text:
Pedestrianisation of Alnwick street poised to move a step closer
The Hairy Bikers rave about 'the best fish and chips ever' on visit to Northumberland
Berwick MP embarks on climate change tour of Northumberland schools
Berwick MP Anne-Marie Trevelyan has launched her tour of local schools ahead of the global COP26 climate conference.
By Ian Smith
Wednesday, 15th September 2021, 1:34 pm
The MP, who as well as being Energy Minister is the COP Champion for Adaptation and Resilience, has been representing the UK in negotiations with other nations in the lead up to COP, being held in Glasgow in November.
Her primary focus is ensuring developing nations, which often bear the brunt of climate shocks, are able to build resilience and adapt the ways in which they live, farm and do business to ensure they are protected in the future.
Ms Trevelyan said: "A lot of my work for COP has been focused on working with countries across the world, but it is important to remember how we can all make changes to protect our environment.
The Berwick MP at Longridge Towers.
"I have been so impressed by how much understanding our children and young people have of the importance of protecting our environment and the wildlife it supports.
"My local focus in the lead up to COP is to get out to as many schools in my constituency as possible, hearing from the pupils, sharing ideas and supporting the work teachers are doing to foster community action on climate change.
"I contacted every school in my constituency offering a visit to talk about COP and the importance of conserving and protecting our planet, and I am delighted so many took me up on the offer.
"It was wonderful to visit Longridge, Belford Primary and Barndale House Schools on the first step of my tour. The pupils have been so passionate and engaging and are a real credit to their teachers, families and communities."
Anne-Marie Trevelyan with Barndale House headteacher Mark Phillips and the Gruffalo.
Northumberland contestants set to feature in new BBC series Take A Hike
Enjoy our headlines with fewer distractions and sign up to a digital subscription today - fewer ads, faster load times and all of the stories you need. Your support for our journalism means we can continue supporting our communities for generations to come. Click 'Subscribe' in the menu to find out more and sign up.
Anne-Marie Trevelyan talking to pupils at Belford Primary School.
Anne-Marie Trevelyan with Belford Primary School headteacher Lorna Rainey.
Anne-Marie TrevelyanBerwickCOP26NorthumberlandGlasgow


Omron releases Sysmac Studio Team Edition, a major update of its machine development environment, providing machine developers with a fully integrated Open Source distributed version control system. Sysmac Studio integrates configuration, programming, simulation, and monitoring in a simple interface that allows engineers to manage vision, motion, control, safety, and robotics in one system.
The Team Edition implementation of Sysmac Studio incorporates a distributed version control system within the platform's rich interface. It allows multiple co-developers to work on same project, with easy project comparison and efficient handling of machine versions. Omron has chosen Git, which is the most popular open source distributed control system, giving development teams the freedom to choose any Git-based cloud web repository.
The version control system allows engineers to work on a local version of a project with full control over the project source code. Improvements and modifications made to the code on the local version can then be merged with a remote server, so other teams can access the code. By using this technique, projects are fully traceable. Changes to code can be identified and applied between projects easily and quickly. It reduces engineering hours, and costs, because engineers do not waste time solving the same problem multiple times.


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Hobbies Frugal Living
Military Divorce and Separation
Lawyers, ID Cards and Housing
Tetra Images / Getty Images
Household Savings
Food Savings
Beauty & Health Care
The Military's Position on Divorce
Military Lawyers
Military ID Cards
Base Housing
Rod Powers
Air Force NCO Academy
Rod Powers was a retired Air Force First Sergeant with 22 years of active duty service.
Military, domestic situations can be confusing because they are governed by a mixed hodgepodge of military regulations, state divorce laws, and procedures, as well as Federal statutes.
In this article, we will try and make a little bit of sense out of the confusion. In the coming weeks, I'll attempt to cover all of the various aspects of a military-related divorce or separation situation, including the "rights" of the military member and family members, military family housing, spousal & child support, ID Cards, the Uniform Services Former Spouse Protection Act, Soldiers & Sailors Civil Relief Act, domestic violence situations, garnishments, divorce jurisdiction, lawyers, and more.
Overall, it's important to realize that the military considers divorce and separation to be a private civil matter, best resolved by the courts. Sometimes military spouses expect too much out of military authorities. They think they can contact their spouse's commander, and the commander will waive a magic wand and make everything better.
In most cases, that's unrealistic -– just as it would be unrealistic to expect the manager at K-Mart to become involved in your marriage situation, should your spouse work there. The commander has limited authority in the area of divorce and separation. The military only becomes involved in domestic situations in very limited, specific ways, through procedures which are authorized under law or military regulation, and usually when it concerns pay, benefits, property, etc., which are regulated by federal law. In a vast majority of the military, domestic situations, the correct procedure is to obtain an attorney, and take it to court, just like everyone else in the United States has to do.
Military personnel and family members all have access to free legal services provided by the "legal office" (JAG). What most people don't realize, however, is the JAG is of very little help when it comes to divorce and separation. At most, the JAG can give you general advice. They cannot prepare divorce or separation documents; they cannot represent you in court, they cannot file legal divorce or separation paperwork for you.
Quite often, even the "general advice" may be of little use, as there is no requirement that a military lawyer is licensed to practice law in the state they are stationed in, so the lawyer's knowledge of divorce laws of that particular state may be limited. For military divorce, separation, or child support situations, you need to consult with a civilian attorney, knowledgeable of the divorce laws of your particular state.
The lawyer you choose should also have extensive experience with military-related family law (because many things are different between military-related and civilian domestic situations). A family lawyer with military-related family law experience will know the provisions of the Servicemembers Civil Relief Act (SSCRA) and the Uniform Services Former Spouse Protection Act (USFSPA), as well as specific provisions required for garnishment of military pay.
Start by calling your local bar association, and ask them for a list of divorce lawyers in your area who specialize in military-related divorce situations. Some lawyers who specialize in military-related divorce situations advertise on the Internet. Many lawyers offer free initial consultations. Take advantage of that. Interview several lawyers and choose the best one for your particular situation.
The issue of ID Cards gets lots of folks into trouble. They mistakenly think that because regulations require them to submit an application for family member ID cards, and because they are listed as the "sponsor," that they can "confiscate" their spouse's ID card anytime they choose. This is not true. Family member ID cards (and the privilege granted by such cards) are an entitlement, granted by congressional law (not the sponsor). In other words, it's Congress who gets to decide who can and cannot have an ID card, not the military "sponsor."
A military member who unlawfully takes a military identification card away from his/her spouse can be charged for Larceny under the provisions of Article 121 of the Uniform Code of Military Justice (UCMJ). All of the services use the same "joint" regulation which governs the issuance of military identification cards. If the military member refuses to sign the application for an ID for a military dependent, the regulation contains provisions where the Personnel Office may indicate such on the application form, and issue the ID card anyway.
In most cases, the nonmilitary spouse will lose his/her ID card (and privilege) once the divorce is final, with two exceptions:
the parties had been married for at least 20 years;
the member performed at least 20 years of service creditable for retired pay, and
there was at least a 20-year overlap of the marriage and the military service.
"20/20/20" former spouse. Full benefits (medical, commissary, base exchange, theater, etc.) are extended to an un-remarried former spouse when:(Note: If an employer-sponsored health care plan covers the former spouse, medical care is not authorized. However, if coverage is terminated, military medical care benefits may be reinstated upon application by the former spouse.
The member performed at least 20 years of service creditable for retired pay, and there was at least a 15-year overlap of the marriage and the military service
"20/20/15" former spouse. The 20/20/15 former spouse qualifies for medical benefits (no commissary, bx, etc.) for one year from the date of the divorce, dissolution or annulment. (Note: If an employer-sponsored health care plan covers the former spouse, medical care is not authorized.)
While on-base family housing is "issued" to the military member, the member does not have the authority to evict his/her military family members (only the installation commander has that authority). In fact, in most cases, when a domestic situation has deteriorated to the point where physical separation is warranted, the first sergeant or commander will usually order the military member to reside in the dormitory (barracks). It is because the military has the authority to house (for free) the military member in the dormitories, but it has no authority to provide free billeting to military spouses.
However, military family housing, by law, can only be occupied by military members who reside with their family members (other than authorized exceptions, such as when the military member is deployed, at sea, or serving in a remote-tour area). The services all have regulations which require the family housing unit to be vacated (usually within 30 days) if the military members stop residing there, or if no family members are residing there. So, in most cases, in the event of a separation, the party remaining in the base housing unit must vacate (unless the remaining party is the military member and other dependents, such as children, remain). The military will not pay for such moves, however.
While the Joint Travel Regulation (JTR), paragraph U5355C authorizes the military to pay for short-distance household good transportation in the event a military member is ordered out of base housing; the regulation prohibits explicitly this provision to be used for "personal problems." The regulation states: "A short distance HHG move, incident to moving to/from Government quarters, is not authorized to accommodate a member's personal problems, convenience, or morale."
Domestic Violence in the U.S. Military
Children of Military Couples and Military Single Parents
Military Divorce and Separation: Spousal and Child Support
Getting Married In The Military
New York Divorce State Laws
How to Find a Free or Cheap Notary, Wherever You Are
Lajes Field, Portugal
Kentucky Divorce Laws
Overview of Edwards Air Force Base in California
U.S. Army Garrison Hohenfels Training Area
Installation Overview—NSA Bahrain, Kingdom of Bahrain
Keesler Air Force Base in Mississippi
Overview of Malmstrom Air Force Base, Montana
Military Home of Record and State of Legal Residence Address
US Military Installation Overview, Fort Knox, Kentucky

redpajama_set_name: RedPajamaCommonCrawl


Buy Allied Brass Dottingham Collection 4-Tier 24-Inch Ladder Towel Bar in Unlacquered Brass from $490.99 at Bed Bath & Beyond. The ladder towel bar from Allied Brass is a perfect addition to any bathroom. The 4 levels of height make it fun to stack decorative towels and allows the towel bar to be user friendly at all heights.The ladder towel bar from Allied Brass is a perfect addition to any bathroom. The 4 levels of height make it fun to stack decorative towels and allows the towel bar to be user friendly at all heights.


text:
Q: AJAX request to https php server from Firefox and Chrome extensions I'm working on extensions for Firefox and Chrome. The data used by my extensions is mostly generated from ajax requests. The type of data being returned is private, so it needs to be secure. My server supports https and the ajax calls are being sent to an https domain. Information is being sent back and forth, and the extensions are working correctly.
My questions are:
Do the extensions actually make secure connections with the server, or is this considered the same as cross domain posting, sending a request from a http page to a https page?
Am I putting my users' information at more risk during the transfers than if the user were to access the information directly from an https web page in the browser?
Thanks in advance!

A: The browser absolutely makes a secure connection when you use HTTPS.  Certainly, a browser would never downgrade the security of your connection without telling you: it will either complete the request as written or it throw some sort of error if it is not possible.
Extensions for both Chrome and Firefox are permitted to make cross-domain AJAX requests.  In Chrome, you simply need to supply the protocol/name of the host as a permission in your manifest.json.  In Firefox, I think you may need to use Components.classes to get a cross-domain requester, as described in the MDN page for Using XMLHttpRequest, but I'm not 100% sure about that. Just try doing a normal request and see if it succeeds; if not, use the Components.classes solution.

redpajama_set_name: RedPajamaStackExchange


text:
Legacy Building
Architect Mark Maddalina '87 makes his mark on the Nazareth campus
by Joanie Eppinga
"I never even considered architecture as a career," says Mark Maddalina '87. When Maddalina graduated from Nazareth with a degree in studio art, he was thinking about painting and graphic design, not buildings.
He would never have believed that one day he'd be asked to design Nazareth's new math and science center, Peckham Hall. Nor would he have expected to be leading the upcoming transformation of Carroll Hall into the Wellness and Rehabilitation Institute. "The only living architect I knew of was Mike Brady from The Brady Bunch," says Maddalina, "and he was fictional."
The new graduate was dabbling in several art forms—"none of them meaningfully"—when a client asked him to repair a job a carpenter had botched. Then the client asked him to build something. Later, the client was unhappy with the architect he'd hired and, Maddalina says, "asked me to do something I was completely unqualified to do at that time." That was when the revelation struck: "I need to go back to school for this."
Maddalina's B.A. turned out to be extremely valuable, serving as a natural foundation for his degree in architecture from the University of Buffalo. "All my art history classes from Nazareth absolutely came with me," he says. "It was a huge advantage—still is—to have an arts background and to know how to draw."
He remembers "fantastic" art history classes with Sister Magdalen LaRow, who showed her classes paintings of stunning buildings and shared her experiences. She told them that once, at the Leaning Tower of Pisa, she had asked why the structure was rough on one side and smooth on the other. A tour guide explained that people were afraid of falling when they were on the leaning side, so they backed up against the tower. Hundreds of backs had polished the marble over the centuries.
That interaction between human and architectural elements intrigued Maddalina, and he brought the concept with him when he designed the new buildings for Nazareth's campus.
On both projects, he knew he wanted to retain the warm, human scale of the campus.
"The buildings at Nazareth are not massive," Maddalina says, "not overwhelming, not brutal. They're a beautiful collegiate Gothic. You get a very comfortable feeling being on the campus, and it was important to us to bring that forward."
Maddalina and his team used the Cloister Walk as their inspiration. "It was always one of my favorite places at Nazareth," he says, "so I'm glad there's a component of this new building that resembles it. The entry canopy has a wood ceiling and a pedestrian scale. It's very inviting."
The team also worked to bring other elements of Nazareth's existing architecture into the new buildings. The choice of brick masonry was deliberate: "We tried to match the Golisano Academic Center and Smyth Hall," Maddalina explains.
He observes that the new math and science building has the most direct comparison to Smyth Hall. Overall, he notes, "I think our project took some of the best elements of the architecture and history of the whole campus and brought them over to Peckham Hall."
Thinking about the past, Maddalina considers architectural styles; thinking about the future, he considers people. As the manager of sustainability at SWBR Architects in Rochester, he always has an overarching theme when he designs a building: discerning what is good for now and for the next generation.
"There are considerations we can bring to every project," Maddalina says. "It's not about a brick or a building, but about a practice that includes human health and comfort. We can invest a little bit more now and benefit long term."
But sometimes, Maddalina says, the best option is doing a bit less, going with the least expensive solution. An emphasis on sustainability can both bring costs down and ensure the longevity of a building. Maddalina reports that LEED certification, a green building standard, is currently being finalized for Peckham Hall.
Nazareth was equally dedicated to sustainability. "They hadn't built a major academic building in roughly 30 years," Maddalina says, "and they were deeply committed to doing this right." That meant emphasizing green construction materials, enhancing energy efficiency, and reducing carbon dioxide emissions.
Maddalina reports that in addition to sharing his emphasis on environmental conscientiousness, the College is a wonderful partner in general. "I don't want my opinion to be suspect because Nazareth is my alma mater, but they're just terrific to work with," he says. "They provide a 'project shepherd' who resolves all issues and is invaluable. They give you the information you need and are an essential part of the conversation."
Teaming for the future was important to Maddalina, but there was another element of working with Nazareth that he loved.
"Every project feels like it has its own purpose, its own soul," he says. "But when you get to go back to your campus, where you consider it to be home, that is powerful, and you take it very seriously.
"Being a student there, I knew a lot of the best spaces," Maddalina continues. "There are places that are unique to Nazareth, and feelings that are attached to those places. There's the Cloister Walk, and a beautiful residential quadrangle with a tree-lined path—how do you see that and not say, 'I want to be a part of this'?"
Maddalina says that contributing to his former school feels incredible, "like a homecoming." He adds that doing it through an abiding art form is especially rewarding, because "graphic arts and advertising are fine, but they don't have the longevity of a building.
"It's a legacy," Maddalina concludes. "Decades from now, I'll be there with little kids who can say, 'Grandpa designed that!'"
Maddalina wishes to acknowledge the key contribution of Peter DeBraal to the new Wellness and Rehabilitation Institute. DeBraal, Maddalina's project manager and friend of 16 years, died recently and unexpectedly.
Joanie Eppinga is a freelance writer and editor in Spokane, Washington.
For more information, see Peckham Hall and the York Wellness and Rehabilitation Institute.


Breaking weeks of public silence since President Barack Obama nominated her to be the fourth woman in the court's history, Kagan called the Supreme Court "a wondrous institution" but one with limited powers under the Constitution. She billed herself as a consensus-builder for the ideologically polarized court and said she'd strive to emulate retiring Justice John Paul Stevens — the man she's been chosen to succeed — by "listening to each party with a mind as open as his ... to render impartial justice."
The 50-year-old solicitor general and former Harvard Law School dean appeared on track for confirmation before the court opens a new term in October as she delivered her brief statement at the end of a day of senatorial speechmaking in a cavernous hearing room on Capitol Hill. However, the deep partisan divide over the court — and Kagan's fitness to serve there — was evident.
Sen. Patrick Leahy, D-Vt., the Judiciary Committee chairman, called Kagan's views "well within the legal mainstream."
Sen. Jeff Sessions of Alabama, the panel's top Republican, countered that her "career has been consumed more by politics than law."
Kagan, who has been cramming in private rehearsals for the thrust and parry of the hearings, faces a long Tuesday of close questioning by senators, friendly and otherwise. She's likely to field queries on a wide range of legal issues as well as her decision as Harvard Law dean to bar military recruiters from the campus career services office because of the Pentagon's ban on openly gay soldiers.
"It's not a coronation but a confirmation process," said Sessions. He said Kagan has "less real legal experience (than) any nominee in at least 50 years." And he said her stance on military recruitment violated the law — a legal conclusion disputed by the White House.
The Supreme Court "has the responsibility of ensuring that our government never oversteps its proper bounds or violates the rights of individuals. But the court must also recognize the limits on itself and respect the choices made by the American people," Kagan said. "I will do my best to consider every case impartially, modestly, with commitment to principle and in accordance with law."
Her remarks seemed designed to blunt conservatives' argument that she would bend the law to suit her own agenda — and also to reassure liberals that she would be a counterweight to what they characterize as a pro-business, conservative-dominated court that has done just that in recent years.
Republicans and Democrats alike used the hearing to rail against "judicial activism." The GOP, seeking to tap into tea party disgust with a federal government many members say has overstepped its bounds, has portrayed Kagan as a prime example of what they charge is Obama's desire to populate the nation's court with liberals willing to stretch the law to achieve preferred policy outcomes.
"Too often, it sounds to me like Ms. Kagan shares the view of President Obama and (former) Justice (Thurgood) Marshall that the Supreme Court exists to advance the agenda of certain classes of litigants," said Sen. Jon Kyl, R-Ariz. "The burden is on the nominee to show that her record demonstrates she can be a fair and impartial justice rather than one who would have an outcome-based approach."
"The rightward shift of the court under Chief Justice Roberts is palpable. In decision after decision, special interests are winning out over ordinary citizens. In decision after decision, this court bends the law to suit an ideology," said Sen. Chuck Schumer, D-N.Y. He said Kagan would bring "moderation and pragmatism to a court that is sorely in need of both."
Stevens was a frequent dissenter in a string of 5-4 rulings handed down by the court under Roberts, and Kagan took the witness stand to succeed him on the retiring justice's last day, when there were several such rulings. In one, a 5-4 majority said the right to bear arms can't be limited by state or local laws any more than by federal legislation. In another, the same majority struck down part of an anti-fraud law enacted in 2002 in response to scandals involving Enron and other corporations.
One Republican on the committee, Sen. Lindsey Graham of South Carolina, said he could say with certainty that Kagan's confirmation wouldn't change the balance of power on the court. But in a reference to Obama, he added, "I hope people will understand that elections do matter."
"I look forward to trying to better understand how you will be able to take political activism, association with liberal causes and park it when it becomes time to be a judge," Graham said.
Outside the hearing room, Sen. Mitch McConnell, R-Ky., the minority leader, suggested Kagan would have a hard time doing so.
"When we look at Elena Kagan's resume, what we find is a woman who has spent much of her adult life working to advance the goals of the Democratic Party," McConnell said on the Senate floor.
Associated Press Writers David Espo and Ann Sanner contributed to this report.


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

OpenStreetMap (OSM) has evolved as a critical source of openly available volunteered geographic information \cite{JokarArsanjani2015}.
The amount of information available in OpenStreetMap is continuously growing.
For instance, the number of geographic entities captured by OSM increased from $5.9 \cdot 10^9$ in March 2020 to $6.7 \cdot 10^9$ in March 2021.
Today, OSM data is used in a plethora of machine learning applications such as road traffic analysis \cite{Keller_2020}, remote sensing \cite{9119753}, and geographic entity disambiguation \cite{TEMPELMEIER2021349}.
Other data-driven OSM applications include map tile generation \cite{4653466} and routing \cite{epub34045}.

OpenStreetMap is a collaborative online project that aims to create a free and editable map and features over 7.6 million contributors as of June 2021.
OSM provides information about its entities in the form of key-value pairs, so-called \emph{tags}. For instance, the tag \texttt{place=city} indicates that an entity represents a city.
OSM tags and keys do not follow any well-defined ontology or controlled vocabulary.
Instead, OSM encourages its contributors to follow a set of best practices for annotation\footnote{\url{https://wiki.openstreetmap.org/wiki/Any\_tags\_you\_like}}.
The number of tags and the level of detail for individual OSM entities is highly heterogeneous \cite{Touya2015}.
For instance, as of June 2021, the size of data available for the country of Germany sums up to 3.3 GB, while only 2.5 GB of data is available for the entire South American continent.
Ultimately, factors including 1) a varying number of tags and details for specific geographic entities, 2) the lack of a well-defined ontology resulting in numerous tags with unclear semantics, and 3) missing values for any given property, substantially hinder the feature extraction for broader OSM usage in machine learning applications.

A central prerequisite to facilitate the effective and efficient use of geographic data in machine learning models is the availability of suitable representations of geographic entities.
Recently, latent representations (embeddings) have been shown to have several advantages in machine learning applications, compared to traditional feature engineering, in a variety of domains \cite{LIU2018183,Xie:2016:LGP:2983323.2983711,Wang:2017:RRL:3132847.3133006}.
First, embeddings can capture semantic entity similarity not explicitly represented in the data.
Second, embeddings facilitate a compact representation of entity characteristics, overall resulting in a significant reduction of memory consumption \cite{TEMPELMEIER2021349}.
Whereas much work has been performed to provide pre-trained embeddings for textual data and knowledge graphs \cite{DBLP:journals/computing/WangZJ20,8047276}, only a few attempts, such as \cite{10.1007/978-3-319-68204-4_14}, aimed to provide such latent representations for geographic entities and captured selected entities only.
From the technical perspective, the creation of OSM embeddings is particularly challenging due to the large scale of OSM (more than 1430 GB of data as of June 2021\footnote{\url{https://wiki.openstreetmap.org/wiki/Planet.osm}}) and the OSM data format (\emph{``protocolbuffer binary format''}\footnote{\url{https://wiki.openstreetmap.org/wiki/PBF_Format}}), requiring powerful computational infrastructure and dedicated data extraction procedures.
Furthermore, the semi-structured data format of OSM tags requires specialized embedding algorithms to capture the semantics of entity descriptions.
As a result of these challenges, currently, no datasets that capture latent representations of OSM entities exist.

The GeoVectors\xspace{} corpus of embeddings presented in this paper is a significant step to enable the efficient use of extensive geographic data in OSM by machine learning algorithms. GeoVectors\xspace facilitates access to these embeddings using semantic technologies.
We utilize established representation learning techniques (word embeddings and geographic representation learning) to capture various aspects of OSM data.
We demonstrate the utility of the GeoVectors\xspace{} corpus in two case studies covering
the tasks of type assertion and link prediction in knowledge graphs.
GeoVectors\xspace{} follows the \emph{5-Star Open Data} best practices in data publishing and reuses existing vocabularies to lift OpenStreetMap entities into a semantic representation.
We provide a knowledge graph that semantically represents the GeoVectors\xspace{} entities and interlinks them with existing resources such as Wikidata, DBpedia, and Wikipedia.
With the provision of pre-computed latent OSM representations, we aim to substantially ease the use of OSM entities for machine learning algorithms and other applications.

To the best of our knowledge, currently, there are no dedicated resources that provide extensive reusable embeddings for geographic entities at a scale comparable to GeoVectors\xspace.
The absence of comprehensive geographic data following a strict schema makes it particularly challenging to process geographic data in machine learning environments.
We address these problems by providing models capable of embedding arbitrary geographic entities in OSM.
Moreover, we enable easy reuse by making both models and encoded data publicly available.

The main contributions of this paper are as follows:
\begin{itemize}
    \item We provide GeoVectors\xspace{} -- a world-scale corpus of embeddings covering over 980 million geographic entities in 188 countries using two embedding models and capturing the semantic and the geographic dimensions of OSM entities.
    \item We introduce an open-source embedding framework for OSM to facilitate the reusable embedding of up-to-date entity representations\footnote{\url{https://github.com/NicolasTe/GeoVectors}}.
    \item We provide a knowledge graph to enable semantic access to GeoVectors\xspace{}.
\end{itemize}
The remainder of this paper is organized as follows.
In Section ~\ref{seC:relevance}, we discuss the predicted impact of GeoVectors\xspace.
Then, in Section ~\ref{sec:generation}, we present the embedding generation framework.
In Section ~\ref{sec:kg}, we present the GeoVectors\xspace knowledge graph.
Next, we describe the characteristics of the GeoVectors\xspace corpus in Section ~\ref{sec:chararcteristics}.
We illustrate the usefulness of GeoVectors\xspace in two case studies in Section ~\ref{sec:case_study} and discuss availability and utility in Section ~\ref{sec:avail}.
In Section ~\ref{sec:related_work}, we discuss related work.
Finally, in Section  ~\ref{sec:conclusion}, we provide a conclusion.
\section{Predicted Impact}
\label{seC:relevance}

GeoVectors\xspace is a new resource.
This section discusses the predicted impact of GeoVectors\xspace regarding the advances of state of the art in geographic embedding datasets, geographic information retrieval, machine learning applications, knowledge graph embeddings
and broader adoption of semantic web technologies.

\textit{Advances of the state of the art:}
We advance the state of the art by providing the first large-scale corpus of pre-trained geographic embeddings.
We carefully select established representation learning techniques to capture both the semantic dimension (What entity type does the OSM entity represent?) and the spatial dimension (Where is the entity located?) and adapt these techniques to OSM data to create meaningful latent representations.
The GeoVectors\xspace corpus is the first dataset that captures the entire OpenStreetMap, thus offering the data on the world scale.
Therefore, GeoVectors\xspace is significantly larger than any existing geographic embedding resources. For instance, the Geonames embedding \cite{10.1007/978-3-319-68204-4_14} provides a dataset containing less than 358 thousand entities, whereas GeoVectors\xspace contains over 980 million entities.

\textit{Impact on geographic information retrieval:}
Geographic information retrieval (GIR) addresses the problems of developing location-aware search systems and addressing geographic information needs \cite{INR-034}.
Recent GIR approaches build on geographic embeddings to address several use cases, including tag recommendation for urban complaint management \cite{10.1145/3357384.3357894},  geographic question answering \cite{10.1145/3442381.3449857}, and POI categorization \cite{10.1145/3397536.3422196}.
While these approaches demonstrate the utility of geographic embeddings for GIR tasks, the laborious generation process hinders the adaption of geographic embeddings for other GIR tasks such as geographic named entity recognition, next location recommendation, or geographic relevance ranking.
In this context, the availability of large-scale and accessible geographic embeddings is a vital prerequisite to stimulate research in the GIR field.
The GeoVectors\xspace corpus presented in this paper addresses these requirements by providing ready-to-use geographic embeddings of the entire OpenStreetMap.

\textit{Impact on machine learning applications}:
Existing machine learning applications use geographic data to address numerous use cases including location recommendation \cite{LIU2018183,Xie:2016:LGP:2983323.2983711}, human mobility prediction \cite{Wang:2017:RRL:3132847.3133006}, and travel time estimation \cite{10.1145/3424346}.
The variety of use cases highlights the general importance of geographic information for machine learning models.
However, these approaches conduct a costly feature extraction process or learn supervised embeddings of geographic entities on task-specific datasets for specific tasks.
In this context, the availability of easy-to-use representations of geographic entities at scale provided by GeoVectors\xspace is crucial to enabling and easing the further development of geographic machine learning models and geographic algorithms.

\textit{Impact on knowledge graph embeddings:}
Knowledge graph embeddings generated without the specific focus on geographic entities have shown success in a large variety of knowledge graph inference and enrichment tasks, including type assertions and link prediction \cite{paulheim2017knowledge}. We envision that GeoVectors\xspace{} can further enhance the quality of embeddings used in the context of these tasks: While geographic entities are part of many popular knowledge graphs such as Wikidata and DBpedia, their specific characteristics are still rarely considered. Existing approaches typically focus on the graph structure, but rarely on the property values assigned to the single nodes \cite{kristiadi2019incorporating}. However, both tags and coordinates of geographic entities bear valuable semantics.
Specifically, the geographic interpretation of coordinates may heavily lift the role of coordinates in knowledge graph embeddings.
In the future, the GeoVectors\xspace{} embeddings can directly support knowledge graph inference and enrichment and creation of geographically aware embeddings from other sources.

\textit{Impact on adoption of semantic web technologies:}
In the context of the Semantic Web, a variety of models and applications, including link prediction, creation of domain-specific knowledge graphs \cite{gottschalk2019eventkg} and Question Answering for event-centric questions \cite{CostaGD20} make use of geographic data.
Semantic technologies have been applied to a variety of domains that require spatio-temporal data, including crime localization, transport data, and historical maps \cite{10.1007/978-3-030-62466-8_23,10.1007/978-3-030-62466-8_26,10.1007/978-3-030-49461-2_24}.
Furthermore, with the increased availability of mobile devices, location-based algorithms such as next location recommendation or trip planning evolved.
Recently, SPARQL extensions for integrated querying of semantic and geographic data have been proposed \cite{10.1007/978-3-030-62419-4_15}.
In this context, the availability of easy-to-use representations of geographic entities at scale is crucial to enable further development of semantic models and geographic algorithms and their adoption in real-world scenarios.
Increasing availability of geographic data accessible through semantic technologies, as facilitated by GeoVectors\xspace, and seamless integration of this data with other semantic data sources in the Linked Data Cloud can attract interested users from various disciplines and application domains, including geography, mobility, and smart cities.

\section{GeoVectors\xspace Framework for Embedding Generation}
\label{sec:generation}

\begin{figure*}[t!]
    \centering
    \includegraphics[width=0.7\textwidth]{OSM-Embedding-Overview-2.pdf}
    \caption{Overview of the embedding generation process.}
    \label{fig:genereation_overview}
\end{figure*}

The GeoVectors\xspace framework facilitates the generation of OSM embeddings that capture geographical (\textit{GV-NLE}\xspace{}) and semantic (\textit{GV-Tags}\xspace{}) similarity of OSM entities.
In this section, we first describe the OSM data model. Then, we provide an overview of the GeoVectors\xspace embedding generation process and
present embedding algorithms that generate the proposed \textit{GV-NLE}\xspace{} and \textit{GV-Tags}\xspace{} embeddings.

\subsection{OpenStreetMap Data Model}
\label{sec:osm-data-model}

The OSM data model distinguishes three entity types: \emph{nodes}, \emph{ways} and \emph{relations}.

\begin{itemize}
    \item \emph{Nodes} represent geographic points. A pair of geographic coordinates gives the point location. Examples of nodes include city centers and mountain peaks.

    \item \emph{Ways} represent entities in the form of a line string, e.g., roads. Ways aggregate multiple nodes that describe the pairwise segments of the line string.

    \item \emph{Relations} represent all other aggregated geographic entities. Relations consist of a collection of arbitrary OSM entities to form, for instance, a polygon. Examples of relations include country boundaries and train routes.
\end{itemize}

Each OSM entity can have an arbitrary number of annotations called \emph{tags}. Tags are key-value pairs that describe entity features. For example, the tag \texttt{place = city} indicates that a particular OSM entity represents a city.
More formally, an \emph{OSM entity} $o = \langle id, type, T \rangle$ consists of an identifier $id$, a $type \in \{\textit{node, way, relation}\}$, and a set of tags $T$. An \emph{OSM snapshot} taken at a time $t$ in a region $r$ is denoted by $s = \langle O, t, r \rangle$, where $O$ is a set of OSM entities within the specified region $r$ at this time.

\subsection{GeoVectors\xspace Embedding Generation Overview}
\label{sec:embedding}

The GeoVectors\xspace{} embeddings reflect semantic and geographic relations of OSM entities, where
semantic relations capture semantic entity similarity, expressed through shared annotations, and geographic relations capture geographic entity proximity.
In general, the relevant relation type is application-dependent.
Therefore, we compute two embedding datasets, one capturing geographic and the other semantic similarity of OSM entities:
\begin{itemize}
    \item (1) \emph{\textit{GV-NLE}\xspace{}} is our geographic embedding model based on the Neural Location Embeddings (NLE) \cite{10.1007/978-3-319-68204-4_14} -- an approach to capture the spatial relations of geographic entities.
    \item (2) \emph{\textit{GV-Tags}\xspace{}} is our semantic embedding model based on fastText \cite{joulin-etal-2017-bag} -- a state-of-the-art word embedding model that we apply on the OSM tags.
\end{itemize}

The embedding generation process that takes as input a set of OSM snapshots and generates the GeoVectors\xspace{} corpus and the GeoVectors\xspace{} knowledge graph is illustrated in Figure \ref{fig:genereation_overview}.
We divide this process into the \emph{training} phase in which we train the GV-NLE model and the \emph{encoding} phase in which we apply embedding models to encode OSM entities.

The training of an embedding model is typically significantly more expensive than the application of the model.
Due to the large scale of OpenStreetMap (as of June 2021, OSM contains more than 7 billion entities), the training of embedding models on the entire corpus is not feasible.
Therefore, in the \emph{training} phase, we sample a subset of OSM entities from OSM snapshots to serve as training data.
We discuss the sampling process in Section \ref{sec:sampling}.
Based on the sampled data, we train our embedding models.
To generate semantic embeddings, we utilize existing pre-trained word embedding models.

In the \emph{encoding} phase, we first load the trained embedding model and then pass all individual entities from an OSM snapshot to the model.
The application of the model can be parallelized by applying the model to each snapshot separately.
The model encodes the OSM entities and stores the generated embedding vectors into an easily processable, tab-separated value file.

We provide an open-source implementation of the embedding framework, including the pre-trained embedding models\footnote{\url{https://github.com/NicolasTe/GeoVectors}}. This framework enables the computation of up-to-date embeddings of individual OpenStreetMap snapshots. We also generate the GeoVectors\xspace knowledge graph that enables semantic access to GeoVectors\xspace and is described in detail in Section \ref{sec:kg}.

We performed the entire extraction and embedding process on a server with 6 TB of memory and 80 Intel(R) Xeon(R) Gold 5215M 2.50GHz CPU cores. Our framework required about four days for data extraction, model training, and  data encoding.

\subsection{Sampling of OSM Training Data for Embedding Algorithms}
\label{sec:sampling}

At the beginning of the training phase, we extract a representative entity subset to use as a training set.
To ensure representativeness, we employ the following conditions:
First, the training set should have a balanced geographic distribution to avoid biases towards specific geographic regions.
Second, the training set should only include meaningful OSM entities.
For instance, many OSM nodes do not provide any tags and only represent spatial primitives for composite entities, such as ways and relations. Such nodes do not correspond to real-world entities and, taken isolated, do not convey any meaningful information. Therefore, we exclude nodes without tags from the training data.

\begin{algorithm}
\begin{flushleft}
\begin{tabular}{lll}
     \texttt{Input:} & $\mathcal{S}$ & OpenStreetMap snapshots  \\

                     & $n$ & Minimum number of training examples \\
     \texttt{Output:} & $\mathcal{R}$ & Set of training examples\\
\end{tabular}
\end{flushleft}
\hrule
\begin{algorithmic}[1]
    \STATE \text{total\_area} $ \leftarrow$ $\sum_{s \in S}\texttt{geo\_area}(s.r)$

    \STATE $\mathcal{R} \leftarrow \{\}$
    \FORALL{$s \in \mathcal{S}$}
        \STATE $n_s \leftarrow n ~\cdot$ \texttt{geo\_area}(s.r) / total\_area
        \STATE linked, tagged, other $\leftarrow$ \texttt{scan\_snapshot}($s$)
        \STATE $\mathcal{T} \leftarrow$ linked
        \IF {$|\mathcal{T}| < n_s$}
             \STATE $\mathcal{T} \leftarrow \mathcal{T} ~\cup$ \texttt{sample}(tagged, $(n_s - |\mathcal{T}|)$)
        \ENDIF
        \IF {$|\mathcal{T}| < n_s$}
             \STATE $\mathcal{T} \leftarrow \mathcal{T} ~\cup$ \texttt{sample}(other, $(n_s - |\mathcal{T}|)$)
        \ENDIF
        \STATE $\mathcal{R} \leftarrow \mathcal{R} \cup \mathcal{T}$
    \ENDFOR
    \RETURN $\mathcal{R}$
\end{algorithmic}
\caption{Sample Training Data}
\label{alg:Sample}
\end{algorithm}

Algorithm \ref{alg:Sample} presents the sampling process to obtain training data.
The input of the algorithm consists of a minimum number $n$ of training samples to be collected and a corpus of OpenStreetMap snapshots $\mathcal{S}$ (e.g., country-specific snapshots).
First, we calculate the total geographic area covered by all snapshots using the $\texttt{geo\_area}(s.r)$ function (line 1), where $s.r$ denotes the region of the OSM snapshot $s$.
To enforce a uniform geographic distribution, we calculate the number of samples extracted from a single snapshot regarding its geographic size.
For each snapshot, we determine the number of samples $n_s$ to be extracted proportionally to the geographic area of the snapshot (line 4).
Then, the \texttt{scan\_snapshot} function divides the snapshot into \emph{linked}, \emph{tagged} and \emph{other} entities (line 5).
Linked entities provide an identity link to external datasets.
As identity links typically indicate good data quality, our algorithm includes all linked entities.
Tagged entities provide at least one tag.
Other entities are entities that neither provide an identity link nor a tag.
Next, the algorithm samples all linked entities (even if their number exceeds $n_s$) into the result set $\mathcal{T}$ (line 6).
If the size of $\mathcal{T}$ does not reach $n_s$, the function \texttt{sample} uniformly selects at maximum $n_s -|\mathcal{T}|$ random samples from the tagged entities (lines 7-9).
If $n_s$ is still not reached, we sample the remaining examples from the other entities (lines 10-12).
Finally, the algorithm returns the union of all snapshot-specific training examples $\mathcal{R}$ (lines 13-15).

\subsection{\textit{GV-NLE}\xspace{} Embedding of OSM Entity Locations}
\label{sec:neural-embed}

The GV-NLE model builds on the neural location embedding (NLE) model \cite{10.1007/978-3-319-68204-4_14} that captures the geographic relations of a set of geographic entities in a latent representation.
The NLE method is an established method to create reusable geographic embeddings.
GV-NLE extends the NLE model with a suitable encoding algorithm to encode previously unseen OSM entities.

\emph{Training:} GV-NLE first constructs a weighted graph representing OSM entities and their mutual distances.
The OSM entities form the nodes of the graph.
The edges encode the geographic distance between OSM entities.
For each node $n$, GV-NLE constructs edges between $n$ and the $k$ geographically nearest neighbor nodes.
Following \cite{10.1007/978-3-319-68204-4_14}, we set $k=50$.
The edge weights represent the haversine distance between two nodes in meters, which measures the geographic distance of two points while taking the earth's curvature into account.
To facilitate an effective distance computation between OSM entities, we employ a Postgres database that provides spatial indexes.
Based on the graph, a weighted DeepWalk algorithm \cite{Perozzi:2014:DOL:2623330.2623732} learns the latent representations of the OSM nodes.
GV-NLE computes a damped weight $w' = \textit{max}(\nicefrac{1}{ln(w)}, e)$, where $w$ denotes the original edge weight, $ln$ the natural logarithm, and $e$ Euler's number.
The use of damped weights further prioritizes short distances between the nodes.
The normalized damped weights serve as a probability distribution for the transition probabilities of the random walk within the DeepWalk algorithm.

\emph{Encoding:} As the original NLE algorithm does not generalize to unseen entities, i.e., entities that are not part of the training set, we extend the NLE model with a suitable encoding algorithm.
The idea of the GV-NLE encoding is to infer a representation of an entity from its geographically nearest neighbors.
We calculate the weighted average of the latent representation of the geographically nearest $k=50$ entities in the training set.

\[
    \textit{GV-NLE}(o) = \frac{1}{\sum_{o' \in N_o} w(o, o')}\sum_{o' \in N_o} w(o, o') \cdot \textit{NLE}(o')
\]
Here, $o$ denotes an OSM entity, $\textit{NLE}(o')$ denotes the latent representation of an entity $o'$ according to the NLE algorithm, $N_o$ denotes the set of the $k$ geographically nearest OSM entities in the training set.
We define the weighting term $w(o, o')$ as
\[
 w(o, o') = ln(1 + \frac{1}{dist(o, o')})
\]
where $dist(o, o')$ denotes the geographic distance between $o$ and $o'$.
$w(o,o')$ assigns a higher weight to geographically closer entities.
We apply a logarithm function to soften high weights of very close entities.

\subsection{\textit{GV-Tags}\xspace{} Embedding of OSM Entity Tags}
\label{sec:fastText}

To infer the \textit{GV-Tags}\xspace{} representations, we adopt fastText, a state-of-the-art word embedding model that infers the latent representation of single words individually \cite{joulin-etal-2017-bag}.
As the tags of OSM entities do not have any natural order, we chose fastText to embed them.

\emph{Training:} Pre-trained word vectors are available at the fastText website\footnote{\url{https://fastText.cc/}}.
As most of the OSM keys are in English, we chose the 300-dimensional English word vectors trained on the Common Crawl, and Wikipedia \cite{grave2018learning}.

\emph{Encoding:}
To encode an OSM entity $o$, we utilize the individual word embeddings of the keys and values that form the entity tags $o.T$. We map entities without any tags to a vector of zeros.

\[
 \textit{GV-Tags}\xspace{}(o) =
\begin{cases}
        \frac{1}{2|o.T|} \cdot \sum_{\langle k,v \rangle \in o.T} ft(k) + ft(v), &  if ~| o.T | > 0\\
        \{0\}^{300},      & \text{otherwise}.
\end{cases}
\]

Here, $\{0\}^{300}$ denotes a 300-dimensional vector of zeros, and $ft(x)$ denotes the fastText word embedding of $x$.

\section{GeoVectors\xspace Embedding Characteristics}
\label{sec:chararcteristics}

In GeoVectors\xspace V1.0, we extracted representations of nodes, ways, and relations from  OpenStreetMap snapshots at country-level from October 2020\footnotemark[11].
We capture all OSM entities having at least one tag.
Entities without any tags typically represent geometric primitives that isolated carry no semantics. Compound OSM entities such as ways and relations typically subsume such geometric primitives and are better suited for the representation.
Table \ref{tab:statistics_continents} summarizes the number of extracted representations regarding their geographic origin.
In addition, Figure \ref{fig:heatmap} provides a visualization of the geographic coverage of the GeoVectors\xspace corpus.
Overall, we observe high geographic coverage.
In total, GeoVectors\xspace contains representations of over 980 million OpenStreetMap entities.

The most significant fraction of extracted representations is located in Europe (430 million), followed by North America (240 million) and Asia (150 million).
The number of representations per region follows the distribution of available volunteered information in OpenStreetMap, most prominent in the regions mentioned above.
Nevertheless, GeoVectors\xspace provides a considerable amount of entity representations for the remaining regions, e.g., 97 million entities for Africa.
We believe that this amount of data is sufficient for many real-world applications.
We expect that the amount of data will further increase in future OSM versions.
We will include this data in future GeoVectors\xspace releases.

\begin{figure*}
    \centering
     \hfill

    \begin{minipage}[t]{0.8\textwidth}
        \begin{minipage}[t]{0.75\textwidth}
        \begin{tikzpicture}
        \node[inner sep=0pt] (picture) {
        \includegraphics[width=\textwidth]{embedding_map}};
    \end{tikzpicture}%
    \end{minipage}
    \begin{minipage}[t]{0.15\textwidth}
    \includegraphics[width=0.7\textwidth]{legend.pdf}
    \end{minipage}
    \end{minipage}
    \hfill
    \caption{Heatmap visualization of geographic embedding coverage. Map image: \textcopyright OpenStreetMap contributors, ODbL.}
    \label{fig:heatmap}
\end{figure*}

\begin{table}[t!]
    \centering
    \caption{Number of OSM entities contained in GeoVectors\xspace by region.}
    \begin{tabular*}{0.46\textwidth}{p{0.13\textwidth}>{\centering}
    p{0.0525\textwidth}>{\centering}
    p{0.0525\textwidth}>{\centering}
    p{0.065\textwidth}>{\centering\arraybackslash}
    p{0.06\textwidth}}
        \toprule
         Continent & No. Nodes & No. Ways & No.\\Relations  & Total \\
         \midrule
         Africa & $ 9.6 \cdot 10^6 $ & $ 8.7 \cdot 10^7 $ & $ 2.4 \cdot 10^5 $ & $ 9.7 \cdot 10^7 $\\
         Antarctica  & $ 6.9 \cdot 10^3 $ & $ 8.4 \cdot 10^4 $ & $ 9.2 \cdot 10^3 $ & $ 1.0 \cdot 10^5 $\\
         Asia        & $ 1.5 \cdot 10^7 $ & $ 1.8 \cdot 10^5 $ & $ 6.7 \cdot 10^5 $ & $ 1.5 \cdot 10^8 $\\
         Australia/Oceania  & $ 5.2 \cdot 10^6 $ & $  7.6 \cdot 10^6 $ & $ 1.7 \cdot 10^5 $ & $ 1.3 \cdot 10^7 $\\
         Europe        & $ 9.6 \cdot 10^7 $ & $ 3.2 \cdot 10^8 $ & $ 5.6 \cdot 10^6 $ & $ 4.3 \cdot  10^8 $\\
         Central-America & $ 4.4 \cdot 10^5 $ & $ 4.1 \cdot 10^6 $ & $ 1.6 \cdot 10^4 $ & $ 4.6 \cdot 10^6 $\\
         North-America  & $ 5.1 \cdot 10^7 $ & $ 1.9 \cdot 10^8 $ & $ 1.8 \cdot 10^6 $ & $ 2.4 \cdot 10^8 $\\
         South-America  & $ 8.3 \cdot 10^5 $ & $ 2.6 \cdot 10^7 $ & $ 3.9 \cdot 10^5 $ & $ 3.5 \cdot 10^7 $\\
         \midrule
         Total & $ 1.8 \cdot 10^8 $ & $ 7.8 \cdot 10^8 $ & $ 9.1 \cdot 10^6 $ & $ 9.8 \cdot 10^8 $ \\
         \bottomrule
    \end{tabular*}
    \label{tab:statistics_continents}
\end{table}

\section{GeoVectors Knowledge Graph}
\label{sec:kg}

\begin{figure*}[t!]
    \centering
    \includegraphics[width=0.7\textwidth]{linked_embeddings_schema.pdf}
    \caption{Schema, prefixes and namespaces of the GeoVectors\xspace{} knowledge graph. $\rightarrowtriangle$ marks a \voc{rdfs}{subClassOf} relation, \linebreak $\rightarrow$ denotes the domain and range of a property.}
    \label{fig:rdf_schema}
\end{figure*}

Semantic access to the GeoVectors\xspace{} embeddings is of utmost importance to facilitate the use of the dataset in downstream semantic applications.
Therefore, GeoVectors\xspace{} is accompanied by a knowledge graph that models the embedding metadata.
This metadata facilitates interlinking of the embeddings with established knowledge graphs such as Wikidata and DBpedia using existing entity links.
This way, the GeoVectors\xspace{} embeddings can be used to enrich geographic entities in these knowledge graphs.
The GeoVectors\xspace{} knowledge graph includes more than $28$ million triples and is made available under a public SPARQL endpoint\footnote{\url{http://geovectors.l3s.uni-hannover.de/sparql}}.

The GeoVectors\xspace{} knowledge graph is based on three established vocabularies. We utilize the LinkedGeoData \cite{10.5555/2590208.2590210} and the Basic Geo vocabulary\footnote{\url{https://www.w3.org/2003/01/geo/wgs84_pos}} to model the spatial dimension of geographic entities, as well as the PROV Ontology \cite{lebo2013prov} for modeling data provenance, i.e., where the geographic entities were extracted from and what they represent. Figure \ref{fig:rdf_schema} illustrates the schema of the GeoVectors\xspace{} knowledge graph, including its prefixes and namespaces.

Each geographic entity in the knowledge graph is typed as \voc{geovec}{Embedded\-Spatial\-Thing}, which encapsulates the classes \voc{geo}{Spatial\-Thing} and \voc{prov}{En\-ti\-ty}. We group the relevant properties shown in Figure \ref{fig:rdf_schema} regarding these three classes:

\begin{itemize}
\item \voc{geo}{SpatialThing}: Each geographic entity is either a node, a way or a
relation and assigned to the respective \textit{LinkedGeoData} class. In addition, the GeoVectors\xspace{} knowledge graph provides the entity's latitude and longitude.
\item \voc{prov}{Entity}: For tracking the origins of an embedding, each geographic entity is linked to the dataset it is extracted from (\voc{prov}{Collection}). Through versioning of these datasets, the GeoVectors\xspace{} corpus and the GeoVectors\xspace{} knowledge graph can be extended in future versions.
\item \voc{geo}{EmbeddedSpatialThing}: The geographic entities are linked to other resources representing the same (\voc{owl}{sameAs}) or a related resource (\voc{dcterms}{related}) in Wikidata, DBpedia and Wikipedia.
\end{itemize}

Listing \ref{berlin_kg} presents the triples describing the geographic entity representing the city of Berlin. These triples provide the geolocation of Berlin, references to its counterparts in Wikidata, DBpedia, Wikipedia and OpenStreetMap, as well as provenance information (the embeddings were extracted from an OSM snapshot from November 2020). Access to the \textit{GV-Tags}\xspace{} and \textit{GV-NLE}\xspace{} embedding is enabled through the Zenodo DOIs \href{https://doi.org/10.5281/zenodo.4321406}{\texttt{10.5281/zenodo.4321406}} and  \href{https://doi.org/10.5281/zenodo.4957746}{\texttt{10.5281/zenodo.4957746}} pointing to the \textit{GV-Tags}\xspace{} and \textit{GV-NLE}\xspace{} embeddings, the entity type (\voc{lgd}{Node}) and its identifier ($240109189$).

\begin{lstlisting}[basicstyle=\scriptsize\ttfamily, caption={RDF representation of Berlin in the GeoVectors Knowledge Graph.}, label=berlin_kg]

geovec:v2_n_240109189 a geovec-s:EmbeddedSpatialThing;
  a lgd:Node;
  geo:longitude "13.3888599"^^xsd:double;
  geo:latitude "52.5170365"^^xsd:double;
  dcterms:identifier 240109189;
  rdfs:label "Berlin";
  dcterms:isPartOf <https://doi.org/10.5281/zenodo.4321406> ;
  dcterms:isPartOf <https://doi.org/10.5281/zenodo.4323008> ;
  owl:sameAs <https://www.wikidata.org/wiki/Q64>;
  dcterms:related
    <https://de.wikipedia.org/wiki/Berlin>;
  dcterms:related
    <http://de.dbpedia.org/resource/Berlin >;
  prov:hadPrimarySource
    <https://www.openstreetmap.org/node/240109189>;
  prov:wasDerivedFrom geovec:v2/collection.
geovec:v2/collection a prov:Collection;
  prov:generatedAtTime "2020-11-10"^^xsd:date;
  owl:versionInfo "1.0".
\end{lstlisting}

\section{Case Studies}
\label{sec:case_study}

To illustrate the utility of the GeoVectors\xspace embeddings, we have conducted two case studies dealing with the type assertion and link prediction tasks. These case studies were selected to demonstrate how widely adopted machine learning models can benefit from the GeoVectors\xspace{} embeddings based on semantic and geographic entity similarity.
Other potential use cases include but are not limited to next trip recommendation, geographic information retrieval, or functional region discovery.

In both case studies, we use the same widely adopted classifiers:
The \textsc{Random Forest} model is a standard random forest classifier. We use the implementation provided by the scikit-learn library\footnote{\url{https://scikit-learn.org/}} with the default parameters.
The \textsc{Multilayer Perceptron} model is a simple feed-forward neural network. The hidden network layers have the dimensions [200, 100, 100] and use the ReLu activation function.
The classification layer uses the softmax activation function.
The network is trained using the Adam optimizer and a categorical cross-entropy loss. We use the default parameters from the Keras API\footnote{\url{https://keras.io/}}.
As the purpose of the case studies is to demonstrate the utility of GeoVectors\xspace, rather than
achieving the highest possible effectiveness of the models,
we adopt the default model hyper-parameters without any further optimization.

\subsection{Case Study 1: Type Assertion}
\label{sec:osm-entity-classification}

The goal of this case study is to assign Wikidata classes to OSM entities, which aligns well with the established task of completing type assertions in knowledge graphs \cite{paulheim2017knowledge}. We expect that this case study particularly benefits from the semantic dimension of the OSM entities as captured by the \textit{GV-Tags}\xspace{} embeddings.

\emph{Test and training dataset generation:}
To obtain a set of relevant Wikidata classes, we first extract all OSM entities that possess an identity link to Wikidata.
All Wikidata classes that are assigned to at least 10,000 OSM entities are selected for this case study.
This way, we obtain 32 Wikidata classes, including ``church building''\footnote{\url{https://www.wikidata.org/wiki/Q16970}} and ``street''\footnote{\url{https://www.wikidata.org/wiki/Q79007}}, as well as more fine-grained classes such as ``village of Poland''\footnote{\url{https://www.wikidata.org/wiki/Q3558970}}.
Finally, we balance the classes by applying random under-sampling and split the data into a training set (80\%, 285k examples) and a test set (20\%, 71k examples).

\emph{Performance}:
Table \ref{tab:perfomance_wikidata} presents the classification performance of the \textsc{Random Forest} and \textsc{Multilayer Perceptron} models using \textit{GV-Tags}\xspace{} and \textit{GV-NLE}\xspace{} in terms of precision, recall and F1-score.

As expected, we observe that the \textit{GV-Tags}\xspace{} embeddings achieve a better performance than the \textit{GV-NLE}\xspace{} embeddings concerning all metrics.
In particular, \textit{GV-Tags}\xspace{} achieves an F1-score of 85.95\% and 83.43\% accuracy using the \textsc{Multilayer Perceptron} model.
The \textsc{Random Forest} model using \textit{GV-NLE}\xspace{} embeddings reaches an F1-score of 50.17\%. This result can be explained by a few classes such as ``village of Poland'' that are correlated with a location.
The results of this case study confirm that the semantic proximity information is appropriately captured by the \textit{GV-Tags}\xspace{} embeddings.

\subsection{Case Study 2: Link Prediction}
\label{sec:country-of-origin-classification}

This case study aims to assign OSM entities to their countries of origin. This task is a typical example of link prediction, where the missing object of an RDF triple is identified \cite{paulheim2017knowledge}.
We expect that this case study particularly benefits from the \textit{GV-NLE}\xspace{} embeddings based on geographic proximity.

\emph{Test and training dataset generation:}
To obtain a set of countries, we sample OSM entities from the country-specific snapshots\footnote{Country-specific snapshots are available at \url{https://download.geofabrik.de/}.} as described in Algorithm \ref{alg:Sample} and preserve the origin information.
In analogy to case study 1, we select all countries with at least 10,000 examples and obtain 88 different countries.
Again, we balance the examples by applying random under-sampling and split the data into a training set (80\%, 687k examples) and a test set (20\%, 171k examples).

\emph{Performance}:
Table \ref{tab:perfomance_countries} presents the classification performance of the \textsc{Random Forest} and \textsc{Multilayer Perceptron} models using \textit{GV-Tags}\xspace{} and \textit{GV-NLE}\xspace{} in terms of precision, recall, and F1-score.
As expected, we observe that \textit{GV-NLE}\xspace{} achieves a better performance than the \textit{GV-Tags}\xspace{} embeddings concerning all metrics on this task.
In particular, the \textit{GV-NLE}\xspace{} embeddings achieve an F1-score of 96.03\% and 94.80\% accuracy using the \textsc{Multilayer Perceptron} classification model.
In contrast, the \textit{GV-Tags}\xspace{} embeddings achieve an F1-score of only 29.91\% and 20.20\% accuracy on this task because the OSM tags of an OSM entity are rarely related to its country of origin.
The results of this case study confirm that the \textit{GV-NLE}\xspace{} embeddings appropriately capture geographic proximity.

\setlength{\tabcolsep}{0.5em}
\begin{table*}[t!]
    \centering
    \caption{Precision, recall and F1-score (macro averages) and accuracy [\%] of type assertion.}
    \begin{tabular}{lcccccccc}
         \toprule
        & \multicolumn{4}{c}{\textit{GV-Tags}\xspace{}}
		& \multicolumn{4}{c}{\textit{GV-NLE}\xspace{}}  \\
		\cmidrule(l{0pt}r{6pt}){2-5} \cmidrule(l{0pt}r{6pt}){6-9}
		    &  Precision &  Recall & F1 & Accuracy
		    &  Precision &  Recall & F1 & Accuracy\\
         \midrule
         \textsc{Random Forest} & 92.80 & 69.07 & 77.37 & 69.06
         & 70.97 & 41.53 & 50.17 & 41.53\\
         \textsc{Multilayer Perceptron} & 90.18 & 83.41 & 85.95 & 83.43
         & 63.70 & 36.68 & 41.69 & 36.66\\
         \bottomrule
    \end{tabular}
    \label{tab:perfomance_wikidata}
\end{table*}

\begin{table*}[t!]
    \centering
    \caption{Precision, recall and F1-score (macro averages)   and accuracy [\%] of link prediction.}
    \begin{tabular}{lcccccccc}
         \toprule
        & \multicolumn{4}{c}{\textit{GV-Tags}\xspace{}}
		& \multicolumn{4}{c}{\textit{GV-NLE}\xspace{}}  \\
		\cmidrule(l{0pt}r{6pt}){2-5} \cmidrule(l{0pt}r{6pt}){6-9}
		    &  Precision &  Recall & F1 & Accuracy
		    &  Precision &  Recall & F1 & Accuracy\\
         \midrule
         \textsc{Random Forest} & 84.38 & 20.25 & 29.91 & 20.28
        & 99.08 & 89.79 & 93.67 & 89.78 \\
         \textsc{Multilayer Perceptron}  & 86.68 & 17.21 & 25.39 & 17.23
         & 96.03 & 94.89 & 95.39 & 94.89\\
         \bottomrule
    \end{tabular}
    \label{tab:perfomance_countries}
\end{table*}

\section{Availability \& Utility}
\label{sec:avail}

The GeoVectors\xspace{} website\footnote{\url{http://geovectors.l3s.uni-hannover.de/}} provides a dataset description, the embedding framework as well as pointers to the following resources:

\begin{itemize}
    \item GeoVectors\xspace{} embeddings: We provide permanent access to the GeoVectors\xspace{} embeddings and the trained models on Zenodo under the Open Database License\footnote{\url{https://opendatacommons.org/licenses/odbl/}}. To facilitate efficient reuse, we provide embeddings in a lightweight \textit{TSV} format.
    \item The GeoVectors\xspace knowledge graph described in Section \ref{sec:kg} can be queried through a public SPARQL endpoint that is integrated into the GeoVectors\xspace{} website\footnotemark[4].
    In addition, we provide an interface for the label-based search of knowledge graph resources.
    The resources can be accessed both via HTML pages and via machine-readable formats. A machine-readable VoID description of the dataset is provided and integrated into the knowledge graph. New dataset releases will imply knowledge graph updates, where each release is accompanied by a new instance of \voc{prov}{Collection}.
    \item The GeoVectors\xspace{} embedding generation framework presented in Section \ref{sec:generation} is available as open-source software on GitHub\footnotemark[2] under the MIT License.
\end{itemize}

In Section \ref{sec:intro}, we have presented the benefits of using geographic embeddings in a variety of domains \cite{LIU2018183,Xie:2016:LGP:2983323.2983711,Wang:2017:RRL:3132847.3133006}. With GeoVectors\xspace{}, we aim at providing access to easily reusable embeddings of geographic entities that can directly support tasks in these and other domains. Due to the task-independent nature of our embedding generation framework, we envision high generalizability of GeoVectors\xspace{} in a variety of application scenarios.

For demonstrating the effectiveness of the GeoVectors\xspace{} embeddings in different scenarios, we have conducted two case studies presented in Section \ref{sec:case_study}, which illustrate that the GeoVectors\xspace embeddings adequately capture both the semantic and geographic similarity of OSM entities. Therefore, we believe that GeoVectors\xspace{} eases the use of OSM data and is of potential use for many machine learning and semantic applications that rely on geographic data.
Finally, the GeoVectors\xspace{} framework can be reused to infer embeddings from arbitrary OSM snapshots.

For sustainability and compliance with up-to-date OSM data, we plan yearly releases of new embeddings versions.

\section{Related Work}
\label{sec:related_work}
This section discusses related work in the areas of geographic embeddings, word embeddings, and knowledge graph embeddings.

\emph{Geographic Embeddings:}
Recently, several algorithms for the creation of domain-specific geographic embeddings emerged.
Popular application domains include location recommendation \cite{LIU2018183,Xie:2016:LGP:2983323.2983711}, human mobility prediction \cite{Wang:2017:RRL:3132847.3133006}, and location-aware image classification \cite{Aodha_2019_ICCV, 9022254}.
In contrast, we provide a corpus of domain-independent embeddings extracted from OpenStreetMap without the supervision of any specific downstream task.
We believe that a wide range of applications can benefit from the GeoVectors\xspace embeddings.
Mai et al. proposed a location-aware knowledge graph embedding algorithm for question answering \cite{https://doi.org/10.1111/tgis.12629}.
However, the authors use a planar geographic projection that is only capable of capturing specific regions. In contrast, the GeoVectors\xspace corpus covers the whole globe and captures the spherical earth surface.

The neural location embeddings (NLE) \cite{10.1007/978-3-319-68204-4_14} were initially proposed to encode entities included in the GeoNames knowledge base.
We adopt NLE to capture the geographic dimension of OSM data.
In our previous work \cite{TEMPELMEIER2021349}, we introduced an embedding algorithm for OSM that learns embeddings of individual entities.
However, learning a latent representation for each entity on a world scale is not feasible.
Therefore, we chose tag and location-based, effective, and efficient algorithms to infer the GeoVectors\xspace representations.

\emph{Word Embeddings:}
A multitude of natural language processing algorithms adopts word embedding models for downstream tasks. \cite{DBLP:journals/computing/WangZJ20} conducted a recent survey on neural word embeddings algorithms.
Recent approaches like BERT \cite{devlin-etal-2019-bert}, and ELMo \cite{Peters:2018} exploit the context information, e.g., the word order in sentences, to infer latent representations.
In contrast, the fastText algorithm \cite{joulin-etal-2017-bag} infers the latent representation of each word individually.
As OSM tags describing geographic entities neither have any natural order nor form any sentences, we choose fastText over BERT and ELMo to create embeddings.

\emph{Knowledge Graph Embeddings:}
Knowledge graph embeddings have recently evolved as an important area to facilitate latent representations of entities and their relations \cite{8047276}.
General-purpose knowledge graphs like Wikidata \cite{Vrandecic:2014}, DBpedia \cite{10.1007/978-3-540-76298-0_52}, and YAGO \cite{10.1007/978-3-030-49461-2_34}, and even specialized KGs like EventKG \cite{gottschalk2019eventkg} and LinkedGeoData \cite{10.5555/2590208.2590210} typically only include the most prominent geographic entities.
Compared to OpenStreetMap, the number of geographic entities captured in such knowledge graphs is relatively low \cite{TEMPELMEIER2021349}.
For instance, as of June 2021, Wikidata contained less than 8.5 million entities with geographic coordinates, while OpenStreetMap contained more than 7 billion entities.
The specific geographic entities or entity types, e.g., roads or shops,  might not be relevant or prominent enough to be captured by the general-purpose knowledge graphs. Nevertheless, these entities play an essential role for various downstream applications, for instance, for land use classification \cite{SCHULTZ2017206} or in the prediction of mobility behavior \cite{10.1371/journal.pone.0188735}.
Consequently, pre-trained embeddings of popular knowledge graphs, such as Wikidata \cite{DBLP:conf/mlsys/LererWSLWBP19} or DBpedia, lack coverage of geographic entities required by spatio-temporal analytics applications.
In contrast, the GeoVectors\xspace embeddings proposed in this work specifically target geographic entities and ensure adequate coverage in the resulting dataset.

\section{Conclusion}
\label{sec:conclusion}
In this paper, we presented GeoVectors\xspace{} -- a linked open corpus of OpenStreetMap embeddings.
GeoVectors\xspace contains embeddings of over 980 million OpenStreetMap entities in 180 countries that capture both their semantic and geographic entity similarity.
GeoVectors\xspace constitutes a unique resource of geographic entities concerning its scale and its latent representations.
The GeoVectors\xspace knowledge graph provides a semantic description of the corpus and includes identity links to Wikidata and DBpedia.
We further provide an open-source implementation of the proposed GeoVectors\xspace embedding framework that enables the dynamic encoding of up-to-date OpenStreetMap snapshots for specific geographic regions.
\subsubsection*{Acknowledgements} This work was partially funded by DFG, German Research Foundation (``WorldKG'', 424985896), the Federal Ministry of Education and Research (BMBF), Germany (``Simple-ML'', 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (``d-E-mand'', 01ME19009B), and the European Commission (EU H2020, ``smashHit'', grant-ID 871477).

\balance
\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


You will always have to dial this telephone code before the number you want to call in India.
How to call India ?
To call India you need to dial like this : 00 + India country code + city area code + the number you wish to call. For example to call India you always need to do 0091 + number, so it looks like 00 91XXXXXXXXX. (00) is the intrnational access code but from Canada it is 011.
What is the 91 country code ?
0091 is India country telephone code. It's the dialing code you need for calling India from another country. This page concern only India dial code. If you want to know others dialing country codes you can find what you search in homepage.
International access code can be replaced by "+" and the dialing code can be written like +91 or like 0091.
You can find below area codes for major cities of India. Before dialing, you must first enter the country code for India and then the area code of the city you want to call.
IDD + India country code + city area code + ?????


text:
/* Code generated by IfcQuery EXPRESS generator, www.ifcquery.com */
#pragma once
#include <vector>
#include <map>
#include <sstream>
#include <string>
#include "ifcpp/model/GlobalDefines.h"
#include "ifcpp/model/BasicTypes.h"
#include "ifcpp/model/BuildingObject.h"
#include "IfcPlanarExtent.h"
namespace IFC4X3
{
	class IFCQUERY_EXPORT IfcAxis2Placement;
	//ENTITY
	class IFCQUERY_EXPORT IfcPlanarBox : public IfcPlanarExtent
	{
	public:
		IfcPlanarBox() = default;
		IfcPlanarBox( int id );
		virtual shared_ptr<BuildingObject> getDeepCopy( BuildingCopyOptions& options );
		virtual void getStepLine( std::stringstream& stream ) const;
		virtual void getStepParameter( std::stringstream& stream, bool is_select_type = false ) const;
		virtual void readStepArguments( const std::vector<std::string>& args, const std::map<int,shared_ptr<BuildingEntity> >& map, std::stringstream& errorStream );
		virtual void setInverseCounterparts( shared_ptr<BuildingEntity> ptr_self );
		virtual uint8_t getNumAttributes() const { return 3; }
		virtual void getAttributes( std::vector<std::pair<std::string, shared_ptr<BuildingObject> > >& vec_attributes ) const;
		virtual void getAttributesInverse( std::vector<std::pair<std::string, shared_ptr<BuildingObject> > >& vec_attributes ) const;
		virtual void unlinkFromInverseCounterparts();
		virtual uint32_t classID() const { return 603570806; }

		// IfcRepresentationItem -----------------------------------------------------------
		// inverse attributes:
		//  std::vector<weak_ptr<IfcPresentationLayerAssignment> >	m_LayerAssignment_inverse;
		//  std::vector<weak_ptr<IfcStyledItem> >					m_StyledByItem_inverse;

		// IfcGeometricRepresentationItem -----------------------------------------------------------

		// IfcPlanarExtent -----------------------------------------------------------
		// attributes:
		//  shared_ptr<IfcLengthMeasure>							m_SizeInX;
		//  shared_ptr<IfcLengthMeasure>							m_SizeInY;

		// IfcPlanarBox -----------------------------------------------------------
		// attributes:
		shared_ptr<IfcAxis2Placement>							m_Placement;
	};
}

redpajama_set_name: RedPajamaGithub


After the partecipation at the Brazilian fair on March, TRIA remained in the area.
In fact toghether with PROINTEC Group, attended Plastimagen in Mexico City.
TRIA was present at the fair in Sao Paulo toghether with its Brazilian branch, that has exhibited locally produced granulators.
Stefano Venturelli new TRIA CEO as of April 1, 2019.
From the 5th to the 8th of March took place in Tunis Plastic Expo 2019, a fair dedicated to plastics. TRIA was present together with Fluides Services.
In December 2018 we completed the process of certification of our Quality System based on the new edition of ISO 9001 issued in 2015, evolution of the 2008 version based on risk analysis (risk-based thinking).
Interplastica, leading plastic exhibition in Moscow took place at the Expocenter from 29 January to 01 February 2019.
After months of preparations, on last 05th March 2018 the SAP go-live was launched.
The TRIA Series 30 granulators are among our most successful machines, we have in fact presented in 2018 the new 7030 model.
To face the increasing sales of last year we decided to restructure a second production site in Cologno Monzese: from last January we are assembling there the series 15, 20 and 30.
The new screenless JS grinders have been used for many years for the in-line recovery with injection moulding machines.
This edition of the K show has been an important moment for TRIA confirming our company growth.
2016 starts with an important change refers to TRIA Board of Directors.
"Dear Core supplier": thus begins the communication we recently received from Plastic Omnium in which we are qualified as one of the few Core suppliers of Inergy Auto Division, company of which PO is the Holding.
DR series replaces DP series, a dust removal system which have already good performance but outdated design.
TRIA will be present as in the previous edition of the fair.
You can visit us at booth E05 - Hall 9.
TRIA was present at the fair at booth 238 together with GRUPO PROINTEC.
TRIA was present at the fair booth E228 with its brazilian branch.
TRIA was present at the fair at EXPO CENTRE NASREC, booth A24 - Hall 5.
TRIA was present at the fair with FLUIDES SERVICES at the booth n.200 - Hall 1.
TRIA was present at the fair, booth 4099 - Exhibit hall.
TRIA was present at the fair, booth A17 - Hall 2.1 .


text:
What are the duties of the President of India?
Why the President and the governor of India is called a puppet of the ministry?
Who is the promoter of Buddhism in India?
Who is the most underrated Cricketer in India?
Who is Milkha Singh? Why is he known as Flying Jat?
Who are the Rohingyas and what's their status in India?
What is the definition of Hereditary and who Discovered it?
Who is Kumar Vishwas? Why he is so much known as poet?
Who are the most economic and best tour operators in India?
What is the significance of the Mountain Railways of India?
What is the difference between throw e and throw new Exception(e) in catch block in java?
What is economics? Which career can a student opt if he studies economics?
What is the salary of ethical hacker in India?
How e-stores are Winning Hearts in India
What is the difference between 'except Exception as e' and 'except Exception, e' in Python?
Who is E Sreedharan and what if he becomes the President of India?
Famous Personalities/LeadersPoliticianspoliticsGeneral Topics
E Sreedharan, popularly known as Metro Man, has been behind changing the face of India. The highly advanced metro trains we are using today are the results of the hard work of this visionary. He has served the Indian Engineering Services (IES) for a huge span and not got retirement to pursue another venture of his life.
Born in Varavoor in the Thrissur district of Kerala in 1932, Shreedharan did his early education at the Basel Evangelical Mission school and then went to Palghat for the further studies and completed his engineering from Kakinada (AP). He commenced his career as a lecturer in Civil Engineering at the Government Polytechnic, Kozhikode and at the Bombay Port Trust as an apprentice for a year.
The Big Phase
The big breakthrough he got when was in charge of Kolkata Metro, especially for its implementation, planning, and design. Sreedharan pegged away in the task, and thus the first ever metro in India came into existence. By surprising everyone, our Metro Man not only finished the high-held project before the timeline but also established the foundation of modern infrastructure engineering in India.
He played a major role in changing the fortune of shipyard by overtaking the production of its first ship named MV Rani Padmini. Shreedharan ensured that its first ship was built during his stint as the chairman and MD and the day of launching came in 1981 when Rani Padmini was all set to soar.
Jaipur Metro
He has also been the part of the chief advisory for the Jaipur Metro.
The big phase in Shreedharan came when he was assigned the task of establishing Delhi Metro. Here also, Shreedharan continued tracking with bullet speed. Being the managing director, he scheduled sections and pushed them towards the end within their respective time and budget.
Race to the Presidentship
Well, anything can happen if people from non-political background grabs the highly honored seat. However, we expect that Sreedharan's carry those qualities, which are required to justify the role of a President rather than being just a political puppet. However, the race to the Presidentship is made harder for those having non-political DNA.
Strong Decisions
He might take some strict decisions including the rejection of death plea of terrorists and criminals. He can play a crucial and active role in signing a few bills like the highly demanded Lokpal (with all its powers), the taxation law, the right of children to free and compulsory education, Inter-state river water dispute etc. It reminds me of the stint of one of my favorite Presidents of India, Mr. APJ Abdul Kalam.
Om Sharma
Published on 12-Dec-2018 10:50:55


Teague's Asphalt Paving is the leading provider of paving services in Houston, Brenham, TX and the surrounding areas. Our asphalt paving contractors are highly trained and experienced and deliver the best caliber of services for your paving needs. We know the industry inside and out and offer the right solutions for projects of all types. Our family owned and operated company understands how to provide personalized services while going above and beyond to complete each project successfully.
Contact the asphalt paving experts of Teague's Asphalt Paving to schedule service by calling us at 1(800) 673-7080 today.
Asphalt is a semi-solid with a black and sticky consistency that is found in crude petroleum. It is actually the distilled residue that is the refined product after crude oil has been processed. Asphalt is used in a variety of different applications including smoothing roads, paving walkways, parking lots, and other types of surfaces. Due to its black coloring, it can often be mistaken for tar, however they each have their own unique components. Asphalt must be milled prior to being used and then it must be ground so it can fix the streets and roads in need of repairs.
At Teague's Asphalt Paving, we understand the best ways in which to use asphalt to best repair cracks in concrete and other surfaces such as parking lots and driveways. We are fully experienced with the various methods of using asphalt for projects both large and small in Houston, Brenham, TX and the surrounding areas.
Asphalt is a black and sticky semi-solid found in crude petroleum, it is the distilled residue that has been refined during the distillation of crude oils. It has many uses to cover and smooth roads, parking lots, and other surfaces such as concrete that has been cracked over time due to extreme weathering conditions in many areas around the country. Asphalt is often confused with tar because of the black coloring, though they are entirely different materials since tar is typically used for roofing jobs. Moreover, asphalt needs to be milled once the top layer is removed and then there is a grinding process to prepare it to fix the many roads and streets in the area that need repairs.


Barbara Hines, I Will Be What I Will Be, 2014, oil on canvas, 72 x 60 in.
wider variety of implements. "There is nothing more that I like than a huge canvas on my driveway and a huge roller on a five-foot pole," Hines says. Spatulas and squeegees are also part of her tool kit. "I use the brushes more to get the focus," she adds.
The freedom Hines feels using these techniques is evident in the work. There is also a sense of experimentation. Filling most of the museum, the exhibition includes works on paper done in mixed media and even a sculptural piece in addition to the paintings. And rather than exclusively recreating Jerusalem's most famous monuments, she focuses more on atmospheric effects. Underpainted with gold and then layered with rich plum and deep indigo, the work replicates the radiance of the city's limestone buildings. "My palette is more about a mood. I think in terms of warm or cool," she explains. There is also a dimensional element to the paintings that is achieved from mixing soil from Israel into her pigments.


We are looking to collect additional and up to date contact information from our patients so we can provide an improved service including text message appointment confirmations and reminders.
Please download the form below and send it to the surgery if you are happy that we contact you in this way.
There is parking at the surgery, however, during busy times you may need to park on the main road, or use the car park in Rusper Road. We politely ask that as a courtesy to residents you do not park in Langley Lane.
If you have a complaint please put it in writing addressed to the Practice Manager or alternatively contact Healthwatch on 0300 012 0122.


For those of you who don't know, an Inverse ETF is an ETF that moves in the opposite direction of whatever it is trying to mimic.
So let's say you think the stock market is going to drop and you want to protect yourself or make money. You could buy an inverse ETF for the S&P500. This will allow for you to make money while the rest of your investments are losing. It can either be a hedge or if you're sitting in cash you can use it to profit. Think of it like you're shorting the entire stock market.
Caution: only very experienced investors should use this! When you short an individual stock you can theoretically lose an infinite amount of money (this has never happened). There are also some inverse ETFs that use leverage to exaggerate the gains or losses. This is even more dangerous! Know what you're investing in!


Kali Parsons: What's today? Hmmmm...happily, I have no idea.
One of my parents recently had a doctor's appointment. You know, one of those you go to once a year. Anyway, one of the routine questions they ask you, particularly as you're getting older, as I understand it, is "What day is today?" This tells them what? That your mind is still intact? That you haven't begun losing your faculties?
Well, all I have to say is that when I'm in my seventies, I don't want to be considered to be losing it if I have no sense of what day it is. I think of that as a sign of being at one with your bliss, truly living in the moment, a sign that you don't need to be aware of and ruled by human definitions of time.
I'm quite positive that I won't have a clue what day it is if I ever have the opportunity to stop working. If my summer breaks are any indication, I will never be able to accurately answer such a question at my annual doctor visit. I celebrate when I lose that skill after the last day of school and I know that I'm truly in the summer zone when I cannot relay what day or date it is without consulting my phone or computer. I say, "Hooray!" for such things.
Now, I must go set an alarm in my phone to remind me when to take the trash/recycling out...after all, I can't think of a single other reason why the day of the week would matter to me...at least till mid-August, anyway. Yay!
I seriously love it when you "like" my blog on facebook...seriously!


View our range of riding skull hats, with a choice of brands and styles to find the one that suits you best. Skull hats offer a great level of protection, and are used by jockeys and cross country competitors. Suitable to be worn by themselves or covered with a hat silk, which come in a variety of colours and finishes.
Amongst the collection at Ayr Equestrian is the Gatehouse HS1 Jockey Skull. This helmet is our best seller and is also regarded as "the best riding hat in the world". The HS1 reaches the highest level of manufacturing testing in the world, which simulates a rider falling in to a fence rail or having a secondary hit from a horse's hoof.
We also have a range of Charles Owen skull hats to choose from. The Charles Owen 4 Star Jockey Helmet is certified to the top four international hat standards, making it a great candidate for extreme situations. Our Charles Owen hats all features ventilation holes, allowing more airflow and a cooler ride. The J3 hat also has adjustable rear padding, allowing you to customise the fit to your individual needs.


text:
Stars arrive for South Indian International Movie Awards
Everybody's excited for the two days mega event.
11 Sep – Fans of Indian movies, get your cameras ready – the stars of your favourite movies are here in Malaysia!
The South Indian movie stars have arrived in the country to attend the South Indian International Movie Awards (SIIMA) 2014, which is taking place this 12 to 13 September at Stadium Negara.
Some of the stars that are set to grace the event include Sridevi, Vijay Yesudas, Unni Krishnan, Isya Talwar, Shivraj Kumar, Kushboo, Danush, Vikram Prabhu, Arya, Shivakartikeyan, Trisha Krishnan, Radhika Sarathkumar and Lakshmi Manchu.
Even Bollywood stars such as Sohail Khan, Bhatia, Boney Kapoor, Saqib Saleem and Huma Quereshi will be in attendance.
The Indian film industry is the biggest in the world and South Indian movies make up the majority of it. South Indian movies can be broken down into four separate segments, namely Kollywood which represents the Tamil segment, Mollywood represents Malayalam, Tollywood represents Telugu and Sandalwood represents the Kannada segment.
On 12 September, the Telugu, Malayalam and Kannada movie awards will be presented while the Tamil awards will be given to their winners on the day after.
In the meantime, SIIMA Facebook page has uploaded several photos of the stars arriving in their hotel in Malaysia today.
Devisri Prasad and Regina Cassandra checking into the hotel.
Lakshmi Manchu too checks into the hotel upon arrival.
Pranitha Subhash and Kriti Kharbanda looking paparazzi-ready with their big sunnies.
Adah Sharma S is all smiles.
Navdeep giving the camera a soulful stare.
Sachiin J. Joshi checking into the hotel.
Cinema Online, 11 September 2014
Arima Nambi (Tamil) (04 Jul 2014)
Jigarthanda (Tamil) (01 Aug 2014)
Sigaram Thodu (Tamil) (12 Sep 2014)
Latest Trailers:
26th FFM to take place this year
Federation of Film Artistes' Association of Malaysia (GAFIM) will be organizing the 26th Malaysian Film Festival
At the Laureus Awards KL red carpet
See red carpet photos of Benedict Cumberbatch, Jamie Foxx, Daniel Brühl, local celebs and more were at Laureus Sports Awards in KL yesterday
"Catching Fire" MTV Movie Awards
"The Hunger Games: Catching Fire" won itself most awards at the teen-voted MTV movie awards show
Malaysia to host South Indian International Movie Awards
South Indian International Movie Awards will take place on 12 to 13 September, 2014 at Stadium Negara
IIFA is back in Malaysia after 13 years
The International Indian Film Academy (IIFA) Awards will once again be held in Malaysia after its first time in the country in 2002
Bollywood star weekend kicks off in Malaysia
16th IIFA weekend event kicks off today in Kuala Lumpur with Anil Kapoor, Hrithik Roshan and more in town
Anil, Priyanka and more at KLCC
Bollywood stars of "Dil Dhadakne Do" were at TGV Cinemas KLCC for the film's premiere yesterday
Bollywood stars rock the IIFA stage
The International Indian Film Academy (IIFA) awards show held at the Stadium Putra was home to the stars last night
"Queen" takes the throne as IIFA Awards' Best Picture
"Queen" and "Haider" won three awards each at the awards show held at Stadium Putra yesterday
Sridevi's best movies of all time
A look back at some of the Bollywood actress' best works that have captured our hearts


The Big Fight: Is Narendra Modi re-positioning himself?
On this special edition of The Big Fight, we debate whether India is going to witness a presidential form of election or are regional political parties too powerful for national parties to make a two-way contest? Also, Narendra Modi seems to have taken a lot of steps in the last one month to reposition himself towards the Centre, making sure he is talking in a more secular language. But in the middle of all this, comes the snooping controversy? How will this affect him?


You're not the only one who can't get enough of the cheese when you're done with a mozzarella salad. Here's a salad that's bursting with flavor and crunch— down to the last bite of the bowl!
crumbs. Freeze for 20 minutes.
fries. Cook for another 5-7 minutes.
and roast on 375oF for approximately 3 minutes.
Keep an eye on it, as it burns easily.
circles, approximately 7-8 inch diameter.
tomatoes and stir fry mixture to the baskets.


text:
Q: How do I move the HTTPERR directory out of my C: Drive I've got an IIS server that is running a bit low on diskspace on the C: drive; I've moved the W3SVC logs from c:\windows\system32\LogFiles to another drive, but there is also a big HTTPERR folder there that I'd like to move somewhere else.  How do I do it?

A: Microsoft have details here but the summary is:
Go into the registry and amend HKEY_LOCAL_MACHINE\System\CurrentControlSet\Services\HTTP\Parameters
Create a String called ErrorLoggingDir and point that somewhere else.  Don't include the HTTPERR path, as the HTTP service will create an HTTPERR subdirectory anyway.
Then you restart the HTTP service (net stop HTTP, net start HTTP) and then start any dependent services - eg W3SVC.  The HTTP service (http.sys) does not appear in the services MMC, so you have to use a command line to start/stop it.

A: Here is some powershell code that will add the registry key:

    $myRegKeyBase = "HKLM:\SYSTEM\CurrentControlSet\services\HTTP\Parameters"
    $myRegKeyName = "ErrorLoggingDir"
    $myRegKeyVal  = "D:\inetpub\logs"
    # Create Key property if it doesn't already exist
    $myRC = New-ItemProperty $myRegKeyBase -Name $myRegKeyName -Value $myRegKeyVal -PropertyType String -ErrorAction SilentlyContinue
    # Update Key property to $iisLogFileDir if it already exists
    Set-ItemProperty $myRegKeyBase -Name $myRegKeyName -Value $myRegKeyVal -ErrorAction SilentlyContinue
    $myProperties = Get-ItemProperty $myRegKeyBase -ErrorAction SilentlyContinue
    Write-Host "regKeyPath: $($myRegKeyBase) regKeyName: $($myRegKeyName) value: $($myProperties.$myRegKeyName)"