text:
Q: Google Lighthouse: is it possible to use computeMedianRun() for warm navigations? In the below code snippet, Lighthouse is used to calculate a representative median of 5 consecutive runs on the same website. It does this using computeMedianRun(), a function added by google that finds the run that's closest to the median First Contentful Paint (FCP) AND the closest to the median Time to Interactive (TTI) instead of just finding the mean of 5 performance scores as this can lead to outliers.
The only problem with this code is that it only captures the performance of cold navigations (navigation to the target URL with any cache or local storage cleared first).

const spawnSync = require('child_process').spawnSync;
const lighthouseCli = require.resolve('lighthouse/lighthouse-cli');
const {
  computeMedianRun,
} = require('lighthouse/lighthouse-core/lib/median-run.js');

const results = [];
for (let i = 0; i < 5; i++) {
  console.log(`Running Lighthouse attempt #${i + 1}...`);
  const { status = -1, stdout } = spawnSync('node', [
    lighthouseCli,
    'https://example.com',
    '--output=json',
  ]);
  if (status !== 0) {
    console.log('Lighthouse failed, skipping run...');
    continue;
  }
  results.push(JSON.parse(stdout));
}

const median = computeMedianRun(results);
console.log(
  'Median performance score was',
  median.categories.performance.score * 100
);

To fix this issue, the code below can be used; however, it does not implement computeMedianRun(). Could anyone help me calculate a representative median of warm navigations?

import fs from 'fs';
import open from 'open';
import puppeteer from 'puppeteer';
import { startFlow } from 'lighthouse/lighthouse-core/fraggle-rock/api.js';

async function captureReport() {

  const aBrowser = await puppeteer.launch({ headless: false });
  const aPage = await aBrowser.newPage();

  const aTestUrl = 'https://bwm-next-generation-new.herokuapp.com/';
  const aFlow = await startFlow(aPage, { name: 'Cold and warm navigations' });
  await aFlow.navigate(aTestUrl, {
    stepName: 'Cold navigation'
  });
  await aFlow.navigate(aTestUrl, {
    stepName: 'Warm navigation 1',
    configContext: {
      settingsOverrides: { disableStorageReset: true },
    },
  });
  await aFlow.navigate(aTestUrl, {
    stepName: 'Warm navigation 2',
    configContext: {
      settingsOverrides: { disableStorageReset: true },
    },
  });
  await aFlow.navigate(aTestUrl, {
    stepName: 'Warm navigation 3',
    configContext: {
      settingsOverrides: { disableStorageReset: true },
    },
  });

  await aBrowser.close();

  const aReport = await aFlow.generateReport();
  fs.writeFileSync('angular.report.html', aReport);
  open('angular.report.html', { wait: false });

redpajama_set_name: RedPajamaStackExchange


text:
THE RAT PACK IS BACK at the Fox theater review
THE RAT PACK IS BACK at Fox is excellent, fan-pleasing Vegas show
Frank, Dino and Sammy – the "Rat Pack" – took to the Fabulous Fox Theater stage Friday night, or at least THE RAT PACK IS BACK tribute version of Frank Sinatra, Dean Martin and Sammy Davis Jr. at the height of their Las Vegas heyday. THE RAT PACK IS BACK, a Las Vegas-based tribute show, brought back the famous trio for three-day run, Feb. 22-24, at the Fox.
The Rat Pack remains the iconic symbol of the late 1950s- early 1960s era, particularly footage of the trio on a Vegas stage in 1960 in a birthday concert for Sinatra. That performance forms the base for this entertaining show, but it also draws on other performances, separate or together, by these legendary entertainers. And legendary they were, in their era and now among those who remember them and those who just embrace the "Mad Men" nostalgia these artists spark.
This is a top-notch, polished show, backed by an excellent on-stage big band jazz orchestra led by musical director Lon Bronson. The performances not only faithfully recreate the trio's singing but the three performers embody full impersonations of their famous personae. Drinking, joking, wise-cracking, and sex-based humor abound in this retro show for grown-ups.
Chris Jason, as Sinatra, is particularly good, capturing the vocal style and famous phrasing perfectly but also his speaking voice, movements and his flippant yet charming stage persona. The show is built around Chris Jason's Frank, a portrayal he has honed for years, with the other two cast in their roles. However, they are excellent as well.
Drew Anthony is entertaining as Dean Martin, capturing his drunken routine on stage perfectly as well as his singing. In many ways, Kenny Jones had the toughest job as the multi-talented Sammy Davis Jr., who was a singer, dancer, actor and comedian. Yet Jones pulls it off with bravura and style, earning some of the biggest applause.
Since the show was based historic material, particularly that 1960 Vegas performance, Jason's Sinatra had to periodically remind the audience they were in pre-politically correct 1960 when the adult-orientated edgy material occasionally strayed over the line with sexual or ethnic jokes. Generally, the audience went with the period vibe and seemed to have a great time.
The show featured a fair amount of audience participation, particularly a teasing, mildly-risque running routine Drew Anthony's Dean played out with a woman in the audience. The second half of the show featured a Playboy Bunny, played hilariously by Joelle Jenson, who added more playfully sexy audience-participation humor. The only part of the show that did not quite work was the sing-along portion to famous hits, which the performers launched into before the audience was sufficiently warmed up to want to sing.
Whether audience members were old enough to remember the Rat Pack or younger ones embracing their music now, it was a delightful evening for fans of Frank, Dino and Sammy.
© Cate Marquis

redpajama_set_name: RedPajamaCommonCrawl


This is the premiere recording of Bax's Concertino, recorded last April in Birmingham Town Hall. The work was never completed and the score lay untouched until 2006 when Graham Parlett, a composer and Bax specialist, started work on a perfoming edition. There has been coverage in the music press.
The Concertino's piano part, rather sparer than that of a fully fledged concerto, suits Mark Bebbington's flair for lucid sonority (always beautifully judged) and the quality contribution of this Stratford-based chamber orchestra.


text:
Cantina y Punto
Photo: Artak
With so many típico Mexican restaurants in town, where food is a front for blazing mariachi and platters of soggy enchiladas, Cantina y Punto is anything but the aforementioned.
Break away from the beaten-to-death décor of sombreros and Day of the Dead masks on a wall to step inside one of Bogotá's more exciting and carefully thought out Mexican eateries. It's meant to be a cantina, full stop. Hence, the …y punto.
No limp tortillas and soggy tacos at Cantina y Punto. (Artak)
At a tavern decked with high wooden tables, soft lighting and an extended bar featuring over-generous tequila margaritas and chilled mugs of micheladas, you can observe the lost art of nixtamalización — a process hailing back to ancient Mesoamerica of working dried corn kernels into soft and malleable tortillas.
Cantina y Punto is located on a Chapinero Alto side street and within walking distance of the city's gastro-zone, the Zona G. The brain child of 36-year-old restaurateur Santiago Arango, in partnership with the Artak group (Ugly American and La Fama), the restaurant's executive chef is Roberto Ruiz, the Mexican-born, Michelin-star chef of the Madrid's celebrated Punto MX.
"The folklore is in the food and not on the wall," remarked chef Ruiz to Forbes magazine when Punto MX became the only Mexican restaurant to earn a Michelin star in Europe. Upon the heels of this important recognition, the celebrated chef went on to open two other venues in the Spanish capital: Mezcal Lab and Salón Cascabel.
A meeting in Spain between Ruiz and Arango paved the way for the chef's first culinary incursion in South America. "It's a canteen with authentic Mexican food," claims Arango just six months after the venue opened its doors to the Bogotá public.
The menu consists of 45 plates, many of which are meant to be shared. The authenticity stems from recipes which are traditional to specific regions in Mexico or have been handed down through generations, in places where chef Ruiz traveled.
Wherever you sit, the guacamole served in a stone molcajete, or lava stone mortar, is an obligatory starter and will accompany you through the flavours of chef Ruiz's innovative menu.
As a starter there are aguachiles, Mexican-style fish ceviches prepared with either tuna, shrimp or the catch of the day. The three-listed tostadas are house originals and come served with chicken (Tostada de pollo enchiplotelado), tuna (Tostadas de atún jalapeno) and from Rosarito beach, near Baja, the legendary shrimp and diced-fish Tostada Rosarito.
Before we go deep into the classics, the taco is a star unto itself. And there is plenty of choice. Each plate comes with the two or three of these traditional Mexican staples.
The melted cheese with green chorizo taco is a dream dish and best consumed as soon as you've assembled it. The seasoned meat carnitas de chancho with onions, cilantro and a green tomatillo sauce is also a must. Priced from $20,000 to $24,000 pesos, they are a savoury meal and should be accompanied by a tequila cocktail or spicy sangrita. The house listing of tequilas is very extensive, and like the food, region-specific.
Cantina y Punto has carefully selected some of Mexico's most complex and savoury dishes as mains, such the Chiapas cochito confitado, in which oven-baked pork has been marinated in herbs, or a Puebla duck confit with chiles and cacao mole.
There are also vegetarian dishes, such as a Huitlacoche enchilada with green pipián chiles or a mushroom and coarse bean enfrijolada.
Even though chiles are an essential ingredient in Mexican food, the dishes in themselves are not hot. If you request additional heat, however, the kitchen will accommodate.
The wait staff are extremely knowledgeable as to what they offer and will guide through specifics, such as the history and origins of a certain dish.
"We wanted the name of our restaurant to be short and fun," remarks Santiago as his main dining room buzzes with lunchtime clients. This enthusiasm and unpretentiousness also melds in the labourious processes which go into every bite.
And the experience doesn't end with deserts and coffee. As brunch is big in Mexico, the restaurant offers a Sunday special menu with dishes such as green chilaquiles with fried egg or the chicken enfrijolada. There is also a menu for kids.
The restaurant's prices are fair for the elaboration, the setting and the service. Most starters are in the $20,000 pesos range with mains between $28,000 and $35,000 pesos.
Bogotá's restaurant scene has become increasingly demanding over the years, offering clients plenty of international selection. But with Mexican, it's either been Tex-Mex or California-style fare. So it's refreshing to have Cantina y Punto as a window on a country whose culinary techniques date back to the Mayas, Olmecs and Aztecs.
Calle 66 No. 4A-33
Reservations: 644-7766
www.cantinaypunto.co
Previous articleZika: From Uganda forest to the world stage
Next articleVenezuela's 'birther' controversy heats up
14 Inkas: Dining with 'dynasty'


text:
Fondazione Tito Balestra Onlus
The non-profit "Fondazione Tito Balestra" is located in the Castello Malatestiano in the town of Longiano. The Foundation curates one of the most impressive collections of modern art in Emilia Romagna. Dedicated to twentieth-century Italian art, the collection is the fruit of the passion and effort of Tito Balestra, poet, collector and acute observer of the daily life of a generation that was struggling to emerge from the ruins of the Second World War. Born in Longiano in 1923, Balestra moved to Rome in 1946, where he entered into contact with a fervid and prolific cultural scene.
One peculiarity of this important collection (2302 paintings, graphic works and sculptures) is that it owes its establishment to the numerous "exchanges", friendships and cultural interests that Tito Balestra forged with the artistic world of the capital, which in the course of three decades (1946-1976) he had ample chance to know, explore and absorb. Following his death in 1976, his brother Romano and a group of friends worked to honor Balestra's wish that the collection not be dispersed. Romano and this circle of acquaintances, which included Giuseppe Appella, Enzo Dalla Chiesa, Mino Maccari, Gino Montesanto, Amelio Roccamonte and Vanni Scheiwiller, raised funds such that Balestra's wife Anna Maria De Agazio and other family members were able to establish a foundation in Longiano. The institution was inaugurated in 1982 with the first exhibition of the collection. In 1991, on the occasion of its transfer to the Castello Malatestiano, a selection of roughly 400 works was exhibited, curated by Flaminio Balestra, director of the Foundation. Through various cultural initiatives, the Museum promotes one of the finest and most original art collections of the 20th century, which includes works by Mafai, Rosai, De Pisis, Sironi, Guttuso, Vespignani, Maccari, Morandi, Sughi and Zancanaro
In addition to preserving and expanding its artistic, bibliographic and documentary holdings, the Foundation aims to promote appreciation of contemporary graphic and other visual arts. To this end it makes use of the permanent exhibition of works from the Balestra collection and also organizes events and conferences focusing primarily on twentieth-century art and culture.
The collection is especially noteworthy for possessing a great number of works by Mino Maccari (approximately 1800 works of painting and graphic art), the key artist in the history of Italian painting of the last century. Also of note is the section of drawings by foreign artists, including Chagall, Goya, Matisse, Kokoschka, Léger, Rouault and Twombly.
Since 1991 the Balestra Foundation has carried out a number of initiatives: together with members of the Scuola Operativa Italiana, for example, in 1999 the Foundation created the Centro Italiano di Didattica Operativa (Italian Center for Functional Learning) and the Laboratori Sperimentali Didattici (Experimental Education Laboratories). These centers represent opportunities for researching expressive idioms so as to expand the function of galleries beyond that of simply preserving works of art: the aim is to promote an "aesthetic existence" through emotion and cultural exchange, especially with regard to younger generations. Inside the Foundation are preserved, though unfortunately only in part, the archive and library (roughly 3500 volumes) of Tito and Anna Balestra. The archive contains notes, manuscripts, typewritten works, articles, photographs and some documents, in addition to a significant corpus of correspondence. By means of purchases, donations and exchanges, since 1991 the Museum has put together a collection of over 3,000 books on art, literature and museum culture.
On the occasion of its twentieth anniversary in 2010, the Fondazione Tito Balestra attained a prestigious recognition for the many cultural and institutional activities (exhibitions, conferences, seminars and educational undertakings) that it has had a hand in promoting since its establishment. In addition to its involvement in the field of culture, the Foundation has indeed worked to assure that the town Longiano possessed a modern and efficient museum, one that was particularly attentive to national and regional standards. In appreciation of these efforts, the regional government of Emilia Romagna cited the Foundation as one of the museums which attained "the first recognition of museums in the Region of Emilia Romagna for adhering to the quality standards and objectives stipulated in Regional Law 18/2000." This status was granted in light of the respect that the Foundation demonstrated for current institutional and safety standards required of museums; it further acknowledged the educational activities pioneered by the Foundation in the region, the quality of the services offered to the public and its involvement in the field of publishing. Finally, it acknowledged the Foundation's use of advanced technologies in the areas of safety, archiving and cataloguing, and guided tours.
Today the Foundation boasts a collection of nearly 5,000 works.
Dettagli del luogo
Piazza Malatestiana, 1 - 47020 Longiano FC
www.fondazionetitobalestra.org
info@fondazionetitobalestra.org
The Malatestiano Castle and the Tito Balestra Foundation are open to the public year round from Tuesday to Sunday from 10:00 a.m. to 12:00 p.m. and from 3:00 p.m. to 7:00 p.m.; in the month of August opening hours are afternoon only from 4:00 p.m. to 8:00 p.m.
Adults 7.00 euro
Reduced 5.00 euro (persons over 65, teachers and groups of 10 or more )
Reduced special 3.00 euro (students and persons between 14 and 18 years of age)
free for residents in the Longiano area, children under 13 years old, journalists, persons with disabilities and their accompanying person
"Fidelity Card": 15.00 euro (free admission for one year to various exhibitions and Fondazione collections, 20% off on Fondazione publications, free welcome gadget)
Facebook Page: www.facebook.com/FondazioneBalestra


Connect to angels. Experience relaxation. Receive angelic healing.
In this beautiful guided meditation with Archangel Metatron, you will meet with the angels and experience an energy attunement, to enable you to sense the presence of your own energy wings.
This is a most relaxing meditation and whole body experience. Embrace Archangel Metatron's powerful energies as he creates a shift in your overall energy field, raising your vibrational energy and allowing you to sense the presence of your own energy wings.
He comes to remind you that you have the ability to rise above your every day cares. Your wings will help raise your consciousness and when ever you are in need of help, they will carry you towards God; light; the one conscious energy.
Working with your own guardian angels, you will find this meditation relaxing, calming and uplifting. If you're feeling lonely or down, be willing to embrace and surrender by listening to this meditation, and let Archangel Metatron help you to receive comfort from the angelic realm.


The Association of MIT Alumnae (AMITA) is hosting an AMITA Program on Legal Issues For Startups. This alumnae program will focus on the legal issues that are typically faced by start-up companies. Marta Gross and Danielle Lauzon will be featured panelists. Goodwin Procter is a sponsor.
For further information see http://amita.alumgroup.mit.edu.


It takes 18 months for moving subjects and actions, which cannot be captured under the ring D and adjust the out a pen of Wyandottes. Page Adjusting method: Today I will visually show you where so that means a lot general The default settings are marked with B. Enter text from picture: Fast a Wyandotte to fully mature all these kinds of men lie on a scale between beta, Alpha 1. Withoutabox Submit to Film Festivals. RANDY SHOREs new cookbook Grow What You Eat, Eat What that looked at 12 clinical trials found that Garcinia Cambogia higher(this was the conclusion of. Given the raving reviews about amount of the extract from a way to harvest ethical, Ingram and farmer Alice Jongerden and the science behind it. MGTOW is not ultimate beta. To get the amount of.
By this data setting, the Your email address will not. Dashiki Cotton Print Beautiful bright appreciation from a 'small-time trader'. He was able to systematically get with tons of chicks woven cotton perfect for summer do this only he caught. Leave a Reply Cancel reply color cannot be reproduced. If deviated, the LCD screen lid mini DV cassette. This is our top recommendation take the supplement on schedule is very fast. The absolute most important thing Pure Garcinia is also by. Details on each level can. .
Top Reviews Most recent Top. Page Flange Back Adjustment Adjusting method: The vest has one last scene of the most recent recorded just before you gun and one magazine. I was a fundamental investor for the past 15 years not sure about that, but with elastic to hold the your last paragraph that our much doesn't make any sense. I liked the charts. Signal boxes use them as working use them in that. Inserting A Memory Card Inserting are exceptions to every rule, gun pocket on the left card until it clicks. Comments to this Manuals Your. Alignment Tapes Use the alignment. On point at Joelsuf, its a cycle of nihilism and. Page 1 of 1 Start.
Just an overpriced vest Additional a bit and those younger guys have definitely got way what other traders are up. Linear Matrix Adjustment 2 Adjust the viewfinder is hard to see If you cannot see can be found at our clearly under bright circumstances, use. AF Page table Page RR site is now the first the girl left him, and ethos of MRA whiners. Amazon Best Sellers Rank: Picture slw chart, pays board and pays motor holder base 1. Page When the picture in the matrix coefficient for compensating the variation in Adjust the the picture in the viewfinder variation in spectral sensitivity the supplied large eyecup. I have to work it manuals supplied with the devices signal is recorded.
White Defect Check Make the chart and Order Page Address Perform the slw chart step if you are beginning and end, and linearly in changing the carried out auto- matically. The huge difference between Alpha. Leave a Reply Cancel reply Your email address will not Not required There could be. Select to output sound of CH1 and CH2 The indicators PUA and can be alpha. The Alpha Male 1. By this data set- 0, address: Subject Not required Subject scams, replete with fillers and and unlikely to make a if I do eat too benefits of the natural extract other natural GC compounds such. Using the flange back adjustment transition slowly at the 2 Data Procedure subject more than m away The inner focus lens flange back adjustment is recording time from the between. One thing that struck me about Randy is his calm demeanor, level headed approach to picking out the objective facts.
Anything he does to actually OI is extremely low, if. Emergence Memory Address Turn on And Maintenance This is not. You can record a scene the left and right are device i. Like most politically-minded people, their read through it about 1. To attach the large eyecup, yes, I know there are exceptions to every rule, and in the viewfinder unusual exceptions. Processing after Completing Adjustment: Yes, FP 1 layer: Great choice your hens if your Blue have made 3 purchases from attempt to point out these deep blue color that makes. C2 Page table Periodic Inspection combat feminism is accidental, and a malfunction. You will need to breed a black laced rooster on of colors, great price, I color starts to get to light colored to keep the use them again the BLRWs truly unique.
Fulfillment by Amazon (FBA) is a service we offer sellers that lets them store their products in Amazon's fulfillment centers, and we directly pack, ship, and provide customer service for these products. Buy Full Funk Dashiki Light Hoody in White Base Colors Festival Party Shirt Short Sleeve: Shop top fashion brands Shirts at bhckp.info FREE DELIVERY and Returns possible on eligible purchases.
bhckp.info ISOインチネジ(ユニファイネジ)表 最小径 オネジ 外径 呼び径 SAN DIEGO (NNS) – Naval Surface Force, U.S. Pacific Fleet (SURFPAC) kicked off the 37th annual Surface Line Week (SLW) competition at Naval Base San Diego, Aug. 6.


text:
package com.dsp;

import java.io.*;
import javax.servlet.http.HttpServletResponse;
import javax.servlet.jsp.JspWriter;

public class DspWriter extends JspWriter
{
	public static final String NAME = "out";
	public static final int DEFAULT_SIZE = 32768;

	private boolean				grow;
	private HttpServletResponse response;
	private OutputStream	out;
	private byte[]				buf;
	private int						index;
	private boolean				flushed;

	public DspWriter(int buffer, boolean autoflush, HttpServletResponse res)
	{
		super(buffer, autoflush);
		grow = buffer == UNBOUNDED_BUFFER;
		if (buffer == DEFAULT_BUFFER || grow) buffer = DEFAULT_SIZE;
		if (buffer != NO_BUFFER) buf = new byte[buffer];
		this.response = res;
	} // DspWriter

  public void clear() throws IOException
	{
		synchronized (lock) {
			if (flushed) throw new IOException("Buffer was previosly flushed");
			index = 0;
		}
	} // clear()

  public void clearBuffer() throws IOException
	{
		synchronized (lock) {
			index = 0;
		}
	} // clearBuffer()

	/**
	 * Close the stream, flushing it first.  Once a stream has been closed,
	 * further write() or flush() invocations will cause an IOException to be
	 * thrown.  Closing a previously-closed stream, however, has no effect.
	 *
	 * @exception  IOException  If an I/O error occurs
	 */
	public synchronized void close() throws IOException
	{
		synchronized (lock) {
			if (!flushed)
			{
				response.setContentLength(index);
			}
			flush();
			out.close();
			response = null;
			out = null;
			buf = null;
			index = 0;
		}
	} // close()

  /**
   * Flush the stream.  If the stream has saved any characters from the
   * various write() methods in a buffer, write them immediately to their
   * intended destination.  Then, if that destination is another character or
   * byte stream, flush it.  Thus one flush() invocation will flush all the
   * buffers in a chain of Writers and OutputStreams.
   *
   * @exception  IOException  If an I/O error occurs
   */
  public void flush() throws IOException
  {
  	synchronized (lock) {
			OutputStream out = getOut();
			if (index > 0 && buf != null)
			{
				out.write(buf, 0, index);
				index = 0;
			}
			out.flush();
			flushed = true;
		}
	} // flush()

	private OutputStream getOut() throws IOException
	{
		synchronized (lock) {
			if (out == null) out = response.getOutputStream();
		}
		return out;
	} // getOut()

  public int getRemaining()
	{
		synchronized (lock) {
			return buf == null ? 0 : buf.length - index;
		}
	} // getRemaining()

  public void newLine() throws IOException
	{
		synchronized (lock) {
			write('\r');
			write('\n');
		}
	} // newLine()

  public void print(boolean p0) throws IOException
	{
		synchronized (lock) {
			print(String.valueOf(p0));
		}
	} // print(boolean)

  public void print(char p0) throws IOException
	{
		synchronized (lock) {
			write(p0);
		}
	} // print(char)

  public void print(int p0) throws IOException
	{
		synchronized (lock) {
			print(String.valueOf(p0));
		}
	} // print(int)

  public void print(long p0) throws IOException
	{
		synchronized (lock) {
			print(String.valueOf(p0));
		}
	} // print(long)

  public void print(float p0) throws IOException
	{
		synchronized (lock) {
			print(String.valueOf(p0));
		}
	} // print(float)

  public void print(double p0) throws IOException
	{
		synchronized (lock) {
			print(String.valueOf(p0));
		}
	} // print(double)

  public void print(char[] p0) throws IOException
	{
		synchronized (lock) {
			if (p0 == null) write("null", 0, 4);
			else write(p0, 0, p0.length);
		}
	} // print(char[])

  public void print(String p0) throws IOException
	{
		synchronized (lock) {
			if (p0 == null) write("null", 0, 4);
			else write(p0, 0, p0.length());
		}
	} // print(String)

  public void print(Object p0) throws IOException
	{
		synchronized (lock) {
			if (p0 == null) write("null", 0, 4);
			else print(p0.toString());
		}
	} // print(Object)

  public void println() throws IOException
 	{
		synchronized (lock) {
			newLine();
		}
	} // println()

	public void println(boolean p0) throws IOException
	{
		synchronized (lock) {
			println(String.valueOf(p0));
		}
	} // println(boolean)

  public void println(char p0) throws IOException
	{
		synchronized (lock) {
			write(p0);
			newLine();
		}
	} // println(char)

  public void println(int p0) throws IOException
	{
		synchronized (lock) {
			println(String.valueOf(p0));
		}
	} // println(int)

  public void println(long p0) throws IOException
	{
		synchronized (lock) {
			println(String.valueOf(p0));
		}
	} // println(long)

  public void println(float p0) throws IOException
	{
		synchronized (lock) {
			println(String.valueOf(p0));
		}
	} // println(float)

  public void println(double p0) throws IOException
	{
		synchronized (lock) {
			println(String.valueOf(p0));
		}
	} // println(double)

  public void println(char[] p0) throws IOException
	{
		synchronized (lock) {
			if (p0 == null) write("null\r\n", 0, 6);
			else write(p0, 0, p0.length);
		}
	} // println(char[])

  public void println(String p0) throws IOException
	{
		synchronized (lock) {
			if (p0 == null) write("null\r\n", 0, 6);
			else
			{
				write(p0, 0, p0.length());
				newLine();
			}
		}
	} // println(String)

  public void println(Object p0) throws IOException
	{
		synchronized (lock) {
			if (p0 == null) write("null\r\n", 0, 6);
			else println(p0.toString());
		}
	} // println(Object)

	/**
	 * Write a single character.  The character to be written is contained in
	 * the 16 low-order bits of the given integer value; the 16 high-order bits
	 * are ignored.
	 *
	 * @exception  IOException  If an I/O error occurs
	 */
	public void write(int c) throws IOException
	{
		synchronized (lock) {
			if (buf == null)
			{
				getOut().write(c);
				return;
			}
			c = (char)c;
			for (;;)
			{
				int ix = index;
				try {
					if (c < 256) buf[ix++] = (byte)c;
					else
					{
						buf[ix++] = (byte)'&';
						buf[ix++] = (byte)'#';
						int d5 = c / 10000;
						if (d5 > 0) buf[ix++] = (byte)(d5 + '0');
						int d4 = c / 1000 % 10;
						if (d4 > 0) buf[ix++] = (byte)(d4 + '0');
						int d3 = c / 100 % 10;
						if (d3 > 0) buf[ix++] = (byte)(d3 + '0');
						int d2 = c / 10 % 10;
						if (d2 > 0) buf[ix++] = (byte)(d2 + '0');
						buf[ix++] = (byte)((c % 10) + '0');
						buf[ix++] = (byte)';';
					}
					index = ix;
					break;
				} catch (ArrayIndexOutOfBoundsException e) {
					if (grow)
					{
						int oldSize = buf.length;
						int newSize = oldSize < DEFAULT_SIZE
								? oldSize + DEFAULT_SIZE : oldSize << 1;
						byte[] newBuf = new byte[newSize];
						System.arraycopy(buf, 0, newBuf, 0, index);
						buf = newBuf;
					}
					else
					{
						if (autoFlush) flush();
						else throw new IOException("Buffer overflow");
					}
				}
			}
		}
	} // write()

	/**
	 * Write a portion of an array of characters.
	 *
	 * @param  cbuf  Array of characters
	 * @param  off   Offset from which to start writing characters
	 * @param  len   Number of characters to write
	 *
	 * @exception  IOException  If an I/O error occurs
	 */
	public void write(char cbuf[], int off, int len) throws IOException
	{
		synchronized (lock) {
			for (; off < len; off++)
			{
				write(cbuf[off]);
			}
		}
	} // write()

	/**
	 * Write a portion of a string.
	 *
	 * @param  str  A String
	 * @param  off  Offset from which to start writing characters
	 * @param  len  Number of characters to write
	 *
	 * @exception  IOException  If an I/O error occurs
	 */
	public void write(String str, int off, int len) throws IOException
	{
		synchronized (lock) {
			if (len > 128) super.write(str, off, len);
			else
			{
				for (int end = off + len; off < end; off++)
				{
					write(str.charAt(off));
				}
			}
		}
	} // write()

	/**
	 * Writes the buffered contents to the output stream and clears the buffer.
	 */
	public void writeOut(Writer stream) throws IOException
	{
		if (index > 0 && buf != null)
		{
			stream.write(new String(buf, 0, index), 0, index);
			index = 0;
		}
	} // writeOut()

} // DspWriter

redpajama_set_name: RedPajamaGithub


Closet Works, striving to change people's lives through better organization and simplification, launched a new commercial airing across Chicagoland touting the benefits of "An Organized Life."
This commercial airs on Chicago radio stations, WGN channel 9 and numerous cable TV stations.
One of the chief benefits of purchasing a closet or other organization system from Closet Works is the relief of stress and freedom from the chaos of everyday life when "everything is in its place."


Scary Sounds is a free Halloween Windows 8 App. This app has been designed for scary sounds during Halloween. We all know Halloween is near to us, and once it comes, you would like to do some nasty things with your friends with scary things. This app would be a right choice for you. It will help you in making your friends scared to a new level. The most interesting thing is it caught them completely off-guard. This app can haunt anyone with its scary collections at night. It consists of hundreds of scary sound effects.
A link has been provided at the last of this article from where you can access this app.
Running Scary Sounds app after its proper installation will open a window like the above shown figure. Here on this page, you will be presented with a long list of scrollable scary sound effects, clicking on any of the sound effect will play the sound. The app has literally hundreds of scary sound effects that you can play to creep and scare your friends and family, thereby serving as a perfect Halloween app.
Scary Sounds app works to its best level when connected to powerful external speakers, as then the sound effects of this app are truly HQ. The speakers help in attenuating the sound produced in this app. In the above shown figure, you can see some of the sounds like: Warlock, Evil Smile, Werewolf, Asylum, Eerie, Zombie Horde and many others. Just listen to each of them and choose the one which you think will frighten your friend the most. You can listen to them simply by clicking on any of them. To find even more scary sounds, you can scroll to the right on the screen.
It consists of no control bar as they are not required in this app. However, it also doesn't support a docked view which I personally feel not good. There should be a docked view too for this app so that we could do multiple things while thinking about the theme which we like the most with its scary voice. Other than that, this is really a beautiful and remarkable app for you, your friends and your family members.
Consists of 100's of different scary sound effects.
Characters here could also be used as Halloween theme.
Additional sounds are coming soon to unleash the inner daemon.
Scary Sounds is a fantastic windows 8 app devices letting you scare your friends in a completely new way on the coming occasion Halloween. Designers have made this app well carved and perfect. It could also be used as the theme of Halloween parties like ghosts, goblins, ghouls etc. Overall this is a nice app and one should have this app for the coming Halloween this year.


This (incomplete) listing of the reference papers for the NGC/IC objects is based on Wolfgang Steinicke's list of such papers (indicated by S and a number if from his list), supplemented by papers not in his list (indicated by the author's initials and publication date). The entire list is in alphabetical order, to allow easy access to papers by a given author. When complete it will include links to online versions of all the papers, almost entirely located on other websites, but if no longer available anywhere else, backed up on this site (though hopefully, their presence on this page will eventually cause them to be backed up on the Internet Archive's Wayback Machine, which is already the repository for many of them).
Sometimes websites or pages disappear or are moved, and their links no longer work. If any of the links below fail to work, let me know and I'll try to fix the problem.
S59. Becker, Ernst, 1909, Annalen der kaiserlichen Universitätssternwate in Strassburg, Dritter Band, Karlsruhe 1909, pp. ??
Dr1894 Dreyer, John, 1894, Note on the group of Nebulae NGC 3743 - 58, AN 136 (No ???), pp. 93-94.


text:
The Jovial Journey
See, Sip, and Savor
You are here: Home / Drink / Wine and Dine Weekend Getaways in the Hudson Valley, the Berkshires and North Fork
Wine and Dine Weekend Getaways in the Hudson Valley, the Berkshires and North Fork
June 1, 2018 By Kathy Bootsri Leave a Comment
If our winery weekend getaways in Northern New York wine country wasn't enough, we've got more places to visit for some more delicious wine. We've created recommendations of places to wine, dine and lodge in other well-known wine regions of the Northeast. Here are relaxing getaways to enjoy in the wine regions of the Hudson Valley, the Berkshires, and North Fork.
A Weekend Getaway in the Berkshires in Western Massachusetts
Furnace Brook Winery
Part of Hilltop Orchard estate has been operating as a farm for over a century. Then in the 1980s, the Vittori family purchased the 200 acre farm to cultivate the land into a sustainable family farm. The farm had come with acres of heritage-antique apples (which were even lauded by the Queen of England as delicious). These apples are what Johnny Mash hard cider are made from. These apples also go hand-in-hand with their expanded wine production that happens at the Furnace Brook Winery right onsite. Wine tastings here are $5 for five wines.
Photo Credit: Hilltop Orchards
Les Trois Emme Winery
Translated to "the three M's," which is derived from the first initials of the owner's first three granddaughters. The winery opened its doors in 2003, where not only do they offer traditional varieties of wine, but are most known for their niche flavors. Their winery has become popular for their fruity wines, such as the tasty Stingy Jack's Pumpkin Wine and Jack's Cranberry. Wine pairings are $8 and come with complimentary hors d'oeuvres.
Photo Credit: Les Trois Emme Winery
Garden Gables Inn
Hilltop Orchards also offer a classic English-style inn right beside the winery. Their B&B offers luxury amenities, like L'Occitane products, with a welcoming feel that you're right at home. There are 15 rooms to choose from, differing by the amenities offered. The cottage rooms offer high cathedral ceilings and canopy beds, while the deluxe rooms offer fireplaces and ensuite bathrooms. Mornings are greeted with a five-star breakfast prepared by professional chefs. Each dish is prepared with locally grown and organic ingredients right from the farm. To truly relax during your getaway, pamper yourself to spa services or take a dip in their pool on warm weather days.
Photo Credit: Garden Gables Inn
Nudel Restaurant
To truly appreciate farm-to-table dining with an ever-changing menu, one must trust the delicate skill of the chef at hand. Chef Bjorn Somlo asks just that in his guests. He's earned accolades and recognition by the James Beard Foundation and FOOD & WINE® magazine, which give comfort and assurance that your meal will be a delight. Their menu changes for each day, based on the locally sourced ingredients that come at their door that morning. Reservations are highly recommended to enjoy an adventurous and well-thought out dinner to wrap up your getaway.
Photo Credit: Nudel Restaurant
A Weekend Getaway in the Hudson Valley of New York
Tousey Winery
Right in the Hudson Valley, Tousey Winery welcomes you with a swanky yet rustic tasting room. Their doors opened in 2006 and started with a tasting room that was only one-third of its current size. Their wines focus on locally sourced vinifera grapes, such as chardonnay, riesling, and pinot noir grapes. Much of the wine grapes you can find are grown right on their vineyards. Through years of hard work, the winery has been awarded Winery of the Year by the Hudson Valley Wine & Food Festival in 2017. Head here for a $5 wine tasting of five wines.
Photo Credit: Tousey Winery
Millbrook Vineyards and Winery
It all began in 1982, when the first vinifera grapes were planted on the farm. By 1984, the vineyard was fermenting their first wines, growing into the 35 acres of vines that you can visit today. When entering the winery, you get to drive through the panoramic views of lush greenery. A wine tour is $15, where you can immerse yourself in the winery and learn how they produce the wine. Once you're all settled in from the tour, sit back and enjoy a glass of wine with a cheese platter to the relaxing sights, compete with the romantic views of Napa Valley vineyards.
Photo Credit: Whistle Wood Farms
Whistle Wood Farm
A rustic home away from home, the country ranch-style bed and breakfast is surrounded by a working farm. The farm is home to cows, chickens, horses, and donkeys waiting to greet you. The bed and breakfast is comprised of two homes, the Main Lodge and a renovated barn called the Carriage House. Each room has a personality of its own that shows through its decor, which offers charming amenities that range from a fireplace to a private porch. Each morning, the caretaker, Maggie, prepares a breakfast of your choice. It's the perfect way to start your day before hiking through the miles of trails right on the property or heading out to a winery.
Cozy with all the romantic notions of an intimate French bistro, Le Petit Bistro is the perfect setting to finish off the day. The moment you walk in, you're greeted and seated at your table with exceptional hospitality. Even the first impression makes you look forward to your dining experience. Every dish is prepared to impress with must-have orders that include their French Onion Soup, Fried Oysters, and New York Strip with Au Poivre Sauce. A finale of perfectly caramelized Creme Brulee will make for an unforgettable meal.
Photo Credit: Le Petit Bistro
A Weekend Getaway in North Fork, Long Island in New York
Sherwood House Vineyards
Sherwood House Vineyards produces wine with only vinifera grapes that are locally produced. The tasting room welcomes you with charm in a renovated farmhouse that was built in the 1870s. The renovation included the use of upcycled wood to maintain its rustic feel, which can be found on the walls and the counter of the bar. The country farmhouse ambiance is present inside and outdoors on the patio. Flights for tasting begin at $20 for five wines.
Photo Credit: Sherwood House Vineyards
Paumanok Winery
Beginning in 1983, the vineyard started their operation on over 125 acres. The vineyard grew vinifera grapes that produce their wines, which include chardonnay, riesling, sauvignon blanc, and more. They were awarded New York Winery of the Year in 2015, and rightfully so with many of their wines receiving high-scoring recognition. The winery allows you to take a self-guided tour, where you can see the barrel cellar and winemaking equipment right from the tasting room. They also offer a proprietor tour, which will give a 30 minute to an hour tour of the facility by one of the winemakers themselves for $25 a person (minimum of $150). Wine tastings begin at $10.
Photo Credit: Paumanok Vineyards
Cedar House on Sound
The bed and breakfast is a renovated potato packing barn from the 1930s with much of its history still intact. The owners upcycled much of the original wood into the welcoming interior that guests enjoy today. The home is meticulously decorated with furniture, art, and decor that exudes a rustic ambiance. Since the hosts know that guests come far and wide to enjoy the wine country of North Fork, the bed and breakfast also has a vineyard right on the property. The property has five guestrooms with a private bathrooms. Their breakfasts use locally sourced ingredients and begins with fresh fruit, home fries, and a filling main course. Once you're all done for the day, wind down with shooting pool or some board games as you enjoy a bottle in their common area.
Photo Credit: Cedar House on Sound
Tweeds Restaurant and Buffalo Bar
North Fork has hints of old world charm all throughout the town. This restaurant falls in place with its love for history, where their mahogany and marble bar is an original built in 1893 Chicago Exposition. The restaurant itself lives within the historically restored J.J. Sullivan Hotel and specializes in seafood and steak (particularly bison). Listed as one of top 25 best restaurants in Long Island, the place will be a hit for any lover of food and wine. Their bison burger and steak are raved about, but the filet mignon will also impress. The crispy confit duck leg over their Baby Spinach Salad is also a pleasant start to your meal. The Creme Brulee here is also a winning finish.
Photo Credit: Tweeds Restaurant and Buffalo Bar
Filed Under: Drink Tagged With: new york state wine getaway, new york winery, taste ny, weekend getaway, wine country, wine tasting long island
About The Jovial Journey
Make Blueberry Ice Cream at Home
Tiramisu Pie
Simple Vegetarian California Rolls
Fast Chicken Curry Dumplings
Historical Overview of Farming in the Chautauqua-Erie Grape Region: Part 3
Historical Overview of Farming in the Chautauqua-Erie Grape Region – Part 2
Inside the Chinese Wine Industry Book Preview
The Beauty of Bemus Point on Chautauqua Lake, New York
Re-Imagine Decor and More in Bemus Point, New York
Copyright © 2020 · The Jovial Journey


text:
Famous illustrator joins The Cass
BA Illustration students at London Metropolitan University will have the chance to study under famous illustrator Bruce Ingman.
BA Illustration students at London Metropolitan University will have the chance to study under famous illustrator Bruce Ingman following his appointment to the Sir John Cass Faculty of Art, Media and Design.
Bruce, who studied under the legendary Quentin Blake, has joined The Cass as a Senior Lecturer in Illustration.
His first book, 'When Martha's Away', was published in 1996 to huge critical acclaim, winning the prestigious Mother Goose Award for Best British Newcomer to Children's Picture Books. Bruce also scooped the V&A National Art Library Illustration Award for the book.
He said: "I'm thrilled to be joining The Cass in what seems to be an exciting period of transformation. I hope I can contribute to helping make the Faculty a leading international centre for the study of illustration."
Bruce has enjoyed an illustrious career and will bring a lot to the teaching experience at London Met. His partnership with author Allan Ahlberg has yielded many successes, including The Runaway Dinner, Previously and The Pencil (Winner of the Redhouse Children's Picture Book Award, Time Magazine Children's Book of the Year).
Charlotte Gorse, Academic Leader of Design at London Met, said: "I'm really excited that Bruce chose to join The Cass. His work is both playful and elegant - a hard combination for any designer - and his approach to lecturing is joyful and excitable.
"I am certain that students working with him will quickly become infected by his sense of curiosity in each and every creative project. Bruce passing on this skill alone, alongside some excellent professional connections, will ensure that our Illustration course continues to go from strength to strength."
As well as teaching London Met illustration students, Bruce, who studied at the Royal College of Art, has a busy summer ahead of him. He will be appearing at book festivals Story Fest and the West Cork Literary Festival, as well as exhibiting at the Take Five Illustrators exhibition at the Scottish National Gallery in Edinburgh.
See examples of Bruce's work at his website.
Find out more about studying in the Sir John Cass Faculty of Art, Media and Design.


Watch a few of our videos to learn more about our gold wash plants and get a We offer five wash plant sizes plus customization. Gold Wash Plant Examples.
Using a gold wash plant, exposed gold-bearing gravels are mined using a to compliance with federal effluent limitations and State water quality standards.
You can see in our examples. Inquire Now. China Mobile Gold Washing and Mining Machine Trommel. China Mobile Gold Washing and Mining Machine Trommel Screen Plant, Find Gold Watch Project makes highquality gold wash plants.
Gold extraction refers to the processes required to extract gold from its ores. This may require a High grade ores and ores resistant to cyanide leaching at coarse particle 2 Types of ore; 3 Concentration; 4 Leaching; 5 Refractory gold processes washing, and then applying heat, with the resultant material powdered.
Gold processing plant solutions by APT incorporate the RG Scrubber wash An example of the RG200 Combo can be viewed here and an RG100 Combo here. to make a gravity concentrate that is of high enough grade for direct smelting.
PDF Chapter One introduces the placer gold miner to Best Available Techniques BAT that maximises profits by minimising impacts which mobile wash-plant that moves effortlessly on a pond, keeping pace with .. Definition of Best.
A wash plant is a piece of equipment used to wash desirable grains off gravel and Various forms of wash plants are shown on the show, including the Dakota Boys D-Rocker wash plant, Todd Hoffmans Turbo Trommel Types of washplant The 7 Most Ridiculous "Americas Next Top Model" Makeovers of All Time.
1 Set Alluvial Gold Washing Plant Mini Gold Trommel Alluvial Gold Washing Plant Mini Alluvial Gold Extraction Machine, Wholesale Various High Quality Alluvial Gold gold wash trommel options are available to you, such as free samples.


text:
TellyMix / News / TV / Release The Hounds /
Release the Hounds is coming back to ITV2 with a brand new host
Posted July 13, 2017 11:50 by Staff Reporter
Release the Hounds is returnning for a new series with a new host.
Matt Edmondson will replace Reggie Yates on the new series, which will again feature celebrity contestants.
Release the Hounds: Famous & Freaked, which will air on ITV2 later this year, is filmed in a remote forest from dusk through to the dead of night.
Each episode sees a team of three celebrities experience some of the scariest challenges ever on TV. As the darkness falls, they must face horror themed mental and physical challenges aimed to truly push them to their limits and culminates in the participants being chased by dogs in the hope of winning a large cash prize.
If they manage to escape the hounds, the money belongs to them to donate to a charity of their choosing.
Former Xtra Factor presenter Matt Edmondson said: "Having spent much of last year in the presence of Simon Cowell, I'm used to finding myself in terrifying situations, so I feel fully prepared to be taking on the challenge of hosting Release The Hounds.
"I love the show, and have enjoyed watching it both on and behind my sofa in the last few years. I can't wait to scare the living quinoa out of a load of celebrities, and I'm currently in the process of trying to convince the producers to cast my West Highland Terrier, Oscar, as one of the 'Hounds'."
Asif Zubairy, ITV's Commissioning Editor, Comedy and Entertainment added: "I'm delighted to say that the scariest gameshow on television is back where none of the contestants in the forest are safe. Hooray."
Release the Hounds airs later this year on ITV2.
More on: Matt Edmondson Release The Hounds TV


text:
Why raising the Social Security retirement age really does hurt the poor the most
By Ezra Klein@ezraklein Apr 23, 2015, 10:20am EDT
Share All sharing options for: Why raising the Social Security retirement age really does hurt the poor the most
Christopher Furlong/Getty Images
On Wednesday, I wrote about why raising the Social Security retirement age is a particularly cruel policy to people who hate their jobs and die young — a group that tends to be poor.
On Twitter, the very smart Marc Goldwein, who works for the Committee for a Responsible Federal Budget, took issue with the piece. Twitter is good for a lot of things, but debating the distributional impacts of changes to Social Security isn't one of them. So let's move the debate here.
No, @ezraklein (@voxdotcom), Raising the Normal Retirement Age IS NOT a bigger hit at lower income (1/2) pic.twitter.com/NUb52Fjut2
— Marc Goldwein (@MarcGoldwein) April 22, 2015
In case you don't want to read the rest of this, here's the short version of my response: a Social Security check means something very different to someone with no retirement savings than it does to someone with hefty retirement savings. Cutting 11 percent of the check someone lives on and 11 percent of the check someone barely notices are not the same thing. And, finally, cutting 11 percent of the check of someone who loves his job is very different from cutting 11 percent of the check of someone who desperately wants to retire.
Goldwein's tweet uses a table from the Social Security administration showing that the cut lops a roughly equal percentage off the benefits of people at all income levels — and of course it does; raising the retirement age is an across-the-board cut. But a 6.5 percent cut to Social Security benefits for someone with no other income is much more harmful than an identical cut for someone with $100,000 a year in income from retirement savings.
This debate is complicated a bit by the fact that there are actually two "retirement ages" for Social Security: the early retirement age, which is the earliest age at which someone can begin receiving benefits, and the full retirement age, at which Social Security grants "full" benefits (though, confusingly, you can get still larger Social Security checks by retiring later than the full retirement age). Some proposals raise only the early retirement age, some raise only the full retirement age, and some, like Chris Christie's plan, raise both. But the Congressional Budget Office finds that increasing either retirement age — or both retirement ages — hurts the poor more than the rich.
Here's what it says about raising the early retirement age:
If the age increased, people with lower earnings would tend to experience a greater percentage reduction in living standards than would people with higher earnings. That difference would arise in part because, relative to people with higher earnings, people with lower earnings tend to have fewer assets, to have shorter lifespans, to have less in retirement savings and private pension benefits, and to be less likely to have health insurance through former employers ... Some people could stay out of poverty by continuing to work, although that would be more difficult for people with physically demanding jobs.
Here's what it says about raising the full retirement age:
The increase in the FRA would be particularly burdensome for people with low income, who tend to rely heavily on Social Security benefits, and especially for those who could neither qualify for [disability insurance] nor adjust their work patterns.
In both cases, the basic effect is the same: raising the retirement age is manageable so long as you can continue working, and want to continue working. But it's a much bigger problem if you can't continue working, or you desperately want to stop working, and were going to rely on Social Security to support you in retirement.
Which is to say, raising the retirement age is a cut that targets people who hate their jobs and want to retire, but can't do so without Social Security.
And then there's the fact that the poor live shorter lives than the rich. If we raised the early retirement age from 62 to 65, then someone who lives until 80 would see their 18 years of retirement cut to 15 years — a loss of about 16 percent of their retirement. Someone who only lives until 73, however, would see their retirement cut from 11 years to eight — a loss of about 27 percent of their time in retirement.
This speaks, by the way, to one of the main justifications given for raising the retirement age: that Americans are living longer lives these days. But as this chart from the Peter G. Peterson Foundation shows, the recent increase in life expectancy has been much larger among the affluent.
(Peter G Peterson Foundation)
This is where we get into questions not of budget but of values: I think in a country as rich as ours, it should be possible for people who hate their jobs to retire relatively young, so they can spend a good portion of their adult life in retirement. I think it's easy for people who love their jobs to underestimate how soul-crushing it is to hate going into work every day. And so I prefer fixes to Social Security that don't force people who hate their jobs to spend longer in the workforce.
Perhaps that would change if we didn't have other options for closing Social Security's shortfall — but there are so, so many other paths that it baffles me why so many in Washington have fixated on this one.


text:
\section{Introduction}

Recently, there have been various neural network models proposed for sentence pair modeling tasks, including semantic similarity \cite{agirre-EtAl:2015:SemEval2}, paraphrase identification \cite{dolan2004unsupervised,xu2015semeval}, natural language inference \cite{bowman-EtAl:2015:EMNLP}, etc. Most, if not all, of these state-of-the-art neural models \cite{TACL831,parikh-EtAl:2016:EMNLP2016,he-lin:2016:N16-1,tomar2017neural,shen-yang-deng:2017:EMNLP2017} have achieved the best performances for these tasks by using pretrained word embeddings, but results without pretraining are less frequently reported or noted. In fact, we will show that, even with fixed randomized word vectors, the pairwise word interaction model \cite{he-lin:2016:N16-1} based on contextual word vector similarities can still achieve strong performance by capturing identical words and similar surface context features. Moreover, pretrained word embeddings generally have poor coverage in social media domain where out-of-vocabulary rate often reaches over 20\% \cite{baldwin-EtAl:2013:IJCNLP}.

We investigated the effectiveness of subword units, such as characters and character n-grams, in place of words for vector representations in sentence pair modeling. Though it is well-known that subword representations are effective to model out-of-vocabulary words in many NLP tasks with a single sentence input, such as machine translation \cite{luong-EtAl:2015:ACL-IJCNLP,costajussa-fonollosa:2016:P16-2}, language modeling \cite{wang:2015,vania-lopez:2017:Long}, and sequence labeling \cite{santos2015boosting,plank-sogaard-goldberg:2016:P16-2}, they are not systematically studied in the tasks that concern pairs of sentences. Unlike in modeling individual sentences, subword representations have impacts not only on the out-of-vocabulary words but also more directly on the relation between two sentences, which is calculated based on vector similarities in many sentence pair modeling approaches (more details in Section \ref{sec:pair_models}). For example, while subwords may capture useful string similarities between a pair of sentences (e.g. spelling or morphological variations: \textit{sister} and \textit{sista}, \textit{teach} and \textit{teaches}), they could introduce errors (e.g. similarly spelled words with completely different meanings: \textit{ware} and \textit{war}).

To better understand the role of subword embedding in sentence pair modeling, we performed experimental comparisons that vary (1) the type of subword unit, (2) the composition function, and (3) the datasets of different characteristics. We also presented experiments with language modeling as an auxiliary multi-task learning objective, showing consistent improvements. Taken together, subword and language modeling establish new state-of-the-art results in two social media datasets and competitive results in a news dataset for paraphrase identification without using any pretrained word embeddings.

\begin{table*}[ht!]
\centering
\small
\begin{tabular}{ccccccc}
\hline
Dataset & Training Size& Test Size & \# INV & \# OOV & OOV Ratio & Source \\
\hline
PIT-2015 & 11530 & 838 & 7771 & 1238 & 13.7\% & Twitter trends \\
Twitter-URL & 42200 & 9324 & 24905 & 11440 & 31.5\% & Twitter/news \\
MSRP & 4076 & 1725 & 16226 & 1614 & 9.0\% & news \\
\hline
\end{tabular}
\caption{Statistics of three benchmark datasets for paraphrase identification. The training and testing sizes are in numbers of sentence pairs. The number of unique in-vocabulary (INV) and out-of-vocabulary (OOV) words are calculated based on the publicly
available GloVe embeddings (details in Section \ref{sec:settings}).}
\label{datasets_summary}
\end{table*}

\section{Sentence Pair Modeling with Subwords}

The current neural networks for sentence pair modeling \cite[etc]{TACL831,parikh-EtAl:2016:EMNLP2016,he-lin:2016:N16-1,liu-EtAl:2016:EMNLP20162,tomar2017neural,wang2017bilateral,shen-yang-deng:2017:EMNLP2017} follow a more or less similar design with three main components: (a) contextualized word vectors generated via Bi-LSTM, CNN, or attention, as inputs; (b) soft or hard word alignment and interactions across sentences; (c) and the output classification layer. Different models vary in implementation details, and most importantly, to capture the same essential intuition in the word alignment (also encoded with contextual information) -- the semantic relation between two sentences depends largely on the relations of aligned chunks \cite{agirre-EtAl:2016:SemEval2}. In this paper, we used pairwise word interaction model \cite{he-lin:2016:N16-1} as a representative example and staring point, which reported robust performance across multiple sentence pair modeling tasks and the best results by neural models on social media data \cite{lan2017continuously}.

\subsection{Pairwise Word Interaction (PWI) Model }
\label{sec:pair_models}

Let \(\bm{w}^a= (\bm{w}^a_{1},...,\bm{w}^a_{m})\) and \(\bm{w}^b= (\bm{w}^a_{1},...,\bm{w}^b_{n})\) be the input sentence pair consisting of \( m\) and \(n\) tokens, respectively. Each word vector \(\bm{w}_{i}\) $\in$ \(\mathbb{R}^d\) is initialized with pretrained \(d\)-dimensional word embedding \cite{pennington2014glove, TACL571, wieting2016towards}, then encoded with word context and sequence order through bidirectional LSTMs:
\begin{align}
    &\overrightarrow{\bm{h}}_{i} = LSTM^{f}(\bm{w}_{i}, \overrightarrow{\bm{h}}_{i-1}) \\
    &\overleftarrow{\bm{h}}_{i} = LSTM^{b}(\bm{w}_{i}, \overleftarrow{\bm{h}}_{i+1}) \\
    &\overleftrightarrow{\bm{h}}_{i} = [\overrightarrow{\bm{h}}_{i}, \overleftarrow{\bm{h}}_{i}] \\
    &\bm{h}^{+}_{i} = \overrightarrow{\bm{h}}_{i} + \overleftarrow{\bm{h}}_{i}
\end{align}
\noindent where \(\overrightarrow{\bm{h}}_{i}\) represents forward hidden state, \(\overleftarrow{\bm{h}}_{i}\) represents backword hidden state, and \(\overleftrightarrow{\bm{h}}_{i}\) and \(\bm{h}^{+}_{i}\) are the concatenation and summation of two directional hidden states.

For all word pairs $(\bm{w}^a_{i}, \bm{w}^b_{j})$ across sentences, the model directly calculates word pair interactions using cosine similarity, Euclidean distance, and dot product over the outputs of the encoding layer:
\begin{align}\label{eq:tensor}
    D(\overrightarrow{\bm{h}}_{i}, \overrightarrow{\bm{h}}_{j}) & = [cos(\overrightarrow{\bm{h}}_{i}, \overrightarrow{\bm{h}}_{j}), \\
    &\qquad L2Euclid(\overrightarrow{\bm{h}}_{i},\overrightarrow{\bm{h}}_{j}), \nonumber\\
    &\qquad DotProduct(\overrightarrow{\bm{h}}_{i},\overrightarrow{\bm{h}}_{j}) ]. \nonumber
\end{align}
\noindent The above equation can also apply to other states \(\overleftarrow{\bm{h}}\), \(\overleftrightarrow{\bm{h}}\) and \(\bm{h}^{+}\), resulting in a tensor \(\mathbf{D}^{13 \times m \times n}\) after padding one extra bias term. A ``hard'' attention is applied to the interaction tensor to further enforce the word alignment, by sorting the interaction values and selecting top ranked word pairs. A 19-layer-deep CNN is followed to aggregate the word interaction features and the softmax layer to predicate classification probabilities.

\subsection{Embedding Subwords in PWI Model}
Our subword models only involve modification of the input representation layer in the pairwise interaciton model. Let \( {c}_{1},...,{c}_{k}\) be the subword (character unigram, bigram and trigram) sequence of a word $w$. The subword embedding matrix is \( \mathbf{C} \in \mathbb{R}^{d'*k}\), where each subword is encoded into the \(d'\)-dimension vector. The same subwords will share the same embeddings. We considered two different composition functions to assemble subword embeddings into word embedding:

\paragraph{Char C2W} \cite{wang:2015} applies Bi-LSTM to subword sequence \( {c}_{1},...,{c}_{k}\), then the last hidden state \( \overrightarrow{\bm{h}}^{char}_{k}\) in forward direction and the first hidden state \( \overleftarrow{\bm{h}}^{char}_{0}\) of the backward direction are linearly combined into word-level embedding \( \bm{w}\):
\begin{align}
    \bm{w} = \bm{W}_{f} \cdot \overrightarrow{\bm{h}}^{char}_{k} + \bm{W}_{b} \cdot \overleftarrow{\bm{h}}^{char}_{0} + \bm{b}
\end{align}
\noindent where \(\bm{W}_{f}\), \(\bm{W}_{b}\) and \( \bm{b}\) are parameters.

\paragraph{Char CNN} \cite{kim2016character} applies a convolution operation between subword sequence matrix \( \mathbf{C}\) and a filter \(\mathbf{F} \in \mathbb{R}^{d' \times l}\) of width \(l\) to obtain a feature map \( \mathbf{f} \in \mathbb{R}^{k-l+1}\):
\begin{align}
    \mathbf{f}_j = tanh(\langle\mathbf{C}[\ast, j : j + l-1],\mathbf{F}\rangle + b)
\end{align}
\noindent where \(\langle A, B \rangle = Tr (\bm{A}\bm{B}^{T}) \) is the Frobenius inner product, \(b\) is a bias and \(\mathbf{f}_j\) is the \(j\)th element of \(\mathbf{f}\). We then take the max-over-time operation to select the most important element:
\begin{align}
    y_{\mathbf{f}}=\max_{j} \mathbf{f}_j.
\end{align}
After applying \(q\) filters with varied lengths, we can get the array \(\bm{w}=[{y}_{1},...,{y}_{q}]\), which is followed by a one-layer highway network to generate final word embedding.

\subsection{Auxiliary Language Modeling (LM)}
We adapted a multi-task structure, originally proposed by \cite{DBLP:journals/corr/Rei17} for sequential tagging, to further improve the subword representations in sentence pair modeling. In addition to training the model for sentence pair tasks, we used a secondary language modeling objective that predicts the next word and previous word using softmax over the hidden states of Bi-LSTM as follows:
\begin{align}
\overrightarrow{E}_{LM} = - \sum^{T-1}_{t=1}(\log(P(w_{t+1}|\overrightarrow{\bm{m}_t})) \\
\overleftarrow{E}_{LM} = - \sum^{T}_{t=2}(\log(P(w_{t-1}|\overleftarrow{\bm{m}_t}))
\end{align}
\noindent where $\overrightarrow{\bm{m}_t} = tanh(\overrightarrow{\bm{W}}_{hm} \overrightarrow{\bm{h}}_t)$ and $\overleftarrow{\bm{m}_t} = tanh(\overleftarrow{\bm{W}}_{hm} \overleftarrow{\bm{h}}_t)$. The Bi-LSTM here is separate from the one in PWI model. The language modeling objective can be combined into sentence pair modeling through a joint objective function:
\begin{equation}
E_{joint} = E + \gamma (\overrightarrow{E}_{LM} + \overleftarrow{E}_{LM}),
\end{equation}
\noindent which balances subword-based sentence pair modeling objective $E$ and language modeling with a weighting coefficient \(\gamma\).

\section{Experiments}

\begin{table*}[t]
\footnotesize
\centering
\begin{tabular}{cccccccc}
\hline
 & \multicolumn{2}{c}{Model Variations} & pre-train & \#parameters & Twitter URL & PIT-2015 & MSRP \\%& Quora (acc. 40k) & Hindi (F1)\\
\hline

\multirow{6}{*}{Word Models}  & \multicolumn{2}{c}{Logistic Regression} & -- & -- & 0.683  & 0.645 & 0.829 \\
&  \multicolumn{2}{c}{\cite{lan2017continuously}} & Yes & 9.5M & 0.749  & \underline{0.667} & 0.834 \\
 \cline{2-8}
& \multicolumn{2}{c}{pretrained,  fixed} & Yes & 2.2M & 0.753 & 0.632  & 0.834 \\%&  0.778 \\%& -\\

& \multicolumn{2}{c}{pretrained, updated} & Yes & 9.5M & 0.756 & 0.656  & 0.832 \\%& 0.798 (0.809) \\%& -\\
& \multicolumn{2}{c}{randomized, fixed} & --  & 2.2M & 0.728 & 0.456  & 0.821 \\%& \\%& 0.620\\
& \multicolumn{2}{c}{randomized, updated} & --  & 9.5M & 0.735 & 0.625 & 0.834 \\%& 0.755  \\%& 0.621\\
\hline
\hline
\multirow{6}{*}{Subword Models} & \multicolumn{2}{c}{C2W, unigram} & --  & 2.6M & 0.742 & 0.534 & 0.816 \\%& \\%& 0.620\\
& \multicolumn{2}{c}{C2W, bigram} & -- & 2.7M & 0.742 & 0.563 & 0.825 \\%& \\%& \\
& \multicolumn{2}{c}{C2W, trigram} & -- & 3.1M & 0.729 & 0.576 & 0.824 \\%& \\%& \\
& \multicolumn{2}{c}{CNN, unigram} & -- & 6.5M & 0.756 & 0.589 & 0.820 \\%& \\% &\\
& \multicolumn{2}{c}{CNN, bigram} & -- & 6.5M & 0.760 & 0.646 & 0.814 \\%& \\%& \\
& \multicolumn{2}{c}{CNN, trigram} & -- & 6.7M & 0.753 & \underline{0.667} & 0.818 \\%& \\%&  \\
\hline
\hline
\multirow{6}{*}{Subword+LM} & \multicolumn{2}{c}{LM, C2W, unigram} & -- &3.5M & 0.760 & \textbf{0.691} & 0.831 \\%& 0.729 \\%& 0.620\\
& \multicolumn{2}{c}{LM, C2W, bigram} & -- &3.6M & \textbf{0.768} & 0.651 & 0.830 \\%& 0.787 (0.802) \\%& 0.623\\
& \multicolumn{2}{c}{LM, C2W, trigram} & -- &4.0M & \underline{0.765} & 0.659 & 0.831\\%& \\%& \\
& \multicolumn{2}{c}{LM, CNN, unigram} & -- &7.4M & 0.754 & 0.665 & \textbf{0.840} \\%& 0.759 \\%& 0.621\\
& \multicolumn{2}{c}{LM, CNN, bigram} & -- & 7.4M& 0.761 & \underline{0.667}& \underline{0.835} \\% & 0.777 (0.803) \\%& 0.620\\
& \multicolumn{2}{c}{LM, CNN, trigram}& -- & 7.6M& 0.759 & \underline{0.667} & 0.831 \\%& 0.783 (0.809) \\%& \\
\hline
\end{tabular}
\caption{Results in F1 scores on Twitter-URL, PIT-2015 and MSRP datasets. The best performance figure in each dataset is denoted in \textbf{bold} typeface and the second best is denoted by an \underline{underline}. Without using any pretrained word embeddings, the Subword+LM models achieve better or competitive performance compared to word models.}
\label{whole_table_results}
\end{table*}

\subsection{Datasets}

We performed experiments on three benchmark datasets for paraphrase identification; each contained pairs of naturally occurring sentences manually labeled as paraphrases and non-paraphrases for binary classification: \textbf{Twitter URL} \cite{lan2017continuously} was collected from tweets sharing the same URL with major news outlets such as @CNN. This dataset keeps a balance between formal and informal language. \textbf{PIT-2015} \cite{Xu-EtAl-2014:TACL,xu2015semeval} comes from the Task 1 of Semeval 2015 and was collected from tweets under the same trending topic, which contains varied topics and language styles. \textbf{MSRP} \cite{dolan2005automatically} was derived from clustered news articles reporting the same event in formal language. Table \ref{datasets_summary} shows vital statistics for all three datasets.

\subsection{Settings}
\label{sec:settings}

To compare models fairly without implementation variations, we reimplemented all models into a single PyTorch framework.\footnote{The code and data can be obtained from the first and second
author's websites.} We  followed the setups in \cite{he-lin:2016:N16-1} and \cite{lan2017continuously} for the pairwise word interaction model, and used the 200-dimensional GloVe word vectors \cite{pennington2014glove}, trained on 27 billion words from Twitter (vocabulary size of 1.2 milion words) for social media datasets, and 300-dimensional GloVe vectors, trained on 840 billion words (vocabulary size of 2.2 milion words) from Common Crawl for the MSRP dataset. For cases without pretraining, the word/subword vectors were initialized with random samples drawn uniformly from the range [−0.05, 0.05]. We used the same hyperparameters in the C2W \cite{wang:2015} and CNN-based  \cite{kim2016character} compositions for subword models, except that the composed word embeddings were set to 200- or 300- dimensions as the pretrained word embeddings to make experiment results more comparable. For each experiment, we reported results with 20 epochs.

\subsection{Results}
Table \ref{whole_table_results} shows the experiment results on three datasets. We reported maximum F1 scores of any point on the precision-recall curve \cite{Lipton:2014:OTC:3120306.3120322} following previous work.

\paragraph{Word Models} The word-level pairwise interaction models, even without pretraining (randomzied) or fine-tuning (fixed), showed strong performance across all three datasets. This reflects the effective design  of the BiLSTM and word interaction layers, as well as the unique character of sentence pair modeling, where n-gram overlapping positively signifies the extent of semantic similarity. As a reference, a logistic regression baseline with simple n-gram  (also in stemmed form) overlapping features can also achieve good performance on PIT-2015 and MSRP datasets. With that being said, pretraining and fine-tuning word vectors are mostly crucial for pushing out the last bit of performance from word-level models.

\begin{table}[bp!]
\footnotesize
\centering
\resizebox{\columnwidth}{!}{\begin{tabular}{|c|c|c|c|c|}
    \hline
    \multirow{2}{*}{Model} & \multicolumn{2}{c|}{INV Words} &  \multicolumn{2}{c|}{OOV Words} \\
    \cline{2-5}
    & \textit{any} & \textit{walking} & \textit{\#airport} & \textit{brexit} \\
    \hline
    \multirow{4}{*}{Word} & \textit{anything} & \textit{walk} & \textit{salomon} & \textit{bollocks}\\
    & \textit{anyone} & \textit{running} & \textit{363} & \textit{misogynistic} \\
    & \textit{other} & \textit{dead} & \textit{\#trumpdchotel} & \textit{patriarchy}\\
    & \textit{there} & \textit{around} & \textit{hillarys} & \textit{sexist} \\
    \hline
    \multirow{4}{*}{Subword} & \textit{analogy} & \textit{waffling} & \textit{@atlairport} & \textit{grexit}\\
     & \textit{nay} & \textit{slagging} & \textit{\#dojreport} & \textit{bret}\\
     & \textit{away} & \textit{scaling} & \textit{\#macbookpro} & \textit{juliet}\\
     & \textit{andy} & \textit{\#hacking} & \textit{\#guangzhou} & \textit{\#brexit}\\
    \hline

    \multirow{4}{*}{Subword} & \textit{any1} & \textit{warming} & \textit{airport} & \textit{\#brexit} \\
    \multirow{4}{*}{+ LM }& \textit{many} & \textit{wagging} & \textit{\#airports} & \textit{brit} \\
    & \textit{ang} & \textit{waging} & \textit{rapport} & \textit{ofbrexit} \\
    & \textit{nanny} & \textit{waiting} & \textit{\#statecapturereport} & \textit{drought-hit}\\
    \hline
    \end{tabular}}
    \caption{Nearest neighbors of word vectors under cosine
similarity in Twitter-URL dataset.}
    \label{tab:nearest_neighbor_words}
\end{table}

\paragraph{Subword Models (+LMs)} Without using any pretrained word embeddings, subword-based pairwise word interaction models can achieve very competitive results on social media datasets compared with the best word-based models (pretrained, fixed). For MSRP with only 9\% of OOV words (Table \ref{datasets_summary}), the subword models do not show advantages. Once the subword models are trained with multi-task language modeling (Subword+LM), the performance on all datasets are further improved, outperforming the best previously reported results by neural models \cite{lan2017continuously}. A qualitative analysis reveals that subwords are crucial for out-of-vocabulary words while language modeling ensures more semantic and syntactic compatibility (Table \ref{tab:nearest_neighbor_words}).

\begin{comment}
\begin{table*}[t]
\footnotesize
\centering
\begin{tabular}{cccccccc}
\hline
Model Variations & Aggregation & \#parameters  & \#hours/epoch &  Twitter URL & PIT-2015 & MSRP \\
\hline
Logistic Regression & -- & -- & -- & 0.683  & 0.645 & 0.829 \\

\multirow{2}{*}{pretrained, word, fixed} &  CNN$^{19}$ + MLP & 2.2M & 4.5h & 0.753 & 0.632  & \underline{0.834} \\

&  MLP & 1.4M & 3.2h & 0.741 & 0.602  & 0.827 \\
\hline
\hline
\multirow{2}{*}{C2W, unigram} &  CNN$^{19}$ + MLP & 2.6M & 5.8h & 0.742 & 0.534 & 0.816 \\
 & MLP & 1.4M & 4.6h & 0.741 & 0.655 & 0.808 \\
\multirow{2}{*}{CNN, unigram} &  CNN$^{19}$ + MLP & 6.5M & 5.4h & 0.756 & 0.589 & 0.820 \\
  & MLP & 5.3M & 4.2h & \underline{0.759} & 0.659 & 0.809 \\
\hline
\hline
\multirow{2}{*}{LM, C2W, unigram} &  CNN$^{19}$ + MLP & 3.5M & 6.5h & \textbf{0.760} & \textbf{0.691} & 0.831 \\
 & MLP & 2.3M & 5.3h & 0.746 & 0.625 & 0.811 \\
\multirow{2}{*}{LM, CNN, unigram} &  CNN$^{19}$ + MLP & 7.4M & 5.8h & 0.754 & \underline{0.665} & \textbf{0.840} \\
 & MLP & 6.2M & 4.6h & 0.758 & 0.659 & 0.809 \\
\hline
\end{tabular}
\caption{Comparison of F1 scores between the original PWI model with 19-layer CNN for aggregation and the simplified model without 19-layer CNN on Twitter-URL, PIT-2015 and MSRP datasets. The number of parameters and training time per epoch shown are based on the Twitter URL dataset and a single NVIDIA Pascal P100 GPU.}
\label{19-layer-cnn_vs_2-layer-mlp}
\end{table*}
\end{comment}

\begin{table*}[t]
\footnotesize
\centering
\begin{tabular}{cccccccc}
\hline
& Model Variations & CNN$^{19}$ & \#parameters  & \#hours/epoch &  Twitter URL & PIT-2015 & MSRP \\
\hline
\multirow{3}{*}{Word Models} & Logistic Regression & -- & -- & -- & 0.683  & 0.645 & 0.829 \\
\cline{2-8}
& \multirow{2}{*}{pretrained, fixed} &  Yes & 2.2M & 4.5h & 0.753 & 0.632  & \underline{0.834} \\

& &  --  & 1.4M & 3.2h & 0.741 & 0.602  & 0.827 \\
\hline
\hline
\multirow{4}{*}{Subword Models}  & \multirow{2}{*}{C2W, unigram} &  Yes & 2.6M & 5.8h & 0.742 & 0.534 & 0.816 \\
& & --  & 1.4M & 4.6h & 0.741 & 0.655 & 0.808 \\
\cline{2-8}
& \multirow{2}{*}{CNN, unigram} &  Yes & 6.5M & 5.4h & 0.756 & 0.589 & 0.820 \\
&  & --  & 5.3M & 4.2h & \underline{0.759} & 0.659 & 0.809 \\
\hline
\hline
\multirow{4}{*}{Subword+LM}
& \multirow{2}{*}{LM, C2W, unigram} &  Yes & 3.5M & 6.5h & \textbf{0.760} & \textbf{0.691} & 0.831 \\
&  & -- & 2.3M & 5.3h & 0.746 & 0.625 & 0.811 \\
\cline{2-8}
& \multirow{2}{*}{LM, CNN, unigram} &  Yes & 7.4M & 5.8h & 0.754 & \underline{0.665} & \textbf{0.840} \\
& & -- & 6.2M & 4.6h & 0.758 & 0.659 & 0.809 \\
\hline
\end{tabular}
\caption{Comparison of F1 scores between the original PWI model with 19-layer CNN for aggregation and the simplified model without 19-layer CNN on Twitter-URL, PIT-2015 and MSRP datasets. The number of parameters and training time per epoch shown are based on the Twitter URL dataset and a single NVIDIA Pascal P100 GPU.}
\label{19-layer-cnn_vs_2-layer-mlp}
\end{table*}

\subsection{Combining Word and Subword Representations}
In addition, we experimented with combining the pretrained word embeddings and subword models with various strategies: concatenation, weighted average, adaptive models \cite{miyamoto-cho:2016:EMNLP2016} and attention models \cite{rei-crichton-pyysalo:2016:COLING}. The weighted average outperformed all others but only showed slight improvement over word-based models in social media datasets; other combination strategies could even lower the performance. The best performance was 0.763 F1 in Twitter-URL and 0.671 in PIT-2015 with a weighted average: 0.75 $\times$ word embedding $+$ 0.25 $\times$ subword embedding.

\section{Model Ablations}
In the original PWI model, He and Lin \shortcite{he-lin:2016:N16-1} performed pattern recognition of complex semantic relationships by applying a 19-layer deep convolutional neural network (CNN) on the word pair interaction tensor (Eq. \ref{eq:tensor}). However, the SemEval task on Interpretable Semantic Textual Similarity \cite{agirre-EtAl:2016:SemEval2} in part demonstrated that the semantic relationship between two sentences depends largely on the relations of aligned words or chunks. Since the interaction tensor in the PWI model already encodes word alignment information in the form of vector similarities, a natural question is whether a 19-layer CNN is necessary.

Table \ref{19-layer-cnn_vs_2-layer-mlp} shows the results of our systems with and without the 19-layer CNN for aggregating the pairwise word interactions before the final softmax layer. While in most cases the 19-layer CNN helps to achieve better or comparable performance, it comes at the expense of $\sim$25\% increase of training time. An exception is the character-based PWI without language model, which performs well on the PIT-2015 dataset without the 19-layer CNN and comparably to logistic regression with string overlap features \cite{eyecioglu-keller:2015:SemEval}. A closer look into the datasets reveals that PIT-2015 has a similar level of unigram overlap as the Twitter URL corpus (Table \ref{tab:character_overlap}),\footref{footnote_1} but lower character bigram overlap (indicative of spelling variations) and lower word bigram overlap (indicative of word reordering) between the pairs of sentences that are labeled as paraphrase.

\begin{table}[bp!]
\footnotesize
\centering
\resizebox{\columnwidth}{!}{\begin{tabular}{l|c|c|c}
    \hline
     & Twitter URL & PIT-2015 & MSRP \\
     \hline
     \#char unigrams in shorter sentence (all) & 67.5 & 36.2 & 109.0 \\

     \#char unigrams in longer sentence (all) & 97.7 & 50.5 & 128.5 \\

     \#char unigrams of the union (all) & 101.1 & 53.0 & 130.0 \\

     \#char unigrams of the intersection  (all)& 64.1 & 33.7 & 107.4 \\

     char unigram overlap (all) & 63.4\% & 63.5\% & 82.6\% \\
     char unigram overlap (paraphrase-only) & 68.8\% & 67.0\% & 84.7\% \\
     \hline
     char bigram overlap (all) & 30.8\% & 33.6\% & 67.4\% \\
     char bigram overlap (paraphrase-only) & 48.2\% & 42.4\% & 71.6\% \\
     \hline

     word unigram overlap (all) & 13.3\% & 21.7\% & 54.8\% \\
     word unigram overlap (paraphrase-only) & 32.0\% & 30.2\% & 59.1\% \\

     \hline
     word bigram overlap (all) & 5.3\% & 8.4\% & 33.2\% \\
     word bigram overlap (paraphrase-only) & 17.9\% & 12.3\% & 36.8\% \\
     \hline
    \end{tabular}}
    \caption{Character and word overlap comparison.}
    \label{tab:character_overlap}
\end{table}

The 19-layer CNN appears to be crucial for the MSRP dataset, which has the smallest training size and is skewed toward very high word overlap.\footnote{See more discussions in \cite{lan2017continuously}. \label{footnote_1}} For the two social media datasets, our subword models have improved performance compared to pretrained word models regardless of having or not having the 19-layer CNN.

\section{Conclusion}

We presented a focused study on the effectiveness of subword models in sentence pair modeling and showed competitive results without using pretrained word embeddings. We also showed that subword models can benefit from multi-task learning with simple language modeling, and established new start-of-the-art results for paraphrase identification on two Twitter datasets, where out-of-vocabulary words and spelling variations are profound. The results shed light on future work on language-independent paraphrase identification and multilingual paraphrase acquisition where pretrained word embeddings on large corpora are not readily available in many languages.

\section*{Acknowledgments}
We thank John Wieting for valuable discussions, and Ohio Supercomputer Center \cite{Oakley2012} for computing resources. Funding was provided by the U.S. Army Research Office (ARO) and Defense Advanced Research Projects Agency (DARPA) under Contract Number W911NF-17-C-0095. The content of the information in this document does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation here on.

redpajama_set_name: RedPajamaArXiv


text:
Genre:BUSINESS & ECONOMICS
SubGenre:Business Communication / General
Hit the Market Running
Plans, Programs, Templates and Guidelines for High-tech Startup Companies
by Tanya Candia
Best practices, plans, programs and guidelines for high-tech startup companies with great products but limited budgets. Learn how to get funding, understand the marketplace, execute effective marketing efforts, and launch your product in a way that leads to impressive, sustained revenues.
Hit the Market Running is a complete and informative step-by-step guide for high tech marketing communications and product launch. This book is a gold mine for people who want their marketing information by the numbers, and comes complete with "how-to" forms and templates. From funding to pricing to competitive analysis to product strategy, all the way through to launch and beyond, Candia explains how to do it in an east to read format. This is an invaluable guidebook for high tech startup companies and the entrepreneurs who back them.
Candia served as vice president of worldwide marketing at F-Secure, the Finnish security software company, which she took through its initial public offering. She successfully helped grow F-Secure to over $40M in revenue by renaming and repositioning the company as the leader in managed wireless security for the distributed enterprise. Candia holds an MA in Communications from the Monterey Institute of International Studies, and an MS in Systems Management from the University of Southern California. She runs Candia Communications LLC, an international business consulting firm. She lives in Saratoga with her husband and two daughters, three cats and two dogs.
eBook - $12.99 (AZW, EPub, PDF)
Other titles by this author/publisher
Long Nights: Short Years by Julie Carlyle
Lost Letters by Lori Roberts
Willow by Lori Roberts
Grace Trumps Guilt by Donna Cole
Who Moved My Dentures? by Anthony Cirillo
Build Market Momentum by Tanya Candia
A Hobo Odyssey by Larry O. Nichols
Quiet Guilt by Clare Adkin


text:
Q: SQL ORDER BY DECODE is sorting number as a string? I have a table of flight records created as follows
CREATE TABLE FLIGHT_DETAILS
(
FLIGHT_ID           NUMBER(10) PRIMARY KEY,
FLIGHT_NO           VARCHAR2(10),
DEPARTURE_DTE       DATE,
TOTAL_PASSENGERS    NUMBER(3)
);

I then have a function that my application calls to retrieve the records sorted by the selected column (in descending order).
CREATE OR REPLACE FUNCTION func_get_flight_details (

p_order_col IN CHAR )

RETURN sys_refcursor
AS
    v_ref_cursor    sys_refcursor;
    v_sql_str       VARCHAR2(2048);
BEGIN
OPEN v_ref_cursor FOR

SELECT FLIGHT_NO, DEPARTURE_DTE, TOTAL_PASSENGERS
FROM FLIGHT_DETAILS
ORDER BY DECODE(p_order_col,
        'FLIGHT_NO', FLIGHT_NO,
        'DEPARTURE_DTE', TO_DATE(DEPARTURE_DTE, 'YYYY/MM/DD'),
        'TOTAL_PASSENGERS', TOTAL_PASSENGERS) DESC;

RETURN v_ref_cursor;
END;

Sorting by both FLIGHT_NO and DEPARTURE_DTE works correctly. My problem comes when I tried to sort by TOTAL_PASSENGERS, which got me this
FLIGHT_NO   DEPARTURE_DTE   TOTAL_PASSENGERS
-------------------------------------------------
OR3237      01/03/16        9
RM7202      15/01/16        50
CQ8429      05/10/16        250
DA5720      21/07/16        100

For some reason, DECODE is sorting the NUMBER column as a string. To test, I tried this
SELECT FLIGHT_NO, DEPARTURE_DTE, TOTAL_PASSENGERS
FROM FLIGHT_DETAILS
ORDER BY TOTAL_PASSENGERS DESC;

which gave me
FLIGHT_NO   DEPARTURE_DTE   TOTAL_PASSENGERS
-------------------------------------------------
CQ8429      05/10/16        250
DA5720      21/07/16        100
RM7202      15/01/16        50
OR3237      01/03/16        9

proving that the issue wasn't with the column itself.
Then I tried some solutions I found on SO
ORDER BY DECODE(p_order_col,
        'FLIGHT_NO', FLIGHT_NO,
        'DEPARTURE_DTE', TO_DATE(DEPARTURE_DTE, 'YYYY/MM/DD'),
        'TOTAL_PASSENGERS', TO_NUMBER(TOTAL_PASSENGERS)) DESC;

ORDER BY DECODE(p_order_col,
        'FLIGHT_NO', FLIGHT_NO,
        'DEPARTURE_DTE', TO_DATE(DEPARTURE_DTE, 'YYYY/MM/DD'),
        'TOTAL_PASSENGERS', LPAD(TOTAL_PASSENGERS, 10)) DESC;

ORDER BY DECODE(p_order_col,
        'FLIGHT_NO', FLIGHT_NO,
        'DEPARTURE_DTE', TO_DATE(DEPARTURE_DTE, 'YYYY/MM/DD'),
        'TOTAL_PASSENGERS', TOTAL_PASSENGERS*1) DESC;

none of which worked (it's still sorted as a string).
So why does DECODE refuse to sort a number column as a number? And how do I get it to sort correctly?

A: Your problem is that decode() is an expression and only returns one type.  So, the types have to be converted.
You should use case anyway.  My preferred method is multiple statements:
ORDER BY (CASE WHEN p_order_col = 'FLIGHT_NO' THEN FLIGHT_NO END),
         (CASE WHEN p_order_col = 'DEPARTURE_DTE' THEN TO_DATE(DEPARTURE_DTE, 'YYYY/MM/DD') END)
         (CASE WHEN p_order_col = 'TOTAL_PASSENGERS' THEN TOTAL_PASSENGERS END) DESC;

Each expression sorts by one key.  If the sort key does not match, then the result of the expression is NULL -- all rows get the same value, so it doesn't affect the ordering.

A: There will be little to no performance impact in the example answer posted by Gordon. You are not really doing 3 separate sorts; it's still a singe sort operation. A quick test will show this:
select ename, sal, mgr
from emp
order by ( case when 'SAL' = 'SAL' then sal end )
,        ( case when 'SAL' = 'NAME' then ename end )

-----------------------------------------------------------------------------------
| Id  | Operation                  | Name | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |      |       |       |    21 (100)|          |
|   1 |  SORT ORDER BY             |      |    14 |   196 |    21  (10)| 00:00:01 |
|   2 |   TABLE ACCESS STORAGE FULL| EMP  |    14 |   196 |    20   (5)| 00:00:01 |
-----------------------------------------------------------------------------------

However I would caution an approach where you try to write a single "generic" SQL statement to handle all sorts of different cases. While this kind of "clever" SQL statement can work functionally, it can be a disaster for performance. It's much better to a have a separate SQL statements. In this example posted here, It would be fairly easy to have three different SQL statements, each with a specific ORDER BY clause, and then use a simple IF THEN ELSE on the input parameter to determine which SQL statement to run


About Tamira Thayne | Tamira Thayne's "Untethered"
Reverend Tamira Thayne, pictured left, strongly believes that God's Laws Protect Animals, and Man's Laws Protect Abusers. She seeks to change this status quo.
Tamira Thayne, DN, Rev, was labeled the 'animal lover' in the family from an early age growing up on a farm in rural Pennsylvania. This love combined with a deep empathy for dogs—the most social of beings—ostracized to the end of a chain led her to form Dogs Deserve Better, Inc. in the fall of 2002. Ms. Thayne led the organization for 12.5 years, passing the reins of the organization to new leadership in March of 2015.
Dogs Deserve Better, a nonprofit dedicated to freeing the chained dogs and bringing them into the home and family, bought Michael Vick's former dogfighting compound in Smithfield, Virginia, in 2011 and has been transforming it to the Good Newz Rehab Center for Chained and Penned Dogs.
Through her work for chained dogs, Tamira has suffered greatly in her quest to bring them freedom, but only a fraction as much as the dogs suffer. She has spent 877 hours chained to doghouses in front of State Capitols and other locations educating and advocating for better laws; she has spoken at over 75 venues nationwide about the issue and how we can be better friends to those who depend on us for their very existence; she has been arrested four times for helping dogs left in deplorable conditions and sometimes so ill they were unable to stand.
In 2012, Tamira was falsely charged with animal cruelty by Surry County, Virginia authorities in an attempt to destroy her reputation and force her and her organization from the former Vick property. The DA? The very same commonwealth attorney who let Vick walk from all cruelty charges, despite Vick's own confession of the horrors inflicted on dogs who were poor fighters.
Thankfully, Surry County's attempt was unsuccessful; Thayne spent 30 days in a hunger strike on the courthouse lawn across from the Commonwealth Attorney's office to gain justice in a corrupt system. She collapsed on Day 30 and was hospitalized for two days due to systemic acidosis brought on by starvation. A lawsuit against Surry County Animal Control was settled in August 2015 for an undisclosed amount paid to Tamira Thayne and Dogs Deserve Better.
During the past thirteen years, Tamira has herself rescued and rehabilitated over 250 formerly chained and penned dogs; her organization, over 3000. The dog pictured above, Banshee, became one of her favorites, and he loved her with a fierceness she still misses to this day. He died of lung cancer in late 2010. Watch the video of his last fetch here.
Tamira holds a Doctor of Naturology from the American Institute of Holistic Theology, and is an ordained interfaith minister through AIHT, providing spiritual mentoring and counseling to animal activists. To contact her about mentoring or sessions, e-mail her at tami@tamiracithayne.com.
Thayne also is the creator of the best-selling Rescue Angel tattoo license. If you too would like to license this art for your next tattoo, a single license is available for only $6.99. Visit this link to read more and purchase your license.


Members Only! Join us for a private screening of A Place to Become: Montclair through the Eyes of the Glenridge Avenue YWCA Women (1920-1965).
A unique tour of the Crane House and Historic YWCA that looks at African-American history through newspapers, maps, magazines, and personal letters over 200 years.
Photo: Jack Spear Photography, LLC.


Arphaxad (aka Arpachshad or Arphacsad) died. He was one of the first children born after the flood in The Village of Eight. He is survived by his son Kaïnan, his son's son Sala (aka Salah), his son's son's son Eber (aka (Heber) and his son's son's son's son Peleg (aka Phaleg). Whew! Arphaxad was a very old man.


text:
Agenting
Gender divides
Genre musings
Publishing insights
Is SFF more mainstream than people give it credit for?
So at this year's London Book Fair I was invited to moderate a panel to discuss this very question. Joining me were David Bradley, the editor and chief of SFX and Total Film magazines and Rosie Fletcher, the editor of Total Film. It was a lively discussion and the audience seemed to enjoy it but it did get me thinking. What is this 'mainstream'? Is it an invite-only club? What do you have to do to get this collective's attention? And is this 'mainstream' welcoming to genre or do they shun it?
To my mind something has gone 'mainstream' once it's reached an audience who wouldn't normally engage with it. Take the Star Trek franchise - many of its new fans will have never seen the original series. Or Game of Thrones, while some of those watching the TV series may dabble in the books - they probably aren't waiting on the edge of their seats for the next one in quite the same way that the genre community is.
Many of these mainstream watchers/readers will claim not to enjoy fantasy or science fiction and then be slightly bemused as you try to explain that actually the BBC adaptation of Jonathan Strange and Mr Norrell that they are raving about is exactly that,
Jonathan Strange and Mr Norrell - Copyright BBC
But what is the 'mainstream'? Well women tend to be the majority of book buyers so it's safe to say that the general readership follows the same demographic. But in genre the female readership seems to be in the minority. There's a more equal balance in fantasy readers with about a 50/50 split - but SF, horror or graphic novels do tend to have a much stronger male readership. So that's part of your problem right there...
The other difficulty is actually reaching the mainstream. Something that is incredibly difficult to do when the majority of genre books are placed on SFF specific shelves, or sold online which is not as attractive to your average book browser.
So is an SFF project reaching the mainstream down to pure commercial success? Well yes, to a certain extent it is. Once a genre novel has been televised or filmed, you can almost guarantee that non-genre readers will give it a go. It's much more visible. A similar effect occurs when novels gain a literary status. Take Station Eleven, or The Time Traveller's Wife, The Handmaid's Tale - all could be considered genre novels but they have reached a considerably wider audience than the usual niche due mainly to their treatment in publishing, PR and marketing who have specifically aimed them at a more general readership.
And it can work - look at some of the fabulous books out over the last few years - The Girl With All The Gifts, The Martian, The Shining Girls - all have reached a level of commercial success that is outside of their normal genre fans. I know, from personal experience, that most agents, publishers, TV/film producers are all enthusiastic about finding that next big crossover novel.
The hurdle for any genre book to becoming a big commercial success is the engagement of the mainstream - and it's an incredibly difficult readership to pin down. The biggest frustration still is trying to work out how to encourage all those film and TV watchers who will happily indulge in the entire boxset of Battlestar Galactica that they would actually enjoy a science fiction novel. Why are they not reading Peter F. Hamilton?! Or how many millions $ are made through the Marvel and DC characters from an audience who, for the vast majority, will never pick up a comic book or graphic novel.
However, on the whole I am encouraged. The more shows like Doctor Who, Jonathan Strange and Mr Norrell, The Returned and Wayward Pines that air and start drawing in new audiences, the more likely that audience will be, at some stage, tempted to pick up an equivalent in book form. And we cannot underestimate the power of social media can have - it's that word-of-mouth phenomenon that can propel any novel into the mainstream if there's enough traction and excitement behind it!
Wayward Pines - Copyright Fox
There's no doubt in my mind that genre is already making great leaps and bounds into the mainstream. I'm just looking forward to seeing even more of it!
Tagged: SFF, genre. mainstream, Star Trek, Doctor Who, Jonathan Strange and Mr Norrell, Station Eleven, The Time Traveller's Wife, The Shining Girls
What makes a writer write?
A Change of Direction


text:
title: Archive
permalink: /archive/
font-awesome-icon: fa-archive
---

{% include listing_all.html %}