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

Words mean different things in different contexts. Sometimes these meanings appear to be distinct, a phenomenon known as \textit{lexical ambiguity}. In English, approximately 7\% of wordforms are \textit{homonymous}, i.e., they have multiple, unrelated meanings\footnote{\citet{dautriche2015weaving} estimates the average rate of homonymy across languages to be 4\%.} (e.g., ``tree bark'' vs. ``dog bark''), and as many as 84\% of wordforms are \textit{polysemous}, i.e., they have multiple, related meanings (e.g., ``pet chicken'' vs. ``roast chicken'') \cite{rodd2004modelling}. But even unambiguous words evoke subtly different interpretations depending on the context of use, i.e., their meanings are dynamic and \textit{context-dependent} \cite{yee2016putting, li2021word}. While the uses of \textit{runs} in ``the boy runs'' vs. ``the cheetah runs'' may not be considered distinct meanings, a human comprehender will likely activate a different mental image when processing each sentence \cite{elman2009meaning}.

These facts present a challenge for computational models of lexical semantics. Any downstream task that involves meaning requires models capable of \textit{disambiguating} among the multiple possible meanings of an ambiguous word in a given context. Further, the \textit{graded} nature of human semantic representations can influence how comprehenders construe events and participants in those events \cite{elman2009meaning, li2021word}. In turn, a number of Natural Language Processing (NLP) tasks could benefit from context-sensitive representations that go beyond discrete sense representations and capture the manner in which humans construe events---including sentiment analysis, bias detection, machine translation, and more \cite{trott2020re}. If an eventual goal of NLP is human-like language understanding, models must be equipped with semantic representations that are flexible enough to accommodate the dynamic, context-dependent nature of word meaning---as humans appear to do \cite{elman2009meaning, li2021word}.

Yet a crucial prerequisite to developing better models is \textit{evaluating} those models along the relevant dimensions of performance. Thus, at the minimum, we need metrics that evaluate a model along two critical dimensions:

\begin{enumerate}[itemsep=-1ex]
    \item \textbf{Disambiguation}: A model's ability to distinguish between distinct meanings of a word.\label{criteria1}
    \item \textbf{Contextual Gradation}: A model's ability to modulate a given meaning in context, in ways that reflect the continuous nature of human judgments.\label{criteria2}
\end{enumerate}

A promising development in recent years is the rise of contextualized word embeddings, produced using neural language models such as BERT \cite{devlin2018bert}, ELMo \cite{peters2018deep}, XLNet \cite{yang2019xlnet}, and more. Advances in these models have yielded improved performance on a number of tasks, including Word Sense Disambiguation (WSD) \cite{boleda2019putting, loureiro2020language}.

WSD satisfies the Disambiguation Criterion above, but not the Contextual Gradation Criterion. In fact, there is still a dearth of metrics for assessing the degree to which contextualized representations match human judgments about the way in which context shapes meaning.

In Section \ref{sec:related}, we describe several related datasets that satisfy at least one of these criteria. In Section \ref{sec:dataset}, we introduce and describe the dataset construction process for RAW-C: Relatedness of Ambiguous Words---in Context.\footnote{The dataset can be found on GitHub: \url{https://github.com/seantrott/raw-c}.} In Section \ref{sec:annotation}, we describe the procedure we followed for collecting human relatedness norms for each sentence pair. In Section \ref{sec:analysis}, we report the results of several analyses that probe how well contextualized embeddings from two neural language models (BERT and ELMo) predict these norms. Finally, in Section \ref{sec:discussion}, we explore possible shortcomings in current models, and propose avenues for future work.

\section{Related Work}\label{sec:related}

Most existing datasets fulfill either the \textbf{Disambiguation} or the \textbf{Contextual Gradation} criterion, but few datasets fulfill both (see \citet{haber2020assessing} for an exception).

Several datasets contain human relatedness and similarity judgments for \textit{distinct} words in isolation (see Section \ref{subsec:simrel}). Others are used for Word Sense Disambiguation, and contain ambiguous words in different sentence contexts, along with annotated sense labels (see Section \ref{subsec:wsd}); as noted in the Introduction, WSD fulfills the Disambiguation Criterion, but not the Contextual Gradation Criterion. Several recent datasets contain graded relatedness judgments for words in different contexts (see Section \ref{subsec:graded}). However, none focus specifically on graded relatedness judgments for \textit{ambiguous} words, controlling both the inflection and part of speech of the target word in question. Finally, one dataset \cite{haber2020assessing} contains similarity judgments for polysemous words in context, but is more limited in size and does not match the sentence frame across the two uses (see Section \ref{subsec:graded_ambig}).

\subsection{De-contextualized Word Similarity and Relatedness}\label{subsec:simrel}

Several datasets contain human judgments of the \textit{similarity} or \textit{relatedness} of (mostly English) word pairs, in isolation (see \citet{taieb2020survey} for a review). This includes SimLex-999 \cite{hill2015simlex}, SimVerb-3500 \cite{gerz2016simverb}, WordSim-353 \cite{finkelstein2001placing}, MTurk-771 \cite{halawi2012large}, MEN \cite{bruni2014multimodal}, and more. These datasets are primarily used for evaluating the quality of static semantic representations, including distributed semantic models such as GloVe \cite{pennington2014glove}, as well as representations that use knowledge bases like WordNet \cite{faruqui2015non}.

However, these resources are (by definition, as decontextualized judgments) not directly amenable to evaluating how well a model incorporates \textit{context} into its semantic representation of a given word.
\subsection{Word Sense Disambiguation}\label{subsec:wsd}

In Word Sense Disambiguation (WSD), a classifier predicts the ``sense'' of an ambiguous word in a given context, often using a contextualized embedding. WSD relies on annotated sense labels, which in turn requires determining whether any given pair of word uses belong to the same or distinct senses---i.e., whether to ``lump'' or ``split''. There is considerable debate about how \textit{granular} word sense inventories should be \cite{hanks2000word, brown2008choosing};\footnote{This also raises deeper philosophical issues about exactly what qualifies as a ``sense'' \cite{hanks2000word, tuggy1993ambiguity, geeraerts1993vagueness, kilgarriff2007word}; answering these questions is beyond the scope of this paper, though see Section \ref{sec:discussion} for a brief discussion.} resources range in granularity from WordNet \cite{fellbaum1998} to the Coarse Sense Inventory, or CSI \cite{lacerra2020csi}. Recent work using coarse-grained sense inventories has achieved success rates of 85\% and beyond \cite{lacerra2020csi, loureiro2020language}.

In terms of the criteria listed above, WSD satisfies the Disambiguation Criterion, but not the Contextual Gradation Criterion. WSD only captures a model's ability to distinguish between distinct senses; it does not assess how meaning is modulated within a given sense category, e.g., that a human comprehender might consider the meaning of \textit{runs} in ``the cheetah runs'' as more similar to ``the jaguar runs'' than to ``the toddler runs'', or that some uses of a sense might be more prototypical than others.

\subsection{Contextualized Word Similarity and Relatedness}\label{subsec:graded}

There have been several recent efforts to address this gap in the literature:

The Stanford Contextual Word Similarity (SCWS) dataset \cite{huang2012improving} contains similarity judgments for 2,003 English word pairs in a sentence context. Approximately 12\% of the pairs contain the same word (e.g., ``pack his bags'' vs. ``pack of zombies''), though not always in the same part of speech; in most cases, the words compared are different (e.g., ``left'' vs. ``abandon''). This dataset is a useful step towards contextualized similarity judgments, but because most pairs contain different words (or the same word in different parts of speech), static word embeddings such as Word2Vec can still perform quite well without considering the context at all \cite{pilehvar2018wic}.

The Word in Context (WiC) dataset \cite{pilehvar2018wic} contains a set of over 7,000 sentence pairs with an overlapping English word, labeled according to the use of that word corresponds to same or different senses. As \citet{pilehvar2018wic} note, the structure of the dataset requires some form of contextualized meaning representation to perform above a random baseline, which makes it more suitable for interrogating contextualized embeddings. However, the task is a binary classification task along the lines of WSD, making it harder to assess the Contextual Gradation Criterion.

The CoSimLex dataset \cite{armendariz2020semeval}, created with the Graded Word Similarity in Context (GWSC) task, contains graded similarity judgments for a number of word pairs across English (340), Croatian (112), Slovene (111), and Finnish (24). Each pair of words is rated in two separate contexts, yielding 1174 scores in total. Sentence contexts were extracted from each language's Wikipedia. Unlike WiC, the word pairs do not actually contain the same word---rather, for any given word pair (e.g., ``beach'' and ``seashore''), there are at least two pairs of sentence contexts with associated similarity judgments. Thus, this dataset can be used to assess graded differences in contextualized meaning representations, but not directly for the \textit{same} ambiguous word.

\subsection{Contextualized Similarity of Ambiguous Words}\label{subsec:graded_ambig}

Finally, one dataset \cite{haber2020assessing, haber2020word} contains graded similarity judgments (as well as co-predication acceptability judgments) for a number of polysemous words in distinct sentential contexts, meeting both \textbf{Contextual Gradation} and the \textbf{Disambiguation} criteria.

The main limitations of this dataset are its size (it contains examples for only 10 polysemes), as well as the fact that the sentence frames are also not always controlled for each polysemous word.

\subsection{Summary}

Most datasets reviewed above allow practitioners to evaluate models on their ability to disambiguate (i.e., the Disambiguation Criterion) or their ability to capture graded differences in word relatedness (i.e., the Contextual Gradation Criterion); one dataset \cite{haber2020assessing, haber2020word} meets both criteria.

But to our knowledge, no datasets contain graded relatedness judgments for ambiguous words in tightly controlled sentence contexts, with inflection and part-of-speech controlled across each use. In Section \ref{sec:dataset} below, we describe the procedure we followed for constructing such a dataset.

\section{RAW-C: Relatedness of Ambiguous Words, in Context}\label{sec:dataset}

Items were adapted from stimuli used in past psycholinguistic studies, which contrasted behavioral responses to homonymous and polysemous words, either in isolated lexical decision tasks \cite{klepousniotou2007disambiguating} or in a disambiguating context \cite{klepousniotou2002processing, klepousniotou2008making, brown2008polysemy}. We selected 115 words in total. For each ambiguous word (e.g., ``bat''), we created four sentences: two each for two distinct meanings of the word. We attempted to match the sentence frames as closely as possible, in most cases altering only a single word\footnote{There were 13 words for which at least one of the four sentences used a different article (``a'' vs. ``an''), in addition to having a different disambiguating word.} across the four sentences to disambiguate the intended meaning:

\begin{enumerate}[itemsep=-1ex]
    \item[1a.] He saw a \textit{fruit} bat.
    \item[1b.] He saw a \textit{furry} bat.
    \item[2a.] He saw a \textit{wooden} bat.
    \item[2b.] He saw a \textit{baseball} bat.
\end{enumerate}

We also labeled each word according to whether the two distinct meanings were judged by lexicographers to be Polysemous or Homonymous. Distinguishing homonymy from polysemy is notoriously challenging \cite{valera2020polysemy}; common tests include determining whether the two meanings share an etymology (polysemy) or not (homonymy), or determining whether the two meanings are conceptually related (polysemy) or not (homonymy). Both tests can be criticized on multiple grounds \cite{tuggy1993ambiguity, valera2020polysemy}, and do not always point in the same direction (e.g., etymologically related words sometimes drift apart, resulting in apparent homonymy).

For our annotation, we consulted both the online Merriam-Webster Dictionary (\url{https://www.merriam-webster.com/}) and the Oxford English Dictionary, or OED (\url{https://www.oed.com/}), and identified whether each dictionary listed the two meanings in question in separate lexical entries (homonymy), or as different senses under the same lexical entry (polysemy).\footnote{Our primary goal with this labelling was not to definitively distinguish homonymy from polysemy; as noted above, there is no single, universal criterion for doing so, and different criteria might be more or less relevant for different purposes. It was simply to specify how lexicographers treat the different words, in case that information is useful for users of the resource.} For example, both dictionaries list the \textit{animal} and \textit{meat} senses of the word ``lamb'' as different senses under the same lexical entry, whereas they list the \textit{animal} and \textit{artifact} senses of the word ``bat'' under different lexical entries. There was one word (``drill'') on which the two dictionaries did not agree; in this case, we labeled the two meanings (``electric drill'' vs. ``grueling drill'') as homonymy (as per the OED).

There were also three words for which neither dictionary distinguished the two meanings (either in terms of homonymy or polysemy). For example, ``best-selling novel'' and ``thick novel'' refer to \textit{cultural} and \textit{physical} artifacts, respectively, but are not listed as distinct senses. Again, this highlights the challenge of distinguishing outright \textit{ambiguity} from \textit{context-dependence}; these items were included in the annotation study described below, but were excluded from the final set of norms, thus resulting in 112 target words altogether.\footnote{The existence of these ``Unsure'' items, as well as items for which the two dictionaries disagreed on the issue of homonymy vs. polysemy, raises the question of whether empirical measurements such as relatedness judgments (or even cosine distance) could help inform lexicographic decisions. As a proof of concept, we trained a logistic regression classifier (using leave-one-out cross-validation) to predict whether two contexts of use belonged to the Same Sense, using Mean Relatedness. The classifier successfully categorized 86.76\% of held-out test items as belonging to the same or different senses. Further, for different sense items only, a trained classifier successfully categorized 79\% of held-out test items as polysemous or homonymous. While only a proof of concept, this demonstration suggests a promising avenue for future research.} Each word was used in four sentences, for a total of six \textit{sentence pairs} (see Table \ref{table:descriptive} for more details). 84 of the target words were nouns, and 28 were verbs (note that Part-of-Speech was always held constant \textit{within} each word).

\begin{table}[H]
\begin{tabular}{lll}
\hline
Ambiguity Type & \#Words & \#Sentence Pairs \\
\hline
Homonymy       & 38      & 228              \\
Polysemy       & 74      & 444
\end{tabular}
\caption{Number of words (and sentence pairs) for each type of ambiguity.}\label{tab:accents}
\label{table:descriptive}
\end{table}

\section{Human Annotation}\label{sec:annotation}

\subsection{Participants}

81 participants were recruited through UC San Diego's undergraduate subject pool for Psychology, Cognitive Science, and Linguistics students. Participants received class credit for participation. Three participants were removed for failing the bot checks at the beginning of the study, and one was removed for failing the catch trials embedded in the experiment, leaving 77 participants in total (59 Female, 16 Male, 2 Non-binary). The median age of participants was 20 (M = 20.22, SD = 2.7), with ages ranging from 18 to 38. 74 participants self-reported as being native speakers of English.

\subsection{Materials}

We used the original set of 115 words described in Section \ref{sec:dataset}, i.e., including the three items labeled ``Unsure''. Each word had four sentences; accounting for order, this resulted in twelve possible sentence pairs (six pairs, with two orders each) for each word, for a total 1380 items.

\subsection{Procedure}

After giving consent, participants answered two questions designed to filter out bots (e.g., ``Which of the following is not a place to swim?'', with the correct answer being ``Chair''). They were then given instructions, which included a description of how the meaning of a word can change in different contexts.

On each page of the study, participants were shown a pair of sentences, with the target word bolded (see Figure \ref{fig:study} for an example). They were asked to indicate how \textbf{related} the uses of that word were across the two sentences, with a labeled Likert scale ranging from ``totally unrelated'' to ``same meaning''.

\begin{figure}[H]
    \centering
    \includegraphics[width=7cm]{experiment_page}
    \caption{Example item from study.}
    \label{fig:study}
\end{figure}

We included two ``catch'' trials in the study to identify participants who did not pay attention. In one, the two sentences were identical, such that the correct answer is ``same meaning''; the other featured a homonym with two different parts of speech (\textit{rose.v} and \textit{rose.n}), such that the correct answer was ``totally unrelated''.

Excluding the catch trials, participants saw 115 sentence pairs total; no word was repeated twice across trials for the same participant. The comparisons any given subject saw for a given word were randomly sampled from the 12 possible sentence pairs, and the order of trials was randomized.\footnote{Based on the suggestion of an anonymous reviewer, we also ran a follow-up norming study to collect estimates of sense frequency bias (sometimes called \textit{dominance}); sense dominance is known to play an important role in the processing of ambiguous words \cite{klepousniotou2007disambiguating, rayner1994effects, binder1998contextual, leinenger2013eye}. These dominance norms are included in the final dataset.}

\section{Analysis and Results}\label{sec:analysis}

The analyses run below were performed on the 112 target words (i.e., excluding the ``Unsure'' items). Human annotations were assigned to a scale from 0 (``totally unrelated'') to 4 (``same meaning'').

\subsection{Analysis of Sentence Pairs}

Before analyzing the responses of human annotators, we first sought to characterize how well two neural language models captured the \textit{categorical} structure in the dataset---i.e., whether their contextualized representations could be used to distinguish same-sense from different-sense uses of the same word, as well as words labelled as different-sense Homonyms from different-sense Polysemes.

We ran every sentence through two language models: ELMo, using the Python AllenNLP package \cite{Gardner2017AllenNLP}, and BERT, using the \texttt{bert-embedding} package.\footnote{\url{https://pypi.org/project/bert-embedding/}} Then, for each sentence pair, we computed the Cosine Distance between the contextualized representations of the target wordform (e.g., \textit{bat} in ``He saw the furry bat'' and ``He saw the wooden bat''). The distribution of Cosine Distances is visualized in Figure \ref{fig:cosines}.

\begin{figure}[H]
    \centering
    \includegraphics[width=7cm]{cosine_distances}
    \caption{Cosine Distances between the target word's contextualized embeddings for both language models, plotted by Same Sense (True vs. False) and Ambiguity Type (Homonymy vs. Polysemy).}
    \label{fig:cosines}
\end{figure}

We also performed several statistical analyses, using the \texttt{lme4} package in R \cite{bates2007lme4}. In each case, we compared a full model to a reduced model using a log-likelihood ratio test. All models had Cosine Distance as a dependent variable, and included Part-of-Speech as a fixed effect, random intercepts for Word and Language Model (i.e., ELMo vs. BERT), and by-Word random slopes for the effect of Same Sense.

Adding a fixed effect of Same Sense significantly improved model fit $[{\chi}{^2}(1)=143.72, p < .001]$, with same-sense uses significantly closer than different-sense uses $[\beta=-.099, SE = 0.005]$. However, adding an interaction between Same Sense and Ambiguity Type (as well as fixed effects of both) did not significantly improve the fit above a model omitting the interaction $[{\chi}{^2}(1)=2.19, p = 0.14]$. In other words, both language models could differentiate same-sense and different-sense uses of an ambiguous word, but their ability to discriminate between Homonymy and Polysemy was marginal at best.

\subsection{Analysis of Human Annotations}\label{subsec:human_analysis}

Our primary goal was understanding the distribution of human relatedness annotations---both in terms of how it reflects the underlying categorical structure of the dataset (e.g., Homonymy vs. Polysemy), as well as the Cosine Distance measures from each language model. As in the section above, we constructed a series of linear mixed effects models and performed log-likelihood ratio tests for each model comparison; in each case, the dependent variable was Relatedness. All models included a fixed effect of Part-of-Speech, by-subject and by-word random slopes for the effect of Same Sense, by-subject random slopes for the effect of Ambiguity Type, and random intercepts for subjects and items.

First, we asked whether participants' relatedness judgments varied across same-sense and different-sense sentence pairs. We added a fixed effect of Same Sense to the base model described above, along with fixed effects for the Cosine Distance measures from BERT and ELMo. This model explained significantly more variance than a model omitting only Same Sense $[{\chi}{^2}(1)=207.11, p < .001]$, with same-sense uses receiving higher relatedness judgments on average $[\beta=1.94, SE = 0.1]$. The median relatedness judgment for same-sense uses was 4 ($M = 3.46, SD = 1.02$), while the median relatedness judgment for different-sense uses was 1 ($M = 1.31, SD = 1.45$).
Second, we asked whether participants' judgments were sensitive to the distinction between Homonymy and Polysemy. We added an interaction between Same Sense and Ambiguity Type (along with a fixed effect of Ambiguity Type) to the model described above. The interaction significantly improved model fit $[{\chi}{^2}(1)=25.45, p < .001]$. The median relatedness for both same-sense homonyms and polysemes was 4, whereas the median relatedness for different-sense homonyms (0) was lower than that for different-sense polysemes (2). Further, as depicted in Figure \ref{fig:relatedness}, there was considerably more variance across polysemous words than homonymous words---this makes sense, given that some polysemous meanings are highly related (e.g., ``pet chicken'' vs. ``roast chicken''), while others are more distant (e.g., ``desperate act'' vs. ``magic act'').

\begin{figure}[H]
    \centering
    \includegraphics[width=7cm]{mean_norms}
    \caption{Mean relatedness judgments for each sentence pair, plotted by by Same Sense (True vs. False) and Ambiguity Type (Homonymy vs. Polysemy).}
    \label{fig:relatedness}
\end{figure}

Third, we asked whether the Cosine Distance measures explained independent variance above and beyond that explained by Same Sense and Ambiguity Type. A full model including all factors explained more variance than a model excluding only the Cosine Distance measure from BERT $[{\chi}{^2}(1)=36.19, p < .001]$, as well as a model excluding only the Cosine Distance measure from ELMo $[{\chi}{^2}(1)=16.92, p < .001]$. This indicates that Relatedness does not vary purely as a function of the categorical structure in the dataset---the graded relatedness judgments were sensitive to subtle differences in context.

\subsection{Inter-Annotator Agreement}

Inter-annotator agreement was assessed by calculating the average Spearman's rank correlation between each participant's responses and the Mean Relatedness for the set of 112 items observed by that participant---where Mean Relatedness was calculated after omitting responses by the participant in question. This answers the question: to what extent do each participant's responses correlate with the consensus rating by the 76 other participants? Using this method, the average correlation was $\rho = 0.79$, with a median of $\rho=0.81$ ($SD = .07$). The lowest agreement was $\rho = 0.55$, and the highest was $\rho = 0.88$.

\subsection{Evaluation of Language Models}\label{evaluation}

To evaluate the language models, we collapsed across the single-trial data and computed the Mean and Median Relatedness for each unique sentence pair. The distribution of Mean Relatedness judgments is depicted in Figure \ref{fig:relatedness}.

As in past work \cite{hill2015simlex}, we computed the Spearman's rank correlation between the distribution of Cosine Distance measures (from each model) and the Mean Relatedness for a given sentence pair. BERT performed slightly better than ELMo ($BERT_{\rho} = -0.58, ELMo_{\rho} = -0.53$).\footnote{Note that larger values of Cosine Distance indicate a \textit{larger} distance between two vectors; thus, a negative correlation is expected between relatedness and Cosine Distance.} Putting this in context, both models performed considerably worse than the average inter-annotator agreement score ($\rho = 0.79$).

We also computed the $R^2$ of a linear regression including the Cosine Distance measures from \textit{both} BERT and ELMo. Combined, both measures explained roughly 37\% of the variance in Mean Relatedness judgments ($R^2 = 0.37$). Surprisingly, this was only slightly more than half the variance explained by a linear regression including only the interaction between Same Sense and Ambiguity Type ($R^2 = 0.66$), as well as a regression including all factors ($R^2 = 0.71$).

By visualizing the residuals from the linear regression with only BERT and ELMo (see Figure \ref{fig:residuals}), we see that Cosine Distance appears to systematically \textit{underestimate} how related participants find same-sense judgments to be (for both Polysemy and Homonymy). Further, we see that Cosine Distance systematically \textit{overestimates} how related participants find different-sense Homonyms to be.

\begin{figure}[H]
    \centering
    \includegraphics[width=7cm]{residuals_test.pdf}
    \caption{Residuals of a linear regression including Cosine Distance measures from both BERT and ELMo, plotted by by Same Sense (True vs. False) and Ambiguity Type (Homonymy vs. Polysemy).}
    \label{fig:residuals}
\end{figure}

\section{Discussion}\label{sec:discussion}

Word meanings are dynamic, dependent on the contexts in which those words appear---and some words are even ambiguous, generating distinct, incompatible interpretations in different situations (e.g., ``fruit \textit{bat}'' vs. ``baseball \textit{bat}'').

RAW-C contains graded relatedness judgments (by human annotators) for ambiguous English words in distinct sentential contexts. Importantly, the ambiguous wordform (e.g., ``bat'') is always matched for both part-of-speech and inflection across each sentence pair; 84 of the target words are nouns, and 28 are verbs. Each word has relatedness judgments for six different sentences pairs (four unique sentences): two same-sense pairs, and four different-sense pairs. Same sense pairs convey the same meaning, according to Merriam-Webster and the OED (e.g., ``fruit bat'' and ``furry bat''), while different sense pairs correspond to meanings listed in either distinct lexical entries (e.g., ``fruit bat'' and ``wooden bat'') or distinct sub-entries (e.g., ``marinated lamb'' and ``baby lamb''). Furthermore, different-sense pairs are labeled according to whether they are related via homonymy or polysemy, a relevant distinction for both lexicographers and psycholinguists---recent evidence suggests that polysemous and homonymous meanings are represented differently in the mental lexicon \cite{klepousniotou2002processing, klepousniotou2007disambiguating}. Finally, the sentential context is always tightly controlled; in most pairs, only one word differs across the two sentences (e.g., ``fruit'' vs. ``furry'').

In Section \ref{sec:analysis}, we reported several primary findings. First, contextualized representations from both BERT and ELMo capture the distinction between same-sense and different-sense uses of a word, but their ability to distinguish between homonymy and polysemy is marginal at best. This contrasts with other recent work \cite{nair2020contextualized}, suggesting that BERT \textit{is} able to differentiate between homonymy and polysemy. One possible explanation for this difference in results is that \citet{nair2020contextualized} used naturally-occurring sentences from Semcor \cite{miller1993semantic}, whereas our sentence contexts were more tightly controlled. Our results indicate that even the presence of a single disambiguating word can trigger nuanced differences in semantic representation in humans, but not necessarily in current neural language models.

Second, we found that both BERT and ELMo explain independent sources of variance in human relatedness judgments, above and beyond Same Sense and Ambiguity Type (i.e., homonymy vs. polysemy). This is encouraging, because it demonstrates a direct benefit of graded (rather than categorical) judgments; for example, among the broad category of different-sense polysemous pairs, some are closely related (e.g., ``marinated lamb'' and ``baby lamb''), and others are considerably less closely related (e.g., ``hostile atmosphere'' and ``gaseous atmosphere''). Overall, contextualized embeddings from BERT were better at predicting human relatedness judgments than those from ELMo---this is consistent with past work \cite{wiedemann2019does} suggesting that BERT outperforms ELMo on tasks involving sense disambiguation.

Importantly, however, both BERT and ELMo failed to capture variance in relatedness judgments that is captured by Same Sense and Ambiguity Type. As depicted in Figure \ref{fig:residuals}, Cosine Distance tended to \textit{underestimate} how related humans find same-sense uses to be, and \textit{overestimate} how related humans find different-senses to be. This is not entirely surprising, given that neither BERT nor ELMo are equipped with discrete sense representations---at most, they produce contextualized embeddings that are amenable to supervised classification or unsupervised clustering. Yet this also illustrates that---at least on this task---humans \textit{do} appear to draw on some manner of (likely fuzzy) categorical representation, such that the difference between two contexts of use is \textit{compressed} for same-sense meanings, and \textit{exaggerated} for different-sense meanings (particularly for homonyms). This suggests several exciting avenues for future work: can neural language models such as BERT be augmented with semantic knowledge or representational schemes that improve their performance on RAW-C or similar tasks? Both possibilities are explored in Section \ref{subsec:future} below.

\subsection{Future Work}\label{subsec:future}

As \citet{bender2020climbing} note, most language models are trained on linguistic form alone. In contrast, human language knowledge is \textit{grounded} in our embodied experience of the world \cite{bisk2020experience}. To the extent that human sense representations are guided by distinct sensorimotor or social-interactional associations, equipping language models with this information ought to facilitate their ability to distinguish between distinct meanings of a word (i.e., the Disambiguation Criterion) and modulate a given meaning in context (i.e., the Contextual Gradation Criterion).

Practitioners could also look to (and in turn, inform) models of the human mental lexicon \cite{nair2020contextualized}. Several promising models attempt to address the \textit{continuous} nature of word meaning, as well as the issue of apparent category boundaries (i.e., word senses) \cite{rodd2004modelling, elman2009meaning}; at this stage, the role of continuity vs. categorical structure in human sense representations remains an open question. Models such as SenseBERT \cite{levine-etal-2020-sensebert} incorporate high-level sense knowledge into internal representations from the beginning, and find improvements on several WSD tasks---would this approach, or others like it, yield an improvement on RAW-C as well?

\subsection{Limitations of Dataset}

RAW-C has multiple limitations, some of which could also be addressed in future work. First, the broad category of ``polysemy'' is often subdivided into different mechanisms or manners of conceptual relation, such as metaphor and metonymy. This distinction is also believed to be cognitively relevant, with some evidence that metaphorically related senses are represented differently than metonymically related ones \cite{klepousniotou2002processing, klepousniotou2007disambiguating, lopukhina2018mental, yurchenko2020metaphor}. Future work could annotate polysemous word pairs for whether they are related by metaphor, metonymy, or another class of semantic relation---annotations could even be made as granular as the specific semantic relation involved (e.g., Animal for Meat) \cite{srinivasan2015concepts}. This finer-grained coding could be used to assess exactly which kinds of semantic relation correlate with the distributional profile of word tokens---i.e., are accessible from linguistic form alone---and which require some external module, whether in the form of grounded world knowledge or a structured knowledge base.

Another possible limitation is the fact that RAW-C contains experimentally controlled minimal pairs, instead of naturally-occurring sentences \cite{nair2020contextualized, haber2020assessing, haber2020word}. On the one hand, naturalistic sentences are useful for evaluating models on WSD ``in the wild'' (and indeed, there are a number of useful datasets for this purpose; see Section \ref{sec:related}). On the other hand, controlled datasets are useful if one's goal is to better understand a particular model or linguistic phenomenon---especially if this also allows a direct comparison with human annotations. For example, our analyses suggest that human sense representations must involve some additional levels of processing or information beyond the statistical regularities in word co-occurrence captured by BERT and ELMo. Moving forward, we hope that experimentally controlled datasets such as RAW-C will serve as a useful complement to existing, more naturalistic datasets.

\section{Conclusion}

We have presented a novel dataset for evaluating contextualized language models: RAW-C (Relatedness of Ambiguous Words, in Context). This resource contains both categorical annotations, derived from expert lexicographers (Merriam-Webster and the OED), as well as graded relatedness judgments from human participants. We found that contextualized representations from BERT and ELMo captured some variance ($R^2=.37$) in these relatedness judgments, but that the distinction between same-sense and different-sense uses, as well as between homonymy and polysemy, explains considerably more ($R^2 = .66$). Finally, we argued that this gap in performance represents an exciting opportunity for further development, and for cross-pollination between experimental psycholinguistics and NLP.

\section{Ethical Considerations}

All responses from human participants were anonymized before analyzing any data. Furthermore, the RAW-C dataset does not contain single-trial data---responses for a given sentence pair have been collapsed across all the human annotators who provided a rating for that pair. All participants provided informed consent, and were compensated in the form of SONA credits (to be applied to various Psychology, Cognitive Science, or Linguistics classes). The project was carried out with IRB approval.

\section*{Acknowledgments}

We are grateful to Susan Windisch Brown and Ekaterini Klepousniotou for making their experimental stimuli available. We also thank the anonymous reviewers for their helpful suggestions, and Nathan Schneider for early feedback on the idea to publish the dataset. Finally, we are grateful to other members of the Language and Cognition Lab (James Michaelov, Cameron Jones, and Tyler Chang) for valuable comments and discussion.

\bibliographystyle{acl_natbib}

redpajama_set_name: RedPajamaArXiv


Free high resolution close up photo of a piece of white tractor feed paper. Tractor feed paper was used for old style dot matrix printers. The paper has a strip on either side with holes in it, and the printers had a spool with points that poked through the holes to feed the paper through the printer. The strips are perforated so they can be removed once the printing is complete. This picture shows the texture of the paper as well as some crinkles. Great background, wallpaper, or texture image for a website, blog, computer, phone, scrapbooking or Photoshop. Free image for any use.


All 300kg weighing scales wholesalers & 300kg weighing scales manufacturers come from members. We doesn't provide 300kg weighing scales products or service, please contact them directly and verify their companies info carefully.
Xiamen Guanghua Electronic Co., Ltd.


Drill chuck/gearbox getting hot to touch.
Discussion in 'Electronics & Electrics' started by xc351, Mar 20, 2019.
Well I have a good old blue ryobi 18v drill that now runs on the lithiums.
Now I do have other ryobi 18v drills but this is my only hammer one. Plus it has 2 built in spirt levels which are supprizing handy.
Is it fixable or anything I can have a squizz at. As it stands I have zero to loose.
If it's old it may just need pulling apart and re-lubricating? I'd take the casing off and have a look.
Old blue one? Relegate it to light duties (I've got the original green one when the first lithiums came out 10 years ago, and it permanently has a counter-sinking bit in it now) and lash out and buy a brushless hammer drill/driver.
And/or if any of the bearings are sealed then you're probably up to replace them as well.
If it's giving an obvious obnoxious whiny sound the gears have likely worn themselves out and it will never be the same again. Unfortunately once the gears are ground out of shape no amount of lubrication will return the box to its former glory.
Might be able to get some replacement gearsets off the net for some common/pro models, but the really cheap sets will definitely be a case of YMMV.
The rotating torque collar slips up and down i can get it to drill perfectly or hammer perfectly just not both at the same time.
I already have enough plain old drills as ive bought 3 or so kits over the years to get extra tools.
I think it would be just over 10 years old, but its dual spirit level is rather handy otherwise would scrap it.
Unless u use it as a hammer drill drilling masonary. Which is what I do reasonably regularly.
I have access to larger sds and kanga drill bug there overkill.
but its dual spirit level is rather handy otherwise would scrap it.
Sounds like a handy feature but you can buy just the spirit things and JB Weld them onto anything. Not as neat as something built in...on the other hand the mechanical wizardry inside those combo drills looks a nightmare to replace parts in.
Well not really as they arnt square flat surfaces to attach them to. Sure you could pack up one side ect but that's just getting messy.
How are the carbon brushes?
Are you saying that in hammer mode, the drill bit doesn't spin at all?
Hammer drills hammer AND rotate. It's the hammer action that does the work, the rotation is there to remove the debris.
The OP's problem is the drill will rotate to drill in wood or steel in rotate mode but in hammer mode it hammers but there's no rotation.
I have various drills that hammer and turn at the same time.
in hammer mode (which is meant to rotate as well) i can hold onto shank (of driver bit thats in there) and apply little pressure and totally stop it. It acts like worlds smallest jackhammer.
That's interesting. I didn't know that. I wonder if all hammer drills are like that.
If its not an SDS hammer (and sounds like it's not) .. Then for some reason you got slippage between ratchet plates and the chuck - which is odd, but would also explain the heat.
Deffinitly not a sds I have one of them as well.
Deffinitly not the chuck slipping.
The adjustment colar has play may be as simple as a c clip/lock but or similar which may of came loose.
Still haven't had time to crack it open I've just been using the sds drill (but it's hardly ideal) with my impact driver and drill picking up the rest of the slack.
I should note the drill has had a damm hard time and other than the crap nicads it came with I have no other gripes about it. 10years for a battery drill is good going.
Just none of the current hammer drills take my fancy.


AB 219 (and SB 836) Ready Mix Hauling Prevailing Wage, including the status of court challenges to these requirements.
Skilled and Trained Workforce Requirements, which mandate the use of apprenticeship program graduates on certain jobs.
NEW law (SB 96) public works contractor registration penalties and small project exemptions, effective as of January 1, 2017.
Legalized marijuana and the workplace.
For more information, visit MCAC's website.


UNDERPINNING the universe is not gravity, as taught today, but something else.
This something is a conscious electro-spiritual power described by the Tibetan word "Fohat," according to Theosophy—a universal force that rules humans, nature and the infinite cosmos.
Science still insists that gravity alone, the weakest force on Earth, runs the entire universe — though admittedly, as with magnetism, they understand very little about it.
A Serbian schoolgirl has amazed medics with her astonishing magnetic hands. Ten-year-old Jelena Momcilov has been picking up cutlery, coins and even metal furniture by just touching them at her family home in Zeljusa, since she first discovered her powers five years ago.


text:
#pragma once
// Project specific
#include <Doremi/Core/Include/EntityComponent/Constants.hpp>
#include <type_traits>
#include <Doremi/Core/Include/EntityComponent/Components/LowerSkeletalAnimationComponent.hpp>
#include <Doremi/Core/Include/EntityComponent/Components/SkeletalAnimationComponent.hpp>

// where we store the actual components/data
template <typename T> class StorageShelf
{
public:
    static StorageShelf<T>* GetInstance();
    T* mItems;

    T* GetPointerToArray() { return mItems; }

private:
    StorageShelf() : mItems(nullptr) { mItems = new T[MAX_NUM_ENTITIES](); }

    StorageShelf(const StorageShelf& t_item) = delete;

    ~StorageShelf() { FreeHelper(); }

    void FreeHelper() { delete[] mItems; }
};

// Temporary fix, when FreeHelper is called and the Template is LowerSkeletalAnimationComponent, do nothing, as the usual destruction crashes, TODOXX,
// TODORT
void StorageShelf<Doremi::Core::LowerSkeletalAnimationComponent>::FreeHelper() {}

// Temporary fix, when FreeHelper is called and the Template is SkeletalAnimationComponent, do nothing, as the usual destruction crashes, TODOXX,
// TODORT
void StorageShelf<Doremi::Core::SkeletalAnimationComponent>::FreeHelper() {}

template <class T> StorageShelf<T>* StorageShelf<T>::GetInstance()
{
    static StorageShelf<T> storageShelf;
    return &storageShelf;
}

// This is the magic about singletons
// Find the right shelf for the needed component
template <class T> static T* GetComponent(EntityID pEntityID)
{
    StorageShelf<T>* tNeededShelf = StorageShelf<T>::GetInstance();

    return &tNeededShelf->mItems[pEntityID];
}

/**
    Fist paremeter is from, second parameter is to
*/
template <class T, class U> static void CloneShelf()
{
    if(sizeof(T) != sizeof(U))
    {
        std::runtime_error("Attempting to memcpy two different sized shelfs!");
    }

    // Get Pointers
    StorageShelf<T>* tFirstShelf = StorageShelf<T>::GetInstance();
    StorageShelf<U>* tSecondShelf = StorageShelf<U>::GetInstance();

    // Memcpy
    // TODO could take parameter of how many entities we have active
    memcpy(tSecondShelf->mItems, tFirstShelf->mItems, sizeof(T) * MAX_NUM_ENTITIES);
}

redpajama_set_name: RedPajamaGithub


text:
Articles/Interview
Life and Living
INTERVIEW WITH HRIDAYESH TRIPATHI
By kslsanjeeb9, December 16, 2007 in Articles/Interview
kslsanjeeb9 0
Honourable Member
Location:Ktm
Hridayesh Tripathi is one of the lawmakers and influential Madheshi leaders who recently quit parliament and the major parties including the Nepali Congress (NC), CPN-UML, CPN-Maoist and Rastriya Prajatantra Party to form a new party in the Terai region. In a recent interview with ekantipur, he shared the reasons behind the decision to launch a new regional party to be led by former NC leader Mahantha Thakur, its demands and future moves. Excerpts:
Q. What's the rationale behind the decision to set up a new regional party in Terai?
Hridayesh Tripathi: The three major parties—the Nepali Congress, the CPN-UML and the CPN-Maoist— not only failed to address the problems of Madhesh, but also completely failed to comprehend them. The government, the state and parliament, too, have proved ineffective in this regard. Similarly, the achievements of the last Madhesh revolt, too, were not commensurate with the sacrifice made by the Madheshi people. This was chiefly because of the lack of a proper and mature leadership. As a result, only minimal gains could be achieved since the revolt was not organised. Even today, Terai lacks a strong party with clear political agendas. Most importantly, the mindset of the state to opt for a military solution or the use of force to tackle the Madhesh problems stirred us to take this decision.
Q. How can you say for sure that the state is mulling over using force to tackle the problems in the Terai region?
Tripathi: I am not making a guess when I am saying that the state is thinking of using force to suppress the Madhes problems. The government's recent move to deploy the Armed Police Force (Special Task Force) in seven Terai districts clearly proves that the state wants a solution through the use of force. Similarly, Maoist Chairman Prachanda (addressing the anniversary of the People's Liberation Army at Maoist cantonment in Chitwan) recently said that the Nepal Army and the Maoist People's Liberation Army should be jointly mobilised in Terai. These developments are going to invite a big accident in the region.
Similarly, a few things that transpired during and after the Madhesh revolt early this year have really moved us. I want to narrate some of them here to show how insensitive the state and the major parties are. Of the 42 people who died during the Madhesh revolt, only one had succumbed to bullets fired by the Maoists. All others were killed in police firing. But the state has not even bothered to declare any one of them a martyr while all 22 people killed during the April uprising were declared martyrs. This shows the state practices discrimination not only against the living Madhesi people but also the dead.
Yet another ordeal: Six Madheshi people, who were severely injured during the agitation had been referred to a hospital in Kathmandu for treatment. But the Young Communist League cadres entered the hospital premises and terrorised them. The injured people seeking treatment in the capital were so terrified that they requested us to take them to a hospital in India. Such incidents have reinforced our conviction that a new political force should be established to push for the cause of the Madheshi people. But at the same time we are also mindful of the fact that the agitation must remain peaceful and under control of responsible political leaders.
Q. So you want the agitation in Terai to be well under your control?
Tripathi: Yes. The people had led the last revolt and the leaders had rallied behind. Hence, it could not yield the desired results.
Q. Speculations have been doing rounds that the leaders of the major parties, especially PM Koirala himself, had been trying to set up a new Madheshi party so that the agitation in the region does not spiral out of control. Is your recent move part of the plan?
Tripathi: No, the Prime Minister is not at all involved in this. In fact, the major parties have become paranoid. They are desperately trying to defame us and our initiative. The Prime Minister has alleged that we took the step at India's behest to destabilise the country. They are simply infuriated by our move.
Moreover, why would any party empower us by weakening itself? We have taken this step being responsible. Forget a separatist movement; we are not even talking about any special rights to the Madheshi people. All we are demanding is an equal right. But the state remains insensitive to the just demands of the Madheshi people.
Q. What are your demands?
Tripathi: As I mentioned before, all we demand is equal rights to the Madhesh. A proportional representation based on population. Now you may say that the state has already agreed to it. Yes, there is a constitutional provision to ensure this right. But what is happening in practice? I want to cite an example to prove my point. Recently, the government came up with an Act to ensure reservation for the Madhesi people in the police force only after a massive recruitment of police personnel. Till date, there is no representation of the Madheshi people in the Nepal Army. The same applies to the Maoist army too. And what is happening in the civil service? A lot of secretaries were appointed only a few days back. Only two of them hailed from Madheshi communities.
The state wants to give our rights in credit, but we are demanding in cash. The major parties still seem reluctant to a true restructuring of the state. They are pushing for a proxy representation. The Reservation Act aims to overshadow the demand for a proportional representation. Some of them are arguing that federalism could lead to the disintegration of the nation. But we see that national disintegration is inevitable if the state is not restructured along the federal lines. Prachanda is now saying that the true state restructuring means integration of two armies. But we are not going to settle for anything less than a proportional representation in all organs of the state and a complete restructuring of the state to guarantee our rights. Hridayesh Tripathi is one of the lawmakers and influential Madheshi leaders
Q. Do you also want the entire Terai as a single autonomous state?
Tripathi: Yes, that's our demand but the Madheshi people have the final right to decide the number of states in Terai. Here I would also like to mention that the Madheshi people are completely opposed to a separatist movement.
Q. While the major parties have opposed your move, the Madheshi People's Rights Forum and some armed groups in the Terai acted promptly to welcome it. Co-chairman of the Rastriya Janshakti Party, Dr Prakash Chandra Lohani, too, has written an article welcoming your move. What are we to make out of it?
Tripathi: Any wise person or group that understands our rationale has hailed our move. But one cannot do anything with those who have ill intentions.
Q. The Madheshi leaders's decision to quit parliament and their parties came like a bolt from the blue. But it wasn't at all a decision taken in haste, was it?
Tripathi: Yes, you guessed it right. It all started in the build up to the Madhesh revolt shortly after the interim constitution was promulgated. The NSP-A had even written a note of dissent against the discriminatory provisions in the constitution. The Madhesh revolt started after a month. The fringe parties in the Seven-Party Alliance had appealed to the major parties to address the problems immediately. But the appeal apparently fell on a deaf ear. Then I resigned from the cabinet. Only then was an emergency meeting of the SPA convened. The Prime Minister then pledged to give some concessions in two installments (two addresses to the nation within a few days span).
The MPs and leaders from Terai met several times and finally the decision to launch a new party was reached.
Q. When will the new party be launched?
Tripathi: Very soon.
Q. Could you be more specific?
Tripathi: The party will be formed within two weeks.
Q. Has the new party's name been decided?
Tripathi: Not yet. We are brainstorming for a name at the moment. It would be finalised within the next few days.
Q. What about the party's hierarchy?
Tripathi: So far, we have agreed that Mahanta Thakur will be our leader.
Q. What about your status in the party?
Tripathi: We are yet to work out the other posts in the party
Q. Who else are going to join you? Could you name some other Madhesi leaders?
Tripathi: I don't want to divulge their names them right now. They are under constant surveillance following our move. When we approached some of the influential Madheshi leaders from the major parties, they said that they supported our cause. They did not want us to meet them at their residences. They want to meet us secretly. They are also being lured with all sorts of promises. The major parties are also talking about reallocation of the ministerial portfolios to stop them from joining us. All I can say right now is that a significant number of Madheshi leaders from different parties will be joining us soon.
Q. What kind of relation do you intend to have with other Terai-based parties and armed groups such as the factions of the MPRF and JTMM?
Tripathi: We want to forge an alliance with all groups, which stand for the cause of the Madhesh. Why only the parties and groups in Terai, we want a good relation with all the major parties, including the Maoists. But that would depend on these parties' stances on the Madhesi issues.
Q. The existing parties and groups in Terai could also treat your party as a competitor?
Tripathi: We don't see any competition. If (Nepal) Sadbhavana Party had genuinely taken up the cause of the Madheshi people then other groups would not have surfaced in Terai, and the region would not be torn by violence like now. Sadbhavana indulged so much in the politics of power that it lost contact with the Madheshi people. A few months back, I had urged the party leadership to launch a movement in Terai sensing the popular mood, but we failed to do so. It was a historic mistake. The Madhesh revolt started soon thereafter. But, the revolt also fizzled out. Had the MPRF been able to lead the Madhesh movement effectively then we would have already achieved what we are striving for. They (MPRF) lacked the political will, maturity and sincerity to take the agitation to a logical end. The Madheshi people are also weary of the armed groups wagging a separatist movement.
Q. Still, don't you think that the new party will only divide the Madheshi votes if you fail to bring the different groups together?
Tripathi: I want to clarify in the first place that we are not thinking about the politics of votes at all. We are not here to play politics for the sake of it. We are established political leaders who have come together to push for the agendas of the Madheshi people. And, we also want to see to it that there is no spillover effect of the Madhesh movement. Our party will also act as a catalyst to bring the different parties together for the good. We have not even set up our party and the three major parties have already stepped up efforts to bury their hatchets and evolve a consensus. This could be seen as an early achievement of our initiative.
Q. What is your take on the national agenda such as the Constituent Assembly elections?
Tripathi: Obviously we believe that an election to the Constituent Assembly is important to transform the nation. But it is unfortunate that everything is happening in this country, except the elections. This is because, I think, the King, Prachanda, Girija Prasad Koirala and Madhav Kumar Nepal do not want the elections. As they have nothing to loose now, they want the status quo to continue. The Madheshi people, janjatis, Dalits and women are the marginalised groups who are actually yearning for the elections since they want the state restructured to ensure their equal representation and participation in the state organs. If you ask me, I don't think the major political actors even want a republican order in the country. The Nepali Congress leadership has now and then floated the idea of ceremonial and baby kings. The Maoists brought a superficial motion on republic declaration as if they only wanted publicity. Now they have started to advocate unity with the royalist nationalists. The Madheshi people, however, do not favor the monarchy since the institution has nakedly discriminated against them for centuries.
Q. When are you going to launch the new Madhesh agitation?
Tripathi: It has in fact already begun with the Madheshi leaders' decision to quit parliament and their parties. However, once we form the party, we will organise a national convention and announce a peaceful agitation, which will establish the rights and authority the Madheshi people.
(By Akhilesh Tripathi & Sanjeev Ghimire)
Posted on: 2007-12-15 09:39:47
Source : http://www.kantipuronline.com/interview.php?&nid=131373

redpajama_set_name: RedPajamaCommonCrawl


Today I'm excited to be sharing another fun Cricut Iron-On project with you. My daughter and I collaborated to create these DIY Ballet Warm-Ups, and have even included a set of hand-lettered Ballet SVG files for you to use. This post is sponsored by Cricut. I'm thrilled to be working with a company that I love and whose products I use on a regular basis!
Recently I've discovered that my youngest daughter has a talent for hand lettering. She's also quite the dancer and has been practicing Ballet since she was 3 years old. This year she decided to move to a more challenging Ballet studio to see if dance is something that she'd like to pursue in college or beyond (long story short, she realized she doesn't want to).
I've always used my kids to help me design the projects that I offer on this site. I think they have a unique perspective and most of the time think of things MUCH better than what I would have dreamt up by myself. They helped me design the Harry Potter pencil pouch iron-ons from last summer and our Disney Halloween t-shirts and costumes.
Since she's our resident ballerina, and an expert hand letterer (is that a word LOL!?) I decided to draft her into the family business. She helped me to create the images for this post! I had fun collaborating with her. We bounced ideas back and forth for sayings for the ballet warm-ups. She'd tell me when something was a stupid idea, or if it was too overdone. In the end we came up with 3 designs that we are really excited about. She drew the ballet shoes and did the hand lettering herself! I think I may have found a new employee!
I've been using my Cricut to make iron-ons for tee shirts for years. I've played around with their different materials so when Cricut introduced their new SportFlex iron on I got soooo excited. I have been using the Lite Iron-On on exercise clothes, and not had bad results, and was intrigued that there was something desinged JUST for that type of material.
Cricut sent me a sample pack of SportFlex to play around with and let me tell you, I'm hooked!! This stuff is so cool. Last week while I was finishing up today's project my husband walked in and asked me where I bought the sweatshirt! It looked so good he didn't think I made it. SCORE!
So how is the SportFlex Iron On Different?
Well, it's designed to stretch for one. It's also designed specifically for polyester and nylon fabrics. Which, are the types of fabrics you see most often in exercise clothing. It's meant to adhere in a way to help it stand up to multiple washings.
What would you use SportFlex for?
Today, I'm going to show you how I made these DIY Ballet Warm-Ups, you can also make anything else "sport" related. Think jerseys for a kids sports team, iron-ons for sports bags and duffles. Backpacks, Decorating your leggings, or even Halloween costumes made from polyester or nylon fabrics. I can think of a million uses.
A lot of people have been using it to create their own personalized running gear. Since I'm not the least bit athletic, I thought that I'd use it to make some ballet clothes for my daughter. However, I do have a fun idea to use it for "running" themed items, that I'll share later next month.
Cricut has also recently come out with a mat specifically designed to work with the EasyPress (if you keep wondering what in the world I'm talking about when I mention the EasyPress, you can CLICK here to read all about the machine and my review of it).
Since the EasyPress is designed to deliver a precise amount of heat in a precise location, the foundation that it's placed on when 'doing it's thing' matters. You want a material that keeps the heat on the iron-on, not passing through it. Since ironing boards are designed to pull the heat out of an iron (and your clothes) they are not ideal to use with the EasyPress. Cricut recommends using either a folded towel or an EasyPress Mat.
It's a mat that is lightweight, it protects your surface from the EasyPress heat and ensures that the heat goes where it was designed to go, to the iron-on itself and.. just as a side note, it works pretty great to keep by your sewing machine to press quilt blocks!
Okay, enough talk, let's get to the tutorial!
Just a note: these are just for personal use, you can't make stuff to sell with them.
Open the Cricut Design Space and click UPLOAD then upload the file. Give it a name and insert it into your canvas in design space.
The files always come in as black, I like to change them to white so that I can see the cut lines better- click on the circle next to the item on the far right-hand side and change the color from black to white.
Size the image as you'd like. Then using your mouse, highlight the entire image and click ATTACH in the lower right-hand corner. This will make sure that your image cuts just the way you see it on the screen.
Load the SportFlex onto the mat shiny side down. Then click MAKE It on your screen. Select "mirror image for iron on" on the screen.
Select SportFlex iron-on on the next screen. Then load the mat into the machine and let it do the work!
Eject the mat from the machine and remove the excess iron-on – this process is referred to as weeding.
I like to also cut the carrier sheet around the iron on, close to the edge, just to make it easier for handling.
What Settings do you use on the EasyPress for SportFlex Iron-On?
(here's a handy chart to print out to use, just right click and select, open image in new tab, then print from there).
Next, place your item that you are applying the iron-on to on the EasyPress mat. Make sure it's flat and there are no wrinkles. My sweatshirt was polyester, so I set my EasyPress to 305 and 30 seconds. If you don't have the mat NO WORRIES, a folded towel works great!
I then pre-heated my sweatshirt by placing the EasyPress on it for 5 seconds.
Next, I placed my image on my sweatshirt, then made sure that my EasyPress Mat was right under the design. I then put a piece of parchment paper OVER the iron on. Since my fabric was polyester and I didn't know how it would react to the direct heat of the EasyPress, I used a pressing sheet a barrier. You can also use a Teflon sheet. You just don't want to melt your fabric with your EasyPress!
After the EasyPress was done, I flipped my sweatshirt over and pressed it for 15 more seconds on the back.
Then I just pulled off the plastic carrier sheet and VOILA! A sweatshirt!
I followed the same instructions for both the leggings and the tank top.
Just look how well that's holding up on those leggings! Stretch and all!!
I'm sure you can tell by the number of Cricut Iron-On posts this month that I'm pretty much obsessed. It's so fun for me to think of an idea for a t-shirt or a tote bag and be able to make it myself! My kids love it too, and are starting to come up with ideas of their own, they are even starting to use the machine themselves.
Hi! I was intrigued by this post hoping that it would have details about the measurements of the cut outs. I am making a pair of leggings for someone and struggling with deciding how long to make it. Any suggestions???
It will depend on what size of leggings you are adding the cut outs to. I'd measure the actual leggings then cut the decals to size.
Hello, I've tried to download the files many times but every time I get a "403 Forbidden" message.
Are you on a phone or tablet? You need to be on a desktop to download it.


text:
id: 587d7fae367417b2b2512be5
title: Convert JSON Data to HTML
challengeType: 6
forumTopicId: 16807
---

## Description

Now that you're getting data from a JSON API, you can display it in the HTML.
You can use a forEach method to loop through the data since the cat photo objects are held in an array. As you get to each item, you can modify the HTML elements.
First, declare an html variable with var html = "";.
Then, loop through the JSON, adding HTML to the variable that wraps the key names in strong tags, followed by the value. When the loop is finished, you render it.
Here's the code that does this:

```js
let html = "";
json.forEach(function(val) {
  const keys = Object.keys(val);
  html += "<div class = 'cat'>";
  keys.forEach(function(key) {
    html += "<strong>" + key + "</strong>: " + val[key] + "<br>";
  });
  html += "</div><br>";
});
```

Note: For this challenge, you need to add new HTML elements to the page, so you cannot rely on `textContent`. Instead, you need to use `innerHTML`, which can make a site vulnerable to Cross-site scripting attacks.

## Instructions

Add a forEach method to loop over the JSON data and create the HTML elements to display it.
Here is some example JSON

```json
[
  {
    "id":0,
      "imageLink":"https://s3.amazonaws.com/freecodecamp/funny-cat.jpg",
      "altText":"A white cat wearing a green helmet shaped melon on its head. ",
      "codeNames":[ "Juggernaut", "Mrs. Wallace", "Buttercup"
    ]
  }
]
```

## Tests

```yml
tests:
  - text: Your code should store the data in the <code>html</code> variable
    testString: assert(code.match(/html\s+?(\+=|=\shtml\s\+)/g));
  - text: Your code should use a <code>forEach</code> method to loop over the JSON data from the API.
    testString: assert(code.match(/json\.forEach/g));
  - text: Your code should wrap the key names in <code>strong</code> tags.
    testString: assert(code.match(/<strong>.+<\/strong>/g));

```

## Challenge Seed

```html
<script>
  document.addEventListener('DOMContentLoaded', function(){
    document.getElementById('getMessage').onclick = function(){
      const req = new XMLHttpRequest();
      req.open("GET",'/json/cats.json',true);
      req.send();
      req.onload = function(){
        const json = JSON.parse(req.responseText);
        let html = "";
        // Add your code below this line

        // Add your code above this line
        document.getElementsByClassName('message')[0].innerHTML = html;
      };
    };
  });
</script>

<style>
  body {
    text-align: center;
    font-family: "Helvetica", sans-serif;
  }
  h1 {
    font-size: 2em;
    font-weight: bold;
  }
  .box {
    border-radius: 5px;
    background-color: #eee;
    padding: 20px 5px;
  }
  button {
    color: white;
    background-color: #4791d0;
    border-radius: 5px;
    border: 1px solid #4791d0;
    padding: 5px 10px 8px 10px;
  }
  button:hover {
    background-color: #0F5897;
    border: 1px solid #0F5897;
  }
</style>

<h1>Cat Photo Finder</h1>
<p class="message box">
  The message will go here
</p>
<p>
  <button id="getMessage">
    Get Message
  </button>
</p>
```

## Solution

```html
<script>
  document.addEventListener('DOMContentLoaded', function(){
    document.getElementById('getMessage').onclick = function(){
      const req = new XMLHttpRequest();
      req.open("GET",'/json/cats.json',true);
      req.send();
      req.onload = function(){
        const json = JSON.parse(req.responseText);
        let html = "";
        // Add your code below this line
        json.forEach(function(val) {
          var keys = Object.keys(val);
          html += "<div class = 'cat'>";
          keys.forEach(function(key) {
          html += "<strong>" + key + "</strong>: " + val[key] + "<br>";
        });
        html += "</div><br>";
        });
        // Add your code above this line
        document.getElementsByClassName('message')[0].innerHTML = html;
      };
    };
  });
</script>

<style>
  body {
    text-align: center;
    font-family: "Helvetica", sans-serif;
  }
  h1 {
    font-size: 2em;
    font-weight: bold;
  }
  .box {
    border-radius: 5px;
    background-color: #eee;
    padding: 20px 5px;
  }
  button {
    color: white;
    background-color: #4791d0;
    border-radius: 5px;
    border: 1px solid #4791d0;
    padding: 5px 10px 8px 10px;
  }
  button:hover {
    background-color: #0F5897;
    border: 1px solid #0F5897;
  }
</style>

<h1>Cat Photo Finder</h1>
<p class="message">
  The message will go here
</p>
<p>
  <button id="getMessage">
    Get Message
  </button>
</p>
```


text:
Fly-tipper told to pay thousands after dumping 'tonne of waste'
Sobeckis was prosecuted at Leeds Magistrates Court
A fly-tipper was ordered to pay more than £2,500 after he dumped a whole tonne of waste on land in Castleford.
Gediminias Sobeckis, 29, abandoned the rubbish on property belonging to Wakefield District Housing (WDH) and was subsequently caught by the authorities.
Sobeckis, who lives in Bramley in Leeds, admitted the offences when he was prosecuted by magistrates in May.
The offence has only now come to light in a report which will go before Wakefield Council next week.
Sobeckis was fined £1,846, and told to pay a £170 victim surcharge, £502.05 in costs and £100 in compensation to WDH.
The report, written by the council's environment portfolio holder Maureen Cummings, said: "After a comprehensive investigation the fly-tipper
was identified, interviewed and prosecuted.
"This is one of the highest fines and costs that Wakefield Council have received in a fly-tipping prosecution."
Figures revealed in May showed that clearing up fly-tipping across the district cost the local taxpayer £200,000 in 2017.
A total of 4,000 reports were made across the 12 months, with Castleford shown to be the most common area for offenders to leave their waste.
10 reasons to visit the 2019 Leeds Jazz Festival this month


Peter creative thinker that specialises in branding and graphic design. He has worked on projects including graphic design, web design, branding & logo design, advertising, marketing, editorial & brochure design.
He wanted a website to showcase some of his work and create an online portfolio where he could send potential clients.
Being a graphic designer Peter came to us knowing exactly what he wanted and how he wanted it. He provide design layouts and everything we needed for a flying start.
We worked closely together creating the website in line with his examples while tweaking it where necessary to deliver a responsive webpage which wasn't initially considered. Peter wanted a dedicated pages for each of projects.
The website we created fitted nicely inline with Peter requirements. As he created a lot of videos for client he wanted to show these of. We achieved this by having the lading pages as videos. Peter also took hosting out with us and continues to update his content.
I work as a graphic designer and design websites but only from a graphics standpoint. When it comes to coding and converting my designs to an actual website I'm at a loss. Webeezy were great, easy to work with and made everything I wanted on my site happen.


1940; 1942; 1949 Steel Lincoln Wheat Cent Set. Circulated. 3 Coins Total.
2018 Mexico Libertad Silver 1 Ounce Onza PCGS MS70 FIRST STRIKE FLAG LABEL nice.
1923-P Peace Dollar Superb Silver Old US Coin , NO RESERVE!!
1891 CARSON CITY SILVER DOLLAR, XF, LOW MINTAGE 1.2mill.!!


text:
Big Bang Theory / Uncategorized
Burst the big bang bubble!
June 5, 2017 July 10, 2017 Bruce CamberLeave a comment
It lacks continuity, symmetry and harmony.
In the Big Board-little universe model using base-2 notation from the Planck Time to the Age of the universe, the entire physical universe is contained within just over 200 notations that are highly-integrated and totally-predictive. Notations are also known as clusters, doublings, groups, sets or steps. Within the first second of the universe (between notations 143 and 144), there is more than enough "natural inflation" from the Planck Charge to get "things" going. As a result of studying and working with this model since December 2011, there are many-many facets to explore, however one of the most important is that this model logically suggests that time is derivative and that the finite and the infinite are perhaps best understood in terms of continuity, symmetry and harmony.1
Continuity. Though an unusual way to define infinity, even with quantum indeterminacy, continuity throughout the universe is the bedrock of science, logic, and rational thought. Numbers clarify this continuity. Carried out a billion places, the universe and its systems around us replicate day after day with utmost precision. Within this model, continuity is more fundamental than time; it begets time.
The complexity of a single molecule
Symmetry. The second face of the infinite is symmetry. Though so much of life is asymmetrical, the deepest examination of any physical thing begins to reveal deeper symmetries. Numbered relations define those symmetries and the universe appears to be tiled and tessellated deeply within every notation throughout the model. Here symmetry is more fundamental than space; it begets space.
Harmony. Speculating, it is hypostatized that two symmetries begin interacting within a notation and then across notations, and though possibly not quite perfect, the interaction of the symmetries perfects the moment for the observer or for the notations involved. Therefore, we have moments of perfection within our experiences of the universe.2
Our studies. At this point in our studies, there is not much more we can say about how the infinite defines the model and what the model says about the very nature of the infinite. These three insights, although reflective of the model, in part come out of a study of a moment of perfection in 1972,3 then from studies of the book, Finite and Infinite: A Philosophical Essay (Austin Farrer, Oxford, Dacre Press, Westminster, 1943), and from an application to a business model.4
These three qualities became the bedrock for our model of the universe and for discussions about the shared nature of the finite and infinite.
What difference does it make? First, it is a clear contrast to the nihilism of big bang cosmology. Building in strength and popularity over the past 30 years, that nihilism has had a lot to do the fraying of our little world. So much is out of control and spinning apart. Money is not the issue. What we believe and how we believe is. Hope is. Charity is. Integrity is.
What is 5000 to 13.8 billion years? The finite and infinite relation has been the focus of humanity for as long as we have been recording our ever-so-short history. In light of 13.8+ billion years, five thousand years of records is, of course, quite short. We've just begun to make sense of it all.
Today in history. The finite is usually associated with physical, limited things. The infinite is often capitalized and associated with godly things, the eternal and everlasting. To our knowledge, Max Tegmark is the first theoretical physicist who has suggested that the concept of the infinite be abandoned. His rationale is that it gets in the way. He cannot make it work for the science he wants to create. Within these many articles, we hope to convince him, Hawking, Guth and so many others to re-engage our simple definition of the infinite. It does not require a religion or religious beliefs. Notwithstanding, it also doesn't fly in the face of those who believe in the Infinite.
We can all begin to tolerate each other.
This is our simple introduction to a very large topic and we will return to this page often to expand its range and its depth.
1 A general introduction was the prior homepage.
2 This construct was introduced within these pages at the end of the year, December 2015.
3 This construct goes back to work in 1972 at both Synectics Education Systems in Cambridge, Massachusetts and the Harvard Philomorphs with Arthur Loeb and Buckminster Fuller.
4 The original construct was used as the foundation of a business model for a weekly television series, Small Business School, that Bruce Camber and Hattie Bryant started in 1994. It aired on PBS-TV stations throughout the USA and on the Voice of America around the world for over 50 seasons (2012).
Heydari, Hoshang


Acquire the edge you need to become a smart investor with this new investment book on art collecting by Scott Zema, owner of Ark Limited Appraisals, Inc., in Seattle, Washington. Three Steps to Investment Success: Buying the Right Art, Antiques, and Collectibles is your ticket to an enjoyable—not to mention profitable—career purchasing art, antiques, and collectibles. Order my book today!
"Brilliant, innovative, and informative! A must-read for the industry and 'meat and potatoes' for the buyers of art, antiques, and collectibles."
Are you a focused, excited collector aware of quality in the valuable properties marketplace and intent on getting the best value both now and into the future? Or is this you; A doctor putting investment funds into 20 years worth of worthless prints? A person impulsively sinking their life savings into worthless fakes?
An estate heir arguing over cheap teacups while dumping the valuable paintings in the trash? An attorney, accountant, or insurance professional scratching their heads, unable to effectively organize their clients assets? If you are any of the latter, you need Three Steps to Invefstment Success: Buying the Right Art, Antiques, and Collectibles.
Buy what you like and don't treat your purchases as investments—that's the standard industry line given interested buyers about the investment potential of art, antiques, and collectibles from dealers, scholars, and other experts in the field who should know better. Sure, art, antiques, or collectibles aficionados should always buy what they like, but armed only with the above mantra most buyers end up unnecessarily purchasing objects without any redeeming investment quality.
There are scads of books describing critical appreciation of these properties or buying and selling with respect to connoisseurship or retailing, but there is no book which embraces and systematically describes investment in these properties—until now! Three Steps to Investment Success: Buying the Right Art, Antiques, and Collectibles is a groundbreaking work that systematically brings the reader from haphazard consumer to purposeful investor.
Please order this fantastic book through Amazon.


The page provides the exchange rate of 50 Hungarian Forint (HUF) to Hong Kong Dollar (HKD), sale and conversion rate. Moreover, we added the list of the most popular conversions for visualization and the history table with exchange rate diagram for 50 Hungarian Forint (HUF) to Hong Kong Dollar (HKD) from Friday, 26/04/2019 till Friday, 19/04/2019. Latest update of 50 Hungarian Forint (HUF) exchange rate.
Convert currency 50 HUF to HKD. How much is 50 Hungarian Forint to Hong Kong Dollar? — 1.36 Hong Kong Dollar.
50 Hungarian Forint to Hong Kong Dollar, convert 50 HUF in HKD.


Socolar and Taylor's aperiodic tile.
As regular readers know I have a love of incomplete or impossible quests. One such quest that comes very close to my work is quest for an aperiodic tile. A shape that can tile the plane, but not periodically. In other words I can cover whatever area I like (the floor of the room or the Milky Way Galaxy) but the tiling will never be a single patch moved about by translations. A big development, a new tile from Joshua Socolar and Joan Taylor, is therefore very exciting, especially as the final answer is still illusive.
Patch of tiling, the red lines show a little of the structure of the tiling. The single grey tile shows how the different pieces of the tile fit together with its neighbours.
As you can see from the patch above the tile forces the formation of a sort of Sierpinsky triangle. It is this structure, occuring at all scales, that ensures that the tilings produced cannot be periodic.
Patch of tiling with the 3d monotile. Note how the tiles fit together at different levels.
Patch of tiling with one tile removed to show internal structure.
This tile therefore, is connected and can lie in no periodic tilings of 3d. However this is not quite the whole story, although the tilings produced will never use a finite region translated around they will only be non-periodic in two directions. In the third direction the tiling can be periodic.
For more details look at Socolar and Taylor's paper and Joan Taylor's original paper (complete with beautiful hand-drawn images). The story has also been covered by the MAKE blog and Technology Review. If you want to make your own images and even tiles I have put my tile models up on Thingiverse and also put the blender file online.
That was a really good blog post,I recently subscribed to your feed.


"Polyurethane PICC lines were found to provide lower rates of infection, dislodgment, thrombus and rupture complications" Seckold et al (2015).
Seckold, T.L., Walker, S. and Dwyer, T. (2015) A comparison of silicone and polyurethane PICC lines and postinsertion complication rates: a systematic review. The Journal of Vascular Access. January 23rd. .
PURPOSE: To determine postinsertion complication rate for peripherally inserted central catheters (PICCs), in particular the difference between silicone and polyurethane lines in general population groups as well as oncology and non-oncology patient groups.
METHODS: A systematic review of prospective and retrospective studies in the English language between January 2000 and October 2013 focusing on postinsertion complication rates for PICCs in the adult population. Joanna Briggs Institute tools were used to extract data from the final 19 articles with information collated relating to catheter type, patient type, overall complication rate, rates of infection, occlusion, dislodgment, phlebitis, thrombus and rupture.
RESULTS: Overall, the PICCs complication rates ranged from 8 to 50%. Although both lines saw similar overall rates upon closer observation, the strengths and weaknesses of both lines are shown. Polyurethane PICC lines were found to provide lower rates of infection, dislodgment, thrombus and rupture complications. Mixed results were found with catheter line occlusions, overall averages showing polyurethane lines slightly higher rates than silicone. Oncology patients however saw opposite results.Phlebitis rates saw the largest division among the postinsertion complication rates, with 6.7% more phlebitis in the general patient group and 14.5% in the oncology group more for those with polyurethane PICC lines compared with the silicone.
CONCLUSIONS: Both silicone and polyurethane PICC lines exhibit nearly identical overall average postinsertion compilation rates; however, it is the type of complications experienced that differ. Overall, oncology patients can expect to experience higher levels of postinsertion complications.


text:
Work on Lansing apartment complex underway; developers project 72 market-rate units
Jared Weber
LANSING — Workers have broken ground on a new apartment complex at the corner of Washington Avenue and Malcolm X Street, and its developer says the project likely will have units available by spring 2023.
The project, called "REO Gateway," will include 72 units "market rate," said Brent Forsberg, an Okemos-based developer. Forsberg's development company, T.A. Forsberg, is leading the effort.
The development is located on the site where the Deluxe Inn once stood and will include a mix of studio, and 1- and 2-bedroom apartments, Forsberg said.
"We were originally looking at different ideas, and with the housing need right now, we thought it was most important we were able to get it built right away and to get housing on the ground," he said.
More Lansing housing news:Rundown homes, out-of-state buyer drive concerns about pending public housing sale
Affordable housing woes:Where will they go?: Women say affordable housing is difficult to find after evictions
Forsberg defined "market rate" rent in Lansing as between 61% and 140% of the area's median income.
According to Zillow, the typical Lansing monthly rent price, as of April, was $1,173 — a 13.3% increase from one year before.
The apartments will include appliances such as washers and dryers, as well as a discounted rate to the gym at City View apartments, in progress at the former Lake Trust building. A parking lot in back will face the Grand River, where Forsberg said an entry point will provide water access for kayakers. In addition, developers plan to install electric vehicle chargers in the parking lot.
Forsberg said they also plan to hire mural artists to paint the building's side "to help promote the culture of REO Town that was built on the idea of graffiti (and) murals as beautification."
REO Gateway is one of several multifamily housing developments coming up in the area between downtown Lansing and REO Town; other projects include the City View apartments and a smaller project in the city's Cherry Hill neighborhood, he said. Forsberg said the downtow developments are a collaboration between his company and Lansing-based Dymaxion Development.
The goal, Forsberg said, is to "reconnect the exchanges" that government officials dismantled in the mid-1960s with the construction of I-496.
"How do we create the connections that reconnect the exchanges that 496 broke in our region? It's looking at the waterways, the trail system, the river trail, the walking trails, the transportation system," Forsberg said. "That's why this project was named the gateway ... between REO Town and downtown."
The I-496 highway project had disproportionate effects on Lansing's Black community, according to the Historical Society of Greater Lansing in a past State Journal story.
More than 840 homes and businesses in the heart of the community were demolished in the mid-1960s to make way for the highway. More than 600 Black families were forced to relocate and find affordable housing options, which came, by some accounts, too late.
More on the legacy of I-496:
Project will tell the story of how I-496 construction impacted Lansing's black community
From 2009: Interstate 496 construction left a complicated legacy
Forsberg said his team assessed the housing need in the area and decided that market-rate units were best to contribute to their goal of creating a "safer, more walkable district in the community."
"It really is about resetting the local economy so the dollars stay in Lansing, in downtown, in REO Town, that allow for that growth to occur. And that's the most important thing is keeping the capital local and not putting it out," Forsberg said. "The money we make off this is going to be reinvested back in Lansing."
"Is it the answer to everything? No," he said. "But it's another piece that brings it closer together (to) connecting parts of our town."
Contact reporter Jared Weber at 517-582-3937 or jtweber@lsj.com.


text:
Anthurium decurrens är en kallaväxtart som beskrevs av Eduard Friedrich Poeppig. Anthurium decurrens ingår i släktet Anthurium och familjen kallaväxter. Inga underarter finns listade.

Källor

Kallaväxter
decurrens

redpajama_set_name: RedPajamaWikipedia


I thought twice about showcasing today's video montage. It features the world famous railway route, the Trans-Mongolian. Many people don't realise, but the Trans-Siberian Railway is actually made up of a few different lines, of which the Trans-Mongolian is one.
Other options include the Trans-Manchurian line, which slopes off to Beijing and the Trans-Mongolian line, which hooks down to — you guessed it — Mongolia.
The reason I thought twice about posting it, was because the slideshow doesn't exactly enthuse me. I've watched it around ten times, and each time I've been distracted by a passing sparrow or the steam rising off my coffee.
However, before I explain my doubting, watch the video.
Now, the real reason I decided to post it, was that despite it being a little melancholy and rather pointlessly dragging your gaze over the tracks, it's actually a fairly good representation of the Trans-Mongolian railway.
The Trans-Mongolian railway is boring.
I stare into their wide eyes, and watch their slithery tonsils swing in the dark void of their throat.
"It was good," I lie. Because it's the most famous railway route in the world, it should be good. People should want to travel along it.
The truth is, I was travelling alone and couldn't leave the confines of the train for five days straight. It's so easy to write that: Five days straight. Five days straight.
What were you doing five days ago? Four days ago? Three? Two? Yesterday? If you're reading this while pulling up to Ulaanbaator, you've probably been on a train.
The same train. With the same people. With the same little toilet. With the same miserable restaurant car. With the same pine forest landscapes. With the same thrum of the engine. With the same narrow aisle. With the same dim bed light.
I made the mistake of leaving myself no choice but to undertake the journey in one foul swoop. My final days in England were spent at the Glastonbury Festival in southern England, and I was eager to get to the Naadam Games in Mongolia.
I genuinely love travelling by train, it's second only to motorbike for my preferred mode of transport. But this was too much.
Take a friend. It's natural that you will get to know your cabin-mates, but pleasantries are no substitute for a solid friend, or lover.
Stop en route. Don't do what I did. Stop along the way at any number of interesting points. Perhaps you can recommend one in the comments?
Take a variety of distractions. Laptop and DVDs. Books. Cards and candy. Magazines and mints. iPad, iPods (other MP3 players are available).
Don't get me wrong. The Trans-Mongolian was one of the most relaxing journeys of my entire life. There was no TV. No internet. No front door. No cooking (or washing up). No chores. Nothing. Nada. Zip.
Just me, and a row of dirty windows.
We stopped occasionally at nondescript stations. I alighted, grabbed an armful of fruits, vegetables, fish and vodka, then clambered back into the Iron Rooster and played swapsies with my cabin-mates.
I accept that the length the Trans-Mongolian is epic by today's standards.
I can look at a map, and like Ewan and Charley, I can say "I crossed that". For them, it was one of the most difficult and rewarding journeys of their lives. For me, it was painfully easy.
Take another look at the montage, does it look extraordinary to you? Do the journeys of Trans-Siberian Railway appeal to you? Discuss your reasons with me and travellers around the world, via the comment thread below. Finally, did Like the video? Consider signing up to Animoto using this special affiliate link, which will help keep TrailofAnts.com alive through the winter.
is a major decision, and we all have put together some facts to assist you out.
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resto de jornadas, es decir, solo va a poder compensarse el saldo positivo real.
De este modo las cámaras digitales dependen tanto del almacenamiento como del procesamiento digital.
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unos jarrones y unas cestas que quitan el hipo.
Le lac Kandawgyi est un des plus grands lacs de Yangon. Situé au centre ville à proximité de la pagode Schwedagon, il est un lieu très agréable pour se promener et contempler les paysages de Yangon au coucher du soleil.
Autour du lac se trouve le Karaweik. Il s'agit d'une double copie de la barge royale. Le Karaweik est en fait un oiseau dans la légende birmane. D'autant plus que ce bateau vous donnera une allure très impressionnant au lac, notamment lorqu'il est allumé le soir.
Le lac Kandawgyi est un lieu très paisible et très serein dans le centre ville lors de votre Séjours tout compris Myanmar.
Te mostramos diez entretenidos dibujos para colorear.
Mi adiestramiento en piscina es por distancias.
cincuenta y siete-Al estar enamorado no hay imposibles.
Relojes Digitales Fashion de Diseño a buen costo.
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