In order to be confident in model predictions, it is critical to quantify the impact of various sources of uncertainty on palaeo ice-sheet reconstructions. The quantification of uncertainty introduced by the choice of climate model or ISM used has been the primary goal of the PLISMIP project presented here and in previous papers 28, 40, 41.
Our results indicate a high level of climate model dependency in reconstructing the Antarctic ice-sheet volume (Fig. 4) and resulting sea level change (Fig. 7). Given the complex nature of the PlioMIP1 climate models used to provide our forcing fields, understanding the reasons for differences in the ISM predictions is non-trivial. It is clear that some of the major variations in the simulated extent and volume of the AIS for the Pliocene scenarios are closely linked to the way temperature, precipitation and ocean temperatures are predicted among the climate models. Warming among the PlioMIP1 climate models in the Antarctic region has been previously attributed to the changes in the cryosphere and surface albedo 42. Over Antarctica, the clear sky albedo is the dominant component of the energy balance, which leads to increased warming in all PlioMIP models south of 75°S (although there is a large range in warming at 90°S (1.5–12 °C)) 42.
The difference in the results from the Pliocene Ice-PRISM3 ensemble (Fig. 5) and the Pliocene Ice-PD ensemble (Fig. 6) also shows the level to which the equilibrium ice-sheet predictions are dependent upon the history of the ice-sheet system (hysteresis); in this case, the specific initial conditions used in the ISM set-up. This is most clearly demonstrated in the results using ANICE and SICOPOLIS driven by the CCSM4 climate model. CCSM4 exhibits some of the coldest sub-shelf ocean temperatures, especially over the Ross Ice Shelf (Supplementary Fig. 3), and has the coldest temperatures (~−25 °C) over West Antarctica of the considered PlioMIP models (Supplementary Fig. 1). This may be in part due to the higher albedo levels in CCSM4 ( α = ~0.6) over West Antarctica relative to the other GCMs helping to promote cooling. In the Pliocene Ice-PRISM3 simulation, the precipitation rates over West Antarctica are not sufficient to create a strong positive SMB (Supplementary Fig. 4), thus the results from CCSM4 are in line with the other GCMs (in that the Pliocene WAIS remains collapsed from the initial PRISM3 starting configuration; Fig. 5). However, when the ISMs are initialised with a modern AIS (Pliocene Ice-PD; Fig. 6), hysteresis is strong due to the fact that the combination of lapse-rate corrected temperatures that are applied and ocean-driven melting are not sufficient to cause a reduction of the WAIS and associated retreat of the ice shelves in either ISM.
As we have shown a broad spread in sea level changes associated with our ensemble of AIS predictions (Fig. 7d), it is useful to understand how these compare to sea level data and also other modelling studies for the mPWP in order to assess which of a number of sources of uncertainty lead to the largest range in predictions of the AIS (Table 1). The results presented here indicate that the spread in sea level change predictions for the mPWP from the PlioMIP ensemble (based on the difference to the modern volume) using the SIA-SSA models is between 2.24 m s.e. (ANICE with HadCM3) and 14.57 m s.e. (SICOPOLIS with IPSLCM5A) for the Pliocene Ice-PRISM3 scenario (the mean is 7.79 m s.e.). Using just the ANICE ISM, the range in sea level change predictions is 7.04 m s.e. for the Pliocene Ice-PRISM3 simulations, and for the SICOPOLIS ISM the range is slightly larger (11.64 m s.e.; Fig. 7d). However, the average difference between the predictions of the two SSA–SIA ISMs using one climate model forcing is only 4.35 m s.e., which shows that the results are more dependent upon the chosen climate model forcing and not the ISM used. However, both have an impact on the determined result. The range in mean sea level change predicted here in both of our Pliocene scenarios is 14.57 to −3.99 m s.e. using only SIA-SSA ISMs. This demonstrates that the choice of initial conditions imposed in the ISMs is an important control on the total range of sea level predictions, as demonstrated previously 28.
Focussing primarily on studies employing SIA-SSA models, Table 1 provides a summary of previous work assessing various sources of uncertainty in predicting the Pliocene AIS (see also ref. 16). Following a similar experimental design to that presented here and initialising from a PRISM3 ice-sheet, ref. 28 predicted a range in mean sea level rise for equilibrium Pliocene ice-sheets of 5.83 m s.e. (7.96–13.79 m s.e.) when considering six different SIA-SSA ISMs but only one climate model forcing (HadCM3). The comparable range shown here incorporating five different GCMs is 12.33 m s.e., supporting the conclusion that climate model dependency on AIS predictions for the Pliocene is high.
Yan et al. perturbed ISM parameterisations (positive degree-day (PDD) factors for ice and snow, enhancement flow factors for the SIA and SSA velocities and the sub-shelf melt parameter) using the Parallel ISM (PISM) driven by climatological forcing from the NorESM climate model 43. They simulated AIS configurations ranging from −4.2 to 6.6 m s.e. volume (causing a 10.8 m s.e. variation due to parameter uncertainty). In the same study, temperature and precipitation were also increased in steps of 1 °C for temperature and 5.1% increase in precipitation per degree of temperature change in order to explore the influence of uncertainty in the Pliocene climate forcing on the modelled AIS. Considering this additional warming, the mass loss of the AIS relates to a global mean sea level rise of 6.2–13.0 m relative to present day (range of 6.8 m s.e.; ref. 43). The impact of uncertainties in dynamic topography 44 and Antarctic bedrock topography 45 have also been considered in a Pliocene ISM framework, however, these mechanisms only cause a small variation in predicted AIS volume and hence mean sea level change (Table 1). Therefore, it is clear from our results that in terms of the range of predictions of global mean sea level change, the climate model chosen has the potential to exert a larger influence on the results than the ISM, ISM parameterisations or changes in topography.
While ideally proxy-based estimates of global mean sea level change for the mPWP could be used to constrain which of the modelled AIS scenarios is more or less likely, significant uncertainty remains in the estimates of Pliocene sea level high stands 4, 46. Although in general all data suggest a higher-than-present sea level indicative of a reduced Greenland ice-sheet and retreated AIS 10, 12, estimates range from between 10 and 30 m above present-day 4– 8, based on a range of global proxy records (e.g. palaeoshorelines), for which the largest uncertainties arise from considering processes such as mantle convection and glacial isostatic adjustment 4, 5, 9, 47. Recent studies inferring sea level changes from oxygen isotopes have offered a more constrained reconstruction of Pliocene sea level, suggesting that high-stands were between 9 and 13.5 m above modern, implying that the EAIS may be less sensitive during the Pliocene than previously considered 48. A further study using a novel approach to constrain mPWP sea level high stands by assessing whether or not particular AIS configurations are consistent with the oxygen isotope record have suggested that AIS contribution to mPWP sea level rise was at most ~13 m 46. However, a recent review 49 of the sources of uncertainty associated with using marine geochemical proxies to infer past changes in sea level (e.g. refs. 46, 48), has shown that diagenesis and potential long-term changes in seawater chemistry likely have large and poorly constrained effects on interpretations of such records. This may limit their ability to make meaningful Pliocene sea level estimates at the present time 49.
The question as to whether or not the EAIS was dynamic or reduced in the Pliocene is a long-standing issue (see ref. 19). Our results show that in around 40% of the simulations for the Pliocene Ice-PRISM3 experiment, substantial ice-sheet reduction is exhibited in the Wilkes and Aurora SGB and around Wilkes Land (Figs. 3e and 4). This is consistent with ice-rafted debris data thought to be indicative of a dynamic Wilkes Land margin region during the Late Pliocene 12, 13. However, this result depends on the boundary conditions imposed in the climate model 28 and the starting conditions of the ISMs. For example, none of the AIS configurations presented in the Pliocene Ice-PD experiments suggest significant retreat into the EAIS SGBs (Fig. 6). It must be acknowledged that this is potentially inconsistent with geological proxy evidence for retreat of portions of the EAIS during the mPWP (e.g. ref. 12). Nevertheless, the results presented here also demonstrate that, while the total AIS volume is most heavily influenced by the choice of climate model, detailed predictions of areal extent are more likely to be influenced by the ISM used and ISM initial conditions (Figs. 5 and 6). For example, Fig. 5 shows that ANICE predictions generally would lead to the conclusion that the Aurora SGB was covered in ice (apart from when using IPSL-CM5A climate forcing), BASISM predictions always suggest an ice-covered region, whereas SICOPOLIS predicts some ice retreat under all climatologies.
There are also additional sources of uncertainty that have been shown previously to impact upon the details of predicted SGB retreat that have not been considered here. When considering the modern AIS under future warming scenarios, it has been shown that the implementation of subgrid interpolation of basal melting at grounding lines within SIA-SSA ISMs may accelerate grounding line retreat 50 and impacts upon the final ice volume predicted in long palaeo-integrations increasing the range in volume predictions 16. None of the ISMs considered here include fine scale representations of the grounding line, for example, subgrid friction schemes 51 or adaptive mesh refinement 52 that has been shown to improve accuracy in a modern setting at very high (<1 km) resolution. DeConto and Pollard also employ a parameterisation of ice-shelf hydrofracturing and ice-cliff collapse of marine-terminating ice margins, which is not included in any of the ISMs here and this could potentially have a large impact on predictions of sea level change for the Pliocene 21, 22 (Table 1). As ISMs that are applicable to palaeo problems evolve (e.g. can be run on a continental scale for long time integrations), it is likely that such fine scale and novel grounding line parameterisations will continue to alter our understanding of potential ice-sheet collapse in the EAIS SGBs and this will be an interesting avenue for future research.
PLISMIP was the first ISM intercomparison project set up to shed light of palaeo ice-sheet variability, with the ambition of helping to understand the sensitivity of the ice-sheets in a warmer-than-modern world 27. To realise this, one of the key aims of the PLISMIP project was to reconstruct the most likely geometry and volume of ice masses on Greenland and Antarctica. The results over Greenland allowed for an improvement to be made in the understanding of the potential extent and configuration of the Greenland Ice Sheet during the Pliocene 40, 41 and a new reconstruction has now been incorporated into the PRISM4 Pliocene reconstruction 53 and the experimental design for PlioMIP2 54. However, results presented here and in other ice-sheet modelling studies for the Pliocene AIS 28 show that the level of uncertainty associated with simulating a marine-based WAIS and also the potential dynamic nature of the grounding line over the EAIS mean that we cannot be confident in using the results from this study to infer a most likely AIS configuration for the mPWP.
Nevertheless, our results unequivocally demonstrate that the climate forcing fields used (and in effect the climate model chosen) can have a significant impact on the predicted equilibrium state ice-sheet for the mPWP. When compared against other sources of uncertainty (e.g. ISM parameterisations and the choice of ISM), the atmospheric forcing field used brings in the largest source of uncertainty in terms of the global mean sea level change associated with volumetric changes in the ice-sheet (Table 1). We also show, however, that the details of the predicted ice-sheet configuration are generally more affected by which ISM is used, rather than the particular climate forcing, with a caveat that this may be affected by some of the ice boundary condition choices used within the PlioMIP models. Therefore it is important that as a community we do not place undue confidence on the ice-sheet reconstructions of one ISM or one climate model, when additional sources of structural uncertainty have not been considered.


UIView
class UIView : UIResponder, NSCoding, UIAppearance, UIAppearanceContainer, UIDynamicItem, UITraitEnvironment, UICoordinateSpace, UIFocusItem, UIFocusItemContainer, CALayerDelegate
-
Create a magic constraint in order to fix size
Declaration
Swift
public func defineSize(width: CGFloat, height: CGFloat)
Parameters
width
width
height
height
-
Create a magic constraint in order to fix height
Declaration
Swift
public func setHeight(height: CGFloat)
Parameters
height
height
-
Create a magic constraint in order to fix width
Declaration
Swift
public func setWidth(width: CGFloat)
Parameters
width
width
-
Create a magic constraint in order to set min width
Declaration
Swift
public func setMinWidth(width: CGFloat)
Parameters
width
min width
-
Create a magic constraint in order to set max width
Declaration
Swift
public func setMaxWidth(width: CGFloat)
Parameters
width
max width
-
Create a magic constraint in order to set min height
Declaration
Swift
public func setMinWidth(height: CGFloat)
Parameters
height
min height
-
Create a magic constraint in order to set max height
Declaration
Swift
public func setMaxWidth(height: CGFloat)
Parameters
height
max height
-
Remove all magic contraints from owner view
Declaration
Swift
open func removeAllMagicConstraints()
-
Create all related NSLayoutConstraint for the view and all subviews in cascade mode
Declaration
Swift
open func applyMagicConstrains()
-
Create a magic constraint in order to center the view horizontally to super view
Declaration
Swift
public func centerHorizontally(to container: UIView? = nil)
Parameters
container
if you want to related the center to another view use container (if you don’t want to center to the parent view)
-
Create a magic constraint in order to center the view vertically to super view
Declaration
Swift
public func centerVertically(to container: UIView? = nil)
Parameters
container
if you want to related the center to another view use container (if you don’t want to center to the parent view)
-
Create a magic constraint in order to center the view vertically & horizontally to super view
Declaration
Swift
public func center(to container:UIView? = nil)
Parameters
container
if you want to related the center to another view use container (if you don’t want to center to the parent view)
-
Create a magic constraint in order to match the view width to super view width
Declaration
Swift
public func matchWidth(toView otherView: UIView? = nil, withPercentage percent: CGFloat = 100)
Parameters
otherView
if you want to related the match to another view use container (if you don’t want to center to the parent view).
percent
use this parameter if you want to match with percent of the related view size, equals is 100
-
Create a magic constraint in order to match the view height to super view height
Declaration
Swift
public func matchHeight(toView otherView: UIView? = nil, withPercentage percent: CGFloat = 100)
Parameters
otherView
if you want to related the match to another view use container (if you don’t want to center to the parent view).
percent
use this parameter if you want to match with percent of the related view size, equals is 100
-
Create a magic constraint in order to match the view height & width to super view height & width
Declaration
Swift
public func matchSize(toView view:UIView? = nil, withPercentage percentage: CGFloat = 100)
Parameters
view
if you want to related the match to another view use container (if you don’t want to center to the parent view).
percent
use this parameter if you want to match with percent of the related view size, equals is 100
-
Undocumented
Declaration
Swift
open var leftPadding: CGFloat { get }
-
Undocumented
Declaration
Swift
open var rightPadding: CGFloat { get }
-
Undocumented
Declaration
Swift
open var topPadding: CGFloat { get }
-
Undocumented
Declaration
Swift
open var bottomPadding: CGFloat { get }
-
Create a magic constraint in order to add left padding to the view
Declaration
Swift
open func setLeftPadding(padding: CGFloat)
Parameters
padding
value of padding
-
Create a magic constraint in order to add right padding to the view
Declaration
Swift
open func setRightPadding(padding: CGFloat)
Parameters
padding
value of padding
-
Create a magic constraint in order to add top padding to the view
Declaration
Swift
open func setTopPadding(padding: CGFloat)
Parameters
padding
value of padding
-
Create a magic constraint in order to add bottom padding to the view
Declaration
Swift
open func setBottomPadding(padding: CGFloat)
Parameters
padding
value of padding
-
Create a magic constraint in order to add paddings to the view
Declaration
Swift
open func setPaddings(left: CGFloat, right: CGFloat, top: CGFloat, bottom: CGFloat)
Parameters
left
value of left padding
right
value of right padding
top
value of top padding
bottom
value of bottom padding
-
Create a magic constraint in order to add paddings to the view
Declaration
Swift
open func setPadding(padding: CGFloat)
Parameters
padding
value of left,right,top & bottom padding
-
Create a magic constraint in order to pin view to another view
Declaration
Swift
public func pin(to otherView: UIView? = nil, withMargin margin: CGFloat = 0 )
Parameters
otherView
view to be pinned to
margin
value of margin
-
Create a magic constraint in order to pin view’s left side to another view’s left side
Declaration
Swift
public func pinLeft(to otherView: UIView? = nil, withMargin margin: CGFloat = 0 )
Parameters
otherView
view to be pinned to
margin
value of margin
-
Create a magic constraint in order to pin view’s right side to another view’s right side
Declaration
Swift
public func pinRight(to otherView: UIView? = nil, withMargin margin: CGFloat = 0 )
Parameters
otherView
view to be pinned to
margin
value of margin
-
Create a magic constraint in order to pin view’s top side to another view’s top side
Declaration
Swift
public func pinTop(to otherView: UIView? = nil, withMargin margin: CGFloat = 0 )
Parameters
otherView
view to be pinned to
margin
value of margin
-
Create a magic constraint in order to pin view’s bottom side to another view’s bottom side
Declaration
Swift
public func pinBottom(to otherView: UIView? = nil, withMargin margin: CGFloat = 0 )
Parameters
otherView
view to be pinned to
margin
value of margin
-
Create a magic constraint in order to pin last subview’s bottom side to view’s bottom side
Declaration
Swift
public func pinLastSubviewToBottom(withMargin margin: CGFloat = 0 )
Parameters
margin
value of margin
-
Create a magic constraint in order to pin first subview’s top side to view’s top side
Declaration
Swift
public func pinFirstSubviewToTop(view: UIView, withMargin margin: CGFloat = 0 )
Parameters
margin
value of margin
-
Create a magic constraint in order to put view below of another view
Declaration
Swift
public func put(belowOf view: UIView, withMargin margin: CGFloat = 0)
Parameters
view
related view
margin
value of margin
-
Create a magic constraint in order to put view on bottom of another view’s last subview
Declaration
Swift
public func put(onBottomOfLastViewOf view: UIView, withMargin margin: CGFloat = 0)
Parameters
view
related view
margin
value of margin
-
Create a magic constraint in order to put view above of another view
Declaration
Swift
public func put( aboveOf view: UIView, withMargin margin: CGFloat = 0)
Parameters
view
related view
margin
value of margin
-
Create a magic constraint in order to put view left of another view
Declaration
Swift
public func put(leftOf view: UIView, withMargin margin: CGFloat = 0, relation: NSLayoutConstraint.Relation = NSLayoutConstraint.Relation.equal)
Parameters
view
related view
margin
value of margin
-
Create a magic constraint in order to put view right of another view
Declaration
Swift
public func put(rightOf view: UIView, withMargin margin: CGFloat = 0, relation: NSLayoutConstraint.Relation = NSLayoutConstraint.Relation.equal)
Parameters
view
related view
margin
value of margin


Contact
I am available to be contacted for questions about my research. Feel free to reach out/look at any of the below links.
atbowen[at]gmail.com LinkedIn GitHub Thesis


My latest mix will be featured on LA's Soul Public Radio's Street Politiks show during Christmas weekend. You can download it
here
if you can't wait :)


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Reports calls to TestNG assertEquals() which have the actual result argument and the expected argument in the wrong order.
For TestNG the correct order is (actual, expected)
.
Such calls will behave fine for assertions which pass but may give confusing error reports on failure.


GrowthRateEstimator.Rd
An estimator of the reproduction number (\(R_t\)) using a renewal equation method.
Version: 0.03
Generated: 2022-05-24T22:12:30.960709
| minWindow | minWindow - the smallest amount of data considered in estimating the growth rate, 4 is a sensible default unless you want bang up to date estimate. - (java expects a int) |
|---|---|
| maxWindow | maxWindow - the largest amount of data considered in estimating the growth rate, 14 is a sensible amount. - (java expects a int) |
The renewal equation method depends on a time series of infections, and on the infectivity profile - a measure of the probability that a secondary infection occurred on a specific day after the primary case, given a secondary infection occurred. A Bayesian framework is then used to update a prior probabilistic estimate of \(R_t\) on any given day with both information gained from the time series of infections in the epidemic to date and the infectivity profile to produce a posterior estimate of \(R_t\). This is based on the article:

A. GrowthRate, N. M. Ferguson, C. Fraser, and S. Cauchemez, ‘A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics’, Am J Epidemiol, vol. 178, no. 9, pp. 1505–1512, Nov. 2013, doi: 10.1093/aje/kwt133.

 and the implementation of it in the R Library EpiEstim. It assumes that infections (or other type of observation) are a Poisson distributed process and that the reproduction number is a gamma distributed quantity. The estimates of \(R_t\) may be aggregated over a specific "window" to provide a more stable estimate.
J$GrowthRateEstimator$strictGrowthRateAndRtEstimator(minWindow, maxWindow, infectivityProfiles)
J$GrowthRateEstimator$defaultGrowthRateAndRtEstimator(minWindow, maxWindow, initialIncidence)
J$GrowthRateEstimator$defaultGrowthRateEstimator(minWindow, maxWindow)
instance$priorIncidenceFromPreviousPosterior(multiplyingSDby)
instance$priorIncidenceFromPosteriorAndGrowthRate(multiplyingSDby)
instance$priorIncidenceFromPosteriorMeanAndCoefVariation(kappa)
instance$priorIncidenceFromPosteriorMeanAndGrowthRateAndCoefVariation(kappa)
instance$withInfectivityProfile(infectivityProfile, replace)
instance$estimateGrowthRate(incidence, dateColName, incidenceColName)
instance$estimateGrowthRateSingle(incidence, dateColName, incidenceColName)
instance$clone()
instance$print()
new()
Produces a GrowthRateEstimator that is configured to behave in a sensible fashion, with sane defaults. In that it estimates an initial poisson rate prior from the first entry and returns a fixed window size and aggregates uncertain SI distributions with a random resampling strategy

J = jepidemic::JavaApi$get() J$GrowthRateEstimator$new(minWindow, maxWindow)
minWindow - the smallest amount of data considered in estimating the growth rate, 4 is a sensible default unless you want bang up to date estimate. - (java expects a int)
maxWindow - the largest amount of data considered in estimating the growth rate, 14 is a sensible amount. - (java expects a int)
R6 GrowthRateEstimator object:
not available
strictGrowthRateAndRtEstimator()
Produces a GrowthRateEstimator that will estimate growth rate and $R_t$, with sane defaults. The $R_t$ estimation is done using bootstrapping, and combining the results using a mixture distribution. This will be relatively slow.
J = jepidemic::JavaApi$get() J$GrowthRateEstimator$strictGrowthRateAndRtEstimator(minWindow, maxWindow, infectivityProfiles)
minWindow - the smallest amount of data considered in estimating the growth rate - 4 is a ssensible default unless you want bang up to date estimate. - (java expects a int)
maxWindow - the largest amount of data considered in estimating the growth rate - 14 is a sensible amount. - (java expects a int)
infectivityProfiles - a numeric matrix with each column containing a discrete probability distribution of the serial interval with \(P_(t=0) = 0\) - (java expects a RNumericArray)
R6 GrowthRateEstimator object: a strict growth rate and \(R_t\) estimator that accounts for uncertainty in the infectivity profile.
not available
defaultGrowthRateAndRtEstimator()
Produces a GrowthRateEstimator that will estimate growth rate and $R_t$, with sane defaults. The $R_t$ estimation is done using a single infectivity profile and does not account for infectivity profile uncertainty. This is comparatively fast.

J = jepidemic::JavaApi$get() J$GrowthRateEstimator$defaultGrowthRateAndRtEstimator(minWindow, maxWindow, initialIncidence)
minWindow - the smallest amount of data considered in estimating the growth rate - 4 is a ssensible default unless you want bang up to date estimate. - (java expects a int)
maxWindow - the largest amount of data considered in estimating the growth rate - 14 is a sensible amount. - (java expects a int)
initialIncidence - a guess at the initial incidence of the time series - (java expects a double)
R6 GrowthRateEstimator object: a growth rate and \(R_t\) estimator that does not account for uncertainty in the infectivity profile.
not available
defaultGrowthRateEstimator()
Produces a GrowthRateEstimator that will estimate growth rate only, with sane defaults, unless an infectivity profile is lated added

J = jepidemic::JavaApi$get() J$GrowthRateEstimator$defaultGrowthRateEstimator(minWindow, maxWindow)
minWindow - the smallest amount of data considered in estimating the growth rate - 4 is a ssensible default unless you want bang up to date estimate. - (java expects a int)
maxWindow - the largest amount of data considered in estimating the growth rate - 14 is a sensible amount. - (java expects a int)
R6 GrowthRateEstimator object: a growth rate and \(R_t\) estimator that does not account for uncertainty in the infectivity profile.
not available
basicGrowthRateEstimator()
Produces a GrowthRateEstimator that will estimate growth rate only, with sane defaults, and estimating using windows between 4 and 14 days

J = jepidemic::JavaApi$get() J$GrowthRateEstimator$basicGrowthRateEstimator()
none
R6 GrowthRateEstimator object: a growth rate and \(R_t\) estimator that does not account for uncertainty in the infectivity profile.
not available
withOutputQuantiles()
Allows you to change the quantiles returned by the estimator. The defaults are c(0.025,0.05,0.25,0.5,0.75,0.95,0.975)
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$withOutputQuantiles(quantiles)
quantiles the desired quantiles of the output - (java expects a RNumericVector)
R6 GrowthRateEstimator object: a fluent method
not available
withSaneDefaults()
Provides a sensible baseline configuration for the estimator that involves estimating the poisson rate using the previous timestep's posterior and growth rate estimates. Multiple posterior estimates are combined using a weighted average based on a discretised normal distributed weight with mean of 7 and SD of 4.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$withSaneDefaults()
none
R6 GrowthRateEstimator object: a fluent method
not available
withInitialIncidence()
Allows configuration of the initial incidence. If this is omitted the initial incidence is guessed from the first entry in the timeseries
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$withInitialIncidence(incidence)
incidence - the prior incidence estimate prior to the the beginning of the time series. - (java expects a double)
R6 GrowthRateEstimator object: a fluent method
not available
withGaussianEstimateWeighting()
Allows configuration of the weighting of estimate combination for the weighted mixture combination. This uses a discretised normal distribution to generate values for the weighting. The weighting is applied to each supported estimate determined by the data window of that estimate. The default value is a mean of 7 and SD of 4
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$withGaussianEstimateWeighting(meanTau, sdTau)
meanTau the mean of the normal distribution of weights - (java expects a double)
sdTau the sd of the normal distribution of weights - (java expects a double)
R6 GrowthRateEstimator object: a fluent method
not available
withEstimateWeighting()
Allows configuration of the weighting of estimate combination for the weighted mixture combination. This assumes the weights are given as a vector where the first entry is the weight for an estimate using a data window of 1. The vector should be the same length as the maxWindow parameter used in configuring the GrowthRateEstimator or 14 if the defaults were used.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$withEstimateWeighting(weights)
weights - a vector of weights for each data window - (java expects a RNumericVector)
R6 GrowthRateEstimator object: a fluent method
not available
combineEstimatesWithWeightedMixture()
Sets the estimator to use a weighted mixture to combine estimates based on the length of different data windows, using weights defined by withEstimateWeighting() or withGaussianEstimateWeighting(). If none is set the weighting defaults to a discretised normal distribution with mean of 7 an SD of 4.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$combineEstimatesWithWeightedMixture()
none
R6 GrowthRateEstimator object: a fluent method
not available
priorIncidenceFromPreviousPosterior()
Select a prior estimate for the poisson rate by taking the previous posterior and multiplying the SD by a constant factor
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$priorIncidenceFromPreviousPosterior(multiplyingSDby)
multiplyingSDby - factor to multiply SD by. Must be greater than or equal to 1. - (java expects a double)
R6 GrowthRateEstimator object: a fluent method
not available
priorIncidenceFromScaledPreviousPosterior()
Select a prior estimate for the poisson rate by taking the previous posterior and multiplying the SD by a factor determined by the difference between the t-1 and t-2 incidence. This allows for rapid rate of change to be detected and less weight given to the prior in this situation
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$priorIncidenceFromScaledPreviousPosterior()
none
R6 GrowthRateEstimator object: a fluent method
not available
priorIncidenceFromPosteriorAndGrowthRate()
Select a prior estimate for the poisson rate by taking the previous posterior and increasing it by the exponential previous posterior growth rate and multiplying the resulting disctribution by a constant factor. This is a mechanistic prior.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$priorIncidenceFromPosteriorAndGrowthRate(multiplyingSDby)
multiplyingSDby - factor to multiply SD by. Must be greater than or equal to 1. - (java expects a double)
R6 GrowthRateEstimator object: a fluent method
not available
priorIncidenceFromScaledPosteriorAndGrowthRate()
Select a prior estimate for the poisson rate by taking the previous posterior and increasing it by the exponential previous posterior growth rate and multiplying the resulting disctribution by a factor determined by the change in t-1 and t-2 indidence. This is a mechanistic prior.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$priorIncidenceFromScaledPosteriorAndGrowthRate()
none
R6 GrowthRateEstimator object: a fluent method
not available
priorIncidenceFromPosteriorMean()
Select a prior estimate for the poisson rate by taking the previous posterior and constructing a prior with mean and sd both equal to the mean of the previous posterior.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$priorIncidenceFromPosteriorMean()
none
R6 GrowthRateEstimator object: a fluent method
not available
priorIncidenceFromPosteriorMeanAndGrowthRate()
Select a prior estimate for the poisson rate by taking the previous posterior and constructing a prior with mean and sd both equal to the mean of the previous posterior plus the growth rate.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$priorIncidenceFromPosteriorMeanAndGrowthRate()
none
R6 GrowthRateEstimator object: a fluent method
not available
priorIncidenceFromPosteriorMeanAndCoefVariation()
Select a prior estimate for the poisson rate by taking the previous posterior and constructing a prior with mean and sd both equal to the mean of the previous posterior.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$priorIncidenceFromPosteriorMeanAndCoefVariation(kappa)
kappa - the coefficient of variation of the prior. - (java expects a double)
R6 GrowthRateEstimator object: a fluent method
not available
priorIncidenceFromPosteriorMeanAndGrowthRateAndCoefVariation()
Select a prior estimate for the poisson rate by taking the previous posterior and constructing a prior with mean and sd both equal to the mean of the previous posterior plus the growth rate.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$priorIncidenceFromPosteriorMeanAndGrowthRateAndCoefVariation(kappa)
kappa - the coefficient of variation of the prior. - (java expects a double)
R6 GrowthRateEstimator object: a fluent method
not available
useAllPosteriorEstimates()
The posterior estimates for each combination of data window and infectivity profile are all retained and combined
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$useAllPosteriorEstimates()
none
R6 GrowthRateEstimator object: a fluent method
not available
usePosteriorEstimatesFromOneWindow()
The posterior estimates for only one data window, but all infectivity profiles, are retained and combined. N.b. the same effect can be achieved by setting the minWindow and maxWindow in the constructor.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$usePosteriorEstimatesFromOneWindow(tau)
tau the window width to retain. - (java expects a int)
R6 GrowthRateEstimator object: a fluent method
not available
usePosteriorEstimatesWithEnoughData()
The posterior estimates for data windows that include a reasonable amount of data are retained and combined. This suppresses noisy estimates made on a very few data points when incidence is low.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$usePosteriorEstimatesWithEnoughData(sumIncidence)
sumIncidence - the minimum number of cases that must be observed in a data window before the estimate is ignored. - (java expects a int)
R6 GrowthRateEstimator object: a fluent method
not available
withInfectivityProfile()
Configure the estimator with an infectivity profile. At least one profile must be added for an estimate of R_t to be made
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$withInfectivityProfile(infectivityProfile, replace)
infectivityProfile - A numeric vector of the daily discrete probability of a secondary infection event occurring given an infected individual. It is assumed that this vector starts at time zero with a probability of zero. - (java expects a RNumericVector)
replace - replace the existing infectivity profile with this new one? if false then the profile is added to a list. - (java expects a boolean)
R6 GrowthRateEstimator object: The estimator itself (a fluent method)
not available
withInfectivityProfileMatrix()
Configure the estimator with an infectivity profile by specifying it as a matrix. This will replace all previously set profiles.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$withInfectivityProfileMatrix(infectivityProfiles)
infectivityProfiles - A numeric array of the daily discrete probability of a secondary infection event occurring given an infected individual in each column. It is assumed that this vector starts at time zero with a probability of zero. - (java expects a RNumericArray)
R6 GrowthRateEstimator object: The estimator itself (a fluent method)
not available
estimateGrowthRate()
Estimates growth rate and, if configured with an infectivity profile, \(R_t\) for a one or more timeseries of infections in an epidemic. If the supplied dataframe is grouped it assumes ach grouping is a timeseries
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$estimateGrowthRate(incidence, dateColName, incidenceColName)
incidence - a dataframe containing at least 2 columns (one containing a date, the other a numeric count of cases). The dataframe may be grouped, in which case grouping is preserved, and each group is treated as a seperate time seriesincidenceColName - the name of the incidence column - (java expects a RDataframe)
dateColName - the name of the date column - (java expects a String)
incidenceColName - the name of the incidence column - (java expects a String)
RDataframe: a dataframe, containing a summary of the estimates of incidence, growth rate and \(R_t\) with mean, sd, and quantiles for each day.
not available
estimateGrowthRateSingle()
Estimates \(R_t\) for a single timeseries of infections in an epidemic.
J = jepidemic::JavaApi$get() instance = J$GrowthRateEstimator$new(...); instance$estimateGrowthRateSingle(incidence, dateColName, incidenceColName)
incidence - a dataframe containing at least 2 columns (one containing a date, the other a numeric count of cases).incidenceColName - the name of the incidence column - (java expects a RDataframe)
dateColName - the name of the date column - (java expects a String)
incidenceColName - the name of the incidence column - (java expects a String)
RDataframe: a dataframe, containing either a summary of the estimates of \(R_t\) with mean, sd, and quantiles for each day, or a full breakdown of the component \(R_t\) estimates.
not available
if (FALSE) { J = jepidemic::JavaApi$get(); instance = J$GrowthRateEstimator$new(minWindow, maxWindow) ## ----------------------------------- ## Method `GrowthRateEstimator$strictGrowthRateAndRtEstimator` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$defaultGrowthRateAndRtEstimator` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$defaultGrowthRateEstimator` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$basicGrowthRateEstimator` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$withOutputQuantiles` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$withSaneDefaults` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$withInitialIncidence` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$withGaussianEstimateWeighting` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$withEstimateWeighting` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$combineEstimatesWithWeightedMixture` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$priorIncidenceFromPreviousPosterior` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$priorIncidenceFromScaledPreviousPosterior` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$priorIncidenceFromPosteriorAndGrowthRate` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$priorIncidenceFromScaledPosteriorAndGrowthRate` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$priorIncidenceFromPosteriorMean` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$priorIncidenceFromPosteriorMeanAndGrowthRate` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$priorIncidenceFromPosteriorMeanAndCoefVariation` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$priorIncidenceFromPosteriorMeanAndGrowthRateAndCoefVariation` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$useAllPosteriorEstimates` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$usePosteriorEstimatesFromOneWindow` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$usePosteriorEstimatesWithEnoughData` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$withInfectivityProfile` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$withInfectivityProfileMatrix` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$estimateGrowthRate` ## ----------------------------------- ## ----------------------------------- ## Method `GrowthRateEstimator$estimateGrowthRateSingle` ## ----------------------------------- }


11:07 pm
Finally I have some time alone, so I've decided to drop ~18mg of 5-MeO-DiPT with around 3g of powdered Syrian Rue, which is in capsules. The plan is to meditate the rest of the night, open up my meridians.
I've taken foxy at this level 8 times before, but never with a MAOI.
11:18
Starting to feel a little light headed, with a sensation at my solar plexus that's too weak at the moment to catagorize.
11:25
I'm going to smoke weed now to help me through the come-up. Tryptamines often have the affect of making me feel depressed while they kick in; the weed helps me with that.
11:30
Colors seem brighter.
The moon is low tonight... just below the treetops.
12:04am
Blood pressure 130/100.
12:18
No audios. No visuals.
12:35
Took an extra 1/3 teaspoon of syrian rue.
...The rue has a definate sedative effect... I feel mellow right now, but I'm not really tripping.
12:42
The rue has given me slight ataxia.
12:51
Feel a slight foxy-ish buzz going on... feels like an extension of the pot, thogugh. My limbs feel light -- like they're composed of air. CEVs are dreamy... a little more visual than when I'm just on weed alone.
12:58
Finally getting visuals!
1-6am
The come up is hard. Only writhing in orgasmic bliss seems to stop the harsh body load. Breathing is heavy and labored.
... I'm already charged up again....
...the body load is TOO MUCH with s. rue! I'm completely tired, but my body wants to writhe continually like I'm about to orgasm... this is very, very annoying!
I can't stop moving for even a second. When I do, within a few seconds I'm back to twisting around on my bed or floor, moaning.  After wacking off, the body load just kept building and building, causing more or less sensory black-out and incesant writhing. I could not stay still, meditate, and enjoy the trip for a second. I basically lost all control of my physical movements (to an extent).  I imagine what I experienced was somewhat similar to what people with Parkinson's go through.
Dreams/hallucinations consisted of very large, squat and round people with severe cases of edema hooked up to blood monitering devices in a nursing home.
Midway through this excrutiating ordeal, when I threw up raisins and an orange in my kitchen sink, I remembered that AMT is also a MAOI. I'd gone through this experience before when I'd done 60mg AMT with ~12mg of Foxy 6 hours into the trip. Upon realizing this, I could almost hit myself.

The rue still has me K.O.ed. I have a slight temporal headache, bilaterally; no energy; my heart rate is ~120 bpm; and tingling extremities.
________________
Afterthoughts
Shit kids, stay away from mixing 5-MeO-DiPT with any MAOI! At ALL costs!!!!
 I really could not take the incessant need to twist and claw at my mattress… nor the visuals of repulsively bloated humans hooked up to blood cleaning machines and catheters. I started screaming at several points that I wanted the trip to be over. If I wasn't a seasoned user of psychedellics, I might have turned myself in to the ER.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


{% load static from staticfiles %} {% load chat_extras %}
Pidgin is our recommended chat client for Windows and Linux users.
Once you have downloaded and installed Pidgin, add a new account by opening the Accounts menu and selecting Manage Accounts, and then click the "Add..." button.
To configure your account, set your options as so:
Under User Options, give yourself a Local alias; this only shows up to you, and identifies your own messages. I like to set it to "Me".
Now click the Add button to finish and connect to the server.
{% include 'chat/annika-intro.html' %}


These blog articles were all written as I was going through the 5-week Foundations portion of the DevAcademy Web Development bootcamp. During this time, I was learning about the very basics of HTML, CSS, and Javascript as well as learning important soft skills to aid me in my learning.


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Dev Blog of Cory Gross
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Summary
A developer in HTML and CSS with hands-on experience in development, testing, and intergration. Supportive and cooperative team player. Looking to learn and prone skills by taking on challenges, while contributing to the community.
A developer in HTML and CSS with hands-on experience in development, testing, and intergration. Supportive and cooperative team player. Looking to learn and prone skills by taking on challenges, while contributing to the community.
August 2019 - Present
Enforcing parking regulations at Colorado School of Mines in order to provide fair and safe access to parking.
May 2018 - Present
Advising PSR Crypto, LLC about cryptocurrencies, pools, and masternodes, while also building their websites.
December 2014 - Present
Working as owner and operator for iTech News. Successfully operating the blog focusing on Apple Products, rumors, and other related thechnologies, with an audience of 7,000+ per week.
Student, Computer Science Freshman
Currently a freshman pursuing computer science with computer engineering specialty.
Graduate
Attended high school and dual-enrolled with a state university. Acheived a 4.19 weighted GPA.
Daul-Enrollment Graduate
Dual-enrolled at this college, worked towards completing core classes before graduating high school. Completed nine classes, totaling 27 credit hours, with a 3.88 GPA
This is the very first website I designed. It is still being used and updated on a regular basis. It is available to view at: https://newsitech.weebly.com.
This CV is written in HTML with CSS for style and effects. To view the plain text of the HTML and CSS go to: https://manley12.github.io/Curriculum-Vitae/plaintext.html


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--- layout: post tag: Algorithm excerpt: In the general case, Integer Programming Problem is NP-complete. However, in a special case, the IP problem can be solved by Linear Programming. ---
In the general case, Integer Programming Problem is NP-complete. However, in a special case, the IP problem can be solved by Linear Programming.
\(\Leftarrow\): If the graph contains an odd cycle, the submatrix corresponding to the cycle has determinant 2, contradiction.
\(\Rightarrow\): Prove by induction. The first two cases are trivial and the same as Example 1. In the last case, all columns of \(M\) has exactly two 1. Now permutate the rows to split \(M\) into two halves, each representing a group. Now we can multiply the bottom half with -1 to make it a directed graph. Multiplying rows by -1 only changes the sign of the determinant. We then apply the result from example 1 to finish the proof.
Here we consider the Linear Programming Problem of the following form \[ \begin{split} \max & {\text{ }c ^T x}\\ \text{s.t. } & Ax \le b, x \ge 0 \end{split} \]
The Linear Programming relaxation of the matching problem is (the original problem restricts \(x\) to be either 0 or 1) \[ \begin{split} \max &{\text{ }1^T x} \\ \text{s.t. } &Ax \le 1\\ & x \ge 0 \end{split} \]
Likewise, the node cover problem can be relaxed to \[ \begin{split} \min & {\text{ } 1 ^T y} \\ \text{s.t. } & A^T y \ge 1\\ & y \ge 0 \end{split} \]
For a bipartite graph, \(A\) is TU ⇒ exists primal-dual pair that is integer, and this is the desired matching/node-cover.


This week we (Jeroen and Niek) enjoyining Full Stack Fest 2017 Barcelona, a week-long full stack development conference with a focus on solving current problems with new & inspiring perspectives.
In this quite short blog we just summarizing briefly the talks and our opinion about the talks.
The first two days are more backend focussed, the last two days have a frontend focus, read more here.
Rating:
Karissa McKelvey (@okdistribute) discussed the topic to share fairly large data sets in the science community in a secure and distributed way. The dat project enables file sharing via peer to peer for example with the beaker browser.
Rating:
Luca Marchesini (@kuzzleio) did a great job by explanining about blockchain and the differences between ethereum and bitcoin. Blockchain; the system where playing by the rules is more profitable than cheating.
Rating:
We love the Terraform framework of HashiCorp but the talk not as good as we hoped, nothing new. The speaker Anton (@antonbabenko) is an expert on the topic and also maintains some great Terraform modules.
Rating:
For us a standard talk by Marcia (@mavi888uy) about migrating an existing monolitic applicaton to smaller components. We loved all the (angry) birds flying around.
Rating:
Good story about cyber security by Dr. Jessica Barker (@drjessicabarker). About how the mood of a user is one of the weakest link. How you can trick people into clicking a bad links. Socially engineering and use stress full situation let a user execute an (maybe) unwanted action.
Key take aways, learn to think as hacker and focus on the why not on how to enforce / restrict users to do something.
Rating:
Comparison with the army knife. Don’t be afraid of new technologies. One tool for everything is not going to work. Example with analytics service, kafka stream, bus. Frank Lyaruu (@lyaruu) did a great job!
Rating:
Good explaination by Sahil (@sahildua2305) about how to train a model and how to deploy them.
Rating:
Not relevant to our needs.
Rating:
Good talk by John Graham-Cummin (@jgrahamc) about how IoT device can be used to do a DDoS attack. About port 1900 showing all kinds of information which is sent to victims address. How to block DDoS attacks by putting the ip-address in a grid to search for patterns.
Rating:
Difference between logs, metric and trace. Good (known) story by Adrian Cole (@adrianfcole).
Rating:
The story about testing in real live situations is not new, good te remember the importance to be prepared for failure. For not much new things in this talk since we here the chaos monkeys stories new for a couple of years. But we will plan our Chaos GameDay soon. Thanks to Aish (@aishrajdahal)
Rating:
Complexity of systems cause a lot of problems. Idris
has a nice feature that Types are also values, so state changes can easialy be checked in compile time. talk.applaud()
should only run after talk.give()
and Idris has a way to check this easily during compile time.
Inspiration for doing more with new languages. Chris Ford (@ctford), thanks!
Rating:
Kelsey Breseman (@ifoundtheme) explains us how complex the eletrical grids works which makes it hard as well to safe for example solar eneregy. It was great to hear the complecity of this relevant topic but unfortunately there was not much relation betwoon the world of of software.
 Learn from that! Talk by Jerome Scheuring
Rating:
There where quite a lot fo lighting talks. In our opinion: hight quality but too many.
docker build
was fun. Docker build takes long, and from now on I can’t ignore it anymore. THANKS!!! aka we should fix the layer thing.Rating:
Awesome DDoS attack of slides by Rich Jones (@GUNdotIO). A lot of cool stuff. A very usefull and insight talk about how unsecure you serverless application can be. Serverless python with zappa and slides


{% blocktrans trimmed %}Device ID attached to the port.
{% endblocktrans %}
{% trans "Device Owner" %}
{% blocktrans trimmed %}Device owner attached to the port.
{% endblocktrans %}
{% trans "Binding: Host" %}
{% blocktrans trimmed %}The ID of the host where the port is allocated.
In some cases, different implementations can run on different hosts.
{% endblocktrans %}
{% trans "MAC Address" %}
{% blocktrans trimmed %}MAC address for the port.
{% endblocktrans %}


News story
The first apprentices that completed a new employer-designed apprenticeship standard attended a graduation event in the House of Commons.
The 15 apprentices, employed by UK Power Networks, are the first to successfully complete one of the new Trailblazer apprenticeships, designed by top employers to meet the needs of industry. The Trailblazer programme forms a key part of the government’s reform of apprenticeships to ensure businesses get the skills they need to succeed.
The apprentices will now put their 18-month Power Network Craftsperson Standard training programme to good use, working as ‘jointers’, connecting and repairing underground electricity cables across London, the South East and East Anglia. Their role will also include working with top energy suppliers to install smart meters in 8.1 million homes across London, the South East and East Anglia.
Following the success of the apprenticeship standard, it was also announced at today’s event that employers in the sector have committed to recruiting over 4000 apprentices this year.
Nadhim Zahawi, Apprenticeship Adviser for the Prime Minister, hosted the event and said:
It is tremendously exciting for me to host this ground-breaking event, to celebrate the first apprentice graduates on a new Standard.
Trailblazers have a massive bearing on the future of apprenticeships, setting out the exact skills, attributes and talents that employers need as well as stamping the mark of quality indelibly on each apprenticeship.
I can’t congratulate these 15 apprentices enough for their dedication to their profession and succeeding in achieving their apprenticeships.
The 15 graduating apprentices are:
Two of the graduating apprentices are father and son Michael and Lee Shannon.
Mick Shannon, 55, of Grays, Essex, joined UK Power Networks’ trainee scheme in 2014. He said:
I am honoured to have completed my apprenticeship alongside my son Lee and I speak on behalf of the whole team when I say that this apprenticeship has been of huge benefit to us all as we deliver in our jobs, giving us all role specific skills and a nationally recognised qualification.
Today’s event reinforces the government’s commitment to 3 million apprenticeships by 2020, providing the skills employers and the economy need for growth. Apprenticeships are available in 1,500 job roles, in more than 170 industries. Apprenticeships develop a motivated, skilled and qualified workforce and improve productivity.
More information about the new standards is available on the apprenticeship standards page .


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Module ecs
ECS (entity compenent system) library for Lua.
Entity Component System library, inspired by the excellent tiny-ecs by bakpakin. Main differences include:
- ability to nest systems (more organisation potential);
- instanciation of systems for each world (no shared state) (several worlds can coexist at the same time easily);
- adding and removing entities is done instantaneously (no going isane over tiny-ecs cache issues);
- ability to add and remove components from entities after they were added to the world (more dynamic entities);
- much better performance for ordered systems (entities are stored in a skip list internally).
And a fair amount of other quality-of-life features.
The goals of this library are similar in spirit to tiny-ecs: simple to use, flexible, and useful. The more advanced features it provides relative to tiny-ecs are made so that you can completely ignore them if you don’t use them.
The module returns a table that contains several functions, world or scene are starting points to create your world.
This library was designed to be reasonably fast; on my machine using LuaJIT, in the duration of a frame (1/60 seconds) about 40000 entities can be added to an unordered system or 8000 to an ordered system. Complexities are documented for each function.
No mandatory dependency. Optional dependency: ubiquitousse.scene, to allow quick creation of ECS-based scenes (ecs.scene).
Usage:
-- Same example as tiny-ecs', for comparaison purposes local ecs = require("ubiquitousse.ecs") local talkingSystem = { filter = { "name", "mass", "phrase" }, process = function(self, e, c, dt) e.mass = e.mass + dt * 3 print(("%s who weighs %d pounds, says %q."):format(e.name, e.mass, e.phrase)) end } local joe = { name = "Joe", phrase = "I'm a plumber.", mass = 150, hairColor = "brown" } local world = ecs.world(talkingSystem) world:add(joe) for i = 1, 20 do world:update(1) end
Functions
| world (...) | Create and returns a world system based on a list of systems. |
| all (...) | Returns a filter that returns true if, for every argument, a field with the same name exists in the entity. |
| any (...) | Returns a filter that returns true if one of the arguments if the name of a field in the entity. |
| scene (name[, systems={}[, entities={}]]) | If uqt.scene is available, returns a new scene that will consist of a ECS world with the specified systems and entities. |
Entity objects
Components.
| Entity.first | First element of the highest layer linked list of entities: { entity, next_element, element_in_lower_layer }. |
| Entity.firstBase | First element of the base layer. |
| Entity.previous | List of hash map (one per skip listlayer) of entities in the system and their previous linked list element. |
| Entity.nLayers | Number of layers in the skip list. |
| Entity.n | Number of elements in the skip list. |
System objects
Modifiable fields.
| System.name | Name of the system. |
| System.systems | List of subsystems. |
| System.interval | If not false , the system will only update every interval seconds. |
| System.active | The system and its susbsystems will only update if this is true . |
| System.visible | The system and its subsystems will only draw if this is true . |
| System.component | Name of the system component. |
| System.default | Defaults value to put into the entities’s system component when they are added. |
Callbacks.
| System:filter (e) [callback] | Called when checking if an entity should be added to this system. |
| System:compare (e1, e2) [callback] | Called when adding an entity to this system determining its order. |
| System:onAdd (e, c) [callback] | Called when adding an entity to the system. |
| System:onRemove (e, c) [callback] | Called when removing an entity from the system. |
| System:onInstance () [callback] | Called when the system is instancied, before any call to System:onAddToWorld (including other systems in the world). |
| System:onAddToWorld (world) [callback] | Called when the system is added to a world. |
| System:onRemoveFromWorld (world) [callback] | Called when the system is removed from a world (i.e., the world is destroyed). |
| System:onDestroy () [callback] | Called when the world is destroyed, after every call to System:onRemoveFromWorld (including other systems in the world). |
| System:onUpdate (dt) [callback] | Called when updating the system. |
| System:onDraw () [callback] | Called when drawing the system. |
| System:process (e, c, dt) [callback] | Called when updating the system, for every entity the system contains. |
| System:render (e, c) [callback] | Called when drawing the system, for every entity the system contains. |
| System:onUpdateEnd (dt) [callback] | Called after updating the system. |
| System:onDrawEnd () [callback] | Called after drawing the system. |
Read-only fields.
| System.world [read-only] | The world the system belongs to. |
| System.w [read-only] | Shortcut to System.world. |
| System.entityCount [read-only] | Number of entities in the system. |
| System.s [read-only] | Map of all named systems in the world (not only subsystems). |
Methods.
| System:add (e, ...) | Add entities to the system and its subsystems. |
| System:remove (e, ...) | Remove entities from the system and its subsystems. |
| System:refresh (e, ...) | Refresh an entity’s systems. |
| System:reorder (e, ...) | Reorder an entity. |
| System:has (e, ...) | Returns true if all these entities are in the system. |
| System:iter () | Returns an iterator that iterate through the entties in this system, in order. |
| System:get (i) | Get the i th entity in the system. |
| System:clear () | Remove every entity from the system and its subsystems. |
| System:update (dt) | Try to update the system and its subsystems. |
| System:draw () | Try to draw the system and its subsystems. |
| System:callback (name, e, ...) | Trigger a custom callback on a single entity. |
| System:emit (name, ...) | Emit an event on the system. |
| System:destroy () | Remove all the entities and subsystems in this system. |
Functions
- world (...)
-
Create and returns a world system based on a list of systems.
The systems will be instancied for this world.
Parameters:
- ... table,... list of (uninstancied) systems
Returns:
-
System
the world system
- all (...)
-
Returns a filter that returns
true
if, for every argument, a field with the same name exists in the entity.Parameters:
- ... string,... list of field names that must be in entity
Returns:
-
function(e)
that returns
true
if e has all the fields - any (...)
-
Returns a filter that returns
true
if one of the arguments if the name of a field in the entity.Parameters:
- ... string,... list of field names that may be in entity
Returns:
-
function(e)
that returns
true
if e has at leats one of the fields - scene (name[, systems={}[, entities={}]])
-
If
uqt.scene
is available, returns a new scene that will consist of a ECS world with the specified systems and entities.When suspending and resuming the scene, the
onSuspend
andonResume
events will be emitted on the ECS world.Note that since
uqt.scene
use require to load the scenes, the scenes files will be cached – including variables defined in them. So if you store the entity list directly in a variable in the scene file, it will be reused every time the scene is entered, and will thus be shared among the different execution of the scene. This may be problematic if an entity is modified by one scene instance, as it will affect others (this should not be a problem with systems due to system instanciation). To avoid this issue, instead you would typically define entities through a function that will recreate the entities on every scene load.Requires:
-
ubiquitousse.scene
Parameters:
- name string the name of the new scene
- systems table/function list of systems to add to the world. If it is a function, it will be executed every time we enter the scene and returns the list of systems. (default {})
- entities table/function list of entities to add to the world. If it is a function, it will be executed every time we enter the scene and returns the list of entities. (default {})
Returns:
-
scene
the new scene
Entity objects
The idea is that entities should only contain data and no code; it’s the systems that are responsible for the actual processing (but it’s your game, do as you want).
This data is referred to, and organized in, “components”.
Components.
This library does not do any kind of special processing by itself on the entity tables and take them as is (no metatable, no methamethods, etc.), so you are free to handle them as you want in your systems or elsewhere.
Since it’s relatively common for systems to only operate on a single component, as a shortcut the library often consider what it calls the “system component”: that is, the component in the entity that is named like System.component (or System.name if it is not set). Though there’s no problem if there’s no system component or if it doesn’t exist in the entity.
- Entity.first
-
First element of the highest layer linked list of entities: { entity, next_element, element_in_lower_layer }.
The default entity
head
is always added as a first element to simplify algorithms; remember to skip it.Fields:
- head
- nil
- Entity.firstBase
- First element of the base layer.
- Entity.previous
-
List of hash map (one per skip listlayer) of entities in the system and their previous linked list element.
Does not contain a key for the
head
entity. This make each linked list layer effectively a doubly linked list, but with fast access to the previous element using this map (and therefore O(1) deletion).Fields:
- {
- Entity.nLayers
- Number of layers in the skip list.
- Entity.n
- Number of elements in the skip list.
Usage:
-- example entity local entity = { position = { x = 0, y = 52 }, -- a "position" component sprite = newSprite("awesomeguy.png") -- a "sprite" component } -- example "sprite" system local sprite = { name = "sprite", filter = "sprite", -- process entities that have a "sprite" component -- systems callbacks that are called per-entity often give you the system component as an argument -- the system component is the component with the same name as the system, thus here the sprite component render = function(self, entity, component) -- component == entity.sprite draw(component) end }
System objects
A system can also be created that do not accept any entity (filter = false
, this is the default): such a system can still be
used to do processing that don’t need to be done per-entity but still behave like other systems (e.g. to do some static calculation each update).
The system also contains callbacks, these define the actual processing done on the system and its entities and you will want to redefine at least one of them to make your system actually do something.
Then you can call System:update, System:draw, System:emit or System:callback at appropriate times and the system will call the associated callbacks on itself and its entities, and then pass it to its subsystems. In practise you would likely only call these on the world system, so the callbacks are correctly propagated to every single system in the world.
Systems are defined as regular tables with all the fields and methods you need in it. However, when a system is added to a world, the table you defined is not used directly, but we use what we call an “instancied system”: think of it of an instance of your system like if it were a class. The instancied system will have a metatable set that gives it some methods and fields defined by the library on top of what you defined. Modifying the instancied system will only modify this instance and not the original system you defined, so several instances of your system can exist in different worlds (note that the original system is not copied on instancing; if you reference a table in the original system it will use the original table directly).
Systems can have subsystems; that is a system that behave as an extension of their parent system. They only operates on the entities already present in their parent subsystem, only update when their parent system updates, etc. You can thus organize your systems in a hierarchy to avoid repeating your filters or allow controlling several system from a single parent system.
The top-level system is called the “world”; it behaves in exactly the same way as other systems, and accept every entity by default.
Usage:
local sprite = { filter = { "sprite", "position" }, -- only operate on entities with "sprite" and "position" components systems = { animated }, -- subsystems: they only operate on entities already filtered by this system (on top of their own filtering) -- Called when an entity is added to this system. onAdd = function(self, entity, component) print("Added an entity, entity count in the system:", self.entityCount) -- self refer to the instancied system end, -- Called when the system is updated, for every entity the system process = function(self, entity, component, dt) -- processing... end } local world = ecs.world(system) -- instanciate a world with the sprite system (and all its subsystems) -- Add an entity: doesn't pass the filtering, so nothing happens world:add { name = "John" } -- Added to the sprite system! Call sprite:onAdd, and also try to add it to its subsystems world:add { sprite = newSprite("example.png"), position = { x=5, y=0 } } -- Trigger sprite:onUpdate and sprite:process callbacks world:update(dt)
Modifiable fields.
- System.name
-
Name of the system.
Used to create a field with the system’s name in
world.s
and determine the associated system component if System.component is not set. If not set, the system will not appear inworld.s
.Do not change after system instanciation.
Type:
string
nil
if no name
- System.systems
-
List of subsystems.
On a instancied system, this is a list of the same subsystems, but instancied for this world.
Do not change after system instanciation.
Type:
table
nil
if no subsystem
- System.interval
-
If not
false
, the system will only update every interval seconds.false
by default.Type:
number
interval of time between each updatefalse
to disable
- System.active
-
The system and its susbsystems will only update if this is
true
.true
by default.Type:
boolean
- System.visible
-
The system and its subsystems will only draw if this is
true
.true
by default.Type:
boolean
- System.component
-
Name of the system component.
Used to determine the associated system component.
If not set, this will fall back to System.name. If this is also not set, then we will give
nil
instead of the system component in callbacks.Type:
string
nil
if no name
- System.default
-
Defaults value to put into the entities’s system component when they are added.
If this is table, will recursively fill missing values. Metatables will be preserved during the copy but not copied themselves.
Changing this will not affect entities already in the system. Doesn’t have any effect if the system doesn’t have a component name.
Type:
any
nil
if no default
Callbacks.
- System:filter (e) [callback]
-
Called when checking if an entity should be added to this system.
Returns
true
if the entity should be added to this system (and therefore its subsystems).If this is a string or a table, it will be converted to a filter function on instanciation using ecs.all.
If this
true
, will accept every entity; iffalse
, reject every entity.Will only test entities when they are added; changing this after system creation will not affect entities already in the system.
By default, rejects everything.
Parameters:
- e table entity table to check
Returns:
-
boolean
true
if entity should be added - System:compare (e1, e2) [callback]
-
Called when adding an entity to this system determining its order.
Returns
true
ife1 <=
e2 (i.e., ife1
should be processed beforee2
in this system). e1 and e2 are two entities.Used to place the entity in the sorted entity list when it is added; changing this after system creation will not change the order of entities already in the system.
By default, new entities are added at the start of the list.
Parameters:
Returns:
-
boolean
true
if e1 <= e2 - System:onAdd (e, c) [callback]
-
Called when adding an entity to the system.
Parameters:
- System:onRemove (e, c) [callback]
-
Called when removing an entity from the system.
Parameters:
- System:onInstance () [callback]
- Called when the system is instancied, before any call to System:onAddToWorld (including other systems in the world).
- System:onAddToWorld (world) [callback]
-
Called when the system is added to a world.
Parameters:
- world System world system
- System:onRemoveFromWorld (world) [callback]
-
Called when the system is removed from a world (i.e., the world is destroyed).
Parameters:
- world System world system
- System:onDestroy () [callback]
- Called when the world is destroyed, after every call to System:onRemoveFromWorld (including other systems in the world).
- System:onUpdate (dt) [callback]
-
Called when updating the system.
Called before any call to System:process or call to subsystems.
Parameters:
- dt number delta-time since last update
- System:onDraw () [callback]
- Called when drawing the system. Called before any call to System:draw or call to subsystems.
- System:process (e, c, dt) [callback]
-
Called when updating the system, for every entity the system contains.
Called after System:onUpdate was called on the system, and before any call to subsystems.
Parameters:
- System:render (e, c) [callback]
-
Called when drawing the system, for every entity the system contains.
Called after System:onDraw was called on the system, and before any call to subsystems.
Parameters:
- System:onUpdateEnd (dt) [callback]
-
Called after updating the system.
Called after System:onDraw, System:process and calls to subsystems.
Parameters:
- dt number delta-time since last update
- System:onDrawEnd () [callback]
- Called after drawing the system. Called after System:onUpdate, System:render and calls to subsystems.
Read-only fields.
- System.world [read-only]
-
The world the system belongs to.
Type:
System
world - System.w [read-only]
-
Shortcut to System.world.
Type:
System
world - System.entityCount [read-only]
-
Number of entities in the system.
Type:
integer
- System.s [read-only]
-
Map of all named systems in the world (not only subsystems). Same for every system from the same world.
Type:
table
{[system.name]=instanciedSystem, ...}
Methods.
- System:add (e, ...)
-
Add entities to the system and its subsystems.
Will skip entities that are already in the system.
Entities are added to subsystems after they were succesfully added to their parent system.
If this is called on a subsystem instead of the world, be warned that this will bypass all the parent’s systems filters. If you do that, since System:remove will not search for entities in systems where they should have been filtered out, the added entities will not be removed when calling System:remove on a parent system or the world. The entity can be removed by calling System:remove on the system System:add was called on.
Complexity: O(1) per unordered system, O(log2(entityCount)) per ordered system.
Parameters:
- e Entity entity to add
- ... Entity... other entities to add
Returns:
-
Entity,...
e,…
the function arguments - System:remove (e, ...)
-
Remove entities from the system and its subsystems.
Will skip entities that are not in the system.
Entities are removed from subsystems before they are removed from their parent system.
If you intend to call this on a subsystem instead of the world, please read the warning in System:add.
Complexity: O(1) per unordered system, O(log2(entityCount)) per ordered system.
Parameters:
- e Entity entity to remove
- ... Entity... other entities to remove
Returns:
-
Entity,...
e,…
the function arguments - System:refresh (e, ...)
-
Refresh an entity’s systems.
Behave similarly to System:add, but if the entity is already in the system, instead of skipping it, it will check for new and removed components and add and remove from (sub)systems accordingly.
Complexity: O(1) per system + add/remove complexity.
Parameters:
- e Entity entity to refresh
- ... Entity... other entities to refresh
Returns:
-
Entity,...
e,…
the function arguments - System:reorder (e, ...)
-
Reorder an entity.
Will recalculate the entity position in the entity list for this system and its subsystems. Will skip entities that are not in the system.
Complexity: O(1) per unordered system, O(log2(entityCount)) per ordered system.
Parameters:
- e Entity entity to reorder
- ... Entity... other entities to reorder
Returns:
-
Entity,...
e,…
the function arguments - System:has (e, ...)
-
Returns
true
if all these entities are in the system.Complexity: O(1).
Parameters:
- e Entity entity that may be in the system
- ... Entity... other entities that may be in the system
Returns:
-
boolean
true
if every entity is in the system - System:iter ()
-
Returns an iterator that iterate through the entties in this system, in order.
Complexity: O(1) per iteration; O(entityCount) for the full iteration
Returns:
-
iterator
iterator over the entities in this system
- System:get (i)
-
Get the
i
th entity in the system.Complexity: O(i)
Parameters:
- i number the index of the entity
Returns:
-
Entity
the entity;
nil
if there is no such entity in the system - System:clear ()
-
Remove every entity from the system and its subsystems.
Complexity: O(entityCount) per system
- System:update (dt)
-
Try to update the system and its subsystems. Should be called on every game update.
Subsystems are updated after their parent system.
Complexity: O(entityCount) per system if system:process is defined; O(1) per system otherwise.
Parameters:
- dt number delta-time since last update
- System:draw ()
-
Try to draw the system and its subsystems. Should be called on every game draw.
Subsystems are drawn after their parent system.
— Complexity: O(entityCount) per system if system:render is defined; O(1) per system otherwise.
- System:callback (name, e, ...)
-
Trigger a custom callback on a single entity.
This will call the
System:name(e, c, …)
method in this system and its subsystems, if the method exists and the entity is in the system.c
is the system component associated with the current system, ande
is the Entity.Think of it as a way to perform custom callbacks issued from an entity event, similar to System:onAdd.
Complexity: O(1) per system
Parameters:
- System:emit (name, ...)
-
Emit an event on the system.
This will call the
System:name(…)
method in this system and its subsystems, if the method exists.Think of it as a way to perform custom callbacks issued from a general event, similar to System:onUpdate.
The called methods may return a string value to affect the event propagation behaviour:
- if a callback returns
"stop"
, the event will not be propagated to the subsystems. - if a callback returns
"capture"
, the event will not be propagated to the subsystems and its sibling systems (i.e. completely stop the propagation of the event).
"stop"
would be for example used to disable some behaviour in the system and its subsystems (likeactive = false
can disable System:onUpdate behaviour on the system and its subsystems)."capture"
would be for example used to prevent other systems from handling the event (for example to make sure an input event is handled only once by a single system).Complexity: O(1) per system
Parameters:
- name string name of the callback
- ... other arguments to pass to the callback
- if a callback returns
- System:destroy ()
- Remove all the entities and subsystems in this system. Complexity: O(entityCount) per system


EBD - Easton Bible DIctionary
Minni
MINNI
"MINNI "only in
Jeremiah 51:27
, as the name of a province in Armenia, which was at this time under the Median kings. Armenia is regarded by some as = Har-minni i.e., the mountainous country of Minni. (See ARMENIA.)
"MINNI "only in Jeremiah 51:27, as the name of a province in Armenia, which was at this time under the Median kings. Armenia is regarded by some as = Har-minni i.e., the mountainous country of Minni. (See ARMENIA.)


Buffer Overflow
Determine length of overflow trigger w/ binary search "A"x1000
Determine exact EIP with pattern_create.rb -l 1000, & pattern_offset.rb -q <pattern>
Determine badchars to make sure all of your payload is getting through
Determine range of addresses for LIBC
Determine offsets for LIBC: system and exit
Determine offset for /bin/sh
Develop exploit
base_libc- system_offset
- exit_offset
- binsh_offset
- system_addr = base_libc + system_off
- exit_addr = base_libc + exit_off
- binsh_addr = base_libc + binsh_off
- buf = A*overflow #will run program x number of times to bruteforce landing at this address
- buf+=system_addr
- buf+=exit_addr
- buf+=binsh_addr
while < num_times_need_to_run:
call([vuln_program, buf])


Are you (or someone you know) currently facing an emergency or crisis situation, or is there the possibility that immediate harm could come to yourself (or others)?
Are you looking for support services for general or specific mental health issues?
Are you looking for services on-campus, off-campus or online?
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Masterson therapy is a psycho-dynamic treatment approach which is particularly beneficial for individuals with borderline personality disorder. Counselling for narcissistic personality disorder Partially reimbursed by private health
insurance and Medicare (with a GP referral- mental health care plan).
ODialectical Behaviour Therapy (DBT) is an e ective treatment for people experiencing frequent and intense changes in emotional state, often leading to self- damaging behaviours. Please consult private health insurance whether services
are covered.
Beyondnow app: A FREE suicide safety planning app. Suicide safety planning involves creating a structured plan – ideally with support from your health professional or someone you trust – that you work through when you’re experiencing
suicidal thoughts, feelings, distress or crisis. Available to download for FREE from the Apple Store or Google Play. BeyondNow is also available to use online.
The Black Dog Institute is a not-for-profit facility for the diagnosis, treatment and prevention of mood disorders such as depression and bipolar disorder.
Is a FREE online interactive self-help program designed to address mild to-moderate symptoms of stress, anxiety, and depression through personalised treatments delivered entirely online.
Is a FREE mobile-based app designed for Australian adults to help monitor and manage their depression or anxiety discreetly. The snapshot app is free to download from the Apple Store or Google Play.
Is an internet based self-help program for people with insomnia that clinical trials have also found to reduce depression and anxiety symptoms. SHUTi costs $170.00 for a 26 week period and $230.00 for a 52 weeks period.
Information, automated assessment (optional) and online self-guided treatment programs for anxiety/depression, and panic disorders. Courses available as self-help for free or with therapist support for a small client fee.
Therapist-assisted or self-help courses for depression, anxiety (GAD, panic, social phobia, OCD) and mixed depression/anxiety for 18+ years. Requires a small client fee. Also has free self-help course for stress.
Information, automated assessment (optional) and online self-guided treatment programs for PTSD. Courses available as self-help for free or with therapist support for a small client fee.
general wellbeing/health (the more entry level/first point of contact psychological service)
REACHOUT
Reachout WorryTime app: Helps users control anxiety by scheduling worry so that it is confined to a specific time each day. Available for download through the Apple store and the Google Play store.
Using simple visuals, the app helps young people reduce the physical symptoms of stress and anxiety by slowing down their heart rate to increase feelings of calmness in the body. Available for download through the Apple store.
A six-week program that helps improve your general health and wellbeing by focusing on improving sleep patterns and promoting physical activity. Available for download through the Apple store.
services specifically designed for lgbtqia+ students
FREEDOM CENTRE ONLINE
FC’s Tumblr and the FC Forum are online peer-support & info services for people under 26 who are same sex attracted, sex and/or gender diverse to get social support, information and meet others who have similar experiences to them.