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← Let me tell you all about about my disease…
Theistic evolution an oxymoron? →
Origins without history
Our house, according to our neighbour (who was there) was built in 1969, but on a 2½ acre plot that was already surrounded on three sides by traditional "devon banks", and on the fourth by a lane. So it's a field with some kind of history, but mostly unknown to us as we've only been here eight years.
So I was pleased a week or so ago to find that a Tithe Map for the parish is now available online, dating from 1840. This was when the ancient system of paying the church tithes for the maintenance of the clergy and the poor was tidied up by Parliament, which ordered that maps and accompanying schedules of who owned what, and who paid rent to whom, should be drawn up.
I was able to see, then, that my property 177 years ago was divided into three lots (739-41 on the map), but under the ownership of one absentee landlord, who rented it to a dairyman (as I later found out) called Isaac Lane. The most fascinating thing was that, as the map shows, there was then actually a small cottage at the opposite end of the plot to the site of our house, in an area that is now an overgrown waste at the foot of a steep slope where we used to dump our stable manure.
Des Res 1840
With that information, certain things about the place make more sense. For one thing, the lane, which lies below a steep bank down from our field, acquires a decent (though ancient and tree-laden) stone wall only where the old cottage stood, which borders a flattish platform which, in retrospect, is just big enough for a humble stone dwelling, of which there now appears no trace, however. With far fewer trees at that time, it would have had a good view over the valley.
Additionally, I've been intrigued over the years by a slight dip at that end of the field, as if in the past livestock were accustomed to ambling up from the lane and eroding the path they took. This, of course, now makes complete sense as that was the "business end" of the smallholding.
Ruined cottage on Stockland turbary – solid, but squalid: marked as 720 on the tithe map.
With the houses marked and a name to go on, I was able to do a bit of research in old census records and so on, and found that the following year the cottage was occupied by a mason named George Strawbridge, his wife, and six children, some of whose history I could trace in parish records. I was also able to find traces of the other nearby houses, tracks and gardens on the ground, including some ruins hidden in nearby woods that I'd not otherwise have known existed. That gave me an idea of what "our" cottage was like – pretty squalid for a poor and large family.
Perhaps that's why the cottage has completely vanished from an Ordnance Survey map of 1888, though in conjunction with the tithe map that provides explanations for otherwise unexplained features of our landscape, such as dips and wobbles in the roads. Even the big oak tree on our boundary, which I've previously estimated to be about 160 years old, is marked on that. One one website it's possible to overlay the 1888 map on to Google Earth for a direct comparison, revealing yet more history.
As always happens, one bit of research leads to another, and some reading drew my attention to the fact that in some areas (like mine) the field boundaries are pretty straight and rectangular, whereas further downhill they are completely irregular (see the plots below "Baggerton" on the map below). This turns out to be because the latter were enclosed during mediaeval times, whereas more marginal upland ground, like mine, was enclosed by Acts of Parliament after 1820. However, the parish bounday (forming the right border of the map) takes a straighter course which dates back to Saxon times, as does the existing ancient boundary hedge of holly and other species that marks it, just down the road, estimated by a species count to have been planted over a thousand years ago.
Armed with all that, I'm now able to say that my plot was probably part of the local mediaeval manor's holdings, enclosed around 200 years ago with the very same banks that now form my boundaries, but fertile enough (unlike the "turbary" land to its left on the map), to be used for agriculture by an on-site cottager, though not sustainably. If I took the effort to clear and dig on the site of the cottage, I might even find some archaeology in the form of masonry, cheap pottery or clay pipes. It's given me a great sense of continuity with our forbears.
I've written about this because it interests me, and might perhaps interest you. But it also occurs to me that it bears somewhat on questions of origins, in two separate areas. For almost all the detailed information I can gain from my spread, and its surrounding landscape, has to be interpreted from the existence of documents which explain it. I would never have suspected the site of an ancient house simply from a stone retaining wall.
Traditional Devon bank, but how old? 100 years? 1000? Actually, 200.
If I'd even noticed that fields formed two patterns – regular or irregular – how would I explain it apart from the knowledge of changes in mediaeval farming and the political background to later land enclosures? For all I would know, my hedges could have been constructed after World War 1 or date back originally to Roman times.
Only parish and census records bring the actual lives of the people back then into relief: I can track George Strawbridge's family to events like the death of his abandoned wife as a pauper some years later. I can chat with our neighbour about how Isaac Lane moved to their place from ours.
Without written history, what exists in the landscape itself tells some fragments of the story – or rather, of many possible stories, depending on the power of imagination. We need to remember that limitation in any account of origins. Archaeology, it is said, is the study of the rubbish history leaves behind – but even that forgets that most of that rubbish never even gets found. So when people complain about the lack of archaeological evidence for the Bible accounts, I will remember that, here in Devonshire, it's the written records that explain the archaeology, and not vice-versa.
In the case of biological origins, there is of course no written history outside what we might glean from inspired Scripture – and in that case, biological history is not its subject matter, as I (like others) have often been at pains to point out. To construct a "history of the world" from the traces left in fossils or deduced from existing genomes is akin to reconstructing the history of England solely from Google Earth and walking round the country lanes or digging the garden.
At best it's prone, in the very nature of things, to error.
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Politics, if Adam had not sinned
The Pope, autonomy, internal law, freedom and autonomy (again)
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Home/Hollywood/Top 5 Will Smith Movies That You Should Watch
HollywoodCinema
Abhishek dodtiSeptember 3, 2021
Thirty years into his career, Willard Carroll Smith Jr., or Will Smith as everyone knows him, is still one of the most successful actors in his generation. Will started his career as a rapper under the name 'the Fresh Prince'. After the fame he gained as a rapper, he starred in his tv show which ran for 6 seasons. After the show ended, Smith turned towards the movie industry, where he starred in multiple blockbusters.
The actor, producer, and rapper have set his mark in Hollywood. He is the only actor to have starred in eight films that grossed more than $100 million in the United States and eleven films that grossed more than $150 million worldwide in a row.
Out of all the movies that he has worked in, here are 5 Will Smith movies that you should watch:
1.Suicide Squad (2016)
Director: David Ayer
Box office collection: $746 million
Cast: Will Smith, Jared Leto, Margot Robbie, Joel Kinnaman, Viola Davis, Jai Courtney, Jay Hernandez, Adewale Akinnuoye-Agbaje, Ike Barinholtz, Scott Eastwood, and Cara Delevingne
The 2016 American superhero film Suicide Squad is based on the same-named DC Comics antihero team.
Following Superman's death, a covert government agency commanded by intelligence officer Amanda Waller manages to convince the US Government to approve Task Force X, a response team of criminals and supervillains. The crew will be deployed to confront metahuman dangers under Waller's command, with nanite bombs implanted in each criminal's neck and detonated remotely. If they succeed, their sentences will be shortened. When an ancient witch threatens to ruin humanity, the squad is dispatched to stop her.
The movie had a spin-off in 2020, Birds of Prey, starring Margot Robbie (Harley Quinn), followed by the standalone sequel The Suicide Squad in 2021.
2.The Pursuit of Happyness (2006)
Director: Gabriele Muccino
Box office collection: $307.1 million
Cast: Will Smith, Thandiwe Newton, and Jaden Smith
IMDb Rating: 8/10
Gabriele Muccino directed the 2006 American biographical drama film The Pursuit of Happyness. The movie is based on a 2006 best seller biography of the same name.
The film depicted a period in Chris Gardner's life when he was homeless for about a year. Chris's wife, fed up with his professional failures, decides to divorce, leaving him penniless with an unpaid internship at a brokerage firm and custody of their son to contend with.
Gardner saw a mural on the wall outside the daycare center where his son attends, which inspired the unique spelling of the film's title.
Columbia Pictures released the picture on December 15, 2006, to mostly positive reviews, with Smith's performance receiving widespread acclaim. Smith was nominated for both an Oscar and a Golden Globe for Best Actor.
3.Aladdin (2019)
Director: Guy Ritchie
Box office collection: $1.051 billion
Cast: Will Smith, Mena Massoud, Naomi Scott, Marwan Kenzari, Navid Negahban, Nasim Pedrad, and Billy Magnussen
Aladdin is a 2019 American musical film directed by Guy Ritchie and produced by Walt Disney Pictures. It is a live-action/CGI adaptation of Disney's 1992 animated picture of the same name, which is based on the titular tale from One Thousand and One Nights.
The plot involves Aladdin, a good-hearted thief, as he falls in love with Jasmine, the princess of Agrabah and a wish-granting Genie (Will Smith). When the villainous Jafar, the grand vizier, attempts to usurp the throne, Jasmine, Aladdin, and Genie must stop him.
Talks are going on for the development of a sequel to the movie.
4.Men in Black (1997)
Director: Barry Sonnenfeld
Cast: Tommy Lee Jones, Will Smith, Linda Fiorentino, Vincent D'Onofrio, and Rip Torn
Men in Black is a 1997 American science fiction action-comedy film. It was written by Ed Solomon and produced by Walter F. Parkes and Laurie MacDonald.
After a government organization made the first contact with aliens in 1961, alien refugees disguise themselves as humans and live in secrecy on Earth. MIB (Men in Black) is a covert agency that polices aliens, protects Earth from extraterrestrial threats, and utilizes memory-erasing neutralizers to keep alien activity hidden. MIB agents' true identities are deleted from public systems, and retired agents are neutralized to live normal lives.
As part of their most recent mission, agents K (Tommy Lee Jones) and J (Will Smith) must save the world from an intergalactic disaster.
After receiving positive feedback and reviews, two movie sequels were released in 2002 and 2012, respectively. A spin-off to the series was also released in 2019.
5.Bad Boys (1995)
Cast: Martin Lawrence, Will Smith, Téa Leoni, Tchéky Karyo, Theresa Randle, and Joe Pantoliano
Michael Bay directed the 1995 American buddy-cop action comedy film Bad Boys was produced by Don Simpson and Jerry Bruckheimer.
The movie follows Marcus Burnett (Martin Lawrence), a family man, and Mike Lowrey (Will Smith), a ladies' man, who are long-time friends and partners in the Miami police department. When they take each other's identities while investigating a narcotics trade, things become problematic.
Critics gave the film mixed reviews, but it was economically successful and garnered a cult following. It inspired the Bad Boys trilogy, which included two sequels, Bad Boys II (2003) and Bad Boys for Life (2020).
Will once said, "In my mind, I've always been an A-list Hollywood superstar. Y'all just didn't know yet." Well, he made it happen what was in his mind.
Here are some other Will Smith movies that you can watch, Gemini Man (2019), I am Legend (2007), Independence Day (1996), Hancock (2008), Ali (2001), Bright (2017).
Stay Tuned For More!!
Edited by Anupama Roy
aladdin bad boys bad boys triology jaden smith men in black men in black series MIB suicide squad the fresh prince the pursuit of happyness will smith
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redpajama_set_name: RedPajamaCommonCrawl


I can't say that I know anything about League of Legends, but I love this build by Eric Tsal.
Rod Gillies is really cranking out some fantastic models for the current Iron Builder competion!
Omar Gonzalez is creating some cool models for his Mecha Racing League.
This one might be digital, but I'm digging the model and the stylized render by Erik.
I'm grooving on Rod Gillies' steampunk automaton.
Chris Perron's neo-classic Ice Planet game is on point!
Lu Sim is back is a pair of mecha. The third iteration of the Aztec mech is beautifully ornate.
This steampunk stagecoach by Nimura貮邑 is quite fine.
nobu_tary has done a fabulous job of recreating this Logikoma.
Ryuhei Kawai's latest mecha are super awesome, but he has sharing restricted on Flickr. Click here to check them out.
This guy is phenomenal! I love how Bangoo H has lit his behemoth from inside.
I love me some airships, and this little scout ship with launching catapult by ska2d2 is perfect.
21301 Birds – A view of the beautiful new Ideas set due in January!


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\section{Introduction}
Markowitz' portfolio selection theory \cite{Markowitz52,Markowitz59} is one of the pillars of theoretical finance. It has greatly influenced the thinking and practice in investment, capital allocation, index tracking, and a number of other fields. Its two major ingredients are (i) seeking a trade-off between risk and reward, and (ii) exploiting the cancellation between fluctuations of (anti-)correlated assets. In the original formulation of the theory, the underlying process was assumed to be multivariate normal. Accordingly, reward was measured in terms of the expected return, risk in terms of the variance of the portfolio.

The fundamental problem of this scheme (shared by all the other variants that have been introduced since) is that the characteristics of the underlying process generating the distribution of asset prices are not known in practice, and therefore averages are replaced by sums over the available sample. This procedure is well justified as long as the sample size, $T$ (i.e. the length of the available time series for each item), is sufficiently large compared to the size of the portfolio, $N$ (i.e. the number of items). In that limit, sample averages asymptotically converge to the true average due to the central limit theorem.

Unfortunately, the nature of portfolio selection is not compatible with this limit. Institutional portfolios are large, with $N$'s in the range of hundreds or thousands, while considerations of transaction costs and non-stationarity limit the number of available data points to a couple of hundreds at most. Therefore, portfolio selection works in a region, where $N$ and $T$ are, at best, of the same order of magnitude. This, however, is not the realm of classical statistical methods. Portfolio optimization is rather closer to a situation which, by borrowing a term from statistical physics, might be termed the ``thermodynamic limit", where $N$ and $T$ tend to infinity such that their ratio remains fixed.

It is evident that portfolio theory struggles with the same fundamental difficulty that is underlying basically every complex modeling and optimization task: the high number of dimensions and the insufficient amount of information available about the system. This difficulty has been around in portfolio selection from the early days and a plethora of methods have been proposed to cope with it, e.g. single and multi-factor models \cite{eltongruber}, Bayesian estimators \cite{jobson1979,jorion1986,frost_savarino,safePO,Jagannathan2003,LedoitWolf2003, LedoitWolf2004,LedoitWolfHoney,DeMiguel2007,Garlappi2007,Golosnoy2007,Kan2007,frahm_memmel,DeMiguel2009}, or, more recently, tools borrowed from random matrix theory \cite{Laloux1999,Plerou1999,Laloux2000,Plerou2002,burda,Potters2005}. In the thermodynamic regime, estimation errors are large, sample to sample fluctuations are huge, results obtained from one sample do not generalize well and can be quite misleading concerning the true process.

The same problem has received considerable attention in the area of machine learning. We discuss how the observed instabilities in portfolio optimization (elaborated in Section \ref{instab}) can be understood and remedied by looking at portfolio theory from the point of view of machine learning.

Portfolio optimization is a special case of regression, and therefore can be understood as a machine learning problem (see Section \ref{reasons}). In machine learning, as well as in portfolio optimization, one wishes to minimize the {\em actual risk}, which is the risk (or error) evaluated by taking the ensemble average. This quantity, however, can not be computed from the data, only the {\em empirical risk} can. The difference between the two is not necessarily small in the thermodynamic limit, so that a small empirical risk does not automatically guarantee small actual risk \cite{VapnikCh71}.

Statistical learning theory \cite{VapnikCh71, Vapnik95, Vapnik98}  finds upper bounds on the generalization error that hold with a certain accuracy. These error bounds quantify the expected generalization performance of a model, and they decrease with decreasing {\em capacity} of the function class that is being fitted to the data. Lowering the capacity therefore lowers the error bound and thereby improves generalization. The resulting procedure is often referred to as regularization and essentially prevents over-fitting (see Section \ref{RPO}).

In the thermodynamic limit, portfolio optimization needs to be regularized. We show in Section \ref{RPO-ES} how the above mentioned concepts, which find their practical application in support vector machines \cite{Boser92, CortesVapnik95}, can be used for portfolio optimization. Support vector machines constitute an extremely powerful class of learning algorithms which have met with considerable success. We show that regularized portfolio optimization, using the expected shortfall as a risk measure, is almost identical to support vector regression, apart from the budget constraint. We provide the modified optimization problem which can be solved by linear programming.

In Section \ref{diversification}, we discuss the financial meaning of the regularizer: minimizing the L2 norm of the weight vector corresponds to a diversification pressure. We also discuss alternative constraints that could serve as regularizers in the context of portfolio optimization.

Taking this machine learning angle allows one to organize a variety of ideas in the existing literature on portfolio optimization filtering methods into one systematic and well developed framework. There are basically two choices to be made: (i) which risk measure to use, and (ii) which regularizer. These choices result in different methods, because different optimization problems are being solved.

While we focus here on the popular expected shortfall risk measure (in Section \ref{RPO-ES}), the variance has a long history as an important risk measure in finance. Several existing filtering methods that use the variance risk measure essentially implement regularization, without necessarily stating so explicitly. The only work we found in this context \cite{safePO} that mentiones regularization in the context of portfolio optimization has not been noticed by the ensuing, closely related, literature. It is easy to show that when the L2 norm is used as a regularizer, then the resulting method is closely related to Bayesian ridge regression, which uses a Gaussian prior on the weights (with the difference of the additional budget constraint). The work on covariance shrinkage, such as \cite{Jagannathan2003,LedoitWolf2003,LedoitWolf2004,LedoitWolfHoney}, falls into the same category. Other priors can be used \cite{DeMiguel2009}, which can be expected to lead to different results (for an insightful comparison see e.g. \cite{Tibshirani96}). Using the L1 norm has been popularized in statistics as the ``LASSO" (least absolute shrinkage and selection operator) \cite{Tibshirani96}, and methods that use any Lp norm are also known as the ``bridge" \cite{Frank93}.

\section{Preliminaries -- Instability of classical portfolio optimization.}
\label{instab}
Portfolio optimization in large institutions operates in what we called the thermodynamic limit, where both the number of assets and the number of data points are large, with their ratio a certain, typically not very small, number. The estimation problem for the mean is so serious \cite{chopraziemba1993,merton1980} as to make the trade-off between risk and return largely illusory. Therefore, following a number of authors \cite{Jagannathan2003,LedoitWolf2003,okhrin2006,kempf_memmel2006,Frahm2008}, we focus on the minimum variance portfolio and drop the usual constraint on the expected return. This is also in line with previous work (see \cite{kondor2007} and references therein), and makes the treatment simpler without compromising the main conclusions. An extension of the results to the more general case is straightforward.

Nevertheless, even if we forget about the expected return constraint, the problem still remains that covariances have to be estimated from finite samples. It is an elementary fact from linear algebra that the rank of the empirical $N\times N$ covariance matrix is the smaller of $N$ and $T$. Therefore, if $T < N$, the covariance matrix is singular and the portfolio selection task becomes meaningless. The point $T = N$ thus separates two regions: for $T > N$ the portfolio problem has a solution, whereas for $T < N$, it does not.

Even if $T$ is larger than $N$, but not {\em much} larger, the solution to the minimum variance problem is unstable under sample fluctuations, which means that it is not possible to find the optimal portfolio in this way. This instability of the estimated covariances, and hence of the optimal solutions, has been generally known in the community, however, the full depth of the problem has only been recognized recently, when it was pointed out that the average estimation error diverges at the critical point $N = T$ \cite{pafka2002,pafka2003,pafka2004}.

In order to characterize the estimation error, Kondor and co-workers used the ratio $q_0^2$ between (i) the risk, evaluated at the optimal solution obtained by portfolio optimization using finite data and (ii) the true minimal risk. This quantity is a measure of generalization performance, with perfect performance when $q_0^2 = 1$, and increasingly bad performance as $q_0^2$ increases. As found numerically in \cite{pafka2003} and demonstrated analytically by random matrix theory techniques in \cite{burda2003}, the quantity $q_0$ is proportional to $(1 - N/T)^{-1/2}$ and diverges when $T$ goes to $N$ from above.

The identification of the point $N = T$ as a phase transition \cite{kondor2007,ciliberti_1} allowed for the establishment of a link between portfolio optimization and the theory of phase transitions, which helped to organize a number of seemingly disparate phenomena into a single coherent picture with a rich conceptual content. For example, it has been shown that the divergence is not a special feature of the variance, but persists under all the other alternative risk measures that have been investigated so far: historical expected shortfall, maximal loss, mean absolute deviation, parametric VaR, expected shortfall, and semivariance  \cite{kondor2007, ciliberti_1,ciliberti_2,hasszan2008}. The critical value of the $N/T$ ratio, at which the divergence occurs, depends on the particular risk measure and on any parameter that the risk measure may depend on (such as the confidence level in expected shortfall). However, as a manifestation of universality, the power law governing the divergence of the estimation error is independent of the risk measure \cite{ kondor2007, ciliberti_1, ciliberti_2}, the covariance structure of the market \cite{pafka2004}, and the statistical nature of the underlying process \cite{hasszan2007}. Ultimately, this line of thought led to the discovery of the instability of coherent risk measures \cite{kondor2008}.

\section{Statistical reasons for the observed instability in portfolio optimization}
\label{reasons}
As mentioned above, for simplicity and clarity of the treatment we do not impose a constraint on the expected return, and only look for the global minimum risk portfolio. This task can be formalized as follows: Given a fixed budget, customarily taken to be unity, given $T$ past measurements of the returns of $N$ assets: $x_i^k$, $i=1, \dots,N$, $k = 1, \dots, T$, and given the risk functional $F(\wx)$, find a weighted sum (the portfolio), $\wx$,\footnote{Notation: bold face symbols are understood to denote vectors.} such that it minimizes the {\em actual} risk
\begin{equation}
R (\w) = \langle F(\wx) \rangle_{p(\x)},
\end{equation}
under the constraint that $\sum_i w_i = 1$. The central problem is that one does not know the distribution $p(\x)$, which is assumed to underly the generation of the data. In practice, one then minimizes the {\em empirical} risk, replacing ensemble averages by sample averages:
\begin{equation}
R_{\rm emp} (\w) = {1 \over T} \sum_{k=1}^T F(\wxk)
\label{emprisk}
\end{equation}
Now, let us interpret the weight vector as a linear model. The model class given by the linear functions has a {\em capacity} $h$, which is a concept that has been introduced by Vapnik and Chervonenkis in order to measure how powerful a learning machine is \cite{VapnikCh71, Vapnik95, Vapnik98}.  (In the statistical learning literature, a learning machine is thought of as having a function class at its disposal, together with an induction principle and an algorithmic procedure for the implementation thereof \cite{Bernhard_thesis}). The capacity measures how powerful a function class is, and thereby also how easy it is to learn a model of that class. The rough idea is this: a learning machine has larger capacity if it can potentially fit more different types of data sets. Higher capacity comes, however, at the cost of potentially over-fitting the data. Capacity can be measured, for example, by the Vapnik-Chervonenkis (VC-) dimension \cite{VapnikCh71}, which is a combinatoric measure that counts how many data points can be separated in all possible ways by any function of a given class.

To make the idea tangible for linear models, focus on two dimensions ($N=2$). For each number of points, $n$, one can choose the geometrical arrangement of the points in the plane freely. Once it is chosen, points are labeled by one of two labels, say ``red" and ``blue". Can a line separate the red points from the blue points for {\em any} of the $2^n$ different ways in which the points could be colored? The VC-dimension is the largest number of points for which this can be done. Two points can trivially be separated by a line. Three points that are not arranged collinear can still be separate for any of the 8 possible labelings. However, for four points this is no longer the case, since there is no geometrical arrangement for which one could not find a labeling that can not be separated by a line. The VC-dimension is 3, and in general, for linear models in $N$ dimensions, it is $N+1$ \cite{Bernhard_thesis, Bernhard_Book1}.

In the regime in which the number of data points are much larger than the capacity of the learning machine, $h/T << 1$, a small empirical risk guarantees small actual risk \cite{VapnikCh71}. For linear functions through the origin that are otherwise unconstrained, the VC-dimension grows with $N$. In the thermodynamic regime, where $N/T$ is not very small, minimizing the empirical risk does not necessarily guarantee a small actual risk \cite{VapnikCh71}. Therefore it is not guaranteed to produce a solution that generalizes well to other data drawn from the same underlying distribution.

In solving the optimizing problem that minimizes the {\em empirical} risk, Eq. (\ref{emprisk}) in the regime in which $N/T$ is not very small, portfolio optimization {\it over-fits} the observed data. It thereby finds a solution that essentially pays attention to the seeming correlations in the data which come from estimation noise due to finite sample effects, rather than from real structure. The solution is thus different for different realizations of the data, and does not necessarily come close to the actual optimal portfolio.

\section{Overcoming the instability}
\label{RPO}
The generalization error can be bounded from above (with a certain probability) by the empirical error plus a confidence term that is monotonically increasing with some measure of the capacity, and depends on the probability with which the bound holds \cite{Vapnik79}. Several different bounds have been established, connected with different measures of capacity, see e.g. \cite{Bernhard_Book1}.

Poor generalization and over-fitting can be improved upon by decreasing the capacity of the model \cite{Vapnik95, Vapnik98}, which helps to lower the generalization error. Support vector machines are a powerful class of algorithms that implement this idea.

We suggest that if one wants to find a solution to the portfolio optimization problem in the thermodynamic regime, then one should not minimize the empirical risk alone, but also constrain the capacity of the portfolio optimizer (the linear model).

How can portfolio optimization be regularized? Portfolio optimization is essentially a regression problem, and therefore we can apply statistical learning theory, in particular the work on support vector regression.

Note first that the capacity of a linear model class for which the length of the weight vector is restricted to $\|w\|^2 \leq A$ has an upper bound which is smaller than the capacity of unconstrained linear models \cite{Vapnik95, Vapnik98}. The capacity is minimized when the length of the weight vector is minimized \cite{Vapnik95, Vapnik98}. Vapnik's concept of {\em structural risk minimization} \cite{Vapnik79} results in the support vector algorithm \cite{Boser92, CortesVapnik95} which finds the model with the smallest capacity that is consistent with the data, that is the model with smallest $\|w\|^2$. This leads to a convex constrained optimization problem \cite{Boser92, CortesVapnik95} which can be solved using linear programming.

\section{Regularized portfolio optimization with the expected shortfall risk measure.}
\label{RPO-ES}
While the original Markowitz' formulation \cite{Markowitz52} measures
risk by the variance, many other risk measures have been proposed
since. Today, the most widely used risk measure, both in practice and in
regulation, is Value at Risk (VaR) \cite{jorion,riskmetrics}. VaR has,
however, been criticized for its lack of convexity, see e.g.
\cite{Artzner99, embrechts,acerbi1}, and an axiomatic approach,
leading to the introduction of the class of coherent risk measures,
was put forward \cite{Artzner99}. Expected shortfall, essentially a
conditional average measuring the average loss above a high threshold,
has been demonstrated to belong to this class
\cite{acerbi2,acerbi3,acerbi4}.

Expected shortfall has been steadily gaining popularity in recent years. The regularization we propose here is intended to cure its weak point, the sensitivity to sample fluctuations, at least for reasonable values of the ratio $N/T$.

Choose the risk functional $F(z) = z \theta(z-\alpha_\beta)$, where
$\alpha_\beta$ is a threshold, such that a given fraction $\beta$ of the
(empirical) loss-distribution over $z$ lies above $\alpha_\beta$. One
now wishes to minimize the average over the remaining tail
distribution, containing the fraction $\nu :=1 - \beta$, and
defines the expected shortfall as
\begin{equation}
ES = \min_{\epsilon}\left[\epsilon + \frac{1}{\nu T} \sum_{k=1}^T {1
\over 2} \left(-\epsilon -\wxk + |-\epsilon -\wxk|\right)\right].
\label{ExpS}
\end{equation}
The term in the sum implements the $\theta$-function, while $\nu$ in
the denominator ensures normalization of the tail distribution. It has
been pointed out \cite{Rockafellar} that this optimization problem
maps onto solving the linear program:
\begin{eqnarray}
&&\min_{\w, {\bf \xi}, \epsilon}  \left[ {1 \over T} \sum_{k=1}^{T}
\xi_k + \nu \epsilon \right] \label{CVaR} \\
&{\rm s.t.}\;\;\; & \wxk + \epsilon + \xi_k \geq 0; \;\;\; \xi_k; \geq
0 \label{ES-constr} \\
&&  \sum_i w_i  = 1.
\end{eqnarray}
We propose to implement regularization by including the minimization of $\|\w \|^2 $. This can be done using a Lagrange multiplier, $C$, to control the trade-off -- as we relax the constraint on the length of the weight vector, we can, of course, make the empirical error go to zero and retrieve the solution to the minimal expected shortfall problem. The new optimization problem reads:

\begin{eqnarray}
&&\min_{\w, {\bf \xi}, \epsilon}  \left[ {1 \over 2}  \|\w \|^2 +  C
\left({1 \over T} \sum_{k=1}^{T} \xi_k + \nu \epsilon \right) \right]
\label{newPO} \\
&{\rm s.t.}\;\;\; & - \wxk \leq \epsilon + \xi_k; \label{con1}\\
&&  \xi_k \geq 0; \;\;\; \epsilon \geq 0;\\
&& \sum_i w_i = 1. \label{b}
\end{eqnarray}
The problem is mathematically almost identical to a support vector regression (SVR) algorithm called $\nu$-SVR. There are two differences: (i) the budget constraint is added, and (ii) the loss function is asymmetric. Expected shortfall is an asymmetric version of the $\epsilon$-intensive loss, used in support vector regression, defined as the maximum of $\{0;| f(\x) - y| - \epsilon \}$, where $f(\x)$ is the interpolant, and $y$ the measured value (response). In that sense $\epsilon$ measures an allowable error below which deviations are discarded.\footnote{The mathematical similarity between minimum expected shortfall {\em without} regularization and the E$\nu$-SVM algorithm \cite{EnuSVM} was pointed out, but incorrectly, in \cite{Takeda2008}. There is an important difference between the two optimization problems. In E$\nu$-SVM, the length of the weight vector, $\| \w \|$, is constrained, which implements capacity control. In the pure expected shortfall minimization, Eq. (\ref{CVaR}), this is not done. Instead, the total
budget $\sum_i w_i$ is fixed. This difference is not correctly identified in the proof of the central theorem (Theorem 1) in \cite{Takeda2008}.}

The use of asymmetric risk measures in finance is motivated by the consideration that investors are not afraid of upside fluctuations. However, to make the relationship to support vector regression as clear as possible, we will first solve the more general symmetrized problem, before restricting our treatment to the completely asymmetric case, corresponding to expected shortfall. In addition, one may argue that focusing exclusively on large negative fluctuations might not be advisable even from a financial point of view, especially when one does not have sufficiently large samples. In a relatively small sample it may happen that a particular item, or a certain combination of items, dominates the rest, i.e. produces a larger return than any other item in the portfolio at each time point, even though no such dominance exists on longer time scales. The probability of such an apparent arbitrage increases with the ratio $N/T$, and when it occurs it may encourage an investor acting on a lopsided risk measure to take up very large long positions in the dominating item(s), which may turn out to be detrimental on the long run. This is the essence of the argument that has led to the discovery of the instability of coherent and downside risk measures \cite{hasszan2008,kondor2008}.

According to the above, let us consider the general case where positive deviations are also penalized. The objective function, Eq. (\ref{newPO}), then becomes
\begin{equation}
\min_{\w, {\bf \xi}, \epsilon}  \left[ {1 \over 2}  \|\w \|^2 +  C
\left({1 \over T} \sum_{k=1}^{T} \left( \xi_k + \xi_k^*\right) + \nu
 \epsilon \right) \right] \label{gen-RES},
\end{equation}
and additional constraints have to be added to Eqs. (\ref{con1}) to (\ref{b}):
\begin{eqnarray}
\wxk \leq \epsilon + \xi_k^*; \;\;\; \xi_k^* \geq 0.\label{con1sym}
\end{eqnarray}
This problem corresponds to $\nu$-SVR, a well understood regression method \cite{Nu-SVM}, with the only difference that the budget constraint, Eq. (\ref{b}) is added here. In the finance context the associated loss might be called {\em symmetric tail average} (STA). Solving the regularized expected shortfall minimization problem, Eqs. (\ref{newPO})--(\ref{b}) is a special case of solving the regularized STA minimization problem, Eq. (\ref{gen-RES}) with the constraints Eqs. (\ref{con1})--(\ref{b}) and (\ref{con1sym}). Therefore, we solve the more general problem first (Section \ref{RSTA}), before providing, in Section \ref{RES-par}, the solution to the regularized expected shortfall, Eqs. (\ref{newPO})--(\ref{b}).

\subsection{Regularized Symmetric Tail Average Minimization}
\label{RSTA}
The solution to the regularized symmetric tail average problem, Eq. (\ref{gen-RES}) with the constraints Eqs. (\ref{con1})--(\ref{b}) and (\ref{con1sym}), is found in analogy to support vector regression, following \cite{Nu-SVM}, by writing down the Lagrangean,
using Lagrange multipliers, $\{ {\bf \alpha}, {\bf \alpha^*}, \gamma, \lambda, {\bf \eta}, {\bf \eta^*} \}$, for the constraints. The
solution is then a saddle point, i.e. minimum over primal and maximum over dual variables. The Lagrangean is different from the one that arises in $\nu$-SVR in
that it is modified by the budget constraint:
\begin{eqnarray}
\!\!\!\!\! \!\!\!\!\! \!\!\!\!\! \!\!\!\!\! \!\!\!\!\! \!\!\!\!\! \!\!\!\!\!  L[\w, {\bf \xi}, {\bf \xi^*}, \epsilon, {\bf \alpha}, {\bf \alpha^*}, \gamma, \lambda, {\bf \eta}, {\bf \eta^*}]
&=&{1 \over 2} \|\w \|^2 + {C \over T} \sum_{k=1}^{T} (\xi_k +
\xi_k^*) + C \nu \epsilon  - \lambda \epsilon + \gamma \left( \sum_i
w_i -1 \right)\nonumber \\
&& + \sum_{k=1}^{T} \alpha_k^* (\wxk - \epsilon - \xi_k^*) -
\sum_{k=1}^{T} \alpha_k (\wxk + \epsilon + \xi_k)
 \nonumber \\
&& - \sum_{k=1}^{T} (\eta_k  \xi_k + \eta_k^*  \xi_k^*) \label{L-sym} \\
&=& F[\w] + \epsilon  \left( C \nu - \lambda -  \sum_{k=1}^{T}
(\alpha_k + \alpha_k^*) \right) -\gamma \label{Lagr-w-constr} \\ &&+
\sum_{k=1}^{T} \left[ \xi_k \left( {C \over T} -\alpha_k -
\eta_k\right) + \xi_k^* \left( {C \over T} - \alpha_k^* - \eta_k^*
\right) \right] \nonumber
\label{L1}
\end{eqnarray}
with
\begin{eqnarray}
F[\w] &=& \w \cdot \left({1 \over 2}  \w - \left(\sum_{k=1}^{T}
(\alpha_k - \alpha_k^*) \xk - \gamma {\bf 1}\right) \right),
\end{eqnarray}
where ${\bf 1}$ denotes the unit vector of length $N$.
Setting the derivative of the Lagrangian w.r.t. $\w$ to zero gives:
\begin{eqnarray}
\w_{\rm opt} = \sum_{k=1}^{T} (\alpha_k - \alpha_k^*) \xk - \gamma
{\bf 1} \label{w-sym}
\end{eqnarray}
This solution for the optimal portfolio is sparse in the sense that,
due to the Karush-Kuhn-Tucker conditions (see e.g. \cite{Bertsekas95}), only those points contribute to the
optimal portfolio weights, for which
the inequality constraints in (\ref{con1}), and the corresponding constraints in Eq. (\ref{con1sym}), are met exactly. The solution of $\w_{\rm opt} $ contains
only those points, and effectively ignores the rest. This
sparsity contributes to the stability of the solution.
Regularized portfolio optimization (RPO) operates, in contrast to
general regression, with a fixed budget. As a consequence, the
Lagrange multiplier $\gamma$ now appears in the optimal solution, Eq.
(\ref{w-sym}). Compared to the optimal solution in support vector (SV) regression, $\w_{\rm SV}$, the solution
vector under the budget constraint, $\w_{\rm RPO}$, is shifted by
$\gamma$:
\begin{equation}
\w_{\rm RPO} = \w_{\rm SV} - \gamma {\bf 1}.
\end{equation}
Let us now consider the dual problem. The dual is, in general, a
function of the dual variables, which are here $\{ {\bf \alpha}, {\bf
\alpha^*}, \gamma, \lambda, {\bf \eta}, {\bf \eta^*} \}$, although we
will see in the following that some of these variables drop out. The
dual is defined as \mbox{$D := \min_{\w, {\bf \xi}, {\bf \xi^*},
\epsilon} L[\w, {\bf \xi}, {\bf \xi^*}, \epsilon, {\bf \alpha}, {\bf
\alpha^*}, \gamma, \lambda, {\bf \eta}, {\bf \eta^*}]$}, and the dual
problem is then to maximize $D$ over the dual variables. We can
replace the minimization over $\w$ by evaluating the Lagrangian at
$\w_{\rm opt}$. For that we have to evaluate
\begin{eqnarray}
F[\w_{\rm opt}] &=& - {1 \over 2}  \| \w_{\rm opt} \|^2 \\
&=& \left[- {1 \over 2}
\left(\sum_{k=1}^{T} (\alpha_k - \alpha_k^*) \xk - \gamma {\bf
1}\right)^2 \right].
\end{eqnarray}
For the other terms in the Lagrangian, we have to consider different cases:
\begin{enumerate}
\item If $\left( C \nu - \lambda -  \sum_{k=1}^{T} (\alpha_k +
\alpha_k^*) \right) < 0$, then $L$ can be minimized by letting
$\epsilon \rightarrow \infty$, which means that $D = -\infty$.
\item If $\left( C \nu - \lambda -  \sum_{k=1}^{T} (\alpha_k +
\alpha_k^*) \right) \geq 0$: The term $\epsilon \left( C \nu - \lambda
-  \sum_{k=1}^{T} (\alpha_k + \alpha_k^*) \right)$ vanishes. Reason:
if equality holds, this is trivially true, and if the inequality holds
strictly then $L$ can be minimized by setting $\epsilon =0$.
\end{enumerate}
Similarly, for the other constraints (the notation $(*)$ means that
this is true for variables with and without the asterisk):
\begin{enumerate}
\item If $\left( {C \over T} -\alpha_k^{(*)} - \eta_k^{(*)} \right) <
0$, then $L$ can be minimized by letting $\xi_k^{(*)} \rightarrow
\infty$, which means that $D = -\infty$.
\item If $\left( {C \over T} -\alpha_k^{(*)} - \eta_k^{(*)} \right)
\geq 0$, then $\xi_k \left( {C \over T} -\alpha_k^{(*)} - \eta_k^{(*)}
\right) = 0$. Reason: If the inequality holds strictly then $L$ can be
minimized by $\xi_k^{(*)} = 0$. If equality holds then it is trivially
true.
\end{enumerate}
By a similar argument, the term $\gamma$ in Eq. (\ref{Lagr-w-constr})
disappears in the Dual. Altogether we have that either $D = - \infty$,
or
\begin{eqnarray}
&& D({\bf \alpha}, {\bf \alpha^*}, \gamma) = \min_{{\bf \xi}, {\bf
\xi^*}, \epsilon} F[\w_{\rm opt}({\bf \alpha}, {\bf \alpha^*},
\gamma)] =  - {1 \over 2}  \| \w_{\rm opt} \|^2  \\
{\rm and} &\;\;\;& \sum_{k=1}^{T} (\alpha_k^* + \alpha_k) \leq C \nu -
\lambda \\
{\rm and} &\;\;\;& \alpha_k^{(*)} + \eta_k^{(*)} \leq {C \over T}.
\end{eqnarray}
Note that the variables $\xi_k^{(*)}, \eta_k^{(*)}, \epsilon, \lambda$
do not appear in $F[\w_{\rm opt}({\bf \alpha}, {\bf \alpha^*},
\gamma)]$. The dual problem is therefore given by
\begin{eqnarray}
\max_{{\bf \alpha}, {\bf \alpha^*}, \gamma} && \left[- {1 \over 2}
\left(\sum_{k=1}^{T} (\alpha_k - \alpha_k^*) \xk - \gamma {\bf
1}\right)^2 \right]. \\
{\rm s.t.}&& \{ \alpha_k, \alpha_k^* \} \in \left[0,{C \over T}\right] \\
&& \sum_{k=1}^{T} (\alpha_k^* + \alpha_k) \leq C\nu.
\end{eqnarray}
We can analytically maximize over $\gamma$ and obtain for the optimal value
\begin{equation}
\gamma = {1 \over N} \left(\sum_{k=1}^{T} (\alpha_k - \alpha_k^*)
\sum_{i=1}^N \xki - 1 \right)  \label{gamma}
\end{equation}
The optimal projection (= optimal portfolio) is given by
\begin{equation}
\!\!\!\!\!\!\!\! \!\!\!\! \!\!\!\! \!\!\!\! \!\!\!\!  \w_{\rm opt} \cdot \x = \sum_{k=1}^{T}  (\alpha_k - \alpha_k^*)  \xk \cdot \x - {1 \over N}
\left(\sum_{k=1}^{T}  (\alpha_k - \alpha_k^*)  \sum_{i=1}^N  \xki - 1
\right) {\bf1} \cdot \x .
\label{RPO-sol-w}
\end{equation}
For $N \rightarrow \infty$ the second term vanishes and the solution is the same as the the solution in support vector regression. Note that the kernel-trick (see e.g. \cite{Bernhard_Book1}), which is
used in support vector machines to find nonlinear models hinges on the
fact that only dot products of input vectors appear in the support vector
expansion of the solution. As a consequence
of the budget constraint, one can no longer use the kernel-trick
(compare Eq. (\ref{RPO-sol-w})). As long as we disregard derivatives, this is
not a problem for portfolio optimization. Keep in mind, however, that the budget
constraint introduces this otherwise undesirable property.

Support vector algorithms typically solve the dual form of the problem
(for a recent survey see \cite{leon2006}), which is in our case given
by
\begin{eqnarray}
\!\!\!\!\!\!\!\! \max_{{\bf \alpha}, {\bf \alpha^*}, \gamma} && - {1 \over 2} \left[
\sum_{k=1}^{T}  \sum_{l=1}^{T} (\alpha_k - \alpha_k^*)(\alpha_l -
\alpha_l^*) \left( \xk \xl - {1\over N} \sum_{i=1}^{N} x^{(k)}_i
\sum_{i=1}^{N} x^{(l)}_i \right)
\right] \label{dual-sym-rpo} \\
\!\!\!\!\!\! {\rm s.t.}&& \{ \alpha_k, \alpha_k^* \} \in \left[0,{C \over T}\right]; \nonumber \\
&& \sum_{k=1}^{T} (\alpha_k^* + \alpha_k) \leq C\nu. \nonumber
\end{eqnarray}
For $N \rightarrow \infty$ the problem becomes {\it identical} to
$\nu$-SVR, which can be solved by linear programming, for
which software packages are available \cite{LOQO}. For finite $N$, it can still be solved with existing
methods, because it is quadratic in the $\alpha_k$'s. Solvers such as
the ones discussed in \cite{bordes-ertekin-weston-bottou-2005} and
\cite{leon2006} can be used, but have to be adapted to this specific problem.

The regularized symmetric tail average minimization problem (Eq. (\ref{gen-RES}) with the constraints Eqs. (\ref{con1})--(\ref{b}) and (\ref{con1sym})) is, as we have shown here, directly
related to support vector regression which uses the $\epsilon$-insensitive loss function. The $\epsilon$-insensitive
loss is stable to local changes for data points that fall outside the range specified by $\epsilon$. This
point is elaborated in Section 3 in \cite{Nu-SVM}, and relates this
method to robust estimation of the mean. It can also be extended to robust estimation of quantiles \cite{Nu-SVM} by scaling
of the slack variables $\xi_k$ by $\mu$ and $\xi_k^*$ by $1-\mu$, respectively.

This scaling translates directly to the portfolio optimization
problem, which is an extreme case: downside risk measures
penalize only loss, not gain. The asymmetry in the loss function
corresponds to $\mu =1$.

\subsection{Regularized expected shortfall.}
\label{RES-par}
By this final change we arrive at the regularized portfolio
optimization problem, Eqs. (\ref{newPO})--(\ref{b}), which we
originally set out to solve. This is now easily solved in analogy to
the previous paragraphs: the slack variables $\xi_k^*$ disappear,
together with the respective Lagrange multipliers which enforce
constraints, including $\alpha_k^*$. The optimal solution is now
\begin{eqnarray}
&\w_{\rm opt} = \sum_{k=1}^{T} \alpha_k  \xk - \gamma {\bf1},
\end{eqnarray}
with
\begin{eqnarray}
\gamma &=& {1 \over N} \left(\sum_{k=1}^{T} \alpha_k \sum_{i=1}^{N}
\xki - 1 \right).
\end{eqnarray}
The dual problem is given by
\begin{eqnarray}
\max_{\alpha_k}&& - {1 \over 2} \left[ \sum_{k=1}^{T}  \sum_{l=1}^{T}
\alpha_k \alpha_l \left( \xk \xl - {1\over N} \sum_{i=1}^{N} x^{(k)}_i
\sum_{i=1}^{N} x^{(l)}_i \right)
\right] \nonumber \\
{\rm s.t.}&& \alpha_k \in \left[0,{C \over T}\right]; \;\;
\sum_{k=1}^{T} \alpha_k \leq C\nu.
\end{eqnarray}
which, like its symmetric counterpart, Eq. (\ref{dual-sym-rpo}), can
be solved by adjusting existing algorithms.

The formalism provides a free parameter, $C$, to set the balance between the original risk function and the regularizer. Its choice may depend on a number of factors, such as the investors time horizon, the nature of the underlying data, and, crucially, on the ratio $N/T$. Intuitively, there must be a maximum allowable value $C_{\rm max}(N/T)$ for $C$, such that when one puts more emphasis on the data, $C > C_{\rm max}(N/T)$, then over fitting will occur with high probability. It would be desirable to know an analytic expression for (a bound on) $C_{\rm max}(N/T)$. In practice, cross-validation methods are often employed in machine learning to set the value of $C$. Those methods are not free of problems (see, for example, the treatment in \cite{bengio}), and the optimal choice of this parameter remains an open problem.

\section{Regularization corresponds to portfolio diversification.}
\label{diversification}
Above, we have controlled the capacity of the linear model by minimizing the L2 norm of the portfolio weight vector. In the finance context, minimizing
\begin{equation}
\| \w \|^2 = \sum_i w_i^2 \simeq {1 \over N_{\rm eff}}
\end{equation}
corresponds roughly to maximizing the effective number of assets, $N_{\rm eff}$, i.e. to exerting a {\it pressure} towards portfolio diversification \cite{Bouchaudpotters}. We conclude that diversification of the portfolio is crucial, because it serves to counteract the observed instability by acting as a regularizer.

Other constraints that penalizes the length of the weight vector could alternatively be considered as a regularizer, in particular any Lp norm. The budget constraint {\em alone}, however, does not suffice as a regularizer, since it does not constrain the length of the weight vector.
Adding a ban on short selling, $w_i \geq 0$, to the budget constraint, $\sum_i w_i = 1$, limits the allowable solutions to a finite volume in the space of weights and is equivalent to requiring that $\sum_i | w_i | \leq 1$.\footnote{This point has been made independently by \cite{DeMiguel2009}.} It thereby imposes a limit on the L1 norm, that is on the sum of the absolute amplitudes of long and short positions.

One may argue that it may be a good idea to use the L1 norm instead of the L2 norm, because that may make the solution sparser. However, the L1 norm has a tendency to make some of the weights vanish. Indeed, it has been shown that in the orthonormal design case (using the variance as the risk measure) an L1 regularizer will set some of the weights to zero, while an L2 regularizer will scale all the weights \cite{Tibshirani96}. The spontaneous reduction of portfolio size has also been demonstrated in numerical simulations \cite{Kondor4}: as one goes deeper and deeper into the regime where $T$ is significantly smaller than $N$, under a ban on short selling, more and more of the weights will become zero. The same ``freezing out" of the weights has been observed in portfolio optimization \cite{scherermartin} as an empirical fact.

It is important to stress that the vanishing of some of the weights does not reflect any structural property of the objective function, it is  just a random effect: as clearly demonstrated by simulations \cite{Kondor4}, for a different sample a different set of weights vanishes. The angle of the weight vector fluctuates  wildly from sample to sample. (The behavior of the solutions is similar for other limit systems as well.) This means that the solutions will be determined by the limit system and the random sample, rather than by the structure of the market. So the underlying instability is merely ``masked", in that the solutions do not run away to infinity, but they are still unstable under sample fluctuations when $T$ is too small. As it is certainly not in the interest of the investor to obtain a portfolio solution which sets weights to zero on the basis of unreliable information from small samples, the above observations speak strongly in favor of using the L2 norm over the L1 norm.

\section{Conclusion}
We have made the observation that the optimization of large portfolios minimizes the empirical risk in a regime where the data set size is similar to the size of the portfolio. In that regime, a small empirical risk does not necessarily guarantee a small actual risk \cite{VapnikCh71}. In this sense naive portfolio optimization over-fits the data. Regularization can overcome this problem by reducing the capacity of the considered model class.

Regularized portfolio optimization has choices to make, not only about the risk function, but also about the regularizer. Here, we have focussed on the increasingly popular expected shortfall risk measure. Using the L2 norm as a regularizer leads to a convex optimization problem which can be solved with linear programming. We have shown that regularized portfolio optimization is then a variant of support vector regression. The differences are an asymmetry, due to the tolerance to large positive deviations, and the budget constraint, which is not present in regression.

Our treatment provides a novel insight into why diversification is so important. The L2 regularizer implements a pressure towards portfolio diversification. Therefore, from a statistical point of view, diversification is important as it is one way to control the capacity of the portfolio optimizer and thereby to find a solution which is more stable, and hence meaningful.

In summary, the method we have outlined in this paper allows for the unified treatment of optimization and diversification in one principled formalism. It shows how known methods from modern statistics can be used to improve the practice of portfolio optimization.

\section{Acknowledgements}
We thank Leon Bottou for helpful discussions and comments on the manuscript. This work has been supported by the ``Cooperative Center for Communication Networks Data Analysis", a NAP project sponsored by the National Office of Research and Technology under grant No. KCKHA005. SS thanks the Collegium Budapest for hosting her during this collaboration, and the community at the Collegium for providing a creative and inspiring atmosphere.

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text:
Lamm Vanda (Budapest, 1945. március 26. –) magyar jogász, jogtudós, egyetemi tanár, a Magyar Tudományos Akadémia rendes tagja. A nemzetközi jog és a nukleáris jog neves kutatója. 1991 és 2010 között az MTA Állam- és Jogtudományi Intézetének igazgatója. 2020. augusztus 1-jétől  a Magyar Tudományos Akadémia alelnöke.

Tanulmányai

1963-ban érettségizett a Hámán Kató Leánygimnáziumban, majd felvették az Eötvös Loránd Tudományegyetem Állam- és Jogtudományi Karára, ahol 1968-ben szerzett állam- és jogtudományi doktorátust summa cum laude eredménnyel. Ezen kívül posztgraduális képzésen vett részt a strasbourgi Faculté Internationale pour l'Enseignement du Droit Comparé-n és a hágai Nemzetközi Jogi Akadémián.

Tudományos pályafutása

1968-ban került a Magyar Tudományos Akadémia Állam- és Jogtudományi Intézetébe, ahol először tudományos segédmunkatárs volt, majd munkatárs, főmunkatárs, végül tudományos tanácsadója lett. 1991-ben az intézet igazgatójává választották.

1979-ben védte meg az állam- és jogtudományok kandidátusa, 1988-ban akadémiai doktori értekezését.

Kutatóintézeti állása mellett 1984 és 1989 között a Nehézipari Műszaki Egyetem nemzetközi jogi tanszékének docense volt, majd 1989-ben megkapta az egyetemi tanári és tanszékvezetői kinevezést. 1999-ben a Széchenyi István Egyetem nemzetközi köz- és magánjogi tanszékének vezetője lett. 1986-ban a New York-i Columbia Egyetemen vendégkutató; több külföldi egyetemen tanított.

Az Acta Juridica Hungarica felelős szerkesztője és az Állam- és Jogtudomány című szakfolyóirat szerkesztője volt. 2000 és 2001 között a Nemzetközi Nukleáris Jogi Társaság elnöke volt, 2002-ben tiszteletbeli elnök lett.

2007-ben az MTA levelező tagjává, 2013-ban rendes taggá választották. 2011 és 2017 között az MTA Gazdaság- és Jogtudományok Osztályának elnökhelyettese, 2017-től 2020-ig osztályelnök. 2020-ban az MTA társadalomtudományi alelnökévé választották. Kutatási területe a nemzetközi jog, ezen belül a nemzetközi bíráskodás, emberi jogok, valamint nukleáris jog.

1981 és 1984 között a nőkkel szembeni diszkrimináció felszámolásával foglalkozó ENSZ-bizottság tagja volt. Ezenkívül megválasztották a hágai Állandó Választottbíróság, az EBESZ Békéltető és Választottbírósága tagjává, valamint az OECD–NEA Nukleáris Jogi Bizottság alelnökévé. 2001 óta tagja az 1904-ben Nobel-békedíjjal kitüntetett Nemzetközi Jogi Intézetnek (Institut de droit international), 2009-2010-ben a társaság alelnöke. Több nemzetközi szervezet, így az ENSZ, a UNDP, az IAEA és az OECD-NEA részére végzett szakértői tevékenységet.

Az Európai Tudományos és Művészeti Akadémia tagja.

Díjai, elismerései
 Akadémiai Ifjúsági Díj (1973)
 Akadémiai Díj (1995)
 Deák Ferenc Díj (2010)
 Professor emerita

Főbb publikációi
A nukleáris energia hasznosításának nemzetközi kérdései (1983)
A hágai Nemzetközi Bíróság döntései (1984)
The Utilization of Nuclear Energy and International Law (1984)
Az államok közötti viták bírói rendezésének története (1990)
A Nemzetközi Bíróság ítéletei és tanácsadó véleményei 1945–1993 (1995)
Jogi lexikon (főszerk., Peschka Vilmossal, 1999)
Nuclear Law under the Sign of Safety and Confidence (szerk., 2002)
A Nemzetközi Bíróság kötelező joghatósági rendszere (2005)
Transformation in Hungarian Law 1989–2006 (szerk., 2007)
Compulsory Jurisdiction in International Law (2014)
A kötelező nemzetközi bíráskodás kilenc évtizede. Székfoglaló előadások a Magyar Tudományos Akadémián (2014)
Emberi jogi enciklopédia (szerk., 2018)

Jegyzetek

Források
MTI Ki Kicsoda 2009, Magyar Távirati Iroda Zrt., Budapest, 2008, 657-658. old.,
Szakmai önéletrajz az MTA JTI honlapján
Adatlap a Magyar Tudományos Akadémia honlapján, publikációs listával
Nótári T. - Török G. (szerk.): Prudentia Iuris Gentium Potestate - Ünnepi tanulmányok Lamm Vanda tiszteletére. Budapest, 2010.

Magyar jogászok
Magyar jogtudósok
Magyar egyetemi, főiskolai oktatók
MTA-tagok
1945-ben született személyek
Budapesten született személyek
Akadémiai díjasok
Magyar nők
Élő személyek

redpajama_set_name: RedPajamaWikipedia


Your career can start at Popeyes if you would like to work for a fast food chain.
How to submit your Popeyes job application form online?
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Employees, no matter part-time or full-time, are provided a wide range of job benefits and employment perks. As soon as you complete the application form, flexible work schedules, career advancement potential and paid training opportunities will be offered to all freshmen. They also draw nice paychecks. For instance, a manager's annual salary is $31,000 – $42,000. In addition, full-time workers have exceptional salary options, paid training programs and advancement potential.
their are some nice people not everyone is nice at times?
Popeyes on Milwaukee ave in Jefferson park. I am trying to apply for a job here but I can't seem to get thru. Mainly because I probably filled out one already I would like to work here.
Ive changed my email and ive been looking for a job here in selma alabama at popeyes chicken….i was hoping they will call me for a job.
Really like the chance to work for you!! Excited that your finally here!
When I fill out an app I make sure its something I'll enjoy doing I'm a people person great communication if dealing with a unhappy customer or employees.
Disponible el jueves viernes y sábado para trabajar.
I am trying too apply for Popeyes through work 4popeyes.com and the internet keeps taking me other places.


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Complete quest "His Greatest Treasure" in Hew's Bane.
A Life Deferred is awarded for completing the quest His Greatest Treasure for Quen in the Thieves Guild. You must first complete A Double Life before starting this quest. This achievement will allow you to purchase a Large Covered Well for your house. It costs 0001500015,000  and can be purchased from Listens-to-Sea in Abah's Landing.
This page was last modified on 16 November 2018, at 05:42.


text:
Breast cancer treatment research wins €800,000 SFI award
by Jenny Darmody
9 Dec 202039 Views
Image: © ryanking999/Stock.adobe.com
The award was funded by Science Foundation Ireland and Puma Biotechnology and will go towards advancing cancer research skills in DCU.
Science Foundation Ireland (SFI) has announced a Strategic Partnerships Award of €800,000 for the National Institute for Cellular Biotechnology (NICB) at Dublin City University (DCU).
SFI provided €400,000 of the funding and the rest was provided by Puma Biotechnology, a biopharma company that specialises in the acquisition and development of novel therapeutics for the treatment of cancer.
The award will fund Acorn, a project partnership between DCU and Puma Biotechnology, which aims to further investigate the activity of the HER2-positive breast cancer drug neratinib, as well as its potential use in the treatment of other cancer types.
The research will investigate whether neratinib works best alone or with other drugs, and whether there are markers in patient blood or tumours which can predict whether they will respond to neratinib treatment.
'A real difference'
The Acorn project is led by Prof John Crown and Dr Denis Collins of DCU. It aims to advance cancer research skills in DCU and bring new findings to clinical trials that will benefit cancer patients in Ireland and globally.
Collins and Crown have studied neratinib for several years, with the first comprehensive in-vitro profile of the drug published by the DCU research group in 2013.
"Our research group at DCU has a long history of industry engagement and producing laboratory data that informs clinical studies," said Crown.
"With the support of SFI and Puma Biotechnology, this Acorn award will create a centre of research excellence in DCU, laying the foundation for novel discoveries that will drive a new wave of clinical investigations."
Announcing the award, SFI director general Prof Mark Ferguson said: "The SFI Strategic Partnership Programme aims to cultivate partnerships across academia and industry to address key research questions and enhance the competitiveness of our economy. The Acorn research programme will enable real progress in cancer research and inform new treatment options for patients with cancer in the future."
Minister for Further and Higher Education, Research, Innovation and Science Simon Harris, TD, said he is delighted to announce the funding for cancer research in Ireland, as more than 3,000 breast cancer cases are diagnosed in the country every year.
"This innovative project has the potential to make a real difference to future treatment options available for cancer patients and I look forward to seeing the impact that this research programme will have for patients and our healthcare system in the future."
Related: biotech, healthcare, research, funding and investment, Science Foundation Ireland, Brand Insights, Dublin City University
Jenny is the Deputy Editor of Siliconrepublic.com
Future Human
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Nearly €12m to be invested in Covid-related research across Ireland
SFI awards €53m to 71 individual-led research projects
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Early results suggest Moderna's Covid vaccine could be 94pc effective


O'SULLIVAN, (née Carroll), Elizabeth (Betty) – Feb. 5, 2019, (Coolock and formerly of Seffin, Birr, Co. Offaly) (Peacefully), surrounded by her loving family at Beaumont Hospital, beloved mother of Jean, Owen, Dermot and Irene; sadly missed by her loving family, her sons, daughters, son-in-law Paddy and, daughters-in-law Bernadine and Marie, grandchildren, great-grandchildren, brother seamus and sisters Maura and Margaret, brothers-in-law, sisters-in-law, nieces, nephews, extended family, neighbours and friends. Reposing at Jennings Funeral Home, Oscar Traynor Rd, Coolock this Thursday from 4pm to 6pm. Removal on Friday morning to St. Brendan's Church, Coolock arriving for 10am Funeral Mass followed by Cremation in Dardistown Crematorium. Family flowers only please, donations if desired to the I.S.P.C.A.


text:
CJN's removal : Nigeria now in full blown dictatorship * Says president's action 'null and void ' * Says Mohammed, Buhari's CJ while Onnoghen remains substantive CJN
Steve Oko January 26, 2019
GARMISCH-PARTENKIRCHEN, GERMANY - JUNE 08: Nigeria's President Muhammadu Buhari arrives to attend a working session with outreach guests at the summit of G7 nations at Schloss Elmau on June 8, 2015 near Garmisch-Partenkirchen, Germany. In the course of the two-day summit G7 leaders are scheduled to discuss global economic and security issues, as well as pressing global health-related issues, including antibiotics-resistant bacteria and Ebola. Several thousand protesters have announced they will seek to march towards Schloss Elmau and at least 17,000 police are on hand to provide security. (Photo by Carl Court/Getty Images)
CJN's removal : Nigeria now in full blown dictatorship
* Says president's action 'null and void '
* Says Mohammed, Buhari's CJ while Onnoghen remains substantive CJN
Speaker of the House of Representatives, Rt. Hon. Dogara, has said that "Nigeria is now in full dictatorship ".
Dogara who stated this while reacting to the unilateral suspension of the Chief Justice of Nigeria, CJN, Justice Waltar Onnoghen by President Muhammadu Buhari, declared the action of the President "null and void ", saying he lacks such powers.
The Speaker said Onnoghen remains the CJN while his purported replacement, Justice Tanko Mohammed is only Buhari's CJ.
He called on the international community and Nigerians to rise in defence of the Nigerian democracy.
Bellow is the full text of the statement obtained by Wawa News Global :
"With the unconstitutional suspension of the Chief Justice of Nigeria (CJN) by President Muhammadu Buhari, the world has been served with notice that Nigeria is now a full-blown dictatorship.
"This did not come as a rude shock except to those that have been blind to the gradual but progressive erosion of democratic values in the polity as President Buhari's Government has never hidden its disdain for the rule of law.
," This did not come as a rude shock except to those that have been blind to the gradual but progressive erosion of democratic values in the polity as President Buhari's Government has never hidden its disdain for the rule of law.
We have watched in disbelief as the Government recklessly deploys institutional prerogatives; routinely flouts the rule of law; subverts and assaults democratic Institutions; refuses to accept opponents as legitimate; suppresses citizens civil liberties(especially those of opponents) and trample underfoot the media. In short, the Government's tyrannical and authoritarian credentials are loathsomely legendary.
No provision in Sections 157 and 292 of the 1999 Constitution as amended supports the President in purporting to suspend the CJN or swearing in an acting CJN. The whole idea of a limited government is that the President's powers is limited by law and it is ultra vires his powers to act in the absence of explicit legislative authorization.
That is representative democracy at its best which our 1999 Constitution as amended guarantees. It is instructive to note that our Constitution does not contemplate or presupposes a situation whereby the Judiciary will have a suspended CJN and an acting CJN at the same time.
Therefore, it is right to posit, as some have done that the President now has his own Chief Judge to do his bidding while Nigeria has a sitting CJN until he is removed in line with the provisions of the Constitution.
The awfully crude annexation of the judiciary by the President in violation of his oath of office and the Constitution cannot be for any other reason except, as alleged by so many, to prepare the judiciary ahead of time for the purpose of conferring some aura of legitimacy to the contraption that the 2019 general elections may after all become.
I therefore call on the President to remember that he has no better legacy to bequeath other than a good name: which cannot be achieved without honour, character and integrity. Honour and integrity demand that he upholds his oath of office by reversing this assault on our Constitution and following the manifestly clear and unambiguous constitutional procedure for the removal of the CJN if he must be removed. Anything short of this demeans all of us.
To our citizens, we must now head the warning of the Irish lawyer cum orator, John Philpot Curran who said, "the condition upon which God had given liberty to man is eternal vigilance: which condition if he breaks, servitude is at once the consequences of his crime, and the punishment of his guilt". Despotism can only prosper in Nigeria if good men and women do nothing.
I also call on all lovers of freedom and democracy all over the world to rise to the occasion and demand of this Government and the President to halt the march to anarchy and bedlam: which dictatorship promotes. The world has an experience in this and it must not allow this unmitigated disaster on Nigeria before it acts to restore sanity.
As of today, Nigeria is now Germany in the wake of the 1933 Reichstag fire. Our democracy is on fire; ignited by the very people who swore to protect and defend it. That this fire must not convert the Chancellor to Fuhrer as it happened in Germany in 1933 depends on our collective response and that of the international community. We must not bow our knees to dictatorship: not now, not ever again".
Previous Biafra referendum imminent as Nnamdi kanu lands UK * To hold diplomat talks with top EU officials
Next Apprehension in presidency as senate moves to impeach Buhari over illegal removal of CJN * APC senators summoned to Aso Rock * PDP alleges plot to bribe senators *Says billions withdrawn from NDDC coffers * Queries authenticity of CCT court order


text:
Tranquilizer Raises Effects of Alcohol
Magazine Issue: Vol. 78 No. #10, September 3, 1960
A version of this article appears in the September 3, 1960 issue of Science News.
Alcohol and Tranquilizers
By Science News December 3, 1977
Tranquilizer Fights Alcohol
By Science News September 28, 1968
Tranquilizers and Alcohol Do Not Mix
By Science News July 11, 1959


text:
Dodo: an experimental companion
April 14, 2007 fromtheheartofeurope	82 Comments
What to say about Dodo Chaplet? Probably the least remembered of the First Doctor's companions – probably the least remembered companion of all, apart from Kamelion – I've been rather fascinated by her appearance in six stories (well, four and a half really) of the show's third season in 1966; sufficiently fascinated to watch/listen to all the series, read all the novelisations, read indeed all the spin-off novels featuring her, pore over Wood and Miles, and to get hold of the interview Nicholas Briggs did with Jackie Lane in 1993, now released by Reeltime on DVD. There's not a lot of information about Lane out there, and also a source of confusion with the actress Jocelyn Lane, ten years older and much better known (including sometimes as "Jackie").
Dodo is mocked by many fans, with one recent survey describing her as the one companion he would want to give "the sharp end of a Dalek gun" to. In particular, the circumstances of both Dodo's introduction to the series and her departure from it must rank among the clumsiest entries and exits for any regular character. She arrives at the TARDIS in Wimbledon Common in 1966, eager to report a traffic accident, and then immediately decides she is happy to leave with the Doctor and Steven, whatever the consequences. Both the Doctor and Steven behave with such extraordinary inconsistency in this brief scene that it is painful. Five stories later, they are back in 1966, and Dodo gets hypnotised by a rogue computer; at the end of episode two she is sent off the the country to recover, and never seen again; not even given a decent farewell – here is Polly trying to explain that away. (Steve Lyons and David Bishop respectively did their best to resolve these peculiar occurrences in their spinoff novels.)
The third season of Doctor Who saw much stress behind the scenes anyway, with three different producers, the longest single-story arc ever (The Daleks' Master Plan), and much trouble with last-minute script changes. There was a great deal of turnover in front of the camera too: Dodo Chaplet was the fourth of five different female companions to feature during the season. There is a rumour that a plan to replace the lead actor by stealth at the end of The Celestial Toymaker (the Doctor is invisible for most of the story, and could therefore have been materialised with a different body at the end) was scotched when someone inadvertently sent Hartnell his renewed contract to sign before it had gone through all the proper channels.
Even under better circumstances, Dodo would have been somewhat in the shadows: both her immediate predecessor and successor as female companion (Jean Marsh and Anneke Wills) had real star quality and experience which Jackie Lane lacked. In fact she was the youngest actor ever to play a female companion, filming her first scenes on 7 January 1966 for broadcast four weeks later, not quite six months after her eighteenth birthday. (Matthew Waterhouse was eighteen and four months when his first scenes were filmed in April 1980, which makes him the youngest companion ever; but he lasted a bit longer in the show than Jackie Lane did, making her the youngest ex-companion ever.) [See correction] She admits quite frankly to Nicholas Briggs that she was given no direction whatever in how to play the character. Indeed, she is perhaps too kind; between her first few scenes, the direction she was given as to what accent to adopt changed drastically, to adopt essentially received pronunciation with occasional outbursts of slang rather than the more demotic tones which she had used at the end of The Massacre. (Widespread fan lore describes her accent there as "Cockney". It clearly isn't – listen for yourself – Jackie Lane is from Manchester.)
Yet, although one can make excuses for the ropey scripts, the lack of direction from the production team, and the failure to define her role properly, the fact is that even from her interview many years later, one feels that Lane's heart wasn't really in it. She had been approached to play Susan two years earlier, but turned it down because she did not want to be committed for a long period of time. She did the nineteen episodes in her contract – her last episode broadcast not quite six months after her first appearance in front of the cameras – and then as far as I can tell never acted again. (IMDB has her in an episode of "Get Smart" in 1969, but I'm pretty sure that must be Jocelyn Lane, not our Jackie.) She did of course later set up an agency for actors doing voiceovers, including both Tom Baker and Janet Fielding among her clients.
I don't want to be unfair. I think that she does quite a lot with limited material. Every single one of her stories shows a new bit of Dodo: in The Ark she is rebellious and mischievous younger sister to Steven's more tightlaced elder brother; in The Celestial Toymaker it is she who tries to feel compassion to the Toymaker's evil minions; in The Gunfighters she is the one who actually co-ordinates getiing Doc Holliday to the right place at the right time (while Steven keep getting captured); in The Savages it is she who comes closest to working out what is really going on in the labs; and in The War Machines I think she does a brilliant job of being brainwashed before her ignominious departure. Yet there's something missing, in terms of a basic spark with the rest of the cast. No longer overshadowed by Steven, she comes into her own to a certain extent in her last story, only to be written out halfway through. Ironically, her last words are to try and assert her own identity; if only her character had been enabled to do so a bit earlier.
One thing she didn't suffer from was the great destruction of old Doctor Who episodes. A surprisingly high proportion of Dodo's episodes actually survive – 11 out of the 19; I haven't calculated, but I am sure that is a larger percentage than any of the Second Doctor's companions, and certain that it is more than for Katarina or Sara Kingdom. Thanks to this, we can be sure about the most striking point about Dodo: she wears a different costume in almost every story. She starts off in schoolgirl uniform for the scene where she wanders into the Tardis on Wimbledon Common, then for The Ark has changed into a rather peculiar and not very flattering mock-medieval tabard; in The Celestial Toymaker, she has this rather attractive circle motif on her T-shirt and skirt (the T-shirt being a bright red, but the viewers of 1966 would not have known that); for The Gunfighters, she and Steven both go native into cowboy costume; and then for her last two stories, she wears a rather businesslike dress (the picture below isn't great, but the best I could find). I have seen a rumour that the original plan was for her to change her hairstyle as well for each story, but she stymied that by getting a haircut over the Christmas break before her first scenes were filmed.
This seems to be the first time we as viewers are invited to really look at one of the regular supporting cast; up to now it has been the Doctor who visually dominates every scene he is in. However, it doesn't work for two reasons. The first is that the clothes on the whole are not very flattering. The second is that style can't really compensate for a lack of substance. I think every other companion, bar Susan, was given a decent build-up for us to understand where they came from and why they might decide to travel with the Doctor. Although Dodo is in fact the first companion since the very beginning to come from our own time (Vicki, Steven, and Sara Kingdom from the future; Katarina from the past) she is oddly enough the one we know least about, and find out least about. She is the girl next door, but one whose parents never let you talk to her and who isn't allowed to discuss anything except the scenery.
There's not a lot of Dodo fan-fiction out there. Such as I have tracked down, it consists of the following:
Fall Fast, Fall Free (Fall With Me) [Steven/Dodo, R] by livii
Put Away Childish Things [One, Steven/Dodo, PG] by bibliophile1887
And from www.whofic.com:
Magnificent Folly
Futures Past
Anyway, I think tha's got her out of my system. Thanks for bearing with me.
doctor whodoctor who: 01doctor who: companions: dodo chaplet
Previous PostI didn't expect that!Next PostWhat Susan says about her home planet
82 thoughts on "Dodo: an experimental companion"
eyemage says:
thank you for taking the time to reveal such a character.
It seems that many of the companions come to inglorious ends.
I personaly hope that eventually the old shows will be remade by fans at least and these story holes will get filled.
I found it interesting that the wiki entry for dodo implies that steven is her ancestor from the previous time trip in france.
thanks again for the entry.
blue_condition says:
Dodo is the prototype for many, many later companions though – ordinary young girl from Earth, with no particular skills or talents. Polly, Jo, Peri, Mel (ok, eidetic memory), Ace (ok, a Wolf of Fenric), Rose…
actually, Victoria too, after a while the fact that she "came from the past" didn't make much difference.
Absolutely – hence my title for the piece.
livii says:
This is a truly fabulous post; an unflinching look at the character and actress without bashing and leaving those of us who rather enjoy her with a good feeling, and possibly you will have convinced a few others too. There have been so very many fantastic companions on the show over all the years, but Dodo really doesn't stack up all that badly in my mind, it's that a lot of the others overshadow her (and hardly anyone has seen her episodes).
(I've come to regret that they named her Dodo – how do you have positive thoughts about such a name?)
I'm very fond of her in The War Games, and her interaction with One; you get the feeling the actors genuinely liked each other, and One looks truly sad when she doesn't come back, and he thinks he'll be all alone. And I feel sort of the same way about her, but then Ben and Polly come aboard and, y'know, the show goes on happily. 🙂
(This is my long-winded nodding and agreeing with you!)
The name "Dodo" would have been survivable if everything else had gone better (indeed, look at Peri – equally stupid name, but lots of other things went better). But it's still an unfortunate choice!
Quite some time later – I'm doing up a quick post for on Dodo, since she did not get represented in their month of Companion love, and I'm hoping to link to this post, and your other Dodo-related posts – they're brilliant, and there's no point in me trying to say any of it better. Is that okay?
davesangel says:
Hey, I like the name "Peri" 😛
http://www.deviantart.com/deviation/46247500/
from a wonderful artist
I haven't seen a lot of the Hartnell stories, and those I have seen I can't really remember well enough to comment upon Dodo with real certainty. However, for some reason, my abiding 'memory' (if that) is of reading an issue of Doctor Who magazine several years ago, which featured a poll about the best DW 'Missing Adventures'. The editor mentioned that in one of these MAs, 'The Man in the Velvet Mask', Dodo contracts a sexually transmitted disease (possibly fatal, can't remember) whilst in post-revolutionary France. I have no idea how true this may be, but it's a bit of an unpleasant way to deal with a companion who wasn't featured as much as perhaps she should have been in the television series…
Hmm… That is only mildly accurate; it is not strictly speaking a disease in the normal sense, and DWM were resorting to easy mockery of a book that is more sophisticated than they wanted to admit. The good thing about the story is that it gives Dodo a sex life at all; Who Killed Kennedy? of course takes this further.
i have always liked Dodo. mainly because the war machines and the gunfighters
seemed to be the only Hartnell stories my PBS station had to air (grew up in the states/maryland public TV) But looking at her stories now she really does seem to be a prototype for the female companions to come – the dr says would you like to go for a ride through all of time and space and the girl says i dont understand what you mean but it sounds like fun and off they go – and like rose,clara and Sarah-Jane and others, she is soon pitching in helping out even saving the day ! in fact the only thing i dont like about the Dodo episodes is Stevens interactions with her – considering his experience with the dr he is way to gruff with her.
so strangely Dodo is most like the girls from the reboot Dr who while polly with mike is more of a lookback


text:
Surprising Science — September 4, 2013
How Much Is That Forest Really Worth? Banks Aim To Find Out
As part of last year's UN Rio+20 Summit, a group of financial institutions are looking into ways to put monetary value on natural resources and add incentives and penalties for their preservation or destruction.
Kecia Lynn
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The World Bank and 43 other financial institutions have begun preliminary work on calculating the monetary worth of the Earth's natural capital, which they declared as "the Earth's natural assets (soil, air, water, flora and fauna), and the ecosystem services resulting from them, which make human life possible." They also want to force companies to reveal how they use these assets in their financial reports, and offer incentives and penalties for their preservation or destruction.
The effort is an output of last year's UN Conference on Sustainable Development — also known as Rio+20 — at which governments committed to better integration of social, political and environmental actions. However, the banks admit that putting a value on natural assets will be a huge challenge. Project member Liesel Van Ast says bankers will "need to address how they will account for natural capital, explain to everyone why they need to do it and then tell them how to do it." The banks have given themselves until 2020 to come up with a functional valuation system.
Read it at The Daily Climate
The Resurrection of Non-Western Civilizations
Shengren, Junzi, Ruxue: The Chinese World is Coming Back in Full Circle"…the creator, when he arises, always finds himself overwhelmingly outnumbered by the inert uncreative mass…" – Arnold J. ToynbeeWhy […]


text:
Visual care
Glasses in the music industry
Musicians, like most artists, tend to pay close attention to their image. Choosing a style or the other helps them create their own identity and enhance what they express with their music. At L'Atelier Óptica, of course, we pay special attention to their glasses and we want to show you some of the choices we like the most.
The rapper does not fear anything when it comes to style. It is clear that she does not like to go unnoticed and she looks spetacular with her fancy pair of Dita Narcissus and her much more sporty-looking Mykita Vice.
We obviously had to include one of the greatest divas of our time. And it seems like Beyoncé shares Nicki Minaj's taste, both singers have chosen the same pair of sunglasses.
If we were to see Elton John without glasses, surely we would not recognize him. The British musician has made outstanding glasses his hallmark. In the photo on the left he appears with an amazing pair by Mykita and in the one on the right with Moscot Bupkes, both of them with colored lenses, a trend that we are seeing a lot lately.
We have seen her with all kinds of glasses, as striking as her outfits. Miley can pull off any look. Our favorites: the sunglasses designed by Salvador Dalí for Courrèges (if you have visited us in our Optician in Madrid, you will understand why).
In an interview, the American actor and musician explained the reason why he does not separate from his glasses. Apparently, he has a congenital vision condition in his left eye. It seems like lately his favorite ones are the Moscot Lemtosh, since we have seen him on numerous occasions wearing them.
Kim Tae-hyung
It is not all about sunglasses, the member of the K-Pop group BTS, better known as V, wears Moscot Genug, a model very similar to the Lemtosh but with finer lines.
If you think of an artist known for their over the top performances and iconic outfits, Lady Gaga immediately comes to mind. The singer has managed to create a whole character around conceptual high fashion looks. She has worn many different sunglasses, but we have been especially drawn to these two Oliver Goldsmith models: Y-Not and Wow.
It is obvious that Bad Bunny doesn't like discreet sunglasses. In the photo, we see the Puerto Rican Trap singer with a colorful limited edition model by Jacques Marie Mage.
It seems like the American singer is a fan of Dita Eyewear sunglasses. For many years now, we have been seeing him with different models from this brand: Grandmaster Two, Mach One, Creator, Midnight Special, Journey …
And apparently, Usher is not the only unconditional fan of Dita. We have also seen Jennifer Lopez with several models from this American brand, such as the Dita Talon or her inseparable Mach One.
The Barbadian artist, however, seems to prefer the Cutler and Gross glasses and wears them both on and off the stage.
Yet another musician who does not separate from his sunglasses is Lenny Kravitz. We have seen him with many aviator style frames, like these by Mykita:
The rapper loves colorful and original sunglasses like the ones he wears in the photo. It is the Togo Flap model of the Italian brand L.G.R.
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A collection of glasses seen on TV and cinema: part 2


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Q: Centroid of volume of revolution Consider a solid generated by the curve $y^2 =ax^2+2bx+c$,rotated about the $x$-axis, and two plane surfaces at right angles to the latter, distance $h$ apart, and with areas $A$ and $B$. To prove that the centroid of the solid is at a distance $h/2 + (B-A)h^2/12V$ from $A$, where $V$ is the volume of the solid.
I have calculated $$V=\frac{(B+A)h}2 -\frac{\pi a h^3}6$$ by integration, but cannot see how to proceed further. Any help would be appreciated.

A: I suggest that you use Pappus's ($2^{nd}$) Centroid Theorem: the volume of a planar area of revolution is the product of the area $A$ and the length of the path traced by its centroid $R$, i.e., $2\pi R$. The bottom line is that the volume is given simply by $V=2\pi RA$. I assume that that you can calculate the area, hence you can get centroid quite easily, i.e., $R=\frac{V}{2\pi A}$. (Be sure to calculate the area between the curve and the axis of revolution.)

A: I have now proved the result, by expressing the stated  result  in terms of integrals and comparing terms in the usual formula for the centroid. This method was somewhat laborious, and did not give any hint of a more elegant method!

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Home > Si-Wan Kim
Si-Wan Kim
NOAA Earth System Research Laboratory
Kim, S., et al. (2016), Modeling the weekly cycle of NOx and CO emissions and their impacts on O3 in the Los Angeles-South Coast Air Basin during the CalNex 2010 field campaign, J. Geophys. Res., 121, 1340-1360, doi:10.1002/2015JD024292.
Page Last Updated: January 18, 2023


Laurie Here - Contemporary Fiction and MORE - Book Reviews: #ArmchairBEA 2014 Wrap-Up Post!
My favorite aspect of the #ArmchairBEA was going around visiting other blogs! I didn't always get to write a comment, but I LOVED reading on the different topics!
I loved the daily pictures! Wish there were more from the open floor of the book sellers, the authors, and more! I want to see it all! How about some video?
I loved the Author Interviews, too! Those were great!
I loved writing something, a Topic every day. That was FUN!
I wouldn't change a thing, except for getting us a better view of the open floor!
I was VERY surprised that WE got to win books! Wow! Thank you to ALL the Sponsors!!
This was so much FUN, I'm going to do it again next year, too! Except this time I am not going to plan on having to read 2 books, do one Blog Tour and one review during the SAME week! This will give me more time to spend at other blogs reading and learning about their topics!


Stone flooring is perhaps the only material can be used to successfully create a seamless connection between indoor and outdoor spaces.
However, not all natural stone is suited to use outside as some can crack and fade in harsh weather conditions and others have polished finishes, which become extremely slippery when wet.
Slate is perfect to use to create the indoor outdoor look, as it is naturally slip resistant, and is also relatively low in price, so is a great budget stone flooring choice. Limestone is also a good option as it is very hardwearing, and also has the benefit of coming in varying colours to add to the natural feel. Additionally, as Sandstone weathers very well, it can be used inside and outside.
Remember, you will need to care for the interior stone flooring and exterior stone flooring slightly differently.
Natural stone is porous and spillages will be absorbed, so your interior stone flooring should have the correct sealant used when it is laid, then reapplied every couple of years. Also avoid using cleaning products that contain vinegars or acids, an all-purpose cleaning solution should do the trick.
The exterior part of your stone flooring won't need to be sealed, just a regular sweep and clean should prevent stains causing problems, such as from moss and algae. To clean it we would recommend a pressure wash every now and then, adding some bleach to particularly stubborn areas.
For more advice on using natural stone to create an indoor outdoor room, speak to the team at the Stone Gallery. As well as offering a wide range of stone flooring in an array of colours and finishes, the Stone Gallery is part of the Art of Living Group, which also includes the Ceramic Tile Warehouse and Park Street Interiors. All based in a triangle in Camberley, the group is on hand to add inspiration to your design ideas.