text:
ALOFT IN A BODY BAG - Isaiah 61 Devotional #5
"...The LORD has anointed Me ... to proclaim ... release from darkness for the prisoners.."
– Isaiah 61:1e (NIV)
As the curtain lifts on Chapter 20 of The Count of Monte Cristo (Alexandre Dumas, 1844), the protagonist, Edmond Dantès, has been imprisoned fourteen years in the darkest dungeons of the Chateau D'If (a 16th-century French Alcatraz ... on steroids!) after having been framed for a political crime he did not commit. And now, the pious Abbé Faria -- Dantès' only dungeon companion, and the only glimmer of hope in his desperate plight -- has died, and is lying still in a body bag waiting for the prison guards to come haul him off.
"They will forget me here," laments Dantès in his despair, "and I shall die in my dungeon like Faria.... None but the dead pass freely from this dungeon ..."
At which moment, our hero has an epiphany. Why not bring on death forthwith? And so, he drags his friend's corpse away through a tunnel and stuffs himself into the body bag instead. When the guards show up, they carry him out and launch him off the cliff on which Chateau D'If is perched, and into the sea below -- their standard method of burying their hapless dead. Dantès, naturally, had provisioned himself with a knife, so he promptly cut his way out of the body bag and swam off to start his new life as the fabulously rich Count of Monte Cristo. (As for how he came by such fabulous riches ... you will have to read the book.)
By happy accident, our monthly trek through Isaiah 61 brings us on Easter Day to the end of verse one: "The LORD has anointed Me ... to proclaim ... release from darkness for the prisoners..."
In previous weeks, we have sounded the depths of this verse truth by truth, the latest having been "...to proclaim freedom for the captives." We explored various means by which the great enemy of our soul holds his captives in bondage, such as: comfort, entertainment, wealth, security, and the fear of man.
However, it is important to note that our Lord, Whose every word is carefully chosen -- He is not given to pointless redundancy -- says that He not only came to proclaim "freedom for the captives," but also "release from darkness for the prisoners." Because, not only are there satanic powers that hold us captive to vice, but there are those that would further imprison the soul in the deep darkness of despair. Think of it this way.... If Captivity is a landslide that blocks our path toward the "life in abundance" our Savior came to gift us, Dark Despair is a Mount Everest landed squarely on top of us, ending all hope of getting there. It is the Chateau D'If, from which dungeon the only way out is in a body bag.
But, just as Abbé Faria provided the only means of escape for Dantès -- with his own body bag -- our Lord Jesus Christ descended into the grave in order that, through His resurrection, He would set us free from the power of death. And, not only death -- which is the ultimate dungeon indeed -- but all other dark prisons that confine us. This is, of course, the miracle of Easter.
But what shall we do with this glorious freedom?
Certainly not to imitate Edmond Dantès, who devotes the remainder of his life to exacting revenge from those who sent him to prison those fourteen years. (The Count of Monte Cristo is a bittersweet tale for, without fail, whenever he executes one of his seemingly flawless plots of vengeance, it has the unintended effect of also bringing down sorrow upon those he holds dear ... and, ultimately, on himself.)
What, then?
"As the Father has sent me, even so I am sending you," Jesus said. (John 20:21) Which means, among other things, that He is sending us "to proclaim ... release from darkness for the prisoners..."
Jesus never hesitated to go where He could find people bound in darkness. He left the multitudes behind and made a special journey to the country of the Gadarenes just to free a single wretched soul held in prison by a legion of devils. And, that is not the only one-on-one house call our Lord made. There was the Samaritan woman, a lonely outcast of five shipwrecked marriages. And the widow of Nain, who had lost her only son and hope of provision.
As we reflect on our Lord's incomprehensible love, which sprung us from Satan's dungeon by means of His own body bag, to the glorious new life of Easter morning, let us not lose sight of His compelling commission in Isaiah 61. "As the Father has sent me, even so I am sending you."
"Dear Father, thank you for Jesus. Thank you for His death and resurrection that set me free, not only from the darkness of sin and death, but also from the many dark prisons in which the enemy would confine me. Please give me a passion to continue Jesus' ministry of setting free those who are oppressed by the devil. And, by Your power I shall!
Originally published as a "Bradstix" devotional on the National Minute of Prayer Facebook page 4/4/2021.


This is for TOL Group Level Three. At the time that it was recorded it was called Level Two. Tree of Life Groups are no longer offered as live facilitated groups. These recordings can still help you on your Journey. Use the recordings as spring board to encounters.


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Still, common practice in a "gentlemans game" should indicate that we help each other, because there will be a day when you will be in need of that some help.
I would've stopped you from throwing from the DZ after anything but your drive.
In the moment, we all forgot what was going on.
I took a triple circle ten on 7 at cc!
I can't find anything in the rule book that states you can't use a disc that went OB. In fact I can't find in the Mando section currently either (now i haven't scoured the rule book for it yet, but I will). I could be thinking of an older version of the rules, because the entire Mando section was basically redone this year. So it may in fact be that now you can go and get a disc that was thrown missing the Mando, but you then can call the person on 30 seconds....The 30 second rule got a big beefy update this year for certain....winds like this past weekend could have made it tough on players to really enforce it.
As far as I understand it, you can retrieve your disc and throw it again as long as you do it within 30 seconds of approaching your lie. I know it use to be 30 seconds from when you placed your mini, but that was changed this year to when you address your lie or something to that effect.
"You are now allowed to retrieve a disc after a stance violation (the excessive time rule still applies of course). "
"The 30-second clock (excessive time) now starts when you reach your disc, rather than after you mark it, since a disc may not be marked. You can no longer stand over your disc holding your mini for an indefinite amount of time."


text:
Q: Is Continuous Function Lebesgue / Borel Measurable? Let $f:\mathbb{R}\to\mathbb{R}$ be continuous then $f$ is Lebesgue measurable
Proof: for $a\in \mathbb{R}$, $E=(a,\infty)$ is an open set, because $f$ is continuous then $f^{-1}(E)$ is open too or:
$$f^{-1}(E)=\{x\in \mathbb{R}:f(x)>a\}$$
And $f$ is Lebesgue measurable, why conclude we that it is Lebesgue measurable and not Borel?

redpajama_set_name: RedPajamaStackExchange


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How to Perfectly Fix A Pedicure Without Messing Up Your Fingers!
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text:
\section{Introduction}

\begin{figure*}[!t]
	\centering
	\includegraphics[scale=3.05]{figures/brat-example2.pdf}
	\caption{An example standardized science exam question densely annotated with one or more fine-grained semantic categories for nearly each word.  This 4-choice multiple choice question (here, under the curriculum topic \textit{``The Interdependence of Life \textgreater The Food Chain \textgreater Decomposers''}) is one of 4,239 drawn from the ARC corpus and densely annotated in this work. \label{fig:example}}
\end{figure*}

Named entity recognition (NER) \cite{grishman1996message} is a common natural language processing task that aims to abstract or categorize common classes of noun phrases in text, such as identifying ``Arthur'' as a \textit{person} or ``Montreal'' as a \textit{location}.  This high-level categorization of important entities in text is a staple of most modern NLP pipelines,
and has a variety of applications for higher-level tasks including information extraction \cite{valenzuela2016odin}, knowledge base population \cite{dredze2010entity}, and question answering \cite{abujabal2017automated}.

\hspace{2em} Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse (typically occurring for between 10\% to 20\% of words in a corpus, see Section~\ref{sec:mentiondensity}), limiting utility to downstream tasks. In this work, we introduce the idea of common entity recognition (CER), which aims to tag all content words in text with an appropriate fine-grained semantic class. CER allows text to be automatically annotated with a much richer set of semantic labels, potentially providing greater utility for downstream applications.  We explore CER in the context of scientific text, and present ScienceExamCER, a training corpus annotated with over 113k common entity annotations drawn from a fine-grained set of over 600 semantic categories, which include named entities, as well as verb groups, properties and quantities, thematic types, and synonyms for key terminology.  We also release an off-the-shelf NER tagger modified to perform multilabel CER tagging. This BERT CER tagger achieves an accuracy of 0.85 F1 on this task, indicating that the tag ontology labels are well-defined and clearly identifiable. These two resources offer new opportunities to explore the impact of dense semantic annotation in downstream tasks.

\hspace{2em} We believe the notion of dense fine-grained semantic tagging to be potentially useful to any application domain, but explore common entity recognition here in the context of scientific text aimed at teaching and evaluating scientific knowledge.
An example of this dense semantic classification in the context of standardized science exams is shown in Figure~\ref{fig:example}.  While CoreNLP \cite{manning2014stanford} does not locate any entities in the sentence \textit{``Rolanda is growing tomato plants in her garden''}, our CER annotation and system abstracts this sentence to \textit{``[Rolanda]$_\textit{Human}$ [is]$_\textit{StateOfBeing}$ [growing]$_\textit{Growth/ActionsForAgriculture}$ [tomato]$_\textit{Food}$ [plants]$_\textit{Plant}$ [in]$_\textit{RelativeLocation}$ her [garden]$_\textit{ManmadeLocation}$''}.

\hspace{2em} We detail corpus and ontology/typology construction in Section 3, including a comparison of mention density with other common corpora.  Automated evaluations of CER performance are shown in Section 4, including an analyses of the training data requirements of this fine-grained classification, as well as an error analysis.

\section{Related Work}

\hspace{2em} Common sets of entity labels (or \textit{typologies}) have expanded from early experiments with a single label, \textit{organization} \cite{rau1991extracting}, to the 7 common MUC-6 types \cite{grishman1996message} typically used by NER systems, including named entities (\textit{person, organization, location}), temporal mentions (\textit{date, time}), and numeric categories (\textit{money, percent}).  Subsets of the MUC-6 types have been included in the typologies of benchmark NER corpora, including CoNLL-2003 \cite{sang2003introduction}, OntoNotes \cite{weischedel2013ontonotes}, and BBN \cite{weischedel2005bbn}.

\hspace{2em} Sekine et al. \shortcite{Sekine2002ExtendedNE} proposed an extended hierarchy of MUC-6 types expanded to include 150 open-domain category labels.  While most of these category labels are named entities, Sekine et al. include 10 measurement categories (e.g. \textit{weight, speed, temperature}) and 3 high-level natural object categories (\textit{animal, vegetable, mineral}) that most closely relate to the 601 fine-grained science categories in this work.  A subsequent version, the Extended Named Entity (ENE) Ontology \cite{sekine-2008-extended}, expands the typology to 200 classes, including 19 fine-grained expansions of the \textit{natural\_object} type, such as \textit{bird} or \textit{reptile}, as well as adding 5 meronym categories, such as \textit{plant\_part}, that further relax the working definition of named entities from proper names to include other categories \cite{nadeau2007survey}.

\hspace{2em} While open-domain typologies are common, domain-specific typologies and corpora are also popular, occasionally making use of existing domain ontologies to reduce the burden in manually generating fine-grained typologies, such as the manual creation of the fine-grained science-domain typology in this work. An extreme example of fine-grained NER is the MedMentions corpus \cite{murty2018hierarchical}, which contains 246k mentions labelled with Universal Medical Language System (UMLS) \cite{bodenreider2004unified} categories, a fine-grained ontology of over 3.5 million medical concepts.
Similarly, large knowledge bases can be filtered to automatically produce fine-grained typologies (as in FIGER \cite{ling2012fine} and HYENA \cite{yosef2012hyena}), or used to bootstrap the entity classification process in manually-generated typologies. Magnini et al. \shortcite{magnini-etal-2002-wordnet} demonstrate combining WordNet predicates \cite{fellbaum1998wordnet} with approximately 200 handcoded rules can achieve an F1 score of 0.85 on recognizing 10 common entity types, while
Ritter et al. \shortcite{ritter2011named} use distantly supervised topic modeling over Freebase entities  \cite{bollacker2008freebase} to perform named entity recognition on social media posts, achieving an F1 score of 0.59 on 10 common entity types.
With respect to larger typologies, Del Corro et al. \shortcite{del2015finet} perform super-fine grained entity typing using the 16k fine-grained WordNet types under the high-level taxonomic categories of \textit{person, organization, and location}, achieving a manually-evaluated precision of 59.9\% on the CoNLL corpus and 28.3\% on New York Times news articles.  For smaller manually-generated typologies, Mai et al.~\shortcite{mai2018empirical} demonstrate a model combining LSTMs, CNNs, CRFs, and dictionary-based methods can achieve an F1 of 83.1 on an in-house corpus labeled with Sekine's \shortcite{sekine-2008-extended} 200-class ENE ontology.

\hspace{2em} NER has historically been approached using a wide variety of methods, including rules \cite{hanisch2005prominer}, feature-based machine learning systems \cite{Mayfield:2003:NER:1119176.1119205}, conditional random fields \cite{greenberg2018marginal}, contextualized embeddings \cite{peters2018deep}, and combinations thereof.
Qu et al. \shortcite{qu-etal-2016-named} demonstrate that it is possible to use a conditional random field model to transfer NER performance between datasets, at least in part.  Ma et al. \shortcite{ma2016label} show embedding models can transfer performance in zero-shot settings on fine-grained named entity classification.  Expanding on this, recent transformer models \cite{peters2018deep,devlin2018bert} have shown strong transfer performance on a variety of text classification tasks including named entity recognition using large pretrained contextualized embeddings that are fine-tuned on comparatively small in-domain corpora.  In this work we make use of an off-the-shelf bidirectional transformer (BERT) NER system modified to support multi-label classification, and demonstrate strong performance on the fine-grained common entity recognition task.

\section{Data and Annotation}

\begin{table}
\centering
\small
\begin{tabular}{lcc}
\textbf{Label}	 			&	\textbf{Examples}	&	\textbf{Prop.}	\\
\hline
StateOfBeing                &   is, are, be             &   4.6\%   \\
LevelOfInclusion            &   which, each, only       &   4.0\%   \\
RelativeLocation            &   inside, under           &   3.0\%   \\
Comparison                  &   identical, difference   &   1.9\%   \\
RelativeDirection           &   forward, upward         &   1.8\%   \\
ProbabilityAndCertainty     &   likely, possible        &   1.6\%   \\
Cause                       &   because, due to         &   1.5\%   \\
AmountComparison            &   most, more, less        &   1.5\%   \\
RelativeTime                &   during, after           &   1.4\%   \\
Creation                    &   produce, make, form     &   1.2\%   \\
PhasesOfWater               &   steam, ice             &   1.1\%   \\
CardinalNumber              &   one, 100                &   1.0\%   \\
ContainBeComposedOf         &   made of, contains       &   1.0\%   \\
IncreaseDecrease            &   increasing, decline     &   0.9\%   \\
Element                     &   oxygen, carbon          &   0.9\%   \\
Plant                       &   tree, crops, weeds      &   0.8\%   \\
Move                        &   placed, motion, travel  &   0.8\%   \\
Use                         &   with, apply             &   0.8\%   \\
AmountChangingActions       &   deplete, extend         &   0.8\%   \\
RelativeNumber              &   many, some, high        &   0.7\%   \\
Temperature                 &   hot, warm, cold         &   0.7\%   \\
Energy                      &   kinetic energy, power   &   0.7\%   \\
CombineAdd                  &   add, absorb, mix        &   0.7\%   \\
LiquidMatter                &   water, oil, droplets    &   0.7\%   \\
Scientist                   &   geologist, Galileo      &   0.6\%   \\
Quality                     &   best, good, useful      &   0.6\%   \\
Size                        &   large, thick, diameter  &   0.6\%   \\
AbilityAvailablity          &   potential, unable       &   0.6\%   \\
ManmadeObjects              &   ball, spoon, paper      &   0.6\%   \\
PrepDirections              &   on, through, along      &   0.6\%   \\
Human                       &   person, astronaut       &   0.6\%   \\
ActionsForAnimals           &   eat, migrate, swim      &   0.6\%   \\
InnerPlanets                &   earth, mars, venus      &   0.6\%   \\
QualityComparison           &   advantage, benefit      &   0.6\%   \\
Mammal                      &   dog, horse, bear        &   0.6\%   \\
Exemplar                    &   including, such as      &   0.5\%   \\
PlantPart                   &   leaves, flower, root    &   0.5\%   \\
PerformActivity             &   conduct, do             &   0.5\%   \\
Result                      &   effect, impact          &   0.5\%   \\
Compound                    &   carbon dioxide          &   0.5\%   \\
BodiesOfWater               &   ocean, lake, pond       &   0.5\%   \\
Help                        &   benefit, support        &   0.5\%   \\
Require                     &   need, must, takes       &   0.5\%   \\
Rock                        &   bedrock, boulder        &   0.5\%   \\
TemporalProperty            &   first, over time        &   0.5\%   \\
EarthPartsGross             &   surface, equator        &   0.5\%   \\
WeatherPhenomena            &   wind, cloud, drought    &   0.5\%   \\
Communicate                 &   explain, describe       &   0.5\%   \\
GeographicFormations        &   mountain, glacier       &   0.5\%   \\
ChangeInto                  &   become, converted       &   0.5\%   \\
Soil                        &   sand, ground, topsoil   &   0.4\%   \\
Color                       &   green, blue, white      &   0.4\%   \\
Star                        &   sun, proxima centauri   &   0.4\%   \\
PhaseChangingActions        &   melt, evaporation       &   0.4\%   \\
Nutrition                   &   food, nutrient          &   0.4\%   \\
AnimalPart                  &   body, organ, eye        &   0.4\%   \\
TimeUnit                    &   day, second, year       &   0.4\%   \\
Method                      &   process, procedure      &   0.4\%   \\
StateOfMatter               &   solid, liquid, gas      &   0.4\%   \\
\end{tabular}
\vspace{-4pt}
\caption{The most frequent subset of the 601 semantic classification labels used to annotate the ScienceExamCER corpus.  Proportion refers to the proportion of mentions in the training set that are labeled with a given category.  The full set of 601 classes is included in the supplementary material. \label{tab:examplesemanticcategories}}
\end{table}

\subsection{Corpus}
We annotate fine-grained semantic classes on standardized science exam questions drawn from the Aristo Reasoning Challenge (ARC) corpus \cite{clark2018think}, which contains 7,787 elementary and middle school ($3^{rd}$ through $9^{th}$ grade) standardized exam questions drawn from 12 US states.  Each question is a 4-way multiple choice question, ranging from short direct questions to detailed multi-step problems grounded in examples.  An example question is shown in Figure~\ref{fig:example}. Question text contains an average of 21 words across 1.7 sentences, while answer candidate text averages 4 words, but can be as short as a single word (as in Figure~\ref{fig:example}).   In this work we draw 4,239 questions from the ARC corpus, consisting of the full training and development folds, to use for our semantic labeling and prediction tasks.

\subsection{Semantic Class Labels}

We conducted a large data-driven analysis of the 4,239 science exam questions with the aim of identifying a set of high-level semantic categories that would provide near total coverage for classifying or grouping nearly all of the 156k words found across the question and answer text in this corpus.
While named entity recognition typically focuses on proper names with specific referents \cite{nadeau2007survey}, in the end we arrived at creating 601 fine-grained categories spanning 6 classes of groups:

{\flushleft\textbf{Taxonomic Groups:}} High-level categories expressing taxonomic membership, such as that a \textit{hummingbird} is a kind of \textit{bird}.  This (or stricter interpretations) is the common form of entity classification in most named entity recognition corpora.

{\flushleft\textbf{Meronym Groups:}} Categories expressing part-of relations, such as that a \textit{fin} is a part of an \textit{aquatic animal}, an \textit{x-axis} is a part of a \textit{graphical representation}, or that an \textit{individual} is a part of a \textit{group}.

{\flushleft\textbf{Action Groups:}} Collections of action words that tend to describe similar ideas.  For example, \textit{decrease, increase, contract, expand, inflate, deflate, accelerate, decelerate, lower, raise} all describe a group of \textit{actions that involve increasing or decreasing quantities}.

{\flushleft\textbf{Thematic word groups:}} Groups of words that surround a particular topic.  For example, \textit{observe, conduct an experiment, compare, study, consider, test, collect, record, gather, examine,} and \textit{research} are some of the words included in the \textit{performing research using the scientific method} semantic class.

{\flushleft\textbf{Properties and Values:}} Common science-domain properties of objects, such as \textit{mass, size,} or \textit{conductivity}, typically grouped with common values they might take, such as \textit{soft, brittle, or hard} in the case of \textit{hardness}.

{\flushleft\textbf{Synonyms:}} Groups of words that tend to express similar ideas in the context of science exams.  For example, \textit{disease, infection, and sick} all convey the notion of \textit{illness}.
\\

\hspace{2em} To identify specific instances of these categories in the science exam domain, we first sorted questions into fine-grained curriculum topics using the 406 detailed science-domain question classification labels of Xu et al. \shortcite{Xu2019MulticlassHQ}, noting that common categories of words tended to emerge upon detailed manual inspection when questions on similar topics were examined together.  We proceeded through several iterations of this process, recording candidate high-level semantic classes, as well as seed words that belonged to those categories.  After assembling a large list of candidate categories, we further enumerated the seed words with encyclopedic knowledge manually through web searches.  For example, while the annotators may have only observed the words \textit{Sun} and \textit{Proxima Centauri} in the corpus for the \textit{Star} category, we would manually expand this to also include other nearby stars such as \textit{Vega}, \textit{Polaris}, and \textit{Wolf 359}.

\hspace{2em} As a final step, we automatically expanded the seed word list to include lexical variations of each manually added word by first using pretrained GLoVe embeddings \cite{pennington2014glove} to compute the top-N most similar words to a given seed word using cosine similarity, then using several low-precision high-recall heuristics to identify words that had the potential to be lexical variations of an existing word on the seed list.  We then generated a frequency histogram of any word present in the corpus that did not yet belong to at least one semantic category, and either placed it in an existing category, or formed a new category for that word and repeated the expansion process for seed words.  This detailed manual category development process required approximately three weeks of annotator time, ultimately arriving at a list of 601 high-level semantic categories, with an extensive list of both manually and automatically populated seed words for each category.  The full list of semantic categories and seed words is included in the \textit{supplementary material}.

\begin{table*}
\centering
\begin{tabular}{lcccccc}
\textbf{Measure}	 						&	\textbf{ScienceExamCER}	&	\textbf{OntoNotes 5}&   \textbf{BBN}    	&   \textbf{GUM}    &	\textbf{CoNLL 2003}	\\
\hline
\textit{Entity Categories}                  &   601                     &   18                  &   64                  &   11              &   4                    \\
\textit{Total Mentions}                     &	~133k${^{*}}$	        &	162k			   	&   172k                &   11k             &	~35k					\vspace{1.5mm}\\

\textit{Words}								&	156k				    &	2.44M				&   1.05M               &   55k             &	264k				\\
~~\textit{Labeled Words}					&	117k				    &	284k				&   257k                &   33k             &	51k					\\
~~\textit{Mention Density (overall)}		&	\textbf{75\%}   	    &	12\%				&   25\%                &   59\%            &	~19\%				\vspace{1.5mm}\\

\textit{Content Words}						&	104k				    &	1.39M				&  677k                 &   34k             &	190k				\\
~~\textit{Labelled Content Words}			&	100k				    &	255k				&  243k                 &   21k             &	50k					\\
~~\textit{Mention Density (Content Words)}	&	\textbf{96\%}		    &	18\%				&  36\%                 &   62\%            &	~27\%				\vspace{1.5mm}\\

\end{tabular}
\caption{Summary statistics including \textit{mention density} for the ScienceExamCER corpus, as well as four other common benchmark corpora.  At 96\%, the ScienceExamCER is significantly more  densely labeled than the next-nearest corpus. (\textit{*} denotes that approximately 16k spans have multiple labels, and as such the total mentions exceeds the total labeled words). \label{tab:summarystatistics}}
\end{table*}

\begin{table}[]
    \centering
    \begin{tabular}{lcc}
         \textbf{Fold}  &   \textbf{Science Questions}  &   \textbf{Words}  \\
         \hline
         Train          &   2,696                &   108,396              \\
         Development    &   674                  &   27,560              \\
         Test           &   869                  &   35,379              \\
    \end{tabular}
    \caption{Summary statistics for the training, evaluation, and test sets used for evaluating semantic category classification. }
    \label{tab:summarystatisticsquestionset}
\end{table}

\subsection{Annotation Procedure}

Annotating a large set of semantic classes onto more than one hundred thousand words presents challenges with annotation consistency and tractability.  It would be challenging for crowdworkers to learn a detailed set of 601 fine-grained semantic categories, and extremely time consuming for research assistants to traditionally annotate a collection at this scale.  To overcome these challenges, we modified the annotation task to automatically preannotate the entire corpus using the large set of bootstrapped seed words associated with each semantic class, effectively preannotating each word with a set of possible semantic category labels.  These preannotated labels are effectively low-precision and high-recall, most often containing the correct label(s) for a given mention, but also containing other incorrect labels that must be manually removed by an annotator.  A total of 226k preannotated mentions were generated (an average of 1.5 per word), which was reduced to 133k mentions (0.9 per word) after incorrect labels were removed by the annotator.  We used the BRAT annotation tool \cite{stenetorp2012brat} for the label removal step.  To ease the annotator's need for switching semantic contexts, questions were presented to the annotator sorted by curriculum topic using the question classification annotation of Xu et al. \shortcite{Xu2019MulticlassHQ}.  The annotation procedure took approximately 2.5 minutes per question, for a total of 200 hours.

\hspace{2em} A clear question with this ``preannotate-then-filter'' annotation protocol is how well this procedure is able to provide both coverage and accurate labels for the words in the corpus.  Our analysis in Section~\ref{sec:mentiondensity} shows that after annotation, 96\% of content words and 75\% of all words have at least one gold semantic category label, suggesting this protocol allows for near-complete coverage of content words at a fraction of the time required to make accurate 601-class annotation judgements at scale.  Both our interannotator agreement (included below) and automatic classification performance are high, suggesting adequate annotated label accuracy.

{\flushleft\textbf{Label distribution:}} Named entity corpora often have many labels in their typologies, but the majority of mentions tend to cluster around a small set of possible labels \cite{choi-etal-2018-ultra}.  The distribution of most frequent labels after annotation is shown in Table~\ref{tab:examplesemanticcategories}. The usage of the 601 total semantic class labels in this corpus is well distributed, with the 356 most-frequent types covering 95\% of the total mentions, while 479 types cover 99\% of mentions.  At the 99\% level, categories (for example, \textit{Geometric Qualities}, such as \textit{angle, slope, or circumference}) still contain 16 mentions, highlighting the scale of the corpus.

{\flushleft\textbf{Interannotator agreement:}} Each question was annotated by a single annotator.  A second annotator was trained in the annotation procedure and re-annotated 50 questions totalling 1,756 tokens.  Between both annotators, a total of 1,369 mentions were annotated with semantic class labels.  Total percent agreement across both annotators was 76\%.\footnote{Because the bootstrapped preannotation procedure reduces the set of possible labels for a given mention from 601 to an average of approximately 2 (the average number of preannotated labels per annotated word), Cohen's Kappa \cite{cohen1960coefficient} would either be artificially inflated (if treating the annotation as a 601 class labeling problem) or reduced (if treating annotation as a 2 class problem).  As such we report raw percent agreement, which (as critiqued by Cohen) has known problems when dealing with highly skewed frequency distributions of labels, particularly when few labels are present.  Here, the number of label categories is high, and (as shown in Table~\ref{tab:examplesemanticcategories}) the frequency of labels is well distributed across the label set.  As such, the inflation of the percent agreement statistic is likely to be minimal. }  Upon inspection, labeling multi-word sequences as either a single mention or multiple smaller mentions was a frequent source of disagreement.  When these cases were removed, percent agreement rose to 83\%.

~\\

\subsection{Mention density comparison}
\label{sec:mentiondensity}

To increase the utility of our common entity corpus for downstream tasks, one of the design goals was to provide at least one high-level semantic category to nearly every word in the corpus.  To measure this we define the notion of the \textit{mention density} of a corpus as the proportion of words that contain at least one entity label.\footnote{Specifically, the proportion of non-punctuation tokens in a BIO-formatted corpus that are labelled with either a B (beginning) or I (inside) tag.}
We compare the mention density of this corpus with the English subsets of the four benchmark named entity recognition corpora listed below:

{\flushleft\textbf{CoNLL}} \cite{sang2003introduction}: The CoNLL 2003 Named Entity Recognition Shared Task corpus, which includes 4 entity labels that are a subset of the MUC-6 typology: \textit{person, location, organization} and \textit{miscellaneous}.
{\flushleft\textbf{OntoNotes 5.0}} \cite{weischedel2013ontonotes}: A large multi-genre corpus of news media, blog, newsgroup, and conversational text, annotated with 18 entity labels, including the MUC-6 types.
{\flushleft\textbf{BBN}} \cite{weischedel2005bbn}: A corpus of news text annotated with 21 course entity types, including 12 named entity types (e.g. \textit{person, organization, product}) and 7 numeric types (e.g. \textit{date, percent, cardinal number}).  The full set of entity labels includes 64 fine-grained types.
{\flushleft\textbf{GUM}} \cite{Zeldes2017}: An open-domain corpus annotated with a collapsed set of OntoNote entities reduced to 11 entity types, such as \textit{person, organization,} or \textit{place}.  Two additional catch-all tags are added, \textit{object} and \textit{abstract}, which provide high-level but minimally informative categorical information for large noun phrases.  Approximately 40.5\% of the labelled words in this corpus are labelled as either \textit{object} or \textit{abstract}.\\

\hspace{2em} The analysis of mention density is shown in Table~\ref{tab:summarystatistics}. Overall, the mention density of this science corpus is 75\%, meaning that 75\% of all words in the corpus are annotated with at least one high-level semantic category.  When considering only content words (here, determined to be \textit{nouns, verbs, adjectives, adverbs, and numbers}), this proportion increases to 96\%.  The mention density for the named entity corpora examined in Table~\ref{tab:summarystatistics} ranges between 12\% and 59\% for all words, and 18\% to 62\% when considering only content words.  At 62\%, the GUM corpus contains the next-nearest mention density to the ScienceExamCER corpus, however a large portion of those mentions (40.5\%) of words) use the high-level \textit{object} or \textit{abstract} labels, and as such are of limited informativeness to downstream tasks.  BBN, the corpus with the next-nearest mention density to GUM, has labels for only 36\% of it content words, and 25\% of all words.

\section{Experimental Results}

\subsection{Model}

Our semantic class labeling task is conceptually similar to named entity recognition or entity typing, only requiring a label for nearly every word in an input sentence.  In light of this, here we use an off-the-shelf named entity recognition model, and show it also performs well on the densely-labeled common entity recognition task.

\hspace{2em} Recently, pretrained bidirectional encoder representation from transformer (BERT) models \cite{devlin2018bert} have shown state-of-the-art performance at both named entity recognition as well as a variety of other token-level classification tasks.  In this work, we use an off-the-shelf implementation of a BERT-based named entity recognition system, BERT-NER\footnote{\url{https://github.com/kamalkraj/BERT-NER}}.  Most approaches to named entity recognition model the task as a single-label prediction task, where each word has at most one label.  We modify the BERT-NER implementation to allow for multi-label predictions using the following method.

\hspace{2em} Given a sentence $S$ consisting of $L$ tokens, such that $S=(x_1, x_2, ..., x_L)$, the original BERT-based token classification model generates $L$ respective $M$-dimensional encodings $(x_1, x_2, ..., x_L)$, one for each token. These encodings then pass through a \textit{softmax} layer and make use of a multi-classes cross entropy loss function that generates a single class prediction per token.
We adapt this system to multi-label classification by using a \textit{sigmoid} function and binary cross entropy in place of the original loss function to allow the classifications for each token to return non-zero values for more than one class.  More formally, our loss function becomes:
\begin{equation}
\begin{aligned}
    L_{multilabel} = {}&-\frac{1}{M}\sum_{m=1}^{M}[\tilde{y}_l^m\cdot{log\sigma(x_l^m)} + {} \\
    &(1-\tilde{y}_l^m)\cdot{log(1-\sigma(x_l^m))}] \\
\end{aligned}
\end{equation}

\begin{equation}
\begin{aligned}
    \sigma(x_l^m)&=\frac{1}{1+e^{-x_l^m}}
\end{aligned}
\end{equation}
where $M$ is the number of total classes, $x_l$ is $M$-dimensional encoding for the $l$-th token in sentence, $\tilde{y}_l$ is the $l$-th token's gold label vector, and $\sigma$ is the \textit{sigmoid} activation function.

{\flushleft\textbf{Folds:}} Because of the expense associated with annotating a large corpus, only the training and development subsets of the ARC corpus were manually annotated with semantic class labels.  As such we repurpose the original development set for testing, and hold out 20\% of the training corpus for development. Summary statistics on these folds are provided in Table~\ref{tab:summarystatisticsquestionset}.

{\flushleft\textbf{Hyperparameters:}} We make use of the pre-trained English \texttt{BERT-Base-cased} model\footnote{\url{https://github.com/google-research/bert}}, with a maximum sequence length of 64.  The threshold for the sigmoid activation layer was tuned on the development set, with a value of 0.4 found to provide good performance. The large number of possible class labels in our task compared with typical named entity recognition datasets, combined with the modified multi-label loss function, necessitated significantly longer training times for the model to converge.  We empirically found that the model tended to converge by 140 epochs, which took approximately 5 hours to train using dual RTX2080Ti GPUs.  Classification of the entire test dataset is comparatively fast, providing semantic class labels at a rate of approximately 900 questions (35,000 words) per minute, enabling the pre-trained model to be run on other science-domain corpora (for example, textbooks, study guides, Simple Wikipedia, or other grade-appropriate knowledge resources) at scale.

\subsection{Evaluation}

\begin{table}
\centering
\begin{tabular}{lcccc}
\textbf{Model} 								& \textbf{Fold} 	&	\textbf{Prec.}  &   \textbf{Recall}     &   \textbf{F1} 	\\
\hline
BERT-NER                                    &   dev             &   0.84            &   0.85                &   0.84            \\
BERT-NER                                    &   test            &   0.85            &   0.86                &   0.85            \\

\end{tabular}
\caption{Performance on the 601-category fine-grained semantic classification task on the development and test folds using the BERT-NER model.  \label{tab:performance}}
\end{table}

The results for our semantic classification task on the ScienceExamCER corpus using the 601-class fine-grained typology are shown in Table~\ref{tab:performance}.  We evaluate entity classification performance using the standard definitions of Precision, Recall, and F1.  Overall classification performance is high, reaching 0.85 F1 on the held-out test set. This suggests the common entity recognition performance is sufficiently high to be useful for a variety of downstream tasks.  To further characterize performance, we investigate how the availability of training data affects this fine-grained classification task, as well as common classes of prediction errors the BERT-NER model makes.

\subsection{Performance vs Training Data}

\begin{figure}[!t]
	\centering
	\includegraphics[scale=0.30]{figures/datadependence1.pdf}
	\caption{Classification performance (F1) versus the number of training epochs when training the model with less data.  Series represent training the model with the entire training set, or randomly subsampled proportions of training data summing to 75\%, 50\%, and 25\% of the original training set size.  Each point represents the average of 5 randomly subsampled training sets.  \label{fig:datadependence}}
\end{figure}

Manually annotating fine-grained mentions in large corpora is expensive and time consuming.  To investigate how classification performance varies with availability of training data, we randomly subsampled smaller training sets from our full training corpus that were 25\%, 50\%, or 75\% as large, corresponding to spending approximately 50, 100, or 150 hours at the manual annotation task, respectively.  The results are shown in Figure~\ref{fig:datadependence}.  With only 25\% of training data available, F1 performance decreases dramatically from 0.85 to 0.50. 50\% of training data decreases classification performance by 7 points, while 75\% of available training data decreases classification performance by 2 points.  This suggests that the scale of training data generated in this work is provides near saturated performance using the BERT-NER model, and that annotating the remainder of available standardized science exam questions in the ARC corpus would likely result in only a minimal increase on classification performance.

\subsection{Error Analysis}

\begin{table}
\centering
\begin{tabular}{lc}
\textbf{Error Class} 									&	\textbf{Prop.}	\\
\hline
\textit{Predicted label also good}						&	24\%	\\
\textit{Model did not generate prediction}				&	24\%	\\
\textit{Multiple gold labels, one found}				&	21\%	\\
\textit{Predicted label semantically near gold label}	&	17\%	\\
\textit{Gold label incorrect}							&	7\%		\\
\textit{Multi-word Expression}							&	6\%		\\
\textit{Predicted label using incorrect word sense}		&	5\%		\\

\end{tabular}
\caption{An analysis of common categories of model prediction errors, as a proportion of the first 100 errors on the test set.  Note that a given errorful prediction may belong to more than one category, and as such the proportions do not sum to 100\%. \label{tab:erroranalysis}}
\end{table}

To better understand the sources of error in our model, we conducted an analysis of the first 100 errorful predictions on the test set, with the results shown in Table~\ref{tab:erroranalysis}.  Nearly one third of errors are due to issues with the annotation, such as a mention missing an additional label that is also good (24\% of errors), or the manually annotated gold label being incorrect (7\% of errors).  For a substantial portion of errors (24\%), no single semantic class rose to meet the activation threshold of the sigmoid layer and the model did not produce a prediction for that word, while, similarly, in 21\% of cases only one label of a multi-label word was produced.  The remaining errors broadly cluster around technical challenges in determining the semantics of each category, including word-sense disambiguation (5\% of errors), locating multi-word expressions (6\% of errors), or predicting a label whose category is semantically similar to the gold label (17\% of errors).

\section{Conclusion}

We present ScienceExamCER, a densely annotated corpus of science exam questions for common entity recognition where nearly every word is annotated with fine-grained semantic classification labels drawn from a manually-constructed typology of 601 semantic classes.  We demonstrate that BERT-NER, an off-the-shelf named entity recognition model, achieves 0.85 F1 on classifying these fine-grained semantic classes on unseen text in a multi-label setting.  The data and code are released with the goal of supporting downstream tasks in question answering that are able to make use of this dense semantic category annotation.

\section{Supplementary Material}

The annotated corpora, fine-grained typology, and pretrained models for this work are available at \url{http://cognitiveai.org/explanationbank/}.  A truncated version of the typology is included in the \texttt{Appendix} below.

\section{Acknowledgements}
This work was supported by the Allen Institute for Artificial Intelligence and the National Science Foundation (NSF Award \#1815948, ``Explainable Natural Language Inference'', to PJ).  We thank Peter Clark for thoughtful discussions on this work and comments on an earlier draft.

\section{Bibliographical References}
\label{main:ref}

\bibliographystyle{lrec}

redpajama_set_name: RedPajamaArXiv


text:
About the projects
The BIAA has been involved in over 100 projects of major academic and cultural significance since its foundation as a research institution more than seven decades ago. Throughout the years, BIAA researchers have engaged in archaeological excavations, surface surveys, epigraphic exploration, architectural studies, restoration projects and museum-based research, spanning a diverse range of topics, territories, disciplines and eras.
Since the early 2000s, as the role of the Institute has expanded to cover a wider range of research in the humanities and social sciences, the BIAA has also sponsored an increasing number of projects in the fields of Cultural Heritage Management, Ottoman and contemporary Turkish history, social anthropology, sociology and political science.
This archive provides information, bibliographic reference, and sources for some of the most significant projects carried out under the auspices of the BIAA from its inception in 1947 until today. These include projects organised directly by the Institute and its academic staff and fellows, projects facilitated by the BIAA and projects funded by the BIAA with large fieldwork grants.
All Archaeology and Related Disciplines Cultural Heritage Management History, Politics and Social Sciences
Safeguarding Archaeological Assets in Turkey (SARAT)
Fragile Landscapes
Migration and Medicine: Bridging the Gap between Patients and Migrant Doctors
Local efforts for climate change adaptation with limited resources: the Case of Kadıköy, Istanbul
Telmessos Survey
Active Learning Protects Heritage and Archaeology
Documentation of sites of religious syncretism in Istanbul
Pisidia Heritage Trail
Aphrodisias Excavations
Details of previous BIAA-funded research projects will soon be available on the Digital Repository

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Multiplying leading diagonal values in Pandas? I am trying to multiply the leading diagonal in a pandas dataframe and I am not sure how to proceed in a computationally reasonable way.
df =       [ 3  4  5
             6  7  8
             9 10 11]

ouput_df = [231 32 5
            60  77 8
             9  10 11]

Explanation - lookoing to 3 * 7 * 11 for the first element, 4 * 8 for the second element, 7 * 11 for the fifth element etc.,
Note: The matrix I am working on is not a square matrix, but a rectangular matrix.

A: Here's one based on NumPy -
def cumprod_upper_diag(a):
    m,n = a.shape
    mask = ~np.tri(m,n, dtype=bool)
    p = np.ones((m,n),dtype=a.dtype)
    p[mask[:,::-1]] = a[mask]
    a[mask] = p[::-1].cumprod(0)[::-1][mask[:,::-1]]
    return a

a = df.to_numpy(copy=False) # For older versions : a = df.values
out = a.copy()
cumprod_upper_diag(out)
cumprod_upper_diag(out.T)
out.ravel()[::a.shape[1]+1] = out.ravel()[::out.shape[1]+1][::-1].cumprod()[::-1]
out_df = pd.DataFrame(out)

A: You can use a sparse diagonal matrix here with some finnicking.  This assumes all non-zero elements in your original matrix, or else this will not work.

from scipy import sparse

a = df.to_numpy()
b = sparse.dia_matrix(a)
c = b.data[:, ::-1]
cp = np.cumprod(np.where(c != 0, c, 1), axis=1)
b.data = cp[:, ::-1]
b.A

array([[231,  32,   5],
       [ 60,  77,   8],
       [  9,  10,  11]], dtype=int64)

A: As Chris mentioned, this is cumprod in reverse order:
# stack for groupby
new_df = df.stack().reset_index()[::-1]

# diagonals meaning col_num - row_num are the same
diags = new_df['level_0']-new_df['level_1']

# groupby diagonals
new_df['out'] = new_df.groupby(diags)[0].cumprod()

# pivot to get the original shape
new_df.pivot('level_0', 'level_1', 'out')

output:
level_1    0   1   2
level_0
0        231  32   5
1         60  77   8
2          9  10  11

A: Here's a method that operates on the DataFrame in place.
df = pd.DataFrame(data=[[3, 4, 5], [6, 7, 8], [9, 10, 11]])
m, n = df.shape
for i in range(-m + 1, n):
    ri, rj = max(-i, 0), min(m - 1, n - i - 1)
    ci, cj = max( i, 0), min(n - 1, m + i - 1)
    np.fill_diagonal(df.values[ri:rj+1,ci:cj+1],
                     df.values.diagonal(i)[::-1].cumprod()[::-1])
print(df)

Result:
     0   1   2
0  231  32   5
1   60  77   8
2    9  10  11


text:
Tom Baurle
At Baurle's behest, WBEN removes war hero David Bellavia from flagship talk show
In what is being described as a 'dumpster fire' at Western New York's most listened to talk radio station, WBEN Newsradio 930 AM, removed David Bellavia as a co-host of the region's leading Baurle and Bellavia program. Sources say that he
Chaos at WBEN, as Baurle and Bellevia are rumored to be feuding
Sources say that Western New York's most listened to talk radio station — WBEN Newsradio 930 AM — is 'imploding with chaos' behind the scenes, and it could lead to drastic programming changes. The station's flagship program, Baurle and Bellavia,


The Eyelash Extensions in a Bottle is a two-step, brush-on lash extension system that instantly builds lashes that reach outrageous lengths. The Extending Fibers are made of nylon and feature a conditioning white-base formula to adhere to lashes on contact. Followed by the Sealing Mascara, fibers are locked into place and lashes are given the look and length of extensions—without the glue. Both formulas contain a nano-peptide complex to nourish and condition so natural lashes appear healthier, lusher, and restored over time. The custom-engineered, dual-sided comb perfectly separates hairs for a clump-free, extension-like finish. This product won't fall into eyes like loose fibers will, leaving you with gorgeous, flirty all-day wear.
Apply the fibers directly to the lashes in an upward sweeping motion.
Allow them to dry completely and repeat until the desired effect is achieved.
This formula is buildable without any clumping, so as many layers as desired may be applied.
Make sure to allow the formula to dry completely before building the next layer for optimal results.


This is a reminder that we will have two weeks free of orchestra rehearsals for the school holiday period. There is a rehearsal this Friday, 25th September 2015. The next rehearsal after that will be 16th October 2015.
You can always check the Rehearsals page for these dates.
On the 19th of September 2015, the Launceston Symphony Orchestra from the LYCO will perform a join concert with the Symphonic Band from the University of Tasmania Community Music Programme.
This will be the first joint concert for these two ensembles since the Folk Songs to Finlandia Concert in 2013.
Please arrive by 7:15pm. As shown on the programme above, the UTCMP Symphonic Band will play first.
Please bring a plate of food to share for supper.
We may need to bring stands – this will be discussed on Friday.


Use this contact form if you missed the retreat and would like to know about future dates for similar retreat when they are offered!
People, it's hard out there!!! Ordinary and extraordinary stresses of life take a toll…the political landscape, especially since last November, weighs heavy on us and the communities we belong to and care about…how do we find relief from suffering? how can we be sustained in our living, our service, and our witness for justice?
Cultivating compassion can allow us to move through these difficult times with less suffering, greater ease, and more energy to bring to service and justice-making. How can we rest in spaciousness, while in prayer and contemplation, while paying attention, and cultivating compassion? How can we find refuge in the Way of Jesus and the Middle Way of the Buddha?
Explore with us the richness in our embodied experience of meditation and prayer, in an open, welcoming space for people from different streams of Christian and Buddhist tradition and community, people from other traditions and spiritual paths, seekers and those simply curious.
Join Heart of Wisdom Monastery, in a diverse and inclusive* community to explore together how these two traditions can intertwine in our lives. We come together in contemplative practice, diving into both Christian and Buddhist contemplative practices during a day of quiet, reflection and refuge.
Retreat participants agree to be present for the entire time of the retreat from 11am-5pm.
> Bring your gloriously whole self on retreat – 'cause that's how we roll at Heart of Wisdom Monastery!


Having spent the last few years, bringing the advertising for its two businesses – clothing and home, and food – together, Marks & Spencer has, in one management reshuffle, shifted strategy quite dramatically.
Patrick Bousquet-Chavanne, the marketing boss for the last six years, is out, as is marketing director for consumer and brand Rob Weston. And they will not be replaced. Instead, responsibility for marketing passes to each department under the leadership of Sharry Crammond in food and Nathan Ansell in clothing and home.
Bousquet-Chavanne and Weston join a long list of executives to be unceremoniously dumped under new chairman Archie Norman and CEO Steve Rowe. And it is hard not to jump to the conclusion that Norman – well known for his no-frills approach – sees advertising as a cost that can be cut. Marketing no longer has a place on the board, and it would be no great surprise under these circumstances to see advertising spend decline.
This shift in strategy might, however, be a good thing. The hope was that bringing its advertising together under one message would provide a halo effect across the business; suddenly people who had never shopped in clothing would be buying skirts and jumpers, while those at home among bedding and lamps would drift over and start buying lunch and dinner.
Yet this doesn't appear to have happened. Over the Christmas quarter, like-for-like food sales fell by 0.4%, while general merchandise sales fell 2.8%. Total UK sales were up by just 1.1%.
Plus advertising the M&S brand is not where it's big issues lie. The retailer may face a range of challenges – from the shift to online shopping to pressures on consumer spending – but its brand strength is not one of them. Its advertising investment has, for the most part, focused on its brand but there are other areas that need to hold its marketers' attention.
The strength of the M&S brand is clear from YouGov BrandIndex. It measures M&S in three separate categories – food, high street retail and high street fashion – and across all of those it is the number one brand in terms of its overall index measure (which takes into account a range of metrics including quality, value and satisfaction). And it comes top in almost every metric, beating competition including John Lewis, Sainsbury's, Next and Debenhams.
Brand Finance's valuation of M&S also shows the brand itself remains strong. In 2018, it was worth $3.8bn to M&S; and while this is down from $4.2bn the year before and around $4.5bn in 2007 it is not where the real problem lies. That is in its enterprise value, which has gone from $22.5bn in 2007 to $9.6bn in 2018.
These figures all suggest that M&S's real issues lie not in how the public perceives the brand, but in the reality of the brand when they go in store or online.
Campaigns such as 'Leading Ladies', 'This is not just food' and more recently 'Adventures In…' have all leant on love for the brand and tapped into the emotional connection it has with consumers. Slowly bringing that messaging together across food and clothing and home, culminating in the 'Spend It Well' campaign last year, seemed to make sense.
But the reality is major brand campaigns like this are expensive and do a job that arguably doesn't need doing at M&S. What it needs to do is focus on product, customer experience and reaching consumers online, whether they increasingly shop. Both marketing and advertising must play a role, but they increasingly need to address the separate issues in its two businesses.
A deeper dive into YouGov BrandIndex shows some clear areas where M&S needs to focus its attention (and in the end its communications) to improve sales. In food, it has a real problem in terms of value; its score of -4.9 puts it 24th on a list of 26 supermarkets, ahead of just Spar and Waitrose. That is a key area the new M&S food team needs to address.
Stuart Machin, who is set to join in May as the new MD for food, is expected to streamline its food offering, which has been increasingly complex in recent years and more expensive. Its products need to reflect how price conscious consumers now are and focus on true innovation.
In clothing and home the shake-up required is more dramatic. As Clive Black, head of research at Shore Capital, politely puts it, food has a "stronger cachet" than clothing. Like-for-like sales have barely been positive in the last six years; for the M&S turnaround to take effect that needs to change.
"There is the need for the greater focus that the new teams will no doubt be concentrating upon, probably leading with product and events over more generic activity. At the heart of success or failure will be product," he adds.
It is only in the last 15 years (since the appointment of Steve Sharp in 2004), that big brand campaigns havr really been back on the agenda at M&S. It didn't advertise on TV at all between the 1970s and 2000s, and so when the 'Your M&S' slogan and logo launched it was a big shift for the brand.
And according to David Haigh, CEO at Brand Finance, it would be little surprise if M&S under Rowe and Norman went back to doing few, if any, big marketing campaigns.
"Archie and his gang may well have decided not to do above-the-line advertising and instead focus on digital, social media and getting the basic proposition right," he says.
If Norman and his team can turn M&S around and improve the proposition, that brand strength will help it find consumers willing to buy. But communicating that turnaround will be key, focusing on specific products and initiatives (much like Tesco has done under Dave Lewis), rather than big emotional brand campaigns.
"Given a good offer, that is well priced and focuses on quality, people will be inclined to buy M&S product. This is a great brand, it can be revived," concludes Haigh.


text:
Community and Family Resources discuss new facility, funding with Supervisors
Gena Johnson, News-Republican Editor
The Boone County Board of Supervisors held its regular meeting Nov. 22. During that time, Community and Family Resources (CFR), headquartered out of Fort Dodge, gave a presentation to the board to ask for possible funding to help contribute to the construction of a new facility for the services they offer. Community and Family Resources, according to an informational pamphlet, states that the organization is dedicated to helping people recover from addiction and mental illness.
The new building would be located in Fort Dodge off of Highway 169, but would serve Boone County residents as well. The cost of the building is estimated at $6,600,000. The nonprofit agency is asking for a gift of $12,553 each year for five years for a total amount of $62,765.
Other counties within the agency's catchment area are: Calhoun, Hamilton, Humboldt, Pocahontas, Story, Webster and Wright.
The programs and services the agency offers are: adult crisis stabilization unit, recovery house, prevention and education, detoxification, residential substance abuse treatment, outpatient substance abuse treatment, problem gambling services and mental health services.
Boone residents receive priority admission into the residential programs. They primarily serve individuals between the ages of 18 and 64.
"Community and Family Resources has been a freestanding substance abuse treatment program for more than 50 years," Community and Family Resources Executive Director Michelle De La Riva said. "We provided comprehensive substance abuse, mental health and gambling treatment and prevention services to individuals and families in the central Iowa area.
"We have a long history of helping people learn healthy behaviors - preventing addition and achieving an approved quality of life."
In fiscal year 2017, the agency treated 2,250 clients for addiction - 7 percent of those were treated in Boone County.
"In fiscal year 2017, we served 1,003 clients in mental health programs," De La Riva said. "22 percent of those were treated for mental health for Boone County residents."
Boone County, she said, might be able to save a significant amount of money due to the recovery treatment programs offered by the agency.
"Recovery treatment in Boone County can lead to a decline in incarceration and recidivism rates of individuals involved in drug related crimes," De La Riva said, adding the county would save money each year for individuals not rearrested for those crimes.
The new building would help with the amount of beds available for these programs. The current three locations for programs in Fort Dodge have capacity, limited accessibility, efficiency and safety and security concerns. The new building with consolidate those locations into one and will help address all those concerns that De La Riva expressed to the board.
"The need for a new facility in Fort Dodge is clear, but we wanted to share with you today why Boone County should invest in the new building," De La Riva said. "In Fiscal year 2017, CFR treated 358 individuals from Boone County, which is 1.3 percent of your population.
"Fifteen services were provided to Boone residents at our Fort Dodge locations, specifically, seven came up for the detox program and 8 for our residential program."
She stated on average, one in five individuals are diagnosed with mental illness every year and one in 10 individuals are diagnosed with a substance use disorder every year.
"Our new facility will ensure that we can continue to be a great resource for your county," De La Riva said. "CFR has already purchased a site for construction and we have actually begun our construction two weeks ago.
"As a not for profit, owning our own building is essential."
The board did not take any action at that time on the gift request from Community and Family resources.


text:
'Mad Max: Fury Road' [Blu ray + DVD + Digital HD]
(Tom Hardy, Charlize Theron, Nicholas Hoult, Hugh Keays-Byrne, Nathan Jones, et al / Blu ray + DVD / R / 2015 / Warner Bros. Home Entertainment)
Overview: A woman rebels against a tyrannical ruler in post apocalyptic Australia in search for her homeland with the help of a group of female prisoners, a psychotic worshiper, and a drifter named Max; who is anything but happy.
Blu ray Verdict: "My name is Max. My world is fire and blood. Once, I was a cop. A road warrior searching for a righteous cause. As the world fell, each of us in our own way was broken. It was hard to know who was more crazy ... me ... or everyone else." These are the first words "spoken" in this completely pulsating movie. Narrated by Max Rockatansky (Hardy), they set the scene perfectly for the colorful, well orchestrated bouts of unbelievable, theatrical carnage to unfold before our very eyes!
But, let's get this straight from the start: this character isn't the "Mad Max" that you grew up with aka Mel Gibson. He doesn't have a dog, he doesn't eat dog food; in fact, he doesn't seem to eat anything! Nobody in the film ever eats anything. Nor are people clamoring for gasoline, they all just seem to have it! That said, let us get into what to expect from our new "hero". After a rip-roaring opening chase sequence, Max is held captive in the Citadel, the city where the ruthlessly totalitarian leader "Joe" is ruling over.
The grotesquely brutal lord maintains tight grip of every valuable resouces across the land. This brings the entire populace crippling under his control. As for Max, he becomes merely a blood bag for Nux (Nick Hoult) one of Joe's War Boys, who is a fatal devout to the cause of their fascist leader, who has promised them the glory of getting into Valhalla, the promised land.
Everything in this cinematic behemoth screams grandeur, even the feverish chase, even the darkness itself seems to spur relentlessly from the ruthless violence. Miller has molded a world where darkness is an escape from the stream of clich�s and retreads, surging into the cinemas, today. It's a visual feast, but surprisingly, with sense. It's explosive in every unimaginable ways, but it's not devoid of a human story, which in this case, is propelled by stellar performances from incredible actors on the movie's payroll.
Tom Hardy is utterly capable as the main hero, but the emotional spine of the narrative mostly runs on Theron's character. At some point, there would be a sense that Max isn't the only one playing under the spotlight, as it gets evident Furiosa is equally as massive as his role is. Hardy here is singularly stunning, playing his role with utmost credibility as he is required. His mission transcends past his emotional torments and he's never pulled himself free from the ghosts if his past. Same can be said with Furiosa, who is not only running from Joe because she wants to get herself free from his cut throat grip, but also to bring every woman into safety, back to freedom where they belong.
Amid these sanity-grabbing action sequences, though, is an emotional streak that keeps Max and Furiousa's humanity alive. This prompts them to survival, into carrying out their similar humane causes, clinging tightly to their only mission even if they get hurled into the barbaric hostilities of their dystopian society. This turns the movie into something visceral, cathartic in some ways that probably only a small fraction of the audience could understand. It will not be enough to merit this achievement with merely a splurge of superlatives. This needs to be experienced, felt, and forever cherished. Watch 'Mad Max: Fury Road' for yourself today and be drawn into a world that you should be so grateful to never have to experience "live" for yourself! This is a Widescreen Presentation (1.85:1) enhanced for 16x9 TVs and comes with the Special Features of:
- Maximum Fury: Filming Fury Road
- Mad Max: Fury on Four Wheels
- The Road Warriors: Max and Furiosa
- The Tools of the Wasteland
- The Five Wives: So Shiny, So Chrome
- Fury Road: Crash & Smash
- I Am A Milker
- Turn Every Grain Of Sand!
- Let's Do This
�Mad Max: Fury Road� will be available on Blu-ray 3D Combo Pack for $44.95, Blu-ray Combo Pack for $35.99 and 2-disc DVD for $28.98. The Blu-ray 3D Combo Pack features the theatrical version of the film in 3D hi-definition, hi-definition and standard definition; the Blu-ray Combo Pack features the theatrical version of the film in hi-definition on Blu-ray; and the DVD features the theatrical version in standard definition.
Blu-ray 3D Combo and Blu-ray Combo Pack include Digital HD with UltraViolet. Fans can also own �Mad Max: Fury Road� on Digital HD via purchase from digital retailers.
www.WBShop.com
'Mad Max: Fury Road' Trailer


text:
Q: SSE/AVX: Choose from two __m256 float vectors based on per-element min and max absolute value I am looking for efficient AVX (AVX512) implementation of
// Given
float u[8];
float v[8];

// Compute
float a[8];
float b[8];

//  Such that
for ( int i = 0; i < 8; ++i )
{
    a[i] = fabs(u[i]) >= fabs(v[i]) ? u[i] : v[i];
    b[i] = fabs(u[i]) <  fabs(v[i]) ? u[i] : v[i];
}

I.e., I need to select element-wise into a from u and v based on mask, and into b based on !mask, where mask = (fabs(u) >= fabs(v)) element-wise.

A: I had this exact same problem just the other day. The solution I came up with (using AVX only) was:
// take the absolute value of u and v
__m256 sign_bit = _mm256_set1_ps(-0.0f);
__m256 u_abs = _mm256_andnot_ps(sign_bit, u);
__m256 v_abs = _mm256_andnot_ps(sign_bit, v);
// get a mask indicating the indices for which abs(u[i]) >= abs(v[i])
__m256 u_ge_v = _mm256_cmp_ps(u_abs, v_abs, _CMP_GE_OS);
// use the mask to select the appropriate elements into a and b, flipping the argument
// order for b to invert the sense of the mask
__m256 a = _mm256_blendv_ps(u, v, u_ge_v);
__m256 b = _mm256_blendv_ps(v, u, u_ge_v);

The AVX512 equivalent would be:
// take the absolute value of u and v
__m512 sign_bit = _mm512_set1_ps(-0.0f);
__m512 u_abs = _mm512_andnot_ps(sign_bit, u);
__m512 v_abs = _mm512_andnot_ps(sign_bit, v);
// get a mask indicating the indices for which abs(u[i]) >= abs(v[i])
__mmask16 u_ge_v = _mm512_cmp_ps_mask(u_abs, v_abs, _CMP_GE_OS);
// use the mask to select the appropriate elements into a and b, flipping the argument
// order for b to invert the sense of the mask
__m512 a = _mm512_mask_blend_ps(u_ge_v, u, v);
__m512 b = _mm512_mask_blend_ps(u_ge_v, v, u);

As Peter Cordes suggested in the comments above, there are other approaches as well like taking the absolute value followed by a min/max and then reinserting the sign bit, but I couldn't find anything that was shorter/lower latency than this sequence of instructions.

Actually, there is another approach using AVX512DQ's VRANGEPS via the _mm512_range_ps() intrinsic. Intel's intrinsic guide describes it as follows:

Calculate the max, min, absolute max, or absolute min (depending on control in imm8) for packed single-precision (32-bit) floating-point elements in a and b, and store the results in dst. imm8[1:0] specifies the operation control: 00 = min, 01 = max, 10 = absolute max, 11 = absolute min. imm8[3:2] specifies the sign control: 00 = sign from a, 01 = sign from compare result, 10 = clear sign bit, 11 = set sign bit.

Note that there appears to be a typo in the above; actually imm8[3:2] == 10 is "absolute min" and imm8[3:2] == 11 is "absolute max" if you look at the details of the per-element operation:
CASE opCtl[1:0] OF
0: tmp[31:0] := (src1[31:0] <= src2[31:0]) ? src1[31:0] : src2[31:0]
1: tmp[31:0] := (src1[31:0] <= src2[31:0]) ? src2[31:0] : src1[31:0]
2: tmp[31:0] := (ABS(src1[31:0]) <= ABS(src2[31:0])) ? src1[31:0] : src2[31:0]
3: tmp[31:0] := (ABS(src1[31:0]) <= ABS(src2[31:0])) ? src2[31:0] : src1[31:0]
ESAC

CASE signSelCtl[1:0] OF
0: dst[31:0] := (src1[31] << 31) OR (tmp[30:0])
1: dst[31:0] := tmp[63:0]
2: dst[31:0] := (0 << 31) OR (tmp[30:0])
3: dst[31:0] := (1 << 31) OR (tmp[30:0])
ESAC

RETURN dst

So you can get the same result with just two instructions:
auto a = _mm512_range_ps(v, u, 0x7);  // 0b0111 = sign from compare result, absolute max
auto b = _mm512_range_ps(v, u, 0x6);  // 0b0110 = sign from compare result, absolute min

The argument order (v, u) is a bit unintuitive, but it's needed in order to get the same behavior that you described in the OP in the event that the elements have equal absolute value (namely, that the value from u is passed through to a, and v goes to b).
On Skylake and Ice Lake Xeon platforms (probably any of the Xeons that have dual FMA units, probably?), VRANGEPS has throughput 2, so the two checks can issue and execute simultaneously, with latency of 4 cycles. This is only a modest latency improvement on the original approach, but the throughput is better and it requires fewer instructions/uops/instruction cache space.

A: clang does a pretty reasonable job of auto-vectorizing it with -ffast-math and the necessary __restrict qualifiers:  https://godbolt.org/z/NMvN1u.  and both inputs to ABS them, compare once, vblendvps twice on the original inputs with the same mask but the other sources in the opposite order to get min and max.
That's pretty much what I was thinking before checking what compilers did, and looking at their output to firm up the details I hadn't thought through yet.  I don't see anything more clever than that.  I don't think we can avoid abs()ing both a and b separately; there's no cmpps compare predicate that compares magnitudes and ignores the sign bit.
// untested: I *might* have reversed min/max, but I think this is right.
#include <immintrin.h>
// returns min_abs
__m256 minmax_abs(__m256 u, __m256 v,  __m256 *max_result) {
    const __m256 signbits = _mm256_set1_ps(-0.0f);
    __m256 abs_u = _mm256_andnot_ps(signbits, u);
    __m256 abs_v = _mm256_andnot_ps(signbits, v);  // strip the sign bit

    __m256 maxabs_is_v = _mm256_cmp_ps(abs_u, abs_v, _CMP_LT_OS);  // u < v

    *max_result = _mm256_blendv_ps(v, u, maxabs_is_v);
    return        _mm256_blendv_ps(u, v, maxabs_is_v);
}

You'd do the same thing with AVX512 except you compare into a mask instead of another vector.
// returns min_abs
__m512 minmax_abs512(__m512 u, __m512 v,  __m512 *max_result) {
    const __m512 absmask = _mm512_castsi512_ps(_mm512_set1_epi32(0x7fffffff));
    __m512 abs_u = _mm512_and_ps(absmask, u);
    __m512 abs_v = _mm512_and_ps(absmask, v);  // strip the sign bit

    __mmask16 maxabs_is_v = _mm512_cmp_ps_mask(abs_u, abs_v, _CMP_LT_OS);  // u < v

    *max_result = _mm512_mask_blend_ps(maxabs_is_v, v, u);
    return        _mm512_mask_blend_ps(maxabs_is_v, u, v);
}

Clang compiles the return statement in an interesting way (Godbolt):
.LCPI2_0:
    .long   2147483647              # 0x7fffffff
minmax_abs512(float __vector(16), float __vector(16), float __vector(16)*):           # @minmax_abs512(float __vector(16), float __vector(16), float __vector(16)*)
    vbroadcastss    zmm2, dword ptr [rip + .LCPI2_0]
    vandps  zmm3, zmm0, zmm2
    vandps  zmm2, zmm1, zmm2
    vcmpltps        k1, zmm3, zmm2
    vblendmps       zmm2 {k1}, zmm1, zmm0
    vmovaps zmmword ptr [rdi], zmm2   ## store the blend result
    vmovaps zmm0 {k1}, zmm1           ## interesting choice: blend merge-masking
    ret

Instead of using another vblendmps, clang notices that zmm0 already has one of the blend inputs, and uses merge-masking with a regular vector vmovaps.  This has zero advantage of Skylake-AVX512 for 512-bit vblendmps (both single-uop instructions for port 0 or 5), but if Agner Fog's instruction tables are right, vblendmps x/y/zmm only ever runs on port 0 or 5, but a masked 256-bit or 128-bit vmovaps x/ymm{k}, x/ymm can run on any of p0/p1/p5.
Both are single-uop / single-cycle latency, unlike AVX2 vblendvps based on a mask vector which is 2 uops.  (So AVX512 is an advantage even for 256-bit vectors).  Unfortunately, none of gcc, clang, or ICC turn the _mm256_cmp_ps into _mm256_cmp_ps_mask and optimize the AVX2 intrinsics to AVX512 instructions when compiling with -march=skylake-avx512.)
s/512/256/ to make a version of minmax_abs512 that uses AVX512 for 256-bit vectors.

Gcc goes even further, and does the questionable "optimization" of
    vmovaps zmm2, zmm1        # tmp118, v
    vmovaps zmm2{k1}, zmm0    # tmp118, tmp114, tmp118, u

instead of using one blend instruction.  (I keep thinking I'm seeing a store followed by a masked store, but no, neither compiler is blending that way).


Workers' Compensation is an employer paid benefit that provides compensation and medical benefits if you are injured or become ill due to a work-related condition. Under Workers' Compensation law, you will receive help if you are injured. It is our intention to see that you receive prompt, competent medical attention and return to work as quickly as possible.
If you are injured on the job, it is the University's policy to offer you modified work (sometime called light duty) while you are recovering from a work-related illness or injury. Such positions will be temporary in nature and will be monitored by your immediate supervisor.


We are working in Textile, supplyer to E.U Countries particularly Greece and German. We are mainly importing 100% cotton fabrics, terry towels, bed sheets sets, garments, undergarments, Shirts.
Greece Directory helps you search for accurate and reliable information in the following Countries.


text:
Q: Magento save order total in checkout page We have a system like gift cards balance usage in the checkout page.While customers on the checkout page customers can use their Gift card balance amount to buy products.
So i have added a a tab before payment tab in which customers have option to enter the amount o use from their gift card balance amount so when they say proceed am getting the entered value in custom Onepage controller and pass it to Onepage.php(Model class) to reduce the total amount.
This is what am doing on the model class.
public function saveCustomDiscount($discount=0)
{
    $this->getQuote()->setGrandTotal($this->getQuote()->getGrandTotal() - $discount);
    $this->getQuote()->setBaseGrandTotal($this->getQuote()->getBaseGrandTotal() - $discount);
    $this->getQuote()->save();
    //$this->getQuote()->collectTotals()->save();
    $order = $this->getQuote()->getData();
    Zend_Debug::dump($order);
    return array();
}

And am calling this function from the controller Action function were am getting the user input.
Here what ever am passing as discount the total amount and base total is not reducing. Its same as original.
Note :
 Before custom order save.
array(51) {
   ---------------
  ["grand_total"] => string(8) "243.7200"
  ["base_grand_total"] => string(8) "243.7200"
  -----------------
}

After custom order save.
array(51) {
   ---------------
   ["grand_total"] => float(223.72)
   ["base_grand_total"] => float(223.72)
  -----------------
}

The data type is changed from string to float when am doing debug. What i have to do, to reduce the order total from the custom discount. Please help me.
Thanks

A: Did you set the discountCode?
i.e.  $quote->setCouponCode(strlen($couponCode) ? $couponCode : '')
                 ->collectTotals()
                 ->save();

Later on, in the Quote, did you apply the discount?
i.e. $order->setDiscountAmount(Mage::getSingleton('checkout/session')->getQuote()->getSubtotalWithDiscount() - Mage::getSingleton('checkout/session')->getQuote()->getSubtotal());
            $order->setShippingAmount(Mage::getSingleton('checkout/session')->getQuote()->getShippingAddress()->getShippingAmount());
            $order->setSubtotalWithDiscount(Mage::getSingleton('checkout/session')->getQuote()->getSubtotalWithDiscount());
            $order->setGrandTotal(Mage::getSingleton('checkout/session')->getQuote()->getGrandTotal());

etc.
EDIT:
I've worked with a team to do a Wordpress / Magento hybrid and it's definitely left some learning lessons on me. Magento was used as a purely transactional engine and we had to do this entire checkout process by hand (including creating a custom WP plugin to handle each step in the cart).
Log this out at the end so we can see what you have (possibly put this in your edited question):
Mage::log("checkoutprocess: getSubtotal: " . $order->getSubtotal());
            Mage::log("checkoutprocess: getTaxAmount: " . $order->getTaxAmount());
            Mage::log("checkoutprocess: getDiscountAmount: " . $order->getDiscountAmount());
            Mage::log("checkoutprocess: getShippingAmount: " . $order->getShippingAmount());
            Mage::log("checkoutprocess: getGrandTotal: " . $order->getGrandTotal());

            Mage::log("checkoutprocess: getBaseSubtotal: " . $order->getBaseSubtotal());
            Mage::log("checkoutprocess: getBaseDiscountAmount: " . $order->getBaseDiscountAmount());
            Mage::log("checkoutprocess: getSubtotalWithDiscount: " . $order->getSubtotalWithDiscount());
            Mage::log("checkoutprocess: getBaseGrandTotal: " . $order->getBaseGrandTotal());


text:
Chinese Parents File Milk Lawsuit
Couple's Claim
Comes as Officials
Oppose Litigation
By Sky Canaves and Juliet Ye
Oct. 1, 2008 12:01 am ET
HONG KONG -- The parents of a 1-year-old boy allegedly sickened by tainted milk powder filed a lawsuit against the manufacturer, according to state media, in what appears to be the first challenge to official efforts to keep the scandal out of China's courts.
China's official media and the family's lawyers said the suit, filed in a court in China's central Henan province, was the first filed in relation to illness caused by milk tainted with the industrial chemical melamine. The suit was filed last week against dairy company Shijiazhuang Sanlu Group Co., but has yet to be accepted by the court. As is common in China, the filing isn't publicly available.


Happy Birthday, sir!! Hope it is a good one!
Ha! Thanks, man. I'm 31, look 16 and feel...drunk at the moment. This growing up thing blows. Do I have to start using phrases like "Hey, that's the bees knees!" and "Damn kids!" now? Is dinner at 4:30? Jim? Kapp?
Wait...it's 10:00. Way past our bedtimes. I need another drink of Grown Up Juice.
dead_poet wrote: Is dinner at 4:30? Jim? Kapp?
Good people to get the old man advice from. But worry not, I'm 33, and I have not done any of those old people things that Jim and Kapp do.


First a Plate, Then a State?
D.C. Shadow Representative Mike Panetta thinks it's time for the statehood movement to step up to the plate.
Panetta wants the District to replace the "Taxation Without Representation" message on its license plates with the phrase "The 51st State." He says the current plates are "about 10 years old at this point, and I think they've lost their effectiveness. More importantly, they are out of line with what we really deserve -- full statehood."
DCist's Martin Austermuhle writes that those plates were first issued in 2000, and more than a million had been distributed by 2006. President Bill Clinton, a lukewarm D.C. rights supporter, had the plates on his official limo, but President George W. Bush had them removed in 2001.
Many representation advocates had hoped President Barack Obama would restore the plates, but he has yet to do so. WTOP's Mark Plotkin took Obama to task for this failure, and others on D.C. issues, in an April Washington Post op-ed.
Panetta and other advocates for D.C. statehood or representation have long recognized that symbolic measures are important, especially since the city draws tens of thousands of visitors each year who are unaware of the District's plight. A poll taken in 2005 showed that 78 percent of Americans believed D.C. residents had full representation in Congress. When told they did not, 82 percent said that they should.
In other Pseudostate of Columbia news, D.C. is getting its very own "state fair." The event will be held in tandem with Columbia Heights Day at Tubman Elementary School Aug. 28. Its creators say that while D.C. "is home to gardeners, cooks, bakers, photographers, [and] artists," but there is no "state fair to celebrate the home-grown talents of the District." They hope to change that.
We Love D.C. says the inaugural D.C. State Fair's photography contest is already underway. You can submit photos online that show "your D.C. spirit and D.C.'s cultural independence."


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Global Mineral Supplements Market size is projected to be valued $22 Billion by 2024; with a CAGR of 8.8% from 2017 to 2025. Mineral supplements especially help people of all ages to lead a healthy life, preventing nutritional deficiency disorders. The demand for mineral supplements or multivitamin is increasing, as individuals are taking these supplements to maintain good health. Moreover, manufacturers are also including certain herbal ingredients along with multivitamins for special purposes to enhance performance, energy and promote immunity.
Growing geriatric population, rise in consumer awareness, increasing self-directed consumers, growing number of maternal mortality rate in underdeveloped economies, and advancements in dietary supplement products are the major factors driving the growth of the global mineral supplements market. However, lack of dosage awareness, and proper regulatory bodies would hamper the market growth. Furthermore, untapped markets, growth in e-commerce, and entry for private labels would create growth opportunities in coming years.
The global mineral supplements market is segmented on the basis of application, end user, and geography. Application is bifurcated as food & beverages, pharmaceuticals, and other applications. While end-user segment includes adult women, adult men, senior citizen, and other end-users.
By geography the market is segmented into North America, Europe, Asia-Pacific and Rest of the World (RoW). The U.S., Canada, and Mexico are covered under North America wherein Europe covers UK, Germany, France, Italy, and Rest of Europe. Asia-Pacific covers China, India, Japan, and Rest of Asia-Pacific. Rest of the world (RoW) covers South America, Middle East and Africa.
The major players operating in this market include Pharmavite LLC, Koninklijke DSM N.V., Nu Skin Enterprises, Herbalife, Ltd., Glanbia Plc., Atrium Innovations Inc., Omega Protein Corporation, Bio-Botanica Inc., BioPharma, Inc., and BASF SE, among others.