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Collaborative Law and Family Mediation
Divorce rarely requires actually going to Court. Bitter and aggressive negotiations can increase the amount of conflict in the situation and is not good for you or your children. Instead of going to Court you can choose to deal with the legal consequences of your separation or divorce using mediation, collaborative law or arbitration. These processes allow you and your former partner to talk through your problems together with professional support and advice. The aim is to work out solutions that are right for you and your family with the right amount of professional support that you need.
These options can give you greater control over the pace and final outcome whereas with Court Proceedings that control is taken away from you and ultimately a decision is imposed by a Judge. Alternatively you can choose to negotiate a settlement in a more traditional way through your solicitors but whilst still maintaining an amicable relationship with your former partner.
Mediation helps couples work together and reach agreed conclusions but is not a form of marriage guidance or counselling. It is not intended to help a couple have a reconciliation but does help a couple decide how to end their relationship.
A trained mediator will assist you and your former partner talk through the issues in relation to money, your children or a combination of both. Solutions are sought over a number of meetings although the number of meetings you require will depend upon your own particular requirements and circumstances.
Most people attending mediation will also have their own Solicitor to take advice from during the process although that Solicitor will not be present at the mediation meetings but can be there if you wish. It is important to make sure that any agreement reached is fair and put into a Court Order that is legally binding.
Legal aid is still available for mediation where a couple are financially eligible whereas generally legal aid is not available for taking legal advice from a Solicitor. Before starting any Court Application an assessment would normally have to be carried out as to whether mediation would be more appropriate than a Court process and this is called a MIAM (mediation information and assessment meeting). Mediation is voluntary and neither you nor your former partner can be forced to attend.
Collaborative law is a process where decisions are made at a series of joint meetings involving you, your former partner and your respective collaborative lawyers. Each lawyer is specially trained to deal with the situation in a more constructive and less adversarial manner. You are in full control of how and when the meetings take place.
The number of meetings that will be needed will depend upon the issues to be resolved. During each meeting you will have support and legal advice with the aim of reaching a practical and amicable solution that will benefit you, your former partner and your children. It is possible that you could take into account factors that a Court might not strictly be entitled to consider in arriving at a financial settlement that is better for the entire family.
You will be able to decide which topics to focus on and whether to involve other experts such as a Financial Adviser or Accountant where necessary.
Family arbitration is a way of a decision being given on any issues you have with your former partner as an alternative to a Judge making that decision. Like a Judge the Arbitrator will make sure that all of the relevant information is collected and will obtain what evidence is needed to be taken into account and then arrive at an "award".
Both you and your partner must agree to use arbitration before this can be used although there is a great deal of flexibility about the way in which the arbitration is run and timescales.
Negotiating your own agreement can be the cheapest and easiest way to reach a settlement following the ending of a relationship. This option is not suitable for everyone but may well work well if you remain on good terms and broadly agree on the issues. If you use this approach it is still important to take legal advice to make sure that you understand the implications of your agreement and that it is legally binding. Having legal advice and support in respect of financial issues can be particularly helpful to avoid making mistakes that you regret in the future.
If mediation, collaborative law or arbitration are not for you it is still possible that your Solicitor can negotiate an agreement. Outcomes often depend on what your Solicitor would expect would be the outcome of a Court process and the likely decision that would be made by a Judge if that approach had to be taken.
Ultimately if an agreement cannot be reached a Judge can make a decision but a Court Application is usually the last resort – and the most expensive!
To find out more or discuss your individual requirements in further detail, our dedicated Family Law solicitors will be delighted to help. Contact us today on 01603 693500 or email us using the 'Make an enquiry' form. Appointments available at our Norwich, North Walsham and Sheringham offices.
Who is this service for?
For individuals and families seeking matrimonial legal advice
Which branches?
Services are available at:
Neale Grearson
Partner, Head of Family and Charities Departments
PA to Neale Grearson
Caroline Eaton
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I am using the pre-population ability of PayPal. All of our users will be Canadian (eh). When the PayPal page is shown all the entries such as first name, last name, address, etc are there but there are a few quirks that I have not been able to get around. The PayPal form defaults to a U.S. user. If the user is Canadian they must hit a button half way down the screen to pull up another screen. Is there any way to have the International screen come up by default and have Candada filled in as the country? Also the phone number fills in correctly in the U.S. screen but in the international screen the format is different and it gets all muddled. Any way around this?
Currently, no. We'll see if we can get those enhancements scheduled.
Please keep us up to date about that issue!
It would be great to redirect non-US users to the international form!
I hope you can do it soon!
Any news on this yet??
I find it a bad that I cannot properly pre-populate the form when I have the country and phone number but as the Guest says they have to click halfway down the form in order to change it, rather unnecessary.
I parsed the country and citizenship variables with GB and then 44 to night_phone_a and the phone number itself as night_phone_b.
When the user clicks on the not from US link it then pre-populates the fields as described above.
Currently setting the International Sign up/Payment page is not available. You can pass the International sign up values with your payment information. Depending on the type of payment button you are using, you can use the following input types, to assist your customers with the sign up process. They will still need to click on the link to switch to the international sign up page.


Picture this, before one of them gets married a group of guys go out to celebrate, get drunk, wake up with no memory of the many things they did the previous night and then have to try fix everything in time for the wedding. While this may seem like a totally original, never seen anything like it before plot to some, others may notice some striking similarities with a little known movie called The Hangover. However, don't worry because A Few Best Men does plenty to set itself apart from The Hangover franchise, like having a story that isn't completely predictable.
When David (Xavier Samuel) returns home his friends are shocked to discover that he is engaged to his holiday romance, Mia (Laura Brent). David then brings his friends to Australia, where Mia is from, so that they can all meet Mia's family a few days before the wedding, but will the English lads fit in? After they there, David goes off with his bride to be while his friends take a detour to get something extra special for the bachelor party. From here there's a bunch of crazy stuff involving sheep, alcohol, gun violence and some missing cocaine, but let's not spoil all that. However, even with all the wild stuff that happens at, and because of, the bachelor party the most unrealistic thing in the movie is how quickly the wedding was put together.
Despite the obvious comparison to The Hangover, A Few Best Men didn't feel like a rip off. The characters are likeable and their friendship seemed much more real than those in The Hangover, particularly David and Tom (Kris Marshall) who seemed more like brothers than friends. As the movie progresses so do the characters and you can see the relationship between Mia and David begin to show cracks as they start to realise how little they know about each other and maybe their holiday romance should have ended at the airport.
The best thing about the movie is that no matter what crazy problem the boys bring on themselves, the characters always seem real. They are believable as a group of people that grew up together and care about each other. They took in David, who doesn't have any family, 'even though everyone though he was gay' and they all just became brothers, which is what motivates them to try keep the wedding on track. Then again, if male bonding and crazy bachelor parties don't get you to the cinema, go for Olivia Newton-John who plays the hilariously crazy mother-in-law.
A Few Best Men hits cinemas today so be sure to check it out if you like comedies, even if The Hangover wasn't your favourite movie you'll definitely find a laugh or two at this English/Australian mix, which is brought to you by the people behind Death at a Funeral (the English version).
Today I saw a list of the top 100 superheroes. I didn't look at which one was number, because I actually want to go down the list and see them all. While I probably won't know some of them, I'm still curious to see were my favourite heroes placed on the list. I then start thinking about making my own list. I remember how long my 100 things about me post took so I'm not going to do 100. Instead I'm going to stick with five, but I'm also going to include my top five villains. I'm already debating on the order so I'm just going to leave the order random.
Anyway, let's start with the heroes.
Superman; I know it sounds boring, but I've always been a fan. The first comic I read was actually a Superman comic so I guess that has a lot to do with it. Some people say he's too powerful, but I don't see the problem with that I don't want to back a weak superhero, besides Krytonite seems to be readily available in the DC universe, plus he's affected by magic.
The Flash; I just really like the character. I have for years. I remember my friend even recorded the movie for me when we were kids. What's cooler than being the faster man in the world? Nothing, that's what.
Nightcrawler; It all started in 2003 when the second X-Men movie came out. He was one of the new characters and I just thought he was really cool. Ever since he's just been one of my favourite superheroes. His powers and the flaws in his powers are just so well thought out. Then again, after seeing The Fly, the remake not the original, I don't think I'm ever going to want to teleport (no one ends up with a fly head in that version, for people who have never seen it).
Batman; You've got to love a spoiled rich kid that uses his money to fight crime. Big fan of gadgets, pure and simple. I think Batman is one of those heroes that everyone knows about whether they want to or not. Who doesn't know his back story?
Iron Man; I generally prefer my superheroes with superpowers, but, to avoid sounding prejudice to non-powered heroes, I've included another. Also, the 2008 Iron Man film was great and I'm looking forward to seeing him in the upcoming Avengers movie.
Dark Phoenix; I was actually going to put Phoenix in the hero section, but I realised I like her better as a villain. She wasn't done justice in the third X-Men movie, but it wasn't completely horrible. Her storyline is a pretty interesting part of the X-Men comic and animated series'.
Black Cat; Forget Batman, Black Cat is the real anti-hero of the comic world. Her philosophy is that she is the Black Cat and Felicia Hardy is just a character she plays.
Juggernaut; I just always remember him from the cartoon when I was a kid. He just stuck with me so he made the list. He too was not done justice in X-Men 3.
Venom; Definitely deserves to be on anyone's list of favourite villains. It's kind of like Dark Phoenix when I think about it. The idea that an external force can just sort of takeover, or at least influence you, is really interesting. Also, both he and Phoenix have infected more than one person.
Doomsday; Though I was let down my how Smallville ended Doomsday's storyline, the character was done very well. However, the comic book version is very different, for one thing there is no Davis Bloome. I do think that adding the human side worked for the show, but either way the villain that killed Superman should be on everyone's favourite super villains list.
Do you agree/disagree? Let me know by voting and leaving a comment below.
Winning an Oscar is pretty much the best thing that can happen to an actor, director or anyone in the film industry. The winners are selected by other members of that field e.g. Best Director is picked by directors. It's basically like all your co-workers saying 'you're the best'. It's the high point of their career, they've peaked and it's all downhill from there. With the exception of those who win multiple Oscars, those bastards.
Melissa Leo's speech was interesting; she managed to say the 'F' word on, what's probably, the most elegant event there is. This is the first year that I've actually watched the Oscars, granted I didn't get to see it all because of the time difference, but I'll catch a repeat. To be honest, I was expecting it to be overly formal and boring, but it wasn't (which is a good thing). I've annoyed people all day, as I desperately try to describe the opening clip that, hosts, James Franco and Anne Hathaway made. It was hilarious, but the funniest part was Franco's grandmother who stood up and proclaimed 'I seen Marky Marky'.
Do you agree with the results? Leave a comment and vote below.
The Grammys, despite what you may have heard on the Simpsons, are a big achievement for performers. Stars gathered to see who the winners were. Lady Gaga proved that she comes in either a pack of six of eight, bad joke aside; she was in an egg for her grand entrance onto the stage. Apparently she had to stay in the egg for three hours before hatching onstage, surprisingly not dressed as a chick. Katy Perry also shocked the audience as she played video clips of her wedding behind her as she performed, until now her wedding pictures had been kept away from the press.
The Grammys has 109 so I've selected a few and a full list can be found at grammy.com.
Surprisingly Bruno Mars beat Michael Jackson, who was also nominated for best male vocal performance, maybe Michael's finally going to get to rest in peace as his career, which outlived him, fizzles out. Train can really sing, the live version of Hey, Soul Sister is great and will be at the end for anyone who wants to listen to it. I'm not really the dancing type so I'd love to hear how people feel about Only Girl winning in that category. Does it get your booty shaking? I was beginning to think I was the last remaining La Roux fan, so I'm really glad there's more out there. I couldn't be happier about Muse winning Best rock album. They deserve it, they made a great album and the title song, The Resistance, will also be at the end.
For a full list of the winners, and losers, head over to the Grammys website, grammy.com, vote and leave a comment below or your band will never make it.
The fighter is a biographical movie based on the career and life of boxer Micky ward. The film stars Mark Wahlberg, Christian Bale and Amy Adams. Wahlberg has been trying to get this movie made since 2005, partially because he really wants an Oscar. Better luck next time Wahlberg, as he failed to get nominated for best actor, but the movie may still win best picture and director David O. Russell is also up for an Oscar.
The movie gives a detailed look at Ward's early career. It also focuses on his brother, who trained him, getting addicted to cocaine. Bale's performance as Ward's sibling, Dicky Eklund, is rumoured to have stole the show as his character faces the more difficult storylines. These rumours appear to be true as Bale did get nominated for an Oscar, best supporting actor, also Amy Adams and Melissa Leo, from The Fighter, have both been nominated for best supporting actress, so it's pretty much just Wahlberg that didn't get a nomination. Anyone else starting to feel sorry for Mark Wahlberg?
The movie hits Irish and UK cinemas this Wednesday, the 2nd of February, so be sure to check it out.
Do you think Mark Wahlberg should get a pity Oscar? Have you seen this film? If so, did you like it?
Yesterday was the RAZZIES, the Golden Raspberry, nominations, but today we have the Oscar nominations, which some would argue are less important, to the world of film, than the RAZZIES. The Oscars has many categories, but the most important are; best picture, best director, best actor, best actress and best visual effects.
Considering that Toy Story 3 technically shouldn't even be in this category, because there is a separate category for animated films, I'd say it definitely has a chance of winning. Although, personally, I would prefer to see Inception get the award. Guess we'll have to wait and see.
Disappointing to see that Christopher Nolan did not get nominated, he's such a great director. For this category my money is on either David Fincher or Darren Aronofsky.
Colin Firth will probably win, but I'm hoping for Eisenberg. By doing The Social Network he broke out of being typecast and I love when that happens. Come on Jack Black break away do something that requires more range.
Portman has this in the bag. Nothing against the other nominees, I'm sure they all did great and it's an achievement to be nominated, but the girl broke bones to make Black Swan, that's commitment and it shows.
Although we now know who the nominees are, we won't know the winners until the actual ceremony which will be on February 27th, the day after the RAZZIES. Who do you think should win? Let me know by leaving a comment and voting below.
I'm going to take a break from writing about remakes, though there will be another one tomorrow, and instead I'm going to talk about the Golden Globes.
It's no surprise that Natalie Portman won Best Performance by an Actress in a Motion Picture for her portrayal of Nina in Black Swan. The movie was so well received that it would have been a complete shock, to me, had she not one. The male version of this award, Best Performance by an Actor in a Motion Picture, went to, Mr. Darcy himself, Colin Firth for his movie The Kings Speech.
Toy Story 3 won Best Animated Feature Film. It's not that I wasn't expecting it to win; it's just that everyone I know who saw it didn't like it. They said it was good but way too sad, some even said they wished it wasn't made. I was hoping something more fun loving would win, but maybe next time. Best Motion Picture Comedy/Musical went to The Kids Are All Right, which is about two lesbians, in a relationship, that each used the same sperm donor to have kids and now those kids, who have reached teenage years, find their biological dad.
Glee got the award for Best Comedy/Musical TV Series. It actually beat the Big Bang Theory in this category, which I wasn't sure it would due to Glee's decline in popularity. The Big Bang Theory didn't completely miss out as Jim Parson, Sheldon, walked away with the award for Best Actor in a Comedy/Musical TV Series. Jane Lynch and Chris Colfer, of Glee, didn't leave empty handed as they won Best Supporting Actress/Actor awards respectively. Isn't it strange that comedy and musical are paired together in the Golden Globes?
Are you happy with the winners? Let me know by leaving a comment below.


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text:
Joseph Levin Saalschütz (Königsberg,  — Königsberg, ) foi um arqueólogo alemão.

Bibliografia
Salomon Wininger: Große Jüdische National-Biographie. Band V, Seite 302.

Ligações externas

Rabinos

redpajama_set_name: RedPajamaWikipedia


text:
using System;

namespace Vertigo.Live
{
    public static partial class Extensions
    {
        public static ILiveList<Tuple<TKey, TData, TData>> Integrate<TKey, TData>(this ILiveList<Tuple<TKey, TData>> source1, ILiveList<Tuple<TKey, TData>> source2)
            where TKey : IComparable<TKey>
        {
            LiveObserver<ICollectionState<Tuple<TKey, TData>, IListDelta<Tuple<TKey, TData>>>> observer1 = null;
            LiveObserver<ICollectionState<Tuple<TKey, TData>, IListDelta<Tuple<TKey, TData>>>> observer2 = null;

            return LiveListObservable<Tuple<TKey, TData, TData>>.Create(
                innerChanged =>
                {
                    source1.Subscribe(observer1 = source1.CreateObserver(innerChanged));
                    source2.Subscribe(observer2 = source2.CreateObserver(innerChanged));
                    return new[] {observer1.GetStateLock, observer2.GetStateLock}.MergeLockers();
                },
                (innerChanged, notified, stateLock, oldState) => null,
                () =>
                {
                    observer1.Dispose();
                    observer2.Dispose();
                });
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
\section{Introduction}\label{Introduction}

In order to derive knowledge of cosmology from observations of the large scale structure of the universe, we must accurately record the angular positions and distances to billions of galaxies and then analyse their statistical distribution. To determine distances in cosmology it is first necessary to have a fiducial cosmological model that allows the observer to calculate quantities such as the angular diameter distance $d_A(z)$ and Hubble function $H(z)$. Inevitably the underlying model the cosmologist has is incorrect in some way. This results in geometric distortions known as the Alcock-Paczynski effect \citep{1979Natur.281..358A} - distances measured along the line of sight look different to those measured perpendicular to the line of sight \citep{2005MNRAS.364..743N, 2011MNRAS.tmp.1599B, 2011arXiv1105.2037K}.

The redshift of a galaxy, $z$, is not a true measure of a galaxy's distance but a measure of the recessional velocity of the galaxy. Galaxies are involved in motions apart from the Hubble flow, such as falling onto a cluster under gravity, these result in a distortion of the perceived distance to a galaxy \citep{1987MNRAS.227....1K}. Because clusters grow under gravity, through studying their growth at different redshifts by isolating these redshift space distortions, we are able to learn how gravity behaves at different epochs. This may help us identify deviations from General Relativity \citep{2010PhRvD..81d3512S, 2009MNRAS.393..297P, 2005PhRvD..72d3529L}. It is well known that these two effects are degenerate in some regimes. Dynamic and geometric distortions can both have an enhancing effect on the galaxy power spectrum in the line of sight.

In order to identify and study these effects it is necessary to find features that are known to be the same physical size in the radial and transverse directions and the same size at all redshifts - a so called cosmic ruler. Unfortunately astrophysical objects are neither uniform nor large enough to perform such tests. The scale of the Baryon Acoustic Oscillations (BAO), i.e. the sound horizon at the epoch of last scattering, $r_s$, (seen on the CMB sky and first detected in the SDSS LRG galaxy distribution by \cite{2005ApJ...633..560E}) can be used as such a standard ruler to test our understanding of cosmology (for a recent review see \cite{2009arXiv0910.5224B}).  This characteristic clustering scale, which emerges from physical processes in the early universe, should be the same in both the radial and transverse directions.

In the transverse direction, measurement of the sound horizon gives a measurement of the angular diameter distance at which the sound horizon is observed, $d_A(z)/r_s$, where
\begin{equation}
d_A(z) = \frac{c}{H_0}\frac{r}{1+z},
\end{equation}
where
\begin{equation}
r(\chi) = \begin{cases}
\frac{\sin (\sqrt{-\Omega_k}\chi)}{\sqrt{|\Omega_k|}} \,\,\, \,\,& \Omega_k > 0\\
\chi \,\,\, \,\, & \Omega_k = 0\\
\frac{\sinh (\sqrt{\Omega_k}\chi)}{\sqrt{|\Omega_k|}} \,\,\, \,\,& \Omega_k < 0 \end{cases}
\end{equation}
$\chi(z)$ is the comoving distance
\begin{equation}
\chi(z) = H_0 \int_0^{z}\frac{dz'}{H(z')}.
\end{equation}
Thus the Hubble function and angular diameter distance are coupled in an FLRW universe. In the radial direction, the same BAO measurement gives $H(z)r_s$. Assuming that the radiation fraction is negligible the Hubble function is
\begin{equation}
H(z) = H_0\sqrt{\{\Omega_m(1+z)^3 + \Omega_k(1+z)^2 + \Omega_{DE} f(z)\}},
\end{equation}
where
\begin{equation}
f(z) = (1+z)^{\{3(1+w_0+w_a))\exp(-3w_a z/(1+z)\}}.
\end{equation}
takes into account an evolving dark energy equation of state, $w$, which can be split into a constant, $w_0$, and time varying component, $w_a$,
 \begin{equation}
 w = w_0 + \frac{w_a}{1+z}.
 \end{equation}

There has been some debate regarding the detection of radial BAO. \citet{2009MNRAS.399.1663G} measured $H(z)$ using the BAO peak in the line of sight two-point correlation function. This was refuted by \citet{2010ApJ...710.1444K} who claimed it was consistent with background noise but used by others to get cosmological constraints \citep{2010arXiv1004.2599Z}. Criticisms of this detection were riposted by \cite{2011MNRAS.412L..98C}. Detections consistent with background noise can still be used to improve cosmological constraints if one assumes the $\Lambda$CDM cosmology. Future projects such as PAU\footnote{http://www.pausurvey.org}, DESpec\footnote{http://eag.fnal.gov/DESpec/Home.html} and BigBOSS\footnote{http://bigboss.lbl.gov} should provide stronger detections of the radial BAO.

Here we investigate how calibrating the BAO scale in the galaxy distribution to that measured in the CMB can remove the degeneracy between geometric and redshift space distortions improving constraints on cosmological parameters. Both of these probes, i.e. probes sensitive to the background expansion and the growth of perturbations, are important if one wants to distinguish the effect of dark energy from modified gravity.

Other similar work has been done on this by \cite{2010PhRvD..81d3512S}. They focused on looking at dark energy versus modified gravity and concluded that CMB calibration of the BAO scale was not a particularly powerful way of telling the difference between the two. Work by \cite{2011arXiv1102.1014S} is also similar, they focus on constraining curvature.

\cite{2010arXiv1004.4810S} make the interesting point that often when we consider constraints from combined probes we merely over plot the likelihood contours even though CMB information is routinely included in each probe. Meaning that it is included more than once in the final interpretation of the results. This highlights the need to understand CMB calibration.

We look at constraints from a  ``small" survey of volume $1(h^{-1}{\rm Gpc})^3$ and a ``large" survey of $20(h^{-1}{\rm Gpc})^3$. This large survey is exemplary of future spectroscopic galaxy redshift surveys like BigBOSS, a proposed all sky spectroscopic galaxy redshift survey and DESpec, a proposed spectroscopic follow up to the photometric Dark Energy Survey\footnote{http://www.darkenergysurvey.org/}. Surveys such as DESpec/BigBOSS will be large and deep enough to use redshift space distortions to measure the growth of structure in the same redshift bins used to measure the Hubble function from radial BAO measurements. We make the assumption that if redshift measurements are accurate enough to measure the radial BAO scale then the precision of these measurements will have little effect on our results (see \cite{2009ApJ...691..241B}). We do not, therefore, consider how uncertainties in redshift measurements will affect our results but instead leave this for further work. Nor do we consider non linear redshift space distortions.

In order to get a feel for the effect of CMB calibration and to avoid adding CMB information via a ``black box", we shall start our investigation with a toy model for the galaxy power spectrum (section \ref{toy model}).  This toy model includes the BAO and geometric and redshift distortions. Later we will investigate the problem with a numerically calculated power spectrum, produced by CAMB \citep{Lewis:1999bs} to allow for a time varying dark energy equation of state \citep{2008PhRvD..78h7303F} (section \ref{camb model}).

In section \ref{fisher section} we investigate the effect of CMB calibration on the parameters used to write our toy model using Fisher information. We show how a prior on just the sound horizon alone reduces the area of the Fisher matrix $1\sigma$ ellipse in the parameter space describing the geometric and redshift distortions.

In section \ref{Nested Sampling} we investigate how nested sampling explores the parameter space and show that derived constraints on parameters are not the same. Through the use of importance sampling (see section \ref{Nested Sampling} for an explanation) we produced combined constraints for our numerical galaxy power spectrum and CMB observations (using a publicly available WMAP chain \citep{Komatsu:2010fb}). We compare these constraints with those from the power spectrum and a prior on the sound horizon alone finding that they are not the same. We present our main results in section \ref{results} and discuss their implications in section \ref{Discussion}.

\section[A Toy Model for the Observed Galaxy Power Spectrum]{A Toy Model for the Observed Galaxy Power Spectrum}\label{toy model}

Here we construct a toy model for the observed galaxy power spectrum which includes BAO `wiggles' in addition to geometric and redshift space distortions. By design this toy model is not explicitly dependant upon cosmology acting as a fitting function for the observed anisotropic power spectrum. In reality, of course, the shape and distortions of the galaxy power spectrum are cosmologically dependant. The aim of this toy model is to focus clearly on quantities that are measured from the large scale structure (LSS) two point correlator. Our toy power spectrum is similar to that used by \cite{2010PhRvD..81d3512S} but simpler, excluding non-linear redshift space distortions.

The Bardeen, Bond, Kaiser, Szalay (BBKS) transfer function (equation \ref{BBKS}) gives an analytically simple power spectrum which is satisfactory for our purposes \citep{1986ApJ...304...15B}.
\begin{equation}\label{BBKS}
\begin{split}T(q) = \frac{\ln[1+2.34q]}{2.34q}
\times[1+3.89q + (16.2q)^2 \\+ (5.47q)^3 +(6.71q)^4]^{-0.25},\end{split}
\end{equation}
where $ q =\frac{k}{\Gamma}$, $\Gamma$, is the power spectrum shape parameter.
This gives us the linear, `no wiggles' part of our power spectrum.
\begin{equation}
P_{nb}(k) = A^*k^{n}T^2_{BBKS}(\frac{k}{\Gamma}).
\end{equation}
$A^*$ is the amplitude of the power spectrum and is independent of cosmology and $\Gamma$ is the shape parameter, which probes the matter density and baryon fraction thus \citep{1995ApJS..100..281S}:
\begin{equation}\label{Gamma}
\Gamma = \Omega_m h\exp\bigg(-\Omega_b - \sqrt{2h}\frac{\Omega_b}{\Omega_m}\bigg).
\end{equation}
$n$ is the spectral index, which for a Harrison-Zeldovich spectrum is equal to $1$ (inflation typically predicts that this number is less than $1$). The spectral index is different from the other parameters in this power spectrum since it can be considered a cosmological parameter in itself and not as such dependant on cosmology, we therefore keep it fixed at $n=1$.

An approximation to the baryon acoustic oscillations can be added to this by multiplying by a slowly varying sinusoidal function whose peaks are harmonics of the horizon scale $\sin(kr_s)$, with some damping given by some exponential part similar to the fitting function used by \cite{2005ApJ...631....1G}
\begin{equation}\label{pk}
P(k) = P_{nb}(k)\bigg(\frac{k}{\Gamma}\bigg)\bigg[1+A_{BAO}k\exp\bigg\{-\bigg(\frac{k}{k_s}\bigg)^{1.4}\bigg\}\sin(r_sk)\bigg].
\end{equation}

However, this is not how the power spectrum appears to the observer. We have to incorporate into our formalism descriptions of the linear dynamic and geometric distortions.  In redshift space, allowing for anisotropies, the power spectrum takes the form
\begin{equation}
P^z(k_\parallel,k_\bot) = \bigg[1+\beta(z)\frac{k_\parallel^2}{k_\parallel^2+k_\bot^2}\bigg]^2 P\bigg(\sqrt{k_\parallel^2 + k_\bot^2}\bigg),
\end{equation}
where
\begin{equation}\label{beta}
 \beta = \frac{\Omega_m^{\gamma(z)}}{b(z)},
\end{equation}
 $\Omega_m(z)^\gamma$ is the equivalent of the logarithmic rate of change of the linear growth factor with respect to redshift. $\gamma$ is dependant on the models of gravity and structure formation used and can be dependant on redshift. This simple parameterisation of the growth of structure has been shown to be effective in capturing the behaviour of a large class of models. Standard GR in a $\Lambda$CDM universe gives a constant $\gamma = 0.55$, whilst the DGP brainworld model is approximated by $\gamma = 0.68$ \citep{2007APh....28..481L}. Some theories of gravity require a $\gamma$ that slowly varies with redshift and in theories of gravity which allow for the presence of anisotropic stress $\gamma$ can be scale dependant. When designing experiments to measure $\gamma$ we will therefore have to consider measuring $\gamma$ in different redshift bins and at different scales in order to constrain deviations from GR. A simple extension to include a dependence on the dark energy equation of state is \citep{2005PhRvD..72d3529L}
 \begin{equation}\label{little_gamma}
 \gamma(z) = 0.55 + 0.05(1+w(z)).
 \end{equation}
 $b(z)$ is the bias, a measure of how well the galaxy distribution follows the underlying dark matter distribution. In reality this will be a function of redshift but for now we shall assume that it is a constant and is equal to unity. $k_\parallel$ and $k_\bot$ are respectively the parallel and perpendicular components of ${\rm k}$.

 \cite{2011ApJ...727L...9J} show, using N body simulations, that strictly linear redshift distortions are very poor at measuring the growth rate in non $\Lambda$CDM cosmologies, even at large scales. However, we will follow the likes of \cite{2011arXiv1102.1014S} in assuming that linear redshift distortions are a good enough approximation at large scales.

Considering the Alcock-Paczynski effect we need to introduce parameters that describe the ratio between the actual and assumed cosmologies in the parallel and perpendicular directions,
\begin{equation}\label{c_par c_perp}
c_\parallel(z)=\frac{H^{fid}(z)}{H(z)}\,\,\,;\,\,\,c_\bot(z)=\frac{d_A(z)}{d_A^{fid}(z)}.
\end{equation}
These parameters relate to the Hubble parameter $H(z)$ and angular diameter distance $d_A(z)$. If our fiducial cosmology is correct (and therefore correctly describing our observed power spectrum) then $c_\parallel = c_\bot = 1$. Thus by measuring the sound horizon in both directions we can determine how well we know our cosmology. It should be noted that there is still a degeneracy between $H(z)$ and $d_A$ since both are dependant upon cosmology and are coupled. Meaning that a result of $c_\parallel \neq c_\bot \neq 1$ does not tell us \textit{how} our fiducial cosmology is wrong, merely that it is.

The observed power spectrum is then described by
\begin{equation}\label{obs_pk}
P^{obs}(k_\parallel, k_\bot;z) = \frac{1}{c_\parallel(z) c^2_\bot(z)}P^z(\tilde{k}_\parallel, \tilde{k}_\bot).
\end{equation}
Where $\tilde{k}_\parallel = k_\parallel / c_\parallel$ and $\tilde{k}_\bot = k_\bot / c_\bot$. The prefactor is needed because geometric distortions lead to a misestimation of the survey volume. The fiducial values for the parameters in this toy model are given in table \ref{base_parameters} and the power spectrum illustrated in figure \ref{fig:pk_plots_print.eps}.

\begin{figure}
\centering
\psfrag{kpar}{$k_{\parallel}$}
\psfrag{kperp}{$k_{\bot}$}
\includegraphics[width = 8cm]{pks_errors.eps}
\caption{The model power spectra used in our analysis, the colourbars have units of ${\rm ln}(h^-1 {\rm Mpc})$. The top left panel is the anisotropic toy model power spectrum (as outlined in section \ref{toy model}), the top right panel gives the error, $\sigma^2$, on this model, for the $20(h^{-1}{\rm Gpc})^3$ `DESpec/BigBOSS' survey with shot noise. The bottom two panels are the CAMB power spectrum (as outlined in section \ref{camb model}) and the error bars associated with it. The $k$bin size is $(1.9\times 10^{-3} h {\rm Mpc}^{-1})^3$. The colour scales used in these plots are logarithmic. }
\label{fig:pk_plots_print.eps}
\end{figure}

\begin{center}
\begin{table
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
$A^*$ &$A_{BAO}$ &$\Gamma$ &$r_s$ &$\beta$ &$c_\parallel$ &$c_\bot$ \\
\hline
   3.80 &   2.00 &   0.15 &  149.86 &   0.52 &   1 &   1 \\
\hline
\end{tabular
\caption{Fiducial values of the parameters of our toy power spectrum used to build the Fisher matrix (equations \ref{pk} - \ref{obs_pk}).}
\label{base_parameters}
\end{table}
\end{center}

\begin{center}
\begin{table}\label{camb_params}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
$\Omega_{cdm}$ &$\Omega_b$ &$\Omega_{DE}$ &$w_0$ &$w_a$ &$H_0$ &$b$ &$A$ \\
\hline
   0.218 &   0.044 &   0.738 &  -0.827 &   -0.753 &  71.5 & 1.0 &   2.3 \\
\hline
\end{tabular}
\caption{Fiducial cosmology for our CAMB power spectrum, based on the best fit parameters for the WMAP with time varying dark energy equation of state.}
\label{fiducial_cosmology}
\end{table}
\end{center}

\section{A more realistic model for the galaxy power spectrum}\label{camb model}

The toy model, by design, is not explicitly dependant on cosmology. The distortion parameters can vary independently of the parameters that describe the shape of the power spectrum. In reality geometric and dynamic space distortions are dependant on the same cosmology that determines the shape of the power spectrum, so they cannot vary independently of the underlying power spectrum.

To build a more cosmologically based model we use a power spectrum generated by CAMB with geometric and redshift space distortions added in the same way as our analytic toy model. There are many parameters in CAMB that one could choose to vary. Here we have chosen parameters that can be related to those in our toy model. In particular, six of the parameters we have chosen determine the distortions on the power spectrum. The numerical model power spectrum is illustrated in figure \ref{fig:pk_plots_print.eps} and the fiducial values of its parameters given in table \ref{fiducial_cosmology}.

In our toy model the amplitude, $A^*$, is a normalising factor, as is the amplitude parameter, $A$, that we vary in CAMB. The amplitude of the BAO, $A_{BAO}$, the power spectrum shape parameter, $\Gamma$ and sound horizon scale, $r_{\rm s}$, are dependant on the fraction of dark matter and of baryons and on $h$, so we vary $\Omega_{\rm cdm}$, $\Omega_{\rm b}$ and $H_0$. We assume a flat cosmology, so $\Omega_{\rm cdm} + \Omega_{\rm b} + \Omega_{\rm DE} = 1$ always. We also vary the linear bias parameter $b$ because the redshift space distortion parameter, $\beta$ is dependant on galaxy biasing. All other parameter values are kept fixed at the default CAMB values.

CMB data, such as that from WMAP or that which will come from Planck, contains more information than just the sound horizon scale and its error. It contains a plethora of cosmological information. Information contained in CMB observations on the geometry and the make up of the universe could also be used to improve parameter constraints. For example, the CMB gives us knowledge of $\Omega_{\rm m}$, this can be used as a prior on $\beta$. Indeed if we want to look for deviations from GR using measurements of $\beta$ it is first necessary to understand the matter fraction, $\Omega_{\rm m}$. In this paper we shall not investigate how explicit priors on parameters such as the matter fraction affect constraints, leaving this for further work. Instead we shall just investigate the prior on the sound horizon.

\section{Fisher Analysis}\label{fisher section}

The Fisher Matrix formalism is a way of estimating the amount of information available in a given parameter space. It is a way of propagating errors in the case of multiple correlated measurements and many parameters.

\cite{2009arXiv0906.0993B} or \cite{2009arXiv0901.0721A} provide a more detailed explanation of the statistics behind the Fisher matrix and also on how they are correctly manipulated. \cite{2007ApJ...665...14S} look at isolating the information cantained in the BAO peaks using Fisher matrices. We try to keep our use of Fisher matrices as simple as possible since later we will do a statistically more robust analysis.

The Fisher information is a measure of how much information an observable (in this case the power spectrum) contains about some unknown parameter $\theta$ (in this case the seven observable parameters in our toy model $A^*,A_{BAO},\Gamma,r_s,\beta,c_\bot,c_\parallel$). It is defined by the expectation value of the derivatives of the logarithm of the likelihood function, $\mathcal{L}$, with respect to parameters $\theta_i$.
\begin{equation}
F_{ij} = - \bigg\langle \frac{\partial^2 \ln \mathcal{L}}{\partial\theta_i \partial\theta_j} \bigg\rangle.
\end{equation}
Assuming that the noise on our data (the power spectrum) is Gaussian. The likelihood function for this observable can be expressed in terms of the theoretical value for that observable evaluated at some point, in our case at a particular $k$ value.

With some manipulation we can express the Fisher matrix as a sum over derivatives of the power spectrum with respect to parameters $\theta_i$ rather than as derivatives of the likelihood function.
\begin{equation}\label{fish_pk}
F_{ij}=\sum_n\frac{1}{\sigma_n^2}\frac{\partial P(k_n)}{\partial\theta_i}\frac{\partial P(k_n)}{\partial\theta_j}
\end{equation}
where $\sigma_n$ is the Gaussian error on $P(k_n)$. The error on the power spectrum is as follows.
\begin{equation}
\delta P = \sqrt{\frac{2}{4\pi k^2 \Delta k V}}\bigg(P + \frac{1}{\bar{n}}\bigg).
\end{equation}
Thus for the limit of large $\bar{n}$ i.e. ignoring shot noise only keeping the cosmic variance term
\begin{equation}
\delta P(k_n) = \sqrt{\frac{2}{4\pi k^2 \Delta k V}}P(k_n),
\end{equation}
where V is the volume of the survey. We will however always include shot noise in our calculations, assuming a constant number density of tracers $\bar{n} =  5\times10^{-4} (h^{-1} {\rm Mpc})^{-3}$. For a more detailed explanation see \cite{1998ApJ...499..555T}. (There is the implicit assumption that uncertainties in the measurement of the power spectrum are independent of cosmology. This assumption is incorrect since knowledge of the Hubble function and angular diameter distance are needed to convert redshifts to galaxy positions and derive a power spectrum from observational data.) We then put this into equation \ref{fish_pk}, $(\delta P)^2 = \sigma^2$, using $\partial P/ P = \partial \ln P$ and recognising that because the effects on the power spectrum that we are interested in are anisotropic we require the full two dimensional anisotropic Fisher matrix, we get:

\begin{equation}
F_{ij} = \sum_{k_\parallel,k_\bot}\frac{\partial \ln \tilde{P}(k_\parallel,k_\bot)}{\partial \theta_i}\frac{\partial\ln \tilde{P}(k_\parallel,k_\bot)}{\partial\theta_j} \frac{2\pi \Delta k^2_{\bot} \Delta k_\parallel}{(2\pi)^3}V.
\end{equation}
The logarithmic derivatives of the toy model power spectrum can be found in the appendix. We have assumed here that the different k-modes of the power spectrum are uncorrelated. In reality a given galaxy redshift survey will only cover part of the sky and may vary in depth. Thus different k-modes of the power spectrum are correlated by a window function and are not independant. The assumption of uncorrelated modes is valid if the $k$-bin $\Delta k >> 1/L$, where L is the smallest linear dimension of the survey, in our case this is $\sim 1 (h^{-1}Gpc)^3$. We used a $k$-bin size of $\Delta k = 1.9 \times 10^{-3} h {\rm Mpc}$ which satisfies this criterion.

In order to reduce the size of our parameter set we have two options. Either ``fix" the parameters we are uninterested in, i.e. assume that we know their values completely. If we think we know the value of a parameter completely then we can just remove that row and column from the Fisher matrix. This is equivalent to adding an infinite prior on that parameter. Or we can marginalise over that parameter. Treating it as a ``nuisance" parameter.

Constraints obtained from a marginalised Fisher matrix will always be weaker or equal to those from an unmarginalised matrix. Marginalisation will have no effect on the error on a parameter only if all other parameters are completely uncorrelated to it. This assumption breaks down because our observed parameters hide shared cosmological information and are therefore correlated. For example all our parameters except $A^*$ are dependant on $\Omega_{\rm m}$.

The procedure we follow to marginalise over unwanted parameters is to first invert the Fisher matrix to get the covariance matrix. Then we remove the row and column corresponding to the ``nuisance" parameter and reinvert to obtain the new reduced Fisher matrix. This is then repeated until we have marginalised over all our ``nuisance" parameters.

In our toy model analysis we demonstrate calibration of LSS information to the CMB by the addition of a prior on the sound horizon scale $r_{\rm s}$. The sound horizon can be calculated using the description by \citet{1996ApJ...471..542H,1998ApJ...496..605E}.

The prior matrix is simply a diagonal matrix made up of $Pr_{i=j} = 1/\sigma^2$, where $\sigma$ is the error on parameter $\theta_i$ and off diagonal components being zero. Adding this matrix to the original Fisher matrix then produces a matrix which includes the prior information.

In Figures \ref{fig:toy_model_error_ellipses_LRG.eps} and \ref{fig:toy_model_error_ellipses_DESpec.eps} we have drawn error ellipses representing the constraints on pairs of parameters (marginalising over all other parameters) in our toy power spectrum from a $1(h^{-1} {\rm Gpc})^3$ volume limited survey (blue). The ellipses are then redrawn including only a prior on the sound horizon of $\sim 1 \%$, i.e. that $\sigma_{r_{\rm s}} = 1.4 \, {\rm Mpc} h^{-1}$ is a Gaussian error bar. This is about the size of the error bar on the the sound horizon scale from WMAP-7. The shrinking on the $c_\bot - c_\parallel$ ellipse implies that calibrating the sound horizon from the CMB to the sound horizon in the BAO features in the galaxy power spectrum can tell us whether we have the correct value for the angular diameter distance and Hubble function. The affect that decreasing the strength of the prior on the sound horizon has on constraints in the $c_\bot - c_\parallel$, $c_\bot - \beta$ and $c_\parallel - \beta$ planes is illustrated in figure \ref{fig:ellipse_size_with_sigma_rs.eps}. Note how calibrating the sound horizon has next to no effect of our constraints on $\beta$.

\begin{figure}
\psfrag{ampl}{$A$}
\psfrag{amplbao}{$A_{BAO}$}
\psfrag{cpar}{$c_{\parallel}$}
\psfrag{cpara}{$c_{\parallel}$}
\psfrag{cperp}{$c_{\bot}$}
\psfrag{beta}{$\beta$}
\psfrag{rs}{$r_s$}
\psfrag{shape}{$\Gamma$}
\centering
\includegraphics[width = 8cm]{TM_LRG.ps}
\caption{An exploration of our toy model parameter space for a $1(h^{-1}{\rm Gpc})^3$ survey, focusing on the linear redshift distortion parameter $\beta$ and the line of sight and perpendicular geometric distortion parameters, $c_\parallel$ and $c_\bot$. Fisher matrix $1\sigma$ contours with (red) and without (blue) a prior on the sound horizon $r_{\rm s}$. Nested sampling $1\sigma$ contours with (black) and without (green) a prior on the sound horizon $r_{\rm s}$. Small galaxy redshift surveys are unable to estimate the values of cosmological parameters acurately alone. It is therefore difficult to approximate the errors on these paramters as being Gaussian.}
\label{fig:toy_model_error_ellipses_LRG.eps}
\end{figure}

\begin{figure}
\psfrag{ampl}{$A$}
\psfrag{amplbao}{$A_{BAO}$}
\psfrag{cpar}{$c_{\parallel}$}
\psfrag{cpara}{$c_{\parallel}$}
\psfrag{cperp}{$c_{\bot}$}
\psfrag{beta}{$\beta$}
\psfrag{rs}{$r_{\rm s}$}
\psfrag{shape}{$\Gamma$}
\centering
\includegraphics[width = 8cm]{TM_DESpec.ps}
\caption{An exploration of our toy model parameter space for a $20(h^{-1}{\rm Gpc})^3$ survey, focusing on the linear redshift distortion parameter $\beta$ and the line of sight and perpendicular geometric distortion parameters, $c_\parallel$ and $c_\bot$.  Fisher matrix $1\sigma$ contours with (red) and without (blue) a prior on the sound horizon $r_s$. Nested sampling $1\sigma$ contours with (black) and without (green) a prior on the sound horizon $r_{\rm s}$. }
\label{fig:toy_model_error_ellipses_DESpec.eps}
\end{figure}

\begin{figure}
\psfrag{cpar}{$c_{\parallel}$}
\psfrag{cperp}{$c_{\bot}$}
\psfrag{beta}{$\beta$}
\psfrag{rs}{$r_{\rm s}$}
\centering
\includegraphics[width = 8cm]{error_on_rs_plot.eps}
\caption{Area of the 1$\sigma$ Fisher ellipse with respect to $\sigma_{r_s}$. Red line is $c_\parallel$-$\beta$, green dotted is $c_\bot$-$\beta$, blue dot dash is $c_\parallel$-$c_\bot$. The vertical black dotted lines show the value for $\sigma_{r_s}$ used as a prior in figures \ref{fig:toy_model_error_ellipses_LRG.eps} and \ref{fig:toy_model_error_ellipses_DESpec.eps} and a prior of $0.1 \%$. The blue dotted and green dotted lines show the area of $c_\bot-\beta$ and $c_\bot-c_\parallel$ ellipses respectively, with no prior on $r_{\rm s}$.}
\label{fig:ellipse_size_with_sigma_rs.eps}
\end{figure}

However, within $d_A(z)$ and $H(z)$ lie all the standard cosmological parameters (the dark energy equation of state appears in the Hubble function and in the angular diameter distance). In order to know how much information we have on those parameters from the original toy power spectrum we need to transform the original Fisher matrix. This is a straight forward coordinate transformation of a rank-2 tensor

\begin{equation}\label{transform}
\tilde{F}_{ij} = F_{rs}\frac{\partial\theta^r}{\partial\theta^i}\frac{\partial\theta^s}{\partial\theta^j}.
\end{equation}

When transforming to cosmological parameters including prior information from the CMB then becomes more complicated since many of the cosmological parameters we are interested in have priors from WMAP and other CMB experiments. They will need to be added to the cosmological parameters Fisher matrix before any further marginalisation if this information is to be included. We could do this by transforming an available Fisher matrix for a CMB probe into our base parameters and adding it to our Fisher matrix as in \citep{2010PhRvD..81d3512S}. However this does not help us \emph{understand} the effect of CMB calibration, it merely assumes it is a necessary step. Here we are trying to investigate the limit of information available before adding a prior from the CMB. To investigate cosmological parameter space we need a more physical toy model and more robust statistical techniques.

\section{Nested Sampling - a more robust exploration of parameter space}\label{Nested Sampling}

We are interested to see how our toy model with one Gaussian prior compares to a full likelihood analysis of a numerically produced power spectrum combined with real CMB information. One commonly used method in cosmology is   Markov-Chain Monte-Carlo (MCMC) by \cite{Lewis:2002ah}. Algorithms such as the Metropolis-Hastings algorithm attempt to optimally explore the parameter space without wasteful calculations at uninteresting points.

Our chosen method of statistical parameter space exploration is `Nested Sampling' \citep{2004AIPC..735..395S,2006ApJ...638L..51M,2009MNRAS.398.1601F,2008MNRAS.384..449F}, a completely different algorithm to Metropolis-Hastings but with much the same aims.

It was first designed for Bayesian evidence evaluation, at which it is more efficient than MCMC since it transforms the multidimensional evidence integral into a one dimensional problem. This allows evidence to be evaluated from a single chain rather than multiple chains as is the case with traditional MCMC. Nested sampling can also be used for parameter estimation since it produces posterior inferences as a by product. Here we use the Multinest implementation of the nested sampling algorithm\footnote{http://ccpforge.cse.rl.ac.uk/gf/project/multinest/} \citep{2009MNRAS.398.1601F,2008MNRAS.384..449F}. There are other packages available, with different variations of the algorithm, such as Cosmonest \citep{2006ApJ...638L..51M}.

The likelihood for each link in the chain is calculated from the $\chi^2$ for that point and fed into the algorithm
\begin{eqnarray}
\chi^2 &=& \sum_{k_\bot, k_\parallel}\frac{(\tilde{P}^{fid}(k_\bot, k_\parallel) - \tilde{P}^{trial}(k_\bot, k_\parallel))^2}{\frac{2}{4\pi k_{\bot}^2\Delta k V} \bigg(\tilde{P}^{trial}(k_\bot, k_\parallel) + \frac{1}{\bar{n}}\bigg)^2},\\
\mathcal{L} &=& \exp{\frac{-\chi^2}{2}}.\label{pk_like}
\end{eqnarray}

We ran chains for  a $1 (h^{-1}{\rm Gpc})^3$ and a $20(h^{-1}{\rm Gpc})^3$ ``DESpec/BigBOSS" style survey, with shot noise $\bar{n} = 5 \times^{-4} {\rm Mpc}^{-1} h$ on our toy model power spectrum and on our numerically produced power spectrum.

CMB information was then included through importance sampling. Importance sampling enables us to combine information from different (independent) experiments (eg \citet{2010MNRAS.409.1100S}. Given a pre calculated Monte Carlo chain of parameter values $\theta$ which explores a likelihood distribution $L$, we can compute parameter constraints relative to a similar distribution $L'$ by reweighting the sample according to the likelihood ratio
\begin{equation}
w_{L'}^i = \frac{L'(\theta^i)}{L(\theta^i)}w_L^i
\end{equation}
For now we are interested in combining CMB and galaxy power spectrum information but we could combine any two independent experiments in this way. For two independent likelihood distributions,$L$ and $L'$, the combined likelihood distribution $\tilde{L}$ is just the product of the two
\begin{equation}
\tilde{L}(\theta)=L(\theta)L'(\theta).
\end{equation}
Which then allows us to simply reweight each point in the chain
\begin{equation}\label{reweight}
\tilde{w}^i = L'^i(\theta^i)w_L^i.
\end{equation}
Now likelihood contours drawn in the parameter space will represent the combined likelihood. It is worth noting that the `Metropolis Hastings' and `Nested sampling' algorithms use different weighting schemes, MCMC points have integer weights whilst the sum of the weights in a Multinest chain is one. For ease of comparison it is necessary to normalise the weights accordingly.

We took a WMAP chain which allowed for a time varying dark energy equation of state and then calculated the likelihood that each point fitted the galaxy power spectrum using equation \ref{pk_like}. We then reweighted each point using equation \ref{reweight} and the WMAP weight. The Alcock-Pazcynski $c_\parallel, c_\bot$ and redshift space distortion $\beta$ parameters were then calculated for each point in the chain by comparing with the fiducial parameter values. This allows us to draw constraints in these planes (figure \ref{fig:all_impsamp.eps}).

To include a prior on the sound horizon in our toy model we assume the likelihood distribution around the true value of $r_s$ is a Gaussian of the same width as the prior used in the Fisher analysis. This is then importance sampled with the toy model, giving rise to the black contours in figures \ref{fig:toy_model_error_ellipses_LRG.eps} and \ref{fig:toy_model_error_ellipses_DESpec.eps}. To include the Gaussian prior in the CAMB model we calculated $r_s$ for each point in the chain using the prescription given in \citet{1996ApJ...471..542H,1998ApJ...496..605E}. We then drew a probability for this value from a Gaussian distribution peaked at the fiducial value for $r_s$ calculated using our fiducial parameter set in table 1, with the same standard deviation as before. Each point was then reweighted using equation \ref{reweight}, giving rise to the black contours in figure \ref{fig:all_impsamp.eps}.

\begin{figure}
\psfrag{cpar}{$c_{\parallel}$}
\psfrag{cperp}{$c_{\bot}$}
\psfrag{beta}{$\beta$}
\centering
\includegraphics[width = 8cm]{DESpec_impsamp_plot.ps}
\caption{Constraints on $\beta$ and $c_\bot$, $c_\parallel$ from nested sampling a CAMB power spectrum. The black ellipse represents the $1\sigma$ confidence limit based on a $20(h^{-1} {\rm Gpc})^3$ `DESpec/BigBOSS'-like survey alone. The purple contour is the $1\sigma$ confidence limit of the prior from WMAP projected onto this parameter space. The blue contours are the $1\sigma$ constraints from `DESpec/BigBOSS' importance sampled with the $1\%$ gaussian prior on $r_s$. The red contours are constraints from WMAP importance sampled with a `DESpec/BigBOSS' CAMB power spectrum projected onto this parameter space.}
\label{fig:all_impsamp.eps}
\end{figure}

\section{Results}\label{results}

When comparing the $68 \%$ confidence limits in figure \ref{fig:toy_model_error_ellipses_LRG.eps} from the Fisher (blue) and nested sampling (green) of the toy model we can see that for smaller survey areas there is some tension between the two methods. The areas bounded by the contours are similar, with the exception of the constraints around $\beta$, but the two sets of contours do not cover exactly the same part of parameter space. The larger survey (figure \ref{fig:toy_model_error_ellipses_DESpec.eps}) is, however, in better agreement. Although in the $\beta-c_\parallel,c_\bot$ planes the areas covered by the contours still differ by about a factor of two.

By design the maximum likelihood is the same in both cases, so the question arises, what is causing the disagreement? The answer can be found by examining the likelihood space in more detail. Let us look at the normalised 1d profile of the parallel Alcock-Paczynski parameter, $c_\parallel$. We can see that for a $1(h^{-1} {\rm Gpc})^3$ survey the likelihood is not Gaussian (figure \ref{fig:1dplot_LRG.eps}), being skewed. This puts asymmetric error bars on our parameters. When we look at the contours for a larger $20(h^{-1} {\rm Gpc})^3$ survey (the kind of volume expected for future spectroscopic surveys such as DESpec and BigBoss) we see that the spaces covered by the two sets of contours are more similar. If we look at the 1d probability distribution around $c_\parallel$ again we can see that it is more Gaussian than in the $1(h^{-1} {\rm Gpc})^3$ case since the marginalised probabilities and mean likelihoods are more similar. This is a pattern which is repeated for all the parameters in our toy model. The larger survey enables us to look closer into the peak of the distribution, which is more Gaussian. As anticipated, prior knowledge of the location and uncertainty in the sound horizon at last scattering improves our ability to constrain the shape of the power spectrum and geometric distortions.

\begin{figure}
\psfrag{ampl}{$A$}
\psfrag{amplbao}{$A_{BAO}$}
\psfrag{cpar}{$c_{\parallel}$}
\psfrag{cperp}{$c_{\bot}$}
\psfrag{beta}{$\beta$}
\psfrag{rs}{$r_s$}
\psfrag{shape}{$\Gamma$}
\centering
\includegraphics[width = 8cm]{1dplot_LRG.eps}
\caption{Normalised probability distributions of $c_\parallel$ using our toy model for a $1(h^{-1} {\rm Gpc})^3$ survey with shot noise, $\bar{n} = 5\times10^{-4}(h^{-1} {\rm Mpc} )^{-3}$. The black solid line is the Gaussian probability distribution based on the Fisher matrix analysis. The solid green line is the Nested Sampling likelihood marginalised over all other parameters but with no prior on the sound horizon. The dot dashed blue line is the normalised 1d marginalised Gaussian probability distribution including a prior on the sound horizon. The dot dashed red line is the Nested Sampling likelihood marginalised over all other parameters and importance sampled with a Gaussian prior on the sound horizon.}
\label{fig:1dplot_LRG.eps}
\end{figure}

\begin{figure}
\psfrag{ampl}{$A$}
\psfrag{amplbao}{$A_{BAO}$}
\psfrag{cpar}{$c_{\parallel}$}
\psfrag{cperp}{$c_{\bot}$}
\psfrag{beta}{$\beta$}
\psfrag{rs}{$r_s$}
\psfrag{shape}{$\Gamma$}
\centering
\includegraphics[width = 8cm]{1dplot_DESpec.eps}
\caption{Normalised 1d probability distributions of the line of sight geometric distortion parameter $c_\parallel$ using our toy model for a $20(h^{-1} {\rm Gpc})^3$ `DESpec/BigBOSS' survey with shot noise, $\bar{n} = 5\times10^{-4}(h^{-1} {\rm Mpc})^{-3}$. The black solid line is the Gaussian probability distribution based on the Fisher matrix analysis. The solid green line is the Nested Sampling likelihood marginalised over all other parameters but with no prior on the sound horizon. The dot dashed blue is the 1d marginalised Gaussian probability distribution including a prior on the sound horizon. The dot dashed red line is the Nested Sampling likelihood marginalised over all other parameters and importance sampled with a Gaussian prior on the sound horizon. The nested sampling probability distribution is more Gaussian than the $1(h^{-1} {\rm Gpc})^3$ case.}
\label{fig:1dplot_DESpec.eps}
\end{figure}

By adding a $~1 \%$ prior onto the sound horizon of $\sigma_{r_s} = 1.4 {\rm Mpc}\,h^{-1}$ in a Fisher matrix analysis we can see how calibrating with the CMB improves constraints in our $c_\bot - c_\parallel$ parameter space for a small $1(h^{-1}{\rm Gpc})^3$ survey by a factor of several and larger $20(h^{-1}{\rm Gpc})^3$ survey by more than a factor of two.

We then conducted a likelihood analysis using nested sampling on our numerically produced power spectrum. A Gaussian prior on the sound horizon improves these constraints by a factor of two for the $20(h^{-1}{\rm Gpc})^3$, `DESpec/BigBOSS'-like survey. The combined constraints from the $20(h^{-1}{\rm Gpc})^3$ survey + WMAP are nearly a factor of 10 times better than the constraints from the survey alone. The WMAP prior contains considerably more information and is more orthogonal than the Gaussian prior on $r_s$.

\section{Discussion}\label{Discussion}

In this paper we have addressed two issues of great interest to current research in Cosmology, in particular in connection with the forthcoming generation of large galaxy redshift surveys. The first issue is physical - the callibration of the BAO using the sound horizon in the CMB. The second issue is statistical - a comparison of parameter fitting using Fisher matrix and Monte Carlo techniques.

We examined if the degeneracy between geometrical (Alcock-Paczynski) and dynamical (redshift space) distortions in the pattern of the galaxy distribution could be lifted. The key point here is to use the BAO in the pattern of the galaxy distribution as a feature of known physical size, the sound horizon $r_{\rm s} \approx 150 {\rm Mpc}$. We have shown, using a toy model, that this indeed can be callibrated using the equivalent feature in the CMB. By adding a prior onto the sound horizon of $\sim 1 \%$ we have shown, using a Fisher matrix analysis, error ellipses for line of sight and tangential distortion parameters shrink by \emph{by a factor of two} for a $20(h^{-1} {\rm Gpc})^3$ `DESpec/BigBOSS'-like survey with shot noise. This improvement is even more marked in smaller surveys, for $1(h^{-1} {\rm Gpc})^3$ the improvement is nearly a factor of 10. When our model for the power spectrum becomes more complicated (using CAMB) and we use the same Gaussian prior of $\sim 1 \%$ on the sound horizon the improvement of the ellipse is by a factor of $2.5$. When we start to include real data (from WMAP), which contains more information than just the sound horizon scale, then the improvement of the error ellipse is by a factor of $21$.

On the statistical side we have carried out a Monte Carlo Nested Sampling analysis on our parameter space. We find that Monte Carlo and Fisher methods can agree reasonably well for large volume ($\sim 20(h^{-1} {\rm Gpc})^3$) surveys with parameter constraints being nearly symetric and 68\% likelihood contours agreeing to within a factor of two. However, for smaller volume surveys the difference between constraints from the two methods can be greater with the areas of 68\% likelihood contours differing by more than a factor of four. The Nested Sampling analysis demonstrates that in general the likelihood distributions in the parameter space our toy model can be non-Gaussian. This undermines a central assumption in the Fisher matrix analysis, where errors are Gaussian by construction. All the likelihood distributions are strongly skewed, with the exception of that around $\beta$. This likelihood falls off more steeply than a Gaussian, having kurtosis. In this example better knowledge of $r_s$ does not help to improve constraints on $\beta$.

The Fisher constraints on the amplitude of the power spectrum for a $1 (h^{-1} {\rm Gpc})^3$ survey demonstrate the unphysicality of the Fisher formalism. Allowed within the $68 \%$ confidence limits are negative and zero values for $A^*$. Clearly in this case the Fisher information can only provide us with an upper limit. The Monte Carlo analysis \emph{does} provide us with an upper and a lower limit on the allowed values of $A^*$.

For most pairs of parameters the Monte Carlo and Fisher constraints are both markedly improved and more similar to each other with the addition of the prior on $r_s$. This is likely to be because the addition of a Gaussian prior makes the combined distribution more Gaussian, in the Monte Carlo case. There is no reason to assume, however, that CMB derived constraints on the sound horizon will be Gaussian.

The discrepancy between the Fisher likelihood space and perhaps more reliable Monte Carlo likelihood space is not a trivial point.
When constraining the parameters of the toy model we allowed the parameters to vary independently but since the distortions are dependant on cosmology they cannot truly be varied independently of the shape of the underlying dark matter power spectrum, crucially in the context of this paper, this includes the shape and scale of the BAO.

There is a large difference between the improvement of constraints when a Gaussian prior on the sound horiazon is used and when we start to include real data (from WMAP). This could be because our parameterisation is different but is probably just because nature is more complex than a toy model. The CMB and also the real world galaxy power spectrum contain complexities and subtleties which are poorly understood.

Importance sampling relies on the two experiments having similar likelihoods, i.e. the chain used covers most of the likelihood volume in both experiments. If it is the case that the experiments provide highly orthogonal information then importance sampling is not appropriate because only the peak of the secondary distribution will be explored. This will give rise to erroneously tight constraints. This may be cause to doubt the constraints we have derived by importance sampling the WMAP chain. It may be the case that if the WMAP chain sampled a wider area then the combined constraints would be broader.

Even relatively fast Monte Carlo algorithms such as Nested Sampling are slower and more computationally demanding than Fisher matrix methods. Therefore the Fisher information remains a useful starting point when exploring the likelihoods of cosmological parameters. Further understanding of the affect of CMB callibration could be attained by expanding the cosmological parameter set used to describe geometrically and dynamically distorted large scale structure measurements

While joint analysis of cosmological data sets can be carried out as a `black box' we have attempted here to gain insight and intuition into the interplay between large scale structure and CMB data, and to quantify the limitations of the Fisher matrix approach relative to Monte Carlo techniques.
This approach can be extended further by more detailed modelling of e.g. non-linear redshift distortions and biasing, and on the statistical testing of different MCMC codes. Given the huge human and financial efforts invovled in the next generation of galaxy surveys it is crucial to \emph{understand} the physical effects and on the other hand to make sure that the statistics are robust. The method presented here can be directly applied to the next generation of spectroscopic surveys such as DESpec and BigBOSS combined with Planck.

\section*{Acknowledgements}
The code written to generate figures \ref{fig:toy_model_error_ellipses_LRG.eps} and \ref{fig:toy_model_error_ellipses_DESpec.eps} used some subroutines taken from Fisher4Cast \cite{2009arXiv0906.0993B}.

We would like to thank the anonymous referee, Enrique Gaztanaga, Adi Nusser, Will Percival and Anais Rassat for helpful comments on the manuscript. O. Lahav and A. J. Hawken would also like to thank the Weizman Institute of Science, where part of this research was carried out. F.B. Abdalla recognises support from the Royal Society in the form of a URF and OL acknowledges a Royal Society Wolfson Research Merit Award.
\bibliographystyle{mn2e}

redpajama_set_name: RedPajamaArXiv


text:
A jingle (sometimes spelled gingle) was a kind of covered carriage formerly used in the city of Cork, Ireland in the 19th and early 20th centuries. It was described as "entirely peculiar to Cork" in 1919.

In 1837 there were 300 jingles running from Cork City to Passage West. The "gingle stand" was located next to an equestrian statue of King George II on Grand Parade, Cork.

In 1873, one writer described them: "The jingle is a covered vis-a-vis, in which you ride with your side in the direction of your onward motion. Over this inside car is reared a flat-topped square tent of black tarpaulin, opening by movable curtains at the rear where you enter the car. The shafts are pitched high on the horse's back […] the body of the vehicle [is placed] at an angle of about 30° with the ground."

In 2018, David Toms wrote an academic paper on Cork hackney drivers; he said that "[jinglemen] were for the most part a precarious working class who were policed by the Corporation, the Hackney Carriage Committee and the by-law governing their livelihoods. As such, the bye-law and the apparatus that implemented it was a form of liberal governmentality and social control over a portion of Cork's working class."

References

Carriages
Transport in Cork (city)
History of Cork (city)


text:
A common brain cell shapes the nervous system in unexpected ways
More than half of our brains are made up of glial cells, which wrap around nerve fibers and insulate them—similarly to how the plastic casing of an electric cable insulates the copper wire within—allowing electrical and chemical impulses to travel faster. In the past, neuroscientists considered the glial cell an essential yet passive helper of nerve cells. But scientists in Rockefeller's Laboratory of Developmental Genetics, headed by Shai Shaham, have accumulated compelling evidence that this cell type plays a much more active and dynamic role in the brain than previously thought.
Shaham's lab now reports in Cell that glial cells can control the shape of specific nerve endings by interacting with them through a previously unknown molecular pathway. "In the nervous system, shape is everything," he says. "The shape a neuron takes dictates which other neurons it connects to and even the strength of those connections. But we know precious little about how that shape is dictated."
For one neuron only: The KCC-3 ion transporter, shown here in green, is expressed in glial cells but only near a particular neuron, shown in red.
Multilingual cells
In this study, Aakanksha Singhvi, a postdoc in the lab and first author of the report, studied a glial cell that encases the nerve endings of 12 neurons in the roundworm Caenorhabditis elegans, including one that responds to temperature—called a thermosensory neuron—and some that respond to odors. The investigators wondered how the glial cell appropriately regulates the requisite shapes of these neurons. They discovered that it uses different molecular mechanisms to control the shape of each neuronal cell type it encloses.
"The glial cells are, surprisingly, multilingual," is how Singhvi puts it. "They can interact with different neurons differently."
She and her colleagues found that the glial cell expresses an ion transporter protein, called KCC-3, specifically on its surface near the thermosensory neuron, but away from other neurons. In this way it affects and maintains the shape of the thermosensory nerve ending only, without affecting the other nerve endings it surrounds.
The researchers also found that KCC-3 affects the function of the thermosensory neuron in addition to its shape. Normally, worms can "remember" the temperature at which they were raised—and if adult worms are given a choice, they will seek out similarly tepid spots. But when the scientists engineered worms in which KCC-3 can't work, they found that these animals don't migrate to their favorite cultivation temperature.
"This idea that glial cells may directly mold neural functions is not new," says Shaham. "Our findings are direct proof that it actually happens."
The researchers found that the local concentration of ions can influence the shape of nerve endings. They showed that KCC-3 modulates extracellular levels of chloride, which then controls the shape and function of neurons. Then they went further and identified the proteins in the neuron whose activity is controlled by chloride ions, which manipulate the cell's skeleton and give the nerve ending its shape.
It was not previously known that glial cells communicate to neurons through specific ions to regulate their shape, and the researchers speculate that glial cells may in fact be able to use ions to mold the shapes and functions of many types of neurons.
KCC-3 is expressed in our ears, retinas, and Schwann cells—glial cells present throughout our peripheral nervous system. People who carry mutations in the gene that produces it suffer from sensory neuropathy, a condition that causes tingling, pain, numbness, or weakness in the hands and feet. The protein is also present in glial cells in the central nervous system, and other research has indicated its function may be relevant for conditions associated with defects in the shape of neurons, including Huntington's disease and epilepsy.
The formation and maintenance of nerve ending shapes are important for learning and memory, and perturbation in shape is associated with neuronal disorders including dementia, schizophrenia, and Alzheimer's disease. In another recent study published in Cell Reports, Shaham and Sean Wallace, a postdoc in the lab, showed that glial cells control the shapes of many nerve endings in worms, suggesting that additional shape pathways remain to be discovered.
The finding that glial cells can modulate nerve endings by expressing an ionic transporter only where it contacts a particular type of neuron is one that opens up many new avenues for research. If it turns out to be a widely used mechanism by which glial cells selectively affect particular neurons, it could be manipulated for therapeutic purposes.
This research was supported by the National Institutes of Health under grants NS081490, HD078703, and 5T32CA967334; and by the American Cancer Society and the Murray Foundation.
Cell 165, 1-13
A Glial K/Cl Transporter Controls Neuronal Receptive Ending Shape by Chloride Inhibition of an rGC
Aakanksha Singhvi, Bingqian Liu, Christine J. Friedman3, Jennifer Fong4, Yun Lu, Xin-Yun Huang, and Shai Shaham

redpajama_set_name: RedPajamaCommonCrawl


Sharing our experiences with mental health issues is important. It provides hope and help to others going through similar experiences. It reassures them there are others going through similar things and that they are not alone. If you are interested in sharing your own story to provide hope and help to others, contact Ben Chambers at ben@chadscoalition.org.
A former CHADS staff member shared his story with his co-workers and friends on social media. The response was overwhelming. He heard messages of support and thanks from people he didn't even know.
Alex, a 22 year-old college student, tells her story to a group of students at SLU. Alex struggles with severe clinical depression and social anxiety disorder.
Dr. Scherrer tells his family's story to a CHADS volunteer. He spoke about his family's history of mental illness as well as his teenage daughter's depression and thoughts of suicide.
A CHADS school outreach specialist recalls one of her most difficult but triumphant experiences while giving a presentation to a middle school class.


text:
Erik JP Drobey
My Have-It-All, Do-It-All Garage
Hopefully you're familiar with my colleagues' lists of money-no-object garages. They're all fun reads, and collectively here at EveryDay Driver, we've amassed quite the (imagined) collection so far:
Scott's Mount Rushmore
Bill's dinner with André (and five interesting cars)
Jason's dream garage
Nate's Powerball-winning shopping-spree
Many of these cars have long been inked on my own bucket-list. Indeed, I can easily assemble a dream garage from among my fellow writers' lists. For my exercise, though, I've decided to pull any of the previously mentioned cars (models regardless of generation) out of my own dream garage. That, sadly, disqualifies the Ferrari 250 GT SWB California (arguably the most beautiful convertible of all time), the Acura NSX (which I fell in love with as soon as I valet-parked one many years ago), and the Mazda RX-7. I also have tried to assemble a garage that could, theoretically, do it all—each representing a standout in its own right, and collectively forming a veritable Voltron of vehicular glory and might. As a result, I haven't stocked my garage full of supercars. For the fun of it, I sourced rough prices for each car.
THE GT CAR THAT WOULD PAIR FABULOUSLY WITH MY VINTAGE 70s LEATHER BLAZER
The Alfa Romeo Montréal
I love Montréal, and not only because I was born near there. It's a glorious city–my favorite in North America–but that Québec ville has got its quirks. Montréal is an old-world/new-world mashup, elegant and cobbled together, a linguistic and cultural mosaic. The Alfa Romeo Montréal (dubbed as such because it debuted there in the 70s) embodies that city's ethos. It's not Alfa's most refined offering, but it's fabuleux all the same. I'm also Italian, so it's emblematic of my own Canadian-Italian origins. I'd get it in the gold depicted here. Because gold.
According to Bringatrailer.com, the average selling price for the Alfa Romeo Montréal since 2020 has been $74,583. What a bargain!
All photos of the Montréal from Motortrend.com.
THE SOMEWHAT PRACTICAL DAILY DRIVER THAT'S NOT AN SUV AND IS A STATION WAGON, DAMMIT
The Porsche Taycan GTS Sport Turismo
I wanted to include a Porsche on this list, and I could have filled the garage with them. In the end, I've chosen the Taycan for this garage (if money is no object, I can always add Caymans and 944s later!). Why? As the badge's first EV offering, the Taycan is still, through and through, a Porsche, which means, according to auto journalists like Tom Voelk, the Taycan sacrifices virtually nothing when it comes to driving dynamics, performance, and poise. The GTS Sport Turismo, with 590hp and plenty of performance, is purportedly equally at home on a road trip and a track, and would also be just fantastic to commute in.
The Taycan GTS Grand Turismo starts at $133,000 before any tax incentives.
Photo from Porsche.com/usa.
THE "I HAVE MONEY AND AM A TRUE DRIVING ENTHUSIAST" WEEKEND TRACK & (BARELY) STREET-LEGAL CAR
The BAC Mono
I mean, if money is no object, this one-person track rocket would be an easy choice. And a heck of a lot of fun to drive, no doubt. Not sure if the BAC Mono is street-label in California; if so, then backroad runs would be spectacular in this car, if overkill (and perhaps a little dangerous).
According to a quick Google search, a BAC Mono would set me back by about $250,000. Sold.
Photo from bac-mono.com.
THE "DID I JUST TAKE UP GARDENING? HOW DID THAT HAPPEN?" CAR/THING
The Ford F150 Lightning XLT
I'm not one for pickup trucks, but I must admit, they're great to have around for hauling. I'd opt for Ford's soon-to-be-released F150 Lightning EV truck, which, by all accounts, would more than meet my needs for the occasional garden-center run and the more frequent trips to the track (pulling a trailer housing the BAC Mono and other track toys).
The XLT starts at $72,474 before incentives.
Photo from Ford.com.
THE CAR I THINK IS JUST GREAT AND WOULD PROBABLY DRIVE MOST OF THE TIME
The Mazda MX-5 Miata (ND2)
Yes, my current daily driver will always have a spot in my dream garage. I am fortunate to own a car that's loads of fun to drive, returns impressive gas-mileage, and would likely remain the car I'd drive the most regardless of which crazy-expensive vehicles were to grace my dream garage.
I paid just over $36,000 for my MX-5, which includes optional Brembo brakes, BBS wheels, and Recaro seats. It's spectacular.
Erik JP Drobey lives in San Francisco. He chronicles some of his culinary and vehicular adventures on Instagram as @zjpd.
The views and opinions expressed here are his own and may not align with the founders of Everyday Driver.


Do you give TMI on First Dates?
Are you providing detailed, personal, even intimate information on every topic that comes up on your first dates with a new person? If so, you might be sending the wrong signals. Some people will feel uncomfortable hearing these details and wonder why you feel compelled to offer them to a person who is still essentially a stranger. And you could put yourself at risk by offering information that could lead to nuisance or even more uncomfortable behavior from the other party when the dating process does not go well.
Neediness. Emotional yearning for something good to happen in your life can compel you to reveal too much too quickly. The thought of not getting what you are hoping for can keep the words and the inappropriate details flowing like water from a faucet. Be aware of an important truth: Few things worth having come quickly. And keep in mind: It\'s a better strategy to leave your date wanting to know more about you and thinking of spending more time with you. So be in the moment on a date, have fun, let your personality and interests shine through—but there is no need to reveal the more personal details of your life.
Unclear boundaries. Did you grow up in a family where personalities, wishes, personal space, and opinions were not respected? A family in which you were not recognized as a distinct person, but one who was constantly asked to think like someone else—your parents or siblings, for example? When unclear boundaries between people are ingrained from childhood, one expression of that is to dump your intimate details onto someone else as if they were requesting the information. In fact, among the kind of people you want to associate with, few people want to hear those details without knowing you better and without an appropriate context for such a conversation.
Depression. The dark cloud of depression can undermine judgment. It\'s one thing to talk about personal issues with a tried and true friend, a trusted family member, or a counselor. It\'s another to dump your stuff on a stranger. Ironically, if you are in counseling or therapy for depression, TMI can be a side effect of that process. While therapy often makes a positive difference in a person\'s life, the process can feel new and all-absorbing, especially the one-on-one talking. Some people seeing a counselor or therapist can take the process out beyond the therapy room and begin to treat others, from old friends to new acquaintances, as confidantes, much like their therapist. If you notice this going on, alert your counselor and make that a point for you two to work on. I am sensitive to that process and can help you develop strategies for relating to others appropriately at the same time as we address the depression.
Analyze your motivations. Start with some of the more common motivations I referred to above, but look for others. What elements in your childhood and teenage development are relevant to this behavior? Is there something in your recent life that has prompted the inappropriate talking?
Seek help. If you are having the kind trouble relating to people described here, look for help in addressing the issue in more depth and developing strategies that can last a lifetime—and begin improving your life in the short term. My Orange Count counseling service offers counseling and talk therapy in the areas of relationships, anxiety, and depression, among other areas.
One of our first interpersonal defenses is privacy, when appropriate. I recommend that you only let people across the drawbridge and into your castle when they have identified themselves clearly and you know for certain that they are friends, not foes. Be aware on dates that you have every right to withhold information about yourself, even when you are asked for it. "Sorry, but I don\'t feel comfortable revealing that," is a completely legitimate reply that can win you respect. You may be with someone who has asked an inappropriate question in a thoughtless moment. Your firm answer may earn you admiration for clear boundaries and strength of personality. In fact, if you get an argument over that answer, you have valuable information on whether to continue dating the person.


PRIMER 150 improves the adhesion of silicone sealants, MS Polymer Technology and SMX on porous surfaces. Especially with increased water stress at the joint use is recommended.
Application temperature: +5 ° C to +25 ° C.
Cleaning: Clean immediately with SURFACE CLEANER or white spirit after use.
12 months from date of manufacture in unopened packaging in a cool (+5 ° C and +25 º C) and dry storage. Opened container tightly closed and short-term use.
FIX ALL ist ein hochwertiger, haftstarker, dauerelastischer Klebstoff / Dichtstoff auf Basis von Hybrid - Polymer mit einem universellem Anwendungsspektrum. Soudal Fix All Classic ist ein Power Klebstoff für viele Einsatzgebiete.


text:
Home » Hardware » NVIDIA GeForce RTX 4080 Will Have 16GB & 12GB Variants
HardwareNews
NVIDIA GeForce RTX 4080 Will Reportedly Have 16GB & 12GB Memory Variants
Nvidia GeForce RTX 4080 graphics card will be available with 12GB or 16GB of GDDR6X memory, according to reports.
By Dinesh Last updated Sep 5, 2022
NVIDIA GeForce RTX 4080 reportedly includes 16GB and 12GB memory versions, leaked PCB details are revealing. There are also more details about the RTX 4090 model, which will be at the top of the range of the series. Check out the complete details below.
NVIDIA GeForce RTX 4080 Comes in 12GB and 16GB Memory Variants
Apparently, the RTX 4080 model is going to have two different classes of PCBs, one that will come with 16GB of GDDR6X memory, while the other will have 12GB of GDDR6X memory. The former is going to have a 12-layer PCB, while the 12GB model would have a 10-layer PCB.
This information is important because it is suggesting that the RTX 4080 with 16GB would have a 256-bit bus, while the 12GB model would come with a trimmed 192-bit bus. If true, Nvidia will have to raise the specifications of other sections to compensate, such as the memory clock speed.
We also have to keep in mind that these are engineering samples and this could change from moment to moment, even PCB models could be discarded.
4080 12G GD6X 10layer PCB aic reference design
4080 16G GD6X 12layer PCB aic
note:4080 12G has a completely different pcb design from 4080 16G
— MEGAsizeGPU (@Zed__Wang) September 5, 2022
According to the data provided by MEGAsizeGPU, the 4090 model will have 24GB of GDDR6X memory with a 14-layer PCB, more complex than that of the RTX 4080.
The RTX 4090 graphics card will have a TBP of 450W, while the RTX 4080 would have a TDP of 350 TBP.
As for the launch, Nvidia should prioritize the cheapest model first, then debuting the more gifted variant if necessary. Remaining in the GPU field, the new competitor Intel has again spoken, through an interview with engineer Tom Petersen on the occasion of IFA 2022, of its top-of-the-range Arc A770 GPU, affirming a superiority in Ray Tracing over the RTX 3060.
According to the information we have, the RTX 4090 is going to be released first in the month of October. We don't know if the RTX 4080 and 4070 will launch this year or have to wait until 2023. We'll keep you posted.
GeForceGeForce RTX 4080NvidiaNVIDIA GeForce RTX 4080RTX 4080