text:
declare namespace f14.L10n {
    /**
     * The key-value object.
     */
    interface IMap<V> {
        [key: string]: V;
    }
    /**
     * Provides commons methods to add and get the localized strings.
     */
    interface IL10NProvider {
        /**
         * Adds the localized data for the specified locale name.
         * @param localeKey A String, representing the language version of the browser. ("en", "en-US", "de", "fr", etc.)
         * @param localeData The localized map. Key is string key; Value is localized string value.
         */
        AddLocale(localeKey: string, localeData: IMap<string>): void;
        /**
         * Returns a localized string for the given key.
         * @param stringKey A String key.
         */
        GetString(stringKey: string): string;
        /**
         * Returns a localized string for the given key and the specified locale.
         * @param localeKey A String, representing the language version of the browser. ("en", "en-US", "de", "fr", etc.)
         * @param stringKey A String key.
         */
        GetStringForLocale(localeKey: string, stringKey: string): string;
        /**
         * Returns the locales map.
         */
        GetLocales(): IMap<IMap<string>>;
    }
    /**
     * Configuration for f14-l10n.
     */
    class Configuration {
        /**
         * Trun on\off debug mode. Default: false.
         */
        DEBUG?: boolean;
        /**
         * Setup default locale name.
         * If a localized string is not found for the current locale, it will search in the default locale.
         */
        DefaultLocale?: string;
        /**
         * Sets the IL10NProvider as window property with property name WindowPartProperty.
         */
        AsWindowPart?: boolean;
        /**
         * This name using if AsWindowPart is true. Default: l10nProvider.
         */
        WindowPartProperty?: string;
        /**
         * Specifed IL10NProvider. Default: DefaultL10NProvider.
         */
        L10nProvider?: IL10NProvider;
    }
    /**
     * Configuration instance.
     */
    var Config: Configuration;
    /**
     * Setup the L10NProvider.
     * @param settings User configuration.
     */
    function Setup(settings?: Configuration): void;
    /**
     * Returns configured IL10NProvider or throws exception.
     */
    function Localizer(): IL10NProvider;
}

redpajama_set_name: RedPajamaGithub


text:
Joseph Muscat
Dr Joseph Muscat has been Prime Minister of Malta, a Mediterranean island between Sicily and Libya and Tunisia, since March 2013. Malta joined the European Union in 2004.
"The problem of migration has struck at the very heart of Europe."
What is the situation with the migrants?
In some years' time we will realise that we are facing a phenomenon of historic proportions. This is not something temporary or that will disappear. This is truly a momentous moment in history. We European leaders are approaching things in a short term and crisis scenario. We are trying to come up with solutions that aren't solutions at the end of the day, but addressing the immediacy of the crisis. What is of paramount importance is to create a context, to create the institutions, to create the tools for a much more wide-ranging engagement with this phenomenon; to actually try to create a system and manage the system.
For the last seven years, Order of Malta medical teams have been embarked on ships of the Italian Navy to provide care to refugee 'boat people' in the Mediterranean. Photo: CISOM
What is going to happen to all these people? Are they going to stay or go back?
I don't think we can just look at this issue as that we have camps in North Africa and so the issue will be solved, or we are sending people back and so the issue will be solved, or we will accept half a million people and the issue will be solved. It's much more complicated than that, and has to be seen in a wider context.
What was your own most recent forum for speaking about this?
I made a speech on the 450th anniversary of the Great Siege of Malta for the Order of St John. I drew parallels with what happened after the Second World War, and at the end of the day we need a Bretton Woods on migration. That is the way forward.
"Levée du siège de Malte assiégé par le général Ottoman Mustapha, en septembre 1565" by Charles Philippe Lariviere (1798-1876), painted slightly before 1843.
Do you think Europe will emerge as a completely different continent?
Describe Europe to me! I cannot come up with a uniform hegemonic description of what Europe is all about. I think that Europe today is completely different to the Europe of 100 years ago, and I think that 50 years from now it will be completely different from today. I don't see this as a threat, but as part of the change that the continent has been used to for the past centuries.
What is Malta's role in that change?
As the smallest member state, we are trying to get our priorities right. First, we are saving lives at sea. We are the only country in Europe devoting 100% of our limited military assets to search-and-rescue and life-saving. Second, we are saying we need a system for legal migration, to allow people to come to Europe legally, but at the same time saying that we cannot accept everyone. By leaving the situation as it is today we are allowing criminals to run the show, and we are not in control. Third, we are advocating a more long-term perspective.
Migrants in a Maltese Detention Facility.
Many Mediterranean islands are receiving refugees. What is the situation in Malta?
Over the past years we have received literally thousands of people. Over the past two years we haven't had so many arrivals, because most people go to Italy. We work in unison with our Italian colleagues, to make sure we are giving assistance and working as a team. We are both doing our part.
Did you meet Prime Minister Renzi?
We are on the same wavelength. We are two prime ministers that, during the next European meetings when the subject will come up, won't need to change our position, which is that of most countries and one of common sense.
Italian Prime Minister Matteo Renzi and Dr Joseph Muscat made a joint call for an EU summit following the loss of the lives of some 700 migrants at sea.
What is the position of Mrs Merkel?
Germany, and France, have a historical mandate, almost an obligation, to take a leadership role, maybe a reluctant leadership role, in Europe. I disagree with those who say that Mrs Merkel has changed her position now. She was very vociferous when we first discussed a European refugee mechanism and she was totally in favour. So she changed her position then, and not now. This did not just happen because of recent events.
Does Europe understand that this issue is probably its most important priority?
What do we mean by Europe? The European commission I would say yes. The member states, well it depends. Most have realised all this, but there is a staunch and strong minority that continue to try to bury their heads in the sand. What happened recently has shown that this is not just a problem of the Mediterranean, it has struck at the heart of Europe. It is not only a European problem, it is a global problem, and it is only a matter of time before the minority accepts this. I am not advocating an open-door policy which means we accept everyone just like that. I am saying there need to be new rules, because the current rules don't work.
What can be done about Syria and the terrorists in Libya and Tunisia?
I can't give you a solution to world problems like that, but there are prominent members of the international community that have turned a blind eye to Libya. We are very close to Libya, and we know the situation there. The truth is that many members of the international community don't care about Libya, and we are one of the few to keep it on the agenda. We are in danger of having a failed state on the doorstep of Europe and we are trying, with the United Nations, to get Libyan people from different factions around the table, and to have a government of national unity.
Armed Forces of Malta and Libyan Navy Engage in Joint Training Operations.
What is your position regarding ISIS ?
We have the same position as the international community. I don't think we are in some clear and present danger. People look at us as a place of dialogue and peace.
What kind of country is Malta now?
Economically we have the highest GDP growth in Europe. We have the second lowest unemployment rate, and expanding investment horizons. When it comes to civil liberty and civil rights we are amongst the most progressive in Europe. We share our natural situation, as a place where people from both North and South meet and work together.
Malta is 98% catholic. Have you met Pope Francis?
Yes, I met the Holy Father last year. His two immediate predecessors both came to the island, and the invitation is always open to him.
Dr Joseph Muscat exchanged gifts with Pope Francis at the Vatican.
How many people live on Malta?
Almost half a million. We are the smallest country in Europe.
What is it like to run such a small country?
I cannot compare it with running a larger country, of which I have no experience! It's challenging. We don't perceive ourselves in matters of size, we like to punch above our weight when showing people what we can do and delivering results. This is a very exciting job.
Are you still very close to the UK?
We have a great deal of influence from the United Kingdom. Now we have been independent for 51 years and we have grown out of that, but definitely both the English influence and the Mediterranean influence can be felt. The Italian influence comes mostly through the media, and the Arabic influence comes through our Semitic Maltese language, so even in our everyday language the influence is felt.
John Ashe, President of the 68th session of the General Assembly, meets with Dr Joseph Muscat. UN Photo/Evan Schneider United Nations, New York 2013.
Who are your strongest allies and friends?
We are spoilt for choice. I cannot really tell you who our enemies are as we don't have enemies. We base our attitudes in foreign relations on an open approach. We will host the leaders of the 28 member states in Europe, who will meet with their African Union counterparts at the Valletta Summit on Migration. This special European Union/African Union summit to discuss migration problems will be held on 11th and 12th November, and Malta was chosen as the location for this conference as a symbol of uniting the continents.
Are you very active at the UN?
Historically we have been very active. We don't have delusions of grandeur. We know our size and our sphere of influence and we try to give our total contribution.
Prime Minister Dr Joseph Muscat delivering his speech at the UN General Assembly.
Do you feel that you are treated like any other state, like France or Italy?
I have never felt otherwise. In this world of ours, and especially in Europe, what really counts are ideas and not geographical size, and we have good ideas.
What was your position during the Greek crisis?
Very clear, that we have to help Greece, and aside from Germany we are the country with the highest exposure to Greek debt. We are the only country to vote twice in parliament unanimously in favour of Greece, but obviously Greece has to do its part, which it wasn't doing. Once again we take a common sense approach.
Prime Minister Dr Joseph Muscat and Greek Prime Minister Alexis Tsipras.
Do you think the Greek situation is settled?
I don't think there is a final solution. This solution has managed to buy more time and to get the situation more in hand.
Do the Maltese feel close to the Greeks?
There is a different mentality. Our colonial past makes us a bit different. Our work ethic, the way we approach business and work, is pretty much Anglo-Saxon. It's not very Mediterranean. The concept of fair play is very Anglo-Saxon and is the mentality here.
Do you have all the advantages, the Mediterranean climate combined with an Anglo-Saxon mentality?
I joke with my friends that we have a Mediterranean lifestyle and an Anglo-Saxon work ethic and thank God it's not the other way round!
Malta's main assets reside in its human capital, international business contacts and innovation.
What are your major concerns?
It is crucial that Europe gets back on track economically, to perform much better than it does today by making it easier to carry out business in Europe, to be less bureaucratic, to open up to the outside world and make it easier for global investors to invest in Europe. Once you create wealth then you can discuss the distribution of wealth. Right now the problem of Europe is that it's not creating the wealth it needs to distribute.
Are the migrants just going to make yet another difficulty for Europe?
Europe needs more talent, it needs more workers, it needs more everything. That's why I say this system must not be allowed to run by criminal gangs, but run by government in order to see what our requirements are, including economic growth. Then we would manage to integrate more people successfully.
Germany under Angela Merkel favours a Willkommenskultur that it has extended to thousands of arriving refugees.
Europe exists as a cultural notion with a common currency, but with no common army or taxes. Can it have a single voice?
Europe is not one, it is a union of 28 different voices. It is a choir and not a single singer. That's fine. It's what we are. It's not a huge drawback, and I don't think it should be like America.
Diversity weakens response to events such as the Ukraine invasion or opening Iran, or even Syria. Wouldn't it be better to have one voice?
Yes, but that would be unnatural, something that the people of Europe would not be looking forward to and that would lead to even an even bigger democratic deficit than there is today. I don't think there is an appetite to do away with the nation state. Look at the issues around Europe and the movements gathering steam. I don't agree with them being very nationalistic, but going into the opposite direction of one Europe with one voice and not leaving the space for national identity leads us into the hands of extremists.
What is the future role of Europe?
Europe should not try to go down the path of becoming one country, because it is not one country and can never be one country. I think Europe should go down the path of living in diversity and trying to coordinate as much as possible. Having one country would be the antithesis of Europe. Europe is diversity, not singularity or hegemony.
A special summit on migration is to be held in Malta, bringing together the European Union, the African Union and key countries involved in the migrant crisis.
You are lucky that one of your two official languages is English, the world Esperanto today, but many countries are confined by their languages and there are even movements towards secession by dialect. What about the problem of language?
We can't have a monopoly on education. People can learn languages. A culture change takes time and the nationalistic mentality is changing because of freedom of movement in Europe. I think the rate of change we will see in the next century will be at the speed of lightning compared to what we have seen over the past 100 years.
Until recently we thought Asian countries were growing in comparison to a shrinking Europe. Was this perception false?
We cannot base economic analysis on the numbers of a couple of weeks or a couple of months. Over the past decade or so in Europe the rate of growth was significantly lower than in Asia. Europe can prosper when other parts of the world prosper too. It is a virtuous circle, not an either/or approach. What we really missed was connection with the economies of other continents, to make it easier for their success to be our success too.
Dr Muscat recently met with President Erdogan to discuss the increasing investment made by Turkey in Malta, migration and the situation in the Mediterranean.
Is Malta still in the Commonwealth?
We are, and we are hosting the Commonwealth Summit in November. Leaders of 53 countries are coming, and the Queen of course – her last visit here was six years ago.
Today there is the celebration that she is the longest reigning monarch. Did you send her a long message?
We will send her a letter, to congratulate her as the head of state of another country, but we follow what is happening.
What newspapers do you read here?
We have our own newspapers, and obviously the international papers.
September 9th, 2015.
www.timesofmalta.com
PreviousColin Bailey
NextNicky Haslam
David Snowdon

redpajama_set_name: RedPajamaCommonCrawl


text:
Villeneuve-du-Paréage – miejscowość i gmina we Francji, w regionie Oksytania, w departamencie Ariège.

Według danych na rok 1990 gminę zamieszkiwało 489 osób, a gęstość zaludnienia wynosiła 43 osób/km² (wśród 3020 gmin regionu Midi-Pireneje Villeneuve-du-Paréage plasuje się na 599. miejscu pod względem liczby ludności, natomiast pod względem powierzchni na miejscu 989.).

Bibliografia

Miejscowości w departamencie Ariège

redpajama_set_name: RedPajamaWikipedia


Huawei E3131 is a Wi-Fi supported modem which can used as modem as well as wi-fi hotspot, E3131 is 21.6 Mbps Modem which allows 5 users to connect Read More Features of E3131 . You can find the unlocking procedure to unlock your E3131 in this article.Huawei E3131 can be easily unlock by just writing the unlock code to the modem, so we need to follow below steps.
Install Firmware Update As Shown Here.


What if The Little Mermaid was a horror movie?
What if the The Little Mermaid wasn't a fired-up ginger hellbent on finding interspecies love on land? What if she was a Coney Island Freak Show attraction with the taste for blood? Then she would be a lot more like director Nicholas Humphries' simple horror short The Little Mermaid. As a rule, we like anything with mermaids in it, and can only hope that this the first of many more horrific mermaid tales to come. Last we heard Sony was moving forward with their own Dark Little Mermaid movie. What ever happened to Joe Wright Little Mermaid we were promise eons ago? That was a match made in supernatural period piece heaven.
Will the live action Little Mermaid be as gruesome as the original?


This is a weekly clinicopathological conference attended by Dr. Samuel Moschella, M.D. (Dermatology), Danielle Miller, M.D. (Dermatology), Dr. Artur Zembowicz, M.D. (Dermatopathology), Winnie W. Ooi, M.D. (Infectious Diseases), Harvard Medical School Dermatology Residents and Tufts Medical School Pathology Residents. Contributors to the conference include Members of Dermatology Department of Lahey Clinic including doctors Mollie MacCormack, Caroline Bovona, M.D., Nancy Burnside, M.D., Emily Fisher, M.D., Siobhan Mannion, M.D., Karen McKoy, M.D., Karen Mitchel, M.D., Suzanne Mosher, M.D., Suzanne Olbricht, M.D., Elizabeth Page, M.D., Pauline Reohr, M.D., Maisie Tam, M.D., Laurie Tolman, M.D..
Golfer's vasculitis: an unusual variant of localized vasculitis.
A challenging case and common diagnostic pitfall: SCC or sebaceous carcinoma?
91 year-old female with a flare of rheumatoid arthritis and cutaneous lesions.


text:
1 October 2003 Use of Common Weeds of Rice as Hosts for the Rice Water Weevil (Coleoptera: Curculionidae)
K. V. Tindall, M. J. Stout
K. V. Tindall,1,2 M. J. Stout2
1vtindal@lsu.edu
2Department of Entomology, Louisiana Agricultural Experiment Station, LSU Ag. Center, Baton Rouge, LA 70803
The rice water weevil, Lissorhoptrus oryzophilus Kuschel, is the most important insect pest in rice, Oryza sativa L., in the United States. Prior research indicates that rice water weevils feed primarily on monocotyledonous plants. Many monocot weeds occur in rice fields, but little is known about rice water weevil-weed interactions in rice. Host utilization of the rice water weevil was evaluated on rice, cultivar 'Cocodrie', and seven weeds commonly found in rice fields in preference and life cycle compatibility tests in the greenhouse. Barnyardgrass, Echinochloa crus-galli Beauv., yellow nutsedge, Cyperus esculentus L., broadleaf signalgrass, Brachiaria platyphylla Nash., and fall panicum, Panicum dichotomiflorum Michx., were more preferred for oviposition than rice. More neonate larvae eclosed on barnyardgrass and yellow nutsedge than eclosed on rice. Densities of late instars feeding on roots of yellow nutsedge and broadleaf signalgrass were significantly lower than densities on rice. Barnyardgrass was also more preferred for adult feeding than rice and all other weeds. Rice water weevils were able to complete their life cycle on all plants examined except hemp sesbania, Sesbania exaltata (Rafin.) Cory, the only dicotyledonous plant species tested in the greenhouse. Several new hosts can be added to an existing list of host plants for the rice water weevil. Field surveys confirmed larvae infested roots of all weed species sampled in the greenhouse, as well as several other weed species. Many of the plants infested with larvae were dicotyledonous plants, suggesting that the host range of rice water weevil is much broader than previously reported.
K. V. Tindall and M. J. Stout "Use of Common Weeds of Rice as Hosts for the Rice Water Weevil (Coleoptera: Curculionidae)," Environmental Entomology 32(5), 1227-1233, (1 October 2003). https://doi.org/10.1603/0046-225X-32.5.1227
Received: 2 June 2003; Accepted: 1 June 2003; Published: 1 October 2003
host preference
host range
insect-weed interactions
Lissorhoptrus oryzophilus Kuschel
Oryza sativa L.
K. V. Tindall, M. J. Stout "Use of Common Weeds of Rice as Hosts for the Rice Water Weevil (Coleoptera: Curculionidae)," Environmental Entomology, 32(5), 1227-1233, (1 October 2003)


text:
package edu.stanford.nlp.io;

import java.io.File;

/**
 * Implements a file filter that uses file extensions to filter files.
 *
 * @author cmanning 2000/01/24
 */
public class ExtensionFileFilter extends javax.swing.filechooser.FileFilter implements java.io.FileFilter {

  private String extension;    // = null
  private boolean recursively;

  /**
   * Sets up Extension file filter by specifying an extension
   * to accept (currently only 1) and whether to also display
   * folders for recursive search.
   * The passed extension may be null, in which case the filter
   * will pass all files (passing an empty String does not have the same
   * effect -- this would look for file names ending in a period).
   *
   * @param ext     File extension (not including period) or null for any
   * @param recurse go into folders
   */
  public ExtensionFileFilter(String ext, boolean recurse) {
    if (ext != null) {
      if (ext.startsWith(".")) {
        extension = ext;
      } else {
        extension = '.' + ext;
      }
    }
    recursively = recurse;
  }

  /**
   * Sets up an extension file filter that will recurse into sub directories.
   * @param ext The extension to accept (with or without a leading period).
   */
  public ExtensionFileFilter(String ext) {
    this(ext, true);
  }

  /**
   * Checks whether a file satisfies the selection filter.
   *
   * @param file The file
   * @return true if the file is acceptable
   */
  @Override
  public boolean accept(File file) {
    if (file.isDirectory()) {
      return recursively;
    } else if (extension == null) {
      return true;
    } else {
      return file.getName().endsWith(extension);
    }
  }

  /**
   * Returns a description of what extension is being used (for file choosers).
   * For example, if the suffix is "xml", the description will be
   * "XML Files (*.xml)".
   *
   * @return description of this file filter
   */
  @Override
  public String getDescription() {
    String ucExt = extension.substring(1).toUpperCase();
    return (ucExt + " Files (*" + extension + ')');
  }

}


text:
MainAll NewsMiddle EastAmerican, Russian Militaries Close to Syria Pact
American, Russian Militaries Close to Syria Pact
American defense official says the United States and Russia could soon sign a pact establishing safety protocols in Syria.
Arutz Sheva Staff, 15/10/15 03:42
Coalition airstrike in Iraq
The United States and Russia could soon sign a pact establishing safety protocols in the skies above Syria as both powers carry out separate bombing campaigns, an American defense official said Wednesday, according to AFP.
"We are nearing completion of a memorandum of agreement that would set up procedures to enhance air safety," the official said, speaking on condition of anonymity. "It could be signed and implemented in the coming days."
The official's comments came shortly after Russia and the United States held a third round of video talks to decide rules so pilots don't inadvertently fly into each other.
"Progress was made during the discussions, which were professional and focused narrowly on the implementation of specific safety procedures," Pentagon spokesman Captain Jeff Davis was quoted by AFP as having said.
The international coalition led by the United States has for more than a year been conducting regular drone and plane strikes in Syria and Iraq, targeting the Islamic State (ISIS) group.
But that mission got much more complex on September 30, when Russia launched its own military campaign to support President Bashar Al-Assad.
Moscow claims it is striking ISIS and other "terrorists", but Washington and its allies accuse Russia of targeting moderate rebels.
Further complicating matters, Russia last week fired cruise missiles into Syria from the Caspian Sea.
The missiles typically fly at much lower altitudes than jets, but Pentagon officials were nonetheless distressed that Russian bombs were in the same air space as coalition planes.
So far, there have been no major incidents in the Syrian skies, though Pentagon officials said coalition planes have had to change course to avoid being in the same space as Russian jets.
And on Tuesday, Colonel Steve Warren, the spokesman for the international coalition, told reporters that coalition and Russian planes came close enough to establish visual contact.
"There's always going to be some risk if there are uncoordinated actors in the battle space," he said.
Officials say the so-called "deconfliction" talks come down to some basic operating procedures, including which language Russian and American pilots will use for communication, the choice of radio frequency for distress calls and the altitude at which warplanes will operate.
Tags:Syria, Russia-Syria, Syrian civil war, Syria-Russia


Nazdar SourceOne is pleased to announce its participation in the upcoming ISS Orlando trade show, September 6-8 at the Orange County Convention Center. Located in booth #610, Nazdar SourceOne will be featuring live print demonstrations from the new Epson SureColor F2100 direct-to-garment printer. Also featured will be the Stahl's Hotronix Auto-Open Heat Press and information on the latest textile screen inks from Wilflex, MagnaColours, International Coatings, and ImageStar Silicone.
Nazdar SourceOne is pleased to announce plans to exhibit at the NBM Indianapolis trade show held at the Indiana Convention Center & Lucus Oil Stadium Halls B/C on September 13-15.
Nazdar SourceOne is pleased to announce the addition of the Epson Sure Color F2000 Series Direct-To-Garment printers to its full line of printing equipment solutions.
Nazdar SourceOne is pleased to announce that Jerry Hall has joined the organization as West Regional Sales Manager for the Textile Inks Business Unit. Jerry will be responsible for leading the Sales Team and driving continued growth in the textile markets for the West Region.
Nazdar SourceOne is pleased to announce the all new ImageStar ProTex chemistry and emulsions. The ImageStar ProTex line of screen printing chemistry and emulsion is specifically designed for the challenges of today's garment printing. The ProTex line includes nine different products, each designed to improve the quality of the print and the efficiency of the screen printing process.
Nazdar SourceOne is pleased to announce David Poto and Bill Geers have joined the Sales team as Account Executives for the SourceOne Graphic Business Unit. In these roles, they will be responsible for developing and increasing graphic sales opportunities within their respective regions. David is part of the Graphic Business Unit West Region and will be based out of Chicago, Illinois. Bill is part of the Graphic Business Unit East Region and will be based out of Cincinnati, Ohio.
Nazdar SourceOne is pleased to announce the addition of Sam Evans and Jordyn Ruhnke to the Nazdar SourceOne Inside Sales team. In the roles as Inside Sales Representative, Jordyn and Sam will be implementing inside sales strategies and campaigns designed to increase the sale of products and services to new and existing customers within the graphic market segment.
Nazdar SourceOne is pleased to announce that both the Nazdar SourceOne Customer Service Team as well as Greg Markus have been awarded a 2015 REGGIE award, presented by the 2 Regular Guys Podcast.
Nazdar SourceOne is pleased to announce that Ashley Barnes has joined the Nazdar SourceOne Inside Sales team as Inside Sales Representative. In her role, Ashley will be implementing Inside Sales strategies and campaigns designed to increase the sale of products and services to new and existing customers within the Graphic Arts Industry.


text:
Mode analysis in optics through fractional transforms
T. Alieva, M.J. Bastiaans
Meet- en Besturingssystemen
Signal transforms and filter banks
The relationship between the mode content and the fractional Fourier and fractional Hankel transforms of a function is established. It is shown that the Laguerre-Gauss spectrum of a rotationally symmetric wavefront can be determined from its fractional Hankel transforms taken at the optical axis.
https://doi.org/10.1364/OL.24.001206
10.1364/OL.24.001206
Fingerprint Dive into the research topics of 'Mode analysis in optics through fractional transforms'. Together they form a unique fingerprint.
Alieva, T., & Bastiaans, M. J. (1999). Mode analysis in optics through fractional transforms. Optics Letters, 24(17), 1206-1208. https://doi.org/10.1364/OL.24.001206
Alieva, T. ; Bastiaans, M.J. / Mode analysis in optics through fractional transforms. In: Optics Letters. 1999 ; Vol. 24, No. 17. pp. 1206-1208.
@article{0ff25b28d86d4b579581d6504217b81a,
title = "Mode analysis in optics through fractional transforms",
abstract = "The relationship between the mode content and the fractional Fourier and fractional Hankel transforms of a function is established. It is shown that the Laguerre-Gauss spectrum of a rotationally symmetric wavefront can be determined from its fractional Hankel transforms taken at the optical axis.",
author = "T. Alieva and M.J. Bastiaans",
doi = "10.1364/OL.24.001206",
journal = "Optics Letters",
publisher = "Optical Society of America (OSA)",
Alieva, T & Bastiaans, MJ 1999, 'Mode analysis in optics through fractional transforms', Optics Letters, vol. 24, no. 17, pp. 1206-1208. https://doi.org/10.1364/OL.24.001206
Mode analysis in optics through fractional transforms. / Alieva, T.; Bastiaans, M.J.
In: Optics Letters, Vol. 24, No. 17, 1999, p. 1206-1208.
T1 - Mode analysis in optics through fractional transforms
AU - Alieva, T.
AU - Bastiaans, M.J.
N2 - The relationship between the mode content and the fractional Fourier and fractional Hankel transforms of a function is established. It is shown that the Laguerre-Gauss spectrum of a rotationally symmetric wavefront can be determined from its fractional Hankel transforms taken at the optical axis.
AB - The relationship between the mode content and the fractional Fourier and fractional Hankel transforms of a function is established. It is shown that the Laguerre-Gauss spectrum of a rotationally symmetric wavefront can be determined from its fractional Hankel transforms taken at the optical axis.
U2 - 10.1364/OL.24.001206
DO - 10.1364/OL.24.001206
JO - Optics Letters
JF - Optics Letters
Alieva T, Bastiaans MJ. Mode analysis in optics through fractional transforms. Optics Letters. 1999;24(17):1206-1208. https://doi.org/10.1364/OL.24.001206


text:
<?php
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if(strlen($core_id)<=0) exit("Must specify core_id parameter");
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if(strlen($tz)<=0) exit("Must specify tz parameter");

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  exit("Must specify valid php tz");
}
$conn=mysqli_connect("", "", "", $db_name);

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if (mysqli_connect_errno()) {
  echo 'Failed to connect to MySQL: ' . mysqli_connect_error();
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  $sql = "INSERT into cores (core_name, name, tz) VALUES ('$core_id', '$name', '$tz')";
  if(!mysqli_query($conn,$sql)) {
	  exit('Error: '.mysqli_error($conn));
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  echo 'Welcome ' . $name . ' ('.$core_id . ') !';

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?>


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The next day, we braved the wait at the North Street Grille for brunch. Order the strawberry pancakes for sure!
Over Longfellow bridge we went to explore Cambridge.
Obligatory grad school nerd visits to MIT and Harvard. The Harvard campus is so beautiful. Expansive lawns, classic red brick buildings, and the imposing library with its grand columns.
We happened upon a festival where the lawn was littered with bubble blowers and hula hoops.
We searched for the Cambridge Brewing Co. After hours of walking, a cold beer sounded pretty nice. Who knew that 1 Kendall Square was the name of a building whose actual address is still a mystery since it is nowhere near Kendall Square or Kendall St. We are thankful for the free wi-fi that allowed us to reach our goal after all.
We were lucky to get a reservation that evening at the Island Creek Oyster bar. This place was clearly happening. Nothing like a cold martini and a plate of oysters with some fresh lemon juice. Alex sang the praises of the lobster roll with its perfectly buttered bun and succulent lobster meat. Also, chowdah.
The next day we followed the Freedom Trail south to Boston Common.
Of course we stopped in the iconic Parker House for the original Boston Cream Pie. I had no idea it's not really a pie at all. Will definitely try to recreate this recipe for you!
Our last dinner was the Chef's Whim at Craigie on Main. For four courses we put ourselves into the chef's power, and he took good care of our stomachs.
Alex declared his birthday trip a success as we settled into our first home-cooked meal in a couple of days. A quiet evening after a whirlwind weekend.


text:
\section{Introduction}
\label{sec:introduction-motivation}
Runways are one of the most valuable resources at an airport and one of the most important factors affecting airport capacity. (Arrival) Runway Occupancy Time (AROT) can be served as an indicator of runway efficiency \cite{spencer2019predictive}\cite{leighfisher2012evaluating}\cite{nikoleris2016effect}\cite{hu2019runway}. Minimizing the runway occupancy time has an operational impact, as stated in \cite{tether2003horndal} \cite{caa1993} as ``saving an average of 5 seconds on every aircraft's runway occupancy would add another 1 - 1.5 movements per hour [at London Heathrow]". A model which can predict the ROT accurately is, therefore, highly desirable.

The availability of surveillance radar data has enabled ROT prediction and analysis research in recent years. Several studies have focused on making the model more general \cite{spencer2019predictive}, in order to be able to be applied to many different airports or to be a component in simulation software. Other works have attempted to make the real-time prediction \cite{friso2018predicting} \cite{dai2020runway} at different distances from the runway threshold. General prediction models which use data from various sources often have an advantage in terms of generalizability. However, the availability of the data sources must be satisfied.

On the other hand, real-time prediction models have often investigated the case of a specific airport. Consequently, the prediction at the airport can be very accurate. However, it may not be possible to apply to other airports due to the differences in data for each airport. In some instances, ROT data are not even available enough for retraining, especially at small and medium airports where traffic is relatively low.

In this research we present a way to have ROT prediction models train on some airports with limited data (3 days, around 1500 samples), and then be able to make predictions on other (target) airports by substituting categorical variables with their numerical equivalences without obtaining a large quantity of data from those target airports. This research can be summarized in Fig. \ref{fig:normal-learning-process} and Fig. \ref{fig:generalized-learning-process}. Fig. \ref{fig:normal-learning-process} depicts a conventional way of training two machine learning models on two different datasets from two airports. However, by substituting the categorical features with the numerical equivalences (will be described later in Section \ref{sec:feature-extraction/engineering}), as shown in Fig. \ref{fig:generalized-learning-process}, we create a generalized dataset that can serve as an input of machine learning algorithms to produce models which can predict AROT in both airport A and airport B. The benefit of the process is significant when airport B is a low traffic airport from which we cannot obtain a great deal of data.

\begin{figure*}
    \centering
    \begin{minipage}{0.495\textwidth}
        \centering
        \includegraphics[width=\textwidth]{figure/normal-learning-process.pdf}
        \caption{A normal way to make prediction model with separated data from two airports}
        \label{fig:normal-learning-process}
    \end{minipage}\hfill%
    \begin{minipage}{0.495\textwidth}
        \centering
        \includegraphics[width=\textwidth]{figure/generalized-learning-process.pdf}
        \caption{A way to make a generalized prediction model for two airport by transforming the dataset with feature equivalences}
        \label{fig:generalized-learning-process}
    \end{minipage}
\end{figure*}

The rest of this paper is organized as follows: we review some related works in Section \ref{sec:related-works}. In Section \ref{sec:data} and \ref{sec:feature-extraction/engineering} we give an overview of the data and the feature selection and engineering process. Section \ref{sec:pred-model} gives the details and the benefit of the prediction model. Section \ref{sec:experiments} details the way we conduct our experiments, which leads to the results in Section \ref{sec:results}. We gave our final conclusions in Section \ref{sec:conclusion}.

\section{Related Works}
\label{sec:related-works}
Runway occupancy time (ROT) has long been an important part in research about runway operation and runway efficiency. One of the early investigations on ROT was the work of Koenig \cite{koenig1978analysis}. The paper used data collected from six airports in the US between 1972 to 1973 to identify the patterns that contribute to high ROT, which in turn causes delay and reduces airport capacity. The author identified the possible influential factors of ROT, such as company procedure, gate location, pilot's knowledge of a specific runway and the design of the runways and runway exits. Some factors are easier to identify than others. Nevertheless, the author, through analysis, concluded that the most important factor is the position of the runway exit taken relative to the terminal gate.

Following the research of Keonig, Weiss and Barrer \cite{weiss1984analysis} conducted an analysis of ROT and the relation of longitudinal separation using data collected at three airports: LaGuardia airport, Newark airport and Boston Logan International airport. The data were collected through visual means at the airport tower and, in some cases, nearby locations where all runways and exits were visible. Although there were some difficulties in data collection, since the threshold crossing time and time clear of the runway had to be estimated visually, nearly 600 data points were collected at each airport. After analyzing the data, the author concluded that the condition of the runway (wet/dry) did not have a significant effect on the difference of average ROTs.

As the data became more abundant and more accessible, thanks to technologies such as Automatic Dependent Surveillance—Broadcast (ADS-B) and Advanced Surface Movement Guidance \& Control System (A-SMGCS), runway occupancy time research using data-driven methods has become increasingly active in recent years. One of the demonstrations of this technique, using surveillance data in measuring ROT, was the work of Kumar et al.\cite{kumar2009runway}. In this study, the authors presented an algorithm to measure the ROT using three days' radar track data extracted from Airport Surface Detection Equipment, Model X (ASDE-X) system. The subsequent analysis on the extract ROTs showed that small aircraft have ROTs as high as large aircraft. Furthermore, identical runways, which have the same measurement in length and width, can exhibit significantly different ROTs.

Kolos-Lakatos\cite{kolos2013influence} also used data from the ASDE-X system to assess the influence of two major elements on runway capacity, that is ROT and wake vortex separation. The data were collected at Boston, Philadelphia, New York La Guardia and Newark airports. The authors found that the size of aircraft is not a strong factor affecting ROT, as small aircraft occupy the runway as long as the large aircraft. The same applies for runway occupancy times in Visual Meteorological Conditions (VMC) and Instrument Meteorological Conditions (IMC). However, runways equipped with high-speed exits showed a reduction in ROT compared to runway equipped with standard 90-degree exits.

Recently, machine learning techniques have been applied in predicting ROT and some studies have shown promising results. Martinez et al.\cite{martinez2018boosted} applied Gradient Boosting Tree Framework to predict, at various distances from runway threshold, the runway exit a flight will take and predicted the arrival ROTs at 2 nautical miles (NM) from runway threshold. The authors managed to get an Area Under the Curve (AUC) of 0.784 in predicting runway exits and a mean absolute error (MAE) of 8 seconds compared to a standard deviation of 14.4 seconds in ROT predictions.

In another work Friso et al.\cite{friso2018predicting} did not make a generic ROT prediction model. Instead, the authors focused on the detection of abnormal AROTs and used a Decision Tree to build the ``what-if" statements. And, from these results, the authors identified 17 abnormal AROT categories and their related precursors. Moreover, the authors also built a real time model to predict abnormal AROTs.

Spencer and Trani\cite{spencer2019predictive} have taken a different approach. Instead of making a model specific to one airport, the authors used statistical modeling and data from 27 airports with over 800,000 data points to model departure and arrival ROT. One of the advantages of the authors' model is the generalizability due to the choice of a small set of general features. However, the lack of real-time aircraft features and the assumption of runway exit information may inhibit the capability of applying this model in a real-time prediction.

Meijers\cite{meijers2020data} recently presented a comprehensive work on the analysis and prediction of ROT using data-driven methods. The work detailed the way to collect ROT data, identify factors affecting ROT and presented a two-step model to predict ROT using the data from 36 airports. The two-step model utilized variants of Neural Network to predict the ROT distribution of landing flight. Hence, there is a lack of interpretability and accumulation of errors in this approach. The author also presented a case study at Keflavík airport where the predictive models suggested locations of new exits which had the potential of reducing the average ROT by up to 23 seconds during peak-hour.

A recent study of Dai and Hansen\cite{dai2020runway} presented a real-time prediction of Runway Occupancy Buffers (ROBs), a new concept proposed by the authors to measure the difference in time when the leading aircraft exits the runway and the trailing aircraft crosses the runway threshold. The authors used several machine learning techniques to predict the ROBs at different distances from the runway thresholds. The best model achieved an R-squared of up to 90\%. A limitation, and an extension of this work, stated by the authors, is applying the model at different airports.

This study is an attempt to combine the benefits of a real-time prediction model and a generalized one, which is also currently a gap in ROT studies. We present a real-time prediction model that can give ATCO arrival runway occupancy time information 75 to 120 seconds before the aircraft crosses the runway threshold. Moreover, the prediction model can also be applied to other airports, which is our focus on this study by applying our pre-processing techniques of substituting categorical features by numerical equivalences.

\section{Data}
\label{sec:data}
The prediction framework makes use of three data categories:
\subsection{Flight data}
The raw flight data are collected using Saab Sensis' Aerobahn system from three airports:
\begin{itemize}
    \item Ronald Reagan Washington National Airport (ICAO: KDCA, IATA: DCA), which has 1232 samples.
    \item Miami International Airport (ICAO: KMIA, IATA: MIA), which has 1628 samples.
    \item Phoenix Sky Harbor International Airport (ICAO: KPHX, IATA: PHX), which has 1722 samples
\end{itemize}
All sets of data range from 16.05.2019 to 18.05.2019 inclusive, that is, three days. The raw data come from two different sources:
\begin{itemize}
    \item The Region Occupancy Reports, which record how long an airplane occupies the regions surrounding and at the airport such as: terminal maneuvering area (TMA), runway, taxiway, ramp and gate. The Region Occupancy Reports also include the information related to a flight, such as origin, destination, airline, aircraft type, gate assigned and runway assigned.
    \item The Surveillance Track Data, which records the position, speed, flight level and heading of an aircraft at a specific point in time.
\end{itemize}
We summarize the data components from these two sources in two tables, Table \ref{tab:region-occupancy-data} and Table \ref{tab:surveillance-track-data}, below.
\begin{table}[htbp]
\caption{Descriptions of data from Region Occupancy Reports}
\centering
\begin{tabularx}{\columnwidth}{|p{2cm}|X|l|}
\hline
\textbf{Name}            & \textbf{Description}                                                    & \textbf{Data type} \\ \hline
Call sign                & The call sign of the flight                                             & Categorical        \\ \hline
Airline                  & The airline that operate the flight                                     & Categorical        \\ \hline
Aircraft type            & Type of the aircraft                                                    & Categorical        \\ \hline
Maximum landing weight   & The maximum landing weight of the aircraft                              & Numerical          \\ \hline
Origination airport code & The origin airport of the flight                                        & Categorical        \\ \hline
Destination airport code & The destination airport of the flight                                   & Categorical        \\ \hline
Flight original date     & The date when the flight depart                                         & Datetime           \\ \hline
Gate assigned            & Arrival gate assigned to the flight                                     & Categorical        \\ \hline
Runway assigned          & Runway the flight used to land                                          & Categorical        \\ \hline
Region occupied name     & The name of the region at the airport defined by the information system & Categorical        \\ \hline
Region type              & The type of the region. It can be: TMA, Runway, Taxiway, Ramp, Gate     & Categorical        \\ \hline
Time entered             & Timestamp when the aircraft enters the region                           & Datetime           \\ \hline
Time exited              & Timestamp when the aircraft completely vacates the region               & Datetime           \\ \hline
Occupancy time           & The time the aircraft occupies the region                               & Numerical          \\ \hline
\end{tabularx}
\label{tab:region-occupancy-data}
\end{table}

\begin{table}[htbp]
\caption{Descriptions of Surveillance Track Data}
\centering
\begin{tabularx}{\columnwidth}{|p{2cm}|X|l|}
\hline
\textbf{Name}            & \textbf{Description}                                  & \textbf{Data type} \\ \hline
Timestamp                & Time when the data is recorded                        & Datetime           \\ \hline
Call sign                & Call sign of the flight                               & Categorical        \\ \hline
Track position           & Position of the flight in X/Y/Z Cartesian coordinates & Numerical          \\ \hline
Origination airport code & The origin airport of the flight                      & Categorical        \\ \hline
Destination airport code & The destination airport of the flight                 & Categorical        \\ \hline
Measured flight level    & Flight level of the aircraft                          & Numerical          \\ \hline
Track heading            & The heading of the aircraft                           & Numerical          \\ \hline
Speed                    & The true air speed of the aircraft                    & Numerical          \\ \hline
\end{tabularx}
\label{tab:surveillance-track-data}
\end{table}

We combine data from these two sources by matching call sign, origination airport code, destination airport code and flight original date, to gather all the information of a given flight for feature extraction and engineering. We also extract the recorded arrival runway occupancy time through Region Occupancy Reports to serve as a target for the prediction model. We will present these in detail in  Section \ref{sec:feature-extraction/engineering}.

\subsection{Weather data}
We collect the weather data from Surface Weather Observation Station (ASOS/AWOS) at the three airports mentioned above during the same three days via \href{https://mesonet.agron.iastate.edu/request/download.phtml}{\color{blue}Iowa Environmental Measonet (IEM)}. The weather data fields are shown in Table \ref{tab:weather-data}.
\begin{table}[htbp]
\caption{Descriptions of weather data}
\centering
\begin{tabularx}{\columnwidth}{|p{2cm}|X|l|}
\hline
\textbf{Name}     & \textbf{Description}                    & \textbf{Data type} \\ \hline
Timestamp         & Time when the weather data are recorded & Datetime           \\ \hline
Temperature       & Air temperature in Fahrenheit           & Numerical          \\ \hline
Visibility        & Visibility in miles                     & Numerical          \\ \hline
Wind direction    & Wind direction in degrees from north    & Numerical          \\ \hline
Wind speed        & Wind speed in knots                     & Numerical          \\ \hline
Pressure altimeter & Pressure altimeter in inches           & Numerical          \\ \hline
\end{tabularx}
\label{tab:weather-data}
\end{table}

\subsection{Supplemental Data}
Furthermore, we also collected additional data as described in Table \ref{tab:supplemental-data} about runways at each airport, using information from the website  \href{https://airnav.com/}{\color{blue}AirNav.com}. The data are measurements of runways such as runway length, runway width, runway altitude and runway true heading. We collected this data as a substitution for the runway assigned feature, which we will explain in detail in Section \ref{sec:feature-extraction/engineering}.
\begin{table}[htbp]
\caption{Descriptions of Supplemental Data}
\centering
\begin{tabularx}{\columnwidth}{|p{2cm}|X|l|}
\hline
\textbf{Name}       & \textbf{Description}                  & \textbf{Data type} \\ \hline
Runway length       & Length of the runway                  & Numerical          \\ \hline
Runway width        & Width of the runway                   & Numerical          \\ \hline
Runway altitude     & Altitude of the runway                & Numerical          \\ \hline
Runway true heading & Heading of the runway from true north & Numerical          \\ \hline
\end{tabularx}
\label{tab:supplemental-data}
\end{table}

\section{Feature Extraction and Engineering}
\label{sec:feature-extraction/engineering}
Using the data sources above, and getting inspiration from some of the past works \cite{spencer2019predictive}\cite{friso2018predicting}\cite{dai2020runway}\cite{martinez2018boosted}, especially the data-driven analysis of Meijers and Hansman\cite{meijers2019data}, we select some of the fields to be the features we use to train our machine learning models. We did not collect and include data about runway exit locations and types, as we are making a real-time AROT prediction model around the final approach fix where the runway exit locations and types information would not be yet known.

The features of the model to predict AROT can be divided into five main groups:
\begin{itemize}
    \item \textbf{Airport features} which is the information of the runway a given aircraft used to land, such as runway (name) assigned, runway length, runway width, runway altitude, runway true heading and the gate assigned to the flight. The runway length, runway width, runway altitude and runway true heading are the numerical equivalences of the runway assigned feature. We also extract the distance from the last trajectory point, when the aircraft fully stops, to the runway it used to land. We use this numerical feature as a substitute for the assigned gate feature, which is categorical.
    \item \textbf{Aircraft features} which are the aircraft's type and maximum landing weight. The maximum landing weight is the numerical equivalence of the aircraft type categorical feature.
    \item \textbf{Aircraft information at/near final approach fix features} which are the distance to the runway threshold, the flight level and the true heading of the aircraft at the time of final approach fix. This information is extracted based on the Surveillance Track Data described above.
    \item \textbf{Weather information features} which are temperature, visibility, wind direction, wind speed and pressure altimeter.
    \item \textbf{Short term runway usage statistics features} which are number of aircraft landing in the last 30 minutes of a specific runway and the last 30 minutes' average AROT.
\end{itemize}

The features of the model are shown in Table \ref{tab:features} below.
\begin{table}[htbp]
\caption{Input Features to Machine Learning Model}
\begin{tabularx}{\columnwidth}{|p{2cm}|X|l|}
\hline
\textbf{Name}                                 & \textbf{Description}                                                                           & \textbf{Data type} \\ \hline
Runway assigned                               & Runway the flight used to land                                                                 & Categorical        \\ \hline
Runway length                                 & Length of the runway                                                                           & Numerical          \\ \hline
Runway width                                  & Width of the runway                                                                            & Numerical          \\ \hline
Runway altitude                               & Altitude of the runway                                                                         & Numerical          \\ \hline
Runway true heading                           & Heading of the runway from true north                                                          & Numerical          \\ \hline
Gate assigned                                 & Arrival gate assigned to the flight                                                            & Categorical        \\ \hline
Distance from last point trajectory to runway & The distance from the last point of the trajectory when the aircraft fully stops to the runway & Numerical          \\ \hline
Aircraft type                                 & Type of the aircraft                                                                           & Categorical        \\ \hline
Maximum landing weight                        & The maximum landing weight of the aircraft                                                     & Numerical          \\ \hline
Distance to the runway threshold              & Distance to of the aircraft to the runway threshold at/near final approach fix                 & Numerical          \\ \hline
Flight level                                  & Flight level of aircraft at/near final approach fix                                            & Numerical          \\ \hline
True heading                                  & True heading of aircraft at/near final approach fix                                            & Numerical          \\ \hline
Temperature                                   & Air temperature in Fahrenheit                                                                  & Numerical          \\ \hline
Visibility                                    & Visibility in miles                                                                            & Numerical          \\ \hline
Wind direction                                & Wind direction in degrees from north                                                           & Numerical          \\ \hline
Wind speed                                    & Wind speed in knots                                                                            & Numerical          \\ \hline
Pressure altimeter                            & Pressure altimeter in inches                                                                   & Numerical          \\ \hline
Number of flight landed last 30 minutes       & Number of flight landed on the runway in the last 30 minutes                                   & Numerical          \\ \hline
Average AROT last 30 minutes                  & Average AROT of flight landed on the runway in the last 30 minutes                             & Numerical          \\ \hline
\end{tabularx}
\label{tab:features}
\end{table}

The target of prediction is the Arrival Runway Occupancy Time (AROT) at each airport. The histograms of the AROTs at those airports are presented below.
\begin{figure}[htbp]
\centerline{\includegraphics[width=0.9\columnwidth]{figure/arot_histograms.pdf}}
\caption{Histogram of Arrival Runway Occupancy Time at three airports: DCA, MIA, PHX}
\label{fig:arot-hist}
\end{figure}

As we mentioned in the Section \ref{sec:introduction-motivation}, one of the advantages of our method is the AROT prediction model can be applied, that is make AROT predictions, for other airports without having to retrain the model with the data at those airports. This generalizability of the prediction model is particularly useful in the case where the AROT data is limited or not available, for example, in small and medium airports where traffic is low. We enable the generalizability by substituting the categorical features of the training data by their numerical equivalences.

Although categorical features are easier for a human to understand, extra processing steps, such as ordinal-encoding, one-hot-encoding, or hashing, are needed to make the data trainable in almost all machine learning algorithms. These extra steps can be eliminated by substituting categorical features by their numerical equivalences. Moreover, methods such as one-hot-encoding can make the post-processed features space increase up to the sum of values in those categorical features. This makes the model more prone to the curse of dimensionality, which is undesirable. We present these categorical features and their numerical equivalences in Table \ref{tab:feature-equivalences} as follows.
\begin{table}[htbp]
\caption{Categorical Features and Numerical Equivalences}
\begin{tabularx}{\columnwidth}{|p{2.5cm}|X|}
\hline
\textbf{Categorical features}    & \textbf{Numerical equivalences}               \\ \hline
Aircraft type                    & Maximum landing weight                        \\ \hline
\multirow{4}{*}{Runway assigned} & Runway length                                 \\ \cline{2-2}
                                 & Runway width                                  \\ \cline{2-2}
                                 & Runway altitude                               \\ \cline{2-2}
                                 & Runway true heading                           \\ \hline
Gate assigned                    & Distance from last point trajectory to runway \\ \hline
\end{tabularx}
\label{tab:feature-equivalences}
\end{table}

In this work we have three different datasets: categorical, numerical and mixed dataset. A categorical dataset is a dataset that has only categorical features, but not their numerical equivalences. A numerical dataset is a dataset has the numerical equivalent features, but not their categorical counterpart. And a mixed dataset is a dataset that has all the features mentioned above, that is, including both the categorical features and their numerical equivalences. From this point on we call a prediction model trained on the categorical dataset a categorical model. Likewise, we a numerical model and a mixed model are trained on a numerical dataset and a mixed dataset respectively. The details of the AROT prediction model, and its benefits, are presented in Section \ref{sec:pred-model}.

\section{AROT Prediction Model}
\label{sec:pred-model}
We train the prediction model utilizing the five groups features shown in Table \ref{tab:features}. As defined in the previous section, we have three types of model: categorical model, numerical model and mixed model. We used a set of features to train a corresponding model, e.g. categorical features to train categorical models using three learning algorithms: Decision Tree Regressor, Random Forest Regressor, and Gradient Boosting Regressor with implementation from Scikit-learn \cite{scikit-learn}. We also employed grid-search (details in Table \ref{tab:hyper-param-ranges}) with the implementation from Scikit-learn for hyper-parameters tuning for each learning algorithm. We present an experiment to compare the performance of these models and show the results in Sections \ref{sec:experiments} and \ref{sec:results}.

One of the main benefits of our model is to be able to make a real-time AROT prediction at, or near, the final approach fix of a runway at an airport. Table \ref{tab:avg-pred-point-measurement} shows the average distance and time for the aircraft to reach the runway threshold at each runway at each airport when the prediction happens. The information is extracted from our dataset. The column ``Average Seconds to Threshold" is the average amount of time in seconds prior to runway threshold crossing that the model can provide an AROT prediction to the ATCO. In other words, using this model, the ATCO can be aware of the AROT 75 to 120 seconds before the aircraft crosses the runway threshold. This can be a valuable time for the ATCO to make a crucial decision if needed. Future work will try to further extend the prediction point beyond the final approach fix.
\begin{table}[htbp]
\caption{Prediction point Average Distance and Time to Threshold}
\label{tab:avg-pred-point-measurement}
\begin{tabularx}{\columnwidth}{|Y|Y|Y|Y|Y|}
\hline
\textbf{Airport} & \textbf{Runway Name} & \textbf{Average Distance from Threshold (NM)} & \textbf{Average Speed (knot)} & \textbf{Average Seconds to Threshold (second)} \\ \hline
\multirow{3}{*}{DCA} & 1   & 4.82 & 166.38 & 105.05 \\ \cline{2-5}
                     & 19  & 6.18 & 179.82 & 124.62 \\ \cline{2-5}
                     & 33  & 3.42 & 163.96 & 75.63  \\ \hline
\multirow{4}{*}{MIA} & 8L  & 4.45 & 157.96 & 102.29 \\ \cline{2-5}
                     & 8R  & 4.45 & 151.81 & 106.61 \\ \cline{2-5}
                     & 9   & 4.29 & 162.61 & 95.79  \\ \cline{2-5}
                     & 12  & 5.97 & 177.9  & 121.96 \\ \hline
\multirow{5}{*}{PHX} & 7R  & 5.75 & 186.15 & 112.08 \\ \cline{2-5}
                     & 8   & 5.51 & 182.63 & 109.2  \\ \cline{2-5}
                     & 25L & 5.63 & 173.17 & 118.05 \\ \cline{2-5}
                     & 25R & 5.56 & 170.78 & 118.37 \\ \cline{2-5}
                     & 26  & 5.6  & 173.01 & 117.47 \\ \hline
\end{tabularx}
\end{table}

In Section \ref{sec:experiments}, we detail the experiments where we compare the performance of the  categorical, numerical and mixed models, and their generalized learning results. We also specify the experiment where we show the generalizability and the benefit of the numerical models.

\section{Experiments}
\label{sec:experiments}
\subsection{Arrival Runway Occupancy Time (AROT) Learning Experiment}
In this experiment we trained three machine learning algorithms mentioned above for three types of dataset (numerical, categorical and mixed) in three different airports (DCA, MIA and PHX) we mentioned in the previous sections.

We employ the cross-validation technique to assess the capability of prediction of unseen data of these models. As the training folds are changed during cross-validation, the hyper-parameters of those learning algorithms must be changed accordingly. In order to obtain the best hyper-parameters for the learning algorithm, we utilize the cross-validation technique again on those training folds. The hyper-parameter set that has the lowest error according to the cross-validation results is used to refit the data in training folds then make predictions on the testing fold.  Table \ref{tab:hyper-param-ranges} shows the hyper-parameters search ranges of the learning algorithms.
\begin{table}[htbp]
\centering
\caption{The search range of learning algorithm's hyper-parameters}
\begin{tabularx}{\columnwidth}{|X|Y|Y|Y|}
\hline
\textbf{Hyper-parameters} & \textbf{Range in Decision Tree Regressor} & \textbf{Range in Random Forest Regressor} & \textbf{Range in Gradient Boosting Regressor} \\ \hline
splitter           & [best, \textbf{random}]                 & N/A                                  & N/A                                         \\ \hline
max\_depth         & [\textbf{3}, \textbf{10}, 30, 100, 300] & [3, \textbf{10}, 30, 100]            & [\textbf{3}, \textbf{10}, \textbf{30}, 100] \\ \hline
min\_sample\_leaf  & [1, 5, \textbf{10}, \textbf{30}]        & [1, \textbf{5}, \textbf{10}, 30]     & [1, \textbf{5}, \textbf{10}, \textbf{30}]   \\ \hline
max\_features      & [sqrt, 10, \textbf{auto}]               & [\textbf{sqrt}, 10, auto]            & [\textbf{sqrt}, \textbf{10}, auto]          \\ \hline
n\_estimators      & N/A                                     & [10, 30, \textbf{100}, \textbf{300}] & [30, 100, \textbf{300}, 900]                \\ \hline
max\_samples       & N/A                                     & [0.1, 0.3, \textbf{None}]            & N/A                                         \\ \hline
learning\_rate     & N/A                                     & N/A                                  & [0.001, 0.003, \textbf{0.01}, 0.03, 0.1]    \\ \hline
subsample          & N/A                                     & N/A                                  & [0.1, \textbf{0.3}, \textbf{1.0}]           \\ \hline
\end{tabularx}
\label{tab:hyper-param-ranges}
\end{table}

We highlighted the best settings of each algorithm in the table above. The best settings showed an inclination towards a generalized model in each algorithm. For example, a shallow tree which has leaves contain a large number of nodes has more generalization capacity than a deep tree which has leaves contains a small number of nodes. Because shallow tree, which has large a number of samples in leaves node, will avoid going into particular cases when making predictions. Our learning algorithm best model setting prefer the shallow tree (small value of \textit{max\_depth} resulted in best models more often than large value). The same conclusion can be drawn when looking at setting \textit{min\_sample\_leaf} when larger values are preferred.

\subsection{Generalized Learning Experiment}
In order to evaluate the generalizability of the prediction model trained on the numerical dataset, we design an experiment where we train the prediction model using the three machine learning algorithms mentioned above, on the source airport(s) numerical data together with a fraction ($\alpha = 0.1 \dots 0.9$) of the target airport data to make AROT predictions on target airport. This model is called the Generalized Model. We also train a prediction model only with a fraction ($\alpha = 0.1 \dots 0.9$) of target airport data with no source airport data to compare with the generalized learning model. This model is called the Normal Model. The experiment is illustrated in Fig. \ref{fig:generalized-learning-experiment}. This figure is the detailed version of Fig.  \ref{fig:normal-learning-process} and Fig. \ref{fig:generalized-learning-process}, when we realize the idea presented in those two previous figures into a measurable experiment.
\begin{figure}[htbp]
\centerline{\includegraphics[width=0.935\columnwidth]{figure/generalized-learning-experiment-v1-4.pdf}}
\caption{Overview of Generalized Learning Experiment}
\label{fig:generalized-learning-experiment}
\end{figure}

We have two versions of this generalized learning experiment. In the first version, the Generalized Model uses the data from a single source airport along with a fraction of the data from the target airport and obtains the prediction errors on the remainder of the target data.  In the second version, the Generalized Model uses the data from two source airports along with a fraction of data from the target airport and obtain the prediction errors on the remainder of the target data. We present the results of the two learning experiments in the next Section \ref{sec:results}.

\section{Results}
\label{sec:results}
\subsection{Unseen Data Prediction Results}
As shown in Fig. \ref{fig:unseen-data-performances}, we can see the numerical models are better than the categorical and mixed models in six out of nine cases (data from DCA and MIA) in terms of median of the predictions. However, among those six cases, the variances of the predictions of the numerical models are better than the others only for the DCA data.

At Phoenix Sky Harbor International Airport (PHX), the categorical models outperformed the numerical models. Moreover, the mixed models' performance (however, not clearly) exceeds both the numerical and categorical models. On one hand, this may suggest that the categorical and numerical equivalent features alone are not adequate for the prediction models in this particular case. On the other hand, this also may suggest that there is a multicollinearity effect in play in this circumstance, as categorical features and their numerical equivalences are just different representations of the same concepts. This phenomenon will be investigated further in upcoming work.

\begin{figure*}[htbp]
\centerline{\includegraphics[width=0.613\textwidth]{figure/unseen_data_results_violinplot.pdf}}
\caption{Results of three dataset (DCA, MIA, and PHX) are shown in three vertical plots (stacked horizontally). Horizontal axis is Mean Absolute Error in second. Vertical axis is different machine learning algorithms apply on the datasets. Dataset type (numerical, categorical, and mixed) is color coded.}
\label{fig:unseen-data-performances}
\end{figure*}

We present the mean and standard deviation AROT at each airport to compare with the Gradient Boosting Regressor prediction mean absolute errors (MAE) in Table \ref{tab:gbr-pred-error} below. Based on the model mean absolute error, we can see the learned model often reduces the uncertainty of the target airport AROT by 32\%-47\%. The prediction error can possibly be greatly reduced by the use of more data than three days data we have used in this work.

\begin{table}[htbp]
\caption{Gradient Boosting Regressor Prediction Error and AROT Range}
\label{tab:gbr-pred-error}
\begin{tabularx}{\columnwidth}{|Y|Y|Y|Y|}
\hline
\textbf{Airport}     & \textbf{AROT Range (second)}                            & \textbf{Model Type} & \textbf{Model's MAE (second)} \\ \hline
\multirow{3}{*}{DCA} & \multirow{3}{*}{$38.15 \pm 5.46$} & numerical           & $3.69 \pm 0.06$                               \\ \cline{3-4}
                     &                              & categorical & $3.78 \pm 0.24$ \\ \cline{3-4}
                     &                              & mixed       & $3.74 \pm 0.2$  \\ \hline
\multirow{3}{*}{MIA} & \multirow{3}{*}{$52.88 \pm 12.65$} & numerical   & $6.84 \pm 0.35$ \\ \cline{3-4}
                     &                              & categorical & $7.03 \pm 0.25$ \\ \cline{3-4}
                     &                              & mixed       & $6.79 \pm 0.3$  \\ \hline
\multirow{3}{*}{PHX} & \multirow{3}{*}{$46.08 \pm 7.57$}  & numerical   & $4.69 \pm 0.12$ \\ \cline{3-4}
                     &                              & categorical & $4.41 \pm 0.15$ \\ \cline{3-4}
                     &                              & mixed       & $4.37 \pm 0.12$ \\ \hline
\end{tabularx}
\end{table}

\subsection{Generalized Learning Results}
In the first version of the generalized learning experiment, we train the Generalized Model on data from only one source airport along with a fraction of data from the target airport, then use that model to make predictions on the remaining target data. The Random Forest Regressor model performs the best, the generalized learning benefits being evident as the error ranges and medians of the Generalized Model are smaller than the Normal Model, when the fraction of target data is small ($\alpha = 0.1 \dots 0.4$) in 4 out of the 6 cases we consider, as shown in Fig. \ref{fig:generalized-learning-one-source-algo-rf}. When the fraction of target data is large ($\alpha = 0.5 \dots 0.9$), the generalized benefits are not very clear.

In the second version of the generalized learning experiment, we train the Generalized Model on data from two source airports along with a fraction of the data from the target airport, then use that model to make predictions on the remaining target data. Both Random Forest Regressor and Gradient Boosting Regressor show the benefit of generalized learning in 4 out of the 6 considered cases when the fraction of target data is small ($\alpha = 0.1 \dots 0.4$), but the benefit is not evident when the fraction of target data is large ($\alpha = 0.5 \dots 0.9$). However, the benefits, when they are present, are are more obvious than the first version. The results are shown in Fig. \ref{fig:generalized-learning-two-sources}.

The fact that the performance increases with a low percentage of new target airport data gives promising initial results for the model's capability to track non-stationary airport environments. The generalized learning benefit is not observed for Washington DCA airport, and for two other airports when fraction of target data is large ($\alpha = 0.5 \dots 0.9$), in both versions of the generalized learning experiment, that is, using one or two source airports. This is likely due to the small quantity of data currently available and a combination of the different delay characteristics of those airports (see Fig. \ref{fig:arot-hist}). The feature equivalences technique cannot sufficiently address these differences. A rigorous transfer learning paradigm could be employed. This will be investigated in future work.
\begin{figure*}[htbp]
    \centering
    \includegraphics[width=0.613\textwidth]{figure/generalized_learning_RandomForestRegressor_boxplot.pdf}
    \caption{Generalized learning results using Random Forest Regressor. The train data is from source airport and a fraction ($\alpha = 0.1 \dots 0.9$) of target airport data. Testing is performed on the remainder ($1 - \alpha$) of target airport data. Evaluation is done using Mean Absolute Error in second.}
    \label{fig:generalized-learning-one-source-algo-rf}
\end{figure*}

\begin{figure*}[htbp]
    \centering
    \includegraphics[width=0.613\textwidth]{figure/generalized_learning_results_two_airport_boxplot.pdf}
    \caption{Generalized learning results of two different machine learning algorithms (Random Forest Regressor and Gradient Boosting Regressor), using source data from two different airports. The train data is from two source airports and a fraction ($\alpha = 0.1 \dots 0.9$) of target airport data. Testing is performed on the remainder ($1 - \alpha $) of target airport data. Evaluation is done using Mean Absolute Error in second. Vertical view shows the results of the same set of source - target data with two different machine learning algorithms.}
    \label{fig:generalized-learning-two-sources}
\end{figure*}

\section{Conclusion}
\label{sec:conclusion}
We have shown that by substituting categorical features with numerical equivalences, we can have a prediction model which exhibits generalized learning capability without sacrificing any appreciable performance. The prediction model gives the ATCO awareness of the AROT of upcoming flights 75 to 120 seconds before the aircraft crosses the runway threshold. The prediction model also reduces the uncertainty of the target airport AROT by 32\%-47\%.

We also have shown the benefit of generalized learning in the majority of test cases. This result enables a prediction model to be deployed at a target airport where AROTs data may not be available or difficult to obtain.

A current potential limitation of this research is the small quantity of training data points used, which can lead to skewed assessments in some cases. We envisage addressing this in future work when more data is available.

\section*{Acknowledgment}
This project was partially funded by Saab Singapore Pte. Ltd. The authors would also like to express their gratitude to Robert Brown and David Sargrad at Saab Sensis, Syracuse, NY for providing the Saab Sensis' Aerobahn data.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
La gare de Châbons est une gare ferroviaire française de la ligne de Lyon-Perrache à Marseille-Saint-Charles (via Grenoble) située sur le territoire de la commune de Châbons, dans le département de l'Isère, en région Auvergne-Rhône-Alpes.

C'est une halte voyageurs de la Société nationale des chemins de fer français (SNCF), desservie par des trains .

Situation ferroviaire
Établie à  d'altitude, la gare de Châbons est située au point kilométrique (PK) 79,455 de la ligne de Lyon-Perrache à Marseille-Saint-Charles (via Grenoble), entre les gares de Virieu-sur-Bourbre et du Grand-Lemps.

Histoire
Longtemps restée désaffectée, la gare n'a plus de personnel d'accueil destiné à l'information des usagers et la vente de billets. Depuis mai 2022 le bâtiment principal de la gare accueille une clinique vétérinaire en vertu de l'application du programme «  » mis en place par la SNCF pour redonner vie aux petites gares de campagne.

Service des voyageurs

Accueil
Cette gare est une simple halte SNCF, un point d'arrêt non géré (PANG) à entrée libre.

Desserte
Châbons est desservie par les trains  de la relation de Saint-André-le-Gaz à Grenoble (ou Grenoble-Universités-Gières).

Intermodalité
Un parc pour les vélos (consignes individuelles en libre accès) et un parking pour les véhicules y sont aménagés.

Notes et références

Voir aussi

Articles connexes
 Liste de gares en France
 Liste des gares d'Auvergne-Rhône-Alpes
 Ligne de Lyon-Perrache à Marseille-Saint-Charles (via Grenoble)
 Schéma de la ligne de Lyon-Perrache à Marseille-Saint-Charles (via Grenoble)

Liens externes

Gare en Isère
Ligne Lyon-Perrache - Marseille-Saint-Charles (via Grenoble)


I think football would become an even better game if someone could invent a ball that kicks back.
— As quoted in Football FAQ: All That's Left to Know About the Clubs, the Players, and the Rivalries (2015), by Dave Thompson.
I could never stay long enough on the shore; the tang of the untainted, fresh, and free sea air was like a cool, quieting thought.
I wish that I could be with you tonight, You give me butterflies inside.
Listen. I wish I could tell you it gets better. But, it doesn't get better. You get better.
I thought maybe we could make gingerbread houses, and eat cookie dough, and go ice skating, and maybe even hold hands.
With a sense of humor like that, you could make a living as a garbage man anywhere in the country.
...maybe someday we could become friends. Friends who ride majestic, translucent steeds, shooting flaming arrows across the bridge of Hemdale.
We have all sorts of words that could describe us. But we get to choose which ones are most important.
If Angels could be jealous of men, they would be so for one reason: Holy Communion.
I'm thinking of you, Wonder where you are tonight, I wish that I could hold you tight. I'm thinking of you. Wish you could stay, But you're so far away, So far away.
Justice could just be clearing a path for people to dream properly.
— "Behind Kanye's Mask", The New York Times (June 11, 2013), by Jon Caramanica.
I went to the worst of bars hoping to get killed but all I could do was to get drunk again.
The night seemed to fade into blossoming dawn, The sun shone anew but the dance lingered on. could we but recall that sweet moment sublime, We'd find that our love is unaltered by time.
If I had a single flower for every time I think of you, I could walk forever in my garden.
My doctor told me that jogging could add years to my life. He was right—I feel ten years older already.
I could survive for months, years, on a crush.
You can't win, Darth. If you strike me down, I shall become more powerful than you could possibly imagine.
— Played by Alec Guinness in Star Wars: Episode IV - A New Hope (1977).
We could have all been killed – or worse, expelled.
— Harry Potter and the Sorcerer's Stone, Chapter 9, said by Hermione Granger (1997).
Baby, I could rule the world, With a girl like you, in my arms.
Inigo Montoya: I donna suppose you could speed things up?
Man in Black: If you're in such a hurry, you could lower a rope or a tree branch or find something useful to do.
Inigo Montoya: I could do that. I have some rope up here, but I do not think you would accept my help, since I am only waiting around to kill you.
Man in Black: That does put a damper on our relationship.
— Played by Mandy Patinkin and Cary Elwes in The Princess Bride (1987).
That is the real spiritual awakening, when something emerges from within you that is deeper than who you thought you were. So, the person is still there, but one could almost say that something more powerful shines through the person.
Neither action nor style could have accomplished the result alone. Both were needed.
To me, baseball is as honorable as any other business. It is the most honest pastime in the world. It has to be, or it could not last a season out.
The night was white - blind with fog, and Kate staggered over every stone and stumbled in every puddle, but she pushed on as fast as she could.
I taught you to fight and to fly. What more could there be?
— Peter Pan (2003), a movie adaption.
My friend, singer Kaya Jones of the Pussycat Dolls, told me she could finally come out of the closet and wear her Trump 2016 campaign shirt.


Age UK is urging older people to claim benefits cash as new research shows that a third of older people are struggling financially.
Everyday living costs are causing considerable hardship for pensioners with 1 in 3 older people (32%) admitting they are struggling financially, according to new research for Age UK.
With well over half (56%) of those surveyed worried about even the most basic living costs such as buying food and keeping warm, Age UK is stepping up its efforts to encourage older people to claim vital benefits cash.
New findings show around a third of older people (32%) are feeling financially worse off than this time last year, with nearly a quarter (24%) of those surveyed admitting they have cut back on luxuries and a fifth (22%) saying they have bought cheaper or less food.
More worryingly, 1 in 5 (21%) poorer pensioners said they cut back on heating their home this winter, despite the bitter weather and health risks of living in a cold home.
Launching today, Age UK's new Let's Talk Money campaign - backed by Money Saving Expert Martin Lewis - has set an ambitious target to get an extra £144 million into the pockets of older people by this time next year.
With as much as £5.5 billion going unclaimed every year, Age UK is warning that millions of older people need help, but are missing out on key benefits to which they are entitled such as Pension Credit, Housing Benefit, Attendance Allowance and help towards paying council tax.
Many pensioners live on low, fixed incomes and have been hit hard by the rising cost of food and energy over the last year. Yet despite 1.7 million pensioners living in poverty and many thousands more struggling to pay the bills, huge numbers of older people are unaware of the help that's available or are reluctant to make a claim.
Thinking they won't get enough to make it worth claiming and not knowing if they would be entitled were the top reasons given to Age UK for failing to make a claim, with other common barriers including people feeling too proud or embarrassed, or believing the claiming process is too complicated or intrusive.
Despite over 4 million pensioners being entitled to Pension Credit - a top up for people on low incomes - a third of those who are eligible don't claim it. Yet if all those who are entitled to Pension Credit put in a claim, it could boost their income by an average of £1,716 a year - well over the cost of the average energy bill which currently stands at £1,420 a year.
And more than two-fifths of those surveyed by Age UK (43%) said that claiming benefits has made a noticeable difference to their quality of life including helping them to worry less about making ends meet, pay for essential items such as food, and see friends and family.
Martin Lewis, creator of Money Saving Expert, commented: 'It's a tragedy that millions of older people who've paid into the system for much of their lives are living in poverty, or on far less than they need, simply because of a lack of information.
Michelle Mitchell, Age UK's Charity Director, commented: 'At Age UK we have helped thousands of older people put in a successful benefits claim over the last year, but there is much more work to be done. People are constantly telling us how much of a difference the extra money makes, how surprised they are by how straightforward the process is with the help of an adviser, and how much less they worry about everyday bills. But there are still huge numbers missing out and in need of help.
'We want any older person who is worried about money to contact us to find out if they could be entitled to some extra help. They can also call our national Age UK Advice line on 0800 169 65 65.
Don't rule yourself out. Around two thirds of claims are successful and over 2.5 million people across Britain receive it.
Even if you only get a little extra each month, receiving Pension Credit may open the door to you receiving help for other things such as rent, council tax or keeping your house warm.
I've been turned down for Pension Credit before. It's not worth it applying again.
Your circumstances could have changed and there's been a change to how entitlement is worked out which means even more people could now be eligible. The first £10,000 of your savings will now be ignored when working out if you can get Pension Credit.


text:
Home/austria/Ozzy Osbourne 70 "DiePresse.com
Ozzy Osbourne 70 "DiePresse.com
November 27, 2018 austria Comments Off on Ozzy Osbourne 70 "DiePresse.com
He likes to delete: When hitting a bat head, he turned himself on an anti-road, as a line with a line and turned into the # 39; almost his wife. But these times are far away: singer Black Sabbath Ozzy Osbourne has been exposed for six years, he does not smoke more and her; play sports. On Monday the Brit celebrates his 70th birthday.
Civil War Growth – John Michael Osbourne – in a family of eight in the Aston classification area in Birmingham. At that time dyslexia was still unidentified, so it became a class clown. "I just made people laugh and make wonderful things," said Osbourne to The Times. "If you make people like you, you have done it for a half." Decades later, he argued in an interview with the "Mirror" that he was often badly damaged by nature on his way home with two boys. "I was scared to tell my father or my mother and it completely missed me." His wife Sharon and Therapy would have helped him later.
Shepherd, factory, star star
At the age of 15, he left school and worked in a market house and car factory before convicted. When a thief went wrong, he spent six weeks in prison. Fortunately, the Beatles encouraged him: when he heard "She Loves You" on the radio, he decided to be a premier star. Years later, he went with the schoolmate and author Tony Iommi and his friends for the Black Sabbath cultural band.
They were stunt advertising; in the name to attract audiences. Although Osbourne has been inviting Satan's worshipers to become a bigger, but he did not want to do something. Her Hitalben is "Paranoid" (1970), "Master of Reality" (1971) and "Sabbath Bloody Sabbath" (1973) now among classical classes and established a base for heavy metal.
Drugs, marriages, illnesses
But in 1979, his band removed because of his excessiveness: There are no drugs but it was not the case, including heroin. His first wife – who has three children – threw it out and was split in 1982. In the same year Osbourne married the Black Sabbath manager, Don Arden. "Sharon was born to the business, you can not smuggle her time because she just says," Dona, I know what you are doing; go. "She took up her role and has since been the main reason for her life since then.
Her marriage has come through something: Osbourne's public concern for two years ago, Sharon's cancer, several disasters, and the different struggle of ten years of suffering. He listened to the "Rolling Stone" music magazine, "I was not as good as a father as an additional multi-child child for my wife."
At Aimee's birthday party, sixteen years old, he drank many vodka until he tried in Sharon's hardship. He woke up in a prison car and did not remember anything: "But that did not stop me," he recognized the Evening Standard. "It was not until I was down to my neck that I was ill without having finally got together."
Top ten of the best metal charts of all times
As an artist soloist Osbourne created a normal Sunday sound; Clips and Clips submit Close Music Playlister Clips Playlister Playlister Clips and Clips submit Music Playlister Clips Playlister Playlister Clips Playlister Blizzard Ozz Share this page Share this with Digg Facebook Google LinkedIn Reddit StumbleUpon Twitter Read more about these links. He established Ozzfest because other festivals had not been invited and wanted to allow new bands, sold 100 million albums and contributed to MTV with his unlucky family in the The Osbournes & Video Viewers prize award series.
In 2013, Black Sabbath returned to the top of the recordings with a "13" and Osbourne return record before they would be happy for their fans in 2016 and 2017 with "The End Tour". In addition, Osbourne wants to be on his way to be no more than four to six weeks at a distance after Farewell "No More Tours II" travels to have more time for the Highlands, his grandsons. But he does not want to turn off: "It's not a job, it's hard, you know?", He praised the Times: "I work a few hours a day, travel around the plane, I have a good time with the band. "
(APA / ADD)


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The boys of summer are heading south as spring training is scheduled to get underway next week.
Spring training 2019 is less than one week away! With the Super Bowl in the rearview mirror and the NCAA March Madness tournament right around the corner, it is time to start thinking about the boys of summer.
The best part about Spring Training for baseball fans is that every team has a chance to win the World Series. Sure, some teams definitely have better odds of capturing the Fall Classic then others, but during spring training hope is always there.
Each year a team or two surprises us.
It is guaranteed to happen. Last year the long shot Oakland A's and Tampa Bay Rays both exceeded expectations in a huge way.
Both were listed at well over 100/1 odds to win the AL Pennant, yet they were two of the best teams in the league. The A's won 97 games in 2018 while the Rays collected 90 wins in the regular season. Who will be this year's team that doesn't draw an early crowd - yet flirts with a postseason bid this September?
While we don't know the answer, the truth is it can be any one of the 20 listed long shots on the Vegas odds board.
Who is No. 1 heading into spring training? That honor belongs to the Boston Red Sox.
According to a Masslive report, the defending champion Boston Red Sox officially begin their title defense when they report to camp on February 13. This team is stacked and if they remain healthy, they have a great shot at repeating as world champs.
Right behind Boston is their long-time rival the New York Yankees. Coming off a 100 win season, the song remains the same in the Bronx – World Series or bust!
A few major changes occurred over this offseason including the Cardinals signing Paul Goldschmidt, A.J.
Pollock heading to the Dodgers and Adam Ottavino joining the Yankees – and that is just a few.
Below is a look at the 2019 spring training edition of the MLB power rankings. Next to each team is there over/under total win projection for the upcoming season.


text:
Accelerando is a studio album by Indian-American pianist Vijay Iyer recorded in 2011 and released on the ACT label in 2012.

Reception

The album received universal acclaim with Metacritic giving it a score of 88 from 9 reviews. Thom Jurek, in his review for AllMusic states, "Accelerando is a triumph in creativity and expert musicianship, and further underscores Iyer's status as a genuine jazz innovator". Writing for All About Jazz, Troy Collins said "Accelerando is a masterful collection that balances high-minded conceptualism with heartfelt conviction". The Guardian review by John Fordham awarded the album 4 stars noting "The world's full of fine jazz piano trios, but Iyer's is way up the A-list". PopMatters editor John Garratt said "Accelerando is an album filled with highlights... There is a reason Vijay Iyer keeps popping up on year-end best-of lists; it's because he synthesizes such disparate styles, some easily palatable and some requiring more effort, into a striking vision of his own".

Track listing
All compositions by Vijay Iyer except as indicated
 "Bode" - 2:18
 "Optimism" - 7:23
 "The Star of a Story" (Rod Temperton) - 5:46
 "Human Nature" (Steve Porcaro, John Bettis) - 9:39
 "Wildflower" (Herbie Nichols) - 4:10
 "Mmmhmm" (Stephen Bruner, Steve Ellison) - 4:33
 "Little Pocket Size Demons" (Henry Threadgill) 7:14
 "Lude" - 4:54
 "Accelerando" - 2:51
 "Actions Speak" - 5:38
 "The Village of the Virgins" (Duke Ellington) - 5:17

Personnel
 Vijay Iyer — piano
Stephan Crump — bass
Marcus Gilmore — drums

References

2012 albums
Vijay Iyer albums
ACT Music albums
Instrumental albums