through 12th grade. BFS is a privately-owned...
Search Associates San Francisco Fair 2018 | February 16, 2018
Search Associates is renowned for helping educators embark upon their first international teaching adventures. On February 9-12, 2018, Senior...
BFIS in Barcelona | February 15, 2018
Benjamin Franklin International School (BFIS) is in the heart of Barcelona and is a truly international community. The K-12 school has 700 students...
Welcome, Heritage International School, Egypt! | February 3, 2018
Here's a shout out to excellent elementary teachers and Canadian-certified secondary teachers: Heritage International School (HIS) has registered...
Search London 2018 | February 2, 2018
Search Associates' London January Fair attracted 172 schools worldwide and over 560 candidates to the Millennium Gloucester Hotel to recruit...
Exciting Positions at Shanghai High School International Division | January 31, 2018
Search Associates candidates can look forward to great opportunities at the highly-regarded and well-established Shanghai High School International...


When you ask a really big question, people can respond like you have asked them a question on a gameshow – they think for three seconds and say "I don't know".
That conversation might be killed at this point. Or with some awareness, it could become something special.
Don't be afraid of an uncomfortable pause. After the person says "I don't know", it's not necessarily the end of their consideration. Let the pause linger enough for the person to think of their answer. Often this is as little as 10 seconds.
As a society, we have become far too expectant of conversation remaining light. We make small talk or witty banter. We keep people at arm's length. We don't throw curveballs. We give pat answers to pat questions.
Why are we so afraid of big questions?
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Opening with a big question can be too much too soon. But be pro-active in asking one after the opening chit-chat.
When did you last ask a big question? And which big question could you ask today?


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text:
Home→Places to See→Thimphu (Capital of Bhutan)
Artist: Jigme Drukpa Title: 'Yak Legbi Ihadar' from CD: 'Endless Songs of Bhutan'
Location: Western Bhutan
Distance from Paro: 65km (1hr 30mins)
Thimphu Attractions
Tashichho Dzong
Tashichho Dzong is Bhutan's most stately and arguably most impressive building. The Fortress of the glorious religion houses the throne room of His Majesty the King, the main secretariat building and the central monk body. Its courtyard is open to visitors during the Thimphu Tshechu and when the monks move to
their winter residence in Punakha.
Thimphu Tshechu Festival
Thimphu Tshechu: Tshechu are religious festivals honouring Guru Rimpoche, the patron saint who brought Buddhism
to Bhutan in the 8th century. He is regarded as the founder of the Nyingmapa sect of Buddhism, the "oldest school" of Lamaism which is still followed very popularly. His biography is highlighted by 12 episodes of the model of the Buddha Shakyamuni's life.
Simtokha Dzong
Simtokha Dzong is a small dzong, located about 3 miles south of the Bhutanese capital of Thimphu. Built in 1629 by Zhabdrung Ngawang Namgyal, who unified Bhutan, the Donzg is the first of its kind built in Bhutan. An important historical monument and former Buddhist monastery, today it houses one of the premier Dzongkha language learning institutes.
Dochula Pass
Dochula Pass is around 3150 meters from sea level and it's mostly covered with white clouds, where on a clear day you can see
a spectacular view of the mighty Himalayan mountain ranges. Dochula Pass serves
as the stop for all the passers-by traveling to and from Punakha and Wandgi for a hot cup of coffee well served from the Dochula Cafeteria located just above
beautiful Dochula Pass.
National Memorial Chorten (Stupa)
The National Memorial Chorten is located near the Jigme Dorji National referral Hospital. It was built in 1974 in memory of the third king of Bhutan, His Majesty Jigme Dorji Wangchuck, 'the father of Modern Bhutan'. The chorten (stupa)
had a major renovation in 2008. The chorten (stupa) features mandalas, statues and altars dedicated to the third Druk Gyalpo of Bhutan.
Changangkha Lhakhang
Constructed in the 15th century, this is one of the oldest temples in the Thimphu Valley, and is dedicated to Avalokiteshvara, the Buddhist emanation of compassion. The temple affords wonderful views over the entire valley.
Buddha Point (Kuensel Phodrang)
Another place from where you can get a good overview of the Thimphu valley is from the Buddha point (Kuensel Phodrang). It is again a short drive from the town. You can pay your obeisance and offer prayers to the Buddha, the largest statue in the country and then walk around and take
in the view of the valley.
Tango Cheri Monastery
It takes about an hour to arrive at the temple and is about a 900 feet climb. Tango
Cheri Monastery is a Buddhist college, and it's the residence of the Desi Tenzin Rabgye, a young boy who is the reincarnation of the 16th-century monk who built Tango.
The National Folk Heritage Museum
The National Folk Heritage Museum displays traditional Bhutanese ways of life in a traditional Bhutanese house. It
provides an interesting window into Bhutanese culture and the domestic lives of the Bhutanese. There are also Bhutanese dances and exhibits held in the Museum
compound.
The National Institute of Traditional Medicine
In Bhutan, equal emphasis is given to both allopathic and traditional medicines. The rich herbal medicines made from medicinal plants abundant in the kingdom are prepared and dispensed here. The Institute is also a training school for traditional medicine practitioners. The complex is closed to visitors due to considerations of hygiene, but one can still walk around
it and visit the showroom.
Zorig Lobdra Choekhor Painting School (school of 13 arts and crafts)
Commonly known as the Painting School, the Institute offers you a glimpse of novices learning 13 traditional arts and crafts of Bhutan. It is a hands-on trip for you. Enjoy
a few moments with the future artists of the country.
The National Library
The National Library was established in the late 1960s primarily to conserve the literary treasures which form a significant part of Bhutan's cultural heritage. It now houses an extensive collection of Buddhist literature mostly in block-printed format, with some works several hundred years old. There is also a small collection of foreign books,
the stock of which mainly comprises works written in English. Take the opportunity to see the world's biggest book stationed in the ground floor.
The Royal Academy of Performing Arts (RAPA)
The Royal Academy of Performing Arts (RAPA) is the home of the Royal Dance troupe. The history of the academy dates back to 1954 when it was started under the command of His
late Majesty the King, Jigme Dorji Wangchuck with the gracious intention to preserve and promote Bhutanese performing arts. The academy was institutionalized in 1967 with a mandate to preserve and promote performing arts traditions. Folk music and dancing-group were introduced in 1970.
VAST (Voluntary Artists' Studio, Thimphu)
Located along Chang Lam is a busy place with after-school and weekend drawing and painting classes for youngsters conducted by volunteer artists. A gallery on the top floor exhibits a mixture of both traditional and contemporary works. There is also a small library and coffee shop where budding artists are encouraged to meet.
Takin Zoo in Motithang
Motithang Takin Preserve, located in the Motithang district of Thimphu, is a wildlife reserve area for takin, the National Animal of Bhutan. Originally a mini-zoo, it was converted into a preserve when it was discovered that the animals refrained from inhabiting the surrounding forest even when set free.
Botanical Garden at Thimphu
The botanical garden in Serbithang, about 15 kilometres from Thimphu, is becoming a popular weekend spot for Thimphu residents who want to get away from the city. Sometimes there are
people seeking solitude who go to the viewpoint from where the hill drops away sharply. From this point one can catch the afternoon breeze, see the Thimphu chu and farmers working in the fields below and the rising hills in the distance.
Paper Factory in Thimphu
This small factory produces watermarked paper as well as cards, lampshades, envelopes, calendars and other items made from traditional Bhutanese paper.
Handicrafts Shops
A wide assortment of colourful, hand woven textiles and other craft products is available for purchase at the
Government-run Handicrafts Emporium and many smaller handicrafts shops around the town. One can pick up small items as souvenirs.
Vegetable market, Rock climbing, Mountain climbing, River rafting etc


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No corporate accounts for car rentals. Expense reports have to be turned In Every single week. These are really cons, just minor annoyances that are completely reasonable.
This guy is a compulsive liar. We all know it.
Hire a new manager. No one has any faith in current management.
We are proud to have 20 years of solid relationships with our hospital and managed healthcare insurance clients across the country. We are also proud to have an admirable team of employees and consultants and we take great strides to ensure they feel well taken care of. As a smaller, boutique agency this is one of our strengths — you're dealing with compassionate, real people, not a corporate bureaucracy. So we take ad hominem attacks such as this quite personally.
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They will work with you as best as possible to make sure you are comfortable.
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text:
COVID-19 Pandemic Updates
Local /
DAIC gives Government high marks
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Your message has been sent. Thank You.
Kenny Green, President of the DAIC at a 2019 DAIC function
A leading Dominican private sector organisation, the Dominica Association of Industry and Commerce (DAIC) says it supports a package of incentives to stimulate the Dominica economy announce by Prime Minister Roosevelt Skerrit earlier this week.
Stating that the initiatives were "geared to sustain and assist the Dominican economy at a sensitive stage during the COVID-19 global pandemic crisis", the DAIC said "the gains made by the country through the health crisis, managed by the Government of the Commonwealth of Dominica and the Ministry of Health, and executed by the front line health care and support workers, are inspirational".
The press release added: "In addition, the general public and the private sector have been strong and resolute in their adherence to the health protocols. Given the uncertainty faced by citizens through this health and economic crisis, it is important to salute the general public and the private sector for continuing to be disciplined.
"The DAIC notes that several initiatives proposed to the Government for its direct membership and the wider Private Sector were included in the support measures presented. These include corporate tax rebates for companies who retained the majority of their employees, duty and VAT relief on personal protective equipment (PPEs), financing for the tourism and manufacturing sectors, the micro, small and medium enterprises (MSMEs) and the self-employed, and facilitating of infrastructure projects to encourage local capacity and income generation.
The DAIC continued: "It is important for there to be consistent and transparent implementation of these measures with the support of all social partners in tandem with the Public Sector. The DAIC pledges its support to the actions needed to deliver these initiatives to the Private Sector and general public by extension in as short a time as possible.
"The DAIC continues to advocate for a careful return to a more normalized business and social climate whilst maintaining vigilance on health protocols and border controls".
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Boomloaded
Court sentences supermarket knife attacker to life imprisonment
01, Mar 2018 0 Comments 56744 Views
A Palestinian jihadist who killed one man and wounded six other people in a German supermarket knife rampage last year was sentenced on Thursday to life in prison.
Ahmad Alhaw, 27, had admitted to the murder and assaults in the river port city of Hamburg.
In delivering the sentence, the judge noted the "particular severity" of the crime, lowering Alhaw's chances of obtaining early release on parole.
Prosecutors had said the attack stemmed from a "radical Islamist" motive, charging that the man had hoped "he would die as a martyr".
His aim was to kill as many German Christians as possible to avenge the suffering of Muslims worldwide, they said.
There was no indication he belonged to the so-called Islamic State group or any other extremist organisation.
The defendant entered a supermarket on July 28 last year,, snatched a 20-centimetre (eight-inch) knife from the shelves, unwrapped it and fatally stabbed a 50-year-old man.
As panic broke out, he slashed and stabbed at others inside and then outside the shop, shouting "Allahu Akbar" (God is greatest), and also wounding a 50-year-old woman and four men.
"I saw him run toward me, covered in blood. He was radiating bloodlust," a 20-year-old supermarket worker who locked himself in a backroom recounted in court.
Outside the supermarket, seven mostly migrant bystanders confronted the screaming attacker, hurling rocks and chairs at him and then subduing him until police arrived.
The men — among them a 30-year-old man of Turkish descent who was wounded in the struggle — have since been celebrated and honoured for their courage.
One of them, Afghan asylum seeker Toufiq Arab, told Bild daily: "I'm not a hero, I only did my duty."
– 'God's will' –
The Islamist attack was Germany's first since December 2016 when Tunisian Anis Amri ploughed a stolen truck through a Berlin Christmas market in an attack that claimed 12 lives.
The knife rampage led to charges that security services had been too complacent in keeping an eye on Alhaw despite multiple warnings that he was radicalising.
It also fuelled growing xenophobia against Muslim migrants after the mass influx of more than one million asylum seekers since mid-2015.
Witnesses told the court they had seen the failed asylum seeker self-radicalise in the lead-up to the attack.
Like Amri, Alhaw was due to have been deported after his asylum application was rejected, but the process was held up by a wait for identity documents.
Having been rejected for asylum in Norway, Sweden and Spain, he had arrived in Germany in 2015 and was moved to a Hamburg migrant accommodation centre, which after the attack was raided by heavily armed police.
A psychiatrist said Alhaw had told him he was initially drawn to the West because of its open lifestyle.
At first he studied the German language, socialised and at times drank alcohol and smoked marijuana, said witnesses.
But Alhaw, who had watched IS propaganda videos online, grew deeply religious and increasingly distrustful of state authorities.
By 2016 he thought that he suddenly understood the Koran completely, rejected all previous temptations, and started looking at all other people as either true believers or non-believers, the psychiatrist said.
Alhaw said the knife attack had not been his decision but God's will.
But he surprised the court in a February 19 hearing when he appeared with his beard shaved off and for the first time showed remorse.
"Unfortunately I can't turn back time," he told the victims and the relatives of the man he killed. "All I can do is to ask you for forgiveness."
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