text:
Fundraising efforts continue for VWMD research at IHMRI
The below article was originally published at ihmri.org.au and has been republished with permission.
The Great Illawarra Walk organisers are aiming to raise $150 thousand to help find a treatment for Chloe Saxby
It is less than two months until the annual Great Illawarra Walk and for the second year, all money raised will go towards research into Vanishing White Matter Disease (VWMD) at the Illawarra Health and Medical Research Institute (IHMRI).
IHMRI Associate Professor Lezanne Ooi has welcomed the fundraising efforts and is urging the community to support Chloe Saxby and her family by joining the walk.
"A dedicated team of researchers is working hard to find a potential treatment for Chloe and other children suffering from VWMD, but research can be slow and expensive. The more resources we have to dedicate to this project, the more research we can do to find some answers," said Associate Professor Ooi.
"The money raised from last year's walk, helped to pay for the salary of a dedicated researcher, Dr Neville Ng, who has helped grow skin cells into stem cells. The stem cells are now being cultivated into brain cells and we have started testing potential drug treatments for VWMD," she said.
Molecular Biologist Dr Tracey Berg has also recently joined Associate Professor Ooi's team on the project.
"We want to continue supporting this important research at IHMRI for Chloe and other children who suffer from this dreadful disease," said Nyree Saxby.
On 2 March, the Great Illawarra Walk will kick off from Shellharbour Village and finish at the Headlands Hotel in Austinmer.
"We have a target of one hundred and fifty thousand dollars this year and we really need your help to reach it," said Nyree Saxby.
"We've just registered our family, and we urge you to make this part of your 2019 'get fit goal'," she added.
Event organiser Chris Lovatt says last year's hot weather prompted them to rethink the two day, 100-kilometre course.
"This year, we really want as many people as possible to take part, it's now only a 40 kilometre walk and there'll be live entertainment at the end of it," said Chris Lovatt.
"Our fundraising target is fifty thousand more than last year, so we really need to get as many local businesses and community teams involved," he added.
Associate Professor Lezanne Ooi says generous donations from the Saxby's, The Great Illawarra Walk and the Illawarra community have allowed staff more laboratory time which has been critical for understanding the disease and what goes wrong in the cells of VWMD patients.
"Mutations in the genes for EIF2B proteins cause VWMD and lead to the loss of certain cell types in the brain called oligodendrocytes and astrocytes," said Associate Professor Ooi.
"Cells from VWMD patients are under stress due to faulty EIF2B proteins, by repairing the function of these proteins and restoring balance to their production we can potentially reduce the symptoms of the disease," she added.
The life expectancy of sufferers of VWMD is between five and ten years from the onset of the disease.
Chloe Saxby was diagnosed with VWMD at just three years of age.
This year she will celebrate her 11th birthday.
CD Strata $235.00 Raised 2nd
Yeah The Mum'z $200.00 Raised 3rd

redpajama_set_name: RedPajamaCommonCrawl


text:
Alaska Airlines Announces First Flight to Ohio
August 30, 2018 Jace Moseley Routes
An Airbus A320 jet painted in the Alaska livery (Photo: AirlineGeeks | Craig Fischer)
Alaska Airlines announced they would be introducing service from its main hub at Seattle-Tacoma International Airport to John Glenn Columbus International in Columbus, Ohio. The route will be the airline's first flight to the state of Ohio as well as the first and only nonstop flight from the Pacific Northwest to Central Ohio.
The outbound to Columbus will operate as AS1692 and depart Seattle at 9:45 a.m. The aircraft will arrive at 5:15 p.m. in Columbus and have an hour on the ground before departing back to Seattle at 6:45 p.m. The return, AS1693, will arrive in Seattle at 8:25 p.m. The flights are scheduled to begin on March 7, 2019, and will operate daily from Seattle operated by Alaska's newly-acquired Airbus A320 aircraft from its merger with Virgin America.
John Kirby, vice president of capacity planning and alliances at Alaska Airlines, said, "This new service to Columbus represents Alaska's first destination in the Buckeye State, and our commitment to offer the most nonstop destinations from our hometown of Seattle."
The addition of Columbus to the route network also means that Alaska will serve 90 destinations out of its main hub in Seattle. The addition of the daily flight to Ohio will mark the 11th Midwest city that will see service from the airline.
Columbus is one of the fastest growing cities in the nation and the largest in Ohio. The city is also home to a rapidly growing technology industry and could be key in more easily connecting it to the major U.S. companies that call Seattle home, including e-retail giant Amazon that has narrowed down its search for a second headquarters to a handful of cities, including Columbus.
Jace Moseley
Jace has been working at AirlineGeeks for nearly three years. Born in Seattle, Boeing and aviation quickly took over his life and he never stopped looking up. Jace enjoys plane spotting and taking trips just so he can fly. He is currently studying at Embry-Riddle Aeronautical University while earning his Bachelors of Science in Air Traffic Management.
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February 26, 2017 James Field
An Airport to Nowhere: The Curious Case of Berlin Brandenburg
July 2, 2017 Andy Nelson
Airbus A321LR Completes First Transatlantic Flight
February 13, 2018 Pablo Diaz
Exclusive: Last Commercially Operating Boeing 747SP Flown to Retirement in Iran
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PHOTO STORY: Up-Close at One of Colorado's Most Scenic Airports


Written by Professor G. Richard Shell, Bargaining for Advantage: Negotiation Strategies for Reasonable People is a top selling negotiation book. As a professor at Wharton School of Business, the book explains the top negotiation strategies used in business. For business owners, a smart negotiation can create more sales and revenues. It can also lower costs, improve delivery time and create new opportunities. In this post, we'll cover some of the key points from Bargaining for Advantage. You don't have to get the Bargaining for Advantage PDF download to learn our favorite negotiation strategies.
Most people think negotiations begin and end at the office. In actuality, business owners are negotiating all the time. They have to deal with with negotiations for buying a car, insurance and a house. But, they also negotiate with with their families, friends and acquaintances. What color couch to buy? What gelato flavors to order? Who's going to stay up late? Of course, they have to negotiate at work with employees and stakeholders too. But, negotiation should not be viewed soley as a business transaction. You are constantly selling yourself and negotiating in all areas of your life. Thus, the art of negotiation is an important skill to learn early on.
Before entering a negotiation, set realistic goals and expectations. The majority of the work that goes into a negotiation requires preparation. If you are readily prepared for a negotiation, you are much more likely to get what you desire. To prepare, decide on what it is that you want. Be specific, including numbers, dates or other measurable attributes. By creating goals for your negotiation, you will have clarity about what you are really willing to compromise on.
Entering a negotiation, be ready to listen first. After preparing your data and information, it is easy to go into a negotiation red hot, and armed. However, start your negotiation by listening carefully to the other side. You are much more likely to learn about what the other party would like most. This will give you much needed leverage and flexibility when it comes to time to speak. In the best case scenario, you will learn something new that helps your negotiation go very smoothly. But, this can only be done by listening to the other party.
After understanding the other party, now is the best time to present your information. Here is your opportunity to share authoritative data and information that sets standards of the negotiation. This can include industry data or trends from a trusted working bulge bracket bank. Offer information that you might assume to be obvious. It is positive to have a number of points that you can agree on. Then, be sure to talk about what matters to you most and options that you have varying priorities for. Of course, a difference in value creates the chance for trade, alternatives or more options. When presenting your information, put everything on the table with your realistic target.
Finally, when both parties have weighed their options, it's time to make a commitment. The closing of the negotiation should be done ethically. It should include compromise and a positive long term relationship. If you need another round of negotiations for verification vs validation, take the time needed for both parties to explore all options. When both sides have come to the best terms, it is time to close. Then, commitments are made and the deal is done. In a good negotiation, neither side loses their ethics or gives up "too much" in the commitment.
Certainly, Bargaining for Advantage gives you an in depth approach to negotiating. We have covered some of our favorite tips. These include negotiating in all aspects of life, preparing diligently and listening carefully to the other party. Moreover, develop your bargaining style by practicing frequently. As a result, you will find advantage in your bargaining and negotiations.


The Secretary, Isheri North Estate Residents Association, Lagos State, Mr. Dayo Adekoya, has been taken to a hospital after he was released by kidnappers.
It was learnt that the landlord was released in the early hours of Monday after an undisclosed cash was paid to his abductors as ransom.
But the Lagos State Police Command denied knowledge of the payment, saying two suspects had been arrested while operatives were on the trail of other fleeing members of the gang.
After five hours of intermittent shooting on the waterways around the estate, the assailants stormed Adekoya's residence around 12.30am on Thursday and seized him at gunpoint.
They also killed three security guards – Dugbe, Oluyole and Friday aka Frayo – and injured two others before escaping with the secretary into the creeks. A few hours after the abduction, they contacted his wife, demanding N80m ransom.
The attack, according to police sources, was carried out by members of a gang which abducted pupils and staff members of the Nigerian Turkish International Colleges on January 13.
In the wake of the incident, panic-stricken residents had fled their houses to hotels and relatives' houses, while an estate supervisor, identified simply as Godwin, was nabbed by the police.
A resident, who did not want his name in print, confided in Punch that a ransom was paid to the kidnappers before Adekoya was released.
"He was released this morning (Monday) after his family paid millions of naira. He has gone for a checkup at a hospital. The government should come to our aid by putting in place the necessary infrastructure and adequate security measures on the estate.
"Nobody will live in peace where their safety is threatened. The police and other security agencies have a lot of work to do. We are tired of having heavy security presence on the estate only when an incident like this occurs," he said.
Lagos Police Public Relations Officer, SP Dolapo Badmos, confirmed Adekoya's release, adding that operatives of the command and Inspector-General of Police Intelligence Response Team were on the trail of the suspects.


text:
The 6 Most Surprising, Important Inventions From World War I
By Simone M. Scully
British soldiers in a WWI trenchAustralian War Memorial
Just over 100 years ago, on the 28th of June, 1914, the Archduke Franz Ferdinand of Austria-Hungary was assassinated in Sarajevo. That set into motion a chain of events that, within several weeks, culminated in the European great powers declaring war on each other. The resulting conflict killed 37 million people.
Once the fighting started, a stalemate emerged on the Western Front, between Germany on the east and the Allies on the west. Both sides hunkered down in trenches, and it was nearly impossible for either side to make any significant progress against the other. With machine guns and artillery on both sides defending the trenches, attackers were quickly mowed down before they could cross "no-man's-land," the short distance between the opposing lines.
"The deadlock on the Western Front forced armies to develop new technologies to overcome it," says Paul Cornish, a historian at the Imperial War Museums in London. Many well-known innovations that we associate with the war today were invented to try to gain an advantage over an entrenched enemy.
"The development of the tank, of poison gas, aerial photography, and sound ranging [a method to determine the coordinates of the enemy from the sound of its guns] can all be seen as efforts to break the trench stalemate," says David Stevenson, professor of international history at the London School of Economics. Each new innovation that gave a slight edge over the other side would then be promptly copied and improved to make it even more deadly. The use of Chlorine gas led chemists to later develop phosgene and mustard gas, for example.
"Arguably, [WWI] was the first war where science and technology were mobilized as part of the war effort," says Jeff Schramm, a Missouri University of Science and Technology historian of technology. "Labs and people all over, at universities and in the industries, were working on stuff for the war. That was unprecedented."
Here are six of the more surprising examples of the technical innovations that came from World War I:
1. Industrial Fertilizer
Shortly before the war, German chemists Fritz Haber and Carl Bosch developed a process to convert atmospheric nitrogen into a biologically available form—ammonia—using high pressure and temperatures. During the war, this allowed Germany to produce artificial nitrates used to create explosives, like TNT. Prior to this, their nitrates came from Chilean guano deposits, which produced a limited supply. Once the war broke out, says Schramm, Germany had only enough natural nitrates to last about six months. "Because of this new process, they could create explosives essentially out of the air, and the war was prolonged for years," he adds.
As deadly as his invention proved to be during the war, Haber's process today actually sustains one third of the population on Earth, as it allows the production of ammonia nitrate fertilizer from nitrogen gas. Nitrogen is a critical component of both our DNA and proteins, and it is by far the most abundant atmospheric gas, but it first has to be "fixed" into a different form before it can be used by living organisms. (Specifically, the nitrogen must be changed from oxidation state 0 to –3.)
In nature, microorganisms possessing nitrogenase enzymes perform the nitrogen fixing. The Haber-Bosch process permitted the mass production of nitrate fertilizers, which sustain mass-scale industrial agriculture today.
2. Drones
World War I caused a tremendous acceleration in the development of aviation. "During the war, air speeds and [altitudes] doubled, engine horsepowers quadrupled, and bomb payloads increased even more," says Stevenson. The war also saw experiments in developing the first pilotless aircraft.
Charles Kettering designed an unmanned "flying bomb," known as the Kettering Aerial Torpedo or the "Kettering Bug," that could hit a target at a range of 40 miles. The craft was launched with a dolly-and-track system; once launched, an onboard gyroscope guided it to its destination. "It was the first cruise missile—or drone if you will—and it was designed to take off, fly for a certain distance, and then stop and dive into the ground," explains Schramm. The first test flight on October 2, 1918, failed because the aircraft climbed too steeply, stalled, and crashed.
Elmer Sperry and Peter Hewitt developed an unmanned aircraft for the US Navy in 1916 and 1917, known as the Hewitt-Sperry Automatic Aircraft. It was just 18.5 feet across and weighed 175 pounds, and it was also piloted with gyroscopes and a barometer to determine its altitude. It flew its first test flight on Long Island on March 6, 1918.
In the end, both were determined to be too unreliable to be useful. Still, by the time the war ended, the US had produced 45 "Kettering Bugs."
A "Kettering Bug" automated plane under construction at the Dayton-Wright Airplane Company 1918Wright State University Libraries
3. Air Traffic Control
Radio had already made its debut before the war, but great strides were made during the war because of how valuable it could be for military communications. Nowhere was this more apparent than in aviation. In the early days of flight, once pilots left the ground, they had no good way of communicating with each other or the people on the ground.
The US Army installed the first two-way radios in planes before the nation's involvement in the war. By 1916 technicians could send a radio signal over a distance of 140 miles, and radio telegraph messages could be exchanged between planes in flight. By the end of 1916, a helmet was designed with a built-in microphone and earphones to block out noise from the aircraft's engine; in the following year, the first human voice was transmitted by radio from a flying plane to an controller on the ground.
The two-way radio systems developed for airplanes and operators on the ground were not only invaluable to fighting of the war, but also became the basis for the technology that would develop into the air-traffic-control system of today.
A Chateau Thierre Aeroplane in flight over France, with trenches visible in backgroundWikimedia
4. Portable X-Rays
Battle casualties overwhelmed the medical services in 1914, but armies quickly developed sophisticated systems for addressing the problems with new innovations made during the war. For example, the Thomas splint for a broken leg had a massive impact on survival rates in an era before antibiotics. Before the Thomas splint was invented, 80 percent of all soldiers died from a broken femur, but by the time of a battle in 1917, over 80 percent survived, reportedly. Blood banks were developed, thanks to the use of sodium citrate to keep blood from coagulating and becoming unusable, allowing battlefield transfusions.
One of the most important medical innovations of the time was the ability to get the diagnostic tools to the front line. When the war broke out, X-ray machines were too bulky and delicate to move. Using funds she raised in Paris, Marie Curie developed small, mobile X-ray machines and installed them in cars and trucks for the French army. She drove some of these portable X-rays to the front herself, and worked with her 17-year-old daughter, Irene, at casualty-clearing stations, using the X-rays to locate fractures, bullets, and shrapnel.
During WWI, physician Antoine De Page helped the allies pioneer the use of "triage," an efficient approach for directing medical resources.Wikimedia
5. Sanitary Napkins & Tissues
In 1914, Ernst Mahler, the head of a small US company called Kimberly-Clark, toured pulp and paper plants in Germany, Austria, and Scandinavia and spotted a new cellulose material called "Cellucotton." It was five times more absorbent than cotton itself and, when mass-produced, half as expensive. Upon returning to the US, Mahler trademarked it; when the US entered the war in 1917, Kimberly-Clark started producing the wadding for surgical bandages.
Meanwhile, enterprising Red Cross nurses started using it for their own personal hygienic use. Once the war ended, Kimberly-Clark re-purchased the surplus of bandages from the military and the Red Cross and created the first commercial sanitary napkins.
The company also ironed out the cellulose material to make smooth tissues, which, in 1924, were released as the first facial tissues: Kleenex.
A 1920 newspaper ad for Kotex, Kimberly-Clark's brand of sanitary padsvia Wikipedia
6. Sun Lamps
The undernourishment of Germans because of the war led an increase of rickets, a disorder caused by a lack of Vitamin D, calcium, or phosphate that leads to the weakening of bones. By the winter of 1918, half of the children in Berlin were suffering from rickets.
At the time, the cause of the ailment was not known, but a Berlin doctor named Kurt Huldschinsky noticed that the children were also very pale, so he conducted an experiment in which he put four children under mercury-quartz lamps that emitted ultraviolet light. The treatment worked: The children's bones became stronger. Ultraviolet light causes the skin to produce Vitamin D, which is necessary for healthy bones. Thus, the sun lamp was born.
Simone M. Scully is a science and culture journalist based in New York City. Follow her on Twitter at @ScullySimone.


text:
\section{Introduction}

Research for dialogue system attracts a lot of attentions recently due to its potential huge value of reducing human cost in many commercial domains, such as restaurant reservation~\cite{dstc3}, travel planning~\cite{hierarchical} and retail service~\cite{retail}.
The majority of dialogue agents with goals are expected to be efficient to complete tasks with as few as possible dialogue turns, in contrast with those chit-chat counterparts~\cite{chat1}. The following two examples show the comparison of efficient and inefficient cases respectively. The senarios is restaurant reservation and the agent's goal is to reserve a table at noon.
\begin{quote}
	Efficient example:\\
	``\textit{Human}: We don't have empty tables at 11 o'clock tomorrow. All are reserved.''
	``\textit{Agent}: How about 12 o'clock? We are also okay then.''
\end{quote}
\begin{quote}
	Inefficient example:\\
	``\textit{Human}: We don't have empty tables at 11 o'clock tomorrow. All are reserved.''
	``\textit{Agent}: What else time is available?''
	``\textit{Human}: 12 o'clock is ok.''\\
	``\textit{Agent}: All right. We want that time.''
\end{quote}
For expressing the same opinion, the inefficient example consumes four turns while the efficient example only needs two. As it can be seen, the efficiency is important for goal-oriented dialogue systems to achieve goals in a rapid way.

Usually, a dialogue system consists of a pipeline of natural language understanding (NLU), dialogue management (DM) and natural language generation (NLG), where the DM part is treat as two separate components: dialogue state tracking (DST) and dialogue control (DC, i.e. dialogue policy selection). The DM part is widely considered to be relevant to the dialogue's efficiency, because it makes decisions on what to say for the next turn. Recently, methods based on reinforcement learning are proposed for the policy selection component to build efficient dialogue systems. However, there are some drawbacks of reinforcement learning based methods. For example, they requires lots of human work to design the learning strategy. Also a real-world environment which is essential for the agent to learn from is expensive, such as from domain experts. Moreover, training the dialogue manager as a two separate components could lead to error propagation issue~\cite{multitask1}.

In addition to reinforcement learning based methods, sequence-to-sequence based methods are also popular recently, because they can learn a dialogue agent purely from data and almost without too many human efforts. The  error propagation issue can also be reduced because they are end-to-end, and they have better scalability for different scenarios. However, it is difficult to build efficient dialogue agents by those methods since their objective functions for training models are usually inclined to general responses, such as \textit{I don't know}, \textit{yes} and \textit{OK}, or often generate the same response for totally different contexts because the contextual information is not well-modeled by those methods~\cite{evaluate2}.

In this paper, we address the problem of learning an efficient dialogue manager from the perspective of reducing manual intervention and error propagation, and propose a new sequence-to-sequence based approach. The proposed end-to-end model contains a novel looking-ahead module for dialogue manager to learn the looking-ahead ability. Our intuition is that by predicting the future several dialogue turns, the agent could make a better decision of what to say for current turn, and therefore goals could be sooner achieved in a long run.

More specifically, our model includes three modules: (1) encoding module, (2) looking-ahead module, and (3) decoding module. At each dialogue turn, three kinds of information, the goals, historical utterances and the current user utterance, are utilized.  First they are encoded by three separate Bidirectional Gated Recurrent Units (BiGRU) models. Then the three encoded embeddings are concatenated to one vector, which is then sent to a new bidirectional neural network that can look ahead for several turns. The decoding module will generate utterances for each turn through a learned  language model. At last, by considering all the predicted future utterances, a new real system utterance for the next turn is re-generated by using an attention model through the same language model.

Our proposed approach has several advantages. First, it is an end-to-end model and does not take too many human efforts for system design. Although the goals should be handcrafted for specific scenario, the number of goals is small and it is a relatively easy work. Moreover, compared with naive sequence-to-sequence based models, our agent can make the dialogue more efficient by modeling the looking-ahead ability. Experimental results show that our model performs better than baselines on two datasets from different domains, which could suggest that our model is also scalable to various domains.

The contributions in this paper include:

\begin{itemize}
  \item We identify the problem that how to make dialogues efficient by exploiting as little as possible manual intervention during system design from the perspective of end-to-end deep learning.
  \item We propose a novel end-to-end and data-driven model that enables the dialgoue agent to learn to look ahead and make efficient decisions of what to say for the next turn.
  \item Experiments conducted on two datasets demonstrate that our model performs better over baselines and can be applied to different domains.
\end{itemize}

\section{Related Work}

In most situations, the dialogue systems require handcrafted definition of dialogue states and dialogue policies~\cite{pomdp,challenge,neg2,jd}. Those methods make the pipeline of dialogue systems clear to design and easy to maintain, but suffer from the massive expensive human efforts and the error propagation issue~\cite{errorpro1,errorpro2}.

Reinforcement learning based methods for dialogue policy selection are widely studied recently~\cite{BBQ,movie,SIGDIAL,continue}. These methods only need human to design the learning strategies and do not require massive training data. However, the expensive domain knowledge and human expert efforts for agents to learn from are necessary~\cite{withhuman,bootstraping}. Therefore, hybrid methods that integrate supervised learning and reinforcement learning are proposed recently~\cite{hybrid,kefu}. Thus, collecting massive training data becomes another manual work.

More recently, end-to-end dialogue systems attract much attention because almost no human efforts are required and they are scalable for different domains~\cite{EACL,IJCNLP,deal,goal3}, especially with sequence-to-sequence based models~\cite{seq2seq}. Although those models have been proved to be effective on chit-chat conversations~\cite{chat1,chat2,chat3}, how to build agents that are goal-oriented with efficient dialogue managers  through end-to-end approaches still remains questionable~\cite{goal1,goal2}, and we investigate the question in this paper.

\begin{figure*}
  \centering
  \includegraphics[height=4.4in,width=6.3in]{model}
  \caption{End-to-end model for learning looking-ahead ability.\label{fig:model}}

\end{figure*}

Our idea of enabling the agent to be efficient by modeling looking-ahead ability is inspired by the AI Planning concept, which is a traditional searching technology in the field of AI, and is suitable for goal-based tasks, such as robotics control~\cite{ai}. Recently, the concept is borrowed to dialogue system communities and integrated into deep learning models. For example, a trade-off method for training the agents neither with real human nor with user simulators is proposed, in order to obtain better policy learning results~\cite{DDQ}. In addition, at earlier time, the planning idea has been utilized for improving the dialogue generation task~\cite{plan1,plan2}.

\section{End-to-end Dialogue Model}

We propose an end-to-end model that contains three modules: (1) encoding module, (2) looking-ahead module, and (3) decoding module. Figure~\ref{fig:model} shows the model architecture. We leverage Bidirectional GRU models~\cite{gru1} to encode agent goals, historical and current utterances. Then the obtained representations by encoding goals and utterances are regarded as inputs of the looking-ahead module, and they are used to predict several future turns. At last the predicted future turns are merged by an attention model and the new real system utterance is generated for the next turn.

Suppose for each dialogue session we have $T$ turns, and we do not distinguish whether it is user's turn or system's turn. If the agent has $S$ goals that are denoted as $g=\{g_1,g_2,...,g_S\}$, each goal is formalized as a binary vector. For example in the restaurant reservation scenario, we can define that each variate in the vector $[1,0]$ corresponds to a yes-no condition, such as the $1$ means agent accepts bar table and the $0$ means agent does not want to change time. As to the utterance information, imagine at turn $t\in\{1,...,T\}$, we denote utterances $\{u_1,...,u_{t-1}\}\in \mathbb{U}$ for historical ones and $u_{t}\in \mathbb{U}$ for current user utterance. Our model predicts the system and user utterances $\{u_{t+1},u_{t+2},...,u_{t+K}\}$ for the next $K$ turns and then a new $u_{t+1}$ is generated as the system utterance after considering all the predicted turns. The model separates the current user utterance from historical ones in order to highlight the user's current states. In general, the model is end-to-end and needs little human intervention or domain knowledge.

\subsection{Encoding Module}

In this module, the agent goals, historical utterances within the dialogue session, and the current user utterance are encoded by using three GRU models which is expected to learn long-range temporal dependencies~\cite{gru}. $GRU^{(g)}$ is defined to encode agent's goals $g$ and the final hidden state $h^{(g)}$ is taken as the representation of goals. The input of $GRU^{(g)}$ is a one-hot binary vector with length $S$.
$GRU^{(u)}$ is used to encode the historical utterances, and $GRU^{(c)}$ is used to encode the current user utterance. $h^{(u)}$ and $h^{(c)}$ are denoted as the final encoded representations of $GRU^{(u)}$ and $GRU^{(c)}$ respectively.

To get the $i$-th hidden state for the three GRUs, respective inputs include the previous hidden state $h_{i-1}^{(g)}$, $h_{i-1}^{(u)}$ or $h_{i-1}^{(c)}$, and the embeddings of current observations, $E(g_i)$, $E(u_i)$ or $E(x_i)$, where $g_i$ is a goal, $u_i$ is an utterance and $x_i$ is a token. For the textual tokens, we use the Word2vec embeddings as their representations~\cite{word2vec}. Then the token embeddings are averaged to represent utterances. The formal denotation of the hidden states for the three GRU models is:

\begin{equation}
h_i^{(g)}=GRU^{(g)}(h_{i-1}^{(g)}, E(g_{i})),
\end{equation}
\begin{equation}
h_i^{(u)}=GRU^{(u)}(h_{i-1}^{(u)}, E(u_{i})),
\end{equation}
\begin{equation}
h_i^{(c)}=GRU^{(c)}(h_{i-1}^{(c)}, E(x_{i})),
\end{equation}
where $E(\cdot)$ represents the embeddings.

The final output of the encoding module is a concatenation of $h^{(g)}$, $h^{(u)}$ and $h^{(c)}$, which is denoted as $h_1^{\overrightarrow{l}}=[h^{(g)},h^{(u)},h^{(c)}]$. $h_1^{\overrightarrow{l}}$ serves as the input of the following looking-ahead module. The right arrow means the initial direction to train the looking-ahead module is from the current to the future.

\subsection{Looking-ahead Module}

With the input of $h_1^{\overrightarrow{l}}$, this module predicts several future dialogue turns. Since the process is sequential, we propose a recurrent  neural network to model the process. In order to exploit the predicted information for later generating a real system utterance, another recurrent neural network is used to backtrack the information from future to current. To reduce the computing cost, the two neural networks share the same parameters, and the whole looking-ahead module looks similar to a bidirectional GRU as shown in Figure~\ref{fig:model}.

We denote the module as $GRU^{(l)}$. $\{h_k^{(l)}|k>0\}$ represent the predicted hidden states for future turns. To get $h_k^{(l)}$, the hidden states from two directions, $h_k^{\overrightarrow{l}}$ and $h_k^{\overleftarrow{l}}$, are concatenated. To calculate each $h_k^{\overrightarrow{l}}$ or $h_k^{\overleftarrow{l}}$, their inputs include the previous hidden state and the previously-predicted hidden state. Formally, suppose we look ahead for $K$ turns, the hidden state of $h_k^{(l)}$ is calculated as following:
\begin{equation}
h_k^{\overrightarrow{l}}=GRU^{\overrightarrow{l}}(h_{k-1}^{\overrightarrow{l}},Wh_{k-1}^{(l)}),
\end{equation}
\begin{equation}
h_k^{\overleftarrow{l}}=GRU^{\overleftarrow{l}}(h_{k+1}^{\overleftarrow{l}},Wh_{k+1}^{(l)}),
\end{equation}
\begin{equation}
h_k^{(l)}=[h_k^{\overrightarrow{l}}, h_k^{\overleftarrow{l}}],
\end{equation}
where $W$ is a weight parameter and $Wh_{k}^{(l)}$ is the hidden state for predicting future turns. If $K=1$, it means our model has no looking-ahead ability and it degrades to a naive goal-based sequence-to-sequence model.

\subsection{Decoding Module}

For generating the real system utterance, as seen in Figure~\ref{fig:model}, the green hidden states $\{Wh_k^{(l)}|k>0\}$ are combined through an attention based model~\cite{attentionlstm}. The formal denotation is:

\begin{equation}
e_{k}=tanh(W^{(a)}Wh_{k}^{(l)})],
\end{equation}
\begin{equation}
v_{k}=\frac{\exp{(e_k)}}{\sum_{k=1}^{K}\exp(e_k)},
\end{equation}
\begin{equation}
r=\sum_{k=1}^{K}{v_{k}}{h_{k}^{(l)}},
\end{equation}
where $W^{(a)}$ is the attention weight parameter and $r$ is the input representation for generating a new $u_{t+1}$ that is regarded as the real system utterance.

Given the hidden state $Wh_k^{(l)}$, the decoding module can also generate the corresponding utterance for learning the looking-ahead ability. We share the parameters of decoding with those in the encoding module, in order to reduce the computing cost~\cite{neuralconvmodel}. The token sequence in $u_{t+k}$ is generated from left to right by selecting the tokens with the maximum probability distribution through a language model learned by the following equation:
\begin{equation}
p_\theta(y_j^{(t+k)}|y_{1,2,...,j-1}^{(t+k)})\propto\exp(E^TWh_k^{(l)}).
\end{equation}

\subsection{Model Training}

To train the proposed model, we define a loss function to maximize three terms: (1) a language model for predicting tokens in language generation, (2) the probability distribution of predicting utterances of future dialogue turns, and (3) a binary classifier to predict if the dialogue will be complete or not. The final joint loss function is formally denoted as:
\begin{equation}
\begin{aligned}
L(\theta)=&-\underbrace{\sum_{u}\sum_i\log p_\theta(x_i|x_{1,...,i-1})}_{language~model~loss}\\&-\alpha\underbrace{\sum_{u,g}\sum_k\sum_i\log p_\theta(y_i^{(t+k)}|y_{1,...,i-1}^{(t+k)},u,g)}_{looking~ahead~prediction~loss}\\&-\beta\underbrace{\sum_c\log p(z_c|c,u_{t+1})}_{dialog~state~prediction~loss},
\end{aligned}
\end{equation}
where
\begin{equation}
u_{t+1}=\arg max_yp_\theta(y|r),
\end{equation}
\begin{equation}
\begin{aligned}
\log p(z_c|c,u_{t+1})&=z_c\log(g(c,u_{t+1}))\\&+(1-z_c)\log(1-g(c,u_{t+1})).
\end{aligned}
\end{equation}
$g(\cdot)$ is a sigmoid function and $z_c$ is the label of the dialogue that current user utterance $c$ belongs to, where 1 means the dialogue ends up with goals achieved while 0 means the goals are not achieved. The three terms are weighted with two hyper-parameters $\alpha$ and $\beta$. We adopt stochastic gradient descent method to minimize $L(\theta)$.

In the looking-ahead module, the hidden state $Wh_k^{(l)}$ is used to generate an utterance $y^{(t+k)}$, and is also used to calculate $h_{k+1}^{\overrightarrow{l}}$ and $h_{k-1}^{\overleftarrow{l}}$. We design an EM-like algorithm to optimize the loss function, as described in Algorithm 1. Line 3-4 optimize the language model, i.e. the first term of $L(\theta)$. Line 5-16 optimize the looking-ahead module, i.e. the second term, among which Line 7-14 are for E-step and Line 15-16 are for M-step. In E-step the language model is fixed for updating all the hidden states $h_k^{(l)}$ in looking-ahead module, and in M-step all the hidden states are fixed for updating the language model. Line 17-18 optimize the third term of $L(\theta)$, which is a binary classifier.

\begin{algorithm}\small
  \caption{Learning algorithm for $L(\theta)$}
  \SetKwInOut{Input}{input}\SetKwInOut{Output}{output}
  \Input{Dialogue utterances $U$, Agent goals $g$, Looking-ahead turns $K$}
  \Output{Agent model $\theta$}
  \nl Randomly initializing parameters\;
  \nl \For{$c\in{U}$, $g$ and historical utterances $\{u\}$}
  {
    \nl \For{$x_i\in{c}$}
    {
      \nl   Optimizing $p_\theta(x_i|x_{1,...,i-1})$\;
    }
    \nl $h_1^{\overrightarrow{l}}=[h^{(g)},h^{(u)},h^{(c)}]$\;
    \nl $u_{t+1}=\arg max_yp_\theta(y|r)$\;
    E-Step: Update $h_k^{(l)}$ with fixed language model\;
    \nl \For{$k=1:K$}{
      \nl   $u_{t+k}=\arg max_yp_\theta(y|h_k^{(l)})$\;
      \nl   $h_{k}^{\overrightarrow{l}}=[h_{k-1}^{\overrightarrow{l}}, Wh_{k-1}^{(l)}]$\;
    }
    \nl $h_K^{\overleftarrow{l}}=h_K^{\overrightarrow{l}}$\;
    \nl \For{$k=K-1:1$}
    {
      \nl   $h_{k}^{\overleftarrow{l}}=[h_{k+1}^{\overleftarrow{l}}, Wh_{k+1}^{(l)}]$\;
    }
    \nl \For{$k=1:K$}
    {
      \nl   $h_k^{(l)}=[h_k^{\overrightarrow{l}}, h_k^{\overleftarrow{l}}]$\;
    }
    M-Step: Update language model with fixed $h_k^{(l)}$\;
    \nl \For{$k=1:K$}
    {
      \nl   Optimizing $p_\theta(y_i^{(t+k)}|y_{1,...,i-1}^{(t+k)})$\;
    }
    \nl $u_{t+1}=\arg max_yp_\theta(y|r)$\;
    \nl Optimizing $p(z_c|c,u_{t+1})$\;
  }
  \nl \Return $\theta$\;
\end{algorithm}

\section{Experiments}

\subsection{Data Collection}

We use two datasets for two different scenarios to evaluate our model. Table~\ref{tab:stat} shows the statistics of two datasets.

\subsubsection{Dataset 1 - Object Division}

Dataset 1 contains crowd-sourced dialogues between humans collected from Amazon Mechanical Turk platform~\cite{deal}. The dataset is for \textit{object division task} and both sides have separate goals of each object's value. We use the textual data and transform their goals to yes-no questions as our binary vectors. The information of each dialogue session's final state, agree or disagree, is used for training the agent.

\subsubsection{Dataset 2 - Restaurant Reservation}

To the best of our knowledge, there is no other public dataset for goal-oriented dialogues where the two sides have different goals. To this end, we construct the Dataset 2 to testify the scalability of our model. The common scenario of restaurant table reservation is chosen.

In this dataset, the two agents are expected to have different goals and they dialogue with each other for looking for the intersection of their goals. We denote Agent A as the role of a customer and Agent B as the restaurant server side. At the beginning of each dialogue session, Agent A is given the available time slot, the number of people, and several other constraints (e.g. can sit at bar or not). All the constraints are regarded as its goals represented by a binary vector. Similarly, Agent B has itself constraints (e.g. whether bar tables are available or not), which are also treat as goals represented by a binary vector. We predefine a pool of `goals' and at the beginning of each dialogue session, the goals for two sides are randomly sampled separately from the pool. The two agents cannot see each other's goals and they dialogue through natural language until a final decision, agreement or disagreement, is reached. In summary, the objective of constructing this dataset is to see if our model can reach the intersection of the two agents' goals in a more efficient way.

To generate dialogues for Dataset 2, we resort to a rule-based method via AI planning search~\cite{planning,dataset}. Watson AI platform~\footnote{https://www.ibm.com/watson/ai-assistant/} is leveraged for natural language understanding by defining intents and entities with examples. A planner is designed for the dialogue manager by defining several states and actions. The goals are represented as part of the states, and the STRIPS algorithm is used to search the shortest path to goals at each turn and return the first planned action for generating the next response. Each action has several handcrafted utterances since the diversity of utterances is not our focus in this paper. Table~\ref{tab:sample} shows a sample dialogue.

\begin{table}\small
  \centering
  \begin{tabular}{|c|c|c|}
    \hline
    Metric & Dataset 1 & Dataset 2 \\
    \hline
    Number of Dialogues & 5,808 & 1,613 \\
    Average Turns per Dialogue & 6.6 & 6.3 \\
    Average Words per Turn & 7.6 & 8.9 \\
    Number of Words & 566,779 & 98,726 \\
    \% Goal Achieved & 80.1\% & 71.5\% \\
    \hline
  \end{tabular}
  \caption{Statistic on the two datasets.}
  \label{tab:stat}
\end{table}
\begin{table}\small
  \centering
  \begin{tabular}{|l|}
    \hline
    Alice: May I reserve a table for 6 people at 17 tomorrow? \\
    Bob: Sorry, we don't have a table at this point. \\
    Alice: Can we sit at the bar then? \\
    Bob: We don't have a bar in the restaurant. \\
    Alice: Can I have more expensive tables then? \\
    Bob: My apologies, we are required not to do that. \\
    Alice: In this case, can I reserve a bigger table? \\
    Bob: Yes, we have VIP rooms but more expensive. \\
    Alice: I want that. \\
    Bob: OK.\\
    Alice: Bye.\\
    \hline
  \end{tabular}
  \caption{Sample of Dataset 2.}
  \vspace{-2ex}
  \label{tab:sample}
\end{table}

\subsection{Training Sample Preparation}

For each dialogue session with $T$ turns, we re-organize the utterances into $T$ samples. For each turn $t=\{1,2,...,T\}$, we can get the current user utterance $c$, and a training sample is created with a historical utterance sequence $\{u_1,u_2,...,u_{t-1}\}$, and the goals $g$ are consistent with the same dialogue session. The future $K$ turns of utterances $\{u_{t+1},u_{t+2},...,u_{t+K}\}$ are used as the supervised information. In total, we get 38,333 and 10,162 samples including training set and test set for the two datasets respectively.

\subsection{Baselines}

Since our model is based on purely data-driven learning, we compare our model with the supervised counterparts.
Our baselines include:

\begin{itemize}
  \item Seq2Seq(goal): This is a naive baseline by adapting the sequence-to-sequence model~\cite{seq2seq} and encoding goals, which removes the looking-ahead module and the supervised information of final state prediction from our model.
  \item Seq2Seq(goal+state): This is a baseline model by removing the looking-ahead module from our proposed model. The parameter $\alpha$ is set to zero.
  \item Seq2Seq(goal+look): This is a baseline model by removing the supervised information of final state prediction from our model. The parameter $\beta$ is set to zero.
  \item Seq2Seq(goal+look+state): This is our proposed model that includes all the modules and supervised information.
\end{itemize}

\subsection{Evaluation Criteria}

In a dialogue system, it could be treat as efficient if it obtains more final goal achievement with as few as possible dialogue turns. Thus we set two criteria for evaluating and comparing models adopted in our experiments: (1) the \textit{goal achievement ratio} that means the ratio of the number of goal achieved dialogue over the number of attempted dialogues), and (2) the \textit{average dialogue turns}.

\subsection{Evaluator}

Our experiments are to achieve goals through conversations, and it is difficult to directly adopt existing simulators~\cite{simulator1}. We refer to the work~\cite{simulator2} and fine-tune it to our task. For each dataset, a naive sequence-to-sequence model that encodes goals is regarded as the user simulator. We run 1000 times of dialogue sessions using the simulator.

Apart from using the simulator, we also invite humans to dialogue with the agents for 100 times each person for each dataset and we report the average results.

\begin{table*}\footnotesize
	\centering
	\begin{tabular}{|c|cc|cc|cc|cc|}
		\hline
		\multirow{3}{*}{Model} &
		\multicolumn{4}{c|}{Dataset 1} &
		\multicolumn{4}{c|}{Dataset 2} \\
		\cline{2-9} & \multicolumn{2}{c|}{\textit{vs. Simulator}} &
		\multicolumn{2}{c|}{\textit{vs. Human}} &
		\multicolumn{2}{c|}{\textit{vs. Simulator}} &
		\multicolumn{2}{c|}{\textit{vs. Human}} \\

		\cline{2-9} & \% Achieved & \# Turns & \% Achieved & \# Turns & \% Achieved & \# Turns & \% Achieved & \# Turns \\
		\hline
		Seq2Seq(goal) & 76.00 & 4.74 & 67.74 & 7.87 & 67.10 & 7.38 & 54.1 & 7.56 \\
		Seq2Seq(goal+state) & 79.41 & 4.74 & 70.97 & 6.35 & 67.37 & 7.42 & 58.1 & 8.04 \\
		Seq2Seq(goal+look) & 80.64 & 6.54 & 74.19 & 5.41 & 83.54 & \textbf{5.82} & 60.3 & \textbf{6.94} \\
		Seq2Seq(goal+look+state) & \textbf{85.07} & \textbf{4.10} & \textbf{77.42} & \textbf{5.02} & \textbf{83.58} & 6.36 & \textbf{61.2} & 7.30 \\
		\hline
	\end{tabular}
	\caption{Performance on two datasets against the user simulator and human.}
	\vspace{-3ex}
	\label{tab:result1}
\end{table*}

\begin{figure}
	\begin{centering}
		\subfloat{\begin{centering}
				\includegraphics[width=1.5in]{11}
				\par\end{centering}}
		\subfloat{\begin{centering}
				\includegraphics[width=1.5in]{12}
				\par\end{centering}}
		\par\end{centering}
	\caption{vs. looking-ahead turns on Dataset 1}~\label{fig:stepdata1}
	\vspace{-4ex}
\end{figure}
\begin{figure}
	\begin{centering}
		\subfloat{\begin{centering}
				\includegraphics[width=1.5in]{21}
				\par\end{centering}}
		\subfloat{\begin{centering}
				\includegraphics[width=1.5in]{22}
				\par\end{centering}}
		\par\end{centering}
	\caption{vs. looking-ahead turns on Dataset 2}~\label{fig:stepdata2}
	\vspace{-4ex}
\end{figure}
\begin{figure}
	\begin{centering}
		\subfloat{\begin{centering}
				\includegraphics[width=1.5in]{h11}
				\par\end{centering}}
		\subfloat{\begin{centering}
				\includegraphics[width=1.5in]{h12}
				\par\end{centering}}
		\par\end{centering}
	\caption{vs. hidden state dimension on Dataset 1}~\label{fig:dimendata1}
	\vspace{-4ex}
\end{figure}

\begin{figure}
	\begin{centering}
		\subfloat{\begin{centering}
				\includegraphics[width=1.5in]{h21}
				\par\end{centering}}
		\subfloat{\begin{centering}
				\includegraphics[width=1.5in]{h22}
				\par\end{centering}}
		\par\end{centering}
	\caption{vs. hidden state dimension on Dataset 2}~\label{fig:dimendata2}
	\vspace{-4ex}
\end{figure}

\subsection{Training Settings}

All the baselines are implemented by PyTorch. One-hot input tokens are embedded into a 64-dimensional space. The goals are encoded by $GRU^{(g)}$ with a hidden layer of size 64. The sizes of hidden states in input utterance encoder $GRU^{(u)}$, $GRU^{(c)}$ and looking-ahead module $GRU^{(l)}$, $h_k^{(l)}$, are all set to 256. A stochastic gradient descent method is employed to optimize the model with a mini-batch size of 32 for supervised learning, an initial learning rate of 1.0, momentum with $\mu=0.1$, and clipping gradients 0.5 in $L^2$ norm. The best model is chosen from the processing of training the model for 400 epochs. After that, the learning rate decays by a factor of 2 for every epoch. The initial hyper-parameters setting in the loss function (Equation (11)) is $\alpha=0.05$ and $\beta=1.0$. Words that appear in the training dataset for less than 5 times are replaced with the `unknown' ($\left<unk\right>$) token. A validation dataset is employed to choose the optimal hyper-parameters.

\subsection{Results and Analysis}

Table~\ref{tab:result1} shows the performance of baselines against user simulator and human on the two datasets. Both reveal that models that learn looking-ahead ability can achieve better performance and deliver more efficient dialogues in terms of both goal achievement ratio and dialogue turns. However, in the table, the dialogue turns of Seq2Seq(goal+look+state) are larger than those of Seq2Seq(goal+look), which may suggest that more dialogue turns lead to more achievement. In spite of this, the looking-ahead ability learned by our model is demonstrated to be effective on the two different scenarios. Moreover, the supervised information of final states (the third term of Equation (11)) is also proven effective in delivering more achievement, which can be seen from the second and last rows of Table~\ref{tab:result1}. Compared to the human evaluation, the results with the simulator generally are better. It is probable that human evaluators tend to be more rigorous and more turns are necessary to achieve goals.

\begin{table}\small
	\centering
	\begin{tabular}{|l|}
		\hline
		\textit{Seq2Seq(goal) Model:}\\
		\hline
		Alice: i just want the book \\
		Bob: no way i have the book and you can take others \\
		Alice: bye\\
		$<$Conversation end$>$ \\
		\hline
		\textit{Seq2Seq(goal+look+state) Model:}\\
		\hline
		Alice: i keep the book you can have the hat and two balls\\
		Bob: i will take the book and you can have others\\
		Alice: can we make a deal? \\
		Bob: ok deal\\
		Alice: okay \\
		Bob: bye\\
		$<$Conversation end$>$ \\
		\hline
	\end{tabular}
	\caption{Example that tends to achieve goals on Dataset 1, vs. simulator.}
	\label{tab:sampledata1}
	\vspace{-2ex}
\end{table}

We also evaluate the models' performance with different parameters. Figure~\ref{fig:stepdata1} and Figure~\ref{fig:stepdata2} show the results of using different looking-ahead turns $K$ in the looking-ahead module on Dataset 1 and Dataset 2 respectively. In most cases, Seq2Seq(goal+look+state) is better than Seq2Seq(goal+look). Setting the number to 3 is the recommended value, since our scenarios are relatively simple and it is enough for the agents to look ahead for 3 dialogue turns. The results are consistent with those in Table~\ref{tab:result1}.

The performance on different dimension sizes of hidden states used in all  the baselines is evaluated and the results are shown in Figure~\ref{fig:dimendata1} and Figure~\ref{fig:dimendata2}. We find that setting the size to 512 yields the optimal results by balancing the performance and the computing cost in most cases.

In addition, we take some case studies for dialogue examples between the agents trained by Seq2Seq(goal+look+state) (proposed model) and the simulator (Seq2Seq(goal)) for the two datasets. \textit{Alice} denotes the agent and \textit{Bob} denotes the simulator. Table~\ref{tab:sampledata1} is a case where our model with the looking-ahead ability (Seq2Seq(goal+look+state)) tends to achieve goals while consume more dialogue turns. Table~\ref{tab:sampledata2} gives an example where our model not only tends to achieve goals but also takes fewer dialogue turns. Based on our observation, the language models for natural language generation is learned well in spite of that confused utterances are generated sometimes.

\begin{table}\small
	\centering
	\begin{tabular}{|l|}
		\hline
		\textit{Seq2Seq(goal) Model:}\\
		\hline
		Alice: can you help me book a table for 2 people at 6pm? \\
		Bob: sorry, we dont have a table at this point \\
		Alice: can i reserve the seats at the bar instead? \\
		Bob: yes you can\\
		Alice: can i book a table for us\\
		Bob: sure i have written down your reservation \\
		Alice: ok \\
		Bob: bye \\
		$<$Conversation end$>$ \\
		\hline
		\textit{Seq2Seq(goal+look+state) Model:}\\
		\hline
		Alice: can you help me book a table for 2 people at 6pm? \\
		Bob: sorry we dont have a table at this point \\
		Alice: can i reserve the seats at the bar instead? \\
		Bob: sure i have written down your reservation \\
		Alice: bye \\
		$<$Conversation end$>$ \\
		\hline
	\end{tabular}
	\caption{Example that costs less dialogue turns under the same goals on Dataset 2, vs. simulator.}
	\label{tab:sampledata2}
	\vspace{-2ex}
\end{table}
\section{Conclusion}

In this paper, we propose an end-to-end model towards the problem of how to learn an efficient dialogue manager without taking too many manual efforts. We model the looking-ahead ability for foreseeing several turns and then the agent can make a decision of what to say that leads the conversation to achieve goals with as few as possible dialogue turns. Experiments on two datasets from different domains demonstrate that our model is efficient in terms of goal achievement ratio and average dialogue turns. Our method is also scalable and can reduce error propagation due to the nature of end-to-end learning.

For the future work, we expect to investigate whether other kinds of abilities, such as reasoning ability, can be modeled for agent towards the problem. In addition to the efficiency issue, the quality of natural language generation should also be paid attention in order to guarantee the quality of overall dialogue system.

\section*{Acknowledgments}
The work is partially supported by SFSMBRP (2018YFB1005100), BIGKE (No. 20160754021), NSFC (No. 61772076 and 61751201), NSFB (No. Z181100008918002), CETC (No. w-2018018) and OPBKLICDD (No. ICDD201901). We thank Tian Lan, Henda Xu and Jingyi Lu for experiment preparation. We also thank the three anonymous reviewers for their insightful comments.

redpajama_set_name: RedPajamaArXiv


text:
How can I write about this book? Is it a novel? Does it matter?
No, it doesn't matter. It was written in the form of occasional entries on a Facebook page, the modern way to keep a diary. But it isn't a Facebook conversation between our two men, because they aren't of the generation that communicates that way. Writing on Facebook is what kept it topical as Doyle wrote it, letting his his characters respond to current news. Or whatever else he felt like writing about that day: there's one surreal moment when it's the Sydney Opera House, because Doyle happens to be in a hotel room overlooking it that day. It reminded me – I'm sure it reminds a lot of Brits of my generation – of the comic dialogues between the working class personas adopted by Peter Cook and Dudley Moore in the 1960s.
Is there more to it than comedy? It's hugely funny – there's at least one joke in each session, and often the dismissive 'Fuck off' that contains so much subtext it made me laugh out loud – but does that mean that it's no more than an update of those Pete'n'Dud sketches? Again, does it matter?
Probably not. There's usually (how usually?) something serious going on that one of the men opens a conversation with. We don't know the names of either of them, but it always seems to be the same man who starts up the conversational thread, and slowly the workings of a relationship emerge. I'll come back to that. The main point, I think, is that it gives Doyle the chance to meditate on current events, celebrity culture and, like any other novelist of a certain age, death. Ok.
And as soon as I wrote that I realised that these really are Facebook entries. Through these two men, who are – again like Pete'n'Dud – less educated than the writer, Doyle can hold up for scrutiny any idea that has occurred to him that day. His working class men are ruthless when it comes to puncturing any sign of pretentiousness – if the one who opens the conversations goes an inch too far he gets an instant 'Fuck off' – so he has created the perfect medium for himself. He can be satirical about the latest state visit to Ireland – Barack Obama or the Queen – but hint at why those visits might actually have a significance beyond the razzmatazz. Or not.
As for the deaths…. These men are old enough to be grandparents, so might be a few years older than Doyle. But he is old enough – and I should know – to notice not only the celebrity deaths like that of Amy Winehouse, but of people who seem to have been around forever. The conversations about death are as comic as any others, but they are like a comedy memento mori, like the Mexican death carnivals with their parades of dancers and garish skull masks.
I mentioned the relationship. They know each other, but only as drinkers in the same pub. If either of them misses a few sessions, the other only finds out why the next time he's in the pub. The one who starts the conversations is careful about the way he floats an idea, cautious about the mockery it might bring down on his head. This is highly recognisable in conversations between men. In my experience – and I currently work alongside working class men – it's more blatant among working men than among politer, more educated men. But don't be fooled. In a book that might seem a million miles from this one (ok, about 200 miles), Kate Fox's Watching the English, the 'Come off it' rule is highly prominent. If you want to go out on a limb conversationally there are a lot of people – or, at least, it always feels as though there are – who are ready to shoot you down.
It leads to a kind of double-think. You hedge your bets in conversations, floating an idea by way of an innocent-sounding question. Or you mock it as you say it. Or you get ready with your reply to the mockery you expect to come…. All of which we see in Two Pints. It doesn't make it exactly realistic – I'll come back to that as well – but it makes it funny. And I think of Roddy Doyle, floating his ideas on his Facebook page, a further step back than usual. This isn't me, he can let himself say, these are just blokes I've made up. Clever.
Just to make sure we know that this is only a joke, he has the rather unnecessary Damien, the grandson. The name says it all (Google it if you don't know what I'm talking about): from small beginnings – he throws his hamster (or gerbil or whatever) into the deep-fat fryer of the local chip-shop – he goes through a hyena, a polar bear and, eventually, a tiger cub he seems to have skinned to make a waistcoat. And the chip-shop owner is one of several with comedy names – his is Gaddafi – that lead to inevitable confusions.
But I was writing about the relationship. All we have to go on is what they say, but Doyle is a playwright and scriptwriter as well as a novelist, so he's used to this. As I've said, only one of the men seems to start off the conversational threads. But it doesn't make him the dominant one. The other has strategies for keeping up his end, through the kind of mockery I've mentioned and through a deft use of the 'Fuck off' technique. And is he the one – it doesn't really matter either way, I know that – who makes a mocking threat at the end of one session because the other didn't mock his wife as he'd just mocked the other's? (Try to keep up.) There are ways to hold your own, as we know in these situations.
But I really see them as Roddy Doyle's alter-egos. He's taking ideas out for an airing, doodling around them with a bit of social comedy and a few satirical riffs on the ridiculousness of the news media – I haven't really touched on that, but it's on nearly every page – and he's having a laugh. Best thing to do.


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text:
Könyvek 99 / 11 - 20. könyv a(z) HARDY (CAPT. C.)— FOREST LIFE IN ACADIE ; and Sketches of Sport and Natural History... kifejezésre.
HARDY (CAPT. C.)— FOREST LIFE IN ACADIE ; and Sketches of Sport and Natural History in the Lower Provinces of the Canadian Dominion. With Illustrations.
The Public Life of the Right Honourable the Earl of Beaconsfield, K.G., Etc ... - 8. oldal
szerző: Francis Hitchman - 1879
First Supplement to the Catalogue of the Young Men's Association Library of ...
1871 - 154 oldal
...9-10970 Hardwicke's science gossip. MC Cooke Ea 3-7154, and 15847 Hardy, Campbell. Forest life in Acadia, sketches of sport and natural history in the lower provinces of the Canadian dominion. NY, 1869. 8° Ga 8-7659 Hardy, ET Widow Freeheart. F's 8. D. v. 45 . Hb 9-10974 Hare, Julius C. The...
Supplement to thr Alphabetical Catalogue of the Liobrary of the Royal ...
John Murray - 1871
...Plates. tim. 8° 1871. See also INDIA, SELECTIONS [Punjab, No. x.]. Hardy, C. Forest Life in Acadie. Sketches of Sport and Natural History in the Lower Provinces of the Canadian Dominion. Plates. 8° 1869. Harlot, T. A briefe and true report of the new found land of Virginia, of the commodities,...
Bibliotheca Americana: A Catalogue of a Valuable Collection of Books ...
Alfred Russell Smith, John Russell Smith - 1871 - 234 oldal
...plates, boards. 4s 6d—half calf gilt. 6s 6d 1829 1315 HARDY'S (Capt. Campbell) Forest Life in Acadie : Sketches of Sport and Natural History in the Lower Provinces of the Canadian Dominion. 8vo, many plates, cloth. 8s 6d (pub. at 18s) 1869 1316 HARLAN (Dr. Richard) Fauna Americana, being...
The Experiences of a Planter in the Jungles of Mysore, 1. kötet
Robert Henry Elliot - 1871
...their Origin, History, Uses and Abuses. 210 Engravings. 8vo, cloth, 11. Is. Forest Life in Acadie. — Sketches of Sport and Natural History in the lower Provinces of the Canadian Dominions, by Captain Campbell Hardy, RA, with Illustrations. Demy 8vo, 18s. Forster (John)— Oliver...
Galvano-therapeutics
William Basil Neftel - 1871 - 161 oldal
...Infantry, NGSNY, formerly Drummer in Co. A, 5th Regiment, NYV, Duryea's Zouaves. Forest Life in Acadie. Sketches of Sport and Natural History in the Lower Provinces of the Canadian Dom1nion. By Capt. CAMPBELL HARDY, author of " Sporting Adventures in the New World." With numerous...
Bibliotheca Californiae, 2. kötet
California State Library - 1871
...Persia, residing at Athens. London, 1798. 2 Voh. bto. HARDY(fop'- C.). Forest-Life in Acadie: Sketehes of Sport, and natural History, in the lower Provinces of the Canadian Dominion. New York, 1869. HARDY (ESTHER L ). Home-Nurse, and Manual for the SickRoom. London, 1863. l'2mo. HARDYNG...
Bibliotheca Americana. A catalogue of a valuable collection of books ...
Alfred Russell Smith - 1871
...plates, boards. 4s Gd — half calf gilt. GsGd 1829 1315 HARDY'S (Capt. Campbell) Forest Life in Acadie : Sketches of Sport and Natural History in the Lower Provinces of the Cana-- dian Dominion. 8vo, many plates, cloth. 8s 6d (pub. at 18s) 1869 1316 HARLAN (Dr. Richard) Fauna...
Thirty Years in the Harem: Or, The Autobiography of Melek-Hanum, Wife of H ...
Malik-Khanam - 1872 - 427 oldal
...Folle-Farine (a New Novel), by Ouida. a vols. Post Svo, clotli, 1f. 11s. 6d. Forest Life in Acadie.— Sketches of Sport and Natural History in the Lower Provinces of the Canadian Dominions, by Captain Campbell Hardy, KA, with Illustrations. Demy Svo, 1Ss. Forster (John)— Oliver...
Struggles and Experiences of a Neutral Volunteer, 2. kötet
John Furley - 1872
...Folle-Farine (a New Novel), by Ouida. 3 vols. Post 8vo, cloth, II. Us. 6d. Forest Life in Acadie.— Sketches of Sport and Natural History in the Lower Provinces of the Canadian Dominions, by Captain Campbell Hardy, RA, with Illustrations. Demy 8vo, 18s. Forster (John)— Oliver...
James Frazer, a reminiscence of the highlands of Scotland in 1843
James Frazer (fict. name.) - 1873
...ENGLISH COUNTIES. Fifty Maps, coloured. New edition, including the railways, demy Svo, in roan tuck, 10s. 6d. HARDY (CAPT. C.)— FOREST LIFE IN ACADIE ; and...Canadian Dominion. With Illustrations. 8vo, cloth, ia«. HAREM LIFE— THIRTY YEARS IN THE HAREM, OR LIFE IN TURKEY. By MADAME KIEEIZI,I-MIHEMKI-PA8HA....


The Problem of Noise by F. C. Bartlett was published as part of the Cambridge Miscellany series in 1934. The volume contains two Heath Clark Lectures delivered by the author at the invitation of the National Institute of Industrial Psychology, printed together with a preface by C. S. Myers. The purpose of the lectures was to provide a concise account of some facts - rather than opinions - about unwanted sounds and their effects.


text:
Q: Alternative to DecimalFormat in Android I'm trying to make a method that receives a double and returns a String formatted with always 2 numbers after the decimal point.
For example:
1990.999 -> "1990.99"
1990.9 -> "1990.90"
I'm developing an app using Android min sdk version 21, so I can't use DecimalFormat. I manage to make something that works like this:
public String currencyFormat(double value) {
        return value < 0
                ? String.format("-" + currencySymbol + "%.02f", value * -1)
                : String.format(currencySymbol + "%.02f", value);
    }

However, the problem here is that if I try for example 1999,369 the result is "1999,37". Is there a way to prevent the round out?

A: You could use this method instead:
public static String currencyFormat(double value) {
    value = Math.floor(value * 100) / 100; // rounds 'value' to 2 decimal places
    return String.format("%.2f", value); // converts 'value' to string
}

redpajama_set_name: RedPajamaStackExchange


text:
All Of Us Are Dead's Cliffhanger Ending, Explained
What did the students see over the ledge of the roof, and why is the government secretly keeping zombies?
by Kadin Burnett
Yang Hae-sung/Netflix
Netflix's South Korean thriller All Of Us Are Dead combines a coming-of-age story with a zombie survival tale. The series is based on the South Korean webtoon Now at Our School and follows a teen girl named Nam On-jo (Park Ji-hu), a student in Class 2-5 at Hyosan High School in the fictional setting of Hyosan City. After a science experiment gone wrong, the school is overtaken by zombies, and she along with the rest of her classmates must fight to evade death and attempt to reach the quarantine camps being operated by the military.
Because her father is a firefighter and former solider, On-jo has a set of survival skills that make her particularly apt to withstand the outbreak. The show also features a handful of other characters, including her neighbor and childhood friend Lee Cheong-san (Yoon Chan-young), class president Choi Nam-ra (Cho Yi-hyun), skilled fighter Lee Su-hyeok (Lomon), resident bully Yoon Gwi-nam (Yoo In-soo), and the wealthy Lee Na-yeon (Lee Yoo-mi).
Season 1 concludes with some survivors reaching safety, but others aren't so lucky. And there's still plenty to make sense of given several cliffhangers and that survival is still far from guaranteed. Let's break down the ending.
Who Dies On All Of Us Are Dead?
Only a handful of characters make it out of Hyosan City. In the beginning, the kids use sports equipment to build a barricade and stave off the zombies, which is an effective strategy until On-jo's father So-ju (Jeon Bae-soo) arrives in an attempt to rescue them. So-ju is forced to sacrifice himself while the rest of the students escape through the tennis courts. Later, Nam-ra is bitten by a zombie but doesn't transform completely. Instead, she becomes what's known as a halfbie (half human, half zombie) and absconds from the group after realizing that she can no longer contain her bloodlust.
All told, the six students still standing at the end are Class 2-5 members On-jo, Su-hyeok, Dae-su, and Hyo-ryung; archery phenom Jang Ha-ri (Ha Seung-ri); and Park Mi-jin (Lee Eun-saem), a senior with a bad temper. However, the final tally doesn't account for the halfbies still stalking the streets — including Cheong-san and Gwi-nam, who were last seen being engulfed in the explosions meant to eradicate the zombies. While it's fairly hard to survive an explosion of that magnitude, it's not completely unreasonable to believe that one or both — having become resilient undead beasts — made it out alive.
What Happened To Nam-ra & The Halfbies?
The fate of halfbies is a big open question at the end of All Of Us Are Dead. After bombs decimate Hyosan City, On-jo and co. sneak out of the quarantine camp they've been staying at for three months and return to their school, where they spot a fire burning on the roof. There, they reunite with Nam-ra. On-jo tries to convince Nam-ra to return with them, but she refuses, citing her status as a halfbie and her desire to stay with the other halfbies currently infesting the school.
Sensing more halfbies are making their way to the roof, Nam-ra leaps from atop the building, knowing that halfbies can take more damage than full zombies. On-jo and the rest of the students peer over the ledge and see something that viewers are never shown, though we do see the group's aghast faces. Are there more halfbies than they expected? Are they more carnivorous than they initially believed? It's possible their shock is simply the result of seeing their friends and loved ones as flesh-eating zombies, but this adds fuel to the idea that Cheong-san or Gwi-nam could still be alive, given that the bombs seem to have only managed to kill fully zombified citizens and not halfbies.
What Does The Government Want With The Zombies?
The whole reason the zombie outbreak began is because of Lee Byeong-chan (Kim Byung-chul), the scientist who tried to chemically enhance his meek son. Obviously things didn't go according to plan: his son, Jin-su (Lee Min-goo), turned into the show's first zombie. At the end of Season 1, government operatives trek into the city to retrieve any and all information about Byeong-chan's research. In doing so, they find Jin-su and his zombified mother chained up.
Upon returning to their military base, it's revealed that not only did the government agents bring Jin-su and his mother with them, they've also detained a halfbie student and a zombified soldier. It's unclear if they're using their subjects to come up with a cure or for something more sinister, being that the original concoction was made to enhance bravery. Either way, the government's decision to house zombies could prove dire in a potential Season 2, given the likelihood of exponential spread.
TikTok Pointed Out A Major 'Emily In Paris' Continuity Error
By Sophie McEvoy
Everything To Know About 'Cobra Kai' Season 6
By Brad Witter
'The Crown' Season 6 Has Suspended Filming "Out Of Respect" For The Queen's Death
By Bustle Staff
The Song At The End Of 'The Last Of Us' Ep 1 Has A Deeper Meaning
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text:
Q: The Death Cure and Newt I'm confused in the Death Cure. So we know Thomas is the cure at this point. Because his blood cells where able to not slow down the virus but to destroy it completely. We know this because he cured Brenda. Which once Teresa figured it that he was the cure then she started talking over the intercom across the whole city. So at this point I believe he knows he's the cure and he's still with Newt at this point. So that brings me to my question.

*

*Since Thomas is known as the cure why wouldn't he have just let Newt bite him once after Newt turned crank then knock him out and then carry him away to safety?

A: Why would you let someone bite you? The possibility of it working seems... slim to say the least, my reasoning behind this is that cranks don't act like vampires.
Yeah, biting him might let Newt consume some of his blood but it wont directly go to his bloodstream - that's why the injected Brenda with Thomas' blood, not gave it to her to drink or something. Sure, it would at some point go into his bloodstream but that wouldn't happen that quickly, and I'm pretty sure you don't wanna stick around in a burning city possibly filled with cranks.
My reasoning behind this is because I'm pretty sure the virus takes place into the bloodstream (judging by the pulsing blue veins being a symptom of it).


True reality comes to life with deep black and natural color. Over 8 million self-illuminating pixels are precisely and individually controlled by the X1 Extreme processor. Sony's OLED TV creates exquisite, unprecedented contrast., Our 4K HDR Processor X1 Extreme includes powerful real-time image processing for ultra-detailed pictures. It takes everything you watch and upscales it to near 4K HDR quality., Unlike most TV speakers, sound comes to you from the entire screen, immersing you in exciting new entertainment experiences. Two actuators behind the TV vibrate the screen to create sound that's truly engaging. It's pictures and sound in perfect harmony., See vivid real-world colors all around you. A TRILUMINOS Display maps from a wider palette, faithfully reproducing every shade and hue in the umbrellas., With 4K X-Reality PRO, every image is upscaled closer to true 4K quality for remarkable clarity. Images are sharpened and refined in real time, revealing extra detail in the books and architecture of the library.
True reality comes to life with deep black and natural color. Over 8 million self-illuminating pixels are precisely and individually controlled by the Picture Processor X1 Ultimate. Sony's 4K OLED HDR TV creates exquisite, unprecedented contrast., With unmatched processing power, the Picture Processor X1 Ultimate detects hundreds of different objects on-screen and intelligently enhances brightness, detail and color so everything you watch is clear, bright and incredibly realistic., Powered by the Picture Processor X1 Ultimate, this best picture quality TV pairs the deep black and natural color of OLED with Pixel Contrast Booster for enhanced color contrast in high luminance. Everything you watch comes to life with fine detail and vi, Just like in a concert hall, sound comes at you from different directions with Acoustic Surface Audio+ . Three actuators and two powerful subwoofers deliver multi-dimensional sound that immerses you in a whole new entertainment experience., Use your BRAVIA(R) as a center speaker and hear dialogue, vocals and instruments with exceptional clarity. Simply connect your home theater system to unleash an immersive cinematic experience.
Designing televisions that faithfully convey the creator's intent is our passion. MASTER Series TVs meet the strict criteria of our top engineers and makes beautiful images a reality. For the first time, creators can explore their imagination and see thei, Be in the moment with true-to-life pictures and acoustics to match. Beautifully detailed images combine with our Sound-from-Picture Reality for a captivating audio-visual performance., With unmatched processing power, the Picture Processor X1 Ultimate detects hundreds of different objects on-screen and intelligently enhances brightness, detail and color so everything you watch is clear, bright and incredibly realistic., True reality comes to life with deep black and natural color. Over 8 million self-illuminating pixels are precisely and individually controlled by the Picture Processor X1 Ultimate. Sony's 4K OLED HDR TV creates exquisite, unprecedented contrast., Powered by the Picture Processor X1 Ultimate, this OLED TV pairs the deep black and natural color of OLED with Pixel Contrast Booster for enhanced color contrast in high luminance. Everything you watch comes to life with fine detail and vivid color.


Welcome to our range of Badger Pest Control Products and Supplies that can be seen below this short write up.
Read here: How to Get Rid of Badgers.
If you prefer to order by telephone or need any advice, please give us a call on 03330 119703. We supply the best and most effective DIY Badger pest control products to buy online for delivery throughout the UK, France, Spain, other parts of Europe and rest of the World.
Badgers have become an increasing garden pest due to the change of law that now makes them a protected species.
We supply Badger Repellent Granules, Electronic Badger Repellers and a Water Jet Spray Repeller.
Our products have been chosen for being the best and most effective in repelling and deterring pest badgers that are causing a mess in your garden.
Badger Repelling and Deterring Granules are a smell deterrent to stop and get rid of badgers in the garden or on the land. 1 x 50g pack will cover up to 50 square metres / 538 square feet.
Ultrasonic Battery Powered Badger Scarer for driving away nuisance badgers from the garden or land. Runs of 2 x 9 volt PP3 batteries. Covers up to 15 metres / 50 feet in front of the device, in an arc of 60 degrees.
Ultrasonic Mains Electric Powered Badger Scarer for repelling and deterring badgers that are coming into the garden or onto the land. Covers up to 15 metres / 50 feet in front of the device, covering a total space of up to 235 square metres / 2500 square feet.
Solar Panel to Power the Ultrasonic Mains Electric Badger Scarer. Easy to use, just plug into the badger repellent unit and start to power the device with free energy from the sun.
Nite Red Eyes Badger Scarer - Solar Powered as seen on the BBC Tv's Countryfile. Effective control of badgers with no running costs. Weatherproof and maintenance free.
Water Jet Spray Badger Scarer to scare away problem badgers in the garden or on the land. Easy to use, just connect to a garden hose and the unit will begin to trigger a 5 second jet of water when a badger comes into the PIR Motion Sensory Detection system.


text:
class BitstreamTrackBuilder final : public TrackBuilder
{
public:
    BitstreamTrackBuilder(DataRate datarate, Encoding encoding);

    int size() const;
    void setEncoding(Encoding encoding) override;
    void addRawBit(bool bit) override;
    void addCrc(int size);

    BitBuffer& buffer();
    DataRate datarate() const;
    Encoding encoding() const;

private:
    BitBuffer m_buffer;
};

redpajama_set_name: RedPajamaGithub


Cargilfield Prep School | P3 join Form 4 for break time!
P3 join Form 4 for break time!
Lots of fun for P3 in the Upper School.
P3 joined upper school break. They had a Form 4 buddy and had great fun doing lots of the activities that are on offer everyday at break time!


Congratulations to National Engineering Manager Craig Barrow and Accounts Specialists Rachel Chettle and Hayley Staniscia on completing the 'WoBurner Tri-for-life' Triathlon at Woburn Abbey on Saturday 8 September.
The team finished the Olympic-distance triathlon in a highly creditable 3 hours, eleven minutes, as a three-person relay called Team Riwal Breast Buddies. Hayley started things off with the 1500-metre open water swim in the Abbey's lake. Craig then completed the 44-kilometre bike ride and Rachel finished with a 10.5-kilometre run through the Abbey's grounds and deer park.
The tired but happy team were raising money as part of Riwal UK's year-long #RiseAboveBreastCancer fundraising campaign.
It is still possible to sponsor the triathlon team, by making a donation through Riwal UK's Just Giving page at https://just.ly/RiwalUKDonateToBreastCancerNow. Every pound donated goes to help make breast cancer a survivable disease for everyone who develops it by the year 2050.


Cambodia is the first country to undertake and launch the VACS in Southeast Asia. The VACS findings, along with key government commitments, were presented at a national launch on October 22, 2014 and included high-level representation from all involved ministries. Participants included the Deputy Prime Minister, Marta Santos Pais, Special Representative for the Secretary General on Violence Against Children, and many more key leaders.
For an overview of the data found in Cambodia's Violence Against Children Survey, download the Cambodia one-pager factsheet.
The average age of the first incident of sexual abuse was 15 for girls.
53% of girls reported experiencing physical violence before age 18.
5% of girls reported experiencing sexual abuse before the age of 18.
The average age of the first incident of sexual abuse was 10 for boys.
54% of boys reported experiencing physical violence before age 18.
6% of boys reported experiencing sexual abuse before the age of 18.
Only 21% of boys who reported experiencing childhood sexual abuse told anyone and only 6% of boys sought help.
In December 2015, Cambodia released the 'Economic Burden of the Health Consequences of Violence Against Children' report.
The report focuses on understanding the consequences of childhood violence and the economic costs incurred by Cambodia at large. The study shows that Cambodia lost at least US$ 168 million in 2013, or 1.1% of its GDP, as a result of the negative impact of some of the health consequences caused by violence against children. It also shows that loss of productivity attributed to childhood violence in 2013 totaled US$ 83.3 million, accounting for 0.55% of the country's GDP. The study findings emphasize the need for investment to strengthen the national child protection system as well as budget allocations and investments in the prevention of and response to violence against children.
The Government of Cambodia has made a number of commitments in response to the data. These will form the basis of a costed, national multi-sectoral action plan to prevent and respond to violence against children in Cambodia. The country's commitments span across sectors, with gender-sensitive strategies integrated into sectoral goals, cross-ministry coordination, and attention to the participation of children.
The code involves increasing the number of Judicial Police Agents at the district level and increasing their capacity and skills on counseling and case management for responding to violence against children.
Specifically in the social and child welfare system at the national and sub-national levels, and the creation of a gender-sensitive child abuse complaint and reporting mechanism.
Especially into early childhood care and development guidelines for parent and caregiver education.
And also ensure the Cambodian National Police' Professional Code of Ethics includes child protection standards.
The National AIDS Authority will mainstream and integrate zero tolerance of violence against children into the national Three Zeroes strategy to respond to HIV and AIDS. In addition to their work on designing and implementing national actions as part of the VACS process, Cambodia is one of the focus countries for the United States Government Action Plan on Children in Adversity, and is also working to align data and actions on gender-based violence and violence against children.


text:
Cet article recense les édifices religieux des Hautes-Alpes, en France, munis d'un clocher-mur.

Liste

Annexes

Références

Liens internes
 Liste d'églises françaises à clocher-mur
 Liste des édifices chrétiens des Hautes-Alpes

Liens externes

Clocher-mur
Alpes-Haute-Provence

redpajama_set_name: RedPajamaWikipedia


text:
Matthew Rossi
Matthew Rossi @MatthewWRossi — Matthew Rossi is a synapsid, perhaps descended from Cynognathus. He was born in Providence, Rhode Island, and grew up there before leaving to see the world and be mistaken for a sasquatch and/or minor singing celebrity in various locales. He currently lives and writes in Edmonton alongside his amazing and beautiful wife and their cats. He's written three collections of speculative fiction, Things That Never Were, Bottled Demon and At Last, Atlantis. He loves playing warriors in World of Warcraft, barbarians in Diablo III, and he's beginning to notice a pattern here.
DMs don't need plan every last detail their D&D campaigns and settings
by Matthew Rossi on January 13, 2022 at 3:30pm @MatthewWRossi
When you're starting a homebrew D&D campaign — building your own adventure in your own world — it's okay to not have everything planned out in advance.
How do you deal with failure?
by Matthew Rossi on January 13, 2022 at 8:00am @MatthewWRossi
I cannot play a ranged caster class in almost any game.
The Queue: I have no idea what this is
Look, all I know is every time Commander Shepard is going anywhere, these guys are in the way, and I feel ridiculously guilty if I run them over in the Mako or the Hammerhead. Why are they on so many different planets? Why do they have weird little arms up front of their legs? Can they pronate or supinate those arms? How useful are they, can they grip tools? Are they sapient beings? What's the deal with them? They're apparently called Space Cows, but… I mean, that's not a cow.
People eat these things. That feels like a mistake.'
Anyway, this is the Queue. I don't think that's what a cow should look like.
Game choice can get overwhelming — even if it's just a cosmetic choice
I love having more options when playing a game, and yet, I often find the options a bit too much.
Say goodbye to Shards of Domination in patch 9.2
If you've been running content from Shadowlands patch 9.1, specifically the Sanctum of Domination raid, then you're probably familiar with Shards of Domination are, and since it's tied so closely to patch 9.1, it doesn't come as a huge surprise that we bid them adieu in patch 9.2.
What makes a game replayable?
It's fair to say I have played the various games of the Mass Effect series multiple times.
When is the Diablo 4 release date? Probably not until 2024 at the earliest
We once thought we would be playing Diablo 4 by 2022, but now it looks like the game is unlikely to arrive for another year or two.
If you like a challenge, WoW Classic's Season of Mastery has Hardcore Raids where death means deleting your character
I've said before that World of Warcraft should steal more ideas from the Diablo franchise, and the WoW Classic Season of Mastery has done exactly that, introducing the Soul of Iron buff/Tarnished Soul debuff system to make Hardcore play possible.
How to get started with D&D without breaking your wallet
So you want to play Dungeons and Dragons -- or any tabletop RPG, really -- but you, like me, are broke and can't afford all those books, and a ton of dice, and minis, and maps, and even more dice.


text:
Q: Making an Alarm application in iOS I am making an alarm application. When I set the alarm the notification fire immediately. I set two schedule notifications functions for alarm. One is for everyday repeat alarm and other is for weekly repeat alarm. My code for everyday repeat alarm is working fine but for weekly repeat alarm , notifications fire immediately and sometimes fire at different day.
Here is code for repeat alarm for weekly basis.
-(void) scheduleLocalNotificationWithWeek:(NSDate *)fireDate
{

    UILocalNotification *localNotif = [[UILocalNotification alloc] init];

    localNotif.fireDate = calculateAlarmDay;
    NSLog(@"%@",localNotif.fireDate);
    localNotif.timeZone = [NSTimeZone localTimeZone];
    localNotif.alertBody = @"Time to wake Up";
    localNotif.alertAction = @"Show me";
    localNotif.soundName = @"Tick-tock-sound.mp3";
    localNotif.applicationIconBadgeNumber = 1;
    localNotif.repeatInterval = NSWeekCalendarUnit;
    [[UIApplication sharedApplication] scheduleLocalNotification:localNotif];

}

and here is a code in which I calculate the day on which it fires e.g if user select sunday than it fires on sunday 28-2-2016 and than next sunday 6-3-2016 and so on..
-(void) repeataftersevendays:(int) day
{
    NSCalendar *calendar = [NSCalendar currentCalendar];
    NSDateComponents *components = [[NSCalendar currentCalendar] components:NSCalendarUnitDay | NSCalendarUnitMonth | NSCalendarUnitYear fromDate:[NSDate date]];

    NSLog(@"%@",components);
    [components setTimeZone:[NSTimeZone localTimeZone]];
    [calendar setTimeZone: [NSTimeZone localTimeZone]];

    finalValue = 0;

    if (weekday == day)
    {
        finalValue=0;
    }
    else if (weekday < day)
    {
        int tempWeekDay = weekday;
        while ( tempWeekDay  < day )
        {
            tempWeekDay++;
            finalValue++;
            NSLog(@"%i",finalValue);
        }
    }
    else
    {
        int tempDay = day;
        while ( weekday  >= tempDay )
        {
            tempDay++;
            finalValue++;
        }
    }

    calculateAlarmDay = [calendar dateByAddingUnit:NSCalendarUnitDay
                                       value:finalValue
                                      toDate:[NSDate date]
                                          options:0];;
    NSLog(@"value is %@",calculateAlarmDay);

}

I am stuck with this stuff from last seven days. Please  tell me the solution. Thanks


Unit Trusts are also known as collective investment schemes. They have grown in popularity in Kenya over the last two decades. There were virtually no unit trusts in 2001 but by 2016, there were 19 unit trust schemes registered by the Capital Markets Authority (CMA).
These investments are the small investor's answer to diversification without the need to have a large amounts of money to invest. In a unit trust fund, money from many investors is pooled together and invested in a portfolio of money market instruments, bonds, shares and other authorized securities depending on the fund's objectives. Investors are known as unit holders and the fund earns income by way of interest, dividends and capital gain when the underlying securities increase in value.
An investor invests in a unit trust fund by buying units at the prevailing price. The price is computed on a daily basis. Investors can sell the units back to the fund at the current buying price.
Management fees are charged to manage the fund, usually ranging from 1 - 3% of the fund balance annually. Some unit trusts also charge an initial joining fee of between 0.5 and 5% of the invested amount.


While Terry Bicyles might be best known for their stellar line of women's products, they also make saddles like the Fly Cromoly Men's Saddle so guys can get in on the company's renowned comfortable ride. Whether you're on the rivet attacking the peloton or wandering through scenic country gravel, this saddle is ready to support you comfortably through any type of ride.
The Fly's most noticeable feature is a large cutout down the middle of the saddle, which lets in cooling air flow and reduces pressure on the perineal artery and surrounding sensitive tissue. This cutout shifts the weighted contact points to your sit bones, which are far more able to support weight comfortably. Say goodbye to numbness and pain, and hello to hours and hours of comfy time in the saddle.
Less apparent, although not less appreciated details include varying density foam that's softer towards the front of the saddle for a boost of cushion and firmer towards the rear of the saddle for increased sit bone support and efficiency through the pedal stroke. The entire top of the saddle is seamless to reduce friction and chafing, and the cover is synthetic for a vegan-friendly ride.


Brent Primus claimed Bellator's lightweight title tonight in dramatic fashion after Michael Chandler appeared to break his ankle early in their fight at Madison Square Garden in New York City.
Primus attempts a head kick to start that doesn't pay off. Again Primus with a high kick as Chandler moves into range.
Jab for Chandler knocks Primus backwards. Strike from Primus and Chandler falls awkwardly to the mat.
He gets back up, but he doesn't seem right and when Primus attacks again Chandler falls over. It seems he's badly hurt his ankle.
He stands again, but whenever he moves his ankle is just buckling under him.
Chandler is trying to battle on regardless and remarkably he lands a punch that drops Primus. Primus gets back up though as Chandler tries to charge forward, but then drops to a knee again.
The referee decides to stop the fight and have a doctor look at the injury. Surely they have to call the fight now – you can't just pause a fight for an injury of this nature.
Sure enough, after taking a minute to examine his limb it's clear that Chandler is in no fit state to continue and the fight is waved off with 2.22mins on the clock.
So we have another shock upset, with Brent Primus being declared Bellator's new lightweight champion due to Chandler's freak injury!
Chandler would have fought on if he was allowed, but his ankle kept folding under him so that would have been a bad idea. It's an unfortunate end to the fight though and these two will surely be locking horns again as soon as the former champion's injury has healed.