White's final months of service at St. Petersburg covered by this reel dealt chiefly with personnel changes within the legation, cases of infringement of rights of naturalized Americans, and day-to-day efforts of the staff on behalf of American visitors. White made his home in Tereoki, Finland, for the summer, and joined his family in Munich in early October. Business conditions continued to dominate many of the letters from America. In a letter to his son on July 7 White recalled his efforts to induce Mr. Depew to admit some cooperative features into the management of the New York Central, and blamed the labor troubles on the short-sightedness of the capitalist class. Holls reported on the work of the New York State constitutional convention on June 25, and George W. Smalley wrote on July 8 about international politics. A detailed account of the death of Professor Vincenzo Botta was sent by a niece who had made her home with him. Cornell topics include Hewett's proposed history, plans for an agriculture and veterinary college, and the arrival of Morse Stephens as a history professor.


Article: Feldbau-Khon, S., Heyman, R., and O'Leary, K. (1998). Major depressive disorder and depressive symptomatology as predictors of husband to wife physical abuse. Violence and Victims, 13, 347-359.
The work of Feldbau-Khon, S., Heyman, R., and O'Leary, K. is described in the article, "Major Depressive Disorder and Depressive Symptomatology as Predictors of Husband to Wife Physical Aggression." The authors tested the link between a husband's depressive symptomatology and the frequency of physical aggression toward his wife. Also tested was the husband's Major Depressive Disorder and the frequency of physical aggression towards his wife.
The authors had four hypotheses. 1: Clinically depressed men are more physically aggressive. 2: Depressive symptomatology is associated with frequency of physical aggression. 3: Depressive symptomatology effects on physical aggression are accounted for by other variables. 4: Depressive symptomatology effects on psychological aggression are accounted for by other variables.
The study assessed physically aggressive men who volunteered for treatment with their wives. Participants responded to newspaper announcements offering free therapy for relationship conflicts. All spouses completed three-hour assessments consisting of structured interviews and self-report measures. Almost one third had moderate levels of depressive symptomatology, but only 11% met the criteria for Major Depressive Disorder (MDD).
Although a significant relationship between increased depressive symptomatology and frequency of physical aggression was found, the relationship was most likely accounted for by self- reported anger.
One weakness with this study is the makeup of the sample. The racial composition of the sample was 1.3% Black, 1.3% Asian, and 97% White. No Hispanics were included in the study although Hispanic men are also sometimes very violent to their wives. The sample should have had more diversity, not be made up almost entirely of Whites. Some races are more naturally violent than others are because its part of their culture. Using mostly whites in this study taints it because different depression could be a major factor affecting the physical violence of whites but perhaps not men of other races.
Another weakness of this study is that it is never mentioned whether or not any of the subjects were taking any sort of medication. Many types of medications, for example any type Serotonin inhibitor, can affect violent behavior.
The fact that the subjects were volunteers raises an issue. The participants responded to an advertisement for free therapy for relationship conflicts. These people wanted help. They were not ordered by the court to obtain therapy. This may suggest that they somehow have a different frame of mind than others who might not seek help, therefore, we cannot make accurate assumptions about the results of the study.
Another issue one must take into consideration is that the participants completed a three-hour assessment for which they were not being paid for. They could have become tired, annoyed, bored, or frustrated. This could have affected their answers to the questions asked in the interview to determine whether or not they had MDD. They could also have not been revealing their true selves in the self-report. The interviews were conducted by grad students and a research assistant, not by the actual researchers or by doctors. There is a possibility they could have overlooked things a professional with more experience would have caught.
One of the strengths of this article is that it stresses the possibility of outside factors affecting the physical violence. An example of this kind of variable is self-reported anger. A relationship where one person is depressed may in fact be a very chaotic relationship where anger and frustration can result in violence. In this case, the violence is may not be a direct result of the depression; instead it may result from the anger and frustration or marital discord that may accompany depression. The study recognizes that there are other factors of life that can affect the physical violence.


Shipping prices are flat rates, depending on the destination of the order and the weight of ordered items.
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NB! Shipments may be delayed due to Norwegian customs declaration.


After a five-year-long restoration, the Royal Botanic Gardens Kew has reopened Temperate House, the largest remaining Victorian glasshouse in the world and a UNESCO World Heritage Site. Covering 4,880 sq m and housing a staggering 1,500 species from temperate regions around the world, this is one of the most striking centres of plant research today. Donald Insall Associates was the project's conservation architect, as part of a multi-disciplinary team charged with repairing, restoring and bringing the historical building back to life.
Originally designed by architect Decimus Burton in the late 1850s, Temperate House served as a place for the preservation and display of the British horticultural collection that was expanding at that time. Influenced by the Industrial Revolution, this large glasshouse project was executed in wrought and cast iron and glass, and took almost 40 years to complete. Through essential new interventions, the restoration process has returned Temperate House to its original design, equipped with electrical systems that are connected to a weather station and internal monitoring network in order to provide the best possible atmospheric conditions.
Composed of 45,000 sheets of float glass, the 180-metre-long structure is organised into five interconnected spaces designed in the form of a dream – a vision of different places, spectacular forms of nature, and threatened wildlife species that existed long before humans. Featuring a 12m high circular mezzanine, the central hall is for the largest plants, bridged by two lower octagonal side wings – the Mexican House and the Himalaya House – and their annexes. A variety of trees and rare species such as Dombeya mauritiana and Encephalartos woodii, in addition to other exotic plants, grow within. Soft light and fresh air fill the space. Creating a place that feels serene and intimate, this beautifully orchestrated building stands within 132 hectares of landscaped gardens overlooking the flowing Thames nearby.
Historically, aside from their practical function, winter gardens have also served an important representational purpose, celebrating plant life and introducing valuable information to the wider public. Today, while more common, they still play an essential role in the exploration and preservation of extinct species, and have always provided us with moments of enchantment through their magnificent appearance and unusual contents. Just as unique in this way, the elegant silhouette of the Temperate House floats on the landscape, shedding new light on the importance of nature and its fragile beauty.


In this job tip, IBEW brother Andy demonstrates how to thread a piece of conduit using rope. The rope holds the threader in place instead of physically holding it. This tip helps it become a one-man operation instead of a two-man one.
To see more job tips, visit the Job Tips section of our website.


LINDA HOYLE released her first solo-album 'Pieces Of Me' in 1971 on the famous Vertigo label, after having fronted the band AFFINITY. Now, some 44 years later, she returns with a stunning follow-up album on which she is backed by a stellar cast of musicians. The album is produced by her long time friend and former bandmate MO FOSTER. 'The Fetch' presents a great collection of songs spanning a variety of musical genres with lyrics that are autobiographical, witty and dark. The iconic ROGER DEAN painted the original artwork. This DOUBLE-LP version (limited to 400 sets on BLACK VINYL) includes a non-CD bonus track.


text:
\section{Introduction}
\label{sec:introduction}
\makeatletter{\renewcommand*{\@makefnmark}{}
\footnotetext{Presented at the ECML-PKDD Workshop on Automating Data Science, 17 Sept 2021.}\makeatother}
In a typical data analytics project, a large amount of time and effort is spent on understanding and cleaning the data. A particular task, which is often performed manually, is to understand how measurements are encoded in quantitative data columns. A \emph{measurement} is a combination of a numeric value and a unit (e.g., litre), which can be encoded by several possible unit symbols for that unit (e.g., \texttt{l} and \texttt{L}).

Consider the following example: Suppose we are given the quantitative data column\footnote{The data is sampled from the ``Freezer\_volume'' column of the Household Electricity Survey (HES) dataset which can be accessed by registering at \url{https://tinyurl.com/ybbqu3n3}.
} in Figure \ref{fig:motivation}(a), which denotes the volume of freezers in various households. The value of each data entry is a measurement encoded by a unit symbol, except the last entry which does not have any unit symbol (we refer to the absence of a unit symbol in an entry as the \emph{missing unit}). As shown in Figure \ref{fig:motivation}(b), the measurements are encoded with two distinct units (litres and cubic feet) and six different unit symbols, some of which including \texttt{ltrs}, and \texttt{Cu} can be considered as \emph{anomalous unit symbols} as they do not follow the standard encodings of units. Moreover, a unit (e.g., litre) can be informative about the \emph{dimension} of a measurement (in this case volume), which is defined as an expression of the dependence of a physical quantity on mutually independent components called \emph{base quantities} in the International Vocabulary of Metrology\footnote{The document is accessible at \url{https://www.bipm.org/documents/20126/2071204/JCGM_200_2012.pdf/f0e1ad45-d337-bbeb-53a6-15fe649d0ff1}}.

\begin{figure}[H]
  \centering
    \includegraphics[width=.7\textwidth]{Fig1}
  \caption{A motivating real-world example that represents our pipeline. a) shows the samples of a raw dataset. b) indicates the intermediate steps required to transform the column. c) denotes the final data column obtained by applying the transformations.}
\label{fig:motivation}
\end{figure}

We define \emph{Unit Canonicalization (UC)} as the task of identifying the dimension of a data column and the units of all its entries so that the data entries can be canonicalized (i.e., so they all are expressed in the same units). See Figure \ref{fig:motivation}(c) for an example. To address the UC problem, one needs to apply transformations such as parsing and identifying the units in the entries, inferring the common unit for the column which is placed in the metadata (e.g., the header), making the entries numeric and scaling the entries where needed. To the best of our knowledge, the UC problem is not addressed by any existing work in the literature (see Sec.\ \ref{sec:related_work} for a detailed discussion). In this work, we propose a probabilistic approach to the UC problem. Our contributions are as follows:
\begin{itemize}
\item We propose a probabilistic model which allows us to extract semantic information about a given data column containing unit symbols (such as its dimension and unit) and canonicalize its entries (Section \ref{sec:methodology}).
\item We make the first quantitative comparison of the existing methods on the unit identification task in real-world tabular data (Section \ref{sec:experiments}).
\item We present the first set of real-world datasets annotated for the units of measurement, to accelerate research in this area.
\end{itemize}

\section{Methodology}
\label{sec:methodology}

\paragraph{\bf{Representing Units:}} We represent units by extending a dictionary of units curated from Wikipedia\footnote{Accessible at \url{https://github.com/marcolagi/quantulum/blob/master/quantulum/units.json}} with information from WikiData \cite{vrandecic2014} and QUDT (Quantities, Units, Dimensions and Data Types Ontology)\footnote{https://www.qudt.org/}. We associate each unit with a dimension and a list of unit symbols. See Appendix \ref{sec:appendix-ontology} for a detailed description.

\paragraph{\bf{The Proposed Model:}} We assume that a column of data $\textbf{y} = \{y_i\}_{i=1}^N$ consisting of $N$ rows has been read in, where each $y_i$ denotes the characters in the $i^{th}$ row. Each $y_i$ is parsed using regular expressions (see Appendix \ref{sec:appendix-parsing}) to a numeric value $v_i$ and a unit symbol $x_i$, which may be missing for some entries, i.e., $x_i$ may be null. Our focus here is on the unit symbol
$x_i$ obtained. Denoting the number of possible dimensions for a column by $K$, our model has the following generative process:
\begin{eqnarray*}
\text{column dimension $t$} &\sim& \mathcal{U}(1,K), \nonumber \\
\text{row unit $u_i$} &\sim& p(u_i|t), \nonumber \\
\text{row label $z_i$}  &=&
\begin{cases}
\text{u}_{\text{i}} & \text{with probability $w_{u_i}^{u_i}$}\ ,\\
\text{m} & \text{with probability $w_{u_i}^m$},\\
\text{a} & \text{with probability $w_{u_i}^a$}\ ,\\
\end{cases} \nonumber\\
\text{row symbol $x_i$} &\sim& p(x_i | z_i),
\end{eqnarray*}
where $\mathcal{U}$ denotes a discrete Uniform
distribution, and $p(u_i|t)$ denotes how likely it is that a row unit $u_i$ represents the dimension $t$. We model $p(u_i|t)$ with an indicator function that assigns a non-zero score only when a unit is a known unit of a dimension. Each row label $z_i$ can be either the same as $u_i$, $m$ or $a$ according to the mixing proportions denoted by $\boldsymbol{W}$, where $m$ and $a$ respectively denote a missing or anomalous unit. Here the mixing proportions $w_{u_i}^{u_i}+ w_{u_i}^m + w_{u_i}^a = 1$ for each row unit $u_i$. Since entries are often expected to be of a regular row label rather than the missing or anomalous labels, we favour regular labels during inference by using lower coefficients for the missing and anomalous labels, i.e. $w_{u_i}^m < w_{u_i}^{u_i}$ and $w_{u_i}^a < w_{u_i}^{u_i}$. These mixing proportions $\boldsymbol{W}$ are assumed to be fixed and known. Even though one could also learn the mixing proportions, this may not be vital as long as the coefficients of the regular labels are larger than the others. Finally, $p(x_i|z_i)$ denotes our observation model which is explained below. Note that the above model is based on ptype \cite{ceritli2020ptype}, but extended to address unit canonicalization by incorporating semantic information
about the units of measurements.

We build the observation model $p(x_i | z_i)$ upon three functions. First, we develop a Categorical distribution for row unit $u_i$ where the categories correspond to the possible unit symbols for that unit, i.e., $p(x_i | z_i = u_i) = \mathcal{C}at(x_i, \pi_{u_i})$ where $\sum_{s=1}^{S_{u_i}} \pi_{u_i}^s = 1$. Here, $\pi_{u_i}^s$ denotes the probability of observing the $s^{th}$ unit symbol for a unit $u_i$, while $S_{u_i}$ denotes the number of known unit symbols for that unit. Secondly, we model missing units with an indicator function, which assigns a non-zero probability only when a unit symbol is missing. Lastly, we adapt the anomaly type in ptype, which is built based on the idea of an \emph{X-factor} proposed by \cite{quinn2009factorial}, to model anomalous unit symbols. Here, we introduce a likelihood function that assigns low but non-zero probabilities to any data value, which in turn allows the model to detect anomalous unit symbols which do not fit any known unit. Note that our model is fed with unit symbols, which are parsed using regular expressions. See Appendix \ref{sec:appendix-parsing} for the details about our parsing step.

\paragraph{\bf{Inference:}} Given the row symbols $\textbf{x}$ in a data column, the initial task is to infer the column dimension $t$, which is cast to as the problem of calculating the posterior distribution of $t$ given $\textbf{x}$, namely $p(t|\textbf{x})$. We then compute a posterior distribution over each row label conditioned on the dimension and the observed value, i.e., $p(z_i|t,x_i)$. Next, we determine the row units by calculating the posterior distribution of each row unit $u_i$ given $t$, $z_i$ and $x_i$, which is also used to predict the column unit. Lastly, we map a unit symbol labelled as anomalous to a known unit symbol and perform unit canonicalization. The derivations are presented for reproducibility in Appendix \ref{sec:appendix-derivations}.

\section{Related Work}
\label{sec:related_work}
We are not aware of existing work specifically on the unit canonicalization problem. The closest related works are described below:

\cite{van2010converting,hignette2009fuzzy,samadian2014automatic} use semantic web technologies to annotate quantitative data columns in terms of units. However, their annotations are based on ontological classes in domain-specific ontologies rather than the dimensions considered in this work. For example, \cite{samadian2014automatic} define the ``High-Systolic-Blood-Pressure-Measurement'' class with \texttt{kilopascal} as its unit. Given a data column and its ontological class, their goal is to represent all entries with the same predefined unit of the corresponding ontological class. \cite{chambers2010reasoning} consider the task of annotating data entries in spreadsheets rather than tabular data. The authors split the label in a header (e.g., ``Total Gallons'') into separate words (e.g., ``Total'' and ``Gallons''), remove word inflections (e.g., obtaining ``Gallon'' from ``Gallons'') and map word stems into known units and dimensions (e.g., ``Gallon'' and ``volume''). Although their approach can be useful when information is explicitly given in a label, it would not work when the information is given implicitly, as in the label of ``Credit Card Charges'' which implicitly implies that the dimension is currency.

Regular expressions have been used for parsing and identifying units in text and unit conversion \cite{pint2019,wolfram2019alpha}. However, they are designed neither to annotate quantitative data columns with dimensions nor to canonicalize the units of their entries. They do not even take the input as a data column, except for the professional version of Wolfram$|$Alpha which is not freely available. \cite{shbita2019parsing} develop a rule-based system named CCUT which maps unit symbols in tabular data to an ontology so that the data entries can be annotated with semantic information, which can be useful for table understanding. Unlike us, the authors do not use the contextual information in the entries of a data column, i.e., the unit symbols in the entries of a data column may be related to each other through the column dimension. Moreover, they do not consider the task of annotating quantitative data columns with dimensions.

Quantulum \cite{quantulum2016} use an ML model to disambiguate unit symbols in unstructured text (e.g., whether ``pound'' in a sentence refers to currency or mass). However, the entries of a data column consist of only numeric values and unit symbols, unlike long sentences where additional information is available through the other words. Similarly, \cite{foppiano2019automatic} propose an ML framework named Grobid-Quantities (GQ) for processing text documents such as PDF files. However, identifying units in general tabular data can be more challenging since units in the scientific papers are more likely to follow standard notations. \cite{williams2020units} apply simple ML techniques for dimension inference (e.g., the cosine similarities between word-embeddings representations of the header and the units), and then assign the most frequent unit for the inferred dimension as the column unit. Although this approach can be useful for dimension inference, it may be misleading for column unit inference as less frequent units can also be used to encode measurements in data columns. Lastly, dimension inference could benefit from Named Entity Recognition (NER) models. However, the set of tags supported by existing NER models are typically limited, e.g., Stanford NER includes only currency and time\footnote{See \url{https://nlp.stanford.edu/software/CRF-NER.shtml} for a complete list of the \\supported tags.}.

\section{Experiments}
\label{sec:experiments}

\paragraph{\bf{Experimental Setup:}} Here, we briefly describe the datasets, baseline methods and evaluation metrics used in our experiments. See Appendix \ref{sec:appendix-exp-setup} for a detailed description. The datasets, their sources and our annotations for dimensions and units can be accessed via \url{https://tinyurl.com/ay44c62r}. The code to reproduce the experiments are publicly available at \url{https://github.com/tahaceritli/puc}.

We conduct experiments on 24 data columns obtained from 16 CSV data files, each of which contains at least one column where the measurements are encoded by units of measurement. Their dimensions were annotated by hand for these sets, resulting in 2 currency, 2 data storage, 6 mass, 3 volume and 11 length columns. We also annotated each data entry in terms of its numeric value and unit symbol. We construct baselines for dimension inference by adapting CCUT, Grobid-Quantities (GQ), Pint, Stanford NER (S-NER) and Quantulum. On dimension inference, we compare our method with CCUT, GQ, Pint and Quantulum. To assess dimension inference, we use the overall accuracy and the Jaccard index. See \cite{ceritli2020ptype} for a detailed description of these metrics and how they are used for type inference. Additionally, we evaluate the runtime of each method per data column (see Figure \ref{fig:units-runtimes} in Supp.\ Mat.). To measure the performance on the unit identification task, we report the accuracies of the methods per dataset and apply paired t-tests to determine whether the predictions of the competitor methods are significantly different from the predictions of our method.

\paragraph{\bf{Results:}} Table \ref{table:units-dimensions-explicit-metrics} presents the performance of the methods on the column dimension inference task. The overall accuracies show that PUC performs better than the competitor methods. We observe a similar trend with the performance per dimension, quantified through the Jaccard index. These improvements are due to our model's extensive knowledge about units, and its structure that takes into account the context shared among data rows. Note that the Jaccard index becomes zero when a method incorrectly labels all the data columns of a particular dimension (e.g., CCUT, GQ and Pint lead to zero Jaccard index for the currency dimension because they do not support currency symbols such as \texttt{\$} and \texttt{£} which occur in the entries of the ``Currency" column of the Zomato dataset).

\vspace*{-5mm}
\begin{table}[ht!]
\centering
\scalebox{0.65}{
\large
\begin{tabular}{l|cccccc}
\toprule
\backslashbox{\textbf{Dimension}}{\textbf{Method}} &  \textbf{CCUT} &  \textbf{GQ} & \textbf{Pint} &   \textbf{S-NER} &  \textbf{Quantulum} & \textbf{PUC} \\
\midrule
Currency&  0.00 &    0.00 &  0.00 & 0.67 &      0.50 &        \textbf{1.00} \\
Data storage&  0.00 &    0.00 &  0.00 &  0.00 &      0.50 &        \textbf{1.00} \\
Length&  0.27 &    0.45 &  0.67 &    0.62 & 0.58 &        \textbf{0.91} \\
Mass&  0.00 &    0.67 &  0.57 &   0.67    & \textbf{1.00} &        \textbf{1.00} \\
Volume&  0.00 &    0.00 &  0.00 &   0.33 & 0.67 &   \textbf{1.00} \\
\bottomrule
\addlinespace[1mm]
Overall & \multirow{2}{*}{0.12} &  \multirow{2}{*}{0.38} &  \multirow{2}{*}{0.50} &    \multirow{2}{*}{0.71}  & \multirow{2}{*}{0.71} &   \multirow{2}{*}{\bfseries  0.96} \\
Accuracy &&&&& \\
\hline
\end{tabular}
}
\vspace*{3mm}
\caption{Performance of the methods for dimension inference using the Jaccard index
and overall accuracy. We highlight the best score in each row by making the highest score bold.}
\label{table:units-dimensions-explicit-metrics}
\end{table}
\vspace*{-10mm}

\begin{wraptable}{r}{5.5cm}
\vspace*{-8mm}
\centering
\scalebox{0.77}{
\begin{tabular}{l|ccccc}
\toprule
\textbf{Dataset} & \textbf{CCUT}&  \textbf{GQ} & \textbf{Pint}&  \textbf{Quant.} &  \textbf{PUC} \\
\midrule
Arabica &  0.77 &               0.17 &  0.66 &      \textbf{1.00} &                0.70 \\
HES &  0.18 &               0.00 &  0.27 &       0.64 &                \textbf{0.98} \\
Huffman                          &  0.06 &               0.53 &  \textbf{1.00} &       \textbf{1.00} &               \textbf{1.00} \\
Maize &  1.00 &               0.30 &  \textbf{1.00} &       \textbf{1.00} &                \textbf{1.00} \\
MBA &  0.00 &               0.00 &  0.00 &       0.84 &                \textbf{0.95} \\
Open Units                       &  0.00 &               0.00 &  \textbf{1.00} &       0.00 &                \textbf{1.00} \\
PHM      &  0.99 &               0.95 &  0.99 &       \textbf{1.00} &                \textbf{1.00} \\
query\_2                          &  0.00 &               0.00 & \textbf{1.00} &       0.00 &                \textbf{1.00} \\
query\_4                          &  0.00 &               0.00 &  \textbf{1.00} &       0.00 &                \textbf{1.00} \\
Robusta &  0.71 &               0.43 &  \textbf{1.00} &       \textbf{1.00} &                \textbf{1.00} \\
Zomato &  0.00 &               0.00 &  0.00 &       0.00 &                \textbf{0.60} \\
143\dots23                     &  0.00 &               0.69 &  0.00 &       0.00 &                \textbf{0.97} \\
143\dots62                    &  0.58 &               0.00 &  0.00 &      \textbf{0.95} &                \textbf{0.95} \\
228\dots96   & \textbf{1.00} &               0.00 &  \textbf{1.00} &      \textbf{1.00} &                \textbf{1.00} \\
3b5\dots ff &  0.60 &               0.00 &  0.00 &      \textbf{1.00} &                \textbf{1.00} \\
\bottomrule
\addlinespace[1mm]
Overall & \multirow{2}{*}{0.39} &  \multirow{2}{*}{0.20} &  \multirow{2}{*}{0.59} &       \multirow{2}{*}{0.62} &   \multirow{2}{*}{\bfseries  0.94} \\
Accuracy &&&&& \\
\hline
\end{tabular}
}
\vspace*{3mm}
\caption{Accuracy of the methods on unit identification. We highlight the best score in each row by making the highest score bold. Quantulum is abbreviated as Quant.}
\label{table:accuracy-unit-identification}

\end{wraptable}
{Table \ref{table:accuracy-unit-identification} presents the accuracy of each method on each dataset (aggregated over columns) and the overall accuracy (averaging over all datasets). PUC performs consistently better than the competitor methods, although they are competitive with our method on some of the datasets. The performance of PUC on the Arabica and Zomato datasets are discussed in Appendix \ref{sec:app-exp-res}. Note that accuracy becomes zero when a method fails to identify the unit of any data entry of a dataset (e.g., Pint cannot recognize the unit symbols in the MBA dataset such as \texttt{LB} and \texttt{OZ} which respectively denote the pound and ounce units). Finally, paired t-tests confirm that PUC is significantly different than each competitor method at the 0.05 level.

\clearpage
\bibliographystyle{splncs04}

\section{Introduction}
\label{sec:introduction}
\makeatletter{\renewcommand*{\@makefnmark}{}
\footnotetext{Presented at the ECML-PKDD Workshop on Automating Data Science, 17 Sept 2021.}\makeatother}
In a typical data analytics project, a large amount of time and effort is spent on understanding and cleaning the data. A particular task, which is often performed manually, is to understand how measurements are encoded in quantitative data columns. A \emph{measurement} is a combination of a numeric value and a unit (e.g., litre), which can be encoded by several possible unit symbols for that unit (e.g., \texttt{l} and \texttt{L}).

Consider the following example: Suppose we are given the quantitative data column\footnote{The data is sampled from the ``Freezer\_volume'' column of the Household Electricity Survey (HES) dataset which can be accessed by registering at \url{https://tinyurl.com/ybbqu3n3}.
} in Figure \ref{fig:motivation}(a), which denotes the volume of freezers in various households. The value of each data entry is a measurement encoded by a unit symbol, except the last entry which does not have any unit symbol (we refer to the absence of a unit symbol in an entry as the \emph{missing unit}). As shown in Figure \ref{fig:motivation}(b), the measurements are encoded with two distinct units (litres and cubic feet) and six different unit symbols, some of which including \texttt{ltrs}, and \texttt{Cu} can be considered as \emph{anomalous unit symbols} as they do not follow the standard encodings of units. Moreover, a unit (e.g., litre) can be informative about the \emph{dimension} of a measurement (in this case volume), which is defined as an expression of the dependence of a physical quantity on mutually independent components called \emph{base quantities} in the International Vocabulary of Metrology\footnote{The document is accessible at \url{https://www.bipm.org/documents/20126/2071204/JCGM_200_2012.pdf/f0e1ad45-d337-bbeb-53a6-15fe649d0ff1}}.

\begin{figure}[H]
  \centering
    \includegraphics[width=.7\textwidth]{Fig1}
  \caption{A motivating real-world example that represents our pipeline. a) shows the samples of a raw dataset. b) indicates the intermediate steps required to transform the column. c) denotes the final data column obtained by applying the transformations.}
\label{fig:motivation}
\end{figure}

We define \emph{Unit Canonicalization (UC)} as the task of identifying the dimension of a data column and the units of all its entries so that the data entries can be canonicalized (i.e., so they all are expressed in the same units). See Figure \ref{fig:motivation}(c) for an example. To address the UC problem, one needs to apply transformations such as parsing and identifying the units in the entries, inferring the common unit for the column which is placed in the metadata (e.g., the header), making the entries numeric and scaling the entries where needed. To the best of our knowledge, the UC problem is not addressed by any existing work in the literature (see Sec.\ \ref{sec:related_work} for a detailed discussion). In this work, we propose a probabilistic approach to the UC problem. Our contributions are as follows:
\begin{itemize}
\item We propose a probabilistic model which allows us to extract semantic information about a given data column containing unit symbols (such as its dimension and unit) and canonicalize its entries (Section \ref{sec:methodology}).
\item We make the first quantitative comparison of the existing methods on the unit identification task in real-world tabular data (Section \ref{sec:experiments}).
\item We present the first set of real-world datasets annotated for the units of measurement, to accelerate research in this area.
\end{itemize}

\section{Methodology}
\label{sec:methodology}

\paragraph{\bf{Representing Units:}} We represent units by extending a dictionary of units curated from Wikipedia\footnote{Accessible at \url{https://github.com/marcolagi/quantulum/blob/master/quantulum/units.json}} with information from WikiData \cite{vrandecic2014} and QUDT (Quantities, Units, Dimensions and Data Types Ontology)\footnote{https://www.qudt.org/}. We associate each unit with a dimension and a list of unit symbols. See Appendix \ref{sec:appendix-ontology} for a detailed description.

\paragraph{\bf{The Proposed Model:}} We assume that a column of data $\textbf{y} = \{y_i\}_{i=1}^N$ consisting of $N$ rows has been read in, where each $y_i$ denotes the characters in the $i^{th}$ row. Each $y_i$ is parsed using regular expressions (see Appendix \ref{sec:appendix-parsing}) to a numeric value $v_i$ and a unit symbol $x_i$, which may be missing for some entries, i.e., $x_i$ may be null. Our focus here is on the unit symbol
$x_i$ obtained. Denoting the number of possible dimensions for a column by $K$, our model has the following generative process:
\begin{eqnarray*}
\text{column dimension $t$} &\sim& \mathcal{U}(1,K), \nonumber \\
\text{row unit $u_i$} &\sim& p(u_i|t), \nonumber \\
\text{row label $z_i$}  &=&
\begin{cases}
\text{u}_{\text{i}} & \text{with probability $w_{u_i}^{u_i}$}\ ,\\
\text{m} & \text{with probability $w_{u_i}^m$},\\
\text{a} & \text{with probability $w_{u_i}^a$}\ ,\\
\end{cases} \nonumber\\
\text{row symbol $x_i$} &\sim& p(x_i | z_i),
\end{eqnarray*}
where $\mathcal{U}$ denotes a discrete Uniform
distribution, and $p(u_i|t)$ denotes how likely it is that a row unit $u_i$ represents the dimension $t$. We model $p(u_i|t)$ with an indicator function that assigns a non-zero score only when a unit is a known unit of a dimension. Each row label $z_i$ can be either the same as $u_i$, $m$ or $a$ according to the mixing proportions denoted by $\boldsymbol{W}$, where $m$ and $a$ respectively denote a missing or anomalous unit. Here the mixing proportions $w_{u_i}^{u_i}+ w_{u_i}^m + w_{u_i}^a = 1$ for each row unit $u_i$. Since entries are often expected to be of a regular row label rather than the missing or anomalous labels, we favour regular labels during inference by using lower coefficients for the missing and anomalous labels, i.e. $w_{u_i}^m < w_{u_i}^{u_i}$ and $w_{u_i}^a < w_{u_i}^{u_i}$. These mixing proportions $\boldsymbol{W}$ are assumed to be fixed and known. Even though one could also learn the mixing proportions, this may not be vital as long as the coefficients of the regular labels are larger than the others. Finally, $p(x_i|z_i)$ denotes our observation model which is explained below. Note that the above model is based on ptype \cite{ceritli2020ptype}, but extended to address unit canonicalization by incorporating semantic information
about the units of measurements.

We build the observation model $p(x_i | z_i)$ upon three functions. First, we develop a Categorical distribution for row unit $u_i$ where the categories correspond to the possible unit symbols for that unit, i.e., $p(x_i | z_i = u_i) = \mathcal{C}at(x_i, \pi_{u_i})$ where $\sum_{s=1}^{S_{u_i}} \pi_{u_i}^s = 1$. Here, $\pi_{u_i}^s$ denotes the probability of observing the $s^{th}$ unit symbol for a unit $u_i$, while $S_{u_i}$ denotes the number of known unit symbols for that unit. Secondly, we model missing units with an indicator function, which assigns a non-zero probability only when a unit symbol is missing. Lastly, we adapt the anomaly type in ptype, which is built based on the idea of an \emph{X-factor} proposed by \cite{quinn2009factorial}, to model anomalous unit symbols. Here, we introduce a likelihood function that assigns low but non-zero probabilities to any data value, which in turn allows the model to detect anomalous unit symbols which do not fit any known unit. Note that our model is fed with unit symbols, which are parsed using regular expressions. See Appendix \ref{sec:appendix-parsing} for the details about our parsing step.

\paragraph{\bf{Inference:}} Given the row symbols $\textbf{x}$ in a data column, the initial task is to infer the column dimension $t$, which is cast to as the problem of calculating the posterior distribution of $t$ given $\textbf{x}$, namely $p(t|\textbf{x})$. We then compute a posterior distribution over each row label conditioned on the dimension and the observed value, i.e., $p(z_i|t,x_i)$. Next, we determine the row units by calculating the posterior distribution of each row unit $u_i$ given $t$, $z_i$ and $x_i$, which is also used to predict the column unit. Lastly, we map a unit symbol labelled as anomalous to a known unit symbol and perform unit canonicalization. The derivations are presented for reproducibility in Appendix \ref{sec:appendix-derivations}.

\section{Related Work}
\label{sec:related_work}
We are not aware of existing work specifically on the unit canonicalization problem. The closest related works are described below:

\cite{van2010converting,hignette2009fuzzy,samadian2014automatic} use semantic web technologies to annotate quantitative data columns in terms of units. However, their annotations are based on ontological classes in domain-specific ontologies rather than the dimensions considered in this work. For example, \cite{samadian2014automatic} define the ``High-Systolic-Blood-Pressure-Measurement'' class with \texttt{kilopascal} as its unit. Given a data column and its ontological class, their goal is to represent all entries with the same predefined unit of the corresponding ontological class. \cite{chambers2010reasoning} consider the task of annotating data entries in spreadsheets rather than tabular data. The authors split the label in a header (e.g., ``Total Gallons'') into separate words (e.g., ``Total'' and ``Gallons''), remove word inflections (e.g., obtaining ``Gallon'' from ``Gallons'') and map word stems into known units and dimensions (e.g., ``Gallon'' and ``volume''). Although their approach can be useful when information is explicitly given in a label, it would not work when the information is given implicitly, as in the label of ``Credit Card Charges'' which implicitly implies that the dimension is currency.

Regular expressions have been used for parsing and identifying units in text and unit conversion \cite{pint2019,wolfram2019alpha}. However, they are designed neither to annotate quantitative data columns with dimensions nor to canonicalize the units of their entries. They do not even take the input as a data column, except for the professional version of Wolfram$|$Alpha which is not freely available. \cite{shbita2019parsing} develop a rule-based system named CCUT which maps unit symbols in tabular data to an ontology so that the data entries can be annotated with semantic information, which can be useful for table understanding. Unlike us, the authors do not use the contextual information in the entries of a data column, i.e., the unit symbols in the entries of a data column may be related to each other through the column dimension. Moreover, they do not consider the task of annotating quantitative data columns with dimensions.

Quantulum \cite{quantulum2016} use an ML model to disambiguate unit symbols in unstructured text (e.g., whether ``pound'' in a sentence refers to currency or mass). However, the entries of a data column consist of only numeric values and unit symbols, unlike long sentences where additional information is available through the other words. Similarly, \cite{foppiano2019automatic} propose an ML framework named Grobid-Quantities (GQ) for processing text documents such as PDF files. However, identifying units in general tabular data can be more challenging since units in the scientific papers are more likely to follow standard notations. \cite{williams2020units} apply simple ML techniques for dimension inference (e.g., the cosine similarities between word-embeddings representations of the header and the units), and then assign the most frequent unit for the inferred dimension as the column unit. Although this approach can be useful for dimension inference, it may be misleading for column unit inference as less frequent units can also be used to encode measurements in data columns. Lastly, dimension inference could benefit from Named Entity Recognition (NER) models. However, the set of tags supported by existing NER models are typically limited, e.g., Stanford NER includes only currency and time\footnote{See \url{https://nlp.stanford.edu/software/CRF-NER.shtml} for a complete list of the \\supported tags.}.

\section{Experiments}
\label{sec:experiments}

\paragraph{\bf{Experimental Setup:}} Here, we briefly describe the datasets, baseline methods and evaluation metrics used in our experiments. See Appendix \ref{sec:appendix-exp-setup} for a detailed description. The datasets, their sources and our annotations for dimensions and units can be accessed via \url{https://tinyurl.com/ay44c62r}. The code to reproduce the experiments are publicly available at \url{https://github.com/tahaceritli/puc}.

We conduct experiments on 24 data columns obtained from 16 CSV data files, each of which contains at least one column where the measurements are encoded by units of measurement. Their dimensions were annotated by hand for these sets, resulting in 2 currency, 2 data storage, 6 mass, 3 volume and 11 length columns. We also annotated each data entry in terms of its numeric value and unit symbol. We construct baselines for dimension inference by adapting CCUT, Grobid-Quantities (GQ), Pint, Stanford NER (S-NER) and Quantulum. On dimension inference, we compare our method with CCUT, GQ, Pint and Quantulum. To assess dimension inference, we use the overall accuracy and the Jaccard index. See \cite{ceritli2020ptype} for a detailed description of these metrics and how they are used for type inference. Additionally, we evaluate the runtime of each method per data column (see Figure \ref{fig:units-runtimes} in Supp.\ Mat.). To measure the performance on the unit identification task, we report the accuracies of the methods per dataset and apply paired t-tests to determine whether the predictions of the competitor methods are significantly different from the predictions of our method.

\paragraph{\bf{Results:}} Table \ref{table:units-dimensions-explicit-metrics} presents the performance of the methods on the column dimension inference task. The overall accuracies show that PUC performs better than the competitor methods. We observe a similar trend with the performance per dimension, quantified through the Jaccard index. These improvements are due to our model's extensive knowledge about units, and its structure that takes into account the context shared among data rows. Note that the Jaccard index becomes zero when a method incorrectly labels all the data columns of a particular dimension (e.g., CCUT, GQ and Pint lead to zero Jaccard index for the currency dimension because they do not support currency symbols such as \texttt{\$} and \texttt{£} which occur in the entries of the ``Currency" column of the Zomato dataset).

\vspace*{-5mm}
\begin{table}[ht!]
\centering
\scalebox{0.65}{
\large
\begin{tabular}{l|cccccc}
\toprule
\backslashbox{\textbf{Dimension}}{\textbf{Method}} &  \textbf{CCUT} &  \textbf{GQ} & \textbf{Pint} &   \textbf{S-NER} &  \textbf{Quantulum} & \textbf{PUC} \\
\midrule
Currency&  0.00 &    0.00 &  0.00 & 0.67 &      0.50 &        \textbf{1.00} \\
Data storage&  0.00 &    0.00 &  0.00 &  0.00 &      0.50 &        \textbf{1.00} \\
Length&  0.27 &    0.45 &  0.67 &    0.62 & 0.58 &        \textbf{0.91} \\
Mass&  0.00 &    0.67 &  0.57 &   0.67    & \textbf{1.00} &        \textbf{1.00} \\
Volume&  0.00 &    0.00 &  0.00 &   0.33 & 0.67 &   \textbf{1.00} \\
\bottomrule
\addlinespace[1mm]
Overall & \multirow{2}{*}{0.12} &  \multirow{2}{*}{0.38} &  \multirow{2}{*}{0.50} &    \multirow{2}{*}{0.71}  & \multirow{2}{*}{0.71} &   \multirow{2}{*}{\bfseries  0.96} \\
Accuracy &&&&& \\
\hline
\end{tabular}
}
\vspace*{3mm}
\caption{Performance of the methods for dimension inference using the Jaccard index
and overall accuracy. We highlight the best score in each row by making the highest score bold.}
\label{table:units-dimensions-explicit-metrics}
\end{table}
\vspace*{-10mm}

\begin{wraptable}{r}{5.5cm}
\vspace*{-8mm}
\centering
\scalebox{0.77}{
\begin{tabular}{l|ccccc}
\toprule
\textbf{Dataset} & \textbf{CCUT}&  \textbf{GQ} & \textbf{Pint}&  \textbf{Quant.} &  \textbf{PUC} \\
\midrule
Arabica &  0.77 &               0.17 &  0.66 &      \textbf{1.00} &                0.70 \\
HES &  0.18 &               0.00 &  0.27 &       0.64 &                \textbf{0.98} \\
Huffman                          &  0.06 &               0.53 &  \textbf{1.00} &       \textbf{1.00} &               \textbf{1.00} \\
Maize &  1.00 &               0.30 &  \textbf{1.00} &       \textbf{1.00} &                \textbf{1.00} \\
MBA &  0.00 &               0.00 &  0.00 &       0.84 &                \textbf{0.95} \\
Open Units                       &  0.00 &               0.00 &  \textbf{1.00} &       0.00 &                \textbf{1.00} \\
PHM      &  0.99 &               0.95 &  0.99 &       \textbf{1.00} &                \textbf{1.00} \\
query\_2                          &  0.00 &               0.00 & \textbf{1.00} &       0.00 &                \textbf{1.00} \\
query\_4                          &  0.00 &               0.00 &  \textbf{1.00} &       0.00 &                \textbf{1.00} \\
Robusta &  0.71 &               0.43 &  \textbf{1.00} &       \textbf{1.00} &                \textbf{1.00} \\
Zomato &  0.00 &               0.00 &  0.00 &       0.00 &                \textbf{0.60} \\
143\dots23                     &  0.00 &               0.69 &  0.00 &       0.00 &                \textbf{0.97} \\
143\dots62                    &  0.58 &               0.00 &  0.00 &      \textbf{0.95} &                \textbf{0.95} \\
228\dots96   & \textbf{1.00} &               0.00 &  \textbf{1.00} &      \textbf{1.00} &                \textbf{1.00} \\
3b5\dots ff &  0.60 &               0.00 &  0.00 &      \textbf{1.00} &                \textbf{1.00} \\
\bottomrule
\addlinespace[1mm]
Overall & \multirow{2}{*}{0.39} &  \multirow{2}{*}{0.20} &  \multirow{2}{*}{0.59} &       \multirow{2}{*}{0.62} &   \multirow{2}{*}{\bfseries  0.94} \\
Accuracy &&&&& \\
\hline
\end{tabular}
}
\vspace*{3mm}
\caption{Accuracy of the methods on unit identification. We highlight the best score in each row by making the highest score bold. Quantulum is abbreviated as Quant.}
\label{table:accuracy-unit-identification}

\end{wraptable}
{Table \ref{table:accuracy-unit-identification} presents the accuracy of each method on each dataset (aggregated over columns) and the overall accuracy (averaging over all datasets). PUC performs consistently better than the competitor methods, although they are competitive with our method on some of the datasets. The performance of PUC on the Arabica and Zomato datasets are discussed in Appendix \ref{sec:app-exp-res}. Note that accuracy becomes zero when a method fails to identify the unit of any data entry of a dataset (e.g., Pint cannot recognize the unit symbols in the MBA dataset such as \texttt{LB} and \texttt{OZ} which respectively denote the pound and ounce units). Finally, paired t-tests confirm that PUC is significantly different than each competitor method at the 0.05 level.

\clearpage
\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


text:
Q: UIAlertView dismisses itself I have a UIAlertView that is called like so
let ac = UIAlertController(title: "Load Discovered!", message: "Please click confirm to confirm the collection of load: "+code, preferredStyle: .alert)
ac.addAction(UIAlertAction(title: "OK", style: .default))
present(ac, animated: true)

This presents itself over a AVCaptureVideoPreviewLayer
My issue is, it dismisses itself almost instantly when it appears, it doesn't wait for the OK input.
I figure it's something to do with the fact it's displaying on top of an AVCaptureVideoPreviewLayer I'm just not sure what to do about it

redpajama_set_name: RedPajamaStackExchange


MAYOR PETE'S BIG WEEKEND. Buttigieg to formally kick off campaign.
ON THE RAG. This week on the gay magazines….
JUSSIE SMOLLETT. Having the time of his life in Hawaii.
TACO TRUCK TAMMY. White woman threatens to call ICE on taco truck workers.
NEW TUNE OF THE DAY. P!nk "Can We Pretend".
TRAINING VIDEO OF THE DAY. How cats are trained for movies like Pet Sematary.
KEEP OR CANCEL OF THE DAY. Queer Eye on hetero trends.
FRIDAY FLASH. Cody by Issa Tall.


text:
We've covered films featuring plenty of the classic Disney characters in our Year in Shorts. We've looked at films with Mickey, films with Pluto, films with Chip and Dale and plenty of films with Donald Duck. (We haven't covered any films with Winnie-the-Pooh as of yet; we'll be getting to those whenever ANOTHER Year of Shorts rolls around.) But we've only looked at one film featuring Goofy. That changes today as we talk about Goofy's other Oscar-nominated short, the 1944 comedy classic, How to Play Football.
As you can guess from the title, Jack Kinney's How to Play Football takes the form of a faux instructional video, which already puts it in my good graces. How to Play Football is actually an entry in a long series of "How To" shorts starring Goofy. While these films seem to be the most beloved of Goofy's films, they were originally born out of necessity more than anything else. In 1938, Pinto Colvig (Goofy's regular voice actor) quite Disney to work for Fleischer, and Disney had to come up with a way to make Goofy shorts without him. The solution was the "How To" shorts, first tested out with 1941's The Art of Skiing and officially debuting with 1942's How to Play Baseball, which, interestingly enough, was rushed into production to play before The Pride of the Yankees. Imagine if Disney still did that today! I'd be a lot more inclined to see a sports movie if they had a Goofy short playing before them. Wouldn't Ford v. Ferrari have been better if it had been preceded by How to Be a Grand Prix Racer? Can you imagine how great it would have been to see a short called How to Box played in front of Creed? And there's no doubt in my mind that a short called How to Recruit a Baseball Team would be by far the most entertaining part of seeing Moneyball.
But I digress. All these shorts follow a pretty basic formula- Goofy would try out various hobbies as a narrator explains the proper way to do them. Judging by the number of shorts they'd make in this format, it certainly paid off well for Disney, even if it couldn't have felt great for Pinto Colvig to know how easily he could be replaced. Imagine that, Disney making an employee feel undervalued. At any rate, his issues with Disney eventually got patched up, and he would return to voicing Goofy before long. But the "How To" shorts would continue, culminating in How to Play Football, generally considered to be the best of the series, remaining one of the most popular Goofy shorts to this day. And really, it's not very hard to see why. Whereas for the most part Disney shorts were known for their state-of-the-art animation and cutesy animals, they could be downright hilarious when they put their minds to it. And How to Play Football is just about as hilarious as it gets. So I guess I should stop goofing around and get to discussing the short itself!
(via The Internet Animation Database)
As the narrator (a spirited Billy Bletcher) introduces us to the basics, a crowd of players, band members and fans (all played by Goofy) swarm the field. It's important to note that How to Play Football was released at a time when football was exclusively a college sport, so some of the information and uniforms are a bit outdated. Of course, if you're watching this film to actually learn how to play football, you might be in a bit of trouble. That said, you don't have to be a football expert to follow the short either. It certainly helps- there are some pretty funny gags about how ridiculous and complicated the game really is- but for the most part the short just uses football as an excuse to throw in as many sight gags as the animators can think of. And it turns out they can think of quite a lot! This short is filled with a lot of great physical comedy, from violent slapstick, absurd body proportions and exaggerated expressions, and just downright absurd cartoon physics. Although this kind of comedy isn't uncommon for animated shorts from those days, it isn't something you typically see in a Disney short. Oh sure, Donald might get a little crazy every now and again, but never quite like this. If it weren't for the fact that the short has a herd of Goofies running around, you might be forgiven for thinking it's a Warner Bros. short. I guess, much like The Emperor's New Groove, it proves that Disney doing Warner Bros. makes for a classic cartoon.
Helping sell the entire thing is Billy Bletcher's narration, which manages to provide a ton of energy to the short without ever getting too over-the-top. Bletcher knows just the right level to deliver his lines, keeping things interesting with ruining the joke. He's a perfect straight man for the absurdity happening on screen, allowing the gags to shine by underplaying his reaction. This is just another ordinary football game, apparently. All of this, combined with some truly top notch animation, makes for a truly great short. (I can see why some might have complaints with the animation in this- there are definitely a couple of moments which seem to be animated in such a way to save time and money- but I'm standing firm. In addition to the great bits of physical comedy, the painted backgrounds are really lovely, as are the painted tableaus sprinkled throughout.) This is one of those rare films which I feel comfortable recommending to just about everyone. And honestly, me talking about it has really done it no justice. This is just one of those films you need to see for yourself.
(via Make a GIF)
But as great as How to Play Football is, it couldn't quite secure itself the Oscar, losing out to the Tom and Jerry short Mouse Trouble. Before our Year in Shorts began, I would have felt that was the correct decision. But now I have to admit that I think How to Play Football was a worthier choice. Still, in the grand scheme of things, it's hard to complain. Both are some damn funny shorts filled with a lot of great physical comedy and quality animation. So if you haven't seen How to Play Football, check it out! And if you have... well, maybe watch Mouse Trouble again. Or hell, watch both! These are crazy times, and I think we all deserve a laugh or two. And those shorts are more than enough to deliver.
"How to Play Football" is available to watch on Disney +
17th Academy Awards A Year in Shorts Best Animated Short Disney Goofy

redpajama_set_name: RedPajamaCommonCrawl


text:
Josef Büchelmeier (* 4. Januar 1948 in Schwäbisch Hall) ist ein deutscher Kommunalpolitiker der SPD.

Ausbildung und Werdegang
Büchelmeier studierte in Tübingen Latein und katholische Theologie. Er war Oberstudienrat am Bildungszentrum Markdorf und am Karl-Maybach-Gymnasium in Friedrichshafen. Vom 10. Mai 2001 bis zum 5. Juni 2009 war er Oberbürgermeister der Stadt Friedrichshafen.

Oberbürgermeister Friedrichshafen
 2001 wurde er bereits im ersten Wahlgang mit 50,02 % der Stimmen gewählt.
 2009 trat er zur Wahl des Oberbürgermeisters nicht mehr an. Zu seinem Nachfolger wurde am 5. April 2009 Andreas Brand gewählt.

Als Oberbürgermeister war er auch Vorsitzender der Zeppelin-Stiftung und als deren Vorsitzender auch Mitglied im Aufsichtsrat der Zeppelin GmbH, Zeppelin Baumaschinen GmbH, ZF Friedrichshafen AG, ZF Saarbrücken GmbH, ZF Passau GmbH, Technische Werke Friedrichshafen GmbH und der Messe Friedrichshafen GmbH.

Gesellschaftliches Engagement

Städtebund Bodensee
2009 wurde auf Initiative von Büchelmeier der "Internationale Städtebund Bodensee" (2011: 24 Mitglieder) in Friedrichshafen gegründet. Seit 2009 ist Büchelmeier Geschäftsführer des Städtebundes Bodensee.

Henry-Dunant-Museums Heiden
Von 2012 bis 2016 war Büchelmeier Mitglied im Vorstand des Henry-Dunant-Museums Heiden.

Rotary Club
Von 2012 bis 2013 war Büchelmeier Präsident RC Friedrichshafen (2011/2012).

Verein Hohentwiel
Von 2016 bis 2022 war Büchelmeier 1. Vorsitzender des Internationalen Vereins "Hohentwiel".

Amici di Imperia
Seit 2015 ist Büchelmeier 1. Vorsitzender des Städtepartnerschaftvereines Amici di Imperia.

Familie
Josef Büchelmeier ist verheiratet und hat zwei Kinder.

Weblinks
 Website von Josef Büchelmeier

Einzelnachweise

Bürgermeister (Friedrichshafen)
Person (Friedrichshafen)
Deutscher
Geboren 1948
Mann

redpajama_set_name: RedPajamaWikipedia


The Arab Uprising: The Unfinished Revolutions of the New Middle East.By Marc Lynch.
THE Arab spring has, inevitably, spawned a gaggle of instant books.
But it was much easier to make sense of the region's upheavals a year ago, in the first flush of excitement, than it is today.
Syria's body-count rising and only Tunisia witnessing a relatively smooth political transition, is there still a single unifying narrative?
Marc Lynch, an American scholar and blogger with ties to the Obama administration, believes there is.
Although alive to the setbacks of the past year, he remains hopeful.
In "The Arab Uprising", he argues that the old status quo in the Middle East has been shattered for good, and that the rest of us had better get used to this brave but baffling new world.
To explain today's events, he goes back to the 1950s, when the revolution of Gamal Abdel Nasser in Egypt set Arab pulses racing.
But that first Arab spring died in 1967, in Israel's humiliating defeat of the Arab armies in the six-day war—a useful warning for today's revolutionaries that success is not preordained.
they are readier to challenge autocratic rulers and, thanks to satellite television, Facebook and Twitter, they are more intimately connected with one another and with the world.
Mr Lynch, who made his name with his 2006 book, "Voices of the New Arab Public", understands what makes Arab opinion tick.
His new book sets out, clearly and forthrightly, to dispel Western (and especially American) illusions about the nature of change in the Arab world.
He does not accept, for instance, that the successes of the Islamists in recent elections in Tunisia, Egypt and Morocco are cause for alarm.
It is inevitable, he contends, that groups like the Muslim Brotherhood will benefit from an opening-up of political space, and their democratic pretensions should be put to the test.
Mr Lynch also thinks the uprisings, by empowering Arab publics, have made the Palestinian issue more important. He supported military intervention in Libya, but opposes it in Syria.
His account is especially useful when it comes to the regional fallout. He sees Turkey and Qatar, which have embraced the new mood, as winners; Israel and Iran as sullen spectators.
in particular, as his critics frequently have not, that these are home-grown uprisings, neither needing nor welcoming American leadership.
But Mr Lynch faults Mr Obama for his failure to advance Israeli-Palestinian peace, to criticise more firmly the suppression of Bahrain's uprising or to develop a more effective public diplomacy.
Is Mr Lynch right in declaring the old status quo beyond recall? This may be too rosy.
But of all the books on the extraordinary events of the past 15 months, this is one of the most illuminating and, for policymakers, the most challenging.


Rosemary Flores, 17, a wrestler for Curtis High School, pinned an opponent during a tournament in January in Harlem. With more young women interested in wrestling, the Public School Athletic League is starting a new city league for girls.
The Taft Educational Complex, in the South Bronx, started a boys' wrestling team in November, and along with the dozens of boys who attended a season-opening meeting, there was one girl, Samantha Torres, 16.
The coach, Josh Lee, asked the wrestlers what motivated them to join, and soon all eyes turned to Ms. Torres.
"I said, 'I want to show everyone that girls aren't weaklings, and that just because I'm a girl doesn't mean I can't wrestle,'" she recalled.
After a silent pause, the room erupted in applause. Once practices and matches began, however, some teammates were uncomfortable working out with Ms. Torres, and some opponents declined to compete for fear of losing to a girl, she said.
But the awkwardness she encountered is often a deterrent for girls seeking to join boys' teams, even though there are no rules barring them. So the city's Public School Athletic League has decided to give the girls a league of their own and is inaugurating a girls' wrestling program with 16 teams in 12 weight classes that officials estimate will include about 300 girls.
The new league will draw from all five boroughs, including Bathgate High School in the Bronx, Hunter College High School in Manhattan, Flushing High School in Queens, Canarsie High School in Brooklyn, and New Dorp High School on Staten Island.
It will be the first league of its kind in New York State, league officials said. The season will begin in March, culminating in a citywide tournament in May, said Eric Goldstein, chief executive of the Education Department's Office of School Support Services.
Officials identified 15 schools with a core group of female wrestlers already competing on the boys' squads, or where coaches and school officials believed there was enough interest from female students to give wrestling a try, Mr. Goldstein said. The 16th team, he said, will be a citywide "catch all" team for girls at schools without programs.
Female wrestling advocates said that starting teams in schools would attract girls who never considered that they could participate in organized wrestling.
"Nothing gets girls to join like seeing a girl throw a boy," said Cheryl Wong, director of women's wrestling at Beat the Streets, a nonprofit that promotes wrestling and that worked with the P.S.A.L. to start the new league.
Ms. Wong, an accomplished freestyle wrestler, has encouraged scores of girls to attend the clinics and practices that Beat the Streets runs as part of its mission to create and strengthen school wrestling teams in the city.
There are 72 girls wrestling on boys' teams in New York City public high schools, according to the National Federation of State High School Associations. That number far outpaces the number of girl wrestlers in the rest of the state, but is far less than the numbers in states that already have separate girl wrestling programs, like Texas (2,416), California (2,008), Washington (1,071) and Hawaii (456).
Rather than use collegiate rules that the boys' league uses, the girls' league will follow the freestyle rules used in Olympic wrestling. (The International Olympic Committee recently announced that it was dropping wrestling from the Summer Games). The style places more emphasis on wrestling on one's feet instead of grappling on the mat.
One of the new teams will be Curtis High School on Staten Island, which already has a dozen girls on its boys' team. At the Mayor's Cup championships, a citywide competition held last month at the Harlem Armory that had a separate girls' division, the Curtis girls won the girls' team championship. One if its stars, Rosemary Flores, 17, who wrestles in the 126-pound class, won an individual championship.
For Ms. Flores, it was her third Mayor's Cup title in a row. She is widely considered the best female wrestler in the city and one of the best in the nation at her weight classification.
Kimberly Cardenas, 18, a senior from Petrides High School on Staten Island who won the girls' 99-pound title at the Harlem tournament, said she was excited that Petrides would field a girls' team.
A version of this article appears in print on 02/28/2013, on page A25 of the NewYork edition with the headline: Girls in Public Schools Get a Wrestling League.


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text:
Q: Syntax Error Assigning Function Output To Postgres Variable I'm trying to write a migration that generate a 'sort name' for each name in a table, where the sort name is identical to the regular name except that ' is replaced with a single space, the name is converted to lowercase and the last word in the name is moved to the front. E.g "John L'Mann" becomes "Mann John L". I have written a temporary function in order to do this:
CREATE FUNCTION generate_sort_name(name text) RETURNS text AS $$
DECLARE
    without_quotes text;
    split_name text[];
    all_but_surname text;
BEGIN
without_quotes := (regexp_replace(name, '\''', 'g'));
split_name := (regexp_split_to_array(lower(without_quotes), '\s+'));
CASE WHEN (length(split_name) = 1) THEN RETURN split_name[1];
    ELSE
    all_but_surname := (array_to_string(split_name[1:length(split_name)-1], ' '));
    return split_name[length(split_name)] || ' ' || all_but_surname;
END
END
$$ LANGUAGE plpgsql;

EDIT: Thank you for the information, I have corrected the lack of semicolons and the double quotes. Now getting syntax error at or near 'END' - I've tried with semicolons following either or both and with and without one at the end of the last 'return' line.


text:
layout: post
title: "A Memory of Light - Robert Jordan"
tags: [big-year, böcker]
---

http://i.imgur.com/rzvo0.jpg

2013-01-13 / 100%

Kanske förtjänar inte boken högsta betyg. Kanske förtjänar inte ens serien i sig högsta betyg. Men det var så förlösande att få läsa SLUTET!

Brandon Sanderson håller ihop det. Jordan hade planerat ett tillfredsställande slut. De flesta verkar nöjda.

redpajama_set_name: RedPajamaGithub


text:
Biografia
Era il figlio di Sir William Thomas Knollys, e di sua moglie, Elizabeth St. Aubyn. Studiò a Guernsey e in seguito entrò al Royal Military College di Sandhurst, dove raggiunse il grado di guardiamarina nel 1854.

Carriera
Nel 1855 Knollys entrò nel Department of the Commissioners of Audit. Nel 1862, divenne segretario al tesoro per il Principe di Galles. Nel 1870, fu nominato segretario privato del Principe di Galles, carica che mantenne fino al 1901.

È stato anche Groom in waiting per il Principe di Galles (1886-1901). Knollys poi divenne segretario privato del sovrano, carica che mantenne fino al 1913. Era conosciuto per la sua lealtà e discrezione in questo ruolo. È stato anche Gentleman Usher della regina Vittoria (1868-1901), e un Lord in waiting per la regina Mary (1910-1924).

Nel 1902 è stato elevato alla nobiltà come Barone Knollys, di Caversham nella Contea di Oxford, ed è diventato un consigliere della Corona nel 1910. Nel 1911 è stato ulteriormente onorato quando fu nominato visconte Knollys, di Caversham nella Contea di Oxford.

Matrimonio
Sposò, l'11 aprile 1887, Arden Mary Tyrwhitt (2 settembre 1860-26 dicembre 1922), figlia di Sir Henry Thomas Tyrwhitt. Ebbero due figli:

 Alexandra Louvima Elizabeth Knollys (?-30 maggio 1958), sposò Allan Keith Mackenzie, ebbero un figlio;
 Edward Knollys, II visconte Knollys (16 gennaio 1895-3 dicembre 1966).

Morte
Morì il 15 agosto 1924.

Onorificenze

Onorificenze britanniche

Note

Collegamenti esterni

Francis Knollys, 01 visconte Knollys
Francis Knollys, 01 visconte iKnollys
Francis Knollys, 01 visconte Knollys
Francis Knollys, 01 visconte Knollys
Francis Knollys, 01 visconte Knollys
Francis Knollys, 01 visconte Knollys
Francis Knollys, 01 visconte Knollys


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