Britain's plan to raise minimum wage backed by review
Companies are now likely to see wage costs rise after next month's snap national election, whatever the outcome.
The United Kingdom's plan to raise the minimum wage to two-thirds of median earnings, taking it to 10.50 pounds ($13.58) an hour, was endorsed by an independent review on Monday that found setting a floor on pay had a negligible effect on job creation.
Companies are now likely to see wage costs rise after next month's snap national election, whatever the outcome.
Conservative Chancellor of the Exchequer Sajid Javid said in September that he would increase the National Living Wage (NLW) to the new target by 2024, provided economic conditions allowed, and expand its reach to all workers over the age of 21, versus the workers over 25 who are covered now.
The opposition Labour Party said it would raise the minimum wage to 10 pounds ($12.93) an hour immediately if it wins power.
More:
An independent review commissioned by the government from economics professor Arindrajit Dube of the University of Massachusetts Amherst in the United States examined the impact of minimum wages in Germany, the US, Britain, and other countries.
"Based on the overall evidence - with a special emphasis on the recent, high-quality evaluations of the NLW and other more ambitious policies internationally - my report concludes that there is room for exploring a higher NLW in the UK up to two-thirds of the median wage," Dube said. "It will also be important to empirically evaluate and recalibrate any such ambitious policy based on new evidence down the road."
Chancellor Javid said of plans to raise the NWL two-thirds by 2024, "The evidence is clear that our approach is the right one." But Labour said Javid's pledge "was an insult to our hard-working people".
"It's a derisory offer which people will have to wait years for," Labour's finance spokesman John McDonnell said. "Labour will immediately introduce a real living wage of 10 pounds an hour for everyone 16 and over, outstripping every publicity stunt figure the Tories invent."


Life isn't all about machine learning! Here's a few of the things I enjoy doing, or have done in the past, when I'm not at a computer running experiments. In no particular order:


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @import uk.gov.hmrc.hmrcfrontend.views.html.helpers.{HmrcStandardFooter, HmrcStandardHeader, HmrcHead, HmrcScripts, HmrcLayout} @import views.html.helper.CSPNonce @this( hmrcLayout: HmrcLayout, hmrcHead: HmrcHead, hmrcStandardHeader: HmrcStandardHeader, hmrcStandardFooter: HmrcStandardFooter, hmrcScripts: HmrcScripts, hmrcReportTechnicalIssueHelper: HmrcReportTechnicalIssueHelper, hmrcLanguageSelectHelper: HmrcLanguageSelectHelper, hmrcTimeoutDialogHelper: HmrcTimeoutDialogHelper ) @(title: String, scriptElement: Option[Html] = None, isUserResearchBannerVisible: Boolean = false, disableSignOut: Boolean = false)(contentBlock: Html)(implicit request: Request[_], messages: Messages, applicationConfig: ApplicationConfig) @head = { @if(!disableSignOut) { @hmrcTimeoutDialogHelper( signOutUrl = applicationConfig.signOut, timeoutUrl = Some(applicationConfig.timeOut), timeout = Some(applicationConfig.timeOutSeconds), countdown = Some(applicationConfig.timeOutCountDownSeconds) ) } } @content = {


(this demonstrates how you can link to a page without setting the "previousPage" - useful if this is not the parent of the page you are linking to, and you'd rather have that page generate a parent link than a back button) (TODO: move this link to another page, so the demo is more meaningful)
(the grandchild page should render "parent links" that can be navigated all the way back the root page)


The book $H_\infty$-Optimal Control and Related Minimax Design Problems frames solving $H_\infty$ optimal control problems in terms of the language of dynamic games, and gives in my opinion quite a transparent derivation. In this post, I will explore the basics of these ideas for a discrete-time linear system. $ \newcommand{\abs}[1]{| #1 |} \newcommand{\bigabs}[1]{\left| #1 \right|} \newcommand{\R}{\mathbb{R}} \newcommand{\E}{\mathbb{E}} \renewcommand{\Pr}{\mathbb{P}} \newcommand{\calE}{\mathcal{E}} \newcommand{\calF}{\mathcal{F}} \newcommand{\calD}{\mathcal{D}} \newcommand{\calN}{\mathcal{N}} \newcommand{\calL}{\mathcal{L}} \newcommand{\calM}{\mathcal{M}} \newcommand{\bbP}{\mathbb{P}} \newcommand{\bbQ}{\mathbb{Q}} \newcommand{\ip}[2]{\langle #1, #2 \rangle} \newcommand{\bigip}[2]{\left\langle #1, #2 \right\rangle} \newcommand{\T}{\mathsf{T}} \newcommand{\Tr}{\mathrm{Tr}} \newcommand{\ind}{\mathbf{1}} \newcommand{\calL}{\mathcal{L}} \newcommand{\norm}[1]{\lVert #1 \rVert} $
Consider the following discrete-time LTI system $$ x_{k+1} = A x_k + B u_k + w_k \:, \:\: x_1 = 0 \:. $$ When we frame the LQR problem, we make a distributional assumption on $w_k$, namely it is driven by (say) a zero-mean independent stochastic process. The resulting LQR controller is optimal under this statistical assumption. While the distributional assumption makes for an elegant theory, it is quite a strong assumption in practice, and it is not obvious how the performance of the LQR controller suffers when the stochastic assumption does not hold.
In $H_\infty$ optimal control, we take a distribution free, adversarial approach. Instead, we aim to design a controller that behaves well in the worst-case. Mathematically, there are many ways to frame this. One such framing is as follows: $$ \begin{align} \min_{u} \max_{w : \norm{w} \leq 1} \sum_{k=1}^{K} x_k^\T Q x_k + u_k^\T R u_k + x_{K+1}^\T Q_f x_{K+1} \:, \label{eq:hinf_opt} \end{align} $$ where the minimum over $u$ is over causal functions $u_k = u_k(x_k, x_{k-1}, x_{k-2}, ...)$ and the maximum over $w$ is over $\ell_2$ bounded signals that satisfy $\sum_{k \geq 0} \norm{w_k}^2 \leq 1$ (the one here is arbitrary). Here, we assume the matrices $Q, Q_f, R$ are positive definite for simplicity. While this optimal control problem appears to be harder than the LQR problem, it turns out that it can be solved with very similar techniques, namely dynamic programming.
A Related Dynamic Game
The approach taken in Başar and Bernhard's book is to first solve a related dynamic game. Define the functional $L_\gamma(u, w)$ as: $$ L_\gamma(u, w) = \sum_{k=1}^{K} x_k^\T Q x_k + u_k^\T R u_k - \gamma^2 w_k^\T w_k + x_{K+1}^\T Q_f x_{K+1} \:. $$ The game we are now interested in solving is: $$ \begin{align} \min_{u} \max_{w} L_\gamma(u, w) \:. \label{eq:game_one} \end{align} $$ Notice how $w$ no longer has any constraints.
Theorem: Define the sequence of matrices: $$ \begin{align*} M_k &= Q + A^\T M_{k+1} \Lambda_k^{-1} A_k \:, \:\: M_{K+1} = Q_f \:, \\ \Lambda_k &= I + (B R^{-1} B^\T - \gamma^{-2} I) M_{k+1} \:, \end{align*} $$ and suppose that $$ \gamma^2 I - M_k \succ 0 \:, \:\: k = 2, ..., K+1 \:. $$ Then the dynamic game $\eqref{eq:game_one}$ has a unique saddle point solution. The solution is given by: $$ \begin{align*} u_k^* &= - R^{-1} B^\T M_{k+1} \Lambda_k^{-1} A x_k \:, \\ w_k^* &= \gamma^{-2} M_{k+1} \Lambda_k^{-1} A_k x_k \:. \end{align*} $$ and its value is $$ \min_u \max_w L_\gamma(u, w) = x_1^\T M_1 x_1 \:. $$
Proof: The proof uses the Issacs's equations, which establish sufficient conditions for a saddle point solution of a dynamic game to exist. We first solve an auxiliary problem. Fix a vector $x$ and positive semi-definite matrix $M$ that satisfies $\gamma^2 I - M \succ 0$. Define $h(u, w)$ to be: $$ h(u, w) = x^\T Q x + u^\T R u - \gamma^2 w^\T w + (A x + B u + w)^\T M (A x + B u + w) \:. $$ Then the mapping $u \mapsto h(u, w)$ is strictly convex for any $w$ and $w \mapsto h(u, w)$ is strictly concave for any $u$. To see this, observe that: $$ \begin{align*} \nabla^2_u h(u, w) &= 2R + B^\T M B \:, \\ \nabla^2_w h(u, w) &= - 2 \gamma^2 I + 2 M \:. \end{align*} $$ This shows that $\nabla^2_u h(u, w)$ is positive definite and $\nabla^2_w h(u, w)$ is negative definite. Consider the game $$ \min_u \max_w h(u, w) \:. $$ We first compute $\max_w h(u, w)$, denoting the unique maximizer as $w^*(u)$: $$ \begin{align*} 0 &= \nabla_w h(u, w) = -2\gamma^2 w + 2 M w + 2 M(A x + B u) \:, \\ \Longrightarrow w^*(u) &= (\gamma^2 I - M)^{-1} M (Ax + Bu) \:. \end{align*} $$ Now we solve for the optimal $u$, noting that $$ \min_u \max_w h(u, w) = \min_u h(u, w^*(u)) \:. $$ First, we note that: $$ A x + B u + w^*(u) = (I + (\gamma^2 I - M)^{-1} M) (A x + B u) = \gamma^2 (\gamma^2 I - M)^{-1} (A x + B u) \:. $$ Hence, $$ \begin{align*} h(u, w^*(u)) &= x^\T Q x + u^\T R u - \gamma^2 (Ax + Bu)^\T M^2 (\gamma^2 I - M)^{-2} (A x + B u) \\ &\qquad + \gamma^4 (Ax + Bu)^\T M (\gamma^2 I - M)^{-2} (A x + B u) \\ &= x^\T Q x + u^\T R u + (A x + Bu)^\T (\gamma^4 M (\gamma^2 I - M)^{-2} - \gamma^2 M^2 (\gamma^2 I - M)^{-2} ) (A x + Bu) \\ &= x^\T Q x + u^\T R u + (A x + Bu)^\T (\gamma^2 M (\gamma^2 I - M)^{-1}) (A x + B u) \\ &:= x^\T Q x + u^\T R u + (A x + Bu)^\T F (A x + B u) \\ &= \begin{bmatrix} x \\ u \end{bmatrix}^\T \begin{bmatrix} Q + A^\T F A & A^\T F B \\ B^\T F A & R + B^\T F B \end{bmatrix} \begin{bmatrix} x \\ u \end{bmatrix} \:. \end{align*} $$ To compute $\min_u h(u, w^*(u))$, we know that partial minimization of a strongly convex quadratic is given by the Schur complement, i.e. $$ \min_u h(u, w^*(u)) = x^\T (Q + A^\T F A - A^\T F B (R + B^\T F B)^{-1} B^\T F A) x \:. $$ Next, by the matrix inversion lemma, $$ \begin{align*} &(I + (B R^{-1} B^\T - \gamma^{-2} I) M)^{-1} \\ &\qquad= ( (I - \gamma^{-2} M) + B R^{-1} B^\T M )^{-1} \\ &\qquad= (I - \gamma^{-2}M)^{-1} - (I - \gamma^{-2}M)^{-1} B (R + B^\T M (I - \gamma^{-2}M)^{-1} B)^{-1} B^\T M (I - \gamma^{-2}M)^{-1} \:. \end{align*} $$ On the other hand, we have $$ \begin{align*} &F - F B(R + B^\T F B)^{-1} B^\T F \\ &\qquad= M (I - \gamma^{-2} M)^{-1} - M (I - \gamma^{-2} M)^{-1} B (R + B^\T M (I - \gamma^{-2} M)^{-1} B)^{-1} B^\T M (I - \gamma^{-2} M)^{-1} \\ &\qquad= M( (I - \gamma^{-2} M)^{-1} - (I - \gamma^{-2} M)^{-1} B (R + B^\T M (I - \gamma^{-2} M)^{-1} B)^{-1} B^\T M (I - \gamma^{-2} M)^{-1} ) \\ &\qquad= M (I + (B R^{-1} B^\T - \gamma^{-2} I) M)^{-1} \\ &\qquad:= M \Lambda^{-1} \:. \end{align*} $$ Above, the last equality follows from the previous calculation. Hence, $$ \min_u h(u, w^*(u)) = x^\T (Q + A^\T M \Lambda^{-1} A) x \:. $$ We also know that the optimal $u^*$ is given as $$ u^* = - (R + B^\T F B)^{-1} B^\T F A x \:. $$ Next we observe that $$ \begin{align*} - R^{-1} B^\T M \Lambda^{-1} A x &= - R^{-1} B^\T (F - F B(R + B^\T F B)^{-1} B^\T F) A x \\ &= -R^{-1} (B^\T F A x - B^\T F B(R + B^\T F B)^{-1} B^\T F A x) \\ &= -R^{-1} (I - B^\T F B(R + B^\T F B)^{-1}) B^\T F A x \\ &= -R^{-1} (R + B^\T F B - B^\T F B) (R + B^\T F B)^{-1} B^\T F A x \\ &= - (R + B^\T F B)^{-1} B^\T F A \:, \end{align*} $$ and therefore we can also write $u^* = - R^{-1} B^\T M \Lambda^{-1} A x$. Similarly, $$ \begin{align*} w^*(u) &= \gamma^{-2} F(A x + Bu) \\ &= \gamma^{-2} F(A x - B(R + B^\T F B)^{-1} B^\T F A x) \\ &= \gamma^{-2} F(I - B(R + B^\T F B)^{-1} B^\T F) Ax \\ &= \gamma^{-2} (F - FB(R + B^\T F B)^{-1} B^\T F) Ax \\ &= \gamma^{-2} M \Lambda^{-1} A x \:. \end{align*} $$ These calculations show that if we set $V_k(x) = x^\T M_k x$, then we have found a solution to the Issacs's equations under the given hypothesis (I am omitting some details here). $\square$
Reduction to $H_\infty$ Optimal Control Problem
Previously, we discussed how to solve a dynamic game involving $L_\gamma(u, w)$. We now sketch the argument for why the solution to this game is also a solution to the original $H_\infty$ optimal control problem $\eqref{eq:hinf_opt}$. Call the original cost in $\eqref{eq:hinf_opt}$ as $J(u, w)$, i.e. $$ J(u, w) = \sum_{k=1}^{K} x_k^\T Q x_k + u_k^\T R u_k + x_{K+1}^\T Q_f x_{K+1} \:. $$ Let $u^\gamma$ denote the minimizing player's solution and $w^\gamma$ denote the maximizing player's solution to $\eqref{eq:game_one}$. Then for any $w$ we obtain, by the saddle point property, $$ J(u^\gamma, w) - \gamma^2 \sum_{k=1}^{K} \norm{w_k}^2 = L_\gamma(u^\gamma, w) \leq L_\gamma(u^\gamma, w^\gamma) = V_1(x_1) = 0 \:. $$ Since this holds for any $w$, we have $$ \max_{w} \frac{J(u^\gamma, w)}{\sum_{k=1}^{K} \norm{w_k}^2} \leq \gamma^2 \:. $$ Now observe that, since the initial condition $x_1 = 0$, the map $w \mapsto \sqrt{J(u, w)}$ is positive homogenous. (Note when $x_1 \neq 0$ what follows does not hold). Hence, $$ \max_{w} \frac{J(u^\gamma, w)}{\sum_{k=1}^{K} \norm{w_k}^2} = \max_{w: \norm{w} \leq 1} J(u^\gamma, w) \:. $$ The last piece remaining is that this $\gamma$ was chosen arbitrary, as long as it satisfied the conditions of the theorem in the previous section so that the solutions $u^\gamma, w^\gamma$ are well defined. Let $\gamma_\star$ denote the smallest $\gamma$ such that those conditions are satisfied. It turns out that the controller $u^{\gamma_\star}$ is the solution to $\eqref{eq:hinf_opt}$, and the value of the dynamic game is $\gamma_\star^2$.
Infinite Horizon Setting
It turns out that these results also generalize to the infinite horizon setting. In the infinite horizon case, one searches for a positive semi-definite $M$, $\Lambda$, and $\gamma$ such that the following conditions hold: $$ \begin{align*} M &= Q + A^\T M \Lambda^{-1} A \:, \\ \Lambda &= I + (B R^{-1} B^\T - \gamma^{-2} I) M \:, \\ 0 & \prec \gamma^2 I - M \:. \end{align*} $$ The controller is then time-invariant, using $M$ in place of $M_k$.


Scheme of the diffraction experiment on free standing liquid crystal (LC) films. The molecular layers are oriented parallel to the film surface, so the X-ray diffraction pattern corresponds to the in-plane order. (adopted from Zaluzhnyy et al. [ 17]—Published by The Royal Society of Chemistry). Used under CC-BY 3.0.


Police seek vehicle involved in fatal hit-and-run in Lahaina
by HNN StaffLAHAINA, Hawaii (HawaiiNewsNow) - Maui police are investigating a fatal hit-and-run pedestrian crash in Lahaina late Tuesday.
The crash happened just before midnight on Honoapiilani Highway near Launiupoko State Park.
Police said a Ford Focus heading north on the highway struck a pedestrian crossing the street and then fled the scene without rendering aid.
The victim has been identified as Luke Hall, 37, who had no local address.
Police said Hall was not in a marked crosswalk when the crash happened.
Investigators said the Focus is believed to be a model from between 2004 and 2007. The vehicle will have damage to its front bumper and hood.
Anyone with information on the crash is asked to call police dispatch at 244-6400.
This is the 21st traffic death in Maui County compared to 16 at the same time last year.
This story will be updated.
Copyright 2019 Hawaii News Now. All rights reserved.


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Introduction
Survival analysis refers to the set of statistical analyses that are used to analyze the length of time until an event of interest occurs. These methods have been traditionally used in analysing the survival times of patients and hence the name. But they also have a utility in a lot of different application including but not limited to analysis of the time of recidivism, failure of equipments, survival time of patients etc. Hence, simply put the phrase survival time is used to refer to the type of variable of interest. It is often also referred by names such as failure time and waiting time.
Such studies generally work with data leading upto an event of interest along with several other characteristics of individual data points that may be used to explain the survival times statistically.
The statistical problem (survival analysis) is to construct and estimate an appropriate model of the time of event occurance. A survival model fulfills the following expectations:
- yield predictions of number of individuals who will fail (undergo the event of interest) at any length of time since the beginning of observation (or other decided point in time).
- estimate the effect of observable individual characteristics on the survival time (to check the relevance of one variable holding constant all others).
It is often observed that the survival models such as proportional hazard model are capable of explaining the survival times in terms of observed characteristics which is better than straight-forward statistical inferences such as rates of event occurence without considering characteristic features of data.
Basics
Assume survival time T is a random variable following some distribution characterized by cumulative distribution function \(F(t, \theta)\), where
- \(\theta\) is the set of parameters to be estimated
- \(F(t, \theta) = P(T \leq t) = \) probability that there is a failure at or before time \(t\), for any \(t \geq 0\)
- \(F(t, \theta) \to 1\) as \(t \to \infty\), since \(F(t, \theta)\) is a cumulative distribution function
- Above tendency leads to an implicit assumption that all candidates would eventually fail. While this assumptions works selectively based on settings (true for patient survival times, not true for time of repayment of loans) and hence needs to be relaxed where it does not hold true.
Survival times are non-negative by definition and hence the distributions (like exponential, Weibull, gamma, lognormal etc.) characterising it are defined for value of time \(t\) from \(0\) to \(\infty\).
Let \(f(t, \theta)\) be the density function correponding to the distribution function \(F(t, \theta)\), then the survival function is given by,
which gives the probability of survival until time \(t\) (\(S(t, \theta) \to 0\) as \(t \to \infty\) because, \(F(t, \theta) \to 1\) as \(t \to \infty\)).
Another useful concept in survival analysis is called hazard rate, defined by,
Hazard rate represents the density of a failure at time \(t\), conditional on no failure prior to time \(t\), i.e., it indicates the probability of failure in the next unit of time, given that no failure has occured yet.
While \(f(t, \theta)\) roughly represents the proportion of original cohort that should be expected to fail between time \(t\) and \(t+1\), hazard rate \(h(t, \theta)\) represents the proportion of survivors until time \(t\) that should be expected to fail in the same time window, \(t\) to \(t+1\).
The relationship betwee the cumulative distribution function and the hazard rate is given by,
and
The fact that \(F(t, \theta)\) is a cdf puts some restrictions on the hazard rate,
- hazard rate is non-negative function
- the integrated hazard in \eqref{5} is finite for finite \(t\) and tends to \(\infty\) as \(t\) approaches \(\infty\).
State Dependence
- Positive state dependence or an increasing hazard rate \(dh(t)/dt \gt 0 \) indicates that the probability of failure during the next time unit increases as the length of time at risk increases.
- Negative state dependence or a decreasing hazard rate \(dh(t)/dt \lt 0 \) indicates that the probability of failure in the next time unit decreases as the length of time at risk decreases.
- No state dependence indicates a constant hazard rate.
Only exponential distribution displays no state dependence.
Censoring and Truncation
A common feature of data on survival times is that they are censored or truncated. Censoring and truncation are statistical terms that refer to the inability to observe the variable of interest for the entire population.
- A standard example to understand this can be understood in the form of a case of an individual shooting at a round target with a rifle and the variable of interest is the distance by which the bullet misses the center of the target.
- If all shots hit the target, this distance can be measure for all the shots and there is no problem of censoring or truncation.
- If some shots miss the target, but we know the number of shots fired, the sample is censored. In this case either the distance of shot from center is known or it is known that it was atleast as large as the radius of the target.
- Similarly if one does not know how many shots were fired but only have information about distance for shots that hit the target, the sample is truncated.
Censored sample has more information than a truncated sample.
Survival times are often censored because not all candidates would fail by the end of time during which the data was collected. This censoring of data must be taken into account while making the estimations because it is not legitimate to drop such observations with unobserved survival times r to set survival times for these observations equal to the length of the follow-up period (when the data was collected).
- Infrequently so, but there is also a chance of getting information about a candidate during a follow-up collection who was not a part of the original population. In such cases the survival time is truncated because there is no information of the candidate or his survival time.
Problem of Estimation
The initial assumption specifies a cumulative distribution function \(F(t, \theta)\), or equivalently a density \(f(t, \theta)\) or hazard \(h(t, \theta)\) that is of a known form except that it depends on a unknown parameter \(\theta\). Estimation of this parameter is first step for the model to make any meaningful prediction about the survival time of new candidate
Consider a case of estimation of parameter for a censored sample which is defined as follows,
- sample has \(N\) individuals with follow-up periods \(T_1, T_2, \cdots, T_N\). These follow-ups may be all equal, but they usually are not.
- \(n\) is number of individuals who fail, numbered \(1, 2, \cdots, n\) and individuals numbered \(n+1, n+2, \cdots, N\) are the non-failures.
- for the candidates who fail, there exists a survival time \(t_i \leq T_i, \, i \in [1, n]\)
- for the non-failures, survival time \(t_i\) is not observed but it is known that it is greater than the length of the follow-up period \(T_i\), \(i \in [n+1, N]\).
If it is assumed that all the outcomes are independent of each other the likelyhood function of the sample is,
Likelyhood function is a general statistical tool that expresses the probability of outcomes observed in terms of unknown parameters that are to be estimated, i.e., it is function of the parameters to be estimated, which serves as a measure of how likely it is that the statistical model, with a given parameter value, would generate the given data.
A common used estimator of \(\theta\) is the Maximum Likelyhood Estimator (MLE) which is defined as the value of \(\theta\) that maximizes the likelyhood function.
The MLE have been shown to display the following desirable properties over a large sample (as the sample size approaches infinity),
- Unbiased
- Efficient
- Normally Distributed
As mentioned, the properties of MLE are only relevant when the sample size is large. It is often observed that the sample sizes in these studies are much smaller and hence reliance on large sample properties of estimator is more tenuous.
The above survival model uses observed survival time \(t_i\) while ignoring the specific timing of the observed returns. So the analysis of the fact of failure or non-failure, ignoring the timing of observed failures, would properly be based on the likelyhood function
Estimation using \eqref{7} is a legitimate procedure and does not cause any bias or inconsistency, but the estimates are inefficient relative to MLEs from \eqref{6}.
The estimates of \(\theta\) gotten by maximizing \eqref{7} will be less efficient (have larger variance) than the estimates of \(\theta\) gotten by maximizing \eqref{6}, atleast for large sample sizes. Hence if the information on time of return is available, it should be used.
If the truncated case of sample is considered, then there is no information on all the individuals who do not fail. Formally, one starts with a cohort of \(N\) candidates, where \(N\) is unknown, and the only observations available are the survival times \(t_i\) for the \(n\) individuals who fail before the end of follow-up period. The \(n\) individuals appear in sample because \(t_i \leq T_i\), and the appropriate density is therefore
And the corresponding likelyhood function which can be maximized to obtain the MLEs is given by,
Explanatory Variables
Information on explanatory variables may or may not be used in estimating survival time models. Some models that are based on the implicit assumption that distribution of survival time is the same for all individuals, do not use explanatory variables.
But practically it is observed that some individuals are more prone to failing than others and hence if information on individual charestistics and environmental variables is available, it should be used.
This information can be incorporated is survival models by letting the parameter \(\theta\) depend on these individual characteristics and a new set of parameters. E.g. exponential model depends on a single parameter, say \(\theta\), and \(\theta\) can be assumed to depend on the individual characteristics as in linear regression.
Non-Parametric Hazard Rate and Kaplan Meier
Before beginning any formal analyses of the data, it is often instructive to check the hazard rate. For this purpose, the time until failure are rounded to the nearest quantized time unit (month, week, day etc.). Following this it is easy to count the number of candidates at risk at the beginning of the said time period (i.e. the number of individuals who have not yet failed or been censored at the beginining of the time unit) and the number of individuals who fail during the time period.
Then, the non-parametric hazard rate can be estimated as the ratio of number of failures during the time period to the number of individuals at risk at the beginning of time period, i.e., if the number of individuals at risk at the beginning of time \(t \, (t = 1, 2, \cdots)\) is denoted by \(r\), and the number of individuals who fail during this time \(t\) is denoted by \(n_t\), then the estimated hazard for time \(t\), \(\hat{h}(t)\) is given by,
Such estimated hazard rates are prone to high variability. Also this high variability makes the purely non parametric estimates unattractive as they are less likely to give an accurate prediction on a new dataset. The parametric models such as exponential, Weibull or lognormal take care of this high variability and makes the model more tractable.
But the plots of non parametric estimates of hazard rate provides a good initial guide as to which probability distribution may work well for a given usecase.
As noted earlier, the hazard function, density function, and distribution function are alternative but equivalent ways of characterizing the distribution of the time until failure. Hence, once the hazard rate is estimated, then implicitly so is the density and the distribution function. It is possible to solve explicitly for the estimated density of distribution function in terms of the estimated hazard function. The resulting estimator (called Kaplan Meier or product limit estimator in statistical literature which is nothing but the non-parametric estimate) of the distribution function is given by,
Models without Explanatory Variables
There are various models that do not consider the explanatory variables, and instead assume some specific distribution such as exponential, Weibull, or lognormal for the length of time until failure. Essentially, the distribution of time until failure is known, except for some unknown parameters that have to be estimated. Hence, models of this type are called parametric models, which are different from the models discussed before as the later have no associated parameters or distribution.
The unknown parameters are estimated by maximizing the likelyhood function of the form \eqref{6}.
In case of exponential distribution, MLEs cannot be written in closed form (i.e. expressed algebraically), and so the maximization of likelyhood function is done numerically.
Once the characteristic parameters have been estimated, one can determine the following (which cannot be determined in case of non-parametric estimates like Kaplan Meier):
- mean time until failures
- proportion of population that should be expected to fail within any arbitrary period of time.
While the advantage of such models lies in the smoothness of predictions, the disadvatage is the fact that it can be wrong and inturn lead to statements that are systematically misleading.
Exponential Distribution
The exponential distribution has density,
and survivor function,
where
- the parameter is constrained, \(\theta \gt 0\)
- mean: \(1 / \theta\) and variance: \(1 / \theta^2\)
- only distribution with a constant hazard rate, specifically \(h(t) = \theta\) for all \(t \geq 0\)
- such hazard rates are generally seen in some physical processes such as radioactive decay.
- it is often not the most reasonable distribution for survival models.
- exponential distribution requires estimation of single parameter \(\theta\).
Consider a sample of \(N\) individuals, of which \(n\) have failed before the end of the follow-up period. The observed failure times be denoted by \(t_i\, (i=1, 2, \cdots, n)\) and the censoring times (length of follow up) for the non-failures de denoted by \(T_i\, (i = n+1, \cdots, N)\). Then the likelyhood function \eqref{6} can be written as
Maximizing \eqref{14} w.r.t. \(\theta\) yields MLE in closed form:
For large samples \(\hat{\theta}\) is normal with mean \(\theta\) and variance
which for large \(N\) is adequately approximated by \(\theta^2/n\).
- Exponential distribution is highly skewed.
- Mean may not be a good measure of central tendency for exponential distribution.
- Median may be more preferrable indicator in most cases.
Logarithm of likelyhood or log-likelyhood is used as a value to measure the goodness of fit. A higher value (more positive or less negative) for this variable indicates that the model fits the data better.
Weibull Distribution
In statistical literature, a very common alternative to the exponential distribution is the Weibull distribution. It is a generalization of the exponential distribution. By using Weibull distribution one can test to check if a simpler exponential model is more appropriate.
- A variable \(T\) has Weibull distribution if \(T^{\tau}\) has an exponential distribution for some value of \(\tau\).
- increasing hazard rate if \(\tau \gt 1\) and decreasing hazard rate if \(\tau \lt 1\). Also, if \(\tau = 1\) the hazard rate is constant and the Weibull distribution reduces to the exponential.
- Weibull distribution has a monotonic hazard rate, i.e it can be increasing, constant or decreasing but it cannot be increasing at first and then decreasing after some point.
The density of Weibull distribution is given by,
and the survivor function is,
The likelyhood function for Weibull distribution can be derived by substituting \eqref{17} and \eqref{18} in \eqref{6}.
Lognormal Distribution
If \(z\) is distributed as \(N(\mu, \sigma^2)\), then \(y = e^z\) has a lognormal distribution with mean
and variance,
where
The density of \(z = ln \, y\) is the density of \(N(\mu, \sigma^2)\) given by,
Generally there is no advantage to working with the density of \(y\) itself, rather than \(ln \, y\). Thus, one can simply assume that log of survival time is distributed normally, and hence the likelyhood function \eqref{6} becomes
- where \(F\) is the cumulative distribution function for \(N(0, 1)\) distribution.
- No analytical solution exists for the maximization of \eqref{23} w.r.t. \(\mu\), and \(\sigma^2\), so it must be maximized numerically.
- the hazard function for lognormal distribution is complicated; it increases first and then decreases.
Other distributions
Although exponential, Weibull and lognormal are among the three most used distributions, there are various other well-known probability distributions possible, such as
- log-logistic
- LaGuerre
- distributions based on Box-Cox power transformation of the normal
There are various ways of measuring how well models fit the data:
- value of likelyhood (or log-likelyhood) function
- maximum difference between the fitted value and actual cumulative distribution function
- standard Kolmogorov-Smirnov test of goodness of fit
- chi-square goodness-of-fit statistic based on predicted and actual failure times.
Over time it has been observed that even though some of these parametric distributions might fit the data better than others and excel on various metrics of good fit of data, these do not give any explaination about the reasons governing the distribution or any insight into the affecting parameters that lead to the different survival times in a population. Hence, these parametric models without the explanatory variables are not considered to be an effective tool for analysis.
Models with Explanatory Variables
-
Explanatory variables are in general added to survival models in an attempt to make more accurate predictions: the practical experiments over time corroborate the fact that individual characteristics, previous experiences and environmental setup helps predict whether or not a person will fail.
-
An analysis of survival time without using the explanatory variables amounts to an analysis of its marginal distribution, whereas an analysis using explanatory variable amounts to an analysis of the distribution of survival time conditional on these variables.
Variance of the conditional distribution is less than the variance of the marginal distribution, i.e. expect more precise distribution from former.
-
Another more fundamental reason may include the interest of understanding the effect of explanatory variables on the survival time.
-
More generally, these variables might be the demographics or environmental characteristics.
Proportional Hazards Model
-
allows one to estimate the effects of individual characteristics on survival time without having to assume a specific parametric form of distribution of time until failure.
-
For an individual with the vector of characteristics, \(x\), the proportional hazards model assumes a hazard rate of the form,
where \(h_0(t)\) is completely arbitrary and unspecified baseline hazard function. Thus, the model assumes that the hazard functions of all individuals differ only by a factor of proportionality, i.e. if an individuals hazard rate is 10 times higher than another’s at a given point of time, then it must be 10 times higher at all points in time. Each hazard function follows same pattern over time.
However, there is no restriction on what this pattern can be, i.e. it puts no restriction on the \(h_0(t)\) curve, which determines the shape of \(h(t \vert x)\) curve. \(\beta\) can be estimated without specifying \(h_0(t)\), and \(h_0(t)\) can be estimated non-parametrically and thus with flexibility.
Consider a sample of \(N\) individuals, \(n\) of whom fail before the end of their follow-up period.  Thus, for individual \(i\), failure time \(t_i\) is observed, with,
A vector \(x_i\) represents individual characteristics for each individual \(i = 1, 2, \cdots, N\), irrespective of whether they failed.
For each observed failure times, \(t_i\), \(R(t_i)\) is defined as set of all individuals who were at risk just prior to time \(t_i\), i.e., it includes the individuals with failure times greater than or equal to \(t_i\), as well as the individuals whose follow-up is at least of length \(t_i\).
Using these definitions, the partial-likelihood function proposed by Cox can be defined for any failure time \(t_i\), as the probability that it is individual \(i\) who fails, given that exactly one individual from set \(R(t_i\)) fails, is given by,
The partial-likelyhood function is formed by multiplying \eqref{26} over all \(n\) failure times,
The estimate of \(\beta\) by maximizing \eqref{27} numerically w.r.t \(\beta\) is the partial maximum-likelyhood estimate. The word partial in partial likelyhood refers to the fact that not all available information is used in estimating \(\beta\), i.e., it only depends on knowing which individuals were at risk when each observed failure occured. The exact numerical values of the failure times \(t_i\) or of the censoring times for the non recedivists are not needed; only their order matters.
Once \(\beta\) is estimated, \(h_0(t)\), the baseline hazard function can be estimated non-parametrically. The estimated baseline hazard function is constant over the intervals between failure times. One can also calculate survivor function \(S_0(t)\) or equivalently the baseline cumulative distribution function \(F_0(t)\), that corresponds to the estimated baseline hazard function.
The estimated survivor function is a step function that falls at each time at which there is a failure.
The point of proportional hazard model is that the survivor function is estimated non-parametrically (i.e. not imposing any structure on its pattern over time, except that it must decrease as \(t\) increases) and estimation of \(\beta\) can proceed seperately from estimation of survivor function.
Split Population Models
The models considered so far assume some cumulative distribution function, \(F(t)\) for the survival time, that gives the probability of a failure upto and including time \(t\), and it approaches one as \(t\) approaches infinity. This basically means that every individual must eventually fail, if they were observed for long enough time. This assumption is not true in all cases.
Split Population Models (or split models) do not imply that every individual would eventually fail. Rather the population is divided into two groups, one of which would never fail.
Mathematically, let \(Y\) be an observable indicator with two values, one implying ultimate failure and zero implying perpetual success. Then,
where \(\delta\) is the proportion of the population that would eventually fail, and \(1 - \delta\) is the proportion that would never fail.
Let \(g(t \vert Y=1)\) be density of survival times for the ultimate failures, and \(G(t \vert Y=1)\) be the corresponding cumulative distribution function. If one considers exponential model to represent them, then
It can also be noted that \(g (t \vert Y = 0)\) and \(G(t \vert Y=0)\) are not defined.
Let \(T\) be the length of the follow up period and let \(R\) be an observable indicator equal to one if there is failure by time \(T\) and zero if there is not. The probability for individuals who do not fail during the follow up period, i.e, the event of \(R = 0\) is given by,
Similarly, probability density for people who fail with survival time \(t\) is given by,
So the likelyhood function is made up of \eqref{29} for those who do not fail and \eqref{30} for those who do. It is given by,
The maximum likelyhood estimate of both \(\theta\) and \(\delta\) can be obtained by maximizing \eqref{32} numerically. It can be noted that when \(\delta = 1\), \eqref{32} reduces to \eqref{14}, the original exponential survival time model.
The split population model can be seen as a model of two seperate subpopulations, one with hazard rate \(\theta\) and other with zero. A more generalized model exists where the subpopulations exist with two non-zero hazard rates namely, \(\theta_1\) and \(\theta_2\). Such models help to account for population that is heterogenous in nature.
Split models can also be based on other distributions such as lognormal etc. Also, it is possible to include explanatory variables into a split model. In such cases, the explanatory variables maybe taken to affect the probabiliy of failure, \(\delta\) or distribution of time until failure.
For example, for a given feature vector \(x_i\) of explanatory variables, using logit/individual lognormal model, \(\delta\) is modeled using,
and parameter \(\mu\) of the lognormal distribution is given by,
Here, the parameter \(\alpha\) gives the effect of \(x_i\) on the probablity of failure, and \(\beta\) gives the effect of \(x_i\) on the time until failure.
Such models are of importance because they let one distinguish between effects of explanatory variable on probability of eventual failure from effects on time until failure who eventually do fail.
Heterogeneity and State Dependence
The two major causes of observed declining hazard rates are:
- state dependence
- heterogeneity
The phenomenon of an actually decreasing hazard rate over time due to an actual change in behavior over time at individual level is referred to as state dependence.
The second possible reason is heterogeneity. This basically means that the hazard rates are different across individuals, i.e., some individuals are more prone to failure than others. Naturally, individuals with higher hazard rates tend to fail earlier, on average, than individuals with lower hazard rates. As a result the average hazard rate of the surviving group will decrease with length of time simply because the most failure prone individuals have been removed already. This is true even without state dependence, i.e, each individual has a constant hazard rate but hazard rate varies across individuals. Even such a group would display decreasing hazard rate.
It is important to understand the difference because a decrease in a hazard rate due to state dependance means a success of the underlying program, while decrease due to heterogeneity does not imply that the program is effective in preventing failure, because it is happening by the virtue of the data at hand.
Time Varying Covariates
Until now explanatory variables affecting the time until failure do not potray changing values over time, but is a possibility that can not be denied.
The types of explanatory variables can be categorizaed as follows:
- variables that do not change over time, e.g race, sex etc.
- variables that change over time but not within a single follow-up period, e.g. number of times followed up etc.
- variables that change continuously over time, such as age, education etc.
The last type of variables make it reasonable to use a statistical model that allows covariates to vary over time. Such incorporation is relatively straightforward in hazard-based models such as proportional hazard models. At each point in time, hazard rate is determined by the values of explanatory variables at that time.
However, it is much more difficult to introduce time-varying components into parametric models because these models are parameterized in terms of density and cumulative distribution function, and the density of distribution function at time \(t\) depends on the whole history of the explanatory variables up to time \(t\). In the presence of time varying covariates, a parameterization of the hazard rate would be much more convenient.
Panel or Longitudinal Data: data on individuals over time without reference to just a single follow-up. Such datasets include a large number of time-varying explanatory variables.


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read MFnumber MTnumber "file_name" [offset]
DeCE can read cross section data from external data files,
and store the data into a specified MF,MT section.
In the current version, the MF number should be 1 or 3, so that
only cross section or average number of neutron data can be read
by the read
command.
The file name must be quoted by the quotation marks.
The data in the external data file are in the table format, like
# Energy Total Capture 1.000000e-03 3.518258e+04 7.921335e+03 2.000000e-03 2.643521e+04 4.590701e+03 5.000000e-03 1.870179e+04 2.295811e+03 1.000000e-02 1.479458e+04 1.437977e+03 2.000000e-02 1.201587e+04 9.583236e+02 5.000000e-02 9.506811e+03 5.829248e+02 1.000000e-01 8.172826e+03 3.250598e+02 1.500000e-01 7.532394e+03 2.282769e+02 ...(snip)... 8.000000e-01 4.944550e+03 4.656816e+01 9.000000e-01 4.735559e+03 3.920111e+01 1.000000e+00 4.550877e+03 3.409828e+01
where the energies are in MeV, and the cross sections in milli-barns as a default. When offset is not given, DeCE reads the first column as the energy, and the second column as the cross section. When offset=2 is specified for example, the second column data will be skipped and the third column data are copied.
The unit of the external data can be changed by the option
ReadXdataConversion
and ReadYdataConversion
.
Often the energy range given in the file does not match the ENDF-6 file. For example, the lower region may overlap with the resonance range where the cross sections in MF=3 should be zero (or background cross sections are given). DeCE tries to adjust the energy boundary at the upper side of the resonance region.
read 3 102 "cross_section.dat" 2 extract 3 102
3.307500+4 7.427800+1 0 0 0 03325 3102 1 7.329602+6 7.329602+6 0 0 1<PHONE_NUMBER> 2 17 2 3325 3102 3 1.000000-5 0.000000+0 2.530000-2 0.000000+0 1.000000+5 0.000000+03325 3102 4 1.000000+5 3.250598-1 1.500000+5 2.282769-1 2.000000+5 1.837162-13325 3102 5 2.500000+5 1.579787-1 3.000000+5 1.379774-1 3.500000+5 1.251382-13325 3102 6 4.000000+5 1.150729-1 4.500000+5 1.024055-1 5.000000+5 9.270790-23325 3102 7 6.000000+5 7.293613-2 7.000000+5 5.763219-2 8.000000+5 4.656816-23325 3102 8 9.000000+5 3.920111-2 1.000000+6 3.409828-2 3325 3102 9 3325 3 099999
In this case, the resonance region goes up to 100 keV, and therefore the data below 100 keV are skipped. Ocassionally, though quite rare, DeCE cannot determine the correct resonance upper boundary. Users should be aware of this.
For the threshold reactions, such as (n,p) when its Q-vale is negative, DeCE calculates the threshold energy and inserts it automatically. However, this is not the case for the inelastic scattering (MT=51-91), and the charged particle emissions where the residual nucleus is left in its discrete levels or continuum (MT=600--849). Because there is no simple way to infer the discrete level energies, users need to supply the excitation energies of the level in the first line. Below an example shows the inelastic scattering data case. The second column is for the 1st excitad state (MF=51), the third is for the 2nd level (MF=52), and so forth.
# 5.392800e-02 1.151270e-01 2.069400e-01 7.687000e-01 1.000000e-01 2.390227e+01 0.000000e+00 0.000000e+00 0.000000e+00 1.500000e-01 4.132186e+01 1.817732e+02 0.000000e+00 0.000000e+00 2.000000e-01 5.647943e+01 2.181649e+02 0.000000e+00 0.000000e+00 2.500000e-01 6.495198e+01 2.142788e+02 1.051117e+02 0.000000e+00 3.000000e-01 7.431213e+01 2.259118e+02 1.355723e+02 0.000000e+00 3.500000e-01 8.305242e+01 2.384692e+02 1.570847e+02 0.000000e+00 4.000000e-01 9.095436e+01 2.506187e+02 1.753431e+02 0.000000e+00 4.500000e-01 9.794954e+01 2.618666e+02 1.916406e+02 0.000000e+00 5.000000e-01 1.040448e+02 2.719887e+02 2.063398e+02 0.000000e+00 6.000000e-01 1.137435e+02 2.886047e+02 2.313577e+02 0.000000e+00 7.000000e-01 1.206132e+02 3.006155e+02 2.509487e+02 0.000000e+00 8.000000e-01 1.211393e+02 2.986678e+02 2.571690e+02 5.829093e+01 1.950000e+01 1.258428e-03 1.358285e-03 1.358676e-03 1.463803e-03 2.000000e+01 1.174476e-03 1.245705e-03 1.246367e-03 1.323415e-03
multiread MFnumber MTstart MTlast "file_name"
DeCE reads all MT numbers from MTstart to MTlast from the same data file "file_name". Since an offset is not given in this operation, DeCE assumes that the data file includes the reactions in the following column order - Energy, Total, Elastic, Non-elastic, Capture, Inelastic, and Fission. For the particle production cross section cases - Energy, (x,n), (x,p), (x,alpha), (x,d), (x,t), (x,h), (x,2n), (x,np), (x,n alpha), (x,nd), (x,nt), (x,nh), (x,2p), (x,p alpha), (x,pd), (x,2alpha), (x,d alpha), (x,3n), (x,2np), (x,2n alpha), (x,np alpha), (x,n2p), (x,4n), (x,5n). For the partial inelastic scattering - Energy, elastic, 1st level, 2nd level, 3rd level ... and the last 40th column is the continuum inelastic.
angdist MFnumber MTnumber "file_name" [ "gamma_branch_file_name" ]
DeCE creates scattering angular distribution data in MF=4, MT=2,
51, 52 ... The MF number should be 4 for the elastic scattering,
while it can be 4 or 6 for the inelastic scattering. The charged
particle angular distributions will be compiled in MF6. The data file may
contain either the differential cross sections or the Legendre
expansion coefficients, which is indicated by the keyword in the data
file (Angdist
or Legcoef
).
The format for the differential angular distribution data is as follows.
# 1.00000e-06 Angdist 1.986060e-01 2.646580e-01 2.795430e-01 1.000000e+00 1.970282e+04 0.000000e+00 0.000000e+00 0.000000e+00 2.000000e+00 1.970282e+04 0.000000e+00 0.000000e+00 0.000000e+00 ... (snip) ... 1.790000e+02 1.970283e+04 0.000000e+00 0.000000e+00 0.000000e+00 1.800000e+02 1.970283e+04 0.000000e+00 0.000000e+00 0.000000e+00 # 1.00000e-05 Angdist 1.986060e-01 2.646580e-01 2.795430e-01 1.000000e+00 1.238493e+04 0.000000e+00 0.000000e+00 0.000000e+00 2.000000e+00 1.238493e+04 0.000000e+00 0.000000e+00 0.000000e+00 ... (snip) ...
In the first line, the neutron incident energy in MeV (default),
the keyword of Angist
, the first level excitation energy,
the second level, etc, are given. The differential cross sections are
given at 180 angular points at every 1 degree. These blocks are
repeated for all incident energies. The unit of scattering cross section
is arbitrary, since they will be normalized.
When Legendre coefficients are given, the keyword
is Legcoef
, and maximum of 50 expansion coefficients are
given.
# 1.00000e-06 Legcoef 1.986060e-01 2.646580e-01 2.795430e-01 0 1.918427e+04 0.000000e+00 0.000000e+00 0.000000e+00 1 3.227445e-08 0.000000e+00 0.000000e+00 0.000000e+00 ... (snip) ... 49 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 # 2.00000e+00 Legcoef 1.986060e-01 2.646580e-01 2.795430e-01 0 1.428037e+01 3.152489e+00 5.780398e+00 8.986116e+00 1 4.024690e+00 4.341183e-01 1.150535e+00 7.207134e-01 2 8.009362e-01-4.895771e-03 1.493828e-01-3.589658e-02 ... (snip) ...
The angular data points and the maximum Legendre coefficients
to be read by DeCE are defined in deceangdist.cpp
When the angular distributions are stored in MF6, the discrete gamma-ray energy spectra are also stored in the same section when gamma_branch_file is provided. The data format of the gamma_branch_file looks
#gammaray 0 27 28 59 0 0 0.000000e+00 1 1 3.394210e-01 0 1.000000e+00 2 1 4.649760e-01 0 1.000000e+00 3 2 8.779470e-01 1 1.420986e-02 0 9.857901e-01
for the case of no internal conversion coefficient (ICC) case. The first four integers mean the particle ID (0 for photon, 1 for neutorn, 2 for proton, 3 for alpha-particle, 4 for deuteron, 5 for triton, and 5 for helium-3). The second number is the number of discrete levels, followed by the Z and A numbers of this isotope. Z and A are not used by DeCE. The following each line shows the level index, number of gamma-ray branches, the level excitation energy, and pairs of the final state level index and branching ratios.
When ICC is provided, the data file contains
#gammaraywithconversion 0 27 28 59 0 0 0.000000e+00 1 1 3.394210e-01 0 1.000000e+00 9.959167e-01 2 1 4.649760e-01 0 1.000000e+00 9.984026e-01 3 2 8.779470e-01 1 1.420986e-02 9.990010e-01 0 9.857901e-01 9.997401e-01
where the decay data are given by triplets (final state level index, branching ratio, and ICC).
multiangdist MFnumber MTstart MTlast "file_name" [ "gamma_branch_file_name" ]
DeCE reads angular distribution data in the specified data file, ranging from MTstart to MTlast.
libread MFnumber MTnumber "file_name"
DeCE imports a specified MF/MT section from another ENDF-6 file, and replace by that, or creates a new section if it does not exist. The external file can be a full ENDF-6 file, or just a section. When MTnumber = 0 all the MTs in the given MFnumber will be imported.
When the ZA and AWR parameters in the external file are different from the current data, DeCE replace them by the current ones, but only in the HEAD part.
multilibread MFnumber MTstart MTlast "file_name"
Repeat libread
for the range of MT numbers.


Author Halted From Digging Into #MeToo Spreadsheet
by AMANDA OTTAWAYBROOKLYN, N.Y. (CN) – A federal judge in Brooklyn Wednesday said he wouldn’t let author Stephen Elliott dig deeper into the potential contributors to a #MeToo-era Google spreadsheet that accused him of rape and other sexual misconduct.
“If [Google] told you they don’t have anything, that’s your answer,” said U.S. Magistrate Judge Sanket Bulsara at a status conference Wednesday, refusing to grant more discovery rights to the plaintiff after a subpoena of Google for the edit history of the document came up empty.
Elliott, who says he was defamed in a 2017 anonymous online spreadsheet nicknamed the “Shitty Media Men” list, subpoenaed Google this past spring for information on the women who contributed to it, as part of his lawsuit against list creator Moira Donegan.
In October 2017, Stephen Elliott’s name appeared on the now-famous #MeToo list, started by Donegan, also a writer, and circulated privately as a crowdsourced document naming men in the media industry who have been accused of sexual misconduct. Donegan has said the spreadsheet was only online for about 12 hours.
 In the “notes” column of the list, Elliott was classified as a man against whom “multiple women allege misconduct.”
It’s unclear who actually typed the accusations against Elliott, or whether Donegan had anything to do with his file specifically beyond creating and sharing the initial list. It is these contributors Elliott seeks to find and potentially sue in addition to Donegan. He’s seeking $1.5 million in damages, as well as written retractions of each accusation.
Attorney Roberta Kaplan, defending Donegan, said Wednesday she doesn’t think Elliott actually believes Donegan typed the accusations.
“He’s looking for the right person to sue,” she said, later adding, “To drag Ms. Donegan into these proceedings through discovery … just to try to figure out who these women were, or men, whoever, who wrote about plaintiff, is really not fair.”
Donegan filed a motion to dismiss in June for failure to state a claim. The parties have fully briefed the motion and await a ruling from U.S. District Judge LaShann DeArcy Hall.
Bulsara said Wednesday it would be too much of a burden on Donegan to force her to go to backup tapes, archives, or her hard drive in an effort to track down more information about the spreadsheet’s contributors.
According to a letter filed by Elliott’s lawyers last month, Google reportedly said it did not have sufficient information to identify the requested records, like a URL for the spreadsheet or email addresses for users who accessed it.
Elliott had been hopeful Bulsara would order further early discovery, and attorney Nick Lewis noted in an email Tuesday the judge had the power to direct a limited deposition, interrogatories or production of computer data.
A Google spokesperson did not return requests for confirmation and comment Tuesday and Wednesday, but the company’s data storage policy says it removes the data its users delete, and that even backup storage is erased after six months.
Copyright © 2019 courthousenews.com


Brazilian GP: Verstappen wins from Gasly, while Ferraris collide
by Scott MitchellMax Verstappen won a crazy Brazilian Grand Prix in which Toro Rosso's Pierre Gasly claimed his first Formula 1 podium in second and the two Ferraris collided with each other.
Poleman Verstappen had to pass Lewis Hamilton on-track twice to score his third victory of 2019, while Gasly beat Hamilton in a drag race to the line after being handed second place when Hamilton and Alex Albon collided on the penultimate lap.
A collision between team-mates Sebastian Vettel and Charles Leclerc caused a safety car and set up a four-lap climax to a thrilling race, with Albon in second because Hamilton had made a switch to soft tyres under the safety and dropped to third.
When Hamilton tried to pass Albon for second on the penultimate lap, they hit each other and the Red Bull spun. Then gave Gasly chance to jump Hamilton and he then somehow resisted the Mercedes on the run to the line in a virtual photo-finish.
Poleman Verstappen had maintained the lead at the start as Hamilton attacked Vettel on the outside into Turn 1 and grabbed second.
Hamilton fell away from Verstappen through the first stint and Verstappen held a lead of around 2.4s initially, which Hamilton had whittled down to 1.8s when he dived into the pitlane at the end of lap 20 of 71.
That committed him to a two-stop strategy as he stayed on soft tyres, and though Red Bull responded one lap later Hamilton's massive pace advantage on new rubber meant he was set to jump Verstappen with relative ease.
He caught Leclerc, who was yet to stop having started 14th, at the end of the lap but still moved ahead of Verstappen because Verstappen was baulked by the Williams of Robert Kubica in the pitlane.
Williams released Kubica but the Pole was slow away and swerved into Verstappen's path at the exit, forcing Verstappen to take evasive action and then sit behind the Williams until they rejoined the circuit.
That allowed Hamilton ahead, but Leclerc put up a stern resistance through the rest of the lap, which meant Verstappen - also on fresh softs - closed back up immediately.
Hamilton passed Leclerc on the outside of the fast left-hander at the end of the middle sector but Verstappen followed him through immediately, darting down the inside of Leclerc into the Juncao left-hander that follows.
Verstappen was then immediately on Hamilton's tail and blasted past into the Senna S, as Hamilton ran out of battery power at the end of the lap and bemoaned his team for a lack of information.
The lead gap gradually extended to more than three seconds and Hamilton grew irate, insisting he could not close the gap and requesting to stop to try to force the undercut again.
Mercedes obliged on lap 43 of 71, but Red Bull covered the move one lap later again and Verstappen retained track position.
He proceeded to keep Hamilton at arm's length until a safety car emerged with 18 laps remaining, when Valtteri Bottas, who was in a frustrated fifth place behind Leclerc, was forced to retire after an apparent engine failure.
Bottas slowed exiting Turn 3 after smoke emerged from the back of his Mercedes before pulling to a halt on the inside at the Descida do Lago. It was initially dealt with using double waved yellow flags but required a safety car when a recovery vehicle was deployed to retrieve the Mercedes.
Hamilton stayed out while Verstappen dived into the pits for another set of softs, and Hamilton opted not to pit next time by either - giving him the lead, but with tyres 11 laps older than Verstappen's.
When the race resumed, Hamilton led Verstappen, Vettel, Albon and Leclerc - with Vettel and Albon sticking with tyres they had only just switched to three laps before the safety car, and Leclerc stopping for another set of softs.
Hamilton backed the pack up aggressively at the restart to thwart Verstappen's attack, but Verstappen drew level on the outside into the first corner and toughed it out to reclaim the lead into the second part of the Senna S.
Behind, Albon aggressively took third from Vettel, then rebuffed the Ferrari's attempt to reclaim the place.
Albon closed in on Hamilton for second but found himself under attack from Vettel again when the Ferrari launched a move around the outside into the first corner with seven laps remaining, but Albon somehow resisted.
Two laps later, Leclerc mugged team-mate Vettel on the inside into the first corner, but when Vettel fought back on the run down to the Descida do Lago he moved left and the two Ferraris hit each other.
The impact broke Leclerc's front right wheel and forced the furious youngster into retirement, while Vettel - who blamed Leclerc - picked up a right-rear puncture.
That triggered a safety car under which Hamilton dived into the pits yet again, dropping him to fourth behind Albon and Gasly.
A two-lap dash to the flag ensued after the safety car period ended, in which Albon and Hamilton collided. Hamilton accepted the blame for the incident, which is under investigation and dropped Albon to 14th.
Gasly then earned his stunning podium finish by keeping Hamilton behind until the Mercedes drew level exiting the penultimate corner, but the Honda-powered Toro Rosso stayed ahead in a near dead heat.
Behind the top three, Carlos Sainz Jr matched McLaren's best result of the year by making the most of the chaos to finish fourth despite starting last after failing to take part in qualifying.
Kimi Raikkonen and Antonio Giovinazzi nailed Alfa Romeo's best result of the season in fifth and sixth, ahead of Daniel Ricciardo, who fought back from a broken front wing and a five-second penalty early on in a collision with Kevin Magnussen to finish seventh.
Lando Norris took eighth for McLaren, with Racing Point's Sergio Perez and Daniil Kvyat completing the points finishers.
Race result
| Pos | Driver | Car | Laps | Gap |
|---|---|---|---|---|
| 1 | Max Verstappen | Red Bull/Honda | 71 | 1h33m14.678s |
| 2 | Pierre Gasly | Toro Rosso/Honda | 71 | 6.077s |
| 3 | Carlos Sainz Jr. | McLaren/Renault | 71 | 8.896s |
| 4 | Kimi Raikkonen | Alfa Romeo/Ferrari | 71 | 9.452s |
| 5 | Antonio Giovinazzi | Alfa Romeo/Ferrari | 71 | 10.201s |
| 6 | Daniel Ricciardo | Renault | 71 | 10.541s |
| 7 | Lewis Hamilton | Mercedes | 71 | 11.139s |
| 8 | Lando Norris | McLaren/Renault | 71 | 11.204s |
| 9 | Sergio Perez | Racing Point/Mercedes | 71 | 11.529s |
| 10 | Daniil Kvyat | Toro Rosso/Honda | 71 | 11.931s |
| 11 | Kevin Magnussen | Haas/Ferrari | 71 | 12.732s |
| 12 | George Russell | Williams/Mercedes | 71 | 13.599s |
| 13 | Romain Grosjean | Haas/Ferrari | 71 | 14.247s |
| 14 | Alexander Albon | Red Bull/Honda | 71 | 14.927s |
| 15 | Nico Hulkenberg | Renault | 71 | 18.059s |
| 16 | Robert Kubica | Williams/Mercedes | 70 | 1 Lap |
| 17 | Sebastian Vettel | Ferrari | 65 | Collision |
| 18 | Charles Leclerc | Ferrari | 65 | Collision |
| 19 | Lance Stroll | Racing Point/Mercedes | 65 | Accident damage |
| - | Valtteri Bottas | Mercedes | 51 | Power Unit |
Drivers' standings
| Pos | Driver | Points |
|---|---|---|
| 1 | Lewis Hamilton | 387 |
| 2 | Valtteri Bottas | 314 |
| 3 | Max Verstappen | 260 |
| 4 | Charles Leclerc | 249 |
| 5 | Sebastian Vettel | 230 |
| 6 | Pierre Gasly | 95 |
| 7 | Carlos Sainz Jr. | 95 |
| 8 | Alexander Albon | 84 |
| 9 | Daniel Ricciardo | 54 |
| 10 | Sergio Perez | 46 |
| 11 | Lando Norris | 45 |
| 12 | Kimi Raikkonen | 43 |
| 13 | Nico Hulkenberg | 37 |
| 14 | Daniil Kvyat | 35 |
| 15 | Lance Stroll | 21 |
| 16 | Kevin Magnussen | 20 |
| 17 | Antonio Giovinazzi | 14 |
| 18 | Romain Grosjean | 8 |
| 19 | Robert Kubica | 1 |
| 20 | George Russell | 0 |
Constructors' standings
| Pos | Constructor | Points |
|---|---|---|
| 1 | Mercedes | 701 |
| 2 | Ferrari | 479 |
| 3 | Red Bull/Honda | 391 |
| 4 | McLaren/Renault | 140 |
| 5 | Renault | 91 |
| 6 | Toro Rosso/Honda | 83 |
| 7 | Racing Point/Mercedes | 67 |
| 8 | Alfa Romeo/Ferrari | 57 |
| 9 | Haas/Ferrari | 28 |
| 10 | Williams/Mercedes | 1 |


{{#if saved}}
Answer both parts of the requirements for this Capability below. You can edit these responses until you submit all your Solution’s Capability Assessment responses from the Capability Dashboard.
All Clarifications should be submitted in Bravo.


Dasuki: Appeal court vacates stringent bail conditions granted former NSA
- The stringent bail conditions granted Dasuki has been vacated
- The former NSA was asked to produce a Level 16 civil servant who must own a property worth N100 million within the FCT
- However, the appeal court says it was wrong for courts to ask a civil servants to stand as surety when bail was granted
The appeal court in Abuja, has on Friday, November 22, vacated stringent bail conditions earlier granted to former National Security Adviser (NSA), Colonel Sambo Dasuki (Rtd).
According to PM News, the court agreed to Dasuki’s request for a variation in the terms attached to the bail earlier granted him.
Consequently, the three-man panel of the court expunged the requirement that Dasuki produced a Level 16 civil servant who must own a property worth N100 million within the Federal Capital Territory (FCT).
Justice Stephen Adah, in the lead judgement, said it was wrong for courts to ask a civil servants to stand as surety when bail was granted.
Meanwhile, recall that Legit.ng reported that Dasuki was granted bail by a Federal High Court.
Justice Ijeoma Ojukwu granted Dasuki N200 million bail with two sureties.
Col. Sambo Dasuki (rtd) had been on trial for alleged diversion of funds meant for procurement of arms to combat the Boko Haram menace to the 2015 campaign of then ruling Peoples Democratic Party (PDP).
The bail granted was the sixth one granted him since his trial commenced.
PAY ATTENTION: Get the Latest Nigerian News Anywhere 24/7. Spend less on the Internet!
Also recall that Legit.ng reported that Dasuki, was granted a N100 million bail by the court of Appeal sitting in Abuja.
The director-general of Department of State Services (DSS), and attorney-general of the federation (AGF) were mentioned as respondents in the suit.
NAIJ.com (naija.ng) -> Legit.ng: Same great journalism, upgraded for better service!
Court grants N500 million bail to Irish national involved in $9.6 million deal
Top Nigeria Corruption Scandals: Joshua Dariye, Olisa Metuh and Sambo Dasuki | Legit TV


WITCHES
Invulnerable ethereal demonic spirits that may appear near places of power. They cannot be destroyed, but present a deadly hazard for the unwary adventurer.
Witches spawn in the following areas in terror and destruction:
· Act 1: Countess Tower
· Act 1: Tamoe Highland
· Act 2: Rocky Waste
· Act 5: Frozen Tundra
Warning: They can even be summoned in the last three areas, if you're suicidal.
 Most spirits are weak, manifesting only as a sobbing on the wind or dancing ghost lights at night.
A witch is a rare sight, but great care should be taken around them. They are invulnerable to any attack or spell but are not aggressive and do not move. However, do not come too close and startle a witch - or else!...
Strategy
Give a witch wide berth to avoid upsetting her. They do stand out from the crowd and their eerie song can be heard up to a screen away. A witch will laugh when she is about to pounce - when you hear this, run away immediately! If she does pounce on top of you, she will deal massive damage (50k physical and 50k magical).
If a witch is completely blocking your path, summon a minion near her. She will kill it and then remain inert for a few seconds, giving you an opportunity to run past.
Minigames
If you're daring enough to listen to the alluring, but dangerous song of a witch, or even slay one, you will gain an incredible potential.
See THIS PAGE for more information about the Minigames.


APIConfiguration
public struct APIConfiguration<T> where T : ResponseBodyParsable
Structure that keeps all information needed from Network Layer for operations.
-
Returns
URL
of the API request.Declaration
Swift
public var requestURL: URL
-
Type of the API request.
Declaration
Swift
public var requestType: NetworkLayer.RequestType
-
Headers for the API request.
Declaration
Swift
public var headers: [String : String]?
-
Request body for the API request.
Declaration
Swift
public var body: [String : Any]?
-
The expected object type from the request.
Declaration
Swift
public var responseBodyObject: T.Type
-
NetworkLayer
priorities the API request by looking that parameter.Declaration
Swift
public var priority: Operation.QueuePriority
-
The desired expiration time for the API request.
Declaration
Swift
public var cachingTime: NetworkLayer.CachingTime
-
NetworkLayer
adds desired queue by looking that parameter.Declaration
Swift
public var isMainOperation: Bool
-
If
true
, the response will be cached by specifiedcachingEndsAt
parameter of the response object.Declaration
Swift
public var autoCache: Bool
-
The timeout value for the request wait time.
Declaration
Swift
public var timeOut: Int
-
init(hostURL:endPoint:requestType:headers:body:responseBodyObject:priority:cachingTime:isMainOperation:autoCache:timeOut:)
Initializes Configuration with the host URL and endpoint separately. Returns nil if request URL cannot be created successfuly.
Declaration
Swift
public init?(hostURL: String, endPoint: String, requestType: NetworkLayer.RequestType = .get, headers: [String:String]? = nil, body: [String:Any]? = nil, responseBodyObject: T.Type, priority: Operation.QueuePriority = Operation.QueuePriority.normal, cachingTime: NetworkLayer.CachingTime = NetworkLayer.CachingTime(seconds: 60 * 60), isMainOperation: Bool = false, autoCache: Bool = false, timeOut: Int = 30)
Parameters
isMainOperation
If
true
, operation will be performed in special queue and return to main queue.autoCache
To use that, override
cachingEndsAt:
method of Response Body Object. Then specified custom caching will be applied for that request. -
init(url:requestType:headers:body:responseBodyObject:priority:cachingTime:isMainOperation:autoCache:timeOut:)
Initializes Configuration with the URL
Declaration
Swift
public init(url: URL, requestType: NetworkLayer.RequestType = .get, headers: [String:String]? = nil, body: [String:Any]? = nil, responseBodyObject: T.Type, priority: Operation.QueuePriority = .normal, cachingTime: NetworkLayer.CachingTime = NetworkLayer.CachingTime(seconds: 60 * 60), isMainOperation: Bool = false, autoCache: Bool = false, timeOut: Int = 30)
Parameters
isMainOperation
If
true
, operation will be performed in special queue and return to main queue.autoCache
To use that, override
cachingEndsAt:
method of Response Body Object. Then specified custom caching will be applied for that request. -
Requests given API configuration by using
NetworkLayer
.Declaration
Swift
public func request(completion: @escaping (_ response: NetworkLayer.Result<T>)->())
Parameters
completion
Completion block which contains error or success case.
response
Response
Enum
which has two cases, whethererror
orsuccess
.


--- layout: default title: Pattern Library ---
Below is a brief outline of the design process and deliverables used to create the RWD Kent State web site.
Developed Goals & Objectives (Research)
View Primary Goals & ObjectivesProduced Sketches and Brief Outline
View SketchesDeveloped Content Outline
View Content/Sitemap in Google DriveCreated Style Tiles and Typography
View Style Tiles View Type ExamplesDeveloped Content Wireframes
View WireframesLaunched Final Website
View Final WebsiteCreated Style Guide / Pattern Library
View Pattern Library


Peaks
The text area contains a list of peaks, each of which is represented
by an m/z value and an intensity separated by a space.
The user can copy, paste, and edit the list as text.
Masses and intensities
The text area contains a list of deconvoluted masses, each of which
is represented a monoisotopic mass, an intensity, and a charge state
separated by spaces.
The user can copy, paste, and edit the list as text.
If the intensity is unknown, a default value 1 can be used. For example,
the user can add a monoisotopic mass 10000 Da with charge state 10 by inserting
a line "10000 1 10" into the text.
Fixed PTMs
To add a fixed PTM, select "C:57.0271464" or "C:58.005479" and click the
"+" button.
Protein sequence
The text area contains a protein sequence, which the user can edit
as text. A mass shift or modification on the protein is represented by a
number enclosed by brackets. For example,
the user can insert
"[79.9663]" into a sequence 'MPRSLKKGPF' to obtained a modified protein
"MPRS[79.9663]LKKGPF",
which has a phosphorylation site on the 4th amino acid.
After the user click the "Submit" button, an annotated protein
sequence will be shown. Alternatively, the user can click the amino
acids in the annotated protein sequence to add mass shifts.


Traversing
This is parent div
This is child div
Click Here
This is parent div
This is child div
Click Here


Choose a lamp:
Choose a pattern:
Randomize Beautiful Sparkles Night Sparkles Dave Worms Disco 1 Disco 2 Disco 3 Disco 4 Rain
Brightness: Low Medium High
Speed:
Psssst - You gotta be on the UENO wi-fi for this to work.


Home Page
About Us
Contact Us
Sales
Welcome to Salmon Cookies reasturant
Location:Irbid-Alhoson
Opening Hours: 6am - 7pm
Phone Number:8888888
Pick one:


[1]
we could also hear a couple of movement in the house.
[2]
we could also hear a fair degree of movement in the house.


The following code provides operations for reading in XML files and parsing them using the parser.
context Components @Operation parseFile() let file = xmf.openFile(xmf.projDir(),"*.xml") in if file <> "" then @WithOpenFile(fout <- file) Components::parse(fout).edit() end else xmf.message("Parse Cancelled") end end end context Components @Operation parse(inch:InputChannel) let grammar = ComponentModel::ComponentModel.compile() in let xin = ParserChannel(inch,grammar) in xin.debug := true; xin.parse("ComponentModel"); // Get the result and resolve the references. xin.result(true) end end end
The package operation parseFile() first opens a browser to select the XML to be imported. If the file exists. This calls the parse() operation, which opens an XMLInputChannel and the XML is read into variable xin. The result (an instance of the ComponentsModel) is returned.


this link will take you to the bottom of the post
General stuff
Hey guys this is my first paragraph of my website! not quite sure what to type here. This whole experience has been pretty exiting so far and im eager to move on!
It is a nice sunny day here in Tannay, Switzerland . It is 20degrees celcius and the sun is shining! I think I may go out for a run!
This is my second heading!
Hey guys! this paragraph has a Line Break in it!
bananas
pears
"These are steaks"
ribeye
tenderloin
Resume
Interests
swimming
weightlifting
martial arts
reading and writing
Experience
Not for sale
end human trafficking now
Stone King
Education
Law
War studies
<EMAIL_ADDRESS>My Facebook!
You've reached the bottom of the post
I will only turn blue on hover
I'm a link to Sam's blogpost!


Allow a taxonomic resolution of plant taxa names listed in the "Leipzig Catalogue of Plants" (LCP). Connects to the LCP table and validates the names of a vector of plant taxa, replacing synonyms by accepted names and removing orthographical errors in plant names.
LCP( splist, genus_search = FALSE, max_distance = 0, encoding = "UTF-8", family_tab = FALSE, order_tab = FALSE, genus_tab = FALSE, infraspecies_tab = FALSE, status = TRUE, save = FALSE, visualize = FALSE, version = "1.1", max.cores = (detectCores() - 1), out_path = getwd() )
| splist | A character vector specifying the input taxon, each element including genus and specific epithet and, potentially, infraspecific rank, infraspecific name and author name |
|---|---|
| genus_search | Logical. If TRUE, the function will apply the fuzzy match algorithm also for the search of the genus name, otherwise as default the search is applied only to the epithet, the infraspecies and the name author. |
| max_distance | is an integer value. It represents the maximum distance (number of characters) allowed for a match when comparing the submitted name with the closest name matches in the LCP |
| encoding | character vector. This value will allow the user to set the specific codification of the strings |
| family_tab | Logical. If TRUE, the function will return the list of plant taxa names belonging to the same family name submitted by the user |
| order_tab | Logical. If TRUE, the function will return the list of plant taxa names belonging to the same order name submitted by the user |
| genus_tab | Logical. If TRUE, the function will return the list of plant taxa names belonging to the same genus name submitted by the user |
| infraspecies_tab | Logical. If TRUE, the function will return also all the infraspecies names found for a submitted plant name |
| status | Logical. If FALSE, the function will return not only the valid epithet for a species name but also all the possible synonyms |
| save | Logical. If TRUE, the function will write the output file as comma-separated format (.csv), saving it into the working directory or in the directory already set through the 'out_path' option |
| visualize | If TRUE the function will visualize the output search on the 'Source Tab' of RStudio. This option has to be turned off (FALSE) if the package is execute in a UNIX environment (from the command line) without having a Graphical User Interface |
| version | A character vector indicating the current version of the package. A new version is under development allowing the package to connect the web API that is under construction |
| max.cores | integer value, indicating the number of CPU cores to be used for the parallelization of the plant name search when a list of plant taxa names is submitted. As default, the maximum number of CPU cores available on the working machine menus one is set |
| out_path | a character vector. Defines the path where the output file has to be saved. |
The Leipzig Catalogue of Plants (LCP) - An improved taxonomic reference list for all known vascular plants
https://idata.idiv.de/ddm/Data/ShowData/1806
LCP("Hibiscus vitifolius")#>#>#> Time difference of 0.2723269 secs#>#>#>#> ID Submitted_Name Order Family Genus Species Infrasp #> 1 1 Hibiscus vitifolius Malvales Malvaceae Hibiscus vitifolius species #> Infraspecies Authors Status LCP_Accepted_Taxon PL_Comparison #> 1 L. valid Hibiscus vitifolius L. identical #> PL_Alternative Score Insertion Deletion Substitution #> 1 matched 0 0 0LCP("Hibiscus abelmoschus var. betulifolius Mast.")#>#>#> Time difference of 0.611408 secs#>#>#>#> ID Submitted_Name Order Family Genus #> 1 1 Hibiscus abelmoschus var. betulifolius Mast. Malvales Malvaceae Hibiscus #> Species Infrasp Infraspecies Authors Status LCP_Accepted_Taxon #> 1 abelmoschus var. betulifolius Mast. synonym Abelmoschus moschatus Medik. #> PL_Comparison PL_Alternative Score Insertion Deletion Substitution #> 1 identical matched 0 0 0LCP(c("Hibiscus abelmoschus var. betulifolius Mast.", "Hibiscus abutiloides Willd.", "Hibiscus aculeatus", "Hibiscus acuminatus"), max.cores = 1)#>#> Time difference of 2.425487 secs#>#>#>#> ID Submitted_Name Order Family Genus #> 1 1 Hibiscus abelmoschus var. betulifolius Mast. Malvales Malvaceae Hibiscus #> 2 2 Hibiscus abutiloides Willd. Malvales Malvaceae Hibiscus #> 3 3 Hibiscus aculeatus Malvales Malvaceae Hibiscus #> 4 4 Hibiscus acuminatus Malvales Malvaceae Hibiscus #> Species Infrasp Infraspecies Authors Status #> 1 abelmoschus var. betulifolius Mast. synonym #> 2 abutiloides species Willd. synonym #> 3 aculeatus species Walter valid #> 4 acuminatus species Cav. synonym #> LCP_Accepted_Taxon PL_Comparison #> 1 Abelmoschus moschatus Medik. identical #> 2 Talipariti tiliaceum (L.) Fryxell identical #> 3 Hibiscus aculeatus Walter identical #> 4 Kosteletzkya acuminata (Cav.) Britten other synonym #> PL_Alternative Score Insertion Deletion Substitution #> 1 matched 0 0 0 #> 2 matched 0 0 0 #> 3 matched 0 0 0 #> 4 Pavonia acuminata (Cav.) Spreng. matched 0 0 0


Options
| name | type | description | default | |
|---|---|---|---|---|
| id | string | The unique id for formula's textarea element. | formula | |
| cursorAnimTime | int | Cursor pointer blinking animation time. (ms) | 160 | |
| cursorDelayTime | int | Cursor pointer blinking delay time. (ms) | 500 | |
| strings | formula | string | Default label text in formula. | Formula |
| strings | validationPassed | string | The label text when result of validation is passed. | Passed |
| strings | validationError | string | The label text when result of validation is error. | Validation Error |
| export | filter(data) | function | You can filter pre-export data as map filtering. | function(data) { return data; } |
| export | item($e) | function | You can bind a trigger or customize for item which be inserted by insert() function. | function($e) { return $e.text(); } |


--- layout: post status: publish published: true title: Cub Nation is One Person Larger author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>excerpt: 'Yesterday morning, my wife gave birth to our second daughter. Mallory Mae was born at 7:01am. She was 9 lbs. 2 oz and 20 inches long. We held off on naming her until the Cubs played in the afternoon because if Ted Lilly threw a no-hitter, ' wordpress_id: 60 wordpress_url: http://ivyenvy.wordpress.com/?p=60 date: '2008-05-04 12:51:48 +0000' date_gmt: '2008-05-04 12:51:48 +0000' categories: - The Cubs tags: - Baby - Bruce Sutter - Cardinals - Mallory Mae - Ted Lilly comments: [] ---
Yesterday morning, my wife gave birth to our second daughter. Mallory Mae was born at 7:01am. She was 9 lbs. 2 oz and 20 inches long. We held off on naming her until the Cubs played in the afternoon because if Ted Lilly threw a no-hitter, we were going to name her Lilly Mae. Of course, it didn't happen, but I'll be glad to always tell her that the day she was born, the Cubs beat the Cardinals.
It's a good thing my parents didn't take the approach towards naming me that we were taking with Mallory. Granted, he didn't throw a no-hitter, but the Cubs won on the day I was born. In fact, I was born probably in the 3rd or 4th inning of the game. But the starting pitcher for the Cubs that day was Bruce Sutter. I like Corey more than Bruce.


Mohsin is a cognitive science student at UC San Diego learning web design in an introductory HCI class.
Mohsin's CSE7 Quarter in Review


B.Tech. IV (CO) Semester –8 |
L |
T |
P |
C |
CO416:INFORMATION TECHNOLOGY AND MANAGEMENT (EIS-I) |
3 |
0 |
0 |
3 |
(05 Hours) |
||||
Information Systems in Organizations |
||||
(15 Hours) |
||||
Hardware, Software, Telecommunications, Internet, Intranets and Extranets- Electronic Commerce – Transaction Processing and Enterprise Resource Planning Systems |
||||
(15 Hours) |
||||
Specialized Business Information Systems: Artificial Intelligence, Expert Systems and Virtual Reality -System Investigation and Analysis |
||||
(07 Hours) |
||||
Design, Implementation, Maintenance and Review Security, Privacy and Ethical Issues in Information Systems and the Internet – Computer Law. |
||||
(Total Contact Time: 42 Hours) |
||||
BOOKS RECOMMENDED |
||||
1). R.M.Stair and George W. Reynolds – “Principles of Information Systems(A Managerial Approach)” 5/E.Thomson Learning, 1997 |


Thank you for your registration!
We are excited to welcome you to Part 3 in our Sufi Healing series. We pray
that this workshop will enhance, cleanse and deepen all the relationships in
your life :)
The next step is payment of your deposit to reserve a spot. Spaces are
limited, so please register now!
Please send your deposit of £60/adult + £30/child (ages 3-12) to:
First Direct
Mrs C A Laird Barclay
40-47-61
60888818
Or use PayPal (send to<EMAIL_ADDRESS>Or you can mail a cheque to:
Claire Laird Barclay
78 Heathcote Grove
London, E4 6SF
Your deposit will cover room & board for the entire weekend (Friday
dinner - Sunday 5pm) and is due NOW in order to reserve your place.
Please plan to attend the entire course (Friday September 21st 6pm — Sunday
5pm).
Tuition for the workshop is an additional payment and can be paid now
for simplicity if you like, but must be paid in full by September 14th, 2018
(one week prior to the start of the workshop) at the latest.
Suggested donation for workshop tuition is £100/adult, £150/couple and £50
for under 18s who want to attend the workshop.
If you cannot afford this, please let me know by replying to this email and
we shall work out an arrangement with you that suits, God-willing! Please
don’t let finances be a barrier to attending. Everyone is welcome and no one
will be turned away for lack of funds.
If you are able to contribute any extra funds to help others attend the
workshop, please do! Thanks to contributions from previous attendees, we
were able to offer scholarship positions for people who otherwise would not
have been able to attend the past 2 workshops and their lives and
relationships are healing as a result!
If you wish to contribute to the scholarship fund, please indicate that in
the "notes" section of the bank transaction and reply to this email to let
me know the intended breakdown of your payment. Every little bit helps!
Please give what you can, thanks!
Lodging: once we receive your registration, we will reserve a room for you.
Double ensuite rooms are available but go very quickly, so if you need one
of those, please send your deposit asap. We will not be holding any rooms
without a deposit, thank you.
Food: If you have indicated dietary specifications in your registration
form, we will note them and do our best to accommodate all your food needs
:)
Please let me know if you have any questions or if there's anything else we
can do to help. We will be in touch with more details of the weekend's
itinerary and a few other tidbits as we near the start of the
workshop.
For now, may we offer our sincere gratitude for your participation in the
third workshop of this series. We look forward to serving you on your
journey toward The One as best we can. May God bless you and keep you on the
path of love.
Yours sincerely,
Sahra & the team!


QuerySubstitutionNames
public enum QuerySubstitutionNames : Int
Enum defining the cases and their index for the substitutions array used by QueryBuilder
to account for variances between the various database engines.
Usage Example:
In this example, a QueryBuilder
for PostgreSQL is initialized.
Parameters are set for PostgreSQL and strings keywords for SQL queries are substituted into the queryBuilder. The QuerySubstitutionNames
enum is used to refer to substitutions by name instead of their position in the substitutions
array.
let queryBuilder = QueryBuilder(withDeleteRequiresUsing: true, withUpdateRequiresFrom: true, createAutoIncrement: createAutoIncrement)
queryBuilder.updateSubstitutions([QueryBuilder.QuerySubstitutionNames.ucase : "UPPER",
QueryBuilder.QuerySubstitutionNames.lcase : "LOWER",
QueryBuilder.QuerySubstitutionNames.len : "LENGTH",
QueryBuilder.QuerySubstitutionNames.numberedParameter : "$",
QueryBuilder.QuerySubstitutionNames.namedParameter : "",
QueryBuilder.QuerySubstitutionNames.double : "double precision"
])
-
The SQL UCASE scalar function to convert a string to upper-case.
Declaration
Swift
case ucase
-
The SQL LCASE scalar function to convert a string to lower-case.
Declaration
Swift
case lcase
-
The SQL LEN scalar function to return the length of a string.
Declaration
Swift
case len
-
The SQL NOW scalar function to return the current date and time.
Declaration
Swift
case now
-
The SQL MID scalar function to return a portion of a string.
Declaration
Swift
case mid
-
The marker for the query numbered parameters.
Declaration
Swift
case numberedParameter
-
The marker for the query named parameters.
Declaration
Swift
case namedParameter
-
The boolean true.
Declaration
Swift
case booleanTrue
-
The boolean false.
Declaration
Swift
case booleanFalse
-
The ALL function for subqueries.
Declaration
Swift
case all
-
The character used to quote identifiers (table name, column name, etc.) with spaces or special characters.
Declaration
Swift
case identifierQuoteCharacter
-
The database type that corresponds to Int32.
Declaration
Swift
case int32
-
The database type that corresponds to Float.
Declaration
Swift
case float
-
The database type that corresponds to Double.
Declaration
Swift
case double
-
The database type that corresponds to char.
Declaration
Swift
case char
-
The database type that corresponds to UUID. Accepts a string representation of UUID.
Declaration
Swift
case uuid
-
Last case, add new values before it.
Declaration
Swift
case namesCount