Product Details: Brand	Cosco Type	Treadmills Features Operation	Automatic Speed Range	0.8 - 16 kph Incline	Yes Incline Levels	0.1 Max. Weight Withstandable	110 kg Pre-Set Programs	5 Preset Programs in 3 Level Intensity, 2 HRC & 2 User Safety Key	Yes Foldable	Yes (Manual) Deck Deck Type	Roller Deck Surface Dimensions	43.18 x 121.92 cm 17 x 48 inches Motor	2.5 HP (Horse Power) DC Display Display Type	Backlight 104 x 57 mm LCD Display Information	Speed, Distance, Time, Caloriess, Program, Level, Lap & Pulse Additional Features Safety key & Hand pulse Shock absorber Wheels for easy transport Weight & Dimensions Weight	61.5 kg Warranty: 1 Year Manufacturer Warranty Assembly & Installation: Installation Service Is Only Available For Customers Of Delhi NCR Region At An Additional Cost Of Rs. 500. No Installation Service Is Provided In The Other Parts Of The Country. Package Contents 1 Treadmill Disclaimer Product once used cannot be exchanged or replaced. Every effort has been made to accurately reproduce the look of this product but there may be upto 5% variation in the colour and design of the actual product received due to varied screen resolution or lighting at time of digital photography. We sell only 100% genuine products. Every effort has been made to ensure that the information on this site is accurate.


MTN Group is near an agreement with the Iranian government to acquire a 49% stake in a state-owned Internet provider as Africa's biggest wireless carrier by sales seeks to expand in a fast-growing yet politically challenging market, according to two people familiar with the matter.
Company officials are planning a meeting with minister of communications & IT Mahmoud Vaezi later this month to finalise the purchase of shares in Iranian Net, said the people, who asked not to be identified because the talks are private.
The move will help smooth the process of repatriating funds from the Middle Eastern country as the government would prefer some profit to be invested locally, one person said.
"MTN continues to see growth potential in Iran and we look at opportunities on an ongoing basis to build on and complement our existing footprint," spokesman Chris Maroleng said in e-mailed comments.
A deal would fuel MTN's expansion in a market that opened up to foreign investors following the lifting of US-led sanctions last year.
The South African carrier already owns a 49% stake in Tehran-based mobile carrier Irancell, and has been repatriating some of the US$1bn that had been trapped in Iran before restrictions were removed. The company also agreed in October to invest about €20m in Snapp.ir, the Islamic Republic's first cab-hailing smartphone application.
The purchase of the stake in Iranian Net is expected to be completed before the end of the first quarter, one of the people said. Further investments in Iran will be identified once the deal has been finalised, the people said. The talks were reported last year by TechRasa, a website focused on Iranian technology news.
US-led international sanctions on Iran were lifted in April following a deal intended to prevent the country from building a nuclear weapon. US President Donald Trump has repeatedly criticised the agreement, putting its future in doubt.
For now, Iran can benefit from a limited amount of Western investment that has started to flow, and some foreign companies like MTN have been able to move funds out of Iran that had been locked inside the country.
Iranian Net was founded in 2011 to provide high-speed broadband Internet services to cities in Iran, but has missed several deadlines on the project due to a lack of capital, one of the people said.
Delta Partners is the financial adviser on the deal and will assist the phone company and Iranian Net to compile a joint business strategy. A Delta spokesman said the company was unable to comment on the details at the moment.
I'm sure this will delight Mr. Trump no end.
If only they also invested more at home to better our connections and reduce price reduction…buy I guess what they have invested may seem enough for now. May all go well in Iran, MTN is our proudly SAn company hey.


text:
<?php

namespace TorrentPier\ServiceProviders;

use TorrentPier\Di;
use TorrentPier\View;

class ViewServiceProviderTest extends \PHPUnit_Framework_TestCase
{
    /**
     * @covers \TorrentPier\ServiceProviders\ViewServiceProvider::register
     */
    public function testRegisterService()
    {
        $di = new Di();
        $di->register(new ViewServiceProvider, [
            'twig' => new \Twig_Environment()
        ]);

        static::assertInstanceOf(View::class, $di->view);
    }
}

redpajama_set_name: RedPajamaGithub


text:
Q: How to download images from "wikimedia search result" using wget? I need to mirror every images which appear on this page:

http://commons.wikimedia.org/w/index.php?title=Special:Search&ns0=1&ns6=1&ns12=1&ns14=1&ns100=1&ns106=1&redirs=0&search=buitenzorg&limit=900&offset=0

The mirror result should give us the full size images, not the thumbnails.
What is the best way to do this with wget?
UPDATE:
I update the solution below.

A: Regex is your friend my friend!
Using cat, egrep and wget youll get this task done pretty fast
Download the search results URI wget, then run
cat DownloadedSearchResults.html | egrep (?<=class="searchResultImage".+href=").+?\.jpg/

That should give you http://commons.wikimedia.org/ based links to each of the image's web page. Now, for each one of those results, download it and run:
cat DownloadedSearchResult.jpg | egrep (?<=class="fullImageLink".*href=").+?\.jpg

That should give you a direct link to the highest resolution available for that image.
Im hoping your bash knowledge will do the rest. Good Luck.

A: It is quite difficult to write all the script in stackoverflow editor, you can find the script at the address below. The script only downloads all images at the first page, you can modify it to automate download process in another page.

http://pastebin.com/xuPaqxKW

A: Came here with the same problem .. found this >> http://meta.wikimedia.org/wiki/Wikix
I don't have access to a linux machine now, so I didn't try it yet.

redpajama_set_name: RedPajamaStackExchange


I haven't had a great need for exploration of throttling options within Bigfix but am now faced with an opportunity. We have a fairly large environment with 50K+ endpoints and 88 relays. As the payload for patches gets bigger, we are seeing smaller sites with slower connections affected especially during business hours. We send out patches at night but many small sites turn their PCs off. The next day, of course, when PCs are turned on relatively at the same time, the network is impacted. I started looking at the different throttling options and wanted to poll the group for ways it has been handled.
A good scenario is we have a site with a 20MB connection. This site has 107 devices connecting to a relay in our datacenter that services 600+ devices.
Thanks in advance for any suggestions and input.
I took the easy route. We have about 11K endpoints with 50 relays world wide. I just set the Throttle Outgoing Traffic (Fixlet ID 163) to 12,500 Kbps on most of my relays. The network team has never complained.
Thanks for the reply. So with those settings at the relay level, does that slow down sites that don't have bandwidth constraints?
Reading the document will give you more information.
The Server can be configured to throttle the cumulative file downloads at any given time. When this throttling setting is enabled, the Server/Relays send out no more than the specified number of bytes per second for all file downloads (including IEM Clients and child IEM Relays). This setting is especially useful if there is a concern in a local area network that too much bandwidth will be used when a patch is sent out to many IEM Clients simultaneously.
BigFix Server cumulative download throttling can be configured by using the _BESRelay_HTTPServer_ThrottleKBPS setting on the Server.


Acne is one of the most common skin problems in humans. Almost 80% of people are suffering from acne at some point in their life. Most people get acne between the ages of 11 to 30 years of age, this is when their hormones are everywhere. So, the question arises that How to get rid of acne naturally? When your body's glands overproduce oils, they combine with the dead skin cells. This blocks the pores and the hair follicles. Then the bacteria begins to grow in the clogged pores, causing a breakout.
First, we will talk about the skin care and then we will talk about the nutritional side of things so that you are able to heal acne from the outside and within. So, let us first start with skincare.
First thing first, if you are suffering from acne, do not touch your face. Constantly, touching your face can lead to breakouts. This is because the bacteria can transfer from your fingertips onto the skin. Although, it is very difficult as people are prone to touching their faces on a regular basis but if your skin is prone to acne, be careful of scratching your face, resting your face on your hands and picking up pimples.
Do not pop a pimple. The risk is that in doing so you will push the infection out of hair follicles into the surroundings. This will cause more inflammation making acne longer to heal. Also, can the bacteria can spread to non affected areas causing even more acne. And then the risk of scars is always there. So, never ever pop a pimple.
Wash your face twice a day. Cleaning the face often will keep the oil production low, kill bacteria and rinse away the dead skin cells. Make sure to wash your face gently because if it is vigorously done, it can irritate the skin and aggravate breakouts.Use any face wash which suits you.
Tea tree oil based face wash, due to their antibacterial power, gently remove sebum, excess dirt and makeup without stripping the skin of its natural oils.
So, wash your face once after getting up in the morning and once before going to sleep at night.
Drink lots of water. As skin is the largest organ of the human body, it is affected when you don't drink ample amounts of water. The toxins, the bodily waste and even the bacteria can be trapped within your skin without proper hydration to flush them out which can increase the possibility of acne. So make it a habit of drinking 8 to 12 hours a day.
Too much exposure to ultraviolet rays damages the skin and prolongs inflammation causing red acne marks. so, if your skin is prone to acne make sure you apply sunscreen whenever you go out in the sun.
Always take a shower after exercise. If you are prone to acne be sure to always take a shower after exercising to remove all dead skin cells that your body has shed off through sweating. If you can not shower after the workout, at least use a soft wet towel to wipe down and remove most of the sweat.
We all know that smoking has negative effects on the skin. Some researchers believe that smoking can cause or aggravate acne breakouts. So, if your skin is prone to acne, it is better to stay away from all kinds of smoke.
Any fabric that comes in contact with your skin on a regular basis, for example- your clothes, towel, pillowcases and sheets should be washed at least once a week to remove all the oil, bacteria and dirt that has buildup on them over time.
This one is for the ladies out there. Unfortunately, makeup can cause acne and may make whiteheads, blackheads, pimples inflammation worse. This does not mean that you can not makeup at all. The best acne treatment approach is to select makeup which is labelled non-comedogenic.
Non-Comedogenic denotes a skincare product of cosmetic that is formulated so as not to cause blocked pores.
If you aren't getting enough sleep chances are that your skin hasn't had the time or the ability to renew it's skin cells. Another factor connecting sleep deprivation to acne is the increase in the cortisol hormone in the body. Cortisol, in turn, causes the skin to secrete more sebum which results in acne.
Sebaceous glands secrete the oily, waxy substance called sebum. An excess amount of sebum along with dirt and bacteria causes the formation of a pimple.
Your skin cells require proper nutrition for functioning well. Getting the proper amount of nutrients from whole grains, fruits, vegetables, proteins, helps your skin regenerate faster and limits unnecessary oil production. Here are some points you should consider.
High sugary foods like sweets, cola, chocolates, packed juices pump up the production of insulin in your body. To bring down these insulin spikes, your body responds by producing androgen hormones which further aggravate acne.
Stay away from oily and greasy foods. The more oily foods you will eat the more oil will your body produce and the more prone to acne your skin will be. So, cut down all the fried and processed foods from your diet. Stick to home-cooked food most of the time. Even at home, instead of using refined oils, switch to better oils like coconut oil or olive oil.
Eat foods which are rich in fibre. Make vegetables, fruits, whole grains, nuts as a part of your daily diet. They will promote digestion by killing the bad bacteria in your gut and will increase the production of probiotics. Including these foods give you essential nutrients that will help you stay away from acne.
We have already discussed how water flushes out the toxins and prevents acne. Cucumber, watermelon, tomatoes, apples, cabbage are all very high in water content.
Zinc is a dietary mineral important for skin development as well as regulating metabolic and hormonal levels. Researchers have found low levels of zinc linked to high acne cases. Beans, mushrooms, lentils, cashews, seeds are some good sources of zinc.
that might be worse than your original acne.
chest, and back (or wherever the acne breakouts are), and allow it dry.
be combined with water, then put it to use on the acne affected areas.
could cause your pores for being infected thus causing more acne.
Recently i read this post…It really great. I loved it. I have acne problem for past 5 years. I tried many product but no one's effect is long last. Now i am just apply all the steps or tips you mention here, it really works for me. Thanks for sharing such a great post with us. My skin is oily.
Will you plz suggest me which suncream i can use??


text:
Locals attend, participate in New York City's St. Patrick's Day Parade
Seth Harrison
Green was the color the day as hundreds of thousands of people, decked out in their St. Patrick's Day finest, lined Fifth Ave. in New York City for the nation's largest and oldest St. Patrick's Day Parade Saturday.
The parade, traditionally held on St. Patrick's Day, was held a day early in honor of Sunday's religious observances.
Tom Menton of Yorktown came down to watch the parade, but not before dying his beard bright green for the occasion. With him was Monica Garrigan, also of Yorktown. "It's a beautiful day," she said, "we come every year, and just love to celebrate the Irish."
PEARL RIVER: 10 things to know about Sunday's St. Patrick's Day parade
GOOD NEWS, EVERYONE: Weather expected to be sunny and mild for weekend
FOOD: Restaurants, pubs that 'go green' on St. Patrick's Day in lower Hudson Valley
Several bands and organizations from Westchester and Rockland Counties marched in the parade, including the Iona College Pipe and Drum Band, the Harrison High School band, the Rockland County Police Emerald Society Pipes and Drum Corps, and the Rockland County AOH Edward V. Larkin County Bagpipe Band.
The parade stepped off on 5th Ave. at 44th Street and made its way north, past St. Patrick's Cathedral at 50th Street, and ended on 5th Ave. at 79th Street. New York City Mayor Bill de Blasio and other officials marched at the top of the parade and stopped at St. Patrick's Cathedral to greet Cardinal Timothy Dolan.
Parade organizers lay claim to it being the oldest parade in nation, with the first parade taking place in 1762, fourteen years before the signing of the Declaration of Independence.
Among the thousands lining the parade route, were countless visitors from Ireland. Meiread Ashe was visiting New York with several friends after starting their trip in Las Vegas.
"We're having a ball", she said, "We came from Vegas, I swear to God I sang with Elvis, and here I am now at the parade."

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction }

	The prosperity of the web has greatly facilitated the dissemination of information. Large-scale knowledge bases (KB), such as Freebase and YAGO, have been built. Knowledge base question answering (KBQA) \cite{webqa-dataset, dongliMCNN} enables people to query the knowledge base with natural language and provides a feasible way of information acquisition. KBQA, which bridges the natural language and the structured knowledge base, involves two subtasks: determining the topic entity mentioned in the question and detecting the relation path from the topic entity to the answer, namely entity linking and relation detection \cite{improved}.
	Relation detection plays a vital role in KBQA and it is difficult because of the ambiguity and high variance of relation expression. As per reported, most of the wrong answers are caused by relation detection\cite{golubcharcnn}.
	\begin{figure*}[!htb]
		\centering
		\includegraphics[width=0.8\textwidth]{./images/traditional_method.pdf}
		\caption{The encoding-comparing framework of KBQA relation detection model based on deep neural networks}
		\label{fig:traditioanlmethodfig}
	\end{figure*}

	Traditionally, relation detection for KBQA is mainly based on the semantic parsing \cite{semanticparsingbao, semanticparsinghe} method, where the natural language question is parsed into a logic query over the knowledge base.
	These methods usually assume predefined lexical triggers, and suffer from lexical chasm \cite{lexicalchasm}.
	Recently, deep learning methods are widely applied in the relation detection for KBQA. Most of deep learning methods fall into the encoding-comparing framework. As is illustrated in Figure \ref{fig:traditioanlmethodfig}, the deep neural networks firstly encode semantics of the question and the relation into vectors of the same dimension, and subsequently vector comparison is performed to measure their semantic similarity.
	Many efforts have been made to the optimization of the relation and the question representation methods.
	Relation representation methods mainly fall into two categories.
	The first category takes the relation as a single semantic unit \cite{dai2016} and represents it with the pre-trained vector representation using network embedding methods such as TransE \cite{transE}. On the other hand, the other category \cite{BiCNN, golubcharcnn,AMPCNN,improved} takes the relation as a sequence of words or characters considering that the relation name comprises meaningful words.
	Yu et al. \cite{improved} model relations from a combined perspective of the word-level and the holistic relation-level representations.
	The question is regarded as a sequence of words, and different encoding methods are adopted to encode it from different granularities.
	Specifically, Yih et al. \cite{BiCNN} model both questions as tri-grams of characters with CNN. He et al. \cite{golubcharcnn} process the questions as sequences of characters with an attention-based LSTM network. Hierarchical residual bi-directional LSTM (HR-Bi-LSTM) is proposed to learn the question representation with different levels of granularity \cite{improved}.
	Attention mechanisms are incorporated to emphasize important units, and learn a relation-dependent representation of question, such as the attentive pooling method \cite{AMPCNN} and the attention-based LSTM network \cite{golubcharcnn}.

	One issue of the encoding-comparing framework is the information loss during the encoding procedure. The question or the relation is firstly mapped into a sequence of word embedding vectors and CNN or LSTM layers are adopted to capture the context information. In order to merge the vector sequence into a single vector of fixed size, max-pooling, average-pooling or attention-weighted average-pooling is adopted.
	The pooling procedure which compressing the question and the relation into fixed-dimensional vectors becomes the bottleneck of information. Crucial information for the semantic similarity measurement can be lost before the comparison.

	There is a new trend to learn interactions between sequences at a low level in tasks of semantic similarity measurement, such as machine reading comprehension \cite{machinereading}, textual entailment \cite{textualentailment}, and answer selection \cite{compare-aggregate}. Competitive results have been reported on these tasks.

	This paper proposes to learn attention-based word-level interactions between question and relation to ameliorate the information loss problem. Rather than encoding the question and the relation into fixed-dimensional vector, we firstly compare semantics of words in the question with the relation and merge the comparison results to measure their semantic similarity.
	Specifically, the question and its candidate relations are represented as sequence of vectors with two bi-directional LSTM (Bi-LSTM) respectively.	Then we learn attention-based soft-alignments between the question words and relation words. A weighted sum of the relation vector sequence is calculated for every word in question.
	Subsequently a multi-kernel CNN layer is involved to compare the question and relation at a fine granularity, and a max-pooling operation is performed to aggregate comparison results. Finally, a logistic layer scores the semantic similarity based on the extracted features.
	With the question represented as a sequence of vectors and the comparison performed on the learned word interactions, information loss caused by the early merging can be avoided to some extent.

	The main contributions of this paper are summarized as follows:
	\begin{itemize}
		\item We propose to learn word-level interactions between the question and the relation, and the attention mechanism is incorporated for a fine-grained alignment between words for relation detection of KBQA.
		\item We propose a method to perform comparison on the word-level interactions and merge the comparison results. Specifically, a CNN layer is incorporated to compare semantic similarity at word-level, and the comparison results are merged with a max-pooling operation.
		\item The proposed model achieves state-of-the-art results on the relation detection tasks \cite{improved} on SimpleQuestions \cite{simpleqadataset} and WebQuestions \cite{webqadataset} dataset, demonstrating the effectiveness of proposed method.
	\end{itemize}

	The rest of paper is organized as follows. Related work is reviewed in Section 2. The ABWIM is introduced in Section 3 in details. Experimental results and analysis are given in Section 4. Finally, we conclude the paper and discuss the future work in Section 5.

	\section{Related Work}
	This paper mainly involves work about the following three aspects: the traditional relation extraction, relation detection of KBQA, and sequence to sequence semantic similarity measurement.
	\subsection{Relation Extraction}
	As a key component of information extraction, relation extraction aims to determine whether the input text describes a type of relation between two corresponding entities. Assuming the relation belongs to a predefined relation set, relation extraction is modelled as a multi-class classification problem. Traditional methods \cite{traditionalRE1,traditionalRE2} focus on feature engineering. In recent years, many deep learning methods, from word embedding \cite{embRE1}, CNN \cite{liukangrelationextraction} to recursive neural network\cite{recursiveRE} and the attention based models \cite{attentionRE2, attentionRE1}, are adopted to extract features automatically. Deep learning methods rely heavily on the labelled data, and manual annotation is laborious and time-consuming. So distant supervision method is firstly introduced by \cite{distantsupervisionRE} to generate training data automatically, and many subsequent works \cite{distantRE1, distantRE2} focus on reducing the impact of labelling errors in the automatically generated training data.

	The relation detection of KBQA differs from the traditional relation extraction in two aspects. On the one hand, there are thousands of relations in the knowledge base, while there are only 74 relations in TAC-KBP2015 task, which has the largest predefined relation set. Some relations are even absent from the training data for the relation detection of KBQA, so it is inappropriate to formulate this task as a classification problem.
	On the other hand, entity information, such as entity embeddings or types, of the two entities used in the relation extraction are not available for the relation detection of KBQA, since there is only one argument (the topic entity) given.
	\subsection{Relation detection for KBQA}
	Open domain KBQA task consists of two subtasks: identifying the topic entity mentioned in the question and determining the answer path (i.e. the path start from the topic entity to the answer node in the knowledge base). Determining the answer path is also the process of relation detection.

	Considering thousands of relations in the open domain question answering tasks, relation detection is processed as a candidate-selection problem. Firstly, candidate relations are generated according to the result of entity linking, and then they are ranked by their semantic similarity with the question. Ranking methods consists of the traditional ones that rely on feature engineering and the deep learning ones that are capable of learning to extract features automatically.
	Much work has been done on the extraction of discriminant features for relation extraction. Features for similarity measurement are extracted from four aspects, namely literal, derivation, synonym, and context, and learning-to-rank \cite{BastLRanking} is adopted for the relation detection. The dependency parsing tree \cite{yaoRD} is explored for relation detection.
	Traditional methods cannot perform well when dealing with the high representational and linguistic variability between questions and relations. For example, the relation word of question ``where is $\langle e\rangle$ located?'' is ``located'', while the relation recorded in the KB is ``containedby''. In this case, traditional methods fail to capture the similarity because of the semantic chasm between symbols.

	Deep learning method is firstly applied to the relation detection of KBQA in \cite{Bordes2014} and since then various models based on deep learning are developed. Most of these methods follow the encoding-comparing paradigm, which maps the question and the candidate relation to vectors respectively, and then computes the similarity between vectors as their semantic similarity.
	For example, Dai et al. \cite{dai2016} taken the relation as a single token and initialized with pre-trained vector learned by TransE \cite{transE}. Some work models the relation detection as a sequence matching and ranking problem, since the relation in KB is denoted by a sequence of meaningful words. Character-level representation is adopted in \cite{golubcharcnn}, to reduce the size of parameters and improve robustness in processing the out-of-vocabulary words.
	A CNN model with attentive max-pooling (AMPCNN) is proposed in \cite{AMPCNN}. Traditional CNN layer is adopted to encode the question, and then a novel attentive max-pooling mechanism is proposed to summarize the question representation aiming at putting more weights on the relation description parts of the question.
	In \cite{improved}, relations are represented using Bi-LSTM with inputs of different granularity (both on the word and the relation-level representations), and the question is represented by a hierarchical residual Bi-LSTM network with different abstract levels.

	However, the aggregation operation such as max-pooling or average-pooling operation, which reduces the question from a sequence of vectors to a single vector, results in information loss before the comparison. The representation methods with attention mechanism concentrate on important units of the sequence. In this paper, comparison is performed before the aggregation operation at a lower level by learning word-level interactions, so as to avoid the information loss before comparison.
	\subsection{Sequence to sequence semantic similarity measurement}
	Sequence to sequence semantic similarity measurement is one of the fundamental tasks in natural language processing (NLP). It is involved in answer selection, natural language inference and machine reading comprehension. Under the traditional encoding-comparing paradigm, CNN \cite{CNNseq2seq} and recurrent neural network (RNN) \cite{LSTMseq2seq, LSTMseq2seq2} are exploited respectively, and attention-based methods \cite{ABCNNseq2seq, attentionSeq2seq} are further introduced to emphasize important units. To solve the problem that fixed-dimensional vector is insufficient to capture information in the input sequences, some efforts are made to the compare-aggregate framework.
	For example, the matching-LSTM model \cite{textualentailment,compareaggSNIanother} and pairwise word interaction model \cite{pairwisesni} are proposed for the natural language inference task, and then the models are extended to machine reading comprehension task \cite{machinereading, multiPerspectiveMatchingReading}. State-of-the-art results are reported on multiple tasks simultaneously within the compare-aggregate framework \cite{compare-aggregate, multiPerspectiveMatchingSNIQA} .
	\def\stackrel{\rightarrow}{\bm{h}}{\stackrel{\rightarrow}{\bm{h}}}
	\def\stackrel{\leftarrow}{\bm{h}}{\stackrel{\leftarrow}{\bm{h}}}

	\section{Method}
	\subsection{Task definition}
	Given a question, its topic entity (identified by entity linking) and candidate relations \(C=\left\{rel_1,rel_2,\dots,rel_{|C|}\right\}\) in knowledge base, relation detection aims to identify the relation (chain) mentioned in the question. That is, finding the chain of relations that connects the topic entity and the answer in the KB.

	The relation detection task is formulated as a point-wise ranking problem.
	For each relation \(r\) in the candidate relation set \(C\), the model computes its semantic similarity with the question \(s(q,r)\), and selects the relation path with the highest score as the answer path, formally:
	\begin{equation}r^+= \argmax_{r\in C}s(q,r)\end{equation}

	\begin{figure*}[!htb]
		\centering
		\includegraphics[width=0.8\textwidth]{./images/framework_6.pdf}
		\caption{The framework of the attention-based word-level interaction model}
		\label{fig:framework}
	\end{figure*}

	\subsection{Model architecture}
	According to the above analysis, the key is to calculate the semantic similarity between the question and the candidate relation.
	The architecture of the model is given in Figure \ref{fig:framework}. The model consists of five components: 1) relation representation layer, which represents relation from diverse granularities with a Bi-LSTM layer. 2) question representation layer, another bi-LSTM is adopted to represent words in the question with both the past and future context in consideration. 3) word-level attention layer, that maps the relation representation to word representation of question with attention mechanism. 4) feature extraction layer, a CNN with multi-kennel \cite{kimcnn} is adopted to compare the question and mapped relation representation with context considered, then max-pooling operation is performed to aggregate the comparison results to extract the comparison features.
	5) output layer, a dense layer computes the final semantic similarity based on features extracted by the CNN layer and the max-pooling operation.

	\subsubsection{Relation representation layer}
	Both the relation and word-level representations \cite{improved} are adopted.
	Relation level representation regards the relation, for example, ``place\_of\_birth'', as a single token, while word-level representation takes the relation as a sequence of words, i.e. ``place of birth''.
	The relation-level representation focuses more on the global information of the relation such as the entity type of objective.
	However, relation-level representation suffers from data sparsity because some relations are absent from the training data and their relation representation is initialized randomly during inference.
	Through modelling the relation as a sequence of words, word-level representation relieves the impact of data sparsity.
	Moreover, it enables us to perform word-level comparison between question and relation.

	In this paper, a candidate relation (chain) \(r=\{r_1, r_{\left|r\right|}\}\), is represented as \(\{r_1^{word}, \dots, r_{\left|r\right|}^{word},\} \cup \{r_1^{rel}, r_{\left|r\right|}^{rel} \}\), where \(\left|r\right|\leq 2\) is the number of relations in the chain.
	And we denote the number of tokens in the representation as \(|R|\).
	Therefore the combined representation of the input relation ``place\_of\_birth'' becomes \{``place'', ``of'', ``birth''\}\(\cup\)\{``place\_of\_birth''\}. Similarly, the input relation chain with two relations ``government\_positions\_held, from'' is represented as \(\{government, position, held, from\} \cup \{government\_positions\_held, from\}\). A lookup table transforms tokens in the sequence from ``one hot representation'' to corresponding embedding vectors of \(d\) dimensions. We have the word embedding vectors \(V\in \mathbb{R}^{|V|\times d}\), and the relation embedding vectors \(V_{rel}\in \mathbb{R}^{|V_{rel}|\times d}\), where \(|V|\) and \(|V_{rel}|\) are the vocabulary size and the number of relations in the KB respectively.

	Then a Bi-LSTM layer is incorporated to represent word with its context in consideration.
	LSTM \cite{LSTM} is a variant of RNN. With the memory cell and the input, forget and output gate to manage the information flow, LSTM avoids the gradient exploding and vanishing problem and is capable of capturing the long range dependencies. The structure of the LSTM cell is illustrated in Figure \ref{fig:LSTM_cell}.

	The forward LSTM cell outputs the encoding result based on the input \(\bm{x}_t\), the memory cell \(\bm{c}_{t-1}\) and the output of the last time \( \stackrel{\rightarrow}{\bm{h}}_{t-1}\). Here we denote the representation procedure in the cell as \(\stackrel{\rightarrow}{\bm{h}}_{t}=lstm(c_{t-1}, \stackrel{\rightarrow}{\bm{h}}_{t-1}, x_t)\). The three gates, namely input gate \(\bm{i}_t\), forget gate \(\bm{f}_t\) and output gate \(\bm{o}_t\), which respectively determine whether to update, reset and output values of the memory cell, are computed as follows
	\begin{equation}
	\bm{i}_t=\sigma\left(\bm{W}_i \bm{x}(t)+\bm{U}_i\stackrel{\rightarrow}{\bm{h}}_{t-1}+\bm{b}_i\right)
	\end{equation}
	\begin{equation}
	\bm{f}_t=\sigma\left(\bm{W}_f \bm{x}(t)+\bm{U}_f\stackrel{\rightarrow}{\bm{h}}_{t-1}+\bm{b}_f\right)
	\end{equation}
	\begin{equation}
	\bm{o}_t=\sigma\left(\bm{W}_o \bm{x}(t)+\bm{U}_o\stackrel{\rightarrow}{\bm{h}}_{t-1}+\bm{b}_o\right)
	\end{equation}
	where \(\bm{W}_*\in \mathbb{R}^{d_c\times d}\), \(\bm{U}_*\in\mathbb{R}^{d_c\times d_c}\) and \(\bm{b}*\in \mathbb{R}^{d_c}\) are parameters to be learned, hyper-parameter
	\(d_c\) is the dimension of LSTM cell and \(\sigma\) is the sigmoid function.
	The value of the memory cell is updated by
	\begin{equation}
	\bm{c}_t=\bm{i}_t\odot\tanh\left(\bm{W}_c \bm{x}(t)+\bm{U}_c\stackrel{\rightarrow}{\bm{h}}_{t-1}+\bm{b}_c\right)+\bm{f}_t\odot \bm{C}_{t-1}
	\end{equation}
	where \(\odot\) denotes element-wise multiplication, \(\bm{W}_c\in \mathbb{R}^{d_c\times d}\), \(\bm{U}_c\in \mathbb{R}^{d_c\times d}\) and \(\bm{b}_c\in \mathbb{R}^{d_c}\) are parameters to be learned. The output of the unit at time step \(t\) is
	\begin{equation}
	\stackrel{\rightarrow}{\bm{h}}_t=\bm{o}_t\odot\tanh(\bm{c}_t)
	\end{equation}
	Similarly, the backward LSTM cell is represented as
	\begin{equation}\stackrel{\leftarrow}{\bm{h}}_t=lstm(\bm{c}_{t+1}, \stackrel{\leftarrow}{\bm{h}}_{t+1}, \bm{x}_t)\end{equation}

	For input vector sequence \(\bm{X}=(\bm{x}_1,\bm{x}_2,\dots,\bm{x}_N)\) with length \(N\), forward LSTM encodes the input \(\bm{x}_t\) with context from \(\bm{x}_1\) to \(\bm{x}_{t-1}\) into vector \(\stackrel{\rightarrow}{\bm{h}}_t\), while backward LSTM encodes \(x_t\) to \(\stackrel{\leftarrow}{\bm{h}}_t\) considering the future contextual information from \(\bm{x}_N\)  to \(\bm{x}_{t+1}\).
	Concatenating \(\stackrel{\rightarrow}{\bm{h}}_t\) and \(\stackrel{\leftarrow}{\bm{h}}_t\), the Bi-LSTM encodes the input \(\bm{x}_t\) with both the past and future information from the sentence in consideration. Then Bi-LSTM layer can be denoted by
	\begin{equation}\bm{H}=Bi-LSTM(\bm{X})=\left[ \left[\begin{array}{cc}
	\stackrel{\rightarrow}{\bm{h}}_{1}  \\
	\stackrel{\leftarrow}{\bm{h}}_{1}   \end{array} \right],
	\left[ \begin{array}{cc}
	\stackrel{\rightarrow}{\bm{h}}_{2}  \\
	\stackrel{\leftarrow}{\bm{h}}_{2}   \end{array} \right],
	\dots,
	\left[ \begin{array}{cc}
	\stackrel{\rightarrow}{\bm{h}}_{N}  \\
	\stackrel{\leftarrow}{\bm{h}}_{N}   \end{array} \right] \right]
	\end{equation}

	\begin{figure}[h]
		\centering
		\includegraphics[width=0.45\textwidth]{./images/LSTM_figure2.png}
		\caption{The structure of the LSTM cell}
		\label{fig:LSTM_cell}
	\end{figure}

	In this paper, the context aware representation of relation can be formally defined as follows
	\begin{equation}
	\bm{R}=Bi-LSTM\left(\left[\bm{r}_1^{word}, \dots, \bm{r}_{|r|}^{word}, \bm{r}_1^{rel}, \dots, \bm{r}_{|r|}^{rel}  \right]\right)
	\end{equation}
	where \(\bm{R}\in \mathbb{R}^{2d_r\times|R|}\), \(d_r\) is the dimension of LSTM cell for the relation representation.

	\subsubsection{Question representation layer}
	Another Bi-LSTM layer is adopted to capture the context-aware representation of the question. For an input question \(q=\left\{w_1, w_2, \dots, w_{|Q|}\right\}\), where \(|Q|\) is the number of words in the question, every word \(w_i\) is mapped to its embedding vector \(\bm{w}_i\) by the lookup table. Therefore, the question is represented as a sequence of word vectors \(\bm{q}=\left\{\bm{w}_1, \bm{w}_2, \dots, \bm{w}_{|Q|}\right\}\). Note that words in questions and relations share the same word embedding. Finally, the context-dependent representation of these question words is defined as follows
	\begin{equation}\bm{Q}=Bi-LSTM(\left[\bm{w}_1, \bm{w}_2, \dots, \bm{w}_{|Q|}\right])\end{equation}
	where \(\bm{Q}\in \mathbb{R}^{2d_q\times |Q|}\), and \(d_q\) is the dimension of the LSTM cell.
	\subsubsection{Attention layer}
	The attention layer aims to learn fine-grained interactions between the question and the relation.
	For every word \(w_i\) in the question, the attention weight between \(w_i\) and the relation word \(w_j\) is calculated as follows
	\begin{equation}\alpha_{i,j}=\bm{r}_i^T \bm{W}_A \bm{q}_j\end{equation}
	where \(\bm{W}_A\in \mathbb{R}^{2d_r\times 2d_q}\) is a learnable matrix.
	Then we have
	\begin{equation}\bm{A}=softmax\left(\bm{R}^T \bm{W}_A \bm{Q}\right)\end{equation}
	where \(\bm{A}\in \mathbb{R}^{|R|\times |Q|}\) is the attention weights between the words in the question and the relation, i.e.
	\begin{equation}
	a_{i,j}=\frac{exp(\alpha_{i,j})}{\sum_{k=1}^{|Q|}\sum_{l=1}^{|R|}exp(\alpha_{k,l})}
	\end{equation}
	Then a weighted sum of relation vectors is calculated with the attention to word \(w_i\),
	\begin{equation}\widehat{\bm{r}}_i=\sum_{k=1}^{|R|}a_{i,k} \bm{r}_k\end{equation}
	It can be seen that \(\widehat{\bm{r}}_i\) encodes parts of the relation that best matches the question word \(w_i\).
	Then overall we have
	\begin{equation}\widehat{\bm{R}}=\bm{R}\bm{A}\end{equation}
	and \(\widehat{\bm{R}}\in \mathbb{R}^{2d_r\times |Q|}\).

	By concatenating the representation of question words and the corresponding weighted representation of the relation, we get the word interactions \(\bm{M}\) between the question and the relation
	\begin{equation}\bm{M}=\left[\left[ \begin{array}{cc}
	\bm{q}_{1}  \\
	\widehat{\bm{r}}_{1}   \end{array} \right],
	\left[ \begin{array}{cc}
	\bm{q}_{2}  \\
	\widehat{\bm{r}}_{2}   \end{array} \right],
	\dots,
	\left[\begin{array}{cc}
	\bm{q}_{N}  \\
	\widehat{\bm{r}}_{N}   \end{array} \right]\right]\end{equation}
	where \(\bm{M} \in \mathbb{R}^{2(d_q+d_r)\times |Q|}\).

	\subsubsection{Feature extraction layer}
	Instead of the explicit element-wise comparison \cite{compare-aggregate}, a CNN layer with multiple kernel sizes \cite{kimcnn} is adopted to compare the semantics of the question and the relation with different context scopes.
	Then the comparison results are aggregated with the max-pooling operation to extract the most discriminant comparison results as features.

	Let \(\bm{m}_j\in \mathbb{R}^{2(d_q+d_r)}\) be the \(j\)-th column of matrix \(\bm{M}\), and \(\bm{m}_{i:i+j}=\bm{m}_i\oplus \bm{m}_{i+1}\oplus \dots \oplus \bm{m}_{i+j}\), \(\bm{m}_{i+j}, \bm{m}_{i:i+j} \in \mathbb{R}^{2(d_q+d_r)}\) represents the concatenation of columns \(\bm{m}_i, \bm{m}_{i+1},\dots,\bm{m}_{i+j}\), where \(\oplus\) is the column concatenation operator. Then the convolution operation involves a filter with kernel size of \(k\), i.e. \(\bm{w}\in \mathbb{R}^{2(d_q+d_r)\times k}\), is performed to generate a feature. For example, a feature \(c_i\) is generated by applying the filter at \(\bm{m}_{i:i+k}\)
	\begin{equation}
	c_i=relu\left(\bm{w}\cdot \bm{m}_{i:i+k}\right)
	\end{equation}
	where \(relu\) is the rectified linear unit \cite{relu} activation.
	By applying the filter at every time step, we get the features along with the sequence
	\begin{equation}\bm{c}=\left[c_1, c_2, \dots, c_{|Q|}\right]\end{equation}
	After that, a max-over-time pooling operation is performed, yielding \(\widehat{c}=max(\bm{c})\) as the feature captured by this filter. The filter can be seen as a pattern matcher, to detect whether there is \(k\)-gram pattern in the sequence regardless its position. Thus this is a procedure of automatic feature extraction. Aiming at the semantic similarity measurement, the filter learns to capture features relevant to the semantic similarity calculation, and the max-pooling operation selects the significant one along the sequence. We adopt \(d_f\) filters thus getting \(d_f\) dimensional semantic similarity features \(\bm{f} \in \mathbb{R}^{d_f} \).
	Kernels with different sizes are used to capture comparison patterns with multiple lengths.

	The CNN layer conducts a fine-grained comparison between the question and the relation, and the max-pooling serves as a primary aggregation operation. With the comparison performed at a low-level representation, our model relieves the information loss problem caused by aggregating the information into a fixed-dimensional vector before comparison.
	\subsubsection{Output layer}
	Given the features \(\bm{f}\) extracted by the CNN layer and max-pooling, the output layer calculates the semantic similarity between the question and the relation.
	\begin{equation}o=\bm{w}_o^T\bm{f}\end{equation}
	where \(\bm{w}_o \in \mathbb{R}^{d_f}\) are parameters to be learned.
	\subsection{Training and inference}
	Training aims to give the golden relation higher score than the false relation. Pairwise ranking loss is defined as
	\begin{equation}
	L=\sum_{q_i \in \mathbb{Q}}\sum_{j\in C_{q_i}}\sigma\left(S(q_i, r_j^-|\theta)-S(q_i,r^+_i|\theta)\right)
	\end{equation}
	where \(\mathbb{Q}\) denotes the questions in the training set, \(C_{q_i}\) is the false candidate relation set, \(r^+_i\) is the golden relation of question \(q_i\), \(S\) denotes ABWIM. \(\theta\) denotes parameters of the network, and it consists of word embeddings, relation embeddings, parameters in the LSTM network for relation and question representation, feature maps in the CNN network and \(\bm{w}_o\) in the output layer.

	Backpropagation method is adopted to update the parameters. Formally, the parameters in \(\theta\) are updated by
	\begin{equation}\theta=\theta-\lambda\frac{\partial L}{\partial \theta}\end{equation}
	where \(\lambda\) is the learning rate. Adadelta optimizer \cite{adadelta} is adopted to adjust the learning rate.
	Dropout is added to the output of embedding, LSTM, and CNN layer so as to avoid the overfitting problem.

	During inference, the semantic similarity \(S(q,r|\theta)\) is calculated for each candidate relation \(r\in C_q\), and the relation with highest semantic similarity score is regarded as the corresponding relation.
	\section{Experiment}
	\subsection{Experimental setting}
	\subsubsection{Datasets and metrics}
	To evaluate the effectiveness of ABWIM, experiments are carried out on the relation detection of two benchmark KBQA tasks, namely SimpleQuestions \cite{simpleqadataset} and WebQuestions \cite{webqadataset}.
	SimpleQuestions is a single-relation question-answering dataset, and its KB is a subset of Freebase and contains 2M entities.
	Questions in this dataset can be answered by referring to single triple from the KB, so the length of relation chain \(|r|=1\).
	WebQuestions is a multi-relation question-answering dataset, and the whole Freebase is used as its KB.
	Similar procedure is adopted to generate the relation detection datasets based on these KBQA datasets.
	Firstly, entity linking is performed to extract the topic entity from the question.
	Then relations and relation chains (only on WebQuestions dataset and chain length no greater than 2) connected to the topic entity are taken as candidate relations.
	After that, labelling the relation which points to the answer node as positive samples and other relations as negative samples.
	Finally, the mention of the topic entity in the question is replaced with a special token \(\langle e\rangle \).
	Yin et al. \cite{AMPCNN} and Yu et al. \cite{improved} released their relation detection datasets\footnote{The datasets are available at https://github.com/Gorov/KBQA\_RE\_data} based on SimpleQuestions and WebQuestions, and we also use the datasets for fair comparison.

	Accuracy metric is used to measure the relation detection result,
	\begin{equation}
	A=\frac{N_c}{N}
	\end{equation}
	where N is the number of questions in the dataset, and \(N_c\) is the number of questions whose relation is correctly identified.
	\subsubsection{Hyper parameters}
	The hyper-parameters of ABWIM are summarized in Table \ref{table: experimentconfig}.
	In the experiment, word embeddings are initialized with the GloVe \cite{glove} with \(d=300\), and embeddings of relations and words that are out of vocabulary are randomly initialized by sampling values uniformly from (-0.25, 0.25).
	Values of embedding are updated during the training process.

	All the experiments are carried out on a machine with a Nvidia GTX1080 GPU, and neural networks are implemented in Keras\footnote{https://github.com/fchollet/keras} with Tensorflow\footnote{https://github.com/tensorflow/tensorflow} as the backend.
	\begin{table}[!htb]
		\centering
		\caption{Hyper parameters of the ABWIM. (SQ and WQ denote the SimpleQustion and WebQuestions datasets respectively)}
		\label{table: experimentconfig}
		\begin{small}
			\begin{tabular}{cccc}
				\hline
				Parameter&Search Space&SQ&WQ\\
				\hline
				Embedding dim. \(d\) & 300 & 300 & 300\\
				Dim. of LSTM \(d_q,d_r\)&	50, 100, 150, 200 & 150 &10{\tiny }0\\
				CNN kernel size & [3], [1,3,5] & [1,3,5] & [1,3,5]\\
				num. of filters & 50, 100, 150, 300 & 150 & 100 \\
				Dropout rate & 0.2, 0.3, 0.35, 0.4, 0.5	& 0.35 &	0.35 \\
				Batch size&	64, 128, 256, 512&256 & 128 \\
				\hline
			\end{tabular}
		\end{small}
	\end{table}

	\subsubsection{Baselines}
	We compare the ABWIM with several baselines.
	All the baseline models fall into the encoding-comparing paradigm, which firstly map the question
	and relation as vectors and then get the semantic similarity by vector comparison.
	These methods vary in the representation procedure, specifically:
	\begin{itemize}
		\item Bi-LSTM: two Bi-LSTM layers represent the question and the relation respectively, and the output of the last timestep is taken as their vector representation.
		\item BiCNN \cite{BiCNN}: both the question and the relation are represented by CNN with the word hash trick on letter-tri-grams.
		\item AMPCNN \cite{AMPCNN}, namely the CNN with attentive the max-pooling.
		CNN is adopted to represent the question and the relation respectively.
		An attentive max-pooling operation is performed to get the vector representation of question, aiming at putting more weights on the words that indicate the relation.
		\item HR-Bi-LSTM (Hierarchical Residual Bidirectional LSTM model) \cite{improved}, word and relation-level representations of relation are adopted, and the question is represented with HR-Bi-LSTM to obtain hierarchies of abstraction.
	\end{itemize}
	\subsection{Result}
	\begin{table}[!htb]
		\centering
		\caption{Comparison of accuracy with the baselines on the SimpleQuestions and WebQuestions}
		\label{table:res0}
		\begin{small}
			\begin{tabular}{cccc}
				\hline
				Model & Relation Input & SimpleQuestions & WebQuestions \\
				\hline
				APMCNN & Words & 91.3 & -\\

				BiCNN & Words & 90.0 & 77.74 \\

				Bi-LSTM & Words & 91.2 & 79.32 \\

				HR-Bi-LSTM & Words + relations & 93.3 & 82.53\\

				Our method & Words + relations & \textbf{93.5} & \textbf{85.32} \\
				\hline
			\end{tabular}
		\end{small}
	\end{table}
	The relation detection results are shown in Table \ref{table:res0}. Our method  comes close to the performance of the state-of-the-art method on SimpleQuestions dataset. On the WebQuestions dataset, our method outperforms the best baseline methods by 2.79 points.
	Questions and relations in the WebQuestion dataset are longer and the information loss problem is more serious, thus ABWIM outperforms the encoding-comparing baseline methods.
	The experimental results demonstrate the effectiveness of ABWIM, which learns the word-level interactions and aggregates the comparison results with multi-kernel CNN networks.

	\subsection{Model Ablation Analysis}
	Ablation experiments are further carried out to analyse the effectiveness of the attention-based word-level interactions and the sequence preprocessing layer.

	\begin{table*}[!htb]
		\centering
		\caption{Ablation experiment results}
		\label{tab: abaltion_res}
		\begin{small}
			\begin{tabular}{cccc}
				\hline
				Analysis content & Model & SimpleQA & WebQA \\
				\hline
				\multirow{3}{*}{Attention and framework}
				&Encoding-comparing model& 92.8 & 78.41 \\
				&Encoding-comparing model with bi-directional attention& 93.2 & 84.44 \\
				&Word-level interaction model without attention & 93.0 & 83.81\\
				\hline
				\multirow{3}{*}{Context representation }
				&Without preprocessing layer &92.9 &82.49\\
				&Gated liner transformation&92.9 &84.71\\
				& Fully CNN network & 93.3 & 84.79\\
				\hline
				-&ABWIM (our method)&\textbf{93.5} & \textbf{85.32} \\
				\hline
			\end{tabular}
		\end{small}
	\end{table*}
	\subsubsection{Attention and word-level interaction mechanisms}
	Ablation experiments for the attention and word-level interaction mechanisms are as follows.
	\begin{itemize}
		\item Encoding-comparing model: questions and relations are represented with Bi-LSTM layers and the final output are taken as their representations.
		Then semantic similarity is measured by the cosine similarity between vectors, formally
		\begin{equation}
		o=\cos(\bm{q}, \bm{r})
		\label{eq:cossim}
		\end{equation}

		\item Encoding-comparing model with bi-directional attention: following the traditional representation-comparison schema, questions and relations are firstly represented as vectors.
		Bi-directional LSTM layer is adopted to perform the context aware representation, and the representation results are merged to vectors with attention mechanism.
		Attention weights for questions are calculated by column wise max-pooling,
		\begin{equation}
		\widehat{a}_i = \max\left(a_{i,1}, a_{i,2},\dots, a_{i, |R|}\right)
		\end{equation}
		and soft-max operation,
		\begin{equation}
		\alpha_i = \frac{e^{\widehat{a}_i}}{\sum_{j=1}^{|Q|} e^{\widehat{a}_j}}
		\end{equation}
		Then the vector representation of the question is
		\begin{equation}
		\widehat{\bm{q}} =\sum_{i=1}^{|Q|}\alpha_i \bm{q}_i
		\end{equation}
		where \(\bm{q}_i\) is the \(i\)-th column of \(\bm{Q}\). Similarly, attention weights of the relation are calculated by
		\begin{equation}
		\widehat{b}_i = \max\left(a_{1,i}, a_{2,i},\dots, a_{|Q|,i}\right)
		\end{equation}
		and
		\begin{equation}
		\beta_i = \frac{e^{\widehat{b}_i}}{\sum_{j=1}^{|R|} e^{\widehat{b}_i}}
		\end{equation}
		Then relation's vector representation is
		\begin{equation}
		\widehat{\bm{r}} =\sum_{i=1}^{|A|}\beta_i \bm{r}_i
		\end{equation}
		Finally, the semantic similarity is calculated as
		\begin{equation}
		o=\cos(\widehat{\bm{q}}, \widehat{\bm{r}})

		\end{equation}
		\item Word-level interaction model without attention: removing the attention mechanism from the ABWIM, i.e. the attention weights are set equally. Formally, all the elements in \(\bm{A}\in \mathbb{R}^{|R|\times |Q|}\) are set to \(\frac{1}{|R|\times |Q|}\).
	\end{itemize}

	\begin{figure}[!htb]
		\centering
		\includegraphics[width=0.45\textwidth]{./images/attention.pdf}
		\caption{Heat-map of the attention weight matrix, which shows the soft alignment between the question (bottom) and relation (left).}
		\label{fig:Attention}
	\end{figure}

	\begin{figure}[!htb]
		\centering
		\includegraphics[width=0.45\textwidth]{./images/maxvpos.pdf}
		\caption{The visualization of the largest value of each dimension in the output of CNN layer. The first 100, second 100, and last 100 dimensions are generated with window size 1, 3 and 5 respectively.}
		\label{fig:maxvpos}
	\end{figure}

	The top of Table \ref{tab: abaltion_res} shows the ablation experiment results.
	First, both tasks benefit from both the bi-directional attention mechanism, especially for WebQA dataset (84.44\% vs 78.41\%). Attention mechanism enables the model to focus on important parts of the sequence and get a better representation.
	Second, comparing the sequences using word-level representations also outperforms the traditional encoding-comparing model, because multiple comparison operations are performed before information in the question is merged into a single vector. Finally, ABWIM, which combines the bi-directional attention and word-level interaction mechanisms, achieves state-of-the-art results on both tasks with accuracy 93.5\% and 85.32\% respectively.

	Visualization of the attention matrix and the positions of maximum values obtained is given to further illustrate how the ABWIM works.
	Visualization of the attention matrix learned is given in Figure \ref{fig:Attention}. The input question is ``what university did $\langle e\rangle$ attend'' and candidate relation path which contains two relation ``/people/person/education,  /education -/education/institution''. Depth of the colour represents the attention weights, {\color{black} and the darker the higher}.
	It shows that ABWIM learns a reasonable alignment between words in the question and the relation. For example,  more attention is paid to the words ``university'' and ``attend'', which are relevant to relation description. And it is also observed that the deep learning method is capable of capturing the semantic similarity between different symbols.
	Visualization of the maximum value in each dimension of the CNN output is given in Figure \ref{fig:maxvpos}. It is observed that the largest values are obtained at the words which are crucial for relation detection. In other words, the CNN layer also learns to focus on important words when performing comparisons.

	\subsubsection{Preprocessing layer}
	Ablation experiments are also carried out to study the effectiveness of the preprocessing Bi-LSTM layer which learns context aware representations. CNN and liner transformation layer are used to perform preprocessing, considering the difficulty in parallelization of the LSTM state computations. Specific ablation experiments are listed as follows.
	\begin{itemize}
		\item Without preprocessing: simply removing the LSTM layers for question and relation preprocessing, and the word-level alignment and comparison are performed on their embeddings.
		\item Gated linear transformation: the question and relation are preprocessed as follows
		\begin{equation}
		\widehat{\bm{Q}}=\sigma\left(\bm{W}_i\bm{Q}\right)\odot\tanh\left(\bm{W}_u\bm{Q}\right)
		\end{equation}
		\begin{equation}
		\widehat{\bm{R}}=\sigma\left(\bm{W}_i\bm{R}\right)\odot\tanh\left(\bm{W}_u\bm{R}\right)
		\end{equation}
		where \(\bm{W}_i\in \mathbb{R}^{d_i\times d}\) and \(\bm{W}_u \in \mathbb{R}^{d_i\times d}\) are parameters to be learned, and \(d_i\) is a hyper-parameter.

		\item Fully CNN: unlike LSTM that depends on the computations of the previous timestep, CNN enables parallelization over every element in a sequence, so it is capable of making full use of the parallel architecture of GPU. We study the performance of fully CNN network on the relation detection of KBQA. The LSTM layer for question and relation preprocessing is replaced with a multi-kernel CNN layer, and the dimension of the CNN output is consistent with that of the original LSTM layer.

	\end{itemize}

	Experimental results with different configurations on the context representation layer are given in the second parts of Table \ref{tab: abaltion_res}. First, ABWIM outperforms several strong baselines, though there is no preprocessing operation. It indicates the effectiveness of learning attention-based word alignments between question and relation. Second, a context aware representation of words can further improve the result and the preprocessing layer is also important for the overall performance. The LSTM layer, which is capable of learning long range dependency,  outperforms the CNN and gated liner transformation. Finally, CNN and gated linear transformation do not rely on the computations of previous timestep, so they can fully utilize the computational capability of GPU and are more faster to be trained and perform inference. We leave the exploration of advanced CNN models with gated linear units \cite{gatedcnn} and residual connections \cite{residuallearning} as a future work.

	\section{Conclusion and future work}
	Relation detection is a key component of KBQA and it is difficult because of the flexible expressions of the relation in natural language and massive relations in the knowledge base.
	In this paper, we propose to learn the attention-based word-level interactions between the question and the relation. Representations of relations are firstly mapped to the semantic space of every word in question with attention mechanism. Then multiple comparisons are performed on the word-level representations, and finally comparison results are merged to evaluate the semantic similarity. With multiple comparisons performed, ABWIM alleviates the bottleneck of compressing the question and the relation into fixed-dimensional vectors.
	Experimental results of relation detection on both SimpleQuestions and WebQuestions show that the proposed model achieves state-of-the-art accuracy.

	In the future work, we will study transfer learning between relation detection of KBQA and the traditional relation extraction task, and further learn to utilize the great amount of training data generated by distant supervision methods to improve the performance of KBQA relation detection.

	\section*{Acknowledgments}
	The authors thank Wenqiang Dong and Weili Zhang for the valuable comments on this work.
	\section*{References}

redpajama_set_name: RedPajamaArXiv


text:
Search For: profane
753. Revelation 18 1. And after these things I saw an angel coming down out of heaven, having great authority, and the earth was lightened by his glory. 2. And he cried out mightily with a great voice, saying, Babylon the great is fallen, is fallen, and is become a habitation of demons, and a hold of every unclean spirit, and a hold of every unclean and hateful bird.
The Book of Leviticus 19
1 Yahweh spoke to Moses, saying, 2 "Speak to all the congregation of the children of Israel, and tell them, 'You shall be holy; for I Yahweh your God am holy. 3 "'Each one of you shall respect his mother and his father. You shall keep my Sabbaths. I am Yahweh your God.
http://www.smallcanonsearch.com/read.php?book=lev&section=19
A Disclosure of the Hidden Treasures of Heaven Contained in the Holy Scripture or Word of the Lord, Together with Amazing Things Seen in the World of Spirits and in the Heaven of Angels 303
303. A man acquires a life by all the things he is persuaded of, that is, which he acknowledges and believes. That of which he is not persuaded, or does not acknowledge and believe, does not affect his mind. And therefore no one can profane holy things unless he has been so persuaded of them that he acknowledges them, and yet denies them. Those who do not acknowledge may know, but are as if they d...
http://www.smallcanonsearch.com/read.php?book=ac&section=303
414. That to "dwell in tents" signifies what is holy of love, is evident from the signification of "tents" in the Word. As in David: Jehovah, who shall abide in Thy tent? Who shall dwell in the mountain of Thy holiness? He that walketh upright, and worketh righteousness, and speaketh the truth in his heart (Ps. 15:1-2), in which passage, what it is to "dwe...
582. After that the sons of God came in unto the daughters of men, and they bare to them. That this signifies that they became Nephilim when they had immersed the doctrinals of faith in their cupidities, is evident from what was said and shown above in verse 2, namely, that the "sons of God" signify the doctrinal things of faith, and that "daughters" signify cupidities. The bir...
623. The earth was filled with violence. That this is said on account of their foul cupidities, and most of all on account of those which come of the love of self, or of inordinate arrogance, is evident from the Word. It is called "violence" when men do violence to holy things by profaning them, as did these antediluvians who immersed the doctrinal things of faith in all kinds of cupidit...
1010. Whoso sheddeth man's blood in man. That this signifies extinguishing charity, and that "in man" is with man, is evident from the signification of "blood"-concerning which above-as being the holy of charity, and from its being said "man's blood in man." This means his internal life, which is not in him, but with him; for the life of the Lord is charity, which is...
1059. This is the sign of the covenant which I have established between Me and all flesh that is upon the earth. That this signifies that the indication of the Lord's presence in charity was not only with the man of the church, but also with the man outside the church, is evident from the signification of "all flesh" as being every man, and consequently the whole human race. That the who...
1183. What is signified in particular by "Erech, Accad, and Calneh, in the land of Shinar," cannot so well be seen, because they are not mentioned in other parts of the Word, except Calneh (in Amos 6:2) but they are varieties of such worship. But as regards the land of Shinar in which these worships were, that external worship wherein is what is profane is signified by it in the Word is...
1368. That by "Ur of the Chaldees" is signified external worship in which there are falsities, is evident from the signification in the Word of "the Chaldeans." It has been shown above, at verse 9, that by "Babel" is signified worship in which interiorly there are evils; but by "Chaldea" is signified worship in which interiorly there are falsities. Consequen...
2220. That "Sodom" is all evil from the love of self, is evident from the signification of "Sodom" in the Word. Although in the following chapter it appears as if the evil of the worst adultery was signified by "Sodom," nevertheless in the internal sense nothing else than evil from the love of self is signified by it. In the Word also the abominations that well forth...
2342. And baked unleavened [bread]. That this signifies purification, is evident from the signification of "unleavened" or "unfermented." In the Word "bread" signifies in general all celestial and spiritual food, thus celestial and spiritual things in general (see n. 276, 680, 1798, 2165, 2177). That these should be free from everything impure was represented by bread...
2986. From the sons of Heth. That this signifies that it was a church from the Gentiles, is evident from the signification of the "sons of Heth." The sons of Heth were not those with whom the church was set up anew, but are those by whom the church is represented; for all things in the Word are representative; and they do not signify the persons who are named, but through them things of...
3399. That in the internal sense "to lie with a woman" denotes to pervert and adulterate truth (here truth Divine, because by the "woman" or Rebekah is represented Divine truth, as shown above) is evident from the fact that by "lying with," by "adultery," and by "harlotry" in the Word, nothing else is signified than perversions of good and falsific...
3402. He that toucheth this man and his woman, dying he shall die. That this signifies that Divine truth and Divine good are not to be opened, and are not even to be approached in faith, from the danger of eternal damnation if they should be profaned, is evident from the signification of "touching this man and his woman," as being to approach the Divine truth and Divine good which are re...
3448. And Ahuzzath his companion, and Phicol the captain of his army. That this signifies the primaries of the doctrine of their faith, is evident from the representation of Abimelech, as being the doctrine of faith that looks to rational things; hence "his companion and the captain of his army" signify these primaries, and indeed the primaries which are of doctrine; for a "captain,...
3479. The Jews who lived before the coming of the Lord, as well as those who lived afterwards, had no other opinion concerning the rituals of their church than that Divine worship consisted solely in external things, and cared naught for what these represented and signified. For they did not know, and were not willing to know, that there was anything internal in worship and in the Word, thus that...
3703. And He said, I am Jehovah the God of Abraham thy father. That this signifies the Lord, that from Him that good comes, is evident from the fact that Jehovah is the Divine being itself of the Lord, who from the Divine good is called "the God of Abraham." (That Abraham represents the Lord as to the Divine good, may be seen above, n. 2172, 2198.) And because the Divine good is that fro...
3898. What these words involve no one can know except from the internal sense-as that false Christs shall arise who shall give signs and wonders; and that if men should say that Christ is in the desert, they should not go out; and that if they should say that He is in the inner chambers, they should not believe it; and that the coming of the Son of man shall be as the lightning which cometh forth...
4031. And to the flock that came together later he did not set them. That this signifies things that are compelled, is evident from the signification of "coming together later." That "coming together first" signifies that which is spontaneous or free, has been shown above (n. 4029). That "coming together later" signifies that which is compulsory or not free, is thereb...
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Home Sports Cricket Can Ravichandran Ashwin Go Past Anil Kumble?
Abhishek Dixit
Zaheer Khan, an ex-India pacer, shared his thoughts about whether Ravichandran Ashwin's off-spinner can surpass Anil's record of 619 wickets and become India's top wicket-taker during Tests.
Zaheer Khan, a former India pacer, shared his thoughts about whether Ravichandran Ashwin will surpass Anil Kumble's record of 619 wickets and become India's top wicket-taker during Tests. Ashwin, who was victorious in the two-match series against New Zealand that India won 1-0, achieved many milestones.
The most notable one was his passing Harbhajan Singh and becoming India's third highest wicket-taker during Tests. Ashwin currently holds 427 wickets in 81 Tests and is just behind Kapil Dev (434) and Anil Kumble (334). It is only a matter time before Ashwin surpasses India's 1983 World Cup winning captain on the elite list. The main question is how he will be able to beat Kumble's record.
Zaheer stated that Ashwin has the time, and if he keeps delivering consistent results like he did with consistency, he can break Kumble's record. To Cricket 14 users, Zaheer Khan, brand ambassador for Baazi Games' fantasy sports vertical, stated that Ashwin had "created a league of his very own."
"Ashwin has made a league for himself and is also a member of the white ball team, which is a great place. He is a natural performer and will do anything to make the batsman switch from backfoot to front foot whenever he has the ball. He has the patience to do it and with his consistency, it would not be surprising that he could become the bowler who can possibly surpass Anil Kumble.
Ashwin took 14 wickets in both Tests against New Zealand. In the Kanpur Test, he took three wickets in each innings. However, he was unstoppable in the second Test in Mumbai. To return with astonishing figures of 8/42 in Test, he picked up four wickets. India won the series by a staggering 372 runs.
Zaheer shared his top takeaways from this match. He said that it was wonderful to witness a dominant victory at home. It felt just like the good times. It was only a matter time when the pitch behaved on the final day of the Test match.
Jayant Yadav's return with the ball after a normal 1st innings really impressed me. He certainly adds to the tradition of bringing five bowlers into a game, especially when there is more left-handers than usual. It's also the addition of new talent to the Indian team that keeps it valuable."
Next, Ashwin will be focusing on the South Africa series where India are scheduled to play three Tests. Although he has lost his place to Ravindra jadeja when the team has been overseas, it is difficult to keep him out the playing XI.
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Bloomer RJ, Cole B, Fisher-Wellman KH. Racial differences in postprandial oxidative stress with and without acute exercise. International Journal of Sport Nutrition and Exercise Metabolism. 2009 Oct;19(5):457-72.
Bloomer RJ, Cole BJ. Relationship between blood lactate and oxidative stress biomarkers following acute exercise. The Open Sports Medicine Journal, 2009, 3, 44-48.


Because anybody gets old, there may be much more desiring somebody to eliminate adore deals with definitely given that they are usually at present excessively uncomfortable for the design. Certainly, if you're likely to remain before the reflection, you will obviously identify exactly how exceptional your personal belly is actually. The actual excess pounds are simply therefore substantial they have actually prolonged completely for your attributes as well as as well as for the back. This is really fairly irritating particularly if you're the kind of one that is actually incredibly vain together with your looks. Without a doubt, you ought to have currently recognized the truth that is generally a consequence of many years associated with eating too much in addition insufficient health and fitness. This utes a good it's great to understand which that there's stay to some method every single child eliminate all of them nevertheless, you will have to starting at the same time.
Among the essential factors that can be done to eliminate adore deals with should be to work together your self along with a number of type of cardiovascular or even cardio exercise. Many of these activities are actually indicated via long stretches associated with entertainment which will require would like you to definitely physical exercise a few drive. Probably the most typical physical exercise as well as exercise routines that many people enjoy tend to be operating, floating around, biking, or even every other activity that needs a great way to obtain atmosphere. With your kinds of measures, you're assured how the system is actually involved set for complete entire body exercise that is required within getting rid of extra calories from fat.
It's just such as essential that you keep track of the food strategy if you wish to eliminate adore deals with. You have to be sure a person will be able to get rid of much more body fat when compared with that which you eat and therefore it's important with regard to you don't overindulge. You know the actual types of meals you have to prevent as well as the types of meals that you ought to require. If you're able to, you need to ensure that you'll stop your own intake associated with things that tend to be wealthy along with carbohydrates as well as calorific content material. Nonetheless, this particular doesn't truly absolutely imply that you'll absolutely get rid of the things which contain these types of ingredients. It is best to be aware that each one of these nutrition are extremely essential therefore you'll need not really include all of them in what you eat.
With that said, many of these strategies received capital t actually function if you don't supply the self-control to take. Routines tend to be useless unless of course they're carried out frequently as well as frequently. Nevertheless, weightloss routine tend to be inadequate when they aren't adopted properly. If you want to eliminate adore deals with it is crucial that you're dedicated, particular as well as dedicated together with your goals.
As you gets old, there might be higher yearning to get gone adore deals with especially given that they are usually currently as well unsightly for your encounter. Because anybody gets old, there may be greater yearning to get gone adore deals with particularly being that they are at present as well unpleasant for the individual encounter.
With regards to dealing with leg discomfort there are various choices that are offered. Leg discomfort remedy is vital in assisting huge numbers of people possibly get rid of or even deal with their own leg issue. It is vital to build up a course which focuses on the main of the leg. If you're unsure associated with the reason for your own serious leg discomfort then you definitely ought to seek advice from your physician prior to discovering your personal remedies.
There are various explanations why individuals experience their own leg. Many people possess problems as a result of prior damage they continual close to their own leg. Other people might experience their own leg because of joint disease or even persistent problems. The reason for your own leg damage will frequently figure out which kind of remedy that's carried out in your leg.
Those who have lately hurt their own leg inside forty eight several hours should think about topping their own leg. Topping the actual leg might help relieve a few of the discomfort since it may slow up the inflammation. The actual blood circulation may decelerate round the leg region when you start in order to glaciers the region. Topping is a good very first part of dealing with a good severe leg damage. In case your leg discomfort is actually the result of a persistent situation after that you should look at taking a look at various applications that will help a person cope with your own discomfort.
Getting anti-inflammatory medication is most effective for those who tend to be coping with discomfort because of joint disease. Leg discomfort remedy with regard to joint disease requires perseverance since you suffer from the actual persistent situation. You will find absolutely no simple solutions with regards to leg remedies, nevertheless, you'll find a course which assists remedy your own discomfort if you're prepared to consider 1.
Right here you are able to find out about the very best moisturizer in it essential oil, through researching things that tend to be genuinely moisturizing. Simply because the actual content label states it's moisturizing doesn't invariably imply that holds true.
Right now, aside from taking a shower within chlorinated drinking water, you will possibly not wish to prevent individuals other activities. They may be a number of your preferred hobbies as well as if you are using the very best moisturizer in it essential oil, you will not need to be worried about this.
Purchasing a chlorine filtration system for the showerhead can help greater than it may seem. Chlorine is extremely drying out. Individual maintenance systems that contains alcoholic beverages will also be really drying out.
The actual inexpensive creams available on the market include petrolatum, nutrient essential oil and/or paraffin. These types of elements aren't moisturizing. They're not really suitable for the actual skin's natural oils. They're not really soaked up to the skin pores. These people block all of them.
Glycerin is much better, however it isn't the very best moisturizer in it essential oil. This boosts the skin's dampness content material through regarding 5%. The very best moisturizer in it with regard to really dried out pores and skin is actually 5 occasions far better.
More than these people many years, you might have observed such things as lanolin, that is essentially sheep's natural oils. It's collected through squeezing the actual made of woll, prior to digesting this. It is considered to be proficient at supplying dampness, without having leading to side effects. It had been the initial "anti-aging" component within Essential oil associated with Olay.
All sorts associated with veggie natural oils, such as olive, grape seedling, jojoba, avocado as well as hand natural oils happen to be marketed previously or even an additional from the many years. Individuals are great elements to consider. However, the very best moisturizer in it essential oil is exclusive.
Such as lanolin, this originates from sheep's made of woll, however it's the fundamental proteins which makes in the made of woll how the producers would like. Keratin meats are simply within the pores and skin, locks, fingernails as well as horns of creatures. 95% from the pores and skin tissue within the human being pores and skin tend to be keratin tissue. A person frequently listen to concerning the need for collagen, however collagen doesn't can be found within the levels associated with pores and skin that people observe.
Additional producers possess attempted to incorporate keratin within their items, however they make use of higher heats as well as severe chemical substances in order to procedure this. Which denaturalizes this and also the skin's tissue cannot utilize it.
1 producer, the actual KERATEC Organization, within Brand new Zealand, trademarked the production procedure which allows the actual proteins to stay energetic. Medical research possess confirmed that it's the very best moisturizer in it with regard to really dried out pores and skin, since it boosts the skin's dampness content material through more than 20% as well as enhances the actual skin's capability to keep dampness.
However, it's not only the very best moisturizer in it essential oil, it's also the very best anti-aging component that's been discovered. This enhances the actual skin's tone through around 40%. Stronger pores and skin appears much less old and wrinkly. Additionally, it encourages the actual manufacturing associated with brand new tissue, something which usually decelerates along with grow older. Individuals tend to be things that allow it to be the very best moisturizer in it with regard to really dried out pores and skin, for me.
Each and every health and fitness aficionado enjoys a bit of gear that needs minimum set up or even set up while offering the optimum fulfillment work out. The actual Professional Match Metal Fitness center provides each. Amongst home gym machines, this rates higher to be the no-fuss method to obtain excellent outcomes.
The actual Professional Match Metal Fitness center, a good torso work out club, occurs inside a couple of element items that may be installed collectively within regarding quarter-hour. The doorway body support guarantees you are able to connect the actual club in order to some thing strong which will maintain unwanted weight. Generally, the actual support, that demands absolutely no resources or even nails, needs to be remaining about the doorway body, despite the fact that the actual club boils down. The actual club, which could endure in order to three hundred lbs, suits within doorway structures which are twenty-four in order to thirty-two in . broad along with molding that's regarding 3.5 in . heavy.
In contrast to a number of other bits of exercise equipment available on the market, the actual Professional Match Metal Fitness center isn't cumbersome as well as stows aside very easily. The actual club is made to assist you to construct power inside your upper body, hands, shoulder blades as well as back again, mainly. Once the club is actually installed inside a doorway body, you can easily get the actual prolonged club as well as perform chin-ups or even pull-ups.
The actual Professional Match Metal Fitness center can also be made to function it's miracle on the ground. The actual bent deals with allow you to perform dips, push-ups as well as sit-ups without having putting an excessive amount of stress in your arms.
A few clients possess cautioned how the club truly needs to be a great match for the specific doorway body to prevent additional difficulties. In case your doorway body is actually under thirty-one in ., for instance, there might be a space between your doorway body as well as steel finishes from the club. This particular locations your own wooden in a danger to become broken. A few purchasers have discovered this simpler to merely make use of the club on the doorway body with no support.
The actual Professional Match Metal Fitness center appears an extremely inexpensive method to truly sculpt your own torso. You will probably spend in between $24 as well as $35, based on exactly where a person help to make the actual buy. Typically, this particular appears an audio expense for that price as well as the opportunity to focus on toning just one a part of the body. The one thing that may screw up your own work out isn't exercising whatsoever.
Pressure incontinence can be a continence trouble that will is affecting much more a lot of women as compared to males since the main factors are frequently with maternity together with childbirth labor. The following version of incontinence comes about in the event the sphincter muscle mass to blame for that move with urine in the urethra gets to be affected and made weaker producing just about every bit of demand relating to the bladder some sort of probable awkward involuntary move with urine.
Very simple offers like joking, shhh, sneezing and pushing to help move a great objective may well position uncommon demand relating to the mid-section which often outcomes with demand relating to the bladder. In the event the sphincter is not really nutritious it's going to incapable to attend that urine that condensed bladder is hoping to push out a. That toughness from this issue may well range between lightweight to help serious goes.
So what on earth will do the following want to do using maternity together with childbirth labor? Properly options experienced a hard maternity you may already have got a great idea with the quality of conflict that the reduced mid-section may well examine. Nevertheless quite possibly usual sheduled delivery may well lengthen that pelvic floors muscle groups you aren't deteriorate these that's that arrange with regard to deficiencies in bladder regulate.
Births which were lengthened, involve forceps and many other equipment and a great episiotomy usually tend to rip and deteriorate that muscle mass set along with the associated risk with preparing a continence issue are generally higher.
That pelvic floors muscle groups are generally vital as they help together with support available the different bodily organs situated that mid-section. When the following muscle mass set aren't able to complete it's occupation than the bodily organs may well switch constructing difficulties with many other bodily organs like the bladder. In most cases, a lot of these muscle groups may be focused as a result of isometric Kegel activities together with their own effectiveness may well bring back to usual clearing away a leading trigger of incontinence.
Nevertheless ones primary cease ought to be to ones physician's office environment to ascertain precisely what that fundamental factors really are generally. Pressure incontinence is normally straightforward to stop and declining that will, the process under way regulate. Jst take into account that comfortably be managed is not really a healthy side effects with the birth and getting older. You may breach the following continence trouble together with you don't need to to maintain along with the issue for any majority ever experience.


The examine through Dr Friedrich Edding is, so far as i will be able to see, the 1st paper to regard the industrial facet of the Refugee challenge in Western Germany in an neutral shape, exhibiting, at the foundation of recent figures, its optimistic in addition to its adverse point and influence. in any case it can be of a few use to underline - from the "bird's-eye view aspect" of Basle - a few of his conceptions and conclusions and to throw a few gentle at the sociological historical past which makes this actual challenge the challenging center of the social and political state of affairs of Western Germany - and possibly of Western Europe to boot. first of all: it needs to be famous that this clinical research helps neither the optimists nor the pessimists. The optimists are guaranteed to admit that the stimulus supplied by means of new manpower and entrepreneurial initiative is greater than counterbalanced by means of loss of capital and by way of the necessity for huge relief within the kind of housing, outfits and cash for thousands of refugees who're previous, unwell or for different purposes not able to paintings. The pessimists are sure to admit that the load of nine million immigrants is an tremendously stimulating problem to Western Germany and that this burden is in part counterbalanced by means of the hot organisations, tools and strategies which owe their origin, software or improvement respectively to the Refugee marketers, cattle-breeders or seed-growers.
Learn:The easy suggestions of this arguable theoryHow string conception builds on physics conceptsThe diversified viewpoints within the fieldString theory's actual implicationsYour plain-English consultant to this complicated medical theoryString concept is without doubt one of the most complex sciences being explored at the present time.
One can assume that about 80 per cent of the refugee labor force have now found some sort of a job. Even if one were to have doubts about the level of their income, as reflected in their tax payments estimated above, one would still have to agree without a doubt that as a group they earn relatively far less than the rest of the population. On the other hand available statistics lead to the conclusion that this same population group as a whole enjoyed before the war a level of production and income on the average by no means under the level of the average German citizen.
However, with the population increase allowed for, pro capita production in Western Germany (May 1951 = 101,5 1 ) still lags far behind that of other countries. (See Diagram 4). Since Western Germany is expected to support its suddenly enlarged population in the near future through its own efforts alone, the following should be included among the key points i~ any readjustment program: 1. Bottlenecks, restricting the further growing of the overall economic volume (at present especially in coal and steel) must be opehed at any cost and in the quickest possible fashion.
After deducting the taxes paid by the refugees during the same period the Ministry concludes with an estimate of about 6 billion DM as the net fiscal burden imposed by the refugees. (See Table V, appendix). 77 billion DM. 1) "Fliichtlingslasten und Verteidigungsbeitrag zwei sich erganzende und begrenzende Belastungen", a study published by the Ministry of Finance of the West German Federal Republic, Bonn, 1951. 28 THE REFUGEES AS A BURDEN ON This last figure then is running at about one fifth of 30 billion DM, the total governmental expenditures for all purposes at all levels for 1951/52.


If you're like most people then you don't like taking painkillers unless you really have to. This is doubly true if you suffer from migraine headaches because the painkillers can be pretty strong. It's not just the strength of these painkillers - the other side is that some people can become addicted to them without even realizing it.
The worry about addiction and potential side-effects is making more people look at alternative treatments every day. Not too long ago people saw "alternative" remedies as a kind of hocus pocus approach to treating any kind of problem. Things have changed though and even doctors are starting to recommend and use alternative treatments with their patients (in combination with the normal treatments.
So why do people use alternative treatments? There's a few good reasons for the fact they're becoming more popular. Some people might suffer with weird side-effects to normal medication so they have no choice but to use the alternative stuff.
Of course, you might just prefer not to use prescription medication because you're worried about using medication over weeks or months might cause problems health problems for you.
The last reason many people are turning to natural migraine remedies is the cost of prescription drugs. If you don't have decent health insurance then you could wind up just not being able to afford to treat your migraine problems with conventional prescription treatments.
* Lay down in a dark room - the less light the better.
* Keep it quiet. Turn off televisions, radios and other noisemakers.
* Make sure you are warm and cozy.
* Lay down and relax as much as possible.
* Don't close your eyes. Instead, cover them with a cloth, such as a scarf or a handkerchief. Closing your eyes actually uses additional muscles and can make it worse.
Keep your breathing as relaxed as possible because this can really help out. If you've had a migraine attack before you know that the pain is going to be bad. This can lead to a type of anxiety attack that can increase your heart rate. More blood pumping around your head is just going to cause more pain.
1. Breathe in slowly and deeply. Then exhale just as slowly.
2. Take a second slow, deep breath. Take the time to feel the air going into your lungs and filling your body. Once you've inhaled as deeply as you can without growing uncomfortable, exhale slowly. Think about the way the air feels as it leaves your body. Be sure to breath out completely before you inhale again.
3. Repeat the entire process four to five more times, focusing on staying relaxed and slow.
4. You should feel far more relaxed and your breathing should be calmer. Your heart should be beating at a normal rate. Ideally, you should be so relaxed that you feel sleepy. Don't fight the feeling. Go ahead and fall asleep.
Lavender oil is the final natural remedy for your migraines. Yes lavender oil. This essential oil has many uses but is really effective when it comes to treating migraines. What's better is that it's 100% natural and available from plenty of stores. When you feel a migraine coming on you can put a drop of lavender oil on your temples or behind your ears. If you're already in the middle of a migraine attack then put a few drops of lavender oil on your pillow and breathe in deeply as you rest - you'll be amazed at how quickly this natural treatment can work. Another nice side effect is that lavender oil can send you off into a deep sleep that allows you to heal without even trying.
There's more than one way to treat your migraine so hopefully these tips help you deal with your migraines more naturally.


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Q: Implementing extract-min in O(1)
Possible Duplicate:
Implement a queue in which push_rear(), pop_front() and get_min() are all constant time operations.

I need to implement a FIFO data structure in which amortized cost of Enqueue, Dequeue and Extract-Min is O(1).
I have thought about using a regular queue which will have O(1) enqueue and dequeue using linked lists and then for Extract-Min I would go through the array to get the min and removie it. the cost for that would be O(n) though and that would not bring the amortized cost to O(1).
Any help or hints would be appreciated.

A: You can implement a queue with an extra member which always points to the node with the least value of the queue. The Enqueue() has to be slight modified as shown by the pseudo code below:
void enqueue( data_t val )
{
  back->m_data = val; // back is a pointer to the end of the queue.

  // m_Min should be initialized with a large value.
  if (back->m_data <= m_Min->m_data)
  {
    m_Min = back;
  }
  else // => back->m_data > m_Min->m_data
  {
    swap(back->m_data, m_Min->m_data);
    m_Min = back;
  }
}

The above modification will ensure that enqueue, dequeue and extract_min all run in O(1).
data_t dequeue( )
{
  data_t front_data = front->m_data;

  prev_front = front;
  front = front->next;

  dump prev_front;
  return front_data;
}

So when front reaches m_Min the queue has only one element which will be the least value.
data_t min()
{
  return m_Min->m_data;
}

EDIT: The if-block in enqueue() is modified from my previous version. The enqueue() basically pushes the new value at the end. But swaps with previous node if it is greater than the current min (which is held in the last node of the queue).
For e.g if the input sequence is 5, 3, 7, 1, 4, 6, 8.
1.
front -> 5 <- back
         ^min

2.
front -> 5 3 <- back.
         ^min

front -> 5 3 <- back.
           ^min

3.
front -> 5 3 7 <- back
           ^min

front -> 5 7 3 <- back
             ^min

4.
front -> 5 7 3 1 <- back
             ^min

front -> 5 7 3 1 <- back
               ^min

5.
front -> 5 7 3 1 4 <- back
               ^min

front -> 5 7 3 4 1 <- back
                 ^min

6.
front -> 5 7 3 4 1 6 <- back
                 ^min

front -> 5 7 3 4 6 1 <- back
                   ^min

7.
front -> 5 7 3 4 6 1 8 <- back
                   ^min

front -> 5 7 3 4 6 8 1 <- back
                     ^min

Say you dequeue 3 items, the queue will look like:
front -> 4 6 8 1 <- back
               ^min

So by the time front reached min, the queue will have only one element which will be min.

A: I would suggest use 2 base data structures together to construct the new one.

First you can use a normal dequeue, which can provide en-Queue and de-Queue in O(1).
Then use a helper stack to track the minimal value's pointer.
Start from the beginning, when insert the 1st item, just deq.push_back() and stack.push();
then for following items:

Enqueue: push back to dequeue, compare the new item with the top of the stack, if new item is smaller, then push it, otherwise, do nothing. All the operation in O(1)

Dequeue: get the head of the dequeue, compare it with the top of the stack, if they are the same, which means the dequed one is the smallest one, then pop the stack, otherwise, do nothing on the stack. All operations in O(1).

Extract-Min: get the top of the stack, it is the smallest. O(1)


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