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The Gentleman tells us, there are no checks in this plan. What has become of his enthusiastic eulogium on the American spirit? We should find a check and controul when oppressed, from that source. In this country, there is no exclusive personal stock of interest. The interest of the community is blended and inseparably connected with that of the individual.--When he promotes his own, he promotes that of the community. When we consult the common good, we consult our own. When he desires such checks as these, he will find them abundantly here. They are the best checks. What has become of his eulogium on the Virginia Constitution? Do the checks in this plan appear less excellent than those of the Constitution of Virginia? If the checks in the Constitution be compared to the checks in the Virginian Constitution, he will find the best security in the former.
The temple of liberty was complete, said he, when the people of England said to their King, that he was their servant. What are we to learn from this? Shall we embrace such a system as that? Is not liberty secure with us, where the people hold all powers in their own hands, and delegate them cautiously, for short periods, to their servants, who are accountable for the smallest mal-administration? Where is the nation that can boast greater security than we do? We want only a system like the paper before you, to strengthen and perpetuate this security.
The Papers of John Marshall. Edited by Herbert A. Johnson et al. Chapel Hill: University of North Carolina Press, in association with the Institute of Early American History and Culture, Williamsburg, Virginia, 1974--.


The average everyday Italian diet is very vegetarian-friendly, and the reason for this is the country's cultural heritage.  The variety of grains, legumes, olive oil, organic eggs, vegetable sauces, hearty soups, condiments, abundant dairy, and heaps of fresh produce grace Italian tables at every meal.  Chef Michael Cummings will prepare some of his top vegetable based spring time dishes.


text:
Facebook App Center Launches With Over 600 Facebook-Connected Apps
by Aldrin Calimlim
A screenshot of the navigation panel of the Facebook iOS app that made the rounds earlier today all but confirmed its imminent arrival. But now, Facebook has announced that its App Center has officially launched. App Center functions as a one-stop shop for apps, both Web-based and mobile, that connect with Facebook one way or another. On your desktop, you can go to facebook.com/appcenter to visit App Center, which Facebook asserts is not a store. Offering free apps for now, the new portal can also be viewed on Facebook's mobile site as well as on its main iOS client. Regardless, though, of whether you're browsing on your computer or on your iDevice, App Center is certain to redirect you to the App Store download page of any app you'd like to install. In the event that an app is already installed on your device, App Center simply launches it and authorizes it under your Facebook account. And following the already existing protocol, any app from the App Center lets you control your privacy and visibility. Facebook and, by extension, apps that feature Facebook integration, may not appeal to everyone. But for those who are keen on using the social network and its supported apps, App Center looks set to be a more streamlined and, of course, more social version of Apple's App Store. It's said to contain only high-quality apps that are actually used by people across the social network, including your friends. Initially housing over 600 apps, Facebook App Center has begun rolling out today to users in the U.S. For the rest of the Facebook-connected world, it's expected to launch in the next few weeks. Will you be "shopping" in Facebook's brand new App Center?

redpajama_set_name: RedPajamaCommonCrawl


text:
Rebirth of the Supreme Celestial Being
Chapter 456 - Wind and Rain Are Coming
Chapter 456 – Wind and Rain Are Coming
"In those days, the Yin family constantly visited the capital and bluntly declared that the Royal Heavenly Capital would change rulers within a hundred years. They even claimed that this change was related to my Xuan clan in the East Lands, so it was understandable that both you and I regarded the prophecy about the Xuan clan's legitimate heir as a deliberate attempt to sabotage our families' relations." Xuan Wushe's eyes sank slightly, as if they contained a dark whirlpool whose depths couldn't be seen. He continued, "However, when I asked Kongsang privately whether this prediction was true, he frankly told me that the Yin family did not fake this prophecy. The Yin family's old ancestor had already fallen as a result of attempting this divination. It's clear that they paid a heavy price for this prediction."
Looking at Yan Zhonghua's dumbfounded expression, Xuan Wushe smiled lightly, as if in self-mockery. "Destiny is hard to disobey, so why should I waste so much effort to oppose heaven? At that time, I planned to drag the voodoo family into the water with me by taking the eldest daughter of the Ying family as my wife, thus producing a legitimate heir while taking this opportunity to turn the Ying family against the Yan family, but I didn't expect…"
He didn't expect for Lin Zhan to have such a fierce temperament. He would rather break than bend; he'd rather end up with nothing than accept a shoddy option. He would rather leave decisively, even if such an act pained him down to his very bones, rather than accept a compromise and serve a husband together with another person.
Contrary to expectations, Xuan Wushe had the wrong idea.
Lin Zhan was precisely such an unique person to begin with. His thoughts were quite different from the vast majority of people in the Nine Lands.
Yan Zhonghua smiled. "Lord Guangling may seem like he has a very good temper, but unexpectedly, he's just as resolute and unyielding as You Ming."
"Ever since we were cultivating in the mountains as youths, You Ming has brazenly displayed his personality. No one dares to provoke him. Anyone with eyes can see his burning intensity, so it's not hard to imagine what his actual personality is like. Lin Zhan, on the other hand, truly caught me off guard."
Yan Zhonghua nodded. He recalled You Ming's characteristic of taking the tiles off the roof after merely three days of not being hit, then thought about that Lord Guangling, who always had a smile on his face as he followed behind Xuan Wushe, and couldn't help sighing.
Yan Zhonghua collected his thoughts and asked, "Just now, you mentioned your eldest son. Could it be that he really appeared?"
"When Lin Zhan left the Nine Lands, he was already three months pregnant. Now, my eldest son is over 20 years old. His location just happens to be inside the sealed Five Continents. Furthermore, Yin Xinghan has already seen through his identity."
Yan Zhonghua's expression changed slightly. "Is the information reliable?"
"Naturally, it's true," answered Yin Xingli, who hadn't not opened his mouth for a long time. "Our spy in the Royal Heavenly Capital reported that Yin Xinghan sent Yan Zizhang a transmission talisman that can travel through worlds. In it, he mentioned the names Lin Xuanzhi and Yan Tianhen, and the spy also heard him bring up the West Phoenix Monarch Feng Jingyu. We don't know the specifics, but today, we received news that Yan Zizhang actually sent the leaders of the Black and White Crow Guards, two of the Heavenly Capital's Four Generals — Lord Chuibi and Yue Suhua — to the Five Continents to kill two people: Lin and Yan."
Yan Zhonghua's eyes became cold, and the surrounding temperature dropped. Frost even crept up some of the leaves near him.
"Over the years, Yan Zizhang has become more and more unbridled."
"Ever since you gave up your claim to the throne, Zizhang has increasingly believed himself to be infallible." Xuan Wushe said, "However, there is good news. Kongsang is currently in the Five Continents and has already made contact with my son. With his help, everything will be much smoother."
Yan Zhonghua inquired, "Lord Chuibi and Yue Suhua are difficult to handle. Do you plan to send someone to the Five Continents?"
Yin Xingli replied, "They sent a prophet there, so naturally, we will also send a prophet."
Yan Zhonghua nodded slightly. "In addition to you, there must also be someone who can fight."
Yin Xingli nodded with a smile. "In terms of strength, who in King Ye's Palace can compare with the Ruler of Snow and Frost, Yan Zhonghua?"
Yan Zhonghua paused, then spoke, "I once made a solemn oath to never leave the Nine Lands."
Not just him, but all the Nine Lands' rulers and kings wouldn't casually leave the Nine Lands.
"Then, what if I told you that You Ming is in the Five Continents right now and can't return to the Nine Lands because of the barrier?" Xuan Wushe asked nonchalantly.
Yan Zhonghua stared blankly for a moment before asking, "When will we leave?"
Yin Xingli's smile deepened. He shook the fan in his hand. "Sure enough, Demon Venerable You Ming's face is the biggest — even more important than your son."
"I haven't heard from You Ming for many years." Yan Zhonghua sighed softly. "I'm afraid that if I see my son, I won't be able to stop myself from breaking the seal for him. When that happens…it won't necessarily be good for him."
"Let them decide for themselves whether it's good or bad." Xuan Wushe's face also became more relaxed. "You haven't seen Ah Hen for so many years. When you see him again, I'm afraid that as his father, you'll have to give him some large gifts."
"I'm afraid he won't want it." Yan Zhonghua asked, "His name is Ah Hen?"
Xuan Wushe nodded. "Ah Zhan adopted him. When Su Mo sent him to Ah Zhan, he told him that the child's name was Yan Tianhen. This is probably a new name that You Ming gave him."
Yan Zhonghua chewed the name in his heart for a moment before saying, "It's a good name, much better than the previous one."
"However, there is one matter you should think through first. If the Five Continents' barrier is broken, then Ah Hen will definitely return to the Nine Lands with you. When that time comes, what kind of status do you want to give him, and how do you want him to think of himself?"
Yan Zhonghua showed a very simple smile and his face, which was as white as jade, seemed much more elegant in this instant. "Back then, I was caught off guard. Now, after 20 years of recuperation while maintaining my resolve for revenge, I'm afraid that Yan Zizhang will no longer be able to cover the sky with a single hand again."
Xuan Wushe thought, Since Yan Zhonghua said this, he must naturally be confident in his ability to protect Yan Tianhen, so I can safely report back to Ah Zhan on a mission successfully completed.
The two people looked at each other and understood the deep meaning in each other's eyes.
This was a very ordinary day. No major event seemed to have occurred in the Nine Lands. However, dark waves would always be brewing in the deep sea before the turbid and violent storm came.
Wind and rain are coming.
Lin Xuanzhi followed Yan Tianhen as he wandered around Heavenly Swamp Mysterious Land for two months.
During this period, Yan Tianhen sneaked into various dangerous places in Heavenly Swamp Mysterious Land under Xia Xiaochan's guidance and harvested plenty of spiritual plants, demonic beasts, and other treasures. He had a lot of fun all the way, and his mood was a lot better.
Now that he was half-Divine Devil, his features underwent some changes. His ears were slightly sharper than before, the color of his pupils were richer, and a circle of red light appeared around his pupils. His long hair became darker, and his figure also lengthened.
The biggest change was his aura.
In reality, Divine Devils were very easy to distinguish, especially when the Divine Devil didn't bother to hide their aura and deliberately indulged themselves by wantonly displaying their demonic Qi.
Yan Tianhen seemed like he deliberately wanted people to find out about his identity. He left demonic Qi wherever he went. This attracted the pursuit of many cultivators, who tried to track him down and kill him.
However, the current Yan Tianhen wasn't the slightest bit afraid of those cultivators. His cultivation had already advanced to Profound Realm and had even attracted divine lightning. He easily broke through the lightning tribulation, and while he was at it, he even refined a lot of medicinal pills that could invigorate Qi and protect the user. He chewed them nonstop, as if he was eating candy, which made Xia Xiaochan tremble with fear. Xia Xiaochan was truly afraid that eventually, he would eat a pill, and it would be the straw that broke the camel's back; the last snowflake whose weight broke the tree branch, and that it would kill Yan Tianhen.
Just like this, they carelessly walked and rested, refining pills and fighting. Two months passed in this manner.
Lin Xuanzhi kept a certain distance from Yan Tianhen. Not only did he have to ensure that Yan Tianhen was within his line of sight, but he also couldn't get too close, or else Yan Tianhen would flip out. This truly required a lot of skill.
No matter how he looked, Feng Jingyu couldn't understand the two people's current situation, so he simply slept on Ling Chigu's head everyday and watched the show, or looked for some spiritual plants that could hasten his recovery to kill time.
"Do you know where the Great Demon-Sealing Array in Thousand Stars Island is located?" Yan Tianhen asked Xia Xiaochan, who was happily eating nibbling on fruit.
Recently, Xia Xiaochan had seen Yan Tianhen kill many people, so he was terrified as hell when facing Yan Tianhen. He hurriedly nodded repeatedly, looking like a chicken pecking at rice. "Of course I know. It will only take me three days to find it. However, I can feel that it is very dangerous inside that barrier, so I can't enter it easily."
Yan Tianhen curled his lips. "You only need to find it. You won't have to worry about the rest."
Xia Xiaochan asked, "Are you planning to leave now?"
"I can't stuff anything else into my storage bag anymore. There are quite a few strange demonic beasts in this mysterious land. Unfortunately, I'm too lazy to disturb them. I might as well leave and make preparations for going to the Demon-Sealing Array later."
Xia Xiaochan subconsciously looked towards Lin Xuanzhi.
Lin Xuanzhi sat cross-legged on a grassy field, his facial features gentle and clear. His clothes were as white as snow and his long hair was like ink, forming a scene right out of a painting.
Xia Xiaochan whispered, "Why don't you have him carry it for you? He has a magic treasure similar to a separate space on him, including every manifestation of nature. I'm afraid it won't be a problem even if he placed the entire Heavenly Swamp Mysterious Land inside it."
Upon hearing Xia Xiaochan's words, killing intent flashed through Yan Tianhen's eyes.
Xia Xiaochan shrank his neck back and cut in, "This is an innate feature unique to my species; I didn't deliberately investigate him. Moreover, I didn't tell anyone. Don't overthink this."
Yan Tianhen felt that it was funny and raised his hand as he flicked Xia Xiaochan's forehead with his finger. "How can you be such a scaredy-cat? At the very least, you're the Golden Cicada King."
Xia Xiaochan replied righteously, "Who let me only be able to find treasures but not fight? I'm a refined Golden Cicada King."
Yan Tianhen shook his head. "I don't see it."
Xia Xiaochan, "…"
Ever since Yan Tianhen recovered his half-Divine Devil bloodline, Ah Bai and Hu Po hadn't dared to casually approach him anymore. This was also an innate instinct of their species.
Yan Tianhen was a little sad at first, but later, he stopped paying attention to these two tiger cubs. He realized that a Divine Devil's aura repelled spiritual beasts on a fundamental level. These two tiger cubs must have felt that his aura was both unfamiliar and terrifying, so they gathered around Lin Xuanzhi's side every day, avoiding dangers while sticking close to benefits.
This was his identity. What did he need to hide?
Even if he restrained his demonic Qi and temporarily fooled Ah Bai and Hu Po, the day when he shows his true colors will always come, so won't the result still be the same when that happens?
It was better not to hide it from the beginning.
After Yan Tianhen had enough rest, he stood up. "Let's go now."
Xia Xiaochan looked at him, opening and closing his mouth several times like he wanted to say something. Finally, he said, "After going out, you have to be careful. You've killed many sects' disciples in this mysterious land. Those who escaped outside must have a grudge against you. They're probably waiting in ambush outside."
Sarah: one mention of You Ming and YZH is willing to break the rules and find him in FC 😂 looks like both of Ah Hen's parents will soon be in the same place *camps for the drama*
Ea: excited for YZH and You Ming's meeting
YZH: I can't leave the Nine Lands, I promised.
XWS: What if I told you You Ming is there?
YZH: …So when do we leave? Now?


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Innovation is a big corporate buzzword, and it's one of the hottest topics on this blog. That's because it's one of the biggest mysteries to business leaders. A new study from Accenture, "Why Low Risk Innovation Is Costly," revealed that fewer than one in five chief executives believes their company's strategic investments in innovation are paying off. Because of the high percentage of failure, nearly half of the executives surveyed said their companies were less likely to risk implementing breakthrough ideas.While some companies are wildly successful with their innovation attempts, we also know that at least 70 percent of change efforts fail and, by definition, innovation ranks as the biggest change effort out there. The question is: What are these unsuccessful CEOs and their people doing wrong with their strategic investment in innovation?. Innovation only happens in the right environment, one where everyone is not only allowed to innovate, but they are actively encouraged to speak up and bring new ideas to the table. This may sound like common sense, but it is far from common practice. it's the struggle of every enterprise organization. Leaders know they need to uncover new ideas and push promising projects forward, yet few know how. Indeed, most enterprises are built to resist the changes they need to grow. The first, and possibly most important, role of a chief innovator is to create a reliable, repeatable process to generate new ideas, test their viability and then either kill or invest in them.
Most corporations don't suffer from a lack of ideas – most just simply don't know how to choose the right projects to pursue. It's an innovator's guide to build a system capable of shepherding ideas worth pursuing and killing the ones that won't cut it. It is undeniable that the world around us is changing. Technology and software have transformed large parts of business; and continues to do so in more and more dramatic ways. Corporate leadership would have to be in a special kind of denial to not see how these changes in technology and economics are impacting their businesses. There is no longer the option to keep our heads in the sand. Companies have to respond. What is more challenging now is deciding exactly what companies should do.


text:
\section{Introduction}
\label{sec-intro}
Biomedical  natural language processing (NLP) that goes beyond simple text processing is increasingly becoming indispensable to
derive value and insights
from vast amounts of unstructured data generated in the form of scientific
articles~\cite{luo2016bridging,cameron2014graph,kavuluru2012up},
clinical narratives~\cite{ctakes,uzuner20112010,kavuluru2015empirical} and health related
social media posts~\cite{sarker2015utilizing,kavuluru2016toward,paul2015social}. Specialized components including named
entity recognition (NER) programs, syntactic parsers, and relation
extractors form the backbone of many high level information extraction and knowledge discovery applications. For most components
in an NLP application pipeline, there is a clear snowball effect of errors in a component in the beginning of the pipeline leading to
more errors in other subsequent components and the final results of the full application.

Resolving lexical mentions in text to
correct named entities from a fixed terminology is essential in building an effective NER program, which is typically the next
step after sentence segmentation and part-of-speech tagging in most NLP pipelines. All other downstream components and the
full application suffer if lexical ambiguities are not correctly resolved. Recent research also shows that resolving ambiguities
provides performance gains in information retrieval and search system design~\cite{zhong2012word}. In this effort, we employ
knowledge-based methods, neural concept and word
vectors learned through unsupervised deep learning approaches, and a straightforward nearest neighbor approach to achieve new
state-of-the-art results over a public gold standard dataset~\cite{jimeno2011exploiting}
in biomedical word sense disambiguation~(WSD).

For this effort, WSD specifically deals with identifying the correct sense of a term, among a set of given candidate senses for that term,
when it is presented in a brief narrative along with its context (surrounding text). For example, consider the ambiguous term `discharge'.
It has two unique senses in biomedicine -- (S1).~The first is the administrative process of releasing a patient from a healthcare facility following an in-patient stay for
some treatment or procedure. (S2).~The second sense pertains to bodily secretions of certain fluids from an orifice or wound.  In our task
the ambiguous term `discharge' is specified along with the sense set \{S1, S2\} and an example context -- ``Low risk patients identified using CADILLAC risk score with STEMI treated
successfully with primary PCI have a low adverse event rate on the third day or later of hospitalization suggesting that an earlier \textbf{discharge} is safe
in properly selected patients.'' Our goal is to identify the correct sense S1 for this specific occurrence of `discharge'.

Next, we outline the organization of the rest of the paper. In Section~\ref{sec-back}, we discuss earlier efforts in biomedical WSD and recent approaches that incorporate word embeddings. Our main methods that employ concept/word embeddings including the nearest neighbor approach are detailed in Section~\ref{sec-meth}. We then present
our main results and takeaways in Section~\ref{sec-res}. Finally, in Section~\ref{sec-conclude}, we conclude with
some future research directions involving recurrent neural architectures for  biomedical WSD.

\section{Background and Related Work}
\label{sec-back}
For a thorough overview of approaches to WSD, we direct the readers
to the survey by Navigli~\cite{navigli2009word}, which suggests mainly three categories -- supervised, knowledge-based,
and unsupervised approaches. Supervised approaches for WSD~\cite{zhong2010makes,stevenson2008disambiguation} use a labeled dataset
along with interesting lexical/syntactic features derived from the context around the term to build machine learned models that predict the correct sense
in unseen test contexts.  Knowledge-based approaches~\cite{jimeno2011exploiting,mcinnes2013evaluating} do not use any corpus but solely rely on thesauri
or sense inventories such as
WordNet and the Unified Medical Language System (UMLS) that contain brief definitions of different senses and corresponding synonyms.
Unsupervised approaches may employ topic modeling~\cite{kim2015link} based methods to disambiguate when the senses are known ahead of time.
Some unsupervised approaches~\cite{wang2015sense} are often referred to as performing word sense \textit{discrimination} or
\textit{induction} as opposed to disambiguation
because they employ clustering approaches where different clusters are expected to represent the different senses, which are not known a priori.

\subsection{WSD in Biomedicine}
\label{sec-wsdbio}
In biomedicine, knowledge-based word sense disambiguation efforts mostly relied on the UMLS knowledge base~\cite{umlsweb}, which
contains over 3.4 million unique concepts expertly sourced from $\approx$ 200 different terminologies in biomedicine and allied fields. The UMLS is maintained
by the US National Library of Medicine (NLM) and is updated every year to reflect new concepts and other changes. For each concept in the UMLS, there is usually
a brief definition and sometimes additional relations (both hierarchical and associative)  connecting it with other concepts. Each concept has a
unique ID called the concept unique identifier (CUI), an alphanumeric string that starts with a `C'.
For example, the sense S1 (administrative process) for `discharge'
in Section~\ref{sec-intro} is represented by CUI C0030685 and sense S2 (body substance) is represented by the CUI C0012621.
S1 has a short definition ``The administrative process of discharging the patient, alive or dead, from hospitals or other health facilities''.
For S2 we notice the definition -- ``In medicine, a fluid that comes out of the body. Discharge can be normal or a sign of disease.''
In the MSH WSD dataset that we use in this effort, the candidate senses for each ambitious word are represented in the form of these unique CUIs.
The task is to identify the correct CUI given a particular context (few sentences) containing an ambiguous term.
For the rest of the manuscript, we use the three terms \textit{CUI}, \textit{concept}, and \textit{sense} synonymously as they refer to the same notion.

Schuemie et al.~\cite{schuemie2005word} present a nice survey of approaches and efforts in biomedical WSD until 2005 including the
well-known NLM WSD dataset~\cite{weeber2001developing}, which has 50 ambiguous terms with 5000 test instances. Disambiguation efforts were also
focused on a small set of 10--15 ambiguous abbreviations~\cite{pakhomov2005abbreviation,xu2012combining} using combinations of supervised and
unsupervised approaches. More recent approaches~\cite{liu2004multi,savova2008word} used supervised models including Naive Bayes, SVMs, logistic
regressors, decision lists with a variety of features using both subsets of the NLM WSD dataset and other smaller datasets.
Berster et al.~\cite{berster2012hyperdimensional} encoded senses, contexts, and ambiguous terms using random indexing
and conducted supervised ten-fold cross validation experiments on the NLM WSD dataset using the binary splatter code method.
McInnes and Pedersen~\cite{mcinnes2013evaluating}
used the network structure of the UMLS (specifically the hypernymic trees) and concept definitions to devise concept relatedness measures which are
in turn used for WSD for the MSH WSD dataset. Chasin et al.~\cite{chasin2014word} demonstrated the
application of topic modeling for a clinical WSD dataset of 50 ambiguous terms curated from the Mayo Clinic~\cite{savova2008word}. Recently, Wang et al.~\cite{wang2016clinical} used an active learning strategy
to involve domain experts in an interactive supervised machine learning framework for biomedical WSD.
Among all the datasets available, the MSH WSD that we use in our current effort is the largest
publicly available dataset~\cite{jimeno2011exploiting} for biomedical WSD (more in Section~\ref{sec-meth})
and also has the least skewed distribution (the average percentage of majority sense is 54\%~\cite{wang2016clinical}).

In a recent approach Jimeno-Yepes and Berlanga~\cite{yepes2015knowledge} used a hybrid approach that combined a knowledge-based component that exploits the UMLS definitions and synonyms
for different concepts
with unlabeled biomedical narratives (from Medline/PubMed)  to derive word-concept probability estimates $P(w|c)$ for any word $w$ and UMLS concept $c$.
They exploited the Naive Bayes formulation and selected the  correct sense as the CUI $c$ that
maximizes $P(T|c) = \prod_{i} P(w_i|c)$,
where $w_i$ is the $i$-th word in the test context $T$ that contains the ambiguous term.
With this approach they achieved an accuracy of 89.1\% on the MSH WSD dataset~\cite{jimeno2011exploiting}.
This result corresponds
to the best performance thus far on the MSH WSD dataset without using supervised models.  Given
we employ this method as a component of our best model, for completeness, we provide its high level summary
in the Appendix.

In this effort, we use recent advances in neural word embeddings to generate new state-of-the-art results on the MSH WSD dataset achieved
without supervised cross validation experiments.
Our methods can be classified as weakly supervised given we employ the well-known biomedical concept mapping tool MetaMap~\cite{metamap}
to generate concept vectors and employ them in combination with the knowledge-based method from Jimeno-Yepes and Berlanga~\cite{yepes2015knowledge}.

\subsection{Neural Embeddings for WSD}
Neural word representations have been shown to capture both semantic and syntactic information and a few recent approaches
learn word vectors~\cite{bengio2003neural, collobert2008unified, mikolov2013distributed} (as elements of $\mathbb{R}^d$, where $d$ is the dimension)
in an unsupervised fashion from textual corpora. These dense word vectors obviate the sparsity issues inherent to the so called \textit{one-hot} representations
of words\footnote{One-hot representations lead to very large dimensionality (typically the size of the vocabulary) resulting in further issues in similarity computations, a phenomenon
often termed as the \textit{curse of dimensionality} \cite[Chapter 1.4]{bishop2006pattern}}.
Chen et al.~\cite{chen2014unified} adapted neural word
embeddings to compute different sense embeddings (of the same word) and showed competitive performance
on the SemEval 2007 WSD dataset~\cite{navigli2007semeval}. Disambiguation is achieved by picking the sense that
maximizes the cosine similarity of the corresponding sense vector with the context vector for an ambiguous term. Recently, Iacobacci et al.~\cite{iacobacci2016embeddings}
evaluated and demonstrated the superiority of neural word embeddings as features in supervised WSD models on the same SemEval dataset.

In a very recent effort Pakhomov et al.~\cite{pakhomov2016corpus} use word embeddings (without corpus enhanced concept
embeddings) for the MSH WSD dataset but only report 77\% accuracy although the central aim of their paper is not limited to WSD.
Their approach relies on vectors of words that co-occur with words in the definitions of different senses (CUIs) in the UMLS.
In our effort, we use a similar framework as Chen et al.~\cite{chen2014unified}  to directly learn sense vectors using
a pure distributional semantics framework that does not rely on word vectors. Additionally, we
employ complementary evidences beyond cosine similarity to achieve further improvements that rival performances typically reported using supervised approaches.

\section{Our Approach}
\label{sec-meth}
There are 203 ambiguous terms in the MSH WSD dataset~\cite{jimeno2011exploiting} with a total of 424 unique
CUIs (from the UMLS), each of which is a unique sense. Thus, on average, the dataset
has $424/203 = 2.08$ senses. There are a total of 38,495 test instances of contexts (a few sentences) each with one of the 203
ambiguous
terms along with the correct sense (CUI). Besides being the largest biomedical WSD dataset, it also
includes a richer set of ambiguities including 106 ambiguous abbreviations, 88 ambiguous noun phrases, and 9 that
are combinations of both. Due to these features, the NLM encourages researchers to use this
dataset over their older dataset (please see \url{https://wsd.nlm.nih.gov}).
Our goal is to directly test on this dataset by employing weakly supervised approaches.
For this, we learn vector representations of words and CUIs using well-known approaches that apply deep neural
networks to NLP tasks.

\subsection{Neural Word and Concept Embeddings}
\label{sec-embed}

We ran the well-known word2vec~\cite{mikolov2013distributed} word embedding
program (the skip-gram model) from Google on over 20 million biomedical citations (titles and abstracts)
from PubMed to obtain word vector representations with a
word window size of ten words and dimensionality $d=300$ with all other parameters set to the default settings.
To learn concept or CUI vectors of the same dimensionality, we curated a dataset of
five million randomly chosen citations (published between 1998 and 2014). For this subset of PubMed, we ran
MetaMap~\cite{metamap} with its WSD option turned on so we obtain unique CUIs for
potential ambiguous terms\footnote{MetaMap uses the UMLS knowledge base of concept
synonyms along with shallow linguistic parsing to map free text to UMLS CUIs. It also has a WSD option which is based on
concept profiles generated through words co-occurring with different
concepts in biomedical literature~\cite{yepes2012integration}.}. The text was passed through MetaMap two adjacent non-stop words at a time,
to capture as many CUIs as possible. Next, we treated these sequences of CUIs in each citation thus obtained through NER
as a semantic version of the free text corpus. We ran word2vec on this corpus of CUI texts,
just like how we ran it on free text articles with the same parameters. As a result we obtained 300 dimensional dense vectors for
each CUI, including all 424 CUIs corresponding to the 203 ambiguous terms in our test set.
This component of our methodology to derive dense concept vectors involves weak supervision because although
MetaMap with its WSD
option is in and of itself not a powerful solution (see Section~\ref{sec-res}), it nevertheless was useful to learn concept vectors that in turn
helped us achieve state-of-the-art results.
This deep neural network based distributional semantics approach to learning CUI vectors aids in modeling complementary aspects of similarity. This is because we use, as a component, the CUI definition based information via
the word-probability estimate based approach~\cite{yepes2015knowledge} outlined earlier.

\subsection{WSD with Word/Concept Embeddings and Knowledge-Based Approaches}
\label{sec-weak}
Our main idea is that besides comparing pairs of word vectors and concept vectors, we
can also compare a word vector with a concept vector given at a high level there is a direct connection between
words and concepts -- words are often lexical manifestations of high level concepts.
The fact that we simply replaced word sequences in free text with the corresponding concept sequences
to generate CUI vectors of the same dimensionality as the word vectors also makes it feasible to compare
word vectors and their compositions to concept vectors. As will see in Section~\ref{sec-res}, this intuition
appears to work as well as other state-of-the-art approaches~\cite{yepes2015knowledge}.

\setlength{\dbltextfloatsep}{8pt plus 1.0pt minus 2.0pt}
\begin{figure*}[ht]
\centering
\includegraphics[scale=0.17]{pipeline.jpg}
\caption{Architecture for WSD approaches from Sections~\ref{sec-embed} and ~\ref{sec-weak} \label{fig-arch}}
\end{figure*}

We establish some notation for the rest of the paper. In any WSD problem, a test instance corresponds to a three tuple
$(T, w, C(w))$ where $T$ is a context, typically a few sentences, that contains the ambiguous term $w$ and
$C(w)$ is the set of different senses that $w$ can assume depending upon the context $T$\footnote{In practice,
there might be cases where the context in $T$ is deemed insufficient even for human judges to pick the right sense.
However, for this paper we assess our performance based on MSH WSD dataset where each instance is assigned a unique sense.}.
Specifically, $C(w)$ in this effort is
the set of different CUIs that capture the different senses for $w$. Our WSD goal is
to construct a function $f(T, w, C(w))$ that maps $T$ to the
CUI $c \in C(w)$ that corresponds to the correct sense in which $w$ was used in $T$.
We have four approaches that apply the embeddings from Section~\ref{sec-embed} to our test set.
We specify them in terms of functions $f^?(T, w, C(w))$ where $?$ indicates symbols that identify the underlying method(s) used, made clear as follows.
\begin{enumerate}[\setlength{\itemsep}{6pt}
\setlength{\labelsep}{6pt}
 \setlength{\IEEElabelindent}{6pt}]
\item Our first approach uses vector cosine similarity with
\[f^c(T, w, C(w)) = \argmax_{c \in C(w)} \cos (\vec{T}_{avg}, \vec{c}), \]
where $\vec{T}_{avg}$ is the average of non-stop words' vectors in the context $T$
and $\vec{c}$ is the context vector for $c$.
This formulation is well-known given cosine similarity is a popular approach to measure semantic similarity of entities (words, concepts, \ldots) represented by the corresponding vectors.

\item Our second approach is based on vector projections with
\[f^p  = \argmax_{c \in C(w)}  \left[ \rho[ \cos (\vec{T}_{avg}, \vec{c})]  \cdot \frac{ \| \mathcal{P} (\vec{T}_{avg}, \vec{c}) \|} {\| \vec{c}\|}  \right], \]
where $\mathcal{P}(\vec{r}, \vec{s})$ refers to the projection of $\vec{r}$ on to $\vec{s}$, $\| \,\, \|$ is the Euclidean norm,
and $\rho$ is the sign function. Using straightforward manipulation based on vector projections in Euclidean spaces~\cite[Chapter 5]{elalg}, we have
\[ \| \mathcal{P} (\vec{T}_{avg}, \vec{c}) \| =  \frac{|\vec{T}_{avg} \bullet \vec{c}|} {\| \vec{c}\|}, \]
which is what we used in our implementation (with $\bullet$ denoting vector dot product).
 Although $f^c$ (approach one) accounts for the overall
directional similarity (thematic orientation) of the vectors, it does not account for the strength or magnitude of association,
an aspect that seems ignored in others' efforts we reviewed for this paper.  By considering the
vector projection of the context vector onto the CUI vector $\vec{c}$, in $f^{p}$ we also account for the magnitude of
the context vector's projection in relation to that of the CUI vector. The sign function $\rho$ is essentially to account for
situations when $90 < \theta \leq 180$, the angle between $\vec{T}_{avg}$ and $\vec{c}$.

\item Our third approach is based on the first two approaches where we set
\[f^{c,p} = \argmax_{c \in C(w)}  \left[ \cos (\vec{T}_{avg}, \vec{c}) \cdot \frac{ \| \mathcal{P} (\vec{T}_{avg}, \vec{c}) \|} {\| \vec{c}\|}  \right]. \]
We simply incorporate both evidences (magnitude and orientation of association) to compare different CUIs.

\item Our final approach uses a probabilistic model developed in an earlier effort by Jimeno-Yepes and Berlanga~\cite{yepes2015knowledge} (as outlined in Section~\ref{sec-wsdbio} and elaborated in the Appendix) that selects the $c$ that maximizes $P(T|c)$.  We involve this knowledge-based approach as a third scoring component and set
\begin{align*}
&f^{c,p,k} \\
&= \argmax_{c \in C(w)}  \left[ \cos (\vec{T}_{avg}, \vec{c}) \cdot \frac{ \| \mathcal{P} (\vec{T}_{avg}, \vec{c}) \|} {\| \vec{c}\|} + P(T|c) \right].
\end{align*}
Although there are different ways of combining evidences from multiple sources of predictive information, we rely on this straightforward combination as a form of unsupervised rank aggregation from two different sources.
\end{enumerate}

The methods discussed thus
far can be summarized using the schematic in Figure~\ref{fig-arch}.

\subsection{WSD with Weak Supervision}
\label{sec-dist}
From methods in Section~\ref{sec-weak}, we have multiple ways of disambiguating CUIs for any ambiguous term given a sample context.
We exploit them to build a  weakly supervised dataset for the 203 ambiguous terms in our test dataset.
For each sentence in an independent corpus of biomedical citations
that contains any ambiguous term from our dataset, we employ methods in Section~\ref{sec-weak}
to assign the predicted CUI. Thus we can create a weakly supervised dataset for each ambiguous term
with thousands of examples if we choose a large corpus. These examples can then be used to train traditional discriminative
models or nearest neighbor models. We emphasize here that we are proposing to label arbitrary sentences (not our test sentences)
in an external corpus based on our methods in Section~\ref{sec-weak}. Hence we still have our full MSH WSD dataset to finally
test the approach we propose here with other models in a fair way.

For the $k$ nearest neighbor ($k$-NN) model, let $\mathcal{D}^w \subseteq \mathcal{D}$ be the set of instances for the ambiguous term
$w$ in the weakly
supervised dataset $\mathcal{D}$. We rank instances $(D, w, c) \in \mathcal{D}^w $ for a given test instance $T$
based on $\cos (\vec{T}_{avg}, \vec{D}_{avg})$, where $c$ is the sense assigned to $D$ from $C(w)$ based on
methods in Section~\ref{sec-weak}. Let $R_k(\mathcal{D}^w)$ be the set of top $k$ instances in $\mathcal{D}^w$ when
ranked in descending order based on $\cos (\vec{T}_{avg}, \vec{D}_{avg})$.
Now the predicted sense for $T$ is chosen based on
\[ f^{k-NN} = \argmax_{c \in C(w)}  \left[ \sum_{(D, w, c) \in R_k(\mathcal{D}^w)} \cos (\vec{T}_{avg}, \vec{D}_{avg}) \right]. \]
The expression in the $\argmax$ boils down to summing up the similarities of the test context with those contexts in the training dataset that have the same assigned CUI $c$.
We subsequently pick the particular $c$ that maximizes that summation. Intuitively, our approach
aggregates evidence from training instances that are
semantically most similar to our test instance. The choice of $k$ also plays an important role in the performance of $k$-NN approaches as we
observe in the next section.

\section{Results and Discussion}
\label{sec-res}
\begin{figure*}[t]
\begin{center}
\begin{tikzpicture}[trim axis right, trim axis left, font=\footnotesize]
  \begin{axis}[legend pos=south east, width=1.41\columnwidth,
  height=0.5\textwidth,
    symbolic x coords={20, 50, 100, 200, 300, 500,  1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000},
    xtick=data,
    xlabel= Number of nearest neighbors $k$ , xlabel style={yshift=-0.5em},
        ylabel= Accuracy, ylabel style={yshift=-0.5em},
                   yticklabel style={/pgf/number format/fixed,
                         /pgf/number format/precision=2}]
    \addplot coordinates {
        (20, 90.38)
        (50, 91.7)
        (100,  92.07)
        (200, 92.68)
        (300, 92.87)
         (500, 93.07)
        (1000, 93.56)
        (1500,  93.77)
         (2000,  93.81)
          (2500,  93.98)
            (3000, 94.29)
        (3500,  94.34)
         (4000,  94.17)
          (4500,  93.9)
          (5000,  92.7)
    };

  \end{axis}
\end{tikzpicture}
\end{center}
\caption{Accuracy of the $k$-NN approach with varying $k$ \label{fig-top}}
\end{figure*}

Our results are shown in Table~\ref{tab-res} based on methods introduced in the previous section.
MetaMap does not perform as well
on this dataset (row 1) even with the WSD option achieving an
accuracy\footnote{Accuracy is the ratio of total number of correctly assigned senses to the total of number of occurrences of the 203 ambiguous terms in the MSH WSD dataset.
The usage of accuracy as the evaluation metric is inline with a few prior efforts on biomedical WSD~\cite{mcinnes2013evaluating,pakhomov2005abbreviation,liu2004multi,pakhomov2016corpus} and is
justified~\cite{chasin2014word} given the notions of precision and recall are equivalent to it in this scenario.}
 of 81.77\%. However, it may not be fair to compare MetaMap with our methods given it
does not try to particularly disambiguate our specific 203 terms, for each of which we are already given candidate concepts
that contain the correct sense.
In row 2 of the table, we show the performance achieved by Jimeno-Yepes and Berlanga
using word-concept probability estimates $P(w|c)$ derived from synonymous names of concepts in the UMLS Metathesaurus.
\setlength{\intextsep}{6pt plus 1.0pt minus 2.0pt}
\begin{table}[h!]
\renewcommand{\arraystretch}{1.4}
\centering
\caption{Performance on MSH WSD Dataset}
      \begin{tabular}{lc}
        \toprule
        Method & Accuracy \\ \midrule
        MetaMap with WSD & 81.77\% \\
        Jimeno-Yepes and Berlanga~\cite{yepes2015knowledge} & 89.10\% \\ \midrule
        Cosine similarity ($f^c$)  & 85.54\% \\
        Projection length proportion ($f^p$)   & 88.68\% \\
        Combining $f^c$ and $f^p$ ($f^{c,p}$) & 89.26\%  \\
        Combining $f^c$, $f^p$, and~\cite{yepes2015knowledge} ($f^{c,p,k}$) & 92.24\%  \\  \midrule
	 Convolutional neural networks & 87.78\%  \\
       $k$-NN with $k=3500$ ($f^{k-NN}$) & 94.34\%  \\
        \bottomrule
              \end{tabular}
\label{tab-res}
\end{table}

Rows 3--6 show performances of our methods that leverage neural word/concept embeddings from Section~\ref{sec-weak}.
The cosine similarity and projection approaches both score above 85\% but when used together, they achieve an accuracy of 89.26\%
which is slightly better than the current best result~\cite{yepes2015knowledge} achieved through unsupervised and knowledge-based approaches. Row 6 shows
an accuracy of 92.24\% achieved by our ensemble method that
combines our word/concept vector approach with the knowledge-based  method by Jimeno-Yepes and Berlanga~\cite{yepes2015knowledge}.
The  time
complexity of these methods is linear in terms of the number of words in the test context $T$ and the number of candidate senses $|C(w)|$,
considering the computation of $\vec{T}_{avg}$ and evaluation of the $\argmax$ expressions for each $c \in C(w)$.

We created a weakly supervised dataset as outlined in Section~\ref{sec-dist} with the same corpus of five million biomedical citations
used for training word and concept vectors (Section~\ref{sec-embed}). From this corpus,
we considered the so called utterances that represent clauses (from the input text) that MetaMap outputs
as distinct fragments with the corresponding CUIs. For each utterance that contains an ambiguous term in our test set,
we apply our best linear method $f^{c,p,k}$ (corresponding to row 6 of Table~\ref{tab-res}) to assign one specific CUI
from all possible candidates. There were seven million such utterances, with an average length of 18 words,
that contained an ambiguous term out of a total of 78 million utterances from the corpus. Given our prior experiences in
convolutional neural networks (CNNs) in biomedical text classification~\cite{rios2015convolutional} that proved superior over
traditional linear classifiers such as support vector machines and logistic regression models, we built 203 multiclass CNN models,
one for each ambiguous term based on this weakly supervised dataset. The configuration of the CNN and its
various hyper parameters were determined as per our prior effort~\cite[Sections 3.2 and 4.2]{rios2015convolutional}.
This setup however resulted in accuracy of 87.78\% which does not match the performance of simpler approaches (rows 4--6  of Table~\ref{tab-res}).

We finally applied the $k$-NN approach outlined in Section~\ref{sec-dist} with the weakly supervised dataset with
the number of nearest neighbors $k \in$ \{20, 50, 100, 200, 300, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000\}.
The corresponding accuracies are plotted as shown in Figure~\ref{fig-top}.
We obtained the best accuracy of 94.34\% when $k=3500$ as shown in the last row of Table~\ref{tab-res}.
Overall, the accuracy rapidly increases as the numbers of neighbors used increase. The gains become smaller as
more neighbors are added, reaching the top score at $k=3500$ after which the accuracy descends abruptly. At $k=5000$, the
accuracy is same as that achieved with $k=300$. This phenomenon is not surprising -- at first more neighbors
contribute to additional evidence, consistency, and robustness against noise in comparing the candidate concepts. However, considering an increasing
number of neighbors at some point also leads to the semantic drift of their content from that of the test context.
So neighbors ranked further down the list negatively affect the prediction given they are not as related to the test context,
thus lowering overall accuracy.
We realize that the value of $k=3500$ is specific to this biomedical dataset and that there could be a value
$3000 < k < 4000$ that achieves a slightly higher accuracy. Our analysis is essentially a proof of concept for the high-level nearly monotonous
nature of performance of $k$-NN based approaches.
Given that there are over 38,000 test instances in our dataset, we believe $k \approx 3500$ is appropriate in domains with similar characteristics
(e.g., average number of senses per word, distributions of senses, and average length of test contexts). However, researchers
may be able to derive more appropriate $k$ values for their domains if they have access to relevant datasets.

Finally, it is well
known that $k$-NN approaches are infamous for high test time complexity because of the nearest neighbor search
in high dimensional space.  Our implementation involves cosine similarity computation with all training
instances for the corresponding ambiguous term. In this effort, on average there are nearly 40,000 training instances created through weak
supervision per ambiguous term.
So given a new test instance $(T, w, C(w))$, cosine similarity (of 300 dimensional vectors)
needs to be computed for the test instance $T$ with about
40,000 contexts to impose the ranking on these potential neighbors. The threshold of a chosen $k$ (say, 3500) can only be
applied after this ranking is created. However, this similarity computation can be parallelized in a straightforward
manner by distributing the similarity computations across multiple processors and pooling the results to incrementally
build the ranked neighbor list. Although real time disambiguation may not be feasible, having the $k$-NN models run overnight
every day to address disambiguation in new articles may be practical. Alternative approaches such as
locality sensitive hashing~\cite{slaney2008locality} that address the dimensionality problems without having to compute
cosine similarities may be helpful to alleviate the situation. Overall, however, it is clear that $k$-NN based
approaches with weakly supervised datasets offer an interesting alternative to purely supervised approaches in biomedical WSD.

\section{Conclusion}
\label{sec-conclude}

Biomedical WSD is an important task with implications for downstream components in NLP applications.
In this effort, we applied recent approaches in neural word embeddings to construct concept embeddings. Our
linear time method uses these embeddings to combine cosine similarity, projection magnitude proportion, and a prior knowledge-based approach
to produce an accuracy of  92.24\%. This is an absolute 3\% improvement over just using the knowledge-based approach, which generated the
previous best result obtained without supervised learning. Based on predictions from our best linear method, we created a new weakly supervised  dataset
and built a  $k$-NN model that achieves an accuracy of 94.34\%.

Our results rival performances achieved by
supervised approaches -- the best published supervised result achieves 93\% macro accuracy over ten fold cross validation
experiments on the MSH WSD dataset with the Naive Bayes model~\cite{jimeno2011exploiting}. Based on additional ten fold cross validation
experiments with support vector machines that use neural word vector features, Jimeno-Yepes~\cite{yepesa2016word} was able to achieve close to 96\% macro accuracy.
However, we cannot directly compare these cross validation results (from the supervised experiments) to the 94.34\% accuracy we obtained without supervision
in this paper. Specifically, in each iteration, the cross validation framework tests only on one-tenth of MSH WSD dataset by training on the remaining nine folds of the dataset. In our method, we test on the full MSH WSD dataset without using any of it for training.

Overall, our results in this paper contribute new evidence that dense neural embeddings
function as useful representations of textual data for biomedical NLP applications.
Furthermore, they also showcase the potential of knowledge-based approaches in learning
better dense vector representations (via MetaMap that uses UMLS) and their complementary contributions to WSD tasks.
We conclude with some limitations and future research directions.
\begin{itemize}[\setlength{\itemsep}{6pt}
\setlength{\labelsep}{6pt}%
 \setlength{\IEEElabelindent}{6pt}]
\item Although linear method's accuracy is above 92.24\%, there is still room for improvement in terms of incorporating
modifications that account for sense level errors. That is, in addition to accuracy, for each ambiguous term, if we
study the errors (false positives, false negatives) associated with each of its senses, we might be able to modify our approaches to account for any common patterns in which errors manifest. This task involves manual qualitative analysis with over 400 unique senses in the MSH WSD dataset and is a important future research direction.

\item In Section~\ref{sec-weak}, the test context $\vec{T}_{avg}$ is the vector formed by element-wise
averaging of the word vectors of the corresponding words in the test context $T$. Although averaging is simple and
intuitive, it may not be the best representation of the semantic content of the narrative in the test context.
As such, more powerful
alternatives that can better represent information in the context sentences might be helpful.
To this end, one option is to directly model paragraphs as fixed size vectors
using a word2vec style unsupervised learning architecture as demonstrated by Le and Mikolov~\cite{le2014distributed}
where paragraph vectors are learned along with word vectors.
\item  A second approach is to consider a weighted average of the word vectors corresponding to
tokens in the context vector where
the weight selected for a word is inversely proportional to its distance from the ambiguous term $w$ in the test context.
Besides word vectors, we can also compute the weighted average of
concept vectors associated with the CUIs (other
than those associated with $w$) in the test context.
The weighted averages of the words and contextual CUIs can then be compared
separately with the candidate concept vectors from $C(w)$ to generate two different scores $\in [0,1]$
whose sum can form the final score to select the correct sense.
\item Both the paragraph vector approach~\cite{le2014distributed} and the weighted averaging approach discussed
earlier do not explicitly model word order when composing test context words to form fixed size vectors that better
capture the semantics of the full context.
Recurrent neural networks (RNNs~\cite[Chapter 3]{DBLP:series/sci/2012-385}), especially with long short-term memory units~\cite{hochreiter1997long}, are a more suitable alternative
for such scenarios but would need training data to set the parameters of the recurrent layer. The dataset created
using weak supervision in Section~\ref{sec-dist} can be used here to estimate RNN parameters corresponding
to the model for each ambiguous term.
\end{itemize}

\section*{Acknowledgments}
Our work is primarily supported by the National Library of Medicine through grant R21LM012274.
We are also supported by the
National Center for Advancing Translational Sciences through grant UL1TR001998 and the Kentucky
Lung Cancer Research Program through grant PO2 41514000040001.  The content is solely the responsibility
of the authors and does not necessarily represent the official views of the NIH.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


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At Morten & Fairchild, our attorneys have significant experience in business law as well as significant experience in various business ventures. Given both our legal and business acumen, our team is particularly qualified to assist business owners at all stages of business. We can help you choose the proper business entity, draft the appropriate agreements and contracts for your business relationships, secure the proper protections for your intellectual property, advise on the best ethical business practices, introduce you to other professionals that are motivated to help your business grow, and much more. Consult with our attorneys to determine whether Morten & Fairchild is the right fit for you and your business.


Sous-titre the National Game of Madagascar.
Maison d' édition 44 pages. (translated in English by Leonard Fox). History and rules of the fascinating / International Fanorona Association. 278-A Meeting St, Charleston, SC 29401. Malagasy board game.


Frankfurt School in partnership with the Pakistan Microfinance Network (PNM) offers a blended version of its very successful FSDF e-Campus online course Certified Expert in Risk Management (CERM).
Frankfurt School's Certified Expert in Risk Management is a comprehensive qualification for financial sector professionals with a special emphasis on the particular requirements of emerging and developing markets. It gives you the tools to measure risks and devise appropriate risk-taking and mitigation strategies that gives your institution the competitive edge in delivering inclusive financial services in a rapidly evolving environment.
Do you want to attend a high class international training in Risk Management but don't have the time to travel abroad? This is your chance!
Frankfurt School of Finance & Management and the Pakistan Microfinance Network are jointly offering a blended learning training on Risk Management.
Blended learning is a combination of online learning and classroom training. The online component gives you the flexibility to learn whenever, whereever it suits you best, while the classroom training offers you the possibility to interact with trainers and peer participants.
In this blended Risk Management training, participants join Frankfurt School's well-established e-learning course Certified Expert in Risk Management, while additionally attending a and webinar series and a three day face-to-face training taking place in Islamabad, Pakistan.
This course should be of interest to Pakistan based financial professionals in retail and small business banks, leasing firms, consumer credit companies and microfinance institutions.
Any travel related costs as well as accomodation have to be covered by the participants individually.
**If less than 16 participants register, enrolled participants can chose to take the pure online course for a reduced fee of EUR 1,170 (instead of the general fee of EUR 1,300), or to get the full tuition fee refunded (minus transaction costs).
You will be certified by Frankfurt School upon completion of the course and passing the final examination.
The online component of the Certified Expert in Risk Management is open to the international audience with arund 100 financial professionals joining from all over the world.
While the online course forum allows you interacting with participants from all over the world, the class-room training component gives you the opportunity to network in person with peer participants based in Pakistan.
You will start the programme attending and following Frankfurt School's Certified Expert in Risk Management online course.
The programme upgrades the Certified Expert in Risk Management with a webinar series, where you will not only gain further insights into the topic but also have the chance to ask questions to your trainers.
During the course four webinars of one hour each will be offered. Attending the sessions is voluntary but highly recommended.
A 3-day classroom training during the programme will be enhancing the Certified Expert in Risk Management online course. This training is scheduled for December 2018.
Attending the classroom training and participating in the case studies is mandatory.
The on-site training will also discuss the application of best-practice methodologies within the specific regulatory and economic context of inclusive financial services in Pakistan.
Dr. Joachim Bald is Frankfurt School's most senior international advisor and a recognized expert in Treasury and Risk Management for banking and microfinance. He serves as Practice Leader at the Frankfurt School Competence Center in Risk Management and regularly manages complex implementation assignments in Eastern Europe, Asia and Africa. Joachim is also on the Board of Directors at Bai Tushum Bank, Kyrgyzstan, and KMF, Kazakhstan.
Dr. Bald maintains a large portfolio of risk management and strategy advisory mandates including recent and ongoing interventions at: Cairo Amman Bank and Etihad Bank – Jordan, RawBank – DR Congo, PRASAC and HKL - Cambodia, Al Amana - Morocco, ENDA - Tunisia, and many more. As a trained econometrician and software developer, Joachim has a knack for financial modeling and crunching large amounts of data. The scoring and rating models he builds actually work!
Joachim started his career in 1990 in the international banking division of Commerzbank AG, Frankfurt. In 1995 he accepted an international faculty appointment at Oregon State University in Corvallis. From 1999 to 2004, he was with Deutsche Börse Systems, the IT subsidiary of the German stock exchange and international derivatives markets operator. From 2001 to 2004, Joachim served as Vice President and managing director of the US subsidiary in Chicago, where he was responsible for operating the electronic futures and options trading platform for the Chicago Board of Trade. Since August 2004, Joachim works full time as an independent consultant from his home office in Oregon.
Joachim's publications include a dissertation and articles on the Russian economic transformation process, Treasury and Liquidity and Cash Management manuals developed for Frankfurt School and Women's World Banking as well as a study on core deposit supply behavior in low-income banking for CGAP. Dr. Bald designed and runs the Certified Expert in Risk Management on-line learning program at the Frankfurt School of Finance & Management. He also teaches the Risk Management for Managers content in the Online Master of Leadership in Development Finance. Joachim holds a Ph.D. in Economics from Ruhr-University Bochum and a Master's Degree in Finance from Dortmund University. Joachim is also a US Certified Management Accountant. He is fluent in English, German, French and Russian.
The trainer for the classroom component will be announced soon.
The Pakistan Microfinance Network (PMN) is Pakistan's national association for retail players in the microfinance industry. The PMN's membership strength today stands at 44 retail microfinance providers (MFPs) that collectively account for over 99% of the total microfinance outreach in Pakistan. PMN members form a diverse group of service providers and include microfinance banks, non-bank microfinance companies, and rural support programs. The PMN's vision is to expand the frontier of formal financial services to all and it pursues a mission of supporting the sector, retail financial service providers, to enhance their scale, quality, diversity and sustainability to achieve the goals of financial inclusion agenda.
One of the primary objectives of PMN is to serve as the knowledge and information hub for Microfinance, and now increasingly for inclusive finance, through collection and analysis of social & financial data from MFPs and disseminating the same. It has also established its credibility as a market facilitator through its robust enabling environment function due to which policy makers & regulators, investors and donors alike became more receptive to its views.
Capacity Building of the microfinance sector is one of the core areas of work for PMN. This function has evolved from a basic in-house and peer learning model to an out-sourced one, using both local and international training programs.


We all thought the long run would get rid of cables from our lives or no less than decrease our want for them. But as a substitute, we obtained much more cables at this time because the units that we will connect with our screens and TVs are gaining quantity: gaming consoles, PCs, TV bins, totally different gamers… It can get sophisticated actual fast. And the issue is, our TVs and screens have solely so many HDMI ports. Sure, you possibly can go and change the cables everytime you run out of inputs, however the very best factor you are able to do is spend money on an HDMI change.
HDMI switchers provide you with a number of inputs in only one HDMI port in your TV/monitor. You can join your switcher to your TV and then join your PS4, Xbox One, and TV field to your switcher and can change between them with one easy button.
Inputs: One of an important components to contemplate when shopping for a switcher is the variety of inputs. This clearly states what number of units you possibly can join without delay, so select in line with your wants.
Supported Audio & Video Formats: Not each switcher helps 4K and 3D so when you have any shows which have 4K decision, you could need to search for 4K assist in your switcher. Likewise, test the supported audio codecs of the product because the audio format your system is utilizing will not be suitable.
Extra Features: HDMI switchers often include further options to make your life slightly bit simpler like distant controllers, auto-switch features and picture-in-picture (PIP) assist. If any of these curiosity you, search for the merchandise which have them.
It actually is a lifesaver and they're often fairly cheap. But due to their low worth, the market is crammed with low-quality merchandise. Today, we're right here that will help you make a high quality buy and keep away from mediocre merchandise.
One of most well-known and greatest HDMI switcher with distant on the market. It is on the dearer aspect, however the options you get from it justify the upper price ticket. It has four ports that are increased than common. When you join or open a brand new system, it mechanically adjustments to that and it additionally comes with a distant. The lights on the entrance let you understand which system is in use. It additionally has 4K and 3D assist. Its picture-to-picture characteristic allows you to use 2 units without delay on a single TV/monitor; one covers a lot of the display screen whereas one other system runs on a small window within the backside right-hand nook. With all that, Zettaguard ZW410 is likely one of the greatest selections for 4K & 3D customers.
This switcher has three inputs and comes with a distant, so that you would not have to rise up out of your sofa whereas utilizing your units. It additionally has 4K and 3D assist and comes with an 18-month guarantee. Its small lights on the entrance let you understand which units are in use. It doesn't have any extra fancy options however it's also decrease in worth, so if you're in search of a great HDIM switcher, that is one other nice alternative.
Even although it was launched 6 years in the past, Fosmon HD continues to be one of many greatest funds selections amongst HDMI switchers and it reveals in its gross sales. It doesn't have further options or 4K assist, however for $15 you get 5 ports, a distant and gold-plated ports to forestall rust. It helps a variety of online game programs, gamers, stream units and audio codecs. For its excessive worth, it is likely one of the greatest HDMI switchers available on the market.
This switcher comes with 2 inputs and 4K assist. The variety of inputs could seem small however this system is extra than simply an HDMI switcher; it's also a splitter. As you possibly can perceive from the "bi-directional" implication in its title, it's also possible to join one system and apply it to 2 totally different shows and change between them. So if you're in any circumstance that requires you to make use of each an HDMI switcher and splitter, this is likely one of the greatest merchandise for that aim.
Kinivo 501BN doesn't even want an introduction because the whopping 10,000 evaluations on Amazon alone ought to persuade you that it's an superior product for its worth. It helps each HDMI-compatible system, has 5 inputs and one output and comes with a distant for straightforward management. Like most different HDMI switchers, it has lights on it to indicate you which of them units are in use. It doesn't have 4K assist however for its large variety of inputs and basic high quality, it is likely one of the greatest merchandise in its class.
If Kinivo's lack of 4K assist bugged you, you will get the identical variety of inputs with 4K assist with Univivi 5×1 HDMI Switcher. It has 5 inputs, has a distant controller and switches between units mechanically once you activate a brand new unit. It wants an exterior energy supply, however that's its solely con.
Gana HDMI Switch three×1 is affordable, unnoticeably small and easy. It's only a cable with three HDMI inputs and 1 output, not even a field like most different HDMI switchers. That is it. It even has 4K assist and is very easy to make use of. If all different extras like auto-switching and distant controllers appear pointless to you and you simply need a easy and small HDMI switcher, look no additional than Gana HDMI Switch three×1.
Smartooo's premium HDMI switcher comes with three inputs and intensive audio and enter assist. With simply three HDMI inputs, the $40 price ticket could seem slightly bit excessive however understand that it has HDMI 2.zero assist and is suitable with 4K at 60Hz. It additionally comes with a distant controller. If you will have any media or online game that runs on 4K at 60 FPS, this can be a strong alternative.
J-Tech's Digital HDMI switcher comes with four inputs, 4K and 3D assist and a distant controller. As a particular characteristic, one of many inputs is an MHL port slightly than an HDMI port. The MHL port permits a smartphone or pill to be related to a TV/monitor (Its adapter is bought individually.). It additionally helps Google's Chromecast.


If you had any doubts about the future of our beloved sport this season, you should have attended one of the many Girls Shred Sessions QParks held in their parks all Europe this winter. With unstoppable motivation (who cares about blizzards?!) and an unstillable hunger for money shots, the finale culminated in the Blue Tomato Kings Park Hochkönig end of March.
Loads of GRILS POWER was unleashed in Mühlbach at the Hochkönig when the Blue Tomato Kings Park hosted a "Girls Only" session including pro coaching, Nikita board test area, goodie session and a film- and photo shooting.
freeski champion and vice champion Regina Rathgeb and Philo Bair.
The mission was obvious: for one day, the female park guests should be in the spotlight. Therefore all ladies were called upon to spend a relaxing day on the mountain among likeminded people, no matter if beginner, intermediate or pro. The Blue Tomato crew took care of the ladies and provided free coffee and cakes. A further highlight was certainly the Nikita board test area. All ladies had the chance to test the brandnew board models – yes, Nikita just recently started to produce snowboards!
At the end of the session every lady had the chance to show her skills at the "Goodie for Trick Session". For every perfectly stomped and highly technical trick there were some nice goodies from Blue Tomato and Nikita to rake in.
Thanks to all the ladies who took part – you were AWESOME! Show up more often in the snowparks, all guys will be thankful.
Tired of riding alone or panting behind the guys?


This all natural face sunscreen with SPF 30 combines organic botanicals of Neem, Shatavari and Rose with non-nano micronized Zinc Oxide help to effectively protect against harmful UVA, UVB and UVC rays. Non-clogging silky formula works to nourish skin, prevent premature aging and guard against breakouts for all year round!
Ionized water, non-nano micronized zinc oxide 18.6%, organic extracts of Neem, Brahmi, Shatavari, Licorice, Vitamin E, Lecithin, Organic essential oil of Rose, and Xanthan Gum.


Este examen de inglés es muy fácil de tomar, lo único que necesitas son tus conocimientos. El examen te debe tomar tan sólo unos 30-35 minutos para terminar. Tómate tu tiempo. Contesta el examen con sólo tus conocimientos.
Al final podrás ver tu resultados y recibirás sugerencias de lecciones que te pueden ayudar a mejorar tu nivel de inglés.
3. I've just seen ... giraffe and ... lion.
4. ... She's my mother.
5. This is ... car.
7. Are you from China?
8. Can you speak Spanish?
9. I live ... the beach.
10. Do you want a cigarette?
13. I want to... down.
14. Do you know the man ... is standing there?
16. I ... read. I ... do my homework.
20. I want ... candies ... there any left?
23. This car is ... than that car.
24. Can you run 50 miles?
26. How do you say this using words?
27. WIll you go to my party?
30. What is the plural of mouse?
31. I want to ... English.
33. Where ... in Mexico?
34. I ... my wife since we were teenagers.
35. When ... the movie begin?
37. Would you mind if I ... the window?
39. Excuse me, ... you tell me the time?
40. Which of the following is NOT correct?
He is sick, I really think you may see him.
You must do your homework!
42. We must ... the window fixed before the storm!
43. Why did the chicken cross the road?
46. I ... living in this house for decades!
53. There was too much interest, it was...!
54. Did our teacher provide ... for us?
58. Would you mind to tell me your name?
60. I don't like coffee, either.


Start getting yourself ready for university your junior year. Most students delay until their older year just before getting serious about college. Rather, make use of junior 12 months to visit distinct universities, read about the scholarships or grants readily available and initiate looking for the educational institutions of your choosing. By using this method will assist you to achieve all things in sufficient time.
Are you presently less than plenty of pressure for the certain course you are not sure you will complete? You should confer with your academic consultant or perhaps to your coach about credit history no credit history. Most schools will allow you to consider a single type in which you will definitely get credit score yet your grade is not going to effect your GPA.
You ought to be polite of your respective roommate's wants. You have to discover ways to assist her or him in order that it helps make your time and effort simpler. Take a seat when you first arrive at school and discover everything you the two assume away from a roommate to help you the two be at liberty.
Use the several resources of your collection. It is a good place to examine, study and relax. When bandar ceme should completely focus, the calm in the collection is a great place to get away from the action from the dorm. The collection is additionally to find one of the most cozy seating on university.
Articulate up frequently inside your foreign words course. Speaking up and volunteering to publish around the blackboard makes it much simpler to get a great quality. Overseas language teachers are considering exactly how much you increase throughout the word. They are not comparing one to other college students. Talking up will help your instructor examine you positively.
In case you are occupied with work and children throughout the daytime, taking nighttime classes is the best option. Evening time school teachers fully grasp that you may have a hectic life and tend to cater their sessions to this. There may be usually a lot less training course work and the instructors are generally speaking a little bit more flexible.
Make contacts while you are in class that you can use when investing in out. Should you enjoy a selected professor's school, make an effort to become familiar with that professor along with driving yourself to become a superb college student. The contacts you are making now will assist you to determine and advance inside your career from the years to come.
Write down a to accomplish checklist the night time before. This is a wonderful way to support prep the human brain for all the learning you need to do the future. You'll get out of bed with a set of purpose instead of a sensation of anxiety that makes the day that quicker to deal with.
If you need career being a college student, talk to your college profession office. These kinds of offices support graduated pupils get jobs, but they also are renowned for putting up part-time prospects on campus and in the surrounding community.
Make sure you understand ways to prevent plagiarism from sprouting up in your documents. During college or university, you can expect to compose a large number of essays and studies. Be sure to realize how to effectively report resources so that you will never need to deal with plagiarism expenses. Instructors have several alternative methods at learning regardless of whether your document is unique or not.
When you are in college or university, get virus safety on the laptop, simply because this is one of the most critical instruments you will probably have throughout your continue to be. The last thing that you will want to have took place is to drop information that you will want on the day in the check coming from a infection.
When of poker online terpercaya , check out the toilet ahead of time. This could seem self-explanatory, but there are so many things to remember on analyze day that it's an easy task to neglect. When evaluating, you want to remove as much interruptions as possible and your kidney could be a large diversion.


text:
Q: Unable to click on Button using Selenium Python I'm trying to click on this button to go to the next page, but for some reason, I'm unable to. I tried xpath, css, and class selectors as well as the data-trekkie-id attribute, but nothing I have tried worked. Any help? Code below:

    <button name="button" type="submit" class="step__footer__continue-btn btn " data-trekkie-id="continue_to_shipping_method_button" aria-busy="false">

            Continue to shipping method

    </button>

A: As per the HTML to invoke click() method on the button with text as Continue to shipping method you need to induce WebDriverWait and you can use either of the following solutions:

*

*Using CSS_SELECTOR:
WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.CSS_SELECTOR, "button.step__footer__continue-btn.btn[data-trekkie-id='continue_to_shipping_method_button']>span.btn__content"))).click()

*Using XPATH:
WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.XPATH, "//button[@class='step__footer__continue-btn btn' and @data-trekkie-id='continue_to_shipping_method_button']/span[@class='btn__content']"))).click()

*Note : You have to add the following imports :
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC

A: This should work:
driver.find_element_by_xpath("//*[contains(local-name(), 'button') and contains(@class, 'step__footer__continue-btn')]").click()

redpajama_set_name: RedPajamaStackExchange


text:
set /p ver=<VERSION
set sourceUrl=-s https://www.nuget.org/api/v2/package

dotnet nuget push artifacts/Es.Serializer.%ver%.nupkg %sourceUrl%
dotnet nuget push artifacts/Es.Serializer.Jil.%ver%.nupkg %sourceUrl%
dotnet nuget push artifacts/Es.Serializer.JsonNet.%ver%.nupkg %sourceUrl%
dotnet nuget push artifacts/Es.Serializer.NetSerializer.%ver%.nupkg %sourceUrl%
dotnet nuget push artifacts/Es.Serializer.ProtoBuf.%ver%.nupkg %sourceUrl%

redpajama_set_name: RedPajamaGithub


You can download the latest sources here as a Maven 2 project.
This entry was posted on Tuesday, July 29th, 2008 at 21:21 and is filed under Software.	You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.
Does it support schema (or others) validations?
Not yet. For 1.2, I'm working on parsing DTD's.
As far as validation goes, I'm not yet convinced what's the best way to proceed. DTD is too limited, XML Schema is very complex and still can't cover all bases.
Right now, I lean towards a pluggable interface which allows you to add any validation you like and where you can say when you want validation (for example, after modifying the XML to make sure it's still valid).


In order to ensure that your fresh meat delivery from our quality butchers at Smithfield market arrives in perfect condition, they are shipped in high quality, durable, temperature controlled packing boxes. The packing boxes have been specifically designed to ensure the contents are kept below +5ºC for a period of 48 hours, to ensure a perfect fresh meat delivery.
The containers are lined with 13mm polyethylene, which is designed to provide effective insulation whilst protecting the contents from any potential impact that may occur during transit. They have been carefully designed by the manufacturer for use by food suppliers and are ideal for use in the transportation of fresh food and other perishable products.
Your fresh meat delivery from Smithfield market, will arrive in perfect condition having been kept at the optimum temperature throughout the journey.


text:
Gary Segal
Diana Tsai
U.S. Bankruptcy Court Holds Illinois Mortgage May Be Avoided for Failure to State Interest Rate and Maturity Date
In a ruling on February 29, 2012, the U.S. Bankruptcy Court for the Central District of Illinois allowed a bankruptcy trustee to avoid an Illinois mortgage as to other creditors of the estate because the mortgage failed to expressly state the maturity date of and interest rate on the underlying debt (In Re Crane, Case 11-90592, U.S. Dist Ct, C.D. IL, February 29, 2012). The court held that the provisions of Section 11 of the Illinois Conveyances Act (the "Act") addressing mortgages of land (765 ILCS 5/11) are not permissive guidelines, as the language of the Act suggests, but rather are mandatory provisions with which a lender must comply in order to provide constructive notice to a trustee in bankruptcy. Mortgages that fail to contain the amount of the underlying debt, maturity date and interest rate, the court held, are avoidable by a bankruptcy trustee. Including these provisions in the mortgage by reference to the promissory note or other loan documents is insufficient. The court in Crane required that the amount of the debt, the maturity date and the underlying interest rate were required to be set forth expressly in the mortgage itself.
This decision is squarely at odds with standard practice in the Illinois mortgage industry. While most Illinois mortgages typically do state the maturity date and the amount of the debt, they do not normally incorporate the interest rate and only cross reference to the promissory note or credit agreement which, in turn, expressly states the rate. As a general rule, lenders prefer not to have their loan pricing in the public record and, furthermore, pricing calculations often fluctuate during the term of the loan based upon a variety of factors that are addressed in detail in the note or credit agreement and would be unwieldy and impractical to restate in the mortgage. Nevertheless, under the Crane decision, failure to state the maturity date and interest rate would permit a bankruptcy trustee to avoid the mortgage. Although the Crane decision does not establish a precedent for other bankruptcy courts or state courts in Illinois, the decision may embolden bankruptcy trustees throughout Illinois to seek to avoid similar mortgages.
The Crane decision has also sent shockwaves throughout the title insurance industry. The major title insurance companies operating in Illinois appear willing to continue to issue loan policies on new loans even if the mortgage fails to state the interest rate—but significantly, they have indicated that they are reserving the right to deny coverage under various exclusions to the standard title policy. Title companies have not given any guidance thus far as to how they will handle claims on existing policies if Crane is upheld.
The court's decision in Crane speaks only to whether the mortgage at issue is specific enough to provide "constructive notice" to the bankruptcy trustee and/or third party purchaser; it does not wholly invalidate mortgages that fail to set forth the items in the Act. At present, it would appear that such mortgages are still valid against the mortgagor/borrower, whose execution of the promissory note and other loan documents is evidence of its receipt of notice of the terms of the underlying debt.
An appeal of the Crane decision was filed by the defendant lender, Gifford State Bank, on April 18, 2012. In addition, legislation to provide that failure to state the interest rate or maturity date does not invalidate or affect the priority of a mortgage has been proposed in the Illinois House of Representatives. Proponents of the legislation are hopeful that the legislation will be introduced and passed before the adjournment of the regular state legislative session scheduled for May 31.
Dykema will continue to provide updates regarding the Crane appeal, related legislative efforts, the positions taken by title insurers and other industry responses in the coming weeks. In the interim, should you have any questions about the Crane decision or its impact on your business, please contact Michael Kurtzon at 312-627-5674, Gary Segal at 312-627-2482, or Diana Tsai at 312-627-2508.
As part of our service to you, we regularly compile short reports on new and interesting developments in our business services program. Please recognize that these reports do not constitute legal advice and that we do not attempt to cover all such developments. Rules of certain state supreme courts may consider this advertising and require us to advise you of such designation. Your comments on this newsletter, or any Dykema publication, are always welcome. © 2012 Dykema Gossett PLLC.
As part of our service to you, we regularly compile short reports on new and interesting developments and the issues the developments raise. Please recognize that these reports do not constitute legal advice and that we do not attempt to cover all such developments. Rules of certain state supreme courts may consider this advertising and require us to advise you of such designation. Your comments are always welcome. © 2020 Dykema Gossett PLLC.