Franz Nissel (* 14. März 1831 in Wien; † 20. Juli 1893 in Bad Gleichenberg) war ein österreichischer Theaterautor. Für das Trauerspiel Agnes von Meran erhielt Nissel den Schiller-Preis.

Leben
Er war der Sohn eines Schauspielerehepaares, sein Vater kam 1844 an das Wiener Hofburgtheater. Er besuchte die Wiener Normalschule und das Schottengymnasium und bildete sich, durch Krankheiten behindert, im Selbststudium fort. Als 1856 sein Schauspiel "Ein Wohlthäter" zur Aufführung kam, widmete er sich ganz der Theaterschriftstellerei. Er heiratete die Opernsängerin Margarethe Konrad (verwitwete Binder von Kriegelstein) und lebte mit ihr in Salzburg und Graz. 1878 wurde sein Stück "Agnes von Meran" mit dem deutschen Schillerpreis ausgezeichnet.

Werke
 (Mit Sigmund Schlesinger) Das Beispiel. Ländliches Gemälde in 3 Aufzügen. Klopf & Eurich, Wien 1852. (Digitalisat)
 Heinrich der Löwe. Historisches Schauspiel in 5 Aufzügen. Klopf & Eurich, Wien 1858. (Digitalisat)
 Ein Wohlthäter. Schauspiel in 3 Aufzügen. Wien 1854. (Digitalisat)
 Die Jakobiten. Trauerspiel in 5 Aufzügen. Wallishausser, Wien 1860. (Digitalisat)
 Perseus von Macedonien. Trauerspiel in 5 Aufzügen. Wallishausser, Wien 1862. (Digitalisat)
 Dido. Ein Trauerspiel in 5 Aufzügen. Bartelmus, Wien 1863. (Digitalisat)
 Agnes von Meran. Trauerspiel in fünf Acten. L. Rosner, Wien 1877. (Digitalisat)
 Mein Leben. Selbstbiographie, Tagebuchblätter und Briefe. Aus dem Nachlass herausgegeben von seiner Schwester Caroline Nissel. J. G. Cotta, Stuttgart 1894. (Digitalisat)
 Ein Nachtlager Corvins. Historisches Lustspiel in drei Akten. Cotta, Stuttgart und Berlin o. J.
 Ausgewählte dramatische Werke, 3 Bände, 1892–1896

Literatur

 Josef Ratislav: Franz Nissel. Sein Leben, seine Werke, sein Nachlass. Dissertation, Universität Wien 1914 (Nachweis im obv).

Weblinks

Autor
Literatur (Deutsch)
Literatur (Österreich)
Literatur (19. Jahrhundert)
Drama
Person (Österreich-Ungarn)
Person (Kaisertum Österreich)
Geboren 1831
Gestorben 1893
Mann

Franz Nissel


සුදෝ සුදු

සුදෝ සුදු
දෙතොල මධූ
සුදෝ සුදු
දෙනෙත මධූ
මධූ… මධූ… මධූ… මධුර මධූ
සූදෝ… සූදෝ… සූදෝ… සූදෝ සුදු

පවන් සලන්න වරල මොනර පිල් කළඹින්
ලවන් දොවන්න සිසිල් සුරත් තොල් සඟලින්
ගිමන් නිවන්න සිහිල් සුසුම් මඳ පවනින්
සවන් සදන්න නීල් දෙපොත් රන් ස්වරයෙන්

කැලූම් මුදන්න නීල නුවන් මිණි පහනින්
තැවුල් දවන්න ළමැද හංස තුඩු පහසින්
උදාව එන්න ඉනිද්‍ර ගිරින් හිරු විලසින්
සදා රැදෙන්න හද දෙවොලේ පුද ලබමින්

සුදෝ සුදූ


Martina Gillian Topley-Bird (née Topley; born 7 May 1975) is an English singer, songwriter and multi-instrumentalist who first gained fame as the featured female vocalist on trip hop pioneer Tricky's debut album, Maxinquaye (1995). She also worked with him on his subsequent albums Nearly God and Pre-Millennium Tension (both 1996). In 2003, Topley-Bird released her debut solo album, Quixotic, which was critically praised and earned her a Mercury Prize nomination.

This was followed by Anything (2004), The Blue God (2008) and Some Place Simple (2010). She has also collaborated with Gorillaz, appearing on their album Demon Days (2005), as well as with Mark Lanegan, Diplo and Massive Attack on Heligoland (2010), which she followed by a world tour with the group. Her song "Sandpaper Kisses" was covered by Stephen Marley and sampled by such artists as Berry Weight and The Weeknd and used in the video game Fahrenheit.

Early life
Martina Topley-Bird was born in London, England, to Charlette Conlon (née Pouncey) and Martin Geoffrey Topley, who died in 1974 before her birth; Martina was named after him. Her stepfather is British direct marketing specialist Drayton Bird, whose surname she adopted in addition to her father's. Her mother is of Salvadoran and Seminole descent, while her father was of untraceable African-American descent. Topley-Bird grew up in a large family, with five siblings and three step-siblings.

Her family later relocated from London to Bristol. "We moved about a bit when I was a child, which wasn't a problem for me", she recalled. "If you're not white and middle-class, then you're slightly different and exotic in those new environments. I thrived, and survived, on those situations." She attended Clifton College, where she was a member of the school choir and took piano lessons.

Topley-Bird grew up listening to R&B, opera and soul music throughout her childhood. As a teenager, she began listening to alternative rock, and became a fan of The Sugarcubes, Faith No More and Jane's Addiction.

Career

Collaboration with Tricky
In 1993, as a teenager at Clifton College, Topley-Bird was discovered by trip hop pioneer Tricky when he saw her sitting on a wall near his house, singing to herself. "That's really how it happened," she recalled. "It's one of those things people are always surprised to find out is true. I remember the graveyard behind the wall. A few weeks later, I went around to his house with some friends. We'd been drinking cider after our GCSEs. We were banging on his door, but he wasn't in. Then Mark Stewart, who lived there, came up to us and said: "Yeah, this is Tricky's house, jump in through the window." So I jumped through, opened the door and we got mashed."

She and Tricky formed a musical partnership, and Topley-Bird collaborated with Tricky as a featured vocalist on his debut album Maxinquaye (1995) (a printing error credited her as "Martine"). Almost all of her vocals on the album were recorded in a single take. In describing the recording sessions, she recalled: "It was totally instinctive. There was no time to drum up an alter ego. I liked the idea that the information people needed about me was what they would hear when they put the record on. Anything else was sort of extraneous. I didn't think there was anything in my biography that would explain my musical choices." Topley-Bird continued her collaboration with Tricky on his follow-up albums Nearly God (1996), Pre-Millennium Tension (1996) and Angels with Dirty Faces (1998).

Solo career
Following a deterioration of her professional and personal relationship with Tricky in 1998, Topley-Bird began pursuing a solo career. It was not until 2003 that she released her debut album, Quixotic through Independiente Records, which she recorded in Los Angeles, California. The record was mainly produced by Topley-Bird and production team AMP9 (Alex McGowan, Steve Crittall, Nick Bird) at Space Eko Recording Studios London. She commented on the ideas underpinning the album, saying: A lot of it is about the notion of physical proximity relative to emotional connections between people. My dad, Martin Topley, died when he was twenty-nine. Between the release of Quixotic and now, I turned twenty-nine, and I started thinking about what I'd achieved in my life as a human being. Also I wanted to focus on the nature of my relationship with him and with my step father because of the way I felt those relationships were affecting my other relationships.
Quixotic peaked at no. 70 on the UK Albums Chart, received positive reviews from critics, and was a finalist for the 2003 Mercury Music Prize. The album finally saw a limited release in the United States in July 2004, when the Palm Pictures label released a re-designed and re-sequenced version under the title Anything, which featured a shorter track listing and all-new cover art. The following year she appeared on the Starbucks compilation album Sweetheart 2005: Love Songs, which features contemporary musicians covering classic love songs. She sang "I Only Have Eyes for You", a song originally by composer Harry Warren and lyricist Al Dubin.

Her second solo album, The Blue God, was released in May 2008. The track "Carnies" was released as the first single on 3 March 2008 and reached No. 20 on the UK Indie Charts. "Poison" was released as the second single and reached No.9 on the Indie Charts. The album received generally positive critical reception; the BBC said it "warps reflections of Ella Fitzgerald, two-tone ska, dark psychedelia and Pentangle's 'acid folk' into unsettling shapes ... It's an often astonishing album and one which, if Tricky's forthcoming comeback can't match it, may curse him to be known as 'that bloke who used to rap with Martina Topley Bird.'"

In 2010, Topley-Bird appeared on the album Heligoland by Massive Attack, lending her voice on the tracks "Psyche" and "Babel". She also toured with Massive Attack as their support act and providing guest vocals at their shows. Her third album, Some Place Simple, was released in July 2010. It contained stripped-down versions of songs from her first two releases, along with four new songs. Topley-Bird performed Maxinquaye live at the Sundance Festival, London, in April 2012, and also appeared on the tracks "Open" and "Secret" on electronic musician Clark's album Iradelphic. In 2013 contributed to a cover of the xx's "Crystalised" with Mark Lanegan and Warpaint, and also toured with Warpaint as a supporting act on their 2013 world tour. On 26 January 2015, she appeared as lead vocalist on title track "The Day is My Enemy", the second single of The Prodigy's, from their album The Day Is My Enemy (2015).

In 2018 Martina Topley-Bird released the single "Solitude", which would subsequently appear on the four-track EP MTB Continued.
In May 2021 Topley-Bird released the single "Pure Heart" from her upcoming album Forever I Wait.

Influences
Topley-Bird cites Ella Fitzgerald and Billie Holiday as vocal influences, as well as Tom Waits and Serge Gainsbourg as instrumental influences. She has cited Bollywood and Asian cinema as an influence on her album artwork as well, including the work of Park Chan-wook.

Personal life
Topley-Bird has an elder sister named Elizabeth.

Topley-Bird had one daughter with Tricky. Mina Mazy (19 March 1995 – 8 May 2019) was born one month after the release of Maxinquaye.

She has said that she doesn't consider herself religious, though she "believes in the need to have a healthy spiritual life".

Discography

Albums
 Quixotic (2003)
 Anything (2004)
 The Blue God (2008)
 Some Place Simple (2010)
 Forever I Wait (2021)

Singles
 "Need One" (2003)
 "Anything" (2003)
 "I Still Feel" (2003)
 "Soul Food" (2004)
 "Carnies" (2008)
 "Poison" (2008)
 "Baby Blue" (2008)
 "Crystalised" (2013) (with Mark Lanegan and Warpaint)
Solitude (2018)
 "Pure Heart" (2021)
 "Hunt" (2021)

Guest appearances

 Tricky – "Overcome", "Ponderosa", "Black Steel", "Hell Is Round the Corner", "Aftermath", "Abbaon Fat Tracks", "Brand New You're Retro", "Suffocated Love", "Strugglin'" and "Feed Me" from Maxinquaye (1995)
 Tricky – "Black Coffee", "Children's Story", "I Be the Prophet", "Judas," and "Poems" from Nearly God (1996)
 Tricky – "Vent", "Christiansands", "Bad Dreams", "Makes Me Wanna Die", "Sex Drive", "Lyrics of Fury" and "My Evil Is Strong" from Pre-Millennium Tension (1996)
 Tricky – "Singing the Blues", "6 Minutes", "Analyze Me", "Talk to Me (Angels with Dirty Faces)", "Carriage for Two" and "Demise" from Angels with Dirty Faces (1998)
 Primus – "Dirty Drowning Man" and "Coattails of a Dead Man" from Antipop (1999)
 David Holmes – "Outrun" and "Zero Tolerance" from Bow Down to the Exit Sign (2000)
 Diplo – "Into the Sun" from Florida (2004)
 Gorillaz – "All Alone" from Demon Days (2005)
 Common – "Everywhere" from Universal Mind Control (2008)
 Leila – "Deflect" and "Why Should I?" from Blood Looms and Blooms (2008)
 Massive Attack – "Babel" and "Psyche" from Heligoland (2010)
 Clark – "Open", "Secret" and "The Pining Pt. 2" from Iradelphic (2012)
 The Prodigy – "The Day Is My Enemy" from The Day Is My Enemy (2015)
 Tricky – "When We Die" (2016)

References

External links

Martina Topley Bird's Some Place Simple – tribute website to the artist
"Girl interrupted" article from The Observer Magazine, 25 May 2003

1975 births
Living people
British trip hop musicians
People educated at Clifton College
English people of Salvadoran descent
British people of Seminole descent
English people of African-American descent
English people of Native American descent
People from St Pancras, London
Musicians from Bristol
21st-century Black British women singers
British women in electronic music
English rock singers
English women pop singers
Black British rock musicians
Independiente Records artists

Martina Topley-Bird


In statistics, Gibbs sampling or a Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm for obtaining a sequence of observations which are approximated from a specified multivariate probability distribution, when direct sampling is difficult.  This sequence can be used to approximate the joint distribution (e.g., to generate a histogram of the distribution); to approximate the marginal distribution of one of the variables, or some subset of the variables (for example, the unknown parameters or latent variables); or to compute an integral (such as the expected value of one of the variables).  Typically, some of the variables correspond to observations whose values are known, and hence do not need to be sampled.

Gibbs sampling is commonly used as a means of statistical inference, especially Bayesian inference.  It is a randomized algorithm (i.e. an algorithm that makes use of random numbers), and is an alternative to deterministic algorithms for statistical inference such as the expectation-maximization algorithm (EM).

As with other MCMC algorithms, Gibbs sampling generates a Markov chain of samples, each of which is correlated with nearby samples. As a result, care must be taken if independent samples are desired. Generally, samples from the beginning of the chain (the burn-in period) may not accurately represent the desired distribution and are usually discarded.

Introduction
Gibbs sampling is named after the physicist Josiah Willard Gibbs, in reference to an analogy between the sampling algorithm and statistical physics. The algorithm was described by brothers Stuart and Donald Geman in 1984, some eight decades after the death of Gibbs, and became popularized in the statistics community for calculating marginal probability distribution, especially the posterior distribution.

In its basic version, Gibbs sampling is a special case of the Metropolis–Hastings algorithm.  However, in its extended versions (see below), it can be considered a general framework for sampling from a large set of variables by sampling each variable (or in some cases, each group of variables) in turn, and can incorporate the Metropolis–Hastings algorithm (or methods such as slice sampling) to implement one or more of the sampling steps.

Gibbs sampling is applicable when the joint distribution is not known explicitly or is difficult to sample from directly, but the conditional distribution of each variable is known and is easy (or at least, easier) to sample from. The Gibbs sampling algorithm generates an instance from the distribution of each variable in turn, conditional on the current values of the other variables.  It can be shown that the sequence of samples constitutes a Markov chain, and the stationary distribution of that Markov chain is just the sought-after joint distribution.

Gibbs sampling is particularly well-adapted to sampling the posterior distribution of a Bayesian network, since Bayesian networks are typically specified as a collection of conditional distributions.

Implementation

Gibbs sampling, in its basic incarnation, is a special case of the Metropolis–Hastings algorithm.  The point of Gibbs sampling is that given a multivariate distribution it is simpler to sample from a conditional distribution than to marginalize by integrating over a joint distribution.  Suppose we want to obtain  samples of  from a joint distribution .  Denote the th sample by .  We proceed as follows:

We begin with some initial value .
We want the next sample. Call this next sample . Since  is a vector, we sample each component of the vector, , from the distribution of that component conditioned on all other components sampled so far. But there is a catch: we condition on 's components up to , and thereafter condition on 's components, starting from  to . To achieve this, we sample the components in order, starting from the first component. More formally, to sample , we update it according to the distribution specified by . We use the value that the th component had in the th sample, not the th sample.
Repeat the above step  times.

Properties
If such sampling is performed, these important facts hold:
 The samples approximate the joint distribution of all variables.
 The marginal distribution of any subset of variables can be approximated by simply considering the samples for that subset of variables, ignoring the rest.
 The expected value of any variable can be approximated by averaging over all the samples.

When performing the sampling:
The initial values of the variables can be determined randomly or by some other algorithm such as expectation-maximization.
It is not actually necessary to determine an initial value for the first variable sampled.
It is common to ignore some number of samples at the beginning (the so-called burn-in period), and then consider only every th sample when averaging values to compute an expectation.  For example, the first 1,000 samples might be ignored, and then every 100th sample averaged, throwing away all the rest. The reason for this is that (1) the stationary distribution of the Markov chain is the desired joint distribution over the variables, but it may take a while for that stationary distribution to be reached; (2) successive samples are not independent of each other but form a Markov chain with some amount of correlation. Sometimes, algorithms can be used to determine the amount of autocorrelation between samples and the value of  (the period between samples that are actually used) computed from this, but in practice there is a fair amount of "black magic" involved.
The process of simulated annealing is often used to reduce the "random walk" behavior in the early part of the sampling process (i.e. the tendency to move slowly around the sample space, with a high amount of autocorrelation between samples, rather than moving around quickly, as is desired). Other techniques that may reduce autocorrelation are collapsed Gibbs sampling, blocked Gibbs sampling, and ordered overrelaxation; see below.

Relation of conditional distribution and joint distribution
Furthermore, the conditional distribution of one variable given all others is proportional to the joint distribution:

"Proportional to" in this case means that the denominator is not a function of  and thus is the same for all values of ; it forms part of the normalization constant for the distribution over .  In practice, to determine the nature of the conditional distribution of a factor , it is easiest to factor the joint distribution according to the individual conditional distributions defined by the graphical model over the variables, ignore all factors that are not functions of  (all of which, together with the denominator above, constitute the normalization constant), and then reinstate the normalization constant at the end, as necessary.  In practice, this means doing one of three things:
If the distribution is discrete, the individual probabilities of all possible values of  are computed, and then summed to find the normalization constant.
If the distribution is continuous and of a known form, the normalization constant will also be known.
In other cases, the normalization constant can usually be ignored, as most sampling methods do not require it.

Inference
Gibbs sampling is commonly used for statistical inference (e.g. determining the best value of a parameter, such as determining the number of people likely to shop at a particular store on a given day, the candidate a voter will most likely vote for, etc.).  The idea is that observed data is incorporated into the sampling process by creating separate variables for each piece of observed data and fixing the variables in question to their observed values, rather than sampling from those variables.  The distribution of the remaining variables is then effectively a posterior distribution conditioned on the observed data.

The most likely value of a desired parameter (the mode) could then simply be selected by choosing the sample value that occurs most commonly; this is essentially equivalent to maximum a posteriori estimation of a parameter. (Since the parameters are usually continuous, it is often necessary to "bin" the sampled values into one of a finite number of ranges or "bins" in order to get a meaningful estimate of the mode.) More commonly, however, the expected value (mean or average) of the sampled values is chosen; this is a Bayes estimator that takes advantage of the additional data about the entire distribution that is available from Bayesian sampling, whereas a maximization algorithm such as expectation maximization (EM) is capable of only returning a single point from the distribution.  For example, for a unimodal distribution the mean (expected value) is usually similar to the mode (most common value), but if the distribution is skewed in one direction, the mean will be moved in that direction, which effectively accounts for the extra probability mass in that direction. (If a distribution is multimodal, the expected value may not return a meaningful point, and any of the modes is typically a better choice.)

Although some of the variables typically correspond to parameters of interest, others are uninteresting ("nuisance") variables introduced into the model to properly express the relationships among variables.  Although the sampled values represent the joint distribution over all variables, the nuisance variables can simply be ignored when computing expected values or modes; this is equivalent to marginalizing over the nuisance variables.  When a value for multiple variables is desired, the expected value is simply computed over each variable separately. (When computing the mode, however, all variables must be considered together.)

Supervised learning, unsupervised learning and semi-supervised learning (aka learning with missing values) can all be handled by simply fixing the values of all variables whose values are known, and sampling from the remainder.

For observed data, there will be one variable for each observation—rather than, for example, one variable corresponding to the sample mean or sample variance of a set of observations.  In fact, there generally will be no variables at all corresponding to concepts such as "sample mean" or "sample variance".  Instead, in such a case there will be variables representing the unknown true mean and true variance, and the determination of sample values for these variables results automatically from the operation of the Gibbs sampler.

Generalized linear models (i.e. variations of linear regression) can sometimes be handled by Gibbs sampling as well.  For example, probit regression for determining the probability of a given binary (yes/no) choice, with normally distributed priors placed over the regression coefficients, can be implemented with Gibbs sampling because it is possible to add additional variables and take advantage of conjugacy.  However, logistic regression cannot be handled this way.  One possibility is to approximate the logistic function with a mixture (typically 7–9) of normal distributions.  More commonly, however, Metropolis–Hastings is used instead of Gibbs sampling.

Mathematical background

Suppose that a sample  is taken from a distribution depending on a parameter vector  of length , with prior distribution .  It may be that  is very large and that numerical integration to find the marginal densities of the  would be computationally expensive. Then an alternative method of calculating the marginal densities is to create a Markov chain on the space  by repeating these two steps:

 Pick a random index
 Pick a new value for  according to

These steps define a reversible Markov chain with the desired invariant distribution . This
can be proved as follows. Define  if  for all  and let  denote the probability of a jump from  to . Then, the transition probabilities are

So

since  is an equivalence relation. Thus the detailed balance equations are satisfied, implying the chain is reversible and it has invariant distribution .

In practice, the index  is not chosen at random, and the chain cycles through the indexes in order. In general this gives a non-stationary Markov process, but each individual step will still be reversible, and the overall process will still have the desired stationary distribution (as long as the chain can access all states under the fixed ordering).

Gibbs sampler in Bayesian inference and its relation to information theory
Let  denote observations generated from the sampling distribution  and  be a prior supported on the parameter space . Then one of the central goals of the Bayesian statistics is to approximate the posterior density

where the marginal likelihood  is assumed to be finite for all .

To explain the Gibbs sampler, we additionally assume that the parameter space  is decomposed as

,
where  represents the Cartesian product. Each component parameter space  can be a set of scalar components, subvectors, or matrices.

Define a set  that complements the . Essential ingredients of the Gibbs sampler is the -th full conditional posterior distribution for each
.

The following algorithm details a generic Gibbs sampler:

Note that Gibbs sampler is operated by the iterative Monte Carlo scheme within a cycle. The  number of samples  drawn by the above algorithm formulates Markov Chains with the invariant distribution to be the target density .

Now, for each , define the following information theoretic quantities:

namely, posterior mutual information, posterior differential entropy, and posterior conditional differential entropy, respectively. We can similarly define information theoretic quantities , , and  by interchanging the  and  in the defined quantities. Then, the following  equations hold.

.

The mutual information  quantifies the reduction in uncertainty of random quantity  once we know , a posteriori. It vanishes if and only if  and  are marginally independent, a posterior. The mutual information  can be interpreted as the quantity that is transmitted from the -th step to the -th step within a single cycle of the Gibbs sampler.

Variations and extensions

Numerous variations of the basic Gibbs sampler exist.  The goal of these variations is to reduce the autocorrelation between samples sufficiently to overcome any added computational costs.

Blocked Gibbs sampler
A blocked Gibbs sampler groups two or more variables together and samples from their joint distribution conditioned on all other variables, rather than sampling from each one individually.  For example, in a hidden Markov model, a blocked Gibbs sampler might sample from all the latent variables making up the Markov chain in one go, using the forward-backward algorithm.

Collapsed Gibbs sampler
A collapsed Gibbs sampler integrates out (marginalizes over) one or more variables when sampling for some other variable.  For example, imagine that a model consists of three variables A, B, and C.  A simple Gibbs sampler would sample from p(A | B,C), then p(B | A,C), then p(C | A,B).  A collapsed Gibbs sampler might replace the sampling step for A with a sample taken from the marginal distribution p(A | C), with variable B integrated out in this case.  Alternatively, variable B could be collapsed out entirely, alternately sampling from p(A | C) and p(C | A) and not sampling over B at all.  The distribution over a variable A that arises when collapsing a parent variable B is called a compound distribution; sampling from this distribution is generally tractable when B is the conjugate prior for A, particularly when A and B are members of the exponential family.  For more information, see the article on compound distributions or Liu (1994).

Implementing a collapsed Gibbs sampler

Collapsing Dirichlet distributions

In hierarchical Bayesian models with categorical variables, such as latent Dirichlet allocation and various other models used in natural language processing, it is quite common to collapse out the Dirichlet distributions that are typically used as prior distributions over the categorical variables.  The result of this collapsing introduces dependencies among all the categorical variables dependent on a given Dirichlet prior, and the joint distribution of these variables after collapsing is a Dirichlet-multinomial distribution.  The conditional distribution of a given categorical variable in this distribution, conditioned on the others, assumes an extremely simple form that makes Gibbs sampling even easier than if the collapsing had not been done.  The rules are as follows:
Collapsing out a Dirichlet prior node affects only the parent and children nodes of the prior.  Since the parent is often a constant, it is typically only the children that we need to worry about.
Collapsing out a Dirichlet prior introduces dependencies among all the categorical children dependent on that prior — but no extra dependencies among any other categorical children. (This is important to keep in mind, for example, when there are multiple Dirichlet priors related by the same hyperprior.  Each Dirichlet prior can be independently collapsed and affects only its direct children.)
After collapsing, the conditional distribution of one dependent children on the others assumes a very simple form: The probability of seeing a given value is proportional to the sum of the corresponding hyperprior for this value, and the count of all of the other dependent nodes assuming the same value.  Nodes not dependent on the same prior must not be counted. The same rule applies in other iterative inference methods, such as variational Bayes or expectation maximization; however, if the method involves keeping partial counts, then the partial counts for the value in question must be summed across all the other dependent nodes.  Sometimes this summed up partial count is termed the expected count or similar. The probability is proportional to the resulting value; the actual probability must be determined by normalizing across all the possible values that the categorical variable can take (i.e. adding up the computed result for each possible value of the categorical variable, and dividing all the computed results by this sum).
If a given categorical node has dependent children (e.g. when it is a latent variable in a mixture model), the value computed in the previous step (expected count plus prior, or whatever is computed) must be multiplied by the actual conditional probabilities (not a computed value that is proportional to the probability!) of all children given their parents.  See the article on the Dirichlet-multinomial distribution for a detailed discussion.
In the case where the group membership of the nodes dependent on a given Dirichlet prior may change dynamically depending on some other variable (e.g. a categorical variable indexed by another latent categorical variable, as in a topic model), the same expected counts are still computed, but need to be done carefully so that the correct set of variables is included.   See the article on the Dirichlet-multinomial distribution for more discussion, including in the context of a topic model.

Collapsing other conjugate priors

In general, any conjugate prior can be collapsed out, if its only children have distributions conjugate to it.  The relevant math is discussed in the article on compound distributions.  If there is only one child node, the result will often assume a known distribution.  For example, collapsing an inverse-gamma-distributed variance out of a network with a single Gaussian child will yield a Student's t-distribution. (For that matter, collapsing both the mean and variance of a single Gaussian child will still yield a Student's t-distribution, provided both are conjugate, i.e. Gaussian mean, inverse-gamma variance.)

If there are multiple child nodes, they will all become dependent, as in the Dirichlet-categorical case.  The resulting joint distribution will have a closed form that resembles in some ways the compound distribution, although it will have a product of a number of factors, one for each child node, in it.

In addition, and most importantly, the resulting conditional distribution of one of the child nodes given the others (and also given the parents of the collapsed node(s), but not given the children of the child nodes) will have the same density as the posterior predictive distribution of all the remaining child nodes.  Furthermore, the posterior predictive distribution has the same density as the basic compound distribution of a single node, although with different parameters.  The general formula is given in the article on compound distributions.

For example, given a Bayes network with a set of conditionally independent identically distributed Gaussian-distributed nodes with conjugate prior distributions placed on the mean and variance, the conditional distribution of one node given the others after compounding out both the mean and variance will be a Student's t-distribution.  Similarly, the result of compounding out the gamma prior of a number of Poisson-distributed nodes causes the conditional distribution of one node given the others to assume a negative binomial distribution.

In these cases where compounding produces a well-known distribution, efficient sampling procedures often exist, and using them will often (although not necessarily) be more efficient than not collapsing, and instead sampling both prior and child nodes separately. However, in the case where the compound distribution is not well-known, it may not be easy to sample from, since it generally will not belong to the exponential family and typically will not be log-concave (which would make it easy to sample using adaptive rejection sampling, since a closed form always exists).

In the case where the child nodes of the collapsed nodes themselves have children, the conditional distribution of one of these child nodes given all other nodes in the graph will have to take into account the distribution of these second-level children.  In particular, the resulting conditional distribution will be proportional to a product of the compound distribution as defined above, and the conditional distributions of all of the child nodes given their parents (but not given their own children).  This follows from the fact that the full conditional distribution is proportional to the joint distribution.  If the child nodes of the collapsed nodes are continuous, this distribution will generally not be of a known form, and may well be difficult to sample from despite the fact that a closed form can be written, for the same reasons as described above for non-well-known compound distributions.  However, in the particular case that the child nodes are discrete, sampling is feasible, regardless of whether the children of these child nodes are continuous or discrete.  In fact, the principle involved here is described in fair detail in the article on the Dirichlet-multinomial distribution.

Gibbs sampler with ordered overrelaxation
A Gibbs sampler with ordered overrelaxation samples a given odd number of candidate values for  at any given step and sorts them, along with the single value for  according to some well-defined ordering.  If  is the sth smallest in the sorted list then the  is selected as the sth largest in the sorted list.  For more information, see Neal (1995).

Other extensions
It is also possible to extend Gibbs sampling in various ways.  For example, in the case of variables whose conditional distribution is not easy to sample from, a single iteration of slice sampling or the Metropolis–Hastings algorithm can be used to sample from the variables in question.
It is also possible to incorporate variables that are not random variables, but whose value is deterministically computed from other variables.  Generalized linear models, e.g. logistic regression (aka "maximum entropy models"), can be incorporated in this fashion. (BUGS, for example, allows this type of mixing of models.)

Failure modes

There are two ways that Gibbs sampling can fail.  The first is when there are islands of high-probability states, with no paths between them.  For example, consider a probability distribution  over 2-bit vectors, where the vectors (0,0) and (1,1) each have probability ½, but the other two vectors (0,1) and (1,0) have probability zero.  Gibbs sampling will become trapped in one of the two high-probability vectors, and will never reach the other one.  More generally, for any distribution over high-dimensional, real-valued vectors, if two particular elements of the vector are perfectly correlated (or perfectly anti-correlated), those two elements will become stuck, and Gibbs sampling will never be able to change them.

The second problem can happen even when all states have nonzero probability and there is only a single island of high-probability states.  For example, consider a probability distribution over 100-bit vectors, where the all-zeros vector occurs with probability ½, and all other vectors are equally probable, and so have a probability of  each.  If you want to estimate the probability of the zero vector, it would be sufficient to take 100 or 1000 samples from the true distribution.  That would very likely give an answer very close to ½.  But you would probably have to take more than  samples from Gibbs sampling to get the same result.  No computer could do this in a lifetime.

This problem occurs no matter how long the burn-in period is.  This is because in the true distribution, the zero vector occurs half the time, and those occurrences are randomly mixed in with the nonzero vectors.  Even a small sample will see both zero and nonzero vectors.  But Gibbs sampling will alternate between returning only the zero vector for long periods (about  in a row), then only nonzero vectors for long periods (about  in a row).  Thus convergence to the true distribution is extremely slow, requiring much more than  steps; taking this many steps is not computationally feasible in a reasonable time period. The slow convergence here can be seen as a consequence of the curse of dimensionality.
A problem like this can be solved by block sampling the entire 100-bit vector at once. (This assumes that the 100-bit vector is part of a larger set of variables.  If this vector is the only thing being sampled, then block sampling is equivalent to not doing Gibbs sampling at all, which by hypothesis would be difficult.)

Software
 The OpenBUGS software (Bayesian inference Using Gibbs Sampling) does a Bayesian analysis of complex statistical models using Markov chain Monte Carlo.

 JAGS (Just another Gibbs sampler) is a GPL program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo.

 Church is free software for performing Gibbs inference over arbitrary distributions that are specified as probabilistic programs.

 PyMC is an open source Python library for Bayesian learning of general Probabilistic Graphical Models.
Turing is an open source Julia library for Bayesian Inference using probabilistic programming.

Notes

References

 Bolstad, William M. (2010), Understanding Computational Bayesian Statistics, John Wiley
  (Contains a basic summary and many references.)

 Gelman, A., Carlin J. B., Stern H. S., Dunson D., Vehtari A., Rubin D. B. (2013), Bayesian Data Analysis, third edition. London: Chapman & Hall.
 Levin, David A.; Peres, Yuval; Wilmer, Elizabeth L. (2008), "Markov Chains and Mixing Times", American Mathematical Society.
 Robert, C. P.; Casella, G. (2004), Monte Carlo Statistical Methods (second edition), Springer-Verlag.

Markov chain Monte Carlo

Gibbs sampling


A Lamar Hunt U.S. Open Cup de 2022 é a 107ª edição da US Open Cup. É a mais antiga competição em andamento nos Estados Unidos e será disputada por 103 equipes dentro do sistema de ligas de futebol nos Estados Unidos. A competição acontecerá após as edições de 2020 e 2021 serem canceladas devido à pandemia da Pandemia de COVID-19.

Os 103 participantes são 25 clubes da Major League Soccer, 23 clubes afiliados na USL Championship, 11 da USL League One, 10 times da NISA, 2 clubes da MLS Next Pro, 10 participantes da USL League Two, 10 times da NPSL e 11 times provenientes dos ligas locais amadoras. O atual campeão da National Amateur Cup , o Lansdowne Bhoys FC , foi convidado para o torneio.

O Atlanta United FC é o atual campeão.

Participantes

 Negrito: Times ainda vivos no torneio.

Primeira Fase
A primeira fase da competição aconteceu nos dias 22 e 23 de março. Com 16 jogos entres os times das divisões USASA (8 times), NPSL (10 times) e USL League Two (10 times). Os times foram pareados de acordo com a distância geográfica.

Segunda Fase
A segunda fase da competição aconteceu nos dias 5, 6 e 7 de abril. Com 31 jogos entres os 16 times campeões do primeiro round, os 2 clubes da MLS Next Pro, os 10 times da NISA, os 11 clubes USL League One e os 23 times da USL Championship. Cada campeão do primeiro round enfrentou um time novato no torneio, e os times restantes foram pareados de acordo com as suas divisões, times da III divisão enfrentam times da II divisão. A escolha dos duelos foi feita pela distância geográfica entre os times.

Terceira Fase
Os campeões da segunda fase, juntamente com os 12 times da MLS que entram nesta fase, serão sorteados em grupos de quatro (12 grupos) ou de seis (8 grupos), dependendo da distribuição geográfica dos times restantes no torneio. Os times da MLS serão alocados em grupos distintos se possível, como também irão enfrentar times de divisões mais baixas. Se o time não se encaixar em nenhum time geograficamente, será realizado um sorteio.

Grupos

Resultados

Quarta Fase
Os vencedores da Terceira Fase e os times que entrarão nessa fase serão agrupados geograficamente em oito grupos com quatro times. Cada grupo terá 1 dos times da MLS que entrará nessa fase. O sorteio acontecerá no dia 21 de abril. E as partidas serão jogadas nos dias 10 e 11 de maio.

Grupos

Resultados

Fase Final
Para a fase final, os campeões do quarto round foram dividos em 4 grupos de acordo com a distância geográfica. Os duelos foram sorteados a partir desses grupos. Esse sorteio serviu até o final do campeoanato.

Oitavas de Final

Quartas de Final

Semifinais

Final

Premiação

2022
Desporto nos Estados Unidos em 2022

Lamar Hunt U.S. Open Cup de 2022


Gaşiye Suresi (Arapça: سورة الغاشية) Kur'an'ın 88. suresidir. Sure 26 ayetten oluşur.

Sure ismini ilk ayette geçen ve örten, bürüyen, kaplayan anlamına gelen gaşiye kelimesinden alır. Kıyamet, cehennem, cennet, cehennem ve ibret alınması gereken nesnelerden bahseder.

Kaynakça

Sureler

Gaşiye Suresi


Die Liste der denkmalgeschützten Objekte in Mládzovo enthält das eine nach slowakischen Denkmalschutzvorschriften geschützte Objekt in der Gemeinde Mládzovo im Okres Poltár.

Denkmäler

Legende

Einzelnachweise

Mladzovo
!


فيتاس بيرنغ (12 أغسطس 1681 - 8 ديسمبر 1741)  مستكشف دنماركي خدم مع البحرية الروسية (حيث عرف باسم إيفان إيفانوفيتش). عرف بأعماله في اكتشاف ألاسكا وجزرها ومضيق بيرنغ الذي حمل اسمه.

فيتوس يوهانسن بيرنغ (1681 – 1741) رحالة بحري وضابط في الجيش الروسي والربان البحري ترأس البعثتين الجغرافيتين العلميتين في شبه جزيرة كامتشاتكا وبلغ ساحل شمال أمريكا واكتشف عددا من جزر أليوت. وقد أطلق اسم بيرينغ على جزيرة ومضيق وبحر.

ولد فيتوس بيرنغ عام 1681 في مدينة هورسنس الدانماركية وتخرج عام 1703 من الكلية الحربية في امستردام وتطوع في العام نفسه للخدمة العسكرية في روسيا برتبة الملازم الثاني ثم خدم في اسطول البلطيق الروسي ابان الحرب الشمالية الكبرى.

في عام 1707 تمت احالته إلى اسطول آزوف الروسي حيث شارك في الحرب مع تركيا ثم عاد إلى بحر البلطيق وترقى عام 1715 إلى النقيب البحري وتزوج من روسية.

خدم بيرنغ في فترة ما بين عام 1716 وعام 1724 في السلاح البحري الروسي قائدا للسفن الحربية واحيل إلى المعاش عام 1724 ثم عاد إلى الخدمة بطلب من القيصر بطرس الأول.

قرر القيصر بطرس الأكبر قبل موته بقليل بتوجيه بعثة جغرافية علمية إلى منطقة الشرق الأقصى وكلف بيرنغ بترأس بعثة كان من شأنها اكتشاف مضيق بحري يفصل بين أمريكا وآسيا أو برزخ يربط بينهما والنزول إلى الجنوب التفافا على القارة الأمريكية.

قام بيرنغ في اعوام 1725 – 1730 بالبعثة العلمية الأولى إلى ساحل كامتشاتكا. وقطع بيرنغ وزملاؤه مسافات هائلة عبر القارة الاوراسية من غربها إلى شرقها ووصلوا إلى دلتا نهر كامتشاكا حيث تم في عام 1728 بناء سفينة «القديس جبرائيل» التي توجهت في اتجاه شمال الشرق ومرت بمضيق يفصل بين آسيا وأمريكا وصولا إلى بحر تشوكوتكا دون أن تكتشف الساحل الأمريكي. ومن فضول تلك البعثة اكتشاف السواحل الشمالية الشرقية لاسيا التي بلغت طولها 3500 كيلومتر ورسم خرائطها والتأكيد على عدم اتصال آسيا بأمريكا.

انيط بيرينغ عام 1733 بمهمة ترأس البعثة العلمية الثانية إلى الشرق الأقصى.

انطلقت سفينتا «القديس بطرس» والقديس بولص" بقيادة الربانين فيتوس بيرينغ واليكسي تشيريكوف في 4 يونيو/حزيران عام 1741 من ميناء اخوتسك الواقع على ساحل بحر اخوتسك في المحيط الهادي متوجهتين إلى الساحل الشرقي لشبه جزيرة كامتشاتكا، وامضتا الشتاء في منطقة خليج افاتشينسكايا حيث تم تأسيس قلعة اطلقت عليها تسمية بيتروبافلوفس أو بالاحرى بطرص وبولص نسبة إلى القديسين والسفينتين.

وتعتبرمدينة بيتروبافلوفسك حاليا عاصمة لإقليم كامتشاكا الروسية.

وواجهت السفينتان في 20 يونيو/حزيران عاصفة بحرية شديدة ابعدتهما بعضهما عن الآخر ولم تتمكن من الاتصال لتقوما بالإبحار المشترك. لكنهما بلغتا كلاهما الساحل الأمريكي الشمالي الغربي. واصيب الربان بيرينغ في طريق العودة بمرض ومات بعد تحطم سفينته. وتم دفن جثمانه في جزيرة مجهولة أطلق عليها فيما بعد اسم بيرينغ. ولم يعد إلى خليج افاتشينسكا في ربيع عام 1742 الا 46 فردا من افراد الطاقم في سفينة «القديس بطرس».

في عام 1876 قام ممثلو الشركة الروسية الأمريكية بنصب صليب خشبي في مكان يرجح ان يقع فيه قبر بيرنغ. ثم اقام اهالي المنطقة في موقع القبر نصبا تذكاريا وصليبا حديديا يبلغ ارتفاعه 3.5 مترا.

روابط خارجية

مراجع

البعثات الروسية في عصر الاستكشاف
أشخاص من هورسينس
بحارة دنماركيون
بحر بيرنغ
دنماركيون مغتربون في جمهورية هولندا
روس من أصل دنماركي
علماء وصف المياه روس
مستكشفو آسيا
مستكشفو ألاسكا
مستكشفو أمريكا الشمالية
مستكشفو الإمبراطورية الروسية
مستكشفو سيبيريا
مستكشفون دنماركيون
مواليد 1681
مواليد في هورسينس
وفيات 1741
وفيات بسبب نقص فيتامين سي
مغتربون من الدنمارك في الإمبراطورية الروسية
علماء وصف المياه من الإمبراطورية الروسية
بحارة دنماركيون في القرن 18

فيتوس بيرنغ


මෝටර් රථ ධාවන තරග (කාර් රේසිං, මෝටර් රේසිං ලෙසින්ද හැඳින්වෙන) යනු, තරග වැදීම සඳහා මෝටර් රථ ධාවනයෙහි යෙදීම සිදුවන්නාවූ ක්‍රීඩාවකි. මෝටර් රථ ධාවන තරගයන්හී විවිධවූ වෙනස් ප්‍රභේද ඇත.

ක්‍රීඩා

මෝටර් රථ ධාවන තරග


Olaylar
 Adrianus'un eziyetleri sırasında gizlenmek zorunda kalan Yahudi bilgeler, Yavne'ye yerleştiler.

Doğumlar

Ölümler

158


Marcus Calpurnius Bibulus (c. 102 – 48 BC) was a politician of the Roman Republic. He was a conservative and upholder of the established social order who served in several magisterial positions alongside Julius Caesar and conceived a lifelong enmity towards him. In 59 BC he was consul alongside Julius Caesar. Their partnership was contentious to the extent that Caesar's supporters assaulted Bibulus in Rome's main forum on the eve of an important vote. Bibulus withdrew from public politics for the rest of his term.

In 51 to 50 BC, he was governor of Syria, where he was effective but alienated the army by taking too much personal credit for the repulse of the Parthians. In 49, after Caesar's Civil War broke out, Bibulus aligned himself with Pompey and was in charge of the fleet tasked with preventing Caesar from shipping his army across the Adriatic. He failed to do so and subsequently failed to effectively cut off Caesar's supplies. On blockade duty in 48 BC, he fell ill and died.

Early years

Marcus Calpurnius Bibulus was a member of the plebeian Calpurnia gens. His cognomen is not previously attested within the gens so he appears to have been the first of the name to reach curile office, but it is unlikely he was a Novus homo, based on the connections he had. He may have descended from the senatorial Pisonian branch of the Calpurnia gens.

He has been described as "earnest and somewhat plodding". He served as curule aedile alongside Julius Caesar in 65 BC. Two curule aediles were appointed each year and they were responsible for maintenance of public buildings (aedēs) and the regulation of public festivals. They also had powers to enforce public order. Caesar overshadowed Bibulus throughout his year in office, particularly in the provision of the ludi Romani, a religious festival involving multiple ceremonies and secular entertainments. He again served alongside Caesar in 62 BC when both were elected praetors, judicial magisterial positions. Bibulus opposed Caesar at every opportunity. Shortly before they took office, Rome was racked by the Catilinarian conspiracy, an attempt by a number of senior aristocrats to overthrow the Republic. During his term in office, Bibulus was called upon to suppress supporters of the Catiline rebellion among the Paeligni, an Italic confederation from what is now Abruzzo.

Bibulus was firmly in the camp of the self-described boni (good men). The boni were the traditionalist senatorial majority of the Roman Republic, politicians who believed that the role of the Senate was being usurped by the legislative people's assemblies for the benefit of a few power hungry individuals. The boni were against anyone who attempted to use these legislative assemblies to reform the state; which was a major policy of the populist Julius Caesar. Caesar nominated himself to stand for the consular elections of 59 BC, with the support of his powerful allies Gnaeus Pompeius Magnus and Marcus Licinius Crassus. Becoming a consul was considered the highest honour of the Roman Republic. Cato and the rest of the boni feared Caesar to be a radical who would destroy the way of the ancestors, the mores. Bibulus was already implacably opposed to Caesar and was married to Porcia, Cato's daughter. The boni bribed the electors heavily in order to ensure that Bibulus would be Caesar's consular colleague. They succeeded; Bibulus narrowly defeated Caesar's preferred consular candidate, Lucius Lucceius.

Consul
Caesar began his term as consul by "presenting a moderate and carefully reasoned bill" to purchase land in order to discharge and settle Pompey's soldiers who had returned from the east some years previously. The Boni, led by Cato, walked out of the Senate en masse to prevent it being heard. Caesar took the bill to the Centuriate Assembly, largely made up of ex-soldiers. Large numbers of Pompey's veterans came to Rome to participate in the expected vote. Bibulus lost popularity by treating them with aristocratic contempt, telling them that he did not care what they wanted. Bibulus was able to secure the support of three plebeian tribunes to block the passage of the bill. However, Pompey and Crassus publicly supported Caesar's bill, and the opposition to Bibulus was such that the tribunes were unwilling to exercise their veto. Immediately before the vote Bibulus ordered it suspended for religious reasons. Caesar, who was also pontifex maximus, the most significant religious official in Rome, ignored this and continued with the vote. Bibulus and two of his tribunes mounted the steps of the Temple of Castor and Pollux and attempted to denounce the bill. The crowd turned on him and his entourage, breaking his fasces (the symbols of his consulship), pushing him to the ground and pouring feces on him. Getting up, Bibulus uncovered his neck and shouted to the crowd to kill him to end his embarrassment. His fellow senators persuaded him to leave and regroup at a nearby temple, as the assembly proceeded to pass the bill.

The following day, Bibulus entered the Senate where he made a formal complaint about the treatment which he had suffered, and appealed to the Senate to annul the law, but this was not acted upon.  He then resisted swearing an oath to uphold the new law, but was eventually convinced to take the oath. After this humiliation, in March 59 BC, Bibulus stopped attending meetings of the Senate, leaving Caesar with complete control over the consulship. He occasionally issued complaints against Caesar and Pompey, which led to attacks on his house from Caesar and Pompey's supporters. For the rest of the year supporters of the First Triumvirate mocked Bibulus by declaring that the two consuls were really "Julius and Caesar". Bibulus returned the insult by referring to his co-consul as the "Queen of Bithynia," an allusion to Caesar's alleged love affair with the King of Bithynia. He also alleged that Caesar had been involved in the first conspiracy of Catiline. Bibulus spent the remainder of his term sequestered in his house where he claimed he was watching for omens, an act that purported to invalidate all legislation passed that year.

By the middle of his consulship Caesar's popularity began to wane, whilst Bibulus's popularity was, according to Cicero, on the rise, mostly due to his vitriolic attacks on Caesar. Seeking to further cause trouble for Caesar and Pompey, in July he directed that the consular elections for 58 BC were to be postponed until 18October. However, in August Lucius Vettius accused Bibulus and one of the consular candidates for 58 BC, Lucius Cornelius Lentulus Crus, of being involved in a plot to assassinate Pompey. Bibulus responded by declaring that he had warned Pompey of the possibility of an assassination attempt on 13May. Vettius was then murdered the day before Bibulus was to be questioned about his alleged involvement in the plot. Lentulus was unsuccessful when the delayed elections finally took place.  Aulus Gabinius and Lucius Calpurnius Piso Caesoninus won the election.

Bibulus attempted to block Caesar's five-year appointment as governor of the provinces of Cisalpine Gaul and Transalpine Gaul by declaring that no public business could be conducted whilst he observed the sky for omens, but was again rebuffed by Pompey and one of the consul-designates who supported Caesar's appointment. At the end of the year Bibulus emerged from his self-enforced retirement and presented himself before the Senate. He took the traditional oath declaring he had done his duty in his consulship. He was then about to justify his actions as consul when the new tribune of the plebs, Publius Clodius Pulcher, used his veto to prevent Bibulus from speaking further.

Senator and governor
Throughout the 50s Bibulus continued to attack Pompey in the Senate, blaming him for the fighting between Publius Clodius and Titus Annius Milo in 56 BC, to the point that Pompey was convinced that Bibulus was in league with plotters who were intent on assassinating him. He also voted against Pompey being granted permission to go to Egypt in person to restore Ptolemy XII Auletes to his throne. Nevertheless, by the end of the 50s, Pompey had been cynically embraced by the boni, who saw in him a champion to bring down Caesar. In 52 BC as a consular senator Bibulus proposed an unconstitutional and illegal resolution, which the Senate accepted, allowing Pompey to serve as sole consul to deal with the breakdown of order in Rome after the murder of Publius Clodius.

As a result of a law passed by Pompey during his sole consulship, proscribing that governorships could not be held by persons who had served as praetor or consul within five years of leaving office, Bibulus was not appointed to a post-consular governorship until 51 BC. He then became governor of Syria. He severely offended the soldiery in Syria by claiming much of the credit due to their commander Gaius Cassius Longinus, whom he outranked. Two years before Crassus had led the Roman army of the east to a calamitous defeat at Carrhae. Longinus had advised Crassus against his misguided actions and when Crassus was killed had taken command and led the survivors in a successful retreat. He then saved the province of Syria by beating the Parthians at Antioch. For this he became the darling of the army. Bibulus arrived to a situation already stabilising. He sent the Senate a report of his pursuit and mopping up of the Parthians and his reorganisation of Syria's defences. The Senate granted Bibulus a thanksgiving of twenty days. With the Parthian threat still present, Bibulus sent two of his sons to Egypt in 50 BC to demand the recall of Roman soldiers who had settled there, but they were killed by the soldiers, who refused to march. When Cleopatra sent him the murderers to be punished, he returned them saying it was up to the Senate to punish them.

Civil war and death

Completing his governorship, Bibulus returned to the west in 49 BC to find that civil war had erupted between Caesar and Pompey. Aligning himself with Pompey, he was placed in charge of Pompey's fleet in the Adriatic, to ensure that Caesar and his troops could not cross from Brundisium in Italy to Epirus to challenge Pompey's army. Having only assembled half the necessary ships, Caesar decided to take seven legions across the Adriatic, and to have the ships return and transport the remaining legions once they arrived at Brindisi. Transporting seven legions across the Adriatic Sea to Greece would ordinarily be difficult due to the winter season.  But because Caesar knew that the calendar was out of step with the astronomical seasons due to his responsibilities as pontifex maximus, he had the advantage of knowing the crossing would be easier than expected. This assisted Caesar to an extent as the Adriatic was sufficiently treacherous to deter Bibulus' war galleys from venturing far from their base at Corfu.

Bibulus was caught by surprise when on the evening of 6November Caesar and his fleet successfully crossed the Adriatic, landing at Palaeste. Although Bibulus was stationed only  south of Palaeste, he had not sent out scouts and his ships were not ready to put to sea to intercept Caesar's transports. When he heard of Caesar's crossing, he ordered his crews to return to their ships, and sailed northward, hoping to capture the ships carrying Caesar's reinforcements. Again too slow, he only managed to arrive for their return journey to Italy, capturing and burning 30 of Caesar's transports. He then maneuvered to prevent any further ships crossing to reinforce or supply Caesar. He only captured one transport, which had been chartered by some private individuals and had refused to obey Bibulus's orders. Enraged, he ordered the entire crew killed.

Bibulus then blockaded all the harbors along the coast, hoping to prevent any further crossings from Italy, and leaving Caesar stranded in Epirus. He found that he could not resupply his ships without abandoning the blockade, and so he attempted to bluff Caesar's legates at Oricum into persuading Caesar to agree to a temporary truce so that he could resupply. When Bibulus refused to guarantee the safety of the envoys Caesar wished to send to discuss a peaceful settlement with Pompey, Caesar realised it was a ploy and pulled out of negotiations. Determined to continue with the blockade, Bibulus pushed himself too hard; he fell ill in early 48 BC and died near Corcyra before the end of winter.

Family
Bibulus was the son of Gaius Calpurnius. Bibulus married twice. From his first marriage he had three sons, including the later statesman Lucius Calpurnius Bibulus. His two eldest sons Marcus and Gaius were killed in Egypt by soldiers whom Aulus Gabinius had left there after restoring Ptolemy Auletes to the throne. He may have had a daughter called Calpurnia, who was the first wife of Roman senator Marcus Valerius Messalla Corvinus. His second wife was Cato's daughter Porcia, whom he married sometime between 58 and 53 BC, by her he had two more children, at least one of them a son who lived to adulthood. The other may have been a daughter but it is not certain. After Bibulus' death Porcia married Brutus, who was to be a ringleader of Caesar's assassins.

Notes

References

Sources

Ancient
 Appian, Civil Wars
 Plutarch, Life of Cato the Younger
 Suetonius, Life of Julius Caesar

Modern
 Bringmann, Klaus, A History of the Roman Republic (2007) Cambridge, UK ; Malden, MA: Polity OCLC 318972410
 Broughton, T. Robert S., The Magistrates of the Roman Republic, Vol II (1952) New York : American Philological Association. OCLC 868514975
 Holland, Tom, Rubicon: The Triumph and Tragedy of the Roman Republic (2004) London: Abacus.
 Holmes, T. Rice, The Roman Republic and the Founder of the Empire, Vol. I (1923) Cambridge: The Clarendon Press. OCLC 2845034
 Holmes, T. Rice, The Roman Republic and the Founder of the Empire, Vol. II (1923) Cambridge: The Clarendon Press. OCLC 163400823
 Holmes, T. Rice, The Roman Republic and the Founder of the Empire, Vol. III (1923) Cambridge: The Clarendon Press. OCLC 889250448
 Morrell, Kit, Pompey, Cato, and the Governance of the Roman Empire (2017)  Oxford: Oxford University Press.
 Smith, William, Dictionary of Greek and Roman Biography and Mythology, Vol I (1867). London: J. Murray. OCLC 490058450
 Syme, Ronald, The Roman Revolution (1939) Oxford: Clarendon OCLC 185188894

100s BC births
48 BC deaths
1st-century BC Roman governors of Syria
1st-century BC Roman consuls
Ancient Roman admirals
Ancient Roman generals
Bibulus, Marcus
Curule aediles
Optimates
Roman Republican praetors
Year of birth uncertain
102 BC births
People of Caesar's civil war

Marcus Calpurnius Bibulus


Les éliminatoires du championnat d'Europe de football 2024 se déroulent à partir du  et déterminent 23 des 24 équipes qualifiées pour l'UEFA Euro 2024.

La phase de groupe s'achèvera le  et désignera 20 des 23 équipes qualifiées. La phase de barrages se tiendra du  au  pour l'attribution des trois dernières places.

L'Allemagne est qualifiée d'office pour la phase finale en tant que pays hôte.

Participants

La Fédération de Russie de football a été suspendue de toutes compétitions organisées par l'UEFA le 28 février 2022 en raison de l'invasion de l'Ukraine par son pays. Elle confirme le 20 septembre 2022 être exclue des éliminatoires du tournoi et de ce fait de la compétition finale.

Équipes participantes :

Format et tirage au sort

Les cinquante-trois équipes nationales de pays membres de l'UEFA, qui sont réparties en sept groupes de cinq et trois autres de six, se disputent vingt places en phase finale. Les deux premiers de chaque groupe sont directement qualifiés. Puis les douze équipes les mieux classées au classement général de la Ligue des nations n'ayant pas terminé dans les deux premières places de leur groupe disputeront les barrages. Celles-ci seront réparties en trois voies de quatre équipes avec des demi-finales et une finale en match simple, les trois vainqueurs seront qualifiés pour la phase finale.

Le tirage au sort de la phase de groupes a eu lieu le 9 octobre 2022 au Festhalle à Francfort.

Composition des chapeaux à l'issue des phases de poules de la Ligue des nations

Légende

Aménagements avant le tirage au sort

Raisons politiques

L'UEFA décide que pour des raisons politiques les duos suivants ne peuvent pas être tirés au sort dans le même groupe :

 Arménie/Azerbaïdjan
 Biélorussie/Ukraine
 Gibraltar/Espagne
 Kosovo/Bosnie-Herzégovine
 Kosovo/Serbie

Raisons climatiques

En raison de conditions hivernales, un groupe peut contenir au maximum 2 des équipes suivantes : Biélorussie, Estonie, Finlande, Îles Féroé, Islande, Lettonie, Lituanie, Norvège.

Raisons géographiques
Pour éviter tout voyage excessif, un groupe peut contenir au maximum une des paires suivantes :
 Kazakhstan avec Andorre, Angleterre, Écosse, Espagne, France, Gibraltar, Îles Féroé, Irlande, Irlande du Nord, Islande, Malte, Portugal, Pays de Galles
 Azerbaïdjan avec Gibraltar, Islande, Portugal
 Islande avec Arménie, Chypre, Géorgie, Israël

Raisons compétitives
 Afin de ne pas entrer en concurrence avec le dernier carré de la Ligue des Nations, les 4 équipes y participant sont tirées au sort dans les quatre premiers groupes de 5 équipes (groupes A-D).
 Si l'Allemagne (pays hôte) se qualifie pour la phase finale de la Ligue des nations, le chapeau LDN sera composé de 3 équipes & le chapeau 1 de 7 équipes

Groupes
La phase de qualification est jouée en groupes selon le système de championnat, chaque équipe rencontrant tous les adversaires du même groupe en matchs aller et retour. Une victoire rapporte trois points, un match nul un point et une défaite zéro point.

Critères

Critères pour le classement des groupes
En cas d'égalité de points de plusieurs équipes à l'issue des matchs de groupe, les critères suivants sont appliqués dans l'ordre indiqué pour établir leur classement :
 plus grand nombre de points obtenus dans les matchs de groupe disputés entre les équipes concernées ;
 meilleure différence de buts dans les matchs de groupe disputés entre les équipes concernées ;
 plus grand nombre de buts marqués lors des matchs de groupe disputés entre les équipes concernées ;
 Si, après l'application des trois premiers critères à plusieurs équipes, plusieurs équipes sont toujours à égalité, les critères suivants s'appliquent :
 meilleure différence de buts dans tous les matchs du groupe ;
 plus grand nombre de buts marqués dans tous les matchs du groupe ;
 plus grand nombre de buts marqués à l'extérieur dans tous les matchs du groupe ;
 plus grand nombre de victoires dans tous les matchs du groupe ;
 plus grand nombre de victoires à l'extérieur dans tous les matchs du groupe ;
 points disciplinaires dans tous les matchs du groupe (barème : -1 point pour un carton jaune, -3 points pour deux cartons jaunes menant à un carton rouge, -3 points pour un carton rouge direct) ;
 Position au classement général de la Ligue des nations.

Critères pour le classement général
Pour déterminer le classement général des éliminatoires, les résultats contre les équipes classées sixièmes ne sont pas pris en compte puis les critères suivants sont appliqués :
 Position des équipes dans chaque groupe ;
 Plus grand nombre de points obtenus ;
 Meilleure différence de buts générale ;
 Plus grand nombre de buts marqués ;
 Plus grand nombre de buts marqués à l'extérieur ;
 Plus grand nombre de victoires ;
 Plus grand nombre de victoires à l'extérieur ;
 Points disciplinaires dans tous les matchs du groupe (-1 point pour un carton jaune, -3 points pour deux cartons jaunes menant à un carton rouge, -3 points pour un carton rouge direct) ;
 Position au classement général de la Ligue des nations.

Légende des classements

Légende des résultats

Groupe A

Groupe B

Groupe C

Groupe D

Groupe E

Groupe F

 Le 16 octobre 2023, le match Belgique-Suède fut arrêté à la mi-temps pour des raisons sécuritaires (Fusillade du 16 octobre 2023 à Bruxelles). Le score de 1-1 à la mi-temps est entériné par l'UEFA. Un point est alors attribué à chaque équipe pour le match nul.

Groupe G

Groupe H

Groupe I

Groupe J

Barrages

Format
À l'issue des éliminatoires, il restera trois places à attribuer pour l'Euro 2024. Les derniers tickets se joueront en mars 2024 entre les 12 meilleures sélections non qualifiées selon les résultats de la Ligue des nations 2022-2023. Les barrages constituent donc un tour de repêchage pour des équipes ayant échoué lors des éliminatoires.

Chaque voie de barrages (A, B & C), contient quatre équipes et se dispute par élimination directe (avec deux «demi-finales» et une «finale», disputées sur un match unique). Les trois vainqueurs de ces groupes décrocheront leurs billets pour l'Euro 2024.

Règles
Sur la base des résultats de la Ligue des nations 2022-23, les barragistes sont sélectionnés selon les critères suivants dans cet ordre :

 Quatre places de barragistes sont données à chaque ligue de la Ligue C à la Ligue A, dans cet ordre
 Les vainqueurs de groupe de la Ligue des nations accèdent aux barrages à moins qu'ils ne soient directement qualifiés pour la phase finale via les éliminatoires
 Si un vainqueur de groupe des Ligues A, B et C est déjà qualifié via les éliminatoires, le pays suivant le mieux classé dans sa ligue et qui n'est pas directement qualifié accède aux barrages
 S'il reste moins de quatre équipes non qualifiées pour une ligue donnée, une place sera accordée à l'équipe la mieux classée de la Ligue D (à moins qu'elle ne soit déjà qualifiée pour la phase finale). Les places restantes sont attribuées aux pays les mieux classés qui ne sont pas déjà qualifiés pour la phase finale selon le classement général de la Ligue des nations
 Les vainqueurs de groupe des Ligues B et C ne peuvent pas être placés dans un groupe de barrage avec des équipes mieux classées

Tableau des barrages

Les oppositions des groupes de barrages sont déterminées comme suit :

 Pour les «demi-finales» de chaque voie, en fonction du classement général de la Ligue des nations 2023 :
 La sélection la mieux classée reçoit la sélection classée quatrième
 La sélection classée deuxième reçoit la sélection classée troisième
 Les hôtes des «finales» sont déterminés par un tirage au sort

Résultats en éliminatoires des équipes suivant le tableau de la Ligue des nations 2023

Le tirage au sort pour déterminer les pays hôtes des finales des barrages a lieu le 23 novembre 2023 à Nyon.

Voie A

Voie B

Voie C

Note : Dans le tableau de chaque voie ci-dessus, l'équipe jouant à domicile est affichée en premier

Liste des 24 qualifiés pour la phase finale

Meilleurs buteurs

Meilleurs passeurs

Classement général
Le classement général des éliminatoires utilisé par l'UEFA déterminera le positionnement des équipes dans les 4 différents chapeaux en vue du tirage au sort de la phase finale.

Note : Pour les trois groupes éliminatoires comprenant six équipes, les résultats contre les équipes classées sixièmes ne sont pas pris en compte.

Mise à jour : le 17 octobre 2023, aprés la  journée.

Références

Notes

Éliminatoires du Championnat d'Europe de football 2024


The Battle of Gravelines is the name given to two battles:

The Battle of Gravelines (1558), in which Spain defeated France in 1558.
The Battle of Gravelines (1588), which saw an indecisive engagement between Spanish and English fleets during the attempted Spanish invasion of England in 1588.

Battle of Gravelines


Udel may refer to:

 a Russian feudal term for an appanage given to a younger son of the tsar or other male member of the imperial family
 UDel, an abbreviation for the University of Delaware
 UDEL, Democratic Liberation Union (Unión Democrática de Liberación) of Nicaragua
 Udel (polymer), high-temperature polymer
 Joan Erbe Udel (1926–2014), U.S. artist

See also

Udel


Palwal (en hindi: पलवल ) es una ciudad de la India, centro administrativo del distrito de Palwal, en el estado de Haryana.

Geografía
Se encuentra a una altitud de 195  a 321 km de la capital estatal, Chandigarh, en la zona horaria UTC +5:30.

Demografía
Según estimación 2011 contaba con una población de 155 331 habitantes.

Referencias

Enlaces externos

Localidades de Haryana

Palwal


Uzungöl, Trabzon ilinin Çaykara ilçesine bağlı turistik mahalledir. Adını kıyısında bulunduğu Uzungöl'den almaktadır. Sınırları dahilinde Uzungöl Tabiat Parkı yer almaktadır.

Tarihçe
Yerleşimin eski adı Şerah'tır. 1681 yılı Avarız Defterinde yerleşimde tamamı Müslüman 12 avarız neferi kaydedilmiştir.  1923 Mübadelesi sırasında Bölümlü doğumlu Papaz Yorgos Papadopulos tuttuğu gezi notlarında yerleşimden "Saraho" adıyla bahsederken, Ofluların Müslümanlığı seçmesi üzerine köyde yaşayan Rumlardan bazılarının Gümüşhane'ye giderek Fitiyana adıyla bir köy kurduklarını belirtmiştir.

Türkiye Cumhuriyetinin kurulmasından sonra Şerah adıyla o dönemki Of kazasının Kadahor (Çaykara) bucağına bağlı görülen yerleşimin adı 1959 yılında, "yabancı kökten geldiği ve iltibasa yol açtığı" gerekçesiyle 7267 sayılı kanunla Uzungöl olarak değiştirilmiş ve 1960 yılı nüfus sayımından itibaren yeni adıyla kayıtlarda yer almaya başlamıştır.

Coğrafya
Uzungöl, Trabzon il merkezine 100 km, Çaykara'ya ise 20 km uzaklıktadır.

Nüfus

Fotoğraf galerisi

Kaynakça

Dış bağlantılar

 Uzaydan Uzungöl
 Uzungöl Tatil ve Gezi Rehberi

Çaykara'nın mahalleleri
Türkiye'deki tatil köyleri

Uzungöl, Çaykara


El arroyo de los Chanchos es un curso de agua uruguayo ubicado en el departamento de Lavalleja, perteneciente a la cuenca hidrográfica de la laguna Merín.

Nace en la cuchilla Grande, desemboca en el arroyo de Godoy tras recorrer alrededor de 19 km.

Chanchos
Chanchos

Arroyo de los Chanchos


Mark William Donaldson (* 6. November 1955 in Palmerston North, Neuseeland) ist ein ehemaliger neuseeländischer Rugby-Union-Spieler auf der Position des Gedrängehalbs und ein Rugbytrainer. Hauptberuflich war er erst Eigentümer eines Sportgeschäfts und wurde später Miteigentümer eines Hotels.

Biografie
Donaldson ging auf dem College Street School und von 1969 bis 1973 auf der New Plymouth Boys' High School zur Schule, wo er während der letzten zwei Jahre in der Ersten Rugbymannschaft der High School spielte. Nach seiner Schulzeit trat er 1974 dem Rugbyverein Palmerston North HSOB RFC bei und absolvierte im selben Jahr Spiele für die U-21-Auswahlmannschaft der Manawatu RU sowie für die Erste Mannschaft von Manawatu. Nach einer Spielzeit für den Celtic RFC in Hastings und für die Hawke's Bay RU im Jahr 1975, kehrte er zum Palmerston North HSOB RFC und der Manawatu RU zurück.

1976 gewann er mit Manawatu gegen die Auckland RFU den Ranfurly Shield, den sie bis 1978 13-mal erfolgreich verteidigten. Im Jahr darauf wurde er im Oktober zum ersten Mal in die neuseeländische Nationalmannschaft (All Blacks) berufen, nachdem er zuvor im selben Jahr für die U-21-Nationalmannschaft aufgelaufen war. Er tourte mit den All Blacks in Frankreich, wo er in den beiden Länderspielen gegen französische Nationalmannschaft spielte.

1978 verteidigte er mit Neuseeland erfolgreich den Bledisloe Cup gegen die australische Nationalmannschaft (Wallabies). Außerdem konnte er mit der Nationalmannschaft im selben Jahr zum ersten Mal in der Geschichte der All Blacks auf ihrer Europatour einen Grand Slam erringen. Dabei verpasste er verletzungsbedingt nur das Spiel gegen die walisische Nationalmannschaft. Im folgenden Jahr verlor er mit den All Blacks den Bledisloe Cup gegen die Wallabies.

1980 wurde er als Mannschaftskapitän mit Manawatu durch den Gewinn der National Provincial Championship (NPC) neuseeländischer Rugbymeister. Des Weiteren gewann er 1981 mit den All Blacks die Länderspielserie gegen die in Neuseeland tourende südafrikanische Nationalmannschaft (Springboks), nachdem Neuseeland zwei der drei Länderspiele für sich entschied. Dabei spielte Donaldson jedoch nur im dritten und letzten, entscheidenden Länderspiel, als er eine Minute vor Schluss für den verletzten David Loveridge beim Spielstand von 22:22 eingewechselt wurde. Kurz vor dem Spielende holte er den entscheidenden Straftritt heraus, den Allan Hewson dann zum 25:22-Endstand kickte. Diese in der neuseeländischen Öffentlichkeit sehr stark umstrittene Tour, aufgrund der herrschenden Apartheid in Südafrika, war der erste Besuch der Springboks seit 1965 und bis 1994 ihr wiederum vorerst letzter.

1983 trat er vorzeitig vom aktiven Rugbysport zurück, kehrte dann aber nach einer kurzen Pause noch in der gleichen Spielzeit zurück, um Manawatu auszuhelfen. Er wechselte 1984 von den Palmerston North HSOBs zum Verein Te Kawau RC, bei dem er sich früh in der Saison verletzte und deshalb eine Berufung für die Manawatu RU verpasste. Ein Jahr später führte er jedoch wieder beide Mannschaften als Kapitän an, bevor er sich erneut verletzte und seine aktive Karriere endgültig beendete.

Von 1990 bis 1992 war er Chef-Trainer der 1988 in die Zweite Division abgestiegenen Manawatu RU.

Weblinks
 Profil auf der Seite der All Blacks

Rugby-Union-Spieler (Neuseeland)
Rugby-Union-Trainer
Neuseeländer
Geboren 1955
Mann
Trainer (Neuseeland)

Mark Donaldson


Yavuz Hilmi Çetin (25 Eylül 1970, Samsun - 15 Ağustos 2001, İstanbul), Türk müzisyen ve gitaristtir. Müzik hayatı boyunca 2 adet albüm çıkarmıştır. 2001 yılında intihar etmiştir.

Hayatı ve kariyeri

İlk yıllar
1970 yılında Samsun'da doğan Yavuz Çetin, gazeteci olan babasının işi nedeniyle çocukluğunu Türkiye'nin çeşitli bölgelerinde geçirir. Baba Erdal Çetin, Milliyet gazetesinde haber şefiydi. Uğur Mumcu, İlhami Soysal ve Fethi Naci gibi isimlerle beraber çalışmıştı.

Müziğe olan ilgisi küçük yaşlarda başlayan Yavuz Çetin, ilk enstrümanı curayla on yaşında tanışır. Müzik aletlerine olan ilgisi curadan sonra bağlama öğrenmeye başlamasıyla devam eder.

Bir süre sadece müzik dinler ve bu süre zarfında elektro gitar sesine hayran kalır. 1985 yılında ilk kez akustik gitarla tanışır ve on yedi yaşında profesyonel müzik yaşamına geçişiyle birlikte, İstanbul'da ve Türkiye'nin güney bölgelerinde çalışarak hayatını sürdürür. Öğrenimini, hayatının akışını şekillendiren o çok sevdiği müzik üzerine yapar.

Ortaöğrenimini Haydarpaşa Lisesi'nde tamamlar. Liseden okul arkadaşı olan Ercan Saatçi ile yaptıkları I'll Cry Again adlı şarkı ile Hey dergisinin yarışmasını kazanır. Marmara Üniversitesi Müzik Bölümü'ne girer. Üniversite hayati boyunca da Elektro Gitarını elinden hiç bırakmaz. Çalıştığı grup Labirent ile katıldığı Yıldız Teknik Üniversitesi'nin düzenlediği müzik yarışmasından birçok ödül alarak çıkarlar.

Müzik kariyeri
Üniversiteyi çalışmalarından dolayı bitiremez. 1990 yılında İstanbul'da müzisyen dostları Batu Mutlugil (Duman grubunun gitaristi olan Batuhan Mutlugil'in babası), Zafer Şanlı ve Kerim Çaplı ile cover grubu olarak tanınan Blue Blues Band'i kurar. 1970 yılların rock ve blues parçalarının ağırlıkta olduğu çalışmaları sürdüren Blue Blues Band grubunda elektro gitar çalıp vokal yapar.

Yaşamı boyunca 1960'lı ve 1970'li yılların rock ve blues müziklerinden etkilenir. Daha sonraları yaptığı beste ve söz çalışmalarına Rock ve Blues müziğinin ruhunu yansıtır. Jimi Hendrix'i ve dünyaya mal olmuş blues şarkılarını da yorumlamaktan her zaman büyük bir keyif alır. 1990'lı yılların ortalarında Fuat Güner ile tanışmasıyla birlikte stüdyo müzisyenliğine başlar. Fuat Guner'in stüdyosunda sürdürdüğü çalışmalarında televizyon ve radyolar için reklam müziklerini gitarıyla seslendirir. Birçok sanatçının albüm kayıtlarına da gitarıyla imzasını atar. Gitarıyla eşlik ettiği albümler arasında İzel'in Bir Küçük Aşk, Kıraç'ın Deli Düş ve Bir Garip Aşk Bestesi; şarkılar arasında da Soner Arıca'nın Ayrılık, Deniz Arcak'ın Kıpır Kıpır albümünden Korku, Bırakın Beni ve Ah Şu Cevapsız Sorular, Turgut Berkes'in Kara Kutu albümündeki Miranda ve Mindos, Sibel Tüzün'ün Tut Beni ve Göksel'in Sabır en bilinenleridir. Göksel'in Sabır şarkısındaki talk box performansının Türkiye'de bir ilk olması, ona talk box kullanan ilk gitarist sıfatını kazandırır. 1996 yılının ortalarında MFÖ grubuyla çalışmaya başlar. Grupla turnelere gider ve katıldığı tüm konserlerde gitarıyla eşlik eder. Bir yandan bar müzisyenliğine devam eder. 1997 yılında Ercan Saatçi prodüktörlüğünde ilk albümü için çalışmalara başlar.

İlk adlı albümünü Stop Müzik'ten çıkarır. Albümünde yer alan Erkeğin Olmak İstiyorum, ayrıca Sinan Çetin'in yönettiği Propaganda filminde kullanılan, Erkan Oğur'un perdesiz gitar performansının da yer aldığı Dünya isimli enstrümantal şarkısı en bilinenleridir. MFÖ ile konserlerde çalmaya ve Yavuz Çetin Group isimli grubuyla bar performansını devam ettirdiği süre içinde ikinci albüm çalışmalarına da başlar. 1999 yıllarının sonlarında TMC ile anlaşır ve ikinci albümü Satılık için stüdyoya girer. Sözü, müziği ve düzenlemeleri kendisine ait bir çalışmaya son kez imza atar. Mart 1999 tarihine çıkması planlanan albüm için tüm çalışmaları bitirir, ancak yaptığı bu son çalışmanın müzikseverlerle buluştuğunu göremeden hayata veda eder.

Ölümü ve sonrası
Yavuz Çetin, Kadıköy'deki Shaft Rock-Blues & Jazz Club'da sahneye çıkıyordu. Gece sahne çalışmalarını sürdürürken, gündüzleri de TMC'den çıkartılacak ve Satılık adını vermeyi düşündüğü ikinci albümünün stüdyo çalışmalarını yürütüyordu. Yoğun depresyon teşhisi konulduğu için bir haftadır hastanede tedavi gören Yavuz Çetin, hafta başında iyileştiği gerekçesiyle taburcu edildi.

Yavuz Çetin, 15 Ağustos 2001 tarihinde, saat 19.00 sularında Boğaziçi Köprüsü'nden atlayarak yaşamına son verdi. 1977 model Peugeot marka otomobili, Boğaziçi Köprüsü üzerinde, Ortaköy ayağına yakın bir noktada bulundu. 34 KBP 09 plakalı otomobilde Yavuz Çetin'in ruhsatı, ehliyeti, 500 dolar ve 190 lira, çeşitli ilaçlar ve 7 tane anahtar bulundu.

Karacaahmet Camii'nde düzenlenen cenaze töreninden sonra Anadoluhisarı'ndaki Yeni Mahalle Mezarlığı'nda defnedildi. 31 yaşındaki gitaristin cenaze töreninde ailesinden yalnızca üvey annesi Sevinç Çetin vardı. Babası gazeteci Erdal Çetin, oğlunun ölüm haberiyle yıkıldığı için törene katılamadı. 1998'de boşandığı eşi Didem Mandabaş ve yedi yaşındaki oğlu Yavuzcan'ın da bulunmadığı törende, cami avlusu, gitaristin dostlarıyla doldu.

Oğlu Yavuzcan Çetin tarafından 5 Temmuz 2014'te YavuzFest adlı 1. Yavuz Çetin Gitar Festivali düzenlendi. Festivalin 2. 3. ve 4.sü 2015, 2016 ve 2017 yıllarında düzenlendi. Son olarak 5 Mayıs 2019 tarihinde YavuzFest düzenlendi.

League of Legends oyununda 8 Temmuz 2015'te çıkan Tahm Kench karakterinin tanıtım müziği, Yavuz Çetin'in Oyuncak Dünya şarkısından esinlenilerek yapılmıştır.

2017'de çekilen Blue isimli belgesel filmle Kerim Çaplı ile birlikte hayatları, müzikal performansları, trajik sonları, tanıklıklar ve orijinal kayıtlarla gösterime sunuldu.

Albümleri
 1997: İlk
 2001: Satılık

Ayrıca bakınız
 Kerim Çaplı
 Batu Mutlugil
 Sunay Özgür
 Blue Blues Band
 Blue
 Blue Extra

Kaynakça

Dış bağlantılar
 Aramızdan ayrılışının 12. yılında Yavuz Çetin'in anısına

1970 doğumlular
Samsun doğumlu sanatçılar
Türk gitaristler
Türk rock şarkıcıları
Anadolu blues
Haydarpaşa Lisesinde öğrenim görenler
20. yüzyıl Türk müzisyenleri
21. yüzyıl Türk müzisyenleri
TMC sanatçıları
2001 yılında ölenler
İstanbul'da ölenler
Türkiye'de intihar edenler
İntihar eden müzisyenler
Anadolu Hisarı Mezarlığı'na defnedilenler
Protest müzik şarkıcıları

Yavuz Çetin


كفرنبل مدينة وناحية إدارية تابعة لمنطقة معرة النعمان في محافظة إدلب في سوريا. تقع غرب معرة النعمان ب13 كم. بلغ عدد سكانها 15,455 نسمة في عام 2004 حسب إحصاء المكتب المركزي للإحصاء. المدينة تعود لعصور تاريخية قديمة فيها الكثير من الشواهد الأثرية ذات الأصول الرومانية، تشتهر المدينة بزراعة الأشجار المثمرة ومن أهمها التين والزيتون  وهي الأكثر إنتاجا في كفر نبل إضافة لزراعات الأخرى. ,;وكلمة كفرنبل أصولها رومانية مقسمة إلى قسمين (كفر) وتعني بالعربية المزرعة، و(نبل) وتعني النبلاء لتصبح كفرنبل بالعربية (مزرعة النبلاء).

تقوم المدينة على أطلال مدينة قديمة وتطالعك الآثار في أماكن عديدة وتحيط ب كفر نبل آثار عدة مدن رومانية هامة مثل ربيعة، شنشراح وسرجيلا والبارة وغيرها.

- اللفظ المحلي للمدينة ( باللهجة العامية ) تأتي النون ساكنة وحرف الباء المضوم مكسور .

- بحسب إحصائيات 2004 :
- القرى التابعة لمدينة كفرنبل : حاس، معرتحرمة , كفرعويد، الفطيرة, حزارين، بسقلا, جبالا، معرتماتر , معرزيتا، سفوهن, كوكبة، كرسعة, قوقفين، ترملا, الفقيع، راشا الشمالية، فليفل, دار الكبيرة، الشيخ مصطفى، الملاجة, معرتصين، كفر موسى, شورلين، لويبدة, أم نير .

أعلام
 رائد الفارس
 فارس البيوش

مراجع

أماكن مأهولة في جبل الزاوية
أماكن مأهولة في منطقة معرة النعمان
بلدات سوريا
تجمعات سنية في سوريا
مدن سوريا
مواقع أثرية في محافظة إدلب

كفرنبل


Olaylar

Doğumlar

Ölümler
 29 Ağustos - Sulzbachlı Bertha, Bizans İmparatoru I. Manuil'un ilk karısı (d. 1110'lar)
 1 Eylül - IV. Hadrianus, tarihteki tek İngiliz asıllı papa (d. 1100)
 tarihi bilinmeyen
 II. Joselin, Urfa Kontluğu'n dördüncü ve son kontu

1159


فيلق بانزر  فيلق مدرع في الفيرماخت الألماني النازي خلال الحرب العالمية الثانية. تم تقديم الاسم في عام 1941، عندما تمت إعادة تسمية الفيلق الآلي ( Armeekorps (mot) أو AK (mot) ) إلى فيلق بانزر. تم إنشاء فيلق بانزر في جميع أوقات الحرب، وكان موجودا في الجيش، و فافن إس إس وحتى لوفتفافه.

الهدف
خضع فيلق بانزر للتحول مع استمرار الحرب. في البداية، كانت القوة الضاربة الرئيسية للفايرماخت، وكانت تتألف من فرق المشاة الآلية ( ID (mot) ) وفرق البانزر. في وقت لاحق من الحرب، كان من الممكن العثور على فيلق بانزر يتكون فقط من فرق المشاة.

خلال الفترة الأولى من الحرب، تم تجميع فرق بانزر السابقة والفيالق الميكانيكية في مجموعات بانزر المختلفة ( Panzergruppen ). تم تسمية مجموعات بانزر (أي لم يتم تخصيصها بأرقام) خلال الحملات في بولندا وفرنسا واليونان، ولم يتم استخدامها على الإطلاق في النرويج والدنمارك في عام 1940، وتم ترقيمها من 1-4 خلال النصف الأول من العام للحرب ضد الاتحاد السوفيتي. في الحالة الأخيرة، تكونت مجموعة بانزر عادة من اثنين أو ثلاثة من فيالق آلية. كانوا عنصر الحركة التشغيلية في مجموعة الجيوش الشمالية ومجموعة الجيوش الوسطى ومجموعة الجيوش الجنوبية. خدم الفيلق الآلي كعنصر قيادة تكتيكي في هيكل القيادة ، حيث عملت الأقسام الفردية كعناصر قتالية تكتيكية.

قائمة فيالق البانزر
الفيالق التالية AK (mot) ولاحقًا Panzerkorps أو تم إعداده كـ فرق بانزر.  تم تخصيص تشكيلات الفيرماخت بالأرقام الرومانية أو الأسماء:

الجيش

 فيلق بانزر الثالث
 فيلق بانزر الرابع
 فيلق بانزر السابع
 فيلق بانزر الرابع عشر
 فيلق بانزر التاسع عشر
 فيلق بانزر الرابع والعشرون
 XXXVIII Panzer Corps
 XXXIX Panzer Corps
 فيلق بانزر XL
 فيلق بانزر XXXXI
 XLVI Panzer Corps
 XLVII Panzer Corps
 فيلق بانزر الحادي والأربعون
 LVI Panzer Corps
 LVII Panzer Corps
 LVIII Panzer Corps (ألمانيا)
 LXXVI Panzer Corps
 فيلق بانزر Feldherrnhalle
 فيلق بانزر جروسدوتشلاند

فافن-إس إس

 فيلق بانزر إس إس 1
 فيلق بانزر إس إس 2
 الثالث (الجرمانية) SS بانزر فيلق
 رابعا SS بانزر فيلق
 السابع SS بانزر فيلق

لوفتفافه

 فيلق المظليين هيرمان جورينج

انظر أيضًا

 Panzerwaffe
 فيلق المدرعة
 تنظيم عسكري

المراجع

 http://www.lexikon-der-wehrmacht.de/Gliederungen/KorpsPz/PzKprps.htm
 http://www.lexikon-der-wehrmacht.de/Gliederungen/KorpsSS/Gliederung.htm
 http://www.lexikon-der-wehrmacht.de/Gliederungen/KorpsSonstige/FallPzKorpsGliederung.htm
 دي بوليو ، تشارلز "دير فورستوس دير بانزغروب 4 آوف لينينغراد"

الوحدات المدرعة والتشكيلات لألمانيا
فيالق ألمانيا في الحرب العالمية الثانية
فيالق بانزر ألمانية في الحرب العالمية الثانية

فيالق البانزر


Naar Fruen gaar paa Eventyr er en stumfilm fra 1913 instrueret af August Blom efter manuskript af Georg Borup.

Handling

Referencer

Eksterne henvisninger

Stumfilm fra Danmark
Danske film fra 1913

Naar Fruen gaar paa Eventyr


Lucia Macari (n. 10 decembrie 1974, Chișinău) este un artist plastic din Republica Moldova.

Biografie
După ce a absolvit Liceul de Arte Plastice "Igor Vieru" și Universitatea de Stat a Artelor la Chișinău, a urmat o specializare la Academia Jan van Eyck din Maastricht, Olanda.

Neosuprarealistă ca stil, a expus în Moldova, România, Ucraina, Voivodina, Ungaria, Rusia, Slovenia, Croația, Olanda, Grecia, Bulgaria, Kazahstan.

Selecție de lucrări
 Alegorie, instalație, 1996;
 Aphros, acțiune, 1996;
 Transformarea sunetului în imagine și a imaginii în sunet, instalație video, 1997.

Distincții
 Premiul 1 la Salonul Tinerilor Artiști, Zagreb, Croația, 1998.

Note

Bibliografie

Nașteri în 1974
Nașteri pe 10 decembrie
Artiști plastici moldoveni
Oameni din Chișinău

Lucia Macari