How we care for and train our dogs is of paramount importance in their future success at forming relationships with us. My German Shepherd Deiter is now two. I filmed his upbringing in the first eighteen months for a new video production dealing with the dog's mind and development. He was obedience-trained from seven weeks, in the main without a collar or lead, simply utilising verbal praise, signals and body language. Discipline was by voice when required. He is now a highly-trained dog and can be walked amongst deer, horses and any other natural prey object for a domestic dog without issue. Most of our dogs become well-mannered and that is why they are our favourite animal – their innate pack or wolf behaviour is what allows us to blend their behaviour into our life for an enjoyable relationship as we, too, are pack animals.
Most experts understand the dog's pack and wolf antecedents and the research by Robert K Wayne PHD in essence finds that the DNA of dogs and wolves is virtually identical – though domestication has certainly altered the dog to being more malleable, otherwise ownership would be as difficult as that of keeping a tame wolf.
However, as a dog and cat behaviour practitioner of over 30 years' experience I regularly see unattractive wolf behaviour in my clients' dogs and the variety that can cause immense conflict between owner and dog and between dog and dog. My job is to first alter the behaviour so the client can keep their dog, or at least manage the behaviour that makes owning the dog easier.
Unfortunately, there has been a trend recently to try and convince us that pack behaviour (dominance) does not exist. Yes, once again, and despite claims that the very limited study of this nature should be used to attempt to overthrow the established and well-documented mainstream behavioural research. It is most untenable. The proponents now wish to transpose the same mad arguments used by other academic 'experts' as to how our children over the past 30 years are supposed to have produced enormous problems of extreme badly behaved children that terrorise society. The fact that there are other well-behaved children is noticeable to all of us and that they now wish to renew outpourings of so-called academic conclusions is deeply sad and troubling.
Thousands of dog owners going peaceably about their business are encountering increasing numbers of badly out of control dogs in public places because the owners have been indoctrinated to believe that any correction is bad so the problem is exacerbated daily. The rule I follow is simple, and based on having trained thousands of dogs successfully – in public places, not school halls. Always reward first but when rewards no longer work then discipline may be required even if only a tiny part of the entire training programme.
Well, welcome to a minor group of academics from Bristol University who are extolling similar parallel work that their child-destroying counterparts did. They desire to prevent trainers and behaviour practitioners from using any discipline on dogs and are campaigning in the media to label as 'wrong' the excellent non-conflictual methods of rehabilitation that I and others have successfully used to help thousands of owners take control of their dogs.
The dogs we deal with exhibit aggressive or other behavioural problems (like excessive attention-seeking, dangerously boisterous behaviour of diving at people in public places or the home or suffering from extreme anxieties because of this wave of feeble happy-clappy-yappy advice. Let's kiss, hug and offer chicken to dogs and all will be fine despite the rising problems dogs exhibit. Dogs love chicken but if the motivation to chase other dogs is greater then unfortunately chicken will come second in their choice of attractions.
It is not arguable that dogs work on a rank structure (dominance). Those of you who own more than one dog or see your dog interact with others in the park see this daily. Dominance can be subtle and does not always mean confrontation. Rank and position maintain peace and order. When dogs step out of line they are admonished by a glare, teeth display or an attack, that's dogs being dogs. Now, just how can these happy-clappy groups (mainly from academia) who have never worked in front-line behaviour state that dogs are not as I describe – replacing the word pack with attachment and believe there is a difference. Well there is – in words but only words.
They are now saying, despite the rising problematic dog count that we need to let them sleep on our beds and sit on our furniture in case we stress them by getting them off. If the dog shows aggression over food, feed them until they burst, they say. The dogs that attack children over toys? Oh, simply supply more toys, they say. The outcome of this irresponsible junk is dogs that do as they wish and are more likely to attack children through dominance aggression because we supply more toy targets for dispute and dominance. Now, you may think this is madness. You're right. It is. These meddlers are purveying nonsense which, in my view, is also dangerous.
My Cairn Terrier Safie sits on my knee when invited and if people with dogs that are well behaved want them on their knee or couch then that's their prerogative. However, the dogs that my fellow behaviourists and I deal with are not dogs that are obedient or well-behaved, but in 70% of cases are aggressive or downright dangerous. Of course the academics, having never worked in the industry, don't even consider the families' needs – whether you want a dog on your bed or furniture covered in wet mud never mind the hygiene issues, especially where children are concerned. They simply have not thought through what individual owners want as their standards in their home. Remember dear academics: people are important too .
The Canine & Feline Behaviour Association believes that more and more owners encountering problem behaviour is a direct result of the confusing counter information distributed. For owners, this makes decision-making confusing and, in time, their dogs become out of control and in hundreds of cases are euthanased because they did not form leadership rules or teach their dogs strict rules whether called pack , boundaries or other terms.
In the last 12 months I have asked on my walks 22 people, whose dogs are very badly behaved and a plain nuisance in the park, why their dog is not trained – especially to come back. With only two exceptions all began to tell me what they don't do . That comment is pertinent to my point. Whilst talking, they are either constantly clicking away or feeding their dog and trying to control it simply by endless food rewards as we are stood. I again ask what do you do – not what you don't do . All I get is a blank look.
They have been brainwashed into becoming ineffective owners by advisors who believe in fantasy moralising. Often these owners have also not let their dog off the lead for over 6 or 12 months because they can't get the dog back thereby ruining its critical socialisation and temperament development period with dogs and causing more aggression problems to other dogs they encounter.
I state the obvious: your dog does not come back despite following the advice for half a year or so – the methods you use don't work – I have observed you. Tell the trainer. They say they have but the trainer will not alter the methods due to inculcated belief systems of non-discipline or being negative being the 'in' word. To me it's inconceivable that a trainer would not show these people what to do by example in public place. The truth is that the trainer cannot achieve a result either and that's the real reason. Of course, there is also the criminal offence under the Trading Standards Act of not doing what you say you can do and charging a fee for it. Not one owner told me how to get the dog back in a way that actually works and they can't reward the dog because it does not come back. All is you hear is No Negative, No Negative, or if you prefer, discipline. No instruction in my view is tantamount to recklessness at some owners' poor dogs' expense.
I point out the actual negative behaviour in their dog they have taught: like upsetting daily most of the people walking their dogs, their dog running off and being hit by a car because it won't recall, or causing a fight by piling on dogs without control. Now, that's what I call negative and can lead, through endless frustration, to their dog developing serious aggression to other dogs. Of course, I am in no way blaming these unfortunate owners. They thought they were taking their beautiful puppy to an expert – they were wrong. I am not referring to lazy owners – these people are dedicated and work hard but are using a belief system that is defective and certainly not positive by result.
I have personally on record instigated some 10,000 programmes on dogs which we have referred to for the last seven years as Intelligent Leadership programmes (ILP) in dogs in the UK. These programmes teach the dog psychologically that you are in charge and moreover link all the attention and praise dogs so enjoy to behaviour you wish to encourage – it's simple and has an outstanding and unequalled success rate.
Members of the CFBA could probably add many thousands more which have been placed on their own variation programmes sometimes called Alpha, Leadership or other title. Of the 10,000 dogs presented to this centre approximately 7,000 were already aggressive to their owners or dogs or both. The majority of owners were considering placing them in rescue or, alternatively, considering euthanasia. The human psychological crisis in the majority of these cases was critical and emotionally catastrophic. The result has been spectacular and only last week a little terrier dog that was due to be put down is now being kept because of the programme above. And, yes, the owner had the dog sleeping on her bed, attacking her visitors and husband over food and it bit people who sat on the couch next to her. Strange that.
Bristol University did an assessment of 19 neutered males all kept in prison-like conditions at rescue centres and after 6 months declared, having visited them with their clipboards, that dogs have little dominance pack behaviour as described by me and other worldwide experts but are simply attached. In other words: don't use rank programmes to alter behaviour, they state. My findings from my own extensive experience with large numbers of dogs are totally supported by virtually all dog behaviour studies where dominance has been investigated, and by the practical experience of the vast majority of dog trainers and behaviour practitioners who have worked in the field for any length of time.
Now if they were to produce quantitative evidence and programmes they do use that work on the dogs I have previously described and that we can inspect – then I would look intelligently at their speculations. They don't. They simply wheel out this irrelevant trial and deliver it as science. Well, if I took 19 male prisoners and neutered them and then drew the parallel that the behaviour in prison they were exhibiting was somehow a correlation of the behaviour of 19 non-castrated males living in normal homes in society and their dominant behaviour one would laugh mockingly at such absurd results. The fact is that this type of minor study is deeply flawed by its study group and environment but, as I said, wheeled out as science we are expected simply to accept.
Where safety can be an issue it is unfortunate if an interpretation arising from a study with few animals damages the long-standing practical experience arising from the handling and training of vast numbers of animals and well-established mainstream research from around the world. One such recent and the largest scale study led by world-renowned Dr Perez-Guisado and Munoz-Serrano of the University of Cordoba, using a sample of 711 dogs (354 males and 357 females of which 594 were pure-bred and 117 mixed breed) has, without equivocation, fully supported my and other experienced practitioners' views about the dog as pack animal within our families. It simply overwhelms the Bristol findings and kicks it not just into touch but way out of the field.
Dr Perez further stated that significant factors that contribute to aggression in the dog are: a lack of basic training, first-time dog ownership, failure to subject the dog to basic obedience training, spoiling or pampering the dog (i.e. having it on the bed/furniture), not using physical punishment when it is required, spaying female dogs, leaving the dog with a constant supply of food. Now I don't know Mr Perez but much of what he states is in my Intelligent Leadership Programmes (with the exception of punishment).
He says "failure to observe all of these modifiable factors will encourage this type of aggressiveness and would conform to what we would colloquially call 'giving our dog a bad education'" He classically refers to the aggression as dominance which, of course, it is.
Dr Perez adds: "to correct the animal's behaviour, the owner should handle it appropriately and re-establish dominance over the dog". Need I say more?
The Canine & Feline Behavioural Association, and other mainstream bodies whose members are practically involved with dealing directly with dogs in real environments, are appropriately concerned when inferences are too lightly taken from small scale studies and whose findings are not in accordance with well established research and experience.
Colin Tennant – Chairman of the Canine and Feline Behaviour Association.


Sometimes because of foxes and other wildlife as well as over-enthusiastic gardeners damaging the pipework, drip-line irrigation is not always the best solution. On this occasion we opted for a reliable system of automated pop-up sprinklers on the lawn that also over-sprayed the borders. Rotary or rotator pop-up sprinklers are designed to mimic natural rainfall without any harsh jets of water. This means they are the ideal option to protect your plants whilst helping them thrive.
Installing a Rosewood irrigation system has allowed the owner to continue to enjoy the garden, into which he has invested much love and care, with much less time and energy.


On short notice the mother of my customer announced she would be coming to town to visit one day after her birthday. Since it was her 70th birthday her daughter managed to organize a surprise party with a special cake.
It was a carrot cake with cream cheese buttercream.
The flowers and writing are fondant cut-outs.


Are you alone within a couple?
Caillebotte shows us two people lost in reverie who have forgotten they once were friends.
Does resentment help you to be heard?
Is coldness getting your needs met?
Is a power struggle what you dreamed of?
Real friendship is safety and paradise. Good therapy protects friendship from endless power struggles, needless accommodations and repetitive scenarios.
Therapy helps you to feel heard; helps you come to terms with how you aren't heard.
Therapy helps you listen to your partner.
Paradise is the ability to put yourself in the place of your partner, to show up in the way the other needs.
One partner comes to therapy, the other not – or not now. Each comes separately or together or both. Whatever it takes to move forward.


Having a history of 102 years, South China Agricultural University (SCAU), has developed into a "project 211" national key university, which was jointly promoted by China Ministry of Agriculture and Guangdong Provincial Government since the period of the 9th and 10th Five-Year Plan (1996-2005). It is situated in Guangzhou City, known as the Flower City. The beautiful and peaceful campus covers an area of more than 8,250 mu (or 550 hectares) with 1,332,000 square meters School Building Area. It is an ideal place for education and scientific research.
At present SCAU has twenty two colleges. They are College of Agriculture, College of Environment and Natural Resources, College of Life Science, College of Economics and Management, College of Engineering, College of Animal Science, College of Veterinary Medicine, College of Horticulture, College of Food Science, College of Forestry, College of Humanities and Law, College of Science, College of Information Science, College of Fine Arts, College of Foreign Studies, College of Water Conservancy and Civil Engineering, College of Public Management, College of Continuing Education, College of International Education and so on. Now more than 40,000 full time students are studying there. Among them, about 36,000 are undergraduate students and 4,000 graduates. There are more than 2,900 faculty and staff at SCAU, including 900 full and associate professors with 209 doctor tutors and 715 master tutors. SCAU has grown into a multi-disciplinary, comprehensive university with 88 undergraduate specialties, 10 first-class discipline doctor's degree programs, 20 first-class discipline master's degree programs, 50 second-class discipline doctor's degree programs including 11 independent majors, 100 second-class discipline master's degree programs including 12 independent majors. Altogether there are 5 types of master academic degrees and 1 type of doctor academic degree.
The College of International Education was founded in July of 2007. It is responsible for the management of international students and international training program, the operation of collaboration programs and courses between SCAU and overseas higher institutions and offering international training program. As a platform for international education of SCAU, the college, based on the principle of having the world in view and pursing excellence, endeavors to cultivate externally-oriented, international senior administration and technology talents and energetically develops international communication and cooperation to enhance the college's internationalism.
Up to now, SCAU has overseas students in more than 40 countries from Asia, Africa, South America, and Europe at undergraduate, master and doctor level. Since it officially launched Chinese language training class in 2008, nearly 100 international students are studying or once studied there. We also offer multi-language classes, including Chinese, English, Spanish and French and etc. at different levels on each semester, which are designed to meet international students' demands for overseas education, daily work or interest. China international center for training in agriculture, cofounded by United Nations Development Program, Food and Agricultural Organization and World Food Council, is committed to train intermediate, senior agricultural administration or technology officials in developing countries in terms of agriculture economic management, agricultural administration and rural leading, rice cropping, crops pests prevention, sugarcane Cultivation and etc. This class has already been organized in 8 phases.
Warmly welcome overseas students to study or further education in beautiful SCAU!


text:
Q: Error in uploading file to a server using Phonegap file transfer I am using phonegap to upload file to a server. I am making android app which takes picture and send to server. I am getting error

"Response  Parse error  syntax error, unexpected T_STRING in this line"

I am also pasting the code
this is my phonegap code.
 navigator.camera.getPicture( cameraSuccess, cameraError, {
                        quality : 75,
                        destinationType : Camera.DestinationType.FILE_URI,
                        sourceType : Camera.PictureSourceType.PHOTOLIBRARY,
                        mediaType: navigator.camera.MediaType.ALLMEDIA,
                        allowEdit : true,
                        targetWidth: 100,
                        targetHeight: 100
                    });

and
move_uploaded_file($_FILES['file']['tmp_name'],'/TeleNoc Sites Images/'.basename($_FILES['file']['name']));

This is my upload script to catch the file on server

A: A common reason to get the 'unexpected T_STRING' error is a missing semi colon in the line before.
Dan

redpajama_set_name: RedPajamaStackExchange


text:
Young Matildas' big win
Staff writer 1430869047
The Young Matildas have topped Group A after a resounding 7-0 win over Indonesia at the AFF Championships, with the unbeaten Aussies now looking forward to the semi-finals.
Coach Ante Juric made several changes to the starting side with another short turnaround after the tight win over Laos.
Young Matildas edge Laos in Vietnam
The move paid dividends from the outset, with the Young Matildas getting on the front foot early and never relinquishing control of the match.
A fine piece of build-up play resulted in Jordan Baker giving the Aussies the lead on ten minutes after her shot cannoned off the cross bar and over the line.
From there the flood gates opened, with Princess Ibini-Isei, Emily Condon and a second to Jordan Baker seeing Australia's U20 side 4-0 up inside the opening 30 minutes.
The half was capped with a superb piece of individual play from Alexandra Chidiac, who received the ball and beat several players before lashing a shot over the keeper's head from outside the area.
It was more of the same in the second stanza with some majestic football breaking down the Indonesian defence and the Young Matildas taking full advantage in attacking areas.
They were rewarded shortly after the restart when captain Olivia Price was found in the area with time to shoot and managed to slot the ball past the keeper to give her side a six goal advantage.
Moments later, Emily Condon was back on the score sheet with her second of the night after a strong run from the left wing to round out the scoring.
Coach Ante Juric was pleased with the improved performance from his side and singled out Alex Chidiac as a star of the future after another strong showing.
"This was an extremely good performance in response to a substandard game last time out," said Juric.
"The girls were positive and played with lots of intent. They did not take the opposition lightly and played to the standards we have set ourselves in this Australian national team.
"All the players delivered, though I must say Alex Chidiac, one of the younger members of the team, is really taking the right steps in progressing into a future superstar in the women's game."
The former Socceroo was thrilled his side took the maximum nine points from the group stage but warned against complacency having qualified for the semi-finals where they'll play Myanmar.
"Three wins from three group games is very pleasing," he said.
"Now with us moving into the last four against Myanmar and it gives the girls an opportunity to experience a game under the pressure of a semi-final.
"It is important to the overall development of the girls and our ultimate goal of qualifying for the World Cup in August."
Australia U20 7 (Jordan Baker 10' 28', Princess Ibini-Isei 25', Emily Condon 31', 59', Alexandra Chidiac 41', Olivia Price 57')

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
The Large Hadron Collider has made significant progress searching for the Higgs boson in 2011. With the latest results from ATLAS \cite{Collaboration2012} and CMS \cite{Collaboration2012a} together with the electroweak precision data from LEP \cite{ALEPHCollaboration2010} only a very small mass window in the region of 125 GeV is not excluded for the standard model Higgs.

The statistical procedure that is used to exclude the existence of a Higgs boson is the $CL_S$ method \cite{ReadCLS}. The method can account for systematic and theoretical uncertainties. A specific concern that was voiced in many discussions \cite{Kilminster2012,Dawson2012} in the theoretical community is the correct treatment of perturbative and parton density uncertainties in this method. In this article we investigate whether the limits obtained with the $CL_S$ method can be considered sufficiently conservative regarding these uncertainties.

The paper is organized as follows. In section \ref{sec:cls} we present a general review of the $CL_S$ method, which is used to calculate exclusion limits in standard model Higgs boson searches. Next, we introduce the concept and use of nuisance parameters. In section \ref{sec:nuisance} we analyze the worst-case and profiling method used to treat nuisance parameters in limit setting procedures. In section \ref{sec:combo} we illustrate the results obtained in the previous sections and support our conclusions with a  exemplary simplified Higgs boson search. Furthermore, we use our example to discuss the common methods of presenting results of Higgs searches. Finally we make a recommendation for the treatment of perturbative uncertainties.
\section{Review of the $\mathbf{CL_{S}}$ method}
\label{sec:cls}

In high energy particle physics a theoretical model or hypothesis is usually represented by a quantum field theoretical Lagrangian density. Experiments such as the LHC test whether these theoretical models are suited to describe observations. Fundamental parameters of the Lagrangian, like coupling strengths or masses, can be extracted by fitting the quantitative predictions of  the model to experimental data.
Experimental results may have discerning power for different models that aspire to explain the same phenomena.
The $CL_S$ method \cite{ReadCLS},  as used by ATLAS and CMS \cite{atlashiggscombo,CMS-PAS-HIG-11-032},  provides a means of setting upper limits on cross-sections derived from a model and constrains the possible range of fundamental parameters.

Cross-section predictions for a specific experiment depend on input parameters, i.e. fundamental parameters of the Lagrangian as well as derived parameters such as scales, parton density functions and detector efficiencies. These parameters can be classified as either nuisance parameters or parameters of interest. A nuisance parameter is any parameter that is not under investigation in an experiment but still has an impact on the predictions. Conversely, parameters of interest are the parameters that are being constrained in the respective analysis.
The Higgs sector of the standard model does not contain a free parameter that affects the cross-sections of processes involving a Higgs boson of a certain mass, leading to unique predictions at a fixed order in perturbation theory. Consequently, there is no fundamental parameter that could be constrained to set a limit on the existence of a Higgs boson.
For testing the model, the experimental collaborations have introduced a deformation of the prediction, scaling the Higgs cross-section with an arbitrary factor $\mu$ that is used as parameter of interest \cite{atlashiggscombo,CMS-PAS-HIG-11-032}. This parameter is referred to as signal-strength modifier. The $CL_S$ method is then used to constrain the value of $\mu$ \cite{ReadCLS}. The standard model Higgs boson hypothesis for a certain Higgs mass is said to be excluded, if $\mu$ is found to be less than one with a certain confidence.
We point out that this procedure entails some conceptual problems.
\begin{itemize}
\item A separation between Higgs (signal) and non-Higgs (background) contributions is only valid if the Higgs width is small with respect to its mass.  For low Higgs masses, $m_h~<~400~\text{GeV}$, such an approximation is valid, as interference terms can safely be neglected. However, in the high Higgs mass regime, $m_h > 400 \text{GeV}$, sizable corrections \cite{Anastasiou2012} have to be taken into account. In general, these corrections will have a different scaling behavior with $\mu$ than the signal contributions.
\item The standard model does not provide a free parameter that could be used to uniformly rescale the cross-section of processes involving the Higgs boson. One way to introduce a physical parameter of interest is to consider more complex Higgs models, such as the strongly interacting light Higgs model \cite{Giudice2007}, which contains additional free parameters. The cross-section should be evaluated consistently from the model, taking into account non-linear scaling of the cross-section from higher order corrections.
\end{itemize}
Model predictions for hadron colliders are hindered by several obstacles.
\begin{itemize}
\item Observables are obtained using a perturbative expansion. This introduces an uncertainty due to missing higher order corrections.
\item Parton distribution functions cannot be extracted from perturbation theory and are obtained from complementary measurements. Consequently, the statistical and systematic uncertainties of these measurements must be propagated to any calculated cross-section.
\end{itemize}
The results of theoretical calculations are commonly stated in combination with the corresponding perturbative uncertainty and an overall parton density function uncertainty \cite{Watt2012,TheNNPDFCollaboration2011}.
Theoretical cross-section uncertainties must be propagated through limit-setting procedures. In the $CL_S$ method they are taken into account using nuisance parameters. In the section \ref{sec:llr} the treatment of nuisance parameters in the CLs method in general is discussed. In section \ref{sec:nuisance} we discuss the use of nuisance parameters for the specific uncertainties stated above.

Experimental collaborations use additional nuisance parameters to account for experiment specific uncertainties, such as detector efficiencies. The focus of this article is on theoretical uncertainties and we will neglect other uncertainties and their nuisance parameters.

\subsection{Likelihood ratios}
\label{sec:llr}
Let $X = \{X_i\}$ be a set of measurements and $g(X_i|\mu, \nu)$ be the probability density for the observation $X_i$ according to a model with parameters of interest $\mu$ and nuisance parameters $\nu = \left\{ \nu_j \right\}$. Then the probability to measure the values $X$ in a single experiment is
\begin{equation}
P = \prod_{i=1}^{N} P(X_i) = \prod_{i=1}^{N} g(X_i|\mu,\nu) dX_i =L(X|\mu,\nu)\prod_{i=1}^{N} dX_i,
\end{equation}
where $P(X_i)$ is the probability to find a measurement in an infinitesimal interval around $X_i$.
The quantity
\begin{equation}
L(X|\mu,\nu) = \prod_{i=1}^{N} g(X_i|\mu,\nu),
\label{eq:L}
\end{equation}
which appears as a joint probability density function, is the likelihood \cite{Fisher,Reid2003,Berger1988,Berger1999,Barndorf-Nielsen1988,Kalbfleisch1985,Reid2010}.

In the case of a collider experiment, $X_i$ is the number of observed events in an experimentally defined region $i$ of the phase-space. The cross-section for an event that the experiments are interested in is very low compared to the total cross-section of any scattering event happening. Therefore the number of interesting events is Poisson distributed and $g(X_{i}\mid \mu,\nu)=e^{-\lambda_i} \lambda_i^{X_i}/X_i!$ is the Poisson probability of observing $X_{i}$ events given the model prediction  $\lambda_i=\lambda_i(\mu,\nu)$. The functional dependence of $g$ on $\lambda_i$ is not written explicitly as we want to stress the implicit dependence on $\mu$ and $\nu$. The set of predictions for all phase-space regions is in the following denoted by $\lambda(\mu,\nu)=\{\lambda_i(\mu,\nu)\}$.

In order to compute $\lambda(\mu,\nu)$ all input parameters are set to specific values. The value of a nuisance parameter is usually constrained by independent experiments or by physical assumptions.
These constraints are modeled as outcomes of an auxiliary measurement by defining the likelihood $\pi(\tilde{\nu}|\nu)$, with a predicted value $\nu$, of the outcome that assigns a probability to every value $\tilde{\nu}$ of a nuisance parameter.
If a nuisance parameter, such as the parton density function uncertainty, has been obtained from a real experiment the confidence intervals and associated distributions are used to determine $\pi$.
For an unmeasured parameter such as the perturbative uncertainty, the distribution $\pi$ has to be modeled from assumptions.

In the $CL_S$ method we define the likelihood for a specific outcome of a measurement as the product of the likelihood to observe the measured number of events $X$ times the likelihood to observe a nuisance parameter value $\tilde\nu$ in an independent experiment.
\begin{equation}
\mathcal{L}(X|\mu,\nu)=\prod_{i}\frac{ e^{-\lambda_i(\mu,\nu)} \lambda_i^{X_i}(\mu,\nu)}{X_i! }\times \prod_{j}\pi_j(\tilde{\nu}_j|\nu_j) \label{eq:priorL}.
\end{equation}
The left product runs over all regions of the phase-space, while the right product enumerates all nuisance parameters used.
We drop the dependence on $\tilde\nu$ on the left-hand side of \ref{eq:priorL} and for this section fix $\tilde\nu$ to the default value of the parameter, e.g. the world-average for a parameter that has been measured in independent experiments or the most plausible value for a theoretical parameter. We explain below how to obtain $\nu$ in the $CL_S$ method.

We assess the agreement of the measurements $X$ with one prediction $\lambda(\mu,\nu)$ relative to the agreement with another prediction $\lambda(\mu',\nu')$ and therefore define the likelihood ratio (LR).
\begin{equation}
LR = \frac{\mathcal{L}(X|\mu,\nu)}{\mathcal{L}(X|\mu',\nu')}.
\end{equation}

A quantity, such as the likelihood ratio, that distinguishes two predictions based on their agreement with a set of data is referred to as test-statistic.
In general there are multiple ways of defining the test-statistic used in the $CL_S$ method \cite{Lyons2008,Gross:1099994} that differ from analysis to analysis. In this work, we follow the prescription by ATLAS and CMS outlined in \cite{atlashiggscombo,CMS-PAS-HIG-11-032} and define our test-statistic, called profiled log-likelihood ratio (LLR), as
\begin{equation}
\label{eq:llr}
q_{\mu}(X)= \left\{ \begin{array}{ll}
	 -2\log\frac{\mathcal{L}(X|\mu,\hat{\nu}_{\mu})}{\mathcal{L}(X|\mu',\hat{\nu}_{\mu'})} \ &, \ \mu \geq \mu' \geq 0 \\
	 0 \ &, \ \text{else}
	 \end{array}\right.
\end{equation}
We choose the nuisance parameters in this definition such that the likelihood of the observation is maximized. This means we obtain $\hat{\nu}_{\mu}$ by performing a constrained maximum likelihood fit, i.e. by finding $\hat{\nu}_{\mu}$  for a given $\mu$ such that
\begin{equation}
\mathcal{L}(X|\mu,\hat{\nu}_{\mu}) \geq \mathcal{L}(X|\mu,\nu) \ \ \ \forall \ \ \nu.
\label{eq:fit1}
\end{equation}
$\mu^{\prime}$ is the value of the parameter of interest that corresponds to the global maximum of the likelihood
function given $X$, i.e. $\mu'$ and $\hat{\nu}_{\mu'}$ are obtained such that
\begin{equation}
\mathcal{L}(X|\mu',\hat{\nu}_{\mu'}) \geq \mathcal{L}(X|\mu,\nu) \ \ \ \forall \ \ \mu,\nu.
\label{eq:fit2}
\end{equation}
The profiled LLR is a positive quantity. The larger it is the more $X$ disagrees with the prediction $\lambda(\mu,\hat\nu_\mu)$ compared to $\lambda(\mu',\hat\nu_{\mu'})$.
To ensure that only positively scaled cross-sections are considered, we constrain $\mu'$ to be non-negative. The fact that the test-statistic is defined to be non-zero only for $\mu'\leq\mu$ implies that the limits on the signal strength are one-sided, i.e. only upper limits are considered.

\subsection{Probability density function of the test-statistic}
\label{sec:fpdf}
As the outcome $X$ of a measurement is subject to statistical fluctuations $q_{\mu}(X)$ will assume different values in independent measurements. The distribution of these values, assuming that the prediction $\lambda(\mu,\hat{\nu}_{\mu})$ describes the expectation value of the measurement outcome $X$,  is referred to as $f(q_{\mu}|\mu,\hat{\nu}_{\mu})$.

Analytic evaluation of $f(q_{\mu}|\mu,\hat{\nu}_{\mu})$ is in general impossible. One way to approximate $f$ is to evaluate $q$ for a large number of simulated toy measurements or replicas \cite{atlashiggscombo}.
First, a replica measurement $X^R$ is obtained by Monte-Carlo generating Poissonian random numbers $X^R_i$ for every considered phase-space region $i$ with an expected number of events $\lambda_i(\mu,\hat\nu_\mu)$. We find the values of the nuisance parameters used to derive $\lambda_i(\mu,\hat\nu_\mu)$ by performing the fit of equation \ref{eq:fit1} to $X$ for a given value of $\mu$.
Next we consider the statistical fluctuation in the auxiliary measurement of the nuisance parameters as in \cite{CMS-PAS-HIG-11-032}. Therefore we generate random numbers $\tilde\nu^R$ distributed according to $\pi(\tilde{\nu}^R|\hat\nu_\mu)$  using a Monte-Carlo.

In the next step the replicated values $X^R$ and $\tilde\nu^R$ are treated as if they were the outcome of a measurement that is entirely independent of the original measurement. For these values we want to compute the test-statistic.
The constrained likelihood fit for the given value $\mu$ as in equation \ref{eq:fit1} is performed to obtain $\hat\nu_\mu^R$. From the unconstrained fit of equation \ref{eq:fit2} we obtain the values $\mu'^R$ and $\hat\nu_{\mu'^R}^R$. Finally, the test-statistic $q_\mu(X^R)$ can be calculated as defined in equation \ref{eq:llr}.

By repeating this procedure for different replicas, we sample the distribution $f(q_{\mu}|\mu,\hat{\nu}_{\mu})$.
In principle, it is possible to determine the distribution with arbitrary precision. However, computational costs increase with the number of considered replicas.

Additionally, the statistical fluctuation of the test-statistic, if the observation follows the background-only ($\mu=0$) prediction, is considered. Consequently, we determine the distribution $f(q_{\mu}|0,\hat{\nu}_0)$ analogue to $f(q_{\mu}|\mu,\hat{\nu}_{\mu})$. The only difference being that the replicas are generated from the prediction $\lambda(0,\hat{\nu}_0)$.

Another computationally less expensive alternative to obtain the distribution  $f(q_{\mu}|\mu,\hat{\nu}_{\mu})$ is to use an approximate analytical expression as described in \cite{Cowan2011}.  This approach, referred to as the Asimov method, is based on results of \cite{Wilks1938,Wald1943} stating that for a large number of samples  $N$ in the measurement $X$ the likelihood ratio can be approximated by
\begin{equation}
q_{\mu}(X) \sim \left(\frac{\mu - \mu'}{\sigma}\right)^2 + \mathcal{O}\left(\frac{1}{\sqrt{N}}\right).
\end{equation}
The assumption underlying this approximation is that $\mu'$ is Gaussian distributed around its true mean $\mu_0$ with a standard deviation $\sigma$. We obtain the standard deviation $\sigma$ by using
\begin{equation}
	\sigma^2 \approx \frac{(\mu-\mu_0)^2}{q_{\mu}(X^A)},
\end{equation}
where $q_{\mu}(X^A)$ is the test-statistic evaluated for the so-called Asimov set.
The Asimov set is defined as a set with infinite statistics corresponding to the prediction $\lambda(\mu,\hat{\nu}_{\mu})$. Therefore, observables evaluated using the Asimov set will equal their true values, i.e. $\mu' = \mu_0$. We can determine the Asimov set by either calculating the expected values of the hypothesis exactly or approximate it by performing a sufficiently high statistics Monte-Carlo simulation of the prediction.

The test-statistic can then be shown \cite{Cowan2011} to follow a non-central $\chi^2$-distribution
\begin{equation}
	f(q_{\mu}|\mu,\hat{\nu}_{\mu}) = \frac{1}{2\sqrt{2\pi q_{\mu}}}\left(e^{-\frac{1}{2}\left(\sqrt{q_{\mu}} + \sqrt{\Lambda}\right)^2} + e^{-\frac{1}{2}\left(\sqrt{q_{\mu}} - \sqrt{\Lambda}\right)^2} \right),
\end{equation}
with
\begin{equation}
\Lambda =  \left(\frac{\mu - \mu_0}{\sigma}\right)^2.
\end{equation}
Similarly, it is possible to obtain the distribution $f(q_{\mu}|0,\hat\nu_0)$ using the Asimov method.

\subsection{The $\mathbf{CL_S}$ value and exclusion limits}
Given a measurement $X$ and the corresponding observed value $q_{\mu}(X)$ of the test-statistic as well as the corresponding  distributions $f(q_{\mu}|\mu,\hat{\nu}_{\mu})$ and $f(q_{\mu}|0,\hat{\nu}_0)$, the statistical significance of the observation, i.e. whether it arose by chance, needs to be determined. For that purpose, the $p$-value with respect to the prediction is evaluated:
\begin{eqnarray}
CL_{S+B}(\mu) = \int_{q_{\mu}(X)}^{\infty} dq_{\mu} f(q_{\mu} | \mu,\hat{\nu}_{\mu}) \label{eq:clspb}
\end{eqnarray}
The $p$-value $CL_{S+B}$ is the cumulative probability of observing a measurement $X'$ with $q_{\mu}(X') \geq q_{\mu}(X)$, assuming that the prediction $\lambda(\mu,\hat\nu_\mu)$ correctly describes the measurement outcome. Therefore, large values of $CL_{S+B}$ suggest a high chance that the observation is compatible with $\lambda(\mu,\hat\nu_\mu)$.

The probability to observe a measurement that has a larger $q_{\mu}$ than the observed one, if the background-only prediction describes the observation, is given by the $p$-value $1-CL_B$.
\begin{eqnarray}
1-CL_{B}(\mu) = \int_{q_{\mu}(X)}^{\infty} dq_{\mu} f(q_{\mu} | 0,\hat{\nu}_{0}) \label{eq:clb}
\end{eqnarray}
This probability is a measure for the disagreement of $X$ with the background-only prediction $\lambda(0,\hat\nu_0)$.
For Higgs boson searches this quantity gives an insight into how frequently a measurement suggesting a higher signal contribution than $X$ would be obtained by background fluctuations.

We define the ratio
\begin{equation}
CL_{S}(\mu) \equiv \frac{CL_{S+B}(\mu)}{1-CL_{B}(\mu)}.
\label{eq:cls}
\end{equation}
as measure of how well $\lambda(\mu,\hat\nu_\mu)$ can be statistically distinguished from $\lambda(\mu',\hat\nu_{\mu'})$ based on $X$. In the literature, this is referred to as $CL_S$ confidence level.
Small values of $CL_S(\mu)$ suggest that $X$ strongly favors $\lambda(\mu',\hat\nu_{\mu'})$ over $\lambda(\mu,\hat\nu_\mu)$. Normalizing to $1-CL_{B}$ ensures sufficiently confident statements, even in the case that fluctuations of the background-only prediction are likely to be similar to the best-fit prediction $\lambda(\mu',\hat\nu_{\mu'})$.

We want to find the minimum $\mu^{\alpha\%}$ of the parameter of interest corresponding to a prediction $\lambda(\mu^{\alpha\%},\hat\nu_{\mu^{\alpha\%}})$ that can be distinguished from $\lambda(\mu',\hat\nu_{\mu'})$ at an $\alpha\%$ $CL_S$ confidence level. To achieve this, we invert the relation $CL_S(\mu) = 1 - \alpha\%$  \cite{Aad2011}, such that
\begin{equation}
\mu^{\alpha\%} = CL_{S}^{-1}(1-\alpha\%).
\end{equation}

Given some measurement $X$, we can define a prediction of the model with $\mu < \mu^{\alpha\%}$ to be indistinguishable from the best-fit prediction at an $\alpha\%$ $CL_S$ confidence level.  Conversely, a prediction corresponding to $\mu > \mu^{\alpha\%}$ is said to be statistically distinguished and excluded at an $\alpha\%$ $CL_S$ confidence level. $\mu^{\alpha\%}$ is referred to as exclusion limit for the parameter of interest $\mu$.

\section{Nuisance parameters for theoretical uncertainties}
\label{sec:nuisance}

The renormalization and factorization scale uncertainty and parton distribution uncertainty have large effects on cross-section predictions for the LHC.
We include these uncertainties in our analysis using nuisance parameters.
We consider nuisance parameters that directly affect the prediction of the signal cross-section:
\begin{equation}
\sigma_{S} = \sigma_{S}^0 \times \nu_{scale} \times \nu_{pdf}
\end{equation}

$\sigma_{S}^0$ is the signal cross-section predicted when choosing the default values for all parameters, $\nu_{scale}$ is the perturbative uncertainty nuisance parameter and $\nu_{pdf}$ is the parton density nuisance parameter.

To use the method as described above and as it is used by ATLAS and CMS \cite{atlashiggscombo,CMS-PAS-HIG-11-032}, it is necessary to choose likelihoods $\pi_{pdf}(\tilde\nu_{pdf}|\nu_{pdf})$ and $\pi_{scale}(\tilde\nu_{scale}|\nu_{scale})$ for the outcomes of auxiliary measurements of the nuisance parameters.

\begin{description}
\item [Parton density uncertainties] \hfill \\
Partonic predictions need to be convoluted with the parton distributions to obtain physical observables \cite{Demartin2010}. These distributions cannot be computed in the framework of perturbative quantum field theory and need to be measured. The uncertainty on parton distributions arises from various sources including experimental uncertainties of said measurements. The providers of the parton distribution functions give recommendations on how to compute the uncertainty on cross-section predictions for experiments \cite{Ball2010,Lai2010,Martin2009,Watt2012}, usually resulting in an interval $[\sigma_S^-,\sigma_S^+]$ of cross-section predictions.

ATLAS and CMS \cite{atlashiggscombo} model the uncertainty due to the parton densities by using a log-normal distribution
\begin{equation}
\pi_{pdf}(\tilde\nu_{pdf}|\nu_{pdf})=\frac{1}{\sqrt{2\pi} \delta_{pdf}} \frac{1}{\tilde\nu_{pdf}} \exp\left(-\frac{(\ln\tilde\nu_{pdf}-\ln\nu_{pdf})^2}{2\delta_{pdf}^2}\right),
\end{equation}
where $\delta$ is computed from the derived uncertainty on the cross-section $\sigma_S^\pm=\sigma_S^0\times\nu^\pm_{pdf}$
\begin{equation}
\delta_{pdf} = \frac{1}{2} \left(\nu^+_{pdf} - \nu^-_{pdf}\right).
\end{equation}
Using a log-normal distribution rather than a normal distribution ensures that the predictions calculated from varying the nuisance parameter cannot become negative.

\item [Perturbative uncertainties] \hfill\\
Perturbative uncertainties are the consequence of missing higher order corrections. For Higgs processes, \cite{Anastasiou2012,Anastasiou2002} choose $\mu_F = \mu_R  = Q=\frac{m_h}{2}$ as a most likely value for the renormalization and factorization scale and estimate the uncertainty on the cross-section by variation of $Q$ in the interval $[\frac{m_h}{4},m_h]$.
This variation of the scale allows to estimate the uncertainty on the predicted value of the cross-section. The interval in between the minimal and maximal variation of the cross-section is denoted by $\left[\sigma_S^0\times\nu^-_{scale},\sigma_S^0\times\nu^+_{scale}\right]$. In our opinion, every value within this range is equally plausible, as no independent measurements of the Higgs cross-section have been carried out in the past. Consequently, we do not consider any value within this range to be preferred.
The central value of the interval does in general not correspond to the preferred scale choice $\mu = \frac{m_h}{2}$ and is of no special significance.

This is supported by studies performed by \cite{Anastasiou2012}, which can be seen in figure \ref{fig:del}. The plot shows a comparison of the LO, NLO and NNLO results for gluon-fusion production of a Higgs together with their corresponding theory uncertainties. As one can see, the error-intervals of the different orders are not centered around the same cross-section even though the spread of the intervals decreases with higher orders.
 To treat the values in the interval equally we model $\nu_{scale}$ with a uniform likelihood:
\begin{equation}
\pi_{scale}(\tilde\nu_{scale}|\nu_{scale}) = \frac{1}{\nu^+_{scale}-\nu^-_{scale}}\left(\theta\left(\nu_{scale}-\nu^-_{scale}\right)-\theta\left(\nu_{scale}-\nu^+_{scale}\right)\right)
\label{eq:fp}
\end{equation}
This was also discussed in \cite{Anastasiou2009,Baglio2010,Baglio2011hc}. Here $\theta$ is the Heaviside function.
The right-hand side of equation \ref{eq:fp} is independent of $\tilde{\nu}_{scale}$, as we cannot define a fluctuation of the auxiliary measurement of the perturbative uncertainty and therefore eliminate $\tilde{\nu}_{scale}$ by setting it to unity.

ATLAS and CMS incorporate the perturbative uncertainty differently and describe $\nu_{scale}$ using a log-normal distribution \cite{atlashiggscombo}, analogue to the parton density uncertainty,
\begin{equation}
\pi_{scale}(\tilde\nu_{scale}|\nu_{scale})=\frac{1}{\sqrt{2\pi} \delta_{scale}} \frac{1}{\tilde\nu_{scale}} \exp\left(-\frac{(\ln\tilde\nu_{scale}-\ln\nu_{scale})^2}{2\delta_{scale}^2}\right)
\end{equation}
$\delta_{scale}$ is related to our definition of the relative variation of the cross-section via
\begin{equation}
\delta_{scale} = \frac{1}{2} \left(\nu^+_{scale} - \nu^-_{scale}\right)
\end{equation}
We point out that this distribution introduces a preference for the central values of the interval. Furthermore, it is possible to find a fit value for $\nu_{scale}$ that is outside of the range  $\left[\nu^-_{scale},\nu^+_{scale}\right]$ considered reasonable.
\end{description}

 \begin{figure}[H]
	\centering
	\includegraphics[width=0.9\textwidth]{rel.pdf}
	\caption{Scale variation of the gluon fusion cross-section at LO, NLO, and NNLO. The LO, NLO and NNLO cross-sections at scale $\mu \in \left[\frac{m_h}{4},m_h\right]$ are normalized to the NNLO cross-section at the central scale $\mu = \frac{m_h}{2}$. Taken from \cite{Anastasiou2012}.}\label{fig:del}
\end{figure}

The interpretation of experimental results is sensitive to the modeling of nuisance parameters and their specific uncertainties. Therefore, various statistical approaches have been devised, to systematically treat them in limit-setting procedures \cite{Junk1999,Lyons2008,Gross:1099994,ReadCLS}.

\begin{description}

\item[The profiling method]\hfill \\
This method is used by the collaborations ATLAS and CMS  \cite{atlashiggscombo,CMS-PAS-HIG-11-032} and we described it in our review of the $CL_S$ method above.
In the profiling method, the nuisance parameters are determined by performing a constrained maximum-likelihood fit, see equation \ref{eq:fit1}, for a given parameter of interest of the prediction $\lambda$ to the observed data. This retains only the functional dependence of $\lambda$ on $\mu$, while $\nu$ is set to the corresponding optimal value $\hat{\nu}_{\mu}$. Therefore, for a given $\mu$ the agreement of $\lambda$ with the observed data $X$ is maximized. Fitting nuisance parameters in likelihood based analyses is discussed in more detail in \cite{Reid2010,Murphy2000}.
\item[The worst-case method]\hfill \\
The simplest approach is to fix a nuisance parameter to a single value that is considered possible when generating the replicas or the Asimov set and evaluating the test-statistic. This method biases the resulting parameter of interest towards the value favored by that choice. By fixing $\tilde{\nu}$ and $\nu$ to unity for our nuisance parameters, the uncertainty on the nuisance parameter is effectively ignored.

The so-called worst-case method chooses a value for $\nu$ from the allowed range and sets $\pi(\tilde{\nu}|\nu) = 1 \ \forall \ \tilde{\nu}$, such that the result of the analysis is maximally biased towards weaker constraints on the parameter of interest. This corresponds to maximally reducing the predicted signal cross-section.

\end{description}

We claim that the worst-case method should be used to treat the perturbative uncertainty to obtain physically reasonable limits. To show the impact of the different approaches to treat nuisance parameter we compute exclusion
limits for a toy Higgs search. We show that using a uniform distribution for $\pi_{scale}(\tilde\nu|\nu)$ yields limits similar to those obtained from using the worst-case method.

\subsection*{Setup of the simulated Higgs search}
We conduct a toy Higgs search using the analysis prescriptions outline before. We simulate idealized measurements using a parton-level Monte-Carlo of proton-proton collisions at a center of mass energy of 8TeV, completely neglecting detector effects.
We focus on the $h\rightarrow\gamma\gamma$-channel, investigating a Higgs boson mass range of 110GeV to 140GeV. We choose the invariant mass distribution of the di-photon system, $\frac{\partial N}{\partial m_{\gamma\gamma}}$, as observable for the $CL_S$ method.

To acquire the prediction for the irreducible background, we use the event generator Pythia8 \cite{Sjostrand2008}, collecting $\mathcal{O}(10^7)$ events which we scale to an integrated luminosity corresponding to  $10\text{fb}^{-1}$.
To comply with NNLO results, we rescale the resulting distribution with a  k-factor computed by \cite{Catani2011}.
We simulate the signal prediction using iHixs \cite{Anastasiou2012,Anastasiou2011} at NNLO with gluon fusion as production mode, for every 1GeV bin in the mass range considered.
We combine the background prediction with the signal for a Higgs mass of 125 GeV in order to simulate a measurement $X$.

The perturbative and parton density uncertainties of the signal prediction can be obtained directly from iHixs. We estimate the perturbative uncertainty using the prescription above determining the maximum allowed upward and downward variation $\nu^{\pm}_{scale}$.
iHixs determines the parton distribution uncertainty using the $90\%$ confidence interval of the MSTW08 set, as argued by \cite{Anastasiou2012}.
We then use the variation $\nu^{\pm}_{pdf}$ of the cross-section observed in the process to determine $\delta_{pdf}$ of the log-normal distribution used for $\pi_{pdf}(\tilde\nu_{pdf}|\nu_{pdf})$.

\subsection*{Effect of nuisance parameters on the $CL_S$ method}
For the purpose of this section we consider only the effect of the perturbative uncertainty on the signal cross-section and drop the index $_{scale}$.
We compare the results obtained from treating a flat distribution for the perturbative uncertainty using the profiling method with the results obtained from using the worst-case method.
Additionally we consider the profiling method treating the perturbative uncertainty with a log-normal likelihood $\pi(\tilde\nu|{\nu})$ for the auxiliary measurement in accordance with the prescription used by ATLAS and CMS  \cite{atlashiggscombo}. We set $\tilde{\nu}$ to the default value one. We neglect the condition $\mu\geq\mu'$ and analyze our simulated observation at a chosen Higgs mass point of $m_h=125$GeV, i.e. on the resonance.

The constrained fit (see equation \ref{eq:fit1}) in the calculation of the likelihood using the profiling method tries to improve the agreement of the prediction $\lambda(\mu,\hat\nu_\mu)$ with our simulated data $X$.
The fit chooses $\hat{\nu}_{\mu}$ such that $\lambda$ is increased for $\mu\leq\mu'$ and decreased for $\mu\geq\mu'$.
If $\pi$ is uniform the nuisance parameter can easily take on extremal values, as all values in the considered range are given the same weight. If $\pi$ is log-normal, the values in the tails are suppressed and the nuisance parameter is biased to take values close to one.

For the worst-case method the nuisance parameter is set to the smallest allowed value. For $\mu$ sufficiently larger than $\mu'$ the profiling method with uniform $\pi$ and the worst-case method result in the same nuisance parameter value.  This is illustrated in Figure \ref{fig:nu}.

The likelihood for a given value $\mu$ is increased if the nuisance parameter is fitted to the observation compared to the likelihood if $\nu$ is fixed to unity. As the uniform $\pi$ does not give different weights to different parameter values in the allowed range the increase is larger than in the log-normal case. The worst-case and the profiling method with uniform $\pi$ yield the same nuisance parameters for sufficiently large $\mu$, the likelihoods are therefore equal in that region. This can be seen in Figure \ref{fig:deltaL}.

\begin{figure}[H]
	\centering
	\subfigure[ ]{\includegraphics[width=0.45\textwidth]{nu.pdf}\label{fig:nu}}
	\subfigure[ ]{\includegraphics[width=0.45\textwidth]{deltaL.pdf}\label{fig:deltaL}}
	\vspace{-0.5cm}
	\caption{a) Nuisance parameter value as function of $\mu$ b) Difference of the likelihood obtained from the specific method to the likelihood obtained from setting the parameters to their default values. The results were obtained with the worst-case (green) and the profiling method using a uniform (blue) or log-normal (red) likelihood $\pi(1|\hat\nu_\mu)$. One can see that the worst-case method and the profiling method with a flat distribution result in equal nuisance parameter values for $\mu > \mu'$. Consequently, we obtain the same likelihood values from both methods in that region.}
\end{figure}

Next, we compute the test-statistic $q_\mu(X)$ using the profiling and worst-case methods, see equation \ref{eq:llr}. Profiling nuisance parameters reduces the difference between the best-fit prediction and the prediction $\lambda(\mu|\hat\nu_\mu)$ corresponding to the considered $\mu$. Consequently, $q_\mu(X)$ is reduced for every $\mu$. The effect is again largest for profiling with a uniform $\pi$. In the vicinity of $\mu\approx\mu'$ the nuisance parameter can compensate for small deviations of the predicted cross-section from the best-fit value and $q_\mu(X)$ is compatible with zero. For the worst-case method $q_\mu(X)$ is also minimal for $\mu=\mu'$ but the best-fit value $\mu'$ is larger than in the profiling method due to the reduced signal cross-section in the prediction. Again, for $\mu$ sufficiently larger than $\mu'$ the worst-case method and the profiling method with uniform $\pi$ give the same results. This is depicted in Figure \ref{fig:llr}.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.9\textwidth]{llr.pdf}
	\caption{The test-statistic obtained with the worst-case (green) and the profiling method using a uniform (blue) or log-normal (red) likelihood $\pi(1|\hat\nu_\mu)$ as a function of $\mu$. As one can see, the worst-case method and the profiling method with a flat $\pi$ result in equal values for the test-statistic in the region $\mu>\mu'$.  Consequently, the exclusion limits obtained from both methods are the same.}\label{fig:llr}
\end{figure}

Re-imposing the constraint $\mu\geq\mu'$ we can compute the distributions of $q_{\mu}(X)$ that are necessary to derive the $CL_S$ value, see section \ref{sec:fpdf}. We compute the $CL_S$ values using the worst-case method and compare them to the $CL_S$ values that we obtain when profiling a uniform distribution as well as a log-normal distribution. For each calculation, the $f$ distributions, which are needed to determine $CL_S$, are evaluated using the replica method as well as the Asimov method.
Figure \ref{fig:cls} shows the different $CL_S$ values as functions of $\mu$. As one can easily see, the values obtained using the Asimov and the replica method agree well for both the profiling and the worst-case method, thanks to the large statistics of our simulation. Furthermore one can see that the exclusion limit $\mu^{95\%}$, i.e. the point where $CL_S$ is smaller than $0.05$, is increased when using a uniform $\pi$ in comparison to using a log-normal $\pi$. Therefore, modeling the perturbative uncertainty with the worst-case method leads to conservative exclusion limits.
\begin{figure}[h]
	\centering
	\includegraphics[width=0.9\textwidth]{cls.pdf}
	\caption{$CL_S$ as a function of $\mu$ obtained with the Asimov (lines) and replica method (dots). The predicted nuisance parameter value was obtained using the profiling method with a uniform $\pi$ (blue), a log-normal $\pi$ (red) and using the worst-case method (green). The exclusion limit $\mu^{95\%}$ is the value of $\mu$ corresponding to $CL_S=0.05$ (black line). One can see that both the profiling of a uniform $\pi$ as well as the worst-case method lead to a higher, i.e. more conservative, exclusion limit in comparison to the profiling of the log-nornal $\pi$.}\label{fig:cls}
\end{figure}

It should be noted that even though the tails of the log-normal distribution are strongly suppressed it is in principle possible to find outliers for the nuisance parameter value in large signal-to-background ratio scenarios. As pointed out before these can be outside of the range of plausible values for the perturbative uncertainties. We do not consider this behavior to be appropriate for this specific uncertainty. Therefore, we recommend to use the worst-case method as defined above.

\section{Discussion of search results}
\label{sec:combo}
\subsection*{Exclusion limits}
The exclusion limits obtained using the $CL_S$ method described in section \ref{sec:cls} are usually presented in exclusion plots.
These show the observed limits obtained from an actual experiment at the same time as the expected limits.

We compute the observed limits $\mu^{95\%}_{obs}$ by applying the $CL_S$ method to a simulated measurement $X$. In order to quantify the sensitivity of the analysis, we determine the exclusion limits obtained from the background only prediction. We produce 5000 replicas (see section \ref{sec:fpdf}) of the background-only prediction $\lambda(0,\hat{\nu}_0)$. The exclusion limit $\mu_R^{95\%}$ for each of the replicas is subsequently computed in the same way as for the simulated measurement using the $CL_S$ method. This set of $\mu^{95\%}_R$ values samples the underlying probability density $m(\mu^{95\%}_R)$, which contains information about the statistical fluctuations of the expected exclusion limits. We calculate the median expected limit $\mu_{med}^{95\%}$ using
\begin{equation}
	\frac{1}{2} = \int_{-\infty}^{\mu_{med}^{95\%}} d \mu^{95\%}_R m(\mu^{95\%}_R).
\end{equation}
To quantify the range of possible statistical deviations from the median we additionally compute the corresponding $\pm1\sigma$ and $\pm2\sigma$ intervals $[\mu_{-N\sigma},\mu_{+N\sigma}]$ using
\begin{equation}
	\frac{1}{2}(1\pm p_N) = \int_{-\infty}^{\mu_{\pm N\sigma}} d \mu^{95\%}_R m(\mu^{95\%}_R),
\end{equation}

\begin{figure}[H]
	\centering
	\subfigure[]{\includegraphics[width=0.45\textwidth]{ExclusionPlot_no.pdf}\label{fig:excl}}
	\subfigure[]{\includegraphics[width=0.45\textwidth]{ExclusionPlot_wn.pdf}\label{fig:exclFF}}
	\caption{Exclusion limits for standard model Higgs with a mass of 125GeV. The solid black line depicts the observed limits. The dashed line represents the median expected limits, while the green (yellow) band shows the interval expected to contain $68\%$ ($95\%$) of the statistical fluctuations of the expected limits. The nuisance parameters are set to unity in the left plot. A constrained fit is performed for the parton density uncertainty and the worst case method was used for the perturbative uncertainty in the right plot. }
\end{figure}

Figures \ref{fig:excl} and \ref{fig:exclFF} show the $95\%$ $CL_S$ confidence limit on the signal strength multiplier $\mu$ as a function of the hypothesized Higgs boson mass.
As our goal is rather to demonstrate the expected limits of a standard model Higgs boson search than to mimic an actual measurement including statistical fluctuations, we use the Asimov set as "observation". This suppresses the statistical fluctuations of the black line.
The hypothetical $95\%$ $CL_S$ exclusion limit on the standard model Higgs prediction, $\mu =1$ is marked with the red horizontal line.
The dashed line represents $\mu_{med}^{95\%}$. The green and yellow bands mark the corresponding $\pm1\sigma$ and $\pm2\sigma$ intervals. These allow to assess the exclusion potential of the experiment. If the dashed curve is below the red line, the experiment is sensitive to the standard model Higgs boson.
If the dashed line is above the red line, the statistics of the experiment is too low to be able to exclude the standard model Higgs boson at $95\%$ $CL_S$ confidence level, even if it is not present in the observed data.

We exclude a Higgs boson with a cross section larger than $\mu^{95\%}$ depicted by the black solid line at $95\%$ $CL_S$ confidence level based on the measurement. If the black line is below one, the existence of a standard model Higgs boson is excluded at a $CL_S$ confidence level of at least $95\%$.
As long as the observed exclusion limit is within the green or yellow bands, it agrees with a $1\sigma$ or $2\sigma$ fluctuation of the expected limits. Large excursions of the black line below the $2 \sigma$ band can indicate problems with modeling of the background.
Conversely, a departure of the observed line above the $2\sigma$ band and the red line, indicates that it is not possible to exclude the standard model Higgs boson in this mass range at a $95\%$ $CL_S$ confidence level.
However, such an exclusion limit should by no means be used to quantify a possible excess over the standard model. In particular, an exclusion limit above $\mu=1$ does not give conclusive evidence that the cross-sections of the physical processes are enhanced above the standard model.

Figure \ref{fig:excl} shows the exclusion limits obtained when no systematic uncertainties are taken into account. In contrast to that, Figure \ref{fig:exclFF} shows the limits that are found when the systematic uncertainties are considered by fitting of the parton density nuisance parameter and using the worst-case method for the perturbative uncertainty. By comparison, it becomes apparent that the sensitivity of the experiment is decreased due to the uncertainties. The bands of the expected exclusion limits are wider in \ref{fig:exclFF} compared to the bands in \ref{fig:excl}, allowing for larger statistical excursions of the limits. Furthermore, including nuisance parameters results in weaker exclusion limits.

\subsection*{The significance of an excess}
To quantify the significance of an excess, the $p$-value corresponding to the probability that the background could fluctuate up to values larger than the observed value needs to be determined. For that purpose, we use
\begin{equation}
q_{0}(X) = -2 \log \frac{\mathcal{L}(X|0,\hat{\nu}_0)}{\mathcal{L}(X|\mu',\hat{\nu}_{\mu'})}
\end{equation}
as test-statistic. We determine the observed value for $q_{0}$ in the usual way and estimate the probability density $f(q_0 | 0,\hat{\nu}_0)$ of $q_0$ using the replica method.
We can then visualize the $p$-value
\begin{equation}
p_0(X) = \int_{q_0(X)}^{\infty} d q_0 f(q_0 |0,\hat{\nu}_0)
\end{equation}
in a separate $p$-value plot, such as figure \ref{fig:pval}. In this plot, smaller $p$-values, i.e. lower probabilities that the observation could be caused by fluctuations of the background, correspond to larger excesses. An excess of at least $5\sigma$ is commonly required to claim a discovery.

\begin{figure}[H]
	\centering
	\includegraphics[width=1.00\textwidth]{pvalue.pdf}
	\caption{$p$-value plot, visualizing the probability that the background rate fluctuates up higher than the observation as a function of the tested Higgs mass. The red dashed lines correspond to $1\sigma$, $2\sigma$ and $3\sigma$ confidence levels respectively (top to bottom).}
	\label{fig:pval}
\end{figure}

\subsection*{Agreement with the standard model}
In order to determine whether an excess of observed events above the background-only prediction indicates enhanced cross-sections, we consider the best fit of the parameter of interest to the observed data. In this analysis we are not so much interested in deriving conservative limits based on the observation but rather want to determine the size of the signal cross-section. To obtain the best fit, we therefore set our nuisance parameters to unity, rather than their worst-case or fitted values, and compute $\mu'$ such that
\begin{equation}
\mathcal{L}(X|\mu',1)\geq\mathcal{L}(X|\mu,1)\hspace{1cm}\forall\mu.
\end{equation}
The results of this computation are presented in the so-called standard model agreement plot, figure \ref{fig:smagree}. For a standard model Higgs in the measurement, the best fit $\mu'$ should be compatible with one at the corresponding Higgs mass. If it significantly differs from one, this may indicate modified cross-sections.

The treatment and assigned values of the nuisance parameters should be stated along with the plot in order to be able to draw conclusions on theoretical models.

\begin{figure}[H]
	\centering
	\includegraphics[width=1.00\textwidth]{agreement.pdf}
	\caption{Standard Model agreement plot showing the best-fit value of the signal strength modifier. The excess has a best-fit value of $\mu'=1$, indicating a standard model Higgs with unmodified cross-sections.}
	\label{fig:smagree}
\end{figure}

Comparing figures \ref{fig:excl}, \ref{fig:exclFF} and \ref{fig:smagree}, one can see that although the exclusion plots show an excess of $\mu^{95\%} \approx 1.7$ at $m_h=125$GeV, corresponding to a significance of more than $3\sigma$, this does not indicate modified cross-sections, as the corresponding standard model agreement plot shows that the respective best-fit value is compatible with unity.
\section{Conclusion}
We investigated the $CL_S$ technique, which is the method used to set exclusion limits on the contributions of a standard model like Higgs boson to observables. The ATLAS and CMS collaborations utilize the profile LLR to take into account nuisance parameters. To compute limits, both the Asimov and the replica method, are used.

We demonstrated that theoretical uncertainties have a significant impact on the exclusion limits derived with the $CL_S$ method. The parton density function uncertainty was modeled in our analysis as a log-normal distribution and treated using the profiling method.

ATLAS and CMS currently model the perturbative uncertainty using a log-normal distribution.
Profiling this specific uncertainty and thereby treating it as if it was the consequence of a insufficiently precise measurement of the renormalisation and factorisation scale is not adequate.
Furthermore, our analysis shows that the limits derived using this technique are not conservative as the impact of the perturbative uncertainty is effectively underestimated.
In our opinion, it is necessary to choose the most conservative signal cross-section plausible according to theoretical considerations.
We therefore recommend to incorporate the perturbative uncertainty using the worst-case method.
High-precision analyses will require calculations at higher order in perturbation theory that reduce the perturbative uncertainty.

An investigation of more physical alternatives to the unphysical signal-strength modifier is a topic of future studies.

\acknowledgments
We thank Babis Anastasiou for his essential support, Ben Kilminster for clarification of the current procedures used by ATLAS and CMS, and Alexander Aeberli for useful discussions.
This research is supported by the ERC Starting Grant for the project "IterQCD".

redpajama_set_name: RedPajamaArXiv


How Changing One Thing In Our Routine, Can Lighten Us Up.
Yesterday I posted on my instagram asking people what they do first thing in the morning.
The answers I got were mostly the usual things of opening our eyes, looking at our phones, heading for a shower. One different answer was, doing 3 yoga poses in bed which I thought must be a really good thing to try and do (if you are interested in yoga).
I sometimes feel it is always good to give our routine a change, an upbeat, something a little more ordinary to add on something new to our day, to our minds, and our attitude to life.
Some ideas I can throw in here are; watering your plants, doing a little prayer, kissing your loved awake or still asleep, meditating, or writing your morning pages.
Writing Morning Pages was my answer on my instagram in response to my question. It has been introduced to me a few years ago first, through a leadership camping training in Switzerland and second, in my writing group club in Amman. The former was used as a method to reflect and write at any time of the day on an empty page without thinking, just writing, letting all our feelings go into writing. And the later was taken from the book 'The Artist Way' and is done first thing in the morning, every day, religiously. It is a method to clear our minds and set our attitude positively to our day by using positive affirmations, positive thoughts, and goals.
Nothing is necessary for writing our Morning Pages, nothing is right or wrong, and no one needs to read them. They can be private or can be torn apart if it makes you feel more comfortable, but the idea is to write everything down every morning, first thing in the morning. Let it all out on empty pages. It is a great new start of the day.
They helped me make new self-realization, reflection, understand feelings, hidden thoughts, aspirations, dreams, neglect, it is just a very great way to understand oneself better, put what is unimportant aside on the pages, and put what we wish to attain from self-motivation on paper to remind us of our basic goals and dreams. Be gentle to my self, today, be gentle to everyone I encounter, smile, be graceful, walk with grace, speak with grace, listen with grace, give, with no expectations, no judgments, live, freely, loudly, happily, share, your dreams, and your love, be generous to yourself and to others… and this is how it goes.
I hope this blog post helps us live more positive. If you do have your own tip to share for a morning ritual, please comment and share.
There is no one better than your own judgement upon yourself. The people surrounding you are your distraction to your self listening. Don't let that possess you. How you feel is the answer to everything you encounter at anytime. Learn to listen to your feelings, learn the difference between your emotions, and learn to act upon them in the very right way. Some will make your hearts feel warmer, some will make your hearts feel colder, listen carefully and listen well, your hearts will lead you to where you belong. Once you understand your own callings, you will understand how to trust your intuition to follow your heart. Be familiar with your inner self, let your faith lead you to what makes you feel happier within. Have a very happy Wednesday.
We live in a world of today to learn about living the moment. Without patience, living any moment will be very difficult. Our heart & mind are both always yearning for what is there yet to come that we miss all the golden moments of the present. This yearning disables our patience & creates a deformation to live with enjoyment. For we can not control the future, we can not predict or create what is there yet to come, we for sure can lead, inspire, attract or drive to a certain future place but we can not guarantee the outcome, therefore our enjoyment is only based on what we imagine can be happiness, but we are never certain of it. As for patience; it is an act of learning to live the now, this very moment of the now, the process of our formation, the drive through the destination, the causes of the outcome, patience teaches us to enjoy the very present moment before enjoying the future that will sooner or latter be also a present moment. Patience is happiness if we only practice it the right way, if we understand that the feeling of being patient is not about having to sit and wait in despair but instead it is to sit in the moment and enjoy the very present process of our life. Be patient, learn to enjoy patience, it is a way to enjoy life. Have a blessed and wonderful Monday.
Today enjoy. Enjoy your moments, your time. Make your decisions based on your understanding, speak when your sure it is the good to be spoken, listen carefully to those who wish to speak to you. Today look at your day as if it was brought to you on a golden tray with crystal lining & sprinkled diamonds. Be aware of your movement, your heart beat, your breathing. Today is your day, it is a day you deserve to recognize yourself, the day you give to yourself, and appreciate yourself. Be kind to you, to the ones near you, and the ones crossing you. Be loving, explore love, give love, and be tranquil. Have a wonderful Saturday filled with your positive fillings.
Follow your heart, but take your brain.


Ecology of Peace Culture Legal Submissions and Correspondence: clauses of international law enabling procreation and consumption above ecological carrying capacity limits are the enablers of all local, national and interational – race, religious, class and gender – resource conflict.
Protected: 21 Apr: EoP Re: Arif Putin to Pena-Taliban: Simple Question: What do you want?
21 Apr: EoP Re: Brian Rose: How do we know when we've actually created change?
18 Apr: EoP RE: Anatoly Golod MEGA News about Cathedral in France etc.
15 Apr: EoP Req for Psych Doctrine Info to HPCSA & PsySSA.
13 Apr: EoP IRGC Q: Rose Sinek: Wanna help EoP TRC Fix the Abel & Kane Cold War Mistake?
TYGAE Contact & Updates information is available at: EoP MILED Clerk. RSS: Posts.


Voting, or exercising one's franchise is a fundamental duty given to every Indian citizen. Voting empowers the country's citizens to elect leaders of their choosing to govern the country and enact policies. Voting allows everyone to participate in the process of governing a country and can be used to bring about a populist change in government and impacts every aspect of a country.
All Indian citizens above the age of 18 years are eligible to vote and contest elections at the state as well as central level, as also for local government bodies. However all those who wish to participate in the electoral process are required to register for the process and provide proof of their eligibility, in the form of a voter's ID card.
A voter's ID card is issued to individuals if they fulfil the criteria as laid down by the laws governing the election process.
Election commissioners, who are responsible for conducting a smooth election process, have made it easier for citizens to apply for voter ID cards or make changes in their cards.
Assam, being one of the largest states in the strategic north eastern region of India, has also made it easier and more convenient for its residents to register for the electoral process, as explained below.
The Assam CEO website(http://ceoassam.nic.in/) currently has no facility available to check your Voter ID status online. However, you can check if your name has been included in the electoral rolls by visiting the NVSP website (http://electoralsearch.in/).You can search using your details or your EPIC number.
Provide your name, father/husband's name, age or date of birth, gender, state, district and assembly constituency. You can also locate your constituency on the map.
Provide your Epic number and state.
This facility is not available in Assam yet, but may be rolled out in the future.
A voter ID card is a government issued photo identity card that is accepted as proof of address and identity universally and thus should be kept safely. However there are situations when your voter ID card may have been misplaced or lost. It could also have been stolen from you. The Electoral Officer in Assam makes provision for issuing a fresh voter ID card in such cases.
Go to the Election Commission of India website and download the Form EPIC-002, which is the application form for issuance of duplicate voter ID card.
In the case of individuals whose voter ID cards have been lost or stolen, an FIR is to be filed at the local police station and a copy of the FIR along with the application form is to be submitted to the Electoral Office.
In the case of individuals with torn or unusable voter ID cards, submit the card along with the form to the local Electoral Office.
After verification of the application has been conducted, you will receive your duplicate voter's ID card through the post or you can collect it in person.
As a registered voter, your voter ID card is the most important document that permits you to vote in regional as well as national elections. It also functions as a government issued identity proof. In the event that you lose or misplace your voter ID card, you can now apply for a duplicate card through a convenient method.
Visit the nearest electoral office and collect a copy of Form EPIC-002, which is the form for issuance of duplicate voter's ID card.
Fill in the form and submit it along with the relevant documents- if you have lost your card, attach a copy of the FIR.
If your card is torn or mutilated, submit your card along with the application form.
Your application will be verified, following which a duplicate voter ID card will be issued to you.
If you have received your voter ID card and it contains errors such as misspelled name or incorrect details with regard to any of the information mentioned, you can correct the errors. It is important that you do this as the voter ID card is issued by the government and the information contained in it is assumed to be correct.
Go to the official website of the Assam Chief Election Officer and click on the "Download Forms" tab.
Select "Form 8", which is the application form for correction of details in voter's ID card.
Download and print the form and fill in the details as requested, taking care to provide the information you wish to change/correct in your voter's ID card.
In the case of change of name, you will have to attach proof of your name change, such as a passport or official government gazette copy along with the application form.
Submit the form and supporting documents to the nearest electoral office, where you will receive an acknowledgment.
After your application has been verified, you will receive an updated voter ID card through the post or by collecting it personally from the electoral office.
If you have noticed that your voter ID card has errors such as name being misspelled, errors in address or any other details, you can now have these errors rectified simply by submitting an application form mentioning the need for changes.
Visit the local electoral office and request for a copy of Form 8, the form for making corrections in your existing voter ID card.
If you wish to correct your name, ensure you provide proof of your name in the form of a passport copy/official government gazette/any recognised documents with the changed name.
For corrections in address or birth date, attach supporting documents with the correct information along with the form.
Submit the filled in form and supporting documents to the electoral office, whereupon you will receive an acknowledgement.
After your application has been verified, you will receive your voter ID card with the corrected information by post, or you can collect it personally from the office.
Most individuals apply for a voter's ID card when they are 18 years of age, with the address mentioned that of their hometown. Individuals sometimes move to other cities or states during the course of their lives for a number of reasons.
As a registered voter, when you change your address, your voter ID card should also reflect this change if it is a long term one. Your name has to be struck off the electoral rolls in your former area of residence and included in the rolls at your present residence.
Go to the official website of the Chief Electoral Officer of Assam and click on the "Download Forms" tab.
Select "Form 8A" from the options presented, which is the form for transposing the entry (shifting the name from one electoral roll to another).
Print and fill in the details as requested and provide the supporting documents as specified in the form.
Submit the duly filled in and signed form with the supporting documents to the nearest electoral office in your current place of residence.
If you have changed your place of residence, you are obliged to change the address as stated in your voter ID card as well. This is done so you can be registered in the electoral rolls in your current place of residence, and so your name is struck off the rolls in your former area of residence. This is important as it reduces the incidence of someone else voting in your name, and permits you to participate in the electoral process even though you have shifted residence.
The Election Commission of Assam has made it easier for individuals to change their address, through the submission of a form to that effect.
Go to the local Electoral Office and collect Form 8A, the form for transposition of entry in the electoral rolls.
Fill in the details and attach proof of your current address as mentioned in the form.
Submit the form and the supporting documents to the electoral office.
A Booth Level Officer (BLO) will visit your home to verify your address details.
On successful completion of the verification, you will receive a voter ID card with your current residence details mentioned. Your name will also be included in the electoral rolls of the constituency in your present area of residence.
Assam, often referred to as a Shangri-La nestled in the picturesque North East of India, is a melting pot of various tribes and sub-tribes with a rich cultural heritage. Elections in Assam are conducted by the office of the chief electoral officer who performs his functions under the supervision of the Election Commission of India. The election department is located at block C, Assam secretariat, Dispur.
Like any other state in India, the political structure of Assam is headed by the governor. However, the governor of Assam is more powerful here than most states in the country. This is because of the presence of ULFA, a terrorist organization that has caused a lot of violence in the state. In addition to the governor, the political structure of the state also comprises of the chief minister and the council of ministers.
The current government in Assam is formed by the Bharatiya Janata Party and the Chief Minister is Sarbananda Sonowal. The legislative assembly elections in the state were last held in 2016.
Harendra Nath Bora is the current election commissioner of Assam. He is a retired IAS officer.
Assam has recorded 76% turnout in the second phase of the panchayat elections. The polls were held in 10 districts of the state. The elections were conducted peacefully except report of disturbances in Cachar, Karimganj and Nalbari districts. The voting went late into the night with the turnout only being measured during the day so it is expected that it could rise even further after the final count is known. During the polling process, certain anomalies were also recorded. Names of registered voters was missing from the electoral roll despite having registered voter ID cards. These issues were resolved at the polling booths and the elections were conducted without much disruptions.
In the second phase, the polls were conducted in the districts of Nalbari, Barpeta, Bongaigaon, Dhubri, South Salmara, Goalpara, Cachar, Hailakandi, Karimganj and Hojai to elect 169 zila parishad, 895 anchalik parishad, 895 gaon panchayats presidents and 8,950 gaon panchayat members from among a total of 35,056 contestants. The first phase of the polls were conducted on December 5 in a total of 16 districts with the turnout being recorded at 82%.
The Assam government has declared 67,480 'D' voters as Indian citizens on Monday. The 'D' voters are doubtful citizens who do not have proper credentials to be the citizens of India as per the government. The list of 'D' voters in the state has been prepared from the time period ranging from 1997 to March 31, 2018. Despite a large number of 'D' voters assured of their Indian citizenship, there are still millions of 'D' voters who are still awaiting judgement from the Supreme Court regarding their citizenship status.
The Chief Election Commissioner (CEC) has said that the Election Commission is working hard to make sure that no eligible voter's name is left behind from the Assam electoral rolls. To make sure that this happens, Rawat said that the Assam state election commission is working closely with the state coordinator NRC. The final electoral roll for the upcoming Lok Sabha elections is set to be published on January 4, 2019. Names of 40 lakh people have already been excluded from the NRC draft that was prepared by the government a few days back.
Rawat also said that the people whose names have been excluded from the NRC draft will get an opportunity to file a claim or an objection regarding the same. He has also asked the Assam state election commission to provide a detailed report on all the aspects of the NRC draft.
Election Commission has in no uncertain terms made it clear that any person in the state of Assam whose name is not present in the NRC list will not be eligible to vote in the upcoming elections. The Election Commission has also asked the chief election commissioner of the state of Assam to present a report on the matter. The Chief Election Commissioner of Assam has 8 days to submit the report as per Chief Election Commissioner, OP Rawat.
The Supreme Court has launched the NRC draft exercise in the state of Assam under which any person who is not an Indian national will be excluded from the list. 40 Lakh people have been already excluded from the list which has caused an uproar and the Bharatiya Janata Party (BJP) has been accused of dividing people for political gains. Concerns in some quarters have also been raised regarding the future of the 4 million who will be ousted due to this exercise. If the government goes ahead with the removal of 4 million people from the country, they will become the largest stateless population in the world.
In a shocking error by the state election commission of Assam, a cop who has 4 family members serving in the army has been listed as a D-voter. Assistant sub-inspector, Shah Alom Bhuyan who is a resident of Barpeta was posted as a security person in the year 1997 at the residence of then chief minister, Sarbananda Sonowal. Bhuyan has alleged that the officers of the state election commission visited his village and removed his name from the electoral roll when they did not find him.
Bhuyan has fighting a legal battle for more than 20 years now and also spent a fortune on the same but to no avail. He has been unable to vote in any election for the past 20 years due to being marked as a D-voter. During the 2001 elections, with the aid of AIUDF member Abdul Rahim Khan, Bhuyan raised the matter in Assam assembly but nothing happened with only false assurances provided. In 2010, an Assamese local newspaper printed an article on Bhuyan highlighting his problem which made people and officials finally notice his problem.
After years of trying, Bhuyan was finally able to get his name into the electoral roll but has been marked a D-voter. The issue has gained momentum in the state with some even claiming that this is happening to him because he belongs to a minority.
The High Court of Assam has passed a decision saying that the Karbi Anglong Autonomous Council (KAAC) can form its own rules and regulations which was already mentioned in the Sixth Schedule to the Constitution of India. The decision has been welcomed by All Karbi Anglong Tribals Students' Union (AKATSU). In a statement that has been released by the vice president Shumshi Kro and the general secretary of AKATSU, Pranjan Kemprai have praised the efforts of all the organisations that filed a PIL asking the freedom to allow KAAC to form their own rules and regulations. The decision by the high court has been termed as historic by the residents of Karbi Anglong. A spokesperson from AKATSU has said that as an organisation it hopes that the Karbi Anglong Autonomous Council will follow the high court's decision and conduct the elections in 6 months.
A. K. Jyoti, Chief Election Commissioner, will lead an Election Commission Team who will visit the three North Eastern States namely Tripura, Nagaland and Meghalaya to review how prepared the states are to take the election polls. This news was released by an official from the Tripura election department. The official further stated that A. K. Jyoti, Chief Election Commissioner, will be accompanied Election Commissioners Om Prakash Rawat and Sunil Arora, Deputy Election Commissioner Sudeep Jain along with other EC officials. They are expected to start this trip on 28 November 2017 and would start from Nagaland and then head to Tripura and Meghalaya. "During its visit, the Commission would hold a series of meetings with the police, district and state election officials besides high-ups and leaders of various political parties," the official added.
Under the theme 'Clean election, Youth for Change and Save Nagaland', The Nagaland Pradesh Youth Congress (NPCC) began a clean election campaign at the Congress Bhawan at Kohima. K Therie, NPCC president who was present in the inauguration function noticed that majority of the voters in Nagaland are in the age group of 18 to 40. Therie felt that if these youth can speak up and exercise their voting rights, Nagaland will see brighter days and a just government.
In his speech, Therie stated that around 1,50,000 youth, including 80,000 educated youth in the State were unemployed. He further stated that the present Government is a party-less Government. Nagaland Baptist Church Council is the one one who initiated 'Clean Election' campaign. If all the church workers in 1800 churches in the State of all denominations participate in this campaign, at least 15 to 20% of the population will be impacted which will be a great beginning.
The Meghalaya Election Department has ramped up efforts to enrol millennials who will be eligible to vote in the upcoming Meghalaya assembly elections next year.
The department seeks to enrol the 2,000-odd millennials and plans to woo them by feting them on National Voter's Day (25th January).
With over 35 lakh individuals in the state as per the 2011 census, there are a scant 17.68 lakh registered voters. The department is hopeful of enrolling millennials before the elections and is also expanding their voter enrolment scheme to entice more voters.
Some individuals in areas have voter ID cards from both Meghalaya and Assam, which has been a cause for concern.


text:
Why You Should File Early This Year
Williams January 18, 2017 Tax Planning and Preparation, UncategorizedLeave a Comment
For an estimated 153 million Americans, January means the beginning of tax season. The official kickoff is Monday, January 23. If reminders about tax time make you want to hit the snooze button until spring, lend your ears. Putting off your taxes can make you a mess come April 18, which is this year's filing deadline. The IRS encourages early filing for lots of reasons; and it's not just because it makes April less of a scramble at their headquarters. Below are three reasons why you should file early this year.
Prepare for the New Rules
Let's be real: Are you up to date on the latest changes in the tax laws? Chances are, the answer to that is no. So by getting a jumpstart on your paperwork, you (or your CPA) will have more time to determine how or whether any changes for the 2016 tax season (such as slight adjustments to rules regarding standard and itemized deductions) could affect your returns. For a list of some of the changes, check out this IRS page.
Do yourself a favor: file early this year.
Get Your Refund Faster
The IRS says it expects to issue more than nine out of 10 refunds in less than 21 days from the time the returns are received. Last year, 73% of taxpayers scored money back, to an average refund of $2,857. So if you're expecting a refund this year, why not try to get that money sooner than later—and start using it to help achieve some of your goals?
Reduce Chances of Fraud
Tax identity fraud is the largest and fastest-growing type of identity theft, according to the Federal Trade Commission, with scammers typically filing a tax return using someone else's personal information and then pocketing their refund. The easiest way to protect yourself is to file early so a thief can't beat you to the punch, according to the National Consumer League, a nonprofit consumer organization.
If you're looking for tax advice that focuses on your particular needs, call Williams & Kunkel CPA today in Flower Mound at 972-446-1040 to have a chat.
Tags:2016 tax planning, April 15, federal taxes, filing


text:
Q: Neo4j data modeling query I am working on a project (platform) where I am using neo4j to make connections between users. There are following different ways where user can connect to other users on the platform.

*

*When a user logs in with Facebook, I get his mutual friends who are already using our platform. There I make a connection in my graph database by creating a new node (current user) and make connections with all existing nodes whom he knows through facebook.

*One user also connects with other user if both are staying in same society/community. So the use case is, once a user updates his residential address (society name, city) than I make a query in graph db and get all nodes who also stay in the same society and create this new user with those nodes with relationship name "Same society".

*Same was user might be connected with other user if both users study in same college or school. I make a connection between two nodes with relationship "Same college/school".
What is the best way to model the above problem in neo4j?  If I do a query in DB to get all the relationship types and shortest path of all the relationships between given two nodes, which model will be optimized for this type of query?

A: *

*Simply create a relationship between users
(user)-[:FRIEND_OF]-(user)
Relationships in neo4j are always directional, but you can ignore the direction because, in this case, it doesn't make much sense. There is no performance penalty for traversing a relationship "backwards".

*Create a node representing your society/community and link all users living there to that node. This has several benefits:

*

*when you add new user to a community you only create one relationship (compared to linking the user directly to all users, which is N relationships), same goes for removing user from community.

*you can create different relationships between the user and community
(user)-[:LIVES]-(community1)
 (user)-[:WORKS]-(community2)

*This is essentially the same as no. 2. You can differentiate between types of communities (schools, etc..) by having different labels/properties.
Finding a shortest path between two nodes in this model will give you their closest connection, the kind of "know via something". You may limit the path by relationship type/community type etc.