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Das Haus Steinweg 60 ist ein denkmalgeschütztes Gebäude in der Stadt Quedlinburg in Sachsen-Anhalt.

Lage
Es befindet sich in der historischen Quedlinburger Neustadt auf der Südseite des Steinwegs. Östlich grenzt das gleichfalls denkmalgeschützte Haus Steinweg 59, westlich das Haus Steinweg 61 an. Im Quedlinburger Denkmalverzeichnis ist es als Kaufmannshaus eingetragen.

Architektur und Geschichte
Das Anwesen besteht aus zwei straßenseitigen, unterschiedlich breiten Fachwerkhäusern. Der rechte, westliche Teil entstand im Jahr 1696 durch den Zimmermeister Andreas Besen, auf den eine der am Gebäude befindlichen Inschriften (A.BESEN Z.M) verweist. Die Fachwerkfassaden verfügen über profilierte Brüstungsbohlen und Füllhölzer. In den Gefachen wurden Zierausmauerungen eingesetzt. Darüber hinaus bestehen Pyramidenbalkenköpfe. 1936 wurde in die westliche Hälfte des Hauses ein Ladengeschäft im Stil des Historismus eingefügt. Bei den Umbau- und Neubauarbeiten der 1930er Jahre wurde auf eine Wiederherstellung der historischen Fassade geachtet.

Hofseitig befinden sich ebenfalls in Fachwerkbauweise errichtete umfangreiche Wirtschaftsbauten.

Literatur
 Landesamt für Denkmalpflege Sachsen-Anhalt (Hrsg.): Denkmalverzeichnis Sachsen-Anhalt. Band 7: Falko Grubitzsch, unter Mitwirkung von Alois Bursy, Mathias Köhler, Winfried Korf, Sabine Oszmer, Peter Seyfried und Mario Titze: Landkreis Quedlinburg. Teilband 1: Stadt Quedlinburg. Fliegenkopf, Halle 1998, ISBN 3-910147-67-4, Seite 255.

Einzelnachweise

Kulturdenkmal in Quedlinburg
Fachwerkhaus in Quedlinburg
Wohngebäude in Quedlinburg
Erbaut in den 1690er Jahren
Steinweg (Quedlinburg)


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The new Guidance and Examples to Promote Traffic Safety through Urban and Street Design by the World Resources Institute Ross Center for Sustainable Cities is now public.
Making urban travel safer is not only about health, but quality of life and creating sustainable, competitive, equitable and smart cities. Providing safe and convenient infrastructure opens up opportunities to all people. Walking and bicycling can thrive, helping curb emissions while offering active, healthy forms of transport. Mass transport can reach more people, helping cut vehicle emissions contributing to global warming and air pollution, while decreasing travel times. These solutions that benefit people also benefit the planet and economic development.
This resource is available on the website (mirror also available).


Tidex Exchange Review 2018 | Safe or a Scam? We've Checked!
If you have invested in crypto, there is a huge possibility that you have come across Tidex exchange during your search for a trading platform.
Even though Tidex is not among the giant players in the digital currency game, we can't ignore the fact that the company is quite popular these days. Unfortunately, the coin market is full of such products right now, which makes it hard to decide whether Tidex is the right company for the job. Because of this, I have decided to drop my Tidex review here, hoping to help someone make an informed decision. Read on!
Tidex is a digital currency exchange that was established in 2017 to allow users to trade in cryptocurrencies and Waves assets. The company was initially based in the UK but later moved its operations to Russia and the US (but you can access its services from any part of the world).
Tidex provides an easy to use platform. With an intuitive user interface and a wide range of functions, you are able to control and monitor your investments better.
A user-friendly interface for desktop, tablet and mobile phone. This is pretty awesome as you can perform transactions on the go.
24/7 customer support to answer queries and resolve complaints.
Apart from these features, Tidex offers high-grade tools such as technical analysis indicators, trading history, charts and raw information to help you decide the best investment for greater profits. If you are a Tidex trader, you can also enroll in their loyalty program whereby you will be rewarded with Waves tokens.
Every participant of the program receives 25 Waves for trades equal to 1BTC and above. You can also trade with other coins but their total value must be equivalent to 1 BTC for you to get the bonus. The main agenda of Tidex is to provide fast, simple, and reliable multifunctional trading. By providing the above benefits, it ensures that users enjoy seamless trading.
Tidex doesn't allow fiat withdrawals or deposits. This means that you cannot send or receive funds on your credit/debit card, PayPal or make a direct bank transfer. This is somehow logical, as the platform doesn't allow the exchange of coins for fiat currency. If you already have an account with Tidex, you can trade in with over 100 coins and several other Wave currencies like WEUR, USDT, and WUSD. Some of the cryptocoins listed on the company's website include Bitcoin, LiteCoin, Bitcoin Cash, Dash, Ethereum, Doge, EOS, etc.
However, some popular coins like Monero, Ripple, Zcash, and OmiseGo are not on the list while those that we rarely hear about, like Oceanlab, Monetha, Enjin Coin, etc. are on the front line. A bit strange. Don't you think? But I don't intend to spread doom on Tidex exchange. My point is they should include those coins that we have seen growing in popularity recently.
When Tidex started its operations, all transactions were free. This was a great way to attract customers to the exchange and it worked as their client base grew tremendously at the time. However, they later introduced a small fee, whereby the company levies 0.1% on market makers and market takers transactions.
Considering that this is what most crypto exchanges charge for transactions, I feel that Tidex is quite okay and affordable for everyone. But you still need to part with high withdrawal and deposit fees depending on the digital coin you are withdrawing or depositing. Some currencies will take almost half of your money.
You don't need any kind of verification to trade in Tidex. All you got to do is create an account and you are in. And you only need an email address and a password to do this.
The process is simple and takes just a few minutes. Once everything is set up, you can start making trades and investments.
Tidex exchange platform looks absolutely legitimate. With a professional interface, interesting trading markets, and a good trading volume, the company looks quite genuine. Furthermore, its collaboration with the Waves team adds more credibility to the exchange. However, there are some issues raised in one BitcoinTalk thread whereby a good number of users feel that listing Monspace (MSD) and OneCoin (a total scam) was a bad move. Now, those of you who have been in this game for some time are aware of the speculations geared towards MSD.
For starters, the currency doesn't have a wallet, block explorer, source code or even a GitHub repository. This gets the crypto community wondering why such a cryptocurrency should be in existence in the first place. Sure, we understand that less popular exchanges must list a few lesser-known currencies to gain popularity and increase trading volume but most Tidex users feel that MSD is a bad decision by the team. As we speak, there are several scam warnings that have been posted across the internet in regards to Monspace, which raises eyebrows on the currency's legitimacy. It is clear that investors are genuinely concerned about this coin and the danger it poses to the digital currency space as a whole.
Is it possible that Tidex is not aware of all these issues surrounding MSD? So far, the company seems to support the currency fully and no one understands why it should be doing so in the first place. Some would strongly suggest that any exchange that openly trades Monspace (including Tidex) be scrutinized so that we can have some light shed into this issue.
Even though there are several coins that may make Tidex users question its legitimacy and reliability, the company doesn't take security and privacy for granted. It has put in place strong internal security measures to ensure that all transactions are safe. Additionally, Tidex claims to monitor all transactions to make sure that none of them is involved in any form of criminal activity.
At the time of writing this review, there has been no hacking attempt on Tidex since its conception. So far, the company seems to hold all the best cards under its sleeve in regards to security.
Clients may be happy to know that their money is secure but not many are satisfied with the exchange's customer support. I was reading reviews on various Bitcoin trading sites and forums and trust me, there is a bunch of negative comments regarding Tidex support team. There are many clients who have crowned the customer support irresponsible as they take ages to respond to inquiries. Others say the method provided to reach these people is not user-friendly. I think Tidex should seriously look into these, as one of the keys to a company's failure is poor client support. Other than this, everything else seems to fall into place pretty nicely.
To contact the support center, you need to log in your Tidex account, type in your query in the space provided and click on the Search button. Tidex should consider availing an email address or even a telephone number because the current procedure of communicating with the support guys is just not working for many customers.
Go to Tidex website and click on the "Create An Account" button.
Key in your email, password, phone number, and nickname in the registration form provided.
If your registration is successful, a screen welcoming you to Tidex will be displayed. Click on the "Log Me In" to proceed.
An authentication code will be generated (in from of a link) and sent to the email address you provided when creating the account. Copy the link and paste it into the requested field. Also, enter your email and password and click on "Login." Alternatively, you can just click on the link sent to your email to log into your account.
Logging into Tidex without confirming your registration will display an error message.
Before signing into your account, make sure you have cryptocoins that are supported by Tidex.
If you already have cryptocurrencies, log in and click on the "Deposits and Withdrawals" button.
Search for the coin you wish to deposit and click on "Deposit".
Copy the coin's deposit address and paste it into your coin's wallet. While at it, keep in mind the minimum deposit and withdrawal fees.
Click on the "Deposits and Withdrawals" button.
Search for the coins you wish to withdraw and click on "Withdraw" to proceed.
A screen will be displayed showing the amount to withdraw, address, available balance, coins that are on open orders, the total balance of cryptocurrencies, withdrawal fee, and the total amount you are about to withdraw. Fill in all the required details and click on "Withdraw" button. You will receive a confirmation link in your email.
Click on the link to confirm the withdrawal. Make sure you have checked all the details carefully because once you have clicked the link, it is impossible to cancel the withdrawal.
You need to have coins in your Tidex account. Let's say you have BTC that you want to trade for WAVES.
Click on BTC and search for WAVES in the list of the coins provided to see the BTC/WAVES market.
A screen will be displayed showing the number of WAVES you will receive and the total amount in BTC. Click on "Buy" to proceed.
A confirmation message will be displayed. Click on "Yes" to confirm the purchase.
Trading fees are understandable but deposit and withdrawal fees are too high.
Needs to deal with more legit coins to win its users trust and confidence.
Support team takes long to respond. Better ways of communication should be introduced.
Simple to navigate. The graphs and charts provided are easy to interpret.
Tidex seems like a great exchange. However, it's still needs to cut down the transaction fees, work on its reputation, and improve its customer support.
Even though Tidex is still pretty new in the crypto market, I think it is a great trading platform. With its easy to use interface and a large collection of coins to trade, Tidex has a lot to offer to both new and advanced investors. Those who trade in large volumes can take advantage of the company's loyalty program to reap more benefits.
Nevertheless, Tidex team has to address some of the issues mentioned earlier including high transaction fees, mistrust from users, and poor customer support. This is the only way they will be able to compete with other players in the crypto market.
Support for more than 100 cryptocoins.
High deposit and withdrawal fee.
Hope you found this Tidex exchange review helpful. Don't forget to check out our website for more reviews on cryptocurrency exchange platforms.


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Prior, L., Hughes, D., and Peckham, S. (2012). The discursive turn in policy analysis and the validation of policy stories Journal of Social Policy 41(2), 271-289
Peckham S and Hutchinson B (2012) Developing primary care: the contribution of primary care research networks Healthcare Policy 8(2), 56-70
Allen, P., Keen, J., Wright, J., Dempster, P., Townsend, J., Hutchings, A., Street, A. and Verzulli, R. (2012) 'Investigating the governance of acute hospitals in England: multi-site case study of NHS Foundation Trusts' Journal of Health Services Research and Policy doi:10.1258/jhsrp.2011.011046
Checkland, K., Harrison, S., Snow, S., McDermott, I., and Coleman, A. (2012). Commissioning in the English National Health Service: what's the problem? Journal of Social Policy, 41(3), 533-550.
Cheraghi-Sohi S, McDonald R, Harrison S (2012) 'The experience of contractual change in UK general practice: A qualitative study of salaried GPs' British Journal of General Practice 62: 198-9
Dowswell G, Harrison S (2012) 'Implementing clinical guidelines: a case study of research in context' in Exworthy M, Hann A, Peckham S, Powell M(eds) Case Studies in British Health Policy. 267-78.Bristol: Policy Press
McDermott, I., Checkland, K., Harrison, S., Snow, S., and Coleman, A. (In Press). Who do we think we are? Analysing the content and form of identity work in the English National Health Service. Journal of Health Organisation & Management
Peckham, S., Mays, N., Hughes, D., Sanderson, M., Allen, P, Prior, L., Entwhistle, V., Thompson, A., and Davies, H. (2012) 'Devolution and patient choice: policy rhetoric versus experience in practice' Social Policy and Administration, 46 (2), 199–218.
Prior, L., Hughes, D., and Peckham, S. (In Press). The discursive turn in policy analysis and the validation of policy stories. Journal of Social Policy.
Schafheutle EI, Hassell K, Ashcroft DM, Hall J, Harrison S (2012) 'How do pharmacy students learn professionalism?' International Journal of Pharmacy Practice 20: 118-28
Walter A, Chew-Graham C, Harrison S (2012) 'Negotiating refusals in primary care consultations: a qualitative study' Family Practice 29(4): 488-96
Allen, P., Bartlett, W., Turner, S., Matchaya, G., Perotin, V. and Zamora, B. (2011) 'Provider diversity in the English NHS: a study of recent developments in four health economies'Journal of Health Services Research and Policy Special Issue on Health Reforms in England Vol 17 Suppl 1 January 2012, 23-30.
Allen, P., Bartlett, W., Perotin, V., Turner, S., Matchaya, G., and Zamora, B. (2011) 'The blurred boundaries between public and private healthcare in the English NHS' International Journal of Public and Private Healthcare Management and Economics 1(3), 1-18.
Allen, P., Bartlett, W., Perotin, V., Zamora, B. and Turner, S. (2011) 'New forms of provider in the English National Health Service' Annals of Public and Cooperative Economics, 82(1), 77-95.
Allen, P., Wright, J., Townsend, J., Dempster, P., Hutchings, A., and Keen, J. (2011) 'Organisational form as a mechanism to involve staff, public and users in public services: a study of the governance of NHS Foundation Trusts' Social Policy and Administration published online 8 NOV 2011, DOI: 10.1111/j.1467-9515.2011.00820.x
Checkland, K., Coleman, A., Harrison, S. (2011). When is a saving not a saving? The micro-politics of budgets and savings under practice-based commissioning. Public Money and Management, 31(4), 241-248.
Checkland, K., Snow, S., McDermott, I., Harrison, S., and Coleman, A. (2011). 'Animateurs' and animation: what makes a good commissioning manager? Journal of Health Services Research and Policy, 17(1), 1-7. DOI: 10.1258/jhsrp.2011.011010
Checkland, K., Snow, S., McDermott, I., Harrison, S., and Coleman, A. (2011). Management practice in primary care organisations: The roles and behaviours of middle managers and GPs. Final Report. NIHR Service Delivery and Organisation programme.
Coleman, A., Checkland, K., McDermott, I., and Harrison, S. (2011). Patient and public involvement in the restructured NHS. Journal of Integrated Care, 19(4), 30-36.
Hughes, D., Petsoulas, C., Allen, P., Doheny, S. and Vincent-Jones, P. (2011) 'Contracts in England's NHS market: economic levers and social embeddedness' Special Issue Health Sociology Review: Transformations in Health Care: Privatisation, Corporatisation and the Market, 20(3), 321-337.
Marks, L., Cave, S., Hunter, D., Mason, J., Peckham, S., and Wallace, A. (2011). Governance for health and wellbeing in the English NHS. Journal of Health Services Research & Policy, 16, 14-21.
Marks, L., Cave, S., Wallace, A., Mason, A., Hunter, D.J., Mason, J.M., and Peckham, S. (2011). Incentivising preventive services in primary care: perspectives on Local Enhanced Services. Journal of Public Health (Oxf), 33(4), 556-64
Peckham, S., Hann, A., Boyce, T. (2011). Health promotion and ill-health prevention: the role of general practice. Quality in Primary Care, 19(5), 317-23.
Petsoulas, C., Allen, P., Hughes, D., Vincent-Jones, P. and Roberts, J. (2011) 'The use of standard contracts in the English National Health Service: A case study analysis' Social Science and Medicine, 73(2), 185-192
Roland M, Rao SR, Sibbald B, Hann M, Harrison S, Walter A, Guthrie B, Desroches C, Ferris TG, Campbell EG (2011) 'Professional values and reported behaviours of doctors in the United States and United Kingdom: quantitative survey' BMJ Quality and Safety 20(6): 522-6
Salisbury, C., Stewart, K., Purdy, S., Thorp, H., Cameron, A., Lart, R., Peckham, S., and Calnan, M. (2011). Making the most of evaluation: a mixed methods study in the English NHS. Journal of Health Services Research & Policy, 16(4), 218-25.
Segar, S., and Rogers, A. (2011). Report from Work Package 2 Qualitative Study of the Healthlines project, research commissioned by the National Institute for Health Research (NIHR).
Turner, S., Allen, P., Bartlett, W. and Perotin, V. (2011). Innovation and the English National Health Service: a qualitative study of Independent Sector Treatment Centres' Social Science and Medicine, 73(4), 522-529.
Willis S, Schafheutle E, Elvey R, Lewis P, Harrison S, Hassell K (2011). Can patient-centred professionalism be engendered in young pharmacists? Pharmaceutical Journal 287, 20/27 Aug, 203-4.
Wright, J., Dempster, P., Keen, J., Allen, P., and Hutchings, A. (2011) 'The New Governance Arrangements for NHS Foundation Trust Hospitals: Reframing Governors as Meta-Regulators' Public Administration published online 17 OCT 2011, DOI: 10.1111/j.1467-9299.2011.01975.x
Checkland, K. & Harrison, S (2010). The impact of QOF on practice organisation and service delivery. In Gillam, S. & Siriwardena, A. (Ed.), The quality and outcomes framework. QOF- transforming general practice. Oxford: Radcliffe.
Checkland, K. & Harrison, S (2010). The impact of the Quality and Outcomes Framework on practice organisation and service delivery: summary of evidence from two qualitative studies. Quality in Primary Care, 18, 139-146.
Coleman, A., Checkland, K., Harrison, S. & Hiroeh, U. (2010). Local histories and local sensemaking: a case of policy implementation in the English National Health Service. Policy and politics, 38(2), 289-306. DOI:10.1332/030557309X462547
Harrison, S. (2010) Oral evidence to House of Commons Health Committee Commissioning. Third Report of Session 2010-2011. Vol II: Ev43-53
Pollitt, C.J., Harrison, S., Dowswell, G., Jerak-Zuiderent, S., & Bal, .R (2010) 'Performance regimes in health care: Institutions, critical junctures and the logic of escalation in England and the Netherlands' Evaluation: the International Journal of Theory and Practice 16(1): 13-29
Roland, M., Sibbald, B., Hann, M., Dowswell, G., Harrison, S., Walter, A., Guthrie, B., Campbell, E.G. (2010) Professional Attitudes and Behaviours of Doctors in the United States. Final Report to Nuffield Trust. Manchester: University of Manchester National Primary Care Research and Development Centre
Schafheutle, E.I., Hassell, K., Ashcroft, D.M., Hall, J., & Harrison, S. (2010). Learning professionalism during the MPharm: the importance of practice exposure and role models. Pharmaceutical Journal 285: 164-5
Schafheutle, E.I., Hassell, K., Ashcroft, D.M., Hall, J., & Harrison, S. (2010). How do pharmacy students learn professionalism, and how is it assessed?' Pharmacy Practice. 8 (suppl 1), 92-93
Schafheutle, E.I., Hassell, K., Ashcroft, D.M., Hall, J., & Harrison, S. (2010). Learning professionalism in the MPharm – What role does 'organisational philosophy' play? International Journal of Pharmacy Practice 18 (Suppl 1): 19-20
Checkland, K. (2009). Being a good clinician is not enough: doctors as employers and practices as organisations. British Journal of General Practice, 59(565), 565-566.
Checkland, K., Coleman, A., Harrison, S. & Hiroeh, U. (2009). We can't get anything done because... Making sense of barriers to Prctice-based Commissioning. Journal of Health Services Research and Policy, 14(1), 20-26. DOI: 10.1258/jhsrp.2008.008043
Checkland, K., Harrison, S. & Coleman, A. (2009). Structural interests in health care: evidence from the contemporary National Health Service. Journal of Social Policy, 38(4), 607-625. DOI: 10.1017/S0047279409003262
Coleman, A., Checkland, K., & Harrison, S. (2009). Still puzzling: patient and public involvement in commissioning. Journal of Integrated Care, 17(6), 23-30. DOI: 10.1108/14769018200900043
Coleman, A., Checkland, K., Harrison. S.,& Dowswell, G. Practice-based Commissioning – Theory, Implementation and Outcome: Final Report. Manchester: University of Manchester National Primary Care Research and Development Centre. 2009.
Coleman, A., Gains, F., Boyd, A., Bradshaw, D., & Johnson, C. (2009). Scrutinising local public service provision: lessons from the experience of health scrutiny 2004-2007. Public Money and Management, 299-306. DOI: 10.1080/09540960903205949
Grant, S., Huby, G., Watkins, F., Checkland, K., McDonald, R., Davies, H., & Guthrie, B. (2009). The impact of pay-for-performance on professional boundaries in UK general practice: an ethnographic study.Sociol Health Illn, 31( 2), DOI: 10.1111/j.1467-9566.2008.01129.x
Harrison S (2009) 'Co-optation, commodification and the medical model: Governing UK medicine since 1991' Public Administration 87(2): 184-97
Harrison S, Checkland K and Coleman A. (2009). Practice-based commissioning in the English National Health Service: House of Commons Health Committee Inquiry into Health Care Commissioning 2009-2010. House of Commons Health Committee.
Harrison, S. and Checkland, K. (2009). Evidence-based practice in UK health policy. In Gabe, J and Calnan, M (Ed.), The new sociology of the health service. (pp. 121-142). Abingdon: Routledge.
Mannion R, Harrison S, Jacobs R, Konteh F, Walshe K, Davies HTO (2009) 'From cultural cohesion to rules and competition: the trajectory of senior management culture in English NHS hospitals, 2001-2008' Journal of the Royal Society of Medicine 102: 1-5
McDonald ER, Checkland KH, Harrison SR, Coleman A. (2009). Rethinking collegiality: Restratification in English General Medical Practice 2004-2008. Social Science and Medicine, 68, 1199-1205. DOI: 10.1016/j.socscimed.2009.01.042
McDonald ER, Harrison SR, Checkland KH. (2009). The new GP contract in English primary health care: an ethnographic study. International Journal of Public Sector Management, 22(1), 21-34.

redpajama_set_name: RedPajamaCommonCrawl


Studios Inc Exhibition Space is pleased to present Floor Samples and Test Patterns, an exhibition featuring resident artist Garry Noland, on view from 11.08.2013 to 12.20.2013 with an opening reception this Friday, 11.08.2013 from 6:00 – 9:00 PM.
Floor Samples and Test Patterns is an exhibition of new floor and wall mounted sculpture incorporating found and re-used materials. Art Historian Kirsten Olds writes of the work: "These…composites express antinomies that have long compelled artistic practice: between the vulgar and the beautiful; high and low culture; fine art and craft…".
Chicago based curator and critic Jamilee Polson Lacy will provide the exhibition essay.


text:
Jan Świtkowski (ur. 23 stycznia 1994 w Lublinie) – polski pływak specjalizujący się w stylu motylkowym i dowolnym. Brązowy medalista mistrzostw świata i Europy. Od 2016 r. zawodnik AZS UMCS Lublin.

Kariera
W sierpniu 2015 roku zdobył brązowy medal mistrzostw świata na dystansie 200 m stylem motylkowym z czasem 1:54,10.

W swojej karierze uczestniczył także w innych seniorskich imprezach najwyższej rangi – w 2012 w mistrzostwach świata na krótkim basenie zajął 14. miejsce na 100 m stylem zmiennym i 11. pozycję na 200 m stylem zmiennym, rok później w mistrzostwach świata, gdzie był 23. na 200 m stylem dowolnym i 30. na 200 m stylem zmiennym, a w 2014 w mistrzostwach Europy uplasował się na 22. miejscu na 200 m stylem dowolnym i 28. pozycję na 100 m stylem motylkowym. Brązowy medalista mistrzostw Europy w 2018 w męskiej sztafecie 4 × 100 metrów stylem dowolnym.

W grudniu 2018 roku na mistrzostwach świata na krótkim basenie w Hangzhou w eliminacjach 200 m stylem zmiennym ustanowił nowy rekord Polski (1:53,60 min) i zakwalifikował się do finału, w którym zajął siódme miejsce z czasem 1:53,96 min.

Świtkowski jest także wielokrotnym medalistą mistrzostw Polski seniorów.

Syn brązowej medalistki mistrzostw Europy juniorów w pływaniu, Joanny Pizoń-Świtkowskiej.

Przypisy

Polscy pływacy
Polscy olimpijczycy (Rio de Janeiro 2016)
Polscy pływacy na igrzyskach olimpijskich
Ludzie urodzeni w Lublinie
Urodzeni w 1994
Pływacy na Letnich Igrzyskach Olimpijskich 2016

redpajama_set_name: RedPajamaWikipedia


text:
Q: Как запретить пользователю вводить в QLineEdit что-либо кроме URL? Как запретить пользователю вводить в QLineEdit что-либо кроме URL?
Ситуация: нужна форма (необязательно QLineEdit, но я не нашёл специальной формы для ссылок), которая не даст пользователю ввести что-либо кроме ссылок. Видел формы, которые разрешают только ввод чисел, а не букв. Нужно что-то типа такого, только для ссылок. При этом при проверке не обязательно подключение к интернету. Знаю про класс QUrl, но его метод isValid() пропускает и нерабочие URL почему-то.

A: Проверка по ходу ввода делается с помощью подклассов QValidator, можно сделать свой на основе QUrl, а можно просто взять QRegExpValidator из коробки и регулярку из гугла с en-so*:
QLineEdit *leUrl= new QLineEdit(this);
QRegExp urlRx{"https?:\\/\\/(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*)"};
leUrl->setValidator(QRegExpValidator(urlRx));

* Не забывая удвоить обратные слеши, дабы компилятор С++ сам не пытался их интерпретировать.

redpajama_set_name: RedPajamaStackExchange


Sarah Hannah CROOKES was born on 20 Nov 1844 in Ingbirchworth and was christened on 4 Sep 1845 in Parish Church of Penistone. Sarah married John HAIGH , son of John HAIGH and Frances WOOD, on 16 Apr 1865 in Denby. John was born on 20 Sep 1836 in Carrhead and was christened on 15 Dec 1836 in Parish Church of Penistone.
Thomas CROOK(E)S was born in 1801/1807 in Aldwark and was christened on 24 May 1801 in Chapelry of Bradfield.
Thomas married Mary Ann DARWENT (DARWIN) on 8 Mar 1824 in Sheffield.
Mary Ann DARWENT (DARWIN) was born in 1796/1801 and was christened on 16 Mar 1802 in Parish Church of Silkstone.


text:
'It was a miracle:' How 2 men saved man's life after he went into cardiac arrest
Derek Maiolo/Steamboat Pilot & Today
Shawn Kindz reveals bruising from a defibrillator, which helped to save his life after he went into cardiac arrest last week near Stagecoach Reservoir. Two men, Brian Boos and Darren Ebaugh, also were instrumental in saving Kindz by performing CPR immediately after he collapsed.
STEAMBOAT SPRINGS — It has been seven years since Oak Creek resident Brian Boos took a CPR class, but when a man collapsed suddenly last week without a pulse near Stagecoach Reservoir, he did not hesitate to provide care.
Boos and another man, Darren Ebaugh, were the two good Samaritans who helped to save the life of Shawn Kindz after he went into cardiac arrest while the three were working on a home in the 23000 block of Postrider Trail.
After what they and first responders consider a miraculous rescue, Kindz has made an almost complete recovery in less than a week.
It serves as an example of the value of knowing how to perform CPR and other basic first aid — skills that all three men said they want to refresh in the wake of the incident.
A local building contractor, Boos had recently sold the house near Stagecoach and was making some repairs to the furnace Tuesday, July 23. He called Kindz, who works with Ferrellgas, and Ebaugh, who works with JDW Construction, to help with the heating system.
Kindz admitted he had been experiencing mild chest pain, like that of heartburn, for the previous three days. He planned to go to the hospital for an examination later in the day but wanted to finish the work at the Stagecoach house first.
Around 11 a.m., Boos remembers talking to Kindz as they were finishing up the project.
"All in one second, he just hit the ground and rolled on his back, eyes glazed over," Boos said. "He was dead when he hit the ground."
Boos yelled to Ebaugh, who was outside by his truck. Ebaugh ran inside and began CPR while Boos dialed 911.
"It's amazing they had cell service out there to even contact dispatch," Fire Chief Chuck Wisecup said, who rushed to the scene with a crew of medics from the Oak Creek Fire Protection District.
Boos and Ebaugh worked together to administer chest compressions and ventilations while they waited for help to arrive.
Once on scene, medics took over CPR, then administered multiple shocks with an electric defibrillator. By that time, Kindz had been dead for about 20 minutes by Boos' estimate.
After three or four shocks, Kindz eventually regained a pulse, according to Wisecup, and medics transported him to the UCHealth Yampa Valley Medical Center in Steamboat Springs.
Back at the house, Boos and Ebaugh took a silent moment to collect themselves, hardly able to believe what had just happened.
"After they took him away in the ambulance, we both knelt down and said a prayer," Boos said.
Kindz would lose his pulse twice on the way to the hospital, but additional defibrillation resuscitated him each time. Once in Steamboat, medical personnel flew him to a hospital in Loveland to receive further treatment.
According to his wife, Jona Ely, a nurse practitioner in Wyoming, Kindz's right coronary artery, which helps supply blood to the heart, was completely blocked. Doctors placed him on a special pump to stabilize his heart and reduce stress to his cardiovascular system.
By Sunday, Kindz was able to leave the hospital and return home with his family. His only noticeable scar from the incident is a large bruise on the right side of his chest, which he attributed to the shocks from the defibrillator.
Ely, his wife of nine years, initially was angry he had not sought treatment for his heart pain sooner. That anger has since melted to a profound thankfulness.
"The best way to learn to appreciate someone is trying to imagine life without them," she said.
Instrumental to Kindz' rescue, according to Wisecup, was the immediate CPR he received from Boos and Ebaugh. In his 36 years as a first responder, this was the first time he led a successful resuscitation mission.
"I firmly believe that early intervention was key to that outcome," he said.
Wisecup encourages everyone to learn CPR and basic first aid. Several organizations in town offer classes, including Colorado Mountain College Steamboat Springs, UCHealth Yampa Valley Medical Center and Old Town Hot Springs.
As a way to thank the two men for their help, Kindz and his wife took them out to dinner Monday, then again for lunch on Tuesday.
Ebaugh could not believe how healthy Kindz appeared.
"It was surreal to have dinner with him," he said. "I never thought I would see him alive again."
Kindz, too, is processing last week's events and how lucky he is to be with his family. The experience has inspired him to give up cigarettes, which he has smoked for more than 30 years.
"I'm lucky — I really am," he said on Tuesday after a follow-up doctor's visit showed he was continuing to make improvements.
"I have another chance at life."
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text:
\section{Introduction}
\label{sec:intro}
Traditional speech technology relies on linguistic expertise and textual information to build acoustic and language models. Recent work has begun to mitigate the need for expert knowledge,
having succeeded at training supervised systems using textual transcripts only \cite{baidudeepspeech,miao2015eesen}. But for the majority of the world's languages, even textual transcripts are sparse or non-existent, and for many languages, the lack of standard orthography would reduce the utility of such transcripts greatly. This is why there is increasing interest in a radically different approach, the so-called \textit{zero resource setting} \cite{glass_2012,jansen_2013}. The aim is to construct speech systems without any textual or linguistic resource. The fact that young children become fluent communicators in their native language(s) before they learn to read and write, and with minimal or no expert supervision, demonstrates that such a system is possible.

Systems constructed with zero expert resources have at least three kinds of applications. They can form the basis for speech services for the perhaps billions of speakers of languages without readily available linguistic or textual resources in sufficient quantities. They can also help field linguists document endangered languages, by providing tools to semi-automatically analyze and annotate audio recordings using automatically discovered linguistic units (phonemes, lexicon, grammar) \cite{addaBulbSLTU2016}. In this case, a realistic scenario is to process raw speech and its translation in a well-resourced language \cite{duong2016,berard_2016,besacier_2006}. Finally, to the extent that they mimic language development, these models can be used to evaluate scientific claims about development, and act as quantitative tools for psychologists and clinicians interested in the impact of sociolinguistic variations in input on subsequent normal or abnormal language and cognitive development \cite{dupoux2016}.

As the previous Zero Resource Speech Challenge \cite{versteegh_2015,versteegh_2016}, the 2017 installment consists of two different sub-challenges. Participants can submit systems that do either \emph{subword modelling} (track one) or \emph{spoken term discovery} (track two). Subword modelling means constructing a representation of speech sounds; we say ``subword'' instead of ``phoneme'' or ``sound'' because the criterion for success does not demand any particular temporal window size. The requirement is to have a representation of the speech signal that is robust to
talker variation, and makes the distinctions necessary to distinguish words.
Spoken term discovery means discovering recurring speech fragments in the audio: the system must output time-stamps delimiting stretches of speech, associated with class labels, ideally corresponding to real words in the language. The systems must be able to do their task without linguistic resources. The 2017 Zero Resource Speech Challenge\footnote{http://zerospeech.com/2017} has two main innovations compared to the 2015 iteration: (1) it tests how well systems' architectures and hyperparameters generalize to unseen languages (the systems are developed with three languages and tested on two `surprise' languages); (2) it tests how well the trained systems' parameters adapt to unseen talkers (varying the amount of speech available for each talker).

\section{The challenge}
\label{sec:overview}

\subsection{Track one: Subword modelling}

The goal of subword modelling is to learn representations appropriate to the sounds of the language (phones or phonemes). As adults, we easily perceive differences between individual phonemes in our native language, substantially better than in non-native languages \cite{lisker1964cross}. This so-called \textit{perceptual tuning} develops substantially during the first year of life \cite{werker1984cross,kuhl1991human}. The input to subword modelling is a set of acoustic features encoding continuous speech. The result is a transformation that applies to new samples of running speech, yielding a new representation (continuous or discrete) of that speech. After training in a particular language from unlabelled speech, the representation is computed for a test set in the same language on novel speakers, to be evaluated on phoneme discrimination based on gold-standard annotations.

\subsection{Track two: Spoken term discovery}

Intuitively, the goal of spoken term discovery is to find words in the speech stream---just as the infant learns the words of its language by listening. The input to candidate systems is a series of speech features. The output is a set of boundaries delimiting the start and end of proposed word tokens discovered in the speech, and category labels indicating proposed word types. These boundaries may, but need not, constitute an exhaustive parse of the speech. The evaluation we apply is a set of scores measuring different aspects of the alignment with the words in the gold-standard transcription.
As is customary in the field of word segmentation, we do not provide a separate test set for this track; we rely on the surprise languages to assess possible hyperparameter overfitting.

\subsection{Generalization across languages}
This challenge is explicitly set up as a `learning to learn' or meta-learning problem: participants select the best architectures and tune their hyperparameters on three development languages on which the evaluation software is provided for a test set. The higher level test is performed on two new surprise languages for which the evaluation is withheld. Participants submit the output of their previously optimized systems for these two corpora, and the organizers perform the evaluation.
Each submission (maximum five per team) is automatically evaluated and made public.

\subsection{Adaptation to new speakers}
The subword modeling task is explicitly set up as a talker adaptation problem. The training data is constructed from a skewed distribution of speakers, and the test set consists only of new speakers appearing in files of various durations (from 1 second to two minutes).
This enables us to evaluate models' ability to generalize and adapt to new speakers on the basis of limited evidence.

\section{Data sets}
\label{sec:data}

Two groups of data sets are provided as part of the challenge:
the \emph{development data} and the \emph{surprise data}. The development data consists of corpora from three different languages (English, French and Mandarin). Each corpus comes with software that does the evaluation for the two tracks. Challenge participants are encouraged to use these resources to tune their hyperparameters using a cross validation approach to maximize generalizability. Each corpus is split into a train and a test set; the subword modelling evaluations are run on the test sets, and the spoken term discovery evaluations on the training sets. The participants then have to submit their systems and their output on all  the data  sets for independent evaluation (run automatically upon submission). The surprise data consists of corpora from two new languages, the identity of which was not revealed to participants. Participants receive only the speech corpora, with no additional resources. As before, each contains a training set (which participants can use to train their systems) and a test set for track one evaluation.

The amount of data in the training part of the development data sets varies from 2.5 to 45 hours (see Table \ref{tab:data}), to ensure that systems can work both with limited data and with large data sets. The statistics of the two surprise languages fall between these two extremes.
The distribution of speakers in the training sets is shaped to reflect what is typically found in natural language acquisition settings: there is a ``family''---a small number of speakers that make up a large proportion of the total speech---and a set of ``outsiders''---a larger number of speakers that each appear in smaller proportions. The test sets consist of many short files, are organized into subsets of differing length (1s, 10s and 120s).

\begin{table*}[t]
\caption{\label{tab:data} Corpus statistics}
\centering

\resizebox{1.6\columnwidth}{!}{
\begin{tabular}{ l l l l l l l | l l }
	  & \multicolumn{6}{c}{Training} &  \multicolumn{2}{|c}{Test} \\
	   & \multicolumn{2}{c}{Relatives} &  \multicolumn{2}{c}{Outsiders}  &  \multicolumn{2}{c|}{Total}   \\ \hline
	   & \#speakers & duration/ speaker &  \#speakers & duration/ speaker &  duration & \#words &  number of files &  duration (min)  \\ \hline
	 \textbf{Development} \\ \hline
	 English & 9 & 165-220min &  60 & 10min & 45h & 370k  & 30658 & 1634 \\
	 French & 10 & 110-195min &  18 & 10min & 24h & 220k  & 23765 & 1061 \\
	 Mandarin & 4 & 20-25min &  8 & 10min &  2h30 & 20k  & 25383 & 1522 \\\hline
	 \textbf{Surprise} \\ \hline
	 S1 -- German & 10 & 85-150min &  20 & 10min &  25h & 213k  & 15243 & 687\\
	 S2 -- Wolof & 4 & 37-42min &  10 & 10min &  4h & 31k  & 7201 & 354 \\
\end{tabular}
}
\end{table*}

The English and French corpora were taken from the LibriVox collection of audio books\footnote{http://librivox.org/} and phone force-aligned using Kaldi \cite{Povey_ASRU2011_2011}. The Mandarin corpus is the one described in \cite{mandarinpaper}, force-aligned using Kaldi. The first surprise language is German, and was taken from LibriVox and force-aligned using Kaldi as well. The second surprise language is Wolof, and is the corpus described in \cite{wolofpaper}.

\begin{table*}[t!]
\caption{\label{tab:track1res} Track 1 within and between talker ABX scores for three test files durations, for the development (average) and surprise languages, for the 14 submitted systems (the mean performance on the surprise set is in square brackets). The three supervised systems are listed separately. See \url{http://zerospeech.com/2017} for the full table including development languages.}

\centering
\resizebox{2.05\columnwidth}{!}{

\begin{tabular}{ l  | l   l  l  l | l  l  l | l l l}
	 \multicolumn{2}{c}{} &\multicolumn{3}{c}{Avg. Development}  &  \multicolumn{3}{c}{S1 -- German} & \multicolumn{3}{c}{S2 -- Wolof}  \\
	 \multicolumn{2}{c}{} &1 s&10 s& 2 min&   1 s & 10 s & 2 min  & 1 s & 10 s & 2 min \\ \hline
	\textbf{Baseline} [18.84]
 & Within & 12 & 12.1 & 12.1 & 14.2 & 13.3 & 12.9 & 14.1 & 14.3 & 14.1 \\
	 & Between  & 23.3 & 23.4 & 23.3 & 27.5 & 27.3 & 27.1 & 30 & 29.5 & 29.5   \\ \hline
	\textbf{Topline} [7.19]
 & Within & 8 & 5.4 & 5.3 & 8.7 & 7.1 & 7.0 & 6.6 & 4.6 & 3.4   \\
	 & Between  & 9.6 & 7.2  & 6.9 & 12.8 & 10.5 & 10.4 & 7.1 & 3.6 & 4.3  \\\hline\hline

	\textbf{H} [9.16
     & Within & 8.5 & 7.6 & 7.4 & 6.5 & 5.6 & 5.3 & 10.9 & 8.8 & 8.4\\
	 & Between  & 10.8 & 9.3 & 9.0 & 11.9 & 10.0 & 9.7 & 13.0 & 10.0 & 9.9 \\\hline
	\textbf{P1} [14.96
     & Within & 10.9 & 8.8 & 8.7 & 8.9 & 6.7 & 6.4 & 13.3 & 11.9 & 11.8\\
	 & Between  & 17.5 & 15.8 & 15.7 & 19.4 & 16.2 & 15.9 & 22.8 & 23.1 & 23.1 \\
	\textbf{P2} [14.9
     & Within & 10.8 & 8.7 & 8.5 & 8.8 & 6.6 & 6.3 & 13.1 & 11.7 & 11.7\\
	 & Between  & 17.5 & 15.8 & 15.7 & 19.2 & 16.3 & 16.0 & 23.3 & 23.3 & 23.1\\
	 \hline
	\textbf{C1} [11.95
     & Within  & 10.0 & 8.4 & 8.3 & 7.6 & 6.22 & 6.3 & 11.7 & 9.9 & 9.8\\
	 & Between  & 14.5  & 12.8 & 12.5 & 15.5 & 12.9 & 12.7 & 17.6 & 16.9 & 16.3 \\
	\textbf{C2} [11.24
     & Within & 10.1 & 8.5 & 8.4 & 7.6 & 6.2 & 6.1 & 11.6 & 9.8 & 9.6\\
	 & Between &  13.9 & 11.9 & 11.7 & 14.7 & 11.7 & 11.6 & 16.9 & 14.7 & 14.4 \\\hline
	\textbf{A1} [12.3
     & Within  & 8.8 & 7.9 & 7.8 & 	6.9& 	6.1& 	6.0& 	9.9& 	9.2& 	9.1 \\
	 & Between  &  15.2 & 14.2 & 14.0 &	16.9 &14.7 &	14.7 &	18.8 &	17.7 &	17.7 \\
    \multicolumn{2}{c}{}

\end{tabular}
~~~~
\begin{tabular}{ l  | l  l  l  l | l  l  l| l l l  }
	 \multicolumn{2}{c}{}&\multicolumn{3}{c}{Avg. Development}  &  \multicolumn{3}{c}{S1 -- German} & \multicolumn{3}{c}{S2 -- Wolof}  \\
	 \multicolumn{2}{c}{}&1 s&10 s& 2 min &  1 s & 10 s & 2 min  & 1 s & 10 s & 2 min \\ \hline
	\textbf{A2} [11.95
     & Within & 8.8  & 7.8 & 7.7 & 	6.8& 	6.0& 	6.0& 	10.1& 	9.6 &	9.6 \\
	 & Between  & 14.5 & 13.4 & 13.3 &	16.0& 	14.0& 	13.9 &	17.9 &	16.9& 	16.6 \\
	\textbf{A3} [11:93
     & Within  & 9.5 & 8.3 & 8.2 &	7.3 &	6.2 &	6.1 &	11.1 &	10.3 &	10.2\\
	 & Between & 14.5 & 13.3 & 13.2 &	15.5 &	13.5 &	13.4 &	17.6 &	16.0 &	16.0 \\
	\textbf{A4} [12.17
     & Within  &  9.7 & 8.5 & 8.5 & 7.6 &	6.4 &	6.2 	&11.6 &	10.9 &	10.7\\
	 & Between   & 14.5 & 13.3 & 13.2 & 15.7&13.7&13.5&17.5&16.1&16.1 \\\hline
	 \textbf{Y1} [12.43
     & Within  & 10.8 & 8.4 & 8.4 & 8.1 & 6.0 & 6.0 & 12.6 & 10.0 & 9.9\\
	 & Between  & 15.3 & 13 & 12.6 & 16.2 & 12.9 & 12.6 & 19.5 & 17.1 & 16.6\\
	 \textbf{Y2} [12.29
     & Within  & 10.7 & 8.4 & 8.2 & 8.2 & 6.2 & 6.2 & 12.7 & 10.1 & 9.9\\
	 & Between  & 15.1 & 12.7 & 12.4 & 16.4 & 13.3 & 13.0 & 19.2 & 17.3 & 16.7\\\hline\hline
	\textbf{YS}  [12.32]
     & Within & 10.7 & 8.3 & 8.1 & 8.0 & 6.0 & 5.9 & 12.9 & 10.8 & 10.6\\
	 & Between & 14.7 & 12.3 &12.1 & 15.8 & 12.4 & 12.3 & 18.7 & 17.4 & 17.0\\\hline
 	\textbf{S1} [8.54]
     & Within  & 8.7 & 8.3 & 7.3 & 6.3 & 5.8 & 5.0 & 9.0 & 8.7 & 7.2\\
	 & Between & 11.4 & 10.4 & 9 & 11.6 & 9.9 & 8.7 & 11.5 & 10.2 & 8.6 \\
	 	\textbf{S2} [6.99]
     & Within  & 7.1 & 6.7 & 6.3 & 5.2 & 4.9 & 4.5 & 6.9 & 7.0 & 6.3\\
	 & Between  & 9.0 & 8.6 & 7.8 & 9.3 & 8.6 & 7.8 & 8.3 & 7.9 & 7.2  \\

\end{tabular}
}
\end{table*}

\section{Evaluation}
\label{sec:evaluation}

\subsection{Evaluation of subword modelling}

Unsupervised subword modeling is a representation learning problem: the system must find speech features that emphasize linguistically relevant properties (phoneme structure) and de-emphasize the linguistically irrelevant ones (speaker identity, emotion, channel, etc). We use a minimal pair ABX task \cite{schatz2013,schatz2014}, which does not require any training, and only requires a dissimilarity metric between speech tokens.  The ABX task is inspired by match-to-sample tasks used in human psychophysics and is a simple way to measure discriminability between two sound categories (where the sounds $A$ and $B$ belong to different categories $x$ and $y$, respectively, and the task is to decide whether the sound $X$ belongs to one or the other). We define the ABX-discriminability of category $x$ from category $y$ as the probability that $A$ and $X$ are further apart than $B$ and $X$ when $A$ and $X$ are from category $x$ and $B$ is from category $y$, according to a dissimilarity funcion $d$. We obtain a symmetric discriminability score by taking the average of the ABX discriminability of $x$ from $y$ and of $y$ from $x$. The dissimilarities provided in this challenge are based on dynamic time warping, the underlying frame-level dissimilarity being either the cosine distance or KL-divergence. For most systems (signal processing, embeddings) the cosine distance usually gives good results, and for others (posteriorgrams) the KL divergence is more appropriate. Participants are allowed to supply their own dissimilarity as long as it was not obtained through supervised training.

We focus on phone triplet minimal pairs: sequences of 3 phonemes that differ in the central sound (not necessarily real words, e.g., ``beg''--``bag'', ``api''--``ati'', etc). Our compound measure sums over all minimal pairs of this type found in the corpus in a structured manner, that depends on the task. For the \textit{within-speaker} task, all of the phone triplets belong to the same speaker (e.g. $A=\textrm{beg}_{T1}$, $B=\textrm{bag}_{T1}$, $X=\textrm{bag}'_{T1}$). The scores for a given minimal pair are first averaged across all of the speakers for which this minimal pair exists. The resulting scores are then averaged over all found contexts for a given pair of central phones (e.g. for the pair /a/-/e/, average the scores for the existing contexts such as /b\_g/, /r\_d/, /f\_s/, etc.). Finally the scores for every pair of central phones are averaged and subtracted from 1 to yield the reported within-talker ABX error rate. For the \textit{across-speaker} task, $A$ and $B$ belong to the same speaker, and $X$ to a different one. $A=\textrm{beg}_{T1}$, $B=\textrm{bag}_{T1}$, $X=\textrm{bag}_{T2}$. The scores for a given minimal pair are first averaged across all of the pairs of speakers for which this contrast can be made. As above, the resulting scores are averaged over all contexts over all pairs of central phones and converted to an error rate.

\subsection{Evaluation of spoken term discovery}

\newcommand{\set}[1]{\left\{#1\right\}}
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\newcommand{C_{\textrm{disc}}}{C_{\textrm{disc}}}
\newcommand{P_{\textrm{disc}}}{P_{\textrm{disc}}}
\newcommand{F_{\textrm{disc}}}{F_{\textrm{disc}}}
\newcommand{F_{\textrm{all}}}{F_{\textrm{all}}}
\newcommand{P_{\textrm{all}}}{P_{\textrm{all}}}
\newcommand{P_{\textrm{all}}}{P_{\textrm{all}}}
\newcommand{P_{\textrm{disc*}}}{P_{\textrm{disc*}}}
\newcommand{P_{\textrm{clus}}}{P_{\textrm{clus}}}
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\newcommand\SetB[3]{\ensuremath{\{\text{\ensuremath{#1 \mid} \parbox[t] {\widthof{\ensuremath{#3}}} {\ensuremath{#2}\}}}}}
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\newcommand{\cover}[1]{\mathrm{cover}(#1)}
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\newcommand{\freq}[1]{\mathrm{freq}(#1)}
\newcommand{\ned}[1]{\mathrm{ned}(#1)}
\newcommand{\card}[1]{\left\vert{#1}\right\vert}

``Spoken term discovery'' is a broad label. We use it to refer to systems that do one or more of three tasks. Systems that do \emph{matching} find fragments that are matched pairwise as being instances of the same sequence of phonemes, attempting to find as many as possible. They can be evaluated based on how similar the matched fragments are, and how much of the corpus they cover. Systems that do \emph{lexicon discovery} group these fragments into sets (rather than just matching pairwise), with the goal of finding a high-quality lexicon of types. They can be evaluated based on how well the sets match on the sequence of phonemes, and how well the sets match the gold-standard lexicon of word types. Systems that do \emph{word segmentation} attempt to find fragments that are aligned with the gold-standard word transcription. By setting out three different types of criteria, the intention is to be open to various types of ``spoken term discovery'' systems, all of which in some sense ``find words.'' The result is that we do three (non-independent) types of evaluations in track two. All of these evaluations are done at the level of the phonemes. Using the aligned phoneme transcription, we convert any discovered fragment of speech into its transcribed string.  If the left or right edge of the fragment contains part of a phoneme, that phoneme is included in the transcription if it corresponds to more than more than 30ms or more than 50\% of its duration.

\subsubsection{Evaluation as matching system}

The evaluation of spoken term discovery systems as matching systems consists of two scores,  \textbf{NED} and  \textbf{coverage}. \textbf{NED,} is the average, over all matched pairs, of the Levenshtein distance between their phonemic transcriptions, divided by the max of their phonemic length.
The second score is the \textbf{coverage,} the fraction of the discoverable part of the corpus that is covered by all the discovered fragments. The discoverable part of the corpus is found by computing the union of all of the intervals corresponding to all of the pairs of ngrams (with n between 3 and 20). This is almost all of the corpus, except for unigram and bigram hapaxes.

\subsubsection{Evaluation as lexicon discovery system}

Six scores are used to evaluate the performance of a spoken term discovery system in terms of lexicon discovery. The first three are \textbf{grouping precision, recall} and \textbf{F-score.} These are defined in terms of $P_{\textrm{clus}}$, the set of all pairs of fragments that are groupes in the same cluster, and $P_{\textrm{goldclus}}$, the set of all non-overlapping pairs of fragments which are both discovered by the system (not necessarily in the same cluster) and have exactly the same gold transcription.

{\small
\begin{align}
 \mbox{Prec:} & \sum_{t\in\types{{P_{\textrm{clus}}}}} w(t,P_{\textrm{clus}})\frac{\card{\nmatch{t, P_{\textrm{clus}}\capP_{\textrm{goldclus}}}}} {\card{\nmatch{t,P_{\textrm{clus}}}}} \label{eq:grouping:precision} \\
 \mbox{Rec:} & \sum_{t\in\types{{P_{\textrm{goldclus}}}}} w(t,P_{\textrm{goldclus}})\frac{\card{\nmatch{t, P_{\textrm{clus}}\capP_{\textrm{goldclus}}}}} {\card{\nmatch{t, P_{\textrm{goldclus}}}}}\label{eq:grouping:recall}
\end{align}
}

Where $t$ ranges over the types of fragments (defined by the transcription) in a cluster $C$, and $occ(t,C)$ is the number of occurrences of that type, $w$ the number of occurrences divided by the size of the cluster. In other words, Prec is a weighted measure of cluster purity and Rec, of the inverse of the cluster's fragmentation. The other three scores are \textbf{type precision, recall,} and \textbf{F-score}. Type precision is the probability that discovered types belong to the gold set of types (real words), whereas type recall is the probability that gold types are discovered. We restrict both sets to words between three and twenty segments long.

\begin{table*}[ht]
\caption{\label{tab:track2res} Track 2 results. Scores are averages over evaluations done on subparts of the corpus: the list of corpus gold utterances is split in groups of 1000 elements/utterances in order to compute the scores in parallel. Thus, the $F$-scores presented are not directly related to the precision and recall scores.}
\centering
\resizebox{1.3\columnwidth}{!}{
\begin{tabular}
{@{}c@{ }|@{ }c@{ }|@{ }c@{ }|@{ }c@{ }|@{ }c@{ }|@{ }c@{ }c@{ }c@{ }|@{ }c@{ }c@{ }c@{ }|@{ }c@{ }c@{ }c@{ }|@{ }c@{ }c@{ }c@{ }}

\multicolumn{5}{c}{} &  \multicolumn{3}{c}{Grouping}   & \multicolumn{3}{c}{Type}  & \multicolumn{3}{c}{Token}   & \multicolumn{3}{c}{Boundary}  \\

& Words & Pairs & NED & Cov &  Pr & Re & F & Pr & Re & F & Pr & Re & F & Pr & Re & F  \\ \hline

\multicolumn{17}{c}{ \textbf{Avg. Training (English, French, Mandarin)} } \\\hline
\textbf{Baseline}	&	4948	&	5842	&	30.0	&	4.1	&	48.7	&	86.6	&	52.3	&	5.5	&	0.3	&	0.6	&	4.4	&	0.2	&	0.2	&	34.1	&	1.5	&	2.9	\\
\textbf{Topline}	&	5779	&	2742085	&	0.0	&	100.0	&	99.9	&	100.0	&	100.0	&	41.3	&	45.9	&	43.4	&	47.8	&	58.4	&	52.2	&	77.9	&	94.4	&	84.9	\\\hline
\textbf{K}	&	24724	&	3775685	&	76.0	&	105.0	&	6.7	&	8.3	&	6.7	&	4.6	&	9.0	&	6.1	&	6.3	&	7.1	&	6.7	&	41.7	&	47.6	&	44.4	\\\hline
\textbf{G1}	&	51377	&	465997	&	71.5	&	60.5	&	3.9	&	67.8	&	7.4	&	3.9	&	6.0	&	4.4	&	3.1	&	2.7	&	2.9	&	25.3	&	31.8	&	27.8	\\
\textbf{G2}	&	51354	&	467369	&	71.4	&	60.4	&	4.0	&	67.6	&	7.5	&	3.9	&	6.0	&	4.4	&	3.1	&	2.7	&	2.8	&	25.2	&	31.8	&	27.7	\\

\multicolumn{17}{c}{ \textbf{S1 -- German} } \\\hline
\textbf{Baseline} & 2973 & 3315 & 30.5 & 3.0 & 54.8 & 94.6 & 64.9 & 5.5 & 0.3 & 0.6 & 4.0 & 0.1 & 0.2 & 28.2 & 1.2 & 2.3 \\
\textbf{Topline} & 7664 & 2588808 & 0.0 & 100.0& 100.0 & 100.0 & 100.0 & 28.4 & 29.5 & 29.0 & 42.3 & 62.7 & 50.5 & 70.0 & 98.3 & 81.8 \\\hline
\textbf{K} & 28675 & 4258731 & 66.4 & 100.0  & 11.8 & 5.9 & 7.9 & 5.7 & 11.2 & 7.5 & 10.3 & 14.3 & 12.0 & 42.6 & 56.5 & 48.6 \\\hline
\textbf{G1} & 60648 & 582009 & 59.9 & 71.8  & 5.7 & 63.8 & 10.4 & 3.2 & 6.8 & 4.3 & 2.4 & 3.1 & 2.7 & 20.6 & 37.2 & 26.6 \\
\textbf{G2} & 60489 & 588162 & 59.9 & 71.8  & 5.8 & 64.0 & 10.7 & 3.2 & 6.8 & 4.3 & 2.4 & 3.1 & 2.7 & 20.6 & 37.2 & 26.5 \\ \hline\hline

\multicolumn{17}{c}{ \textbf{S2 -- Wolof} } \\\hline
\textbf{Baseline} & 462 & 545 & 33.5 & 3.2 & 39.1 & 72.1 & 32.8 & 2.3 & 0.1 & 0.2 & 1.6 & 0.0 & 0.1 & 25.3 & 1.0 & 2.0 \\
\textbf{Topline} & 2472 & 602339 & 0.0 & 100.0  & 100.0 & 100.0 & 100.0 & 43.0 & 47.6 & 45.2 & 55.6 & 65.6 & 60.2 & 81.3 & 93.2 & 86.9 \\\hline
\textbf{K} & 3593 & 439321 & 72.2 & 100.0  & 4.7 & 5.4 & 5.0 & 4.6 & 10.0 & 6.3 & 4.9 & 5.2 & 5.0 & 42.4 & 44.3 & 43.3 \\\hline
\textbf{G1} & 5468 & 37191 & 56.8 & 47.8 & 7.6 & 43.3 & 12.8 & 5.9 & 8.2 & 6.9 & 6.1 & 4.2 & 4.9 & 29.6 & 29.6 & 29.6 \\
\textbf{G2} & 5460 & 37273 & 56.8 & 47.8  & 7.7 & 44.0 & 13.0 & 6.0 & 8.3 & 7.0 & 6.1 & 4.2 & 4.9 & 29.7 & 29.6 & 29.6 \\
\end{tabular}
}

\end{table*}

\subsubsection{Evaluation as word segmentation system}

Six scores are used to evaluate the performance of a spoken term discovery system as a system for word segmentation. The first three are \textbf{token precision, recall,} and \textbf{F-score.} Token precision is the probability that discovered fragment tokens are in the gold set of word fragments, and token recall the probability that the gold fragments are discovered.
Again, tokens are restricted to be between three and twenty segments long.
Finally, \textbf{boundary precision, recall,} and \textbf{F-score} are similarly defined with respect to the set of discovered and gold word boundaries.

\section{Description of submitted systems}
We received sixteen systems for track one, and three for track two. For two track one submissions, the authors declared that the systems were not intended as submissions, and are thus not included in the present overview, although the results can be found on the leaderboard on the Challenge website (\url{www.zerospeech.com/2017}).

\subsection{Track 1}

The baseline features are the MFCC features provided to participants; they are evaluated using the ABX score using the cosine similarity. The topline system is a supervised HMM-GMM phone recognition system, with a bigram language model, trained with a Kaldi recipe \cite{Povey_ASRU2011_2011}. The phone posteriors are evaluated using the KL divergence.

The submitted systems follow four strategies. The first strategy consists in performing a bottom-up frame-level clustering, following up on the success of \cite{chen15} with this simple strategy.
In system \textbf{[H]}, from \textbf{Heck et al.} \cite{heck}, PLP representations are used, as well as the output of three learned feature transformations, to be the input to the same frame-level clustering algorithm used in \cite{chen15} (the parallel Dirichlet process Gaussian mixture model sampler of \cite{pdpgmm}). The transformations (LDA, LDA+MLLT; LDA+MLLT+fMLLR) are aimed at making the representation talker invariant. The training requires labels, which are obtained from an initial clustering using the DPGMM algorithm. The submitted system is a
combination of the raw posteriorgrams (not smoothed by any language model) from the four DPGMMs obtained by clustering frames in each of the three transformed representations, plus the raw PLP features.
System \textbf{[P1]}, from \textbf{Pellegrini et al.} \cite{pellegrini}, uses $k$-means clustering on ZCA-transformed (whitened using PCA and rotated back to the original axes) MFCC features. In system \textbf{[P2]}, this is followed by a step of centroid re-estimation: the centroids are re-estimated based only on the data points for which the label is the same as that of its left and right neighbors. The distance from point to all cluster centroids is the feature vector in both.

A second strategy constructs language-independent embeddings by training neural networks in an unsupervised way. The systems of \textbf{Chen et al.} \cite{chen} first cluster frames separately on each language, using the same DPGMM algorithm as \cite{heck} and \cite{chen15}. The cluster labels are then used as targets for a multitask neural network, where the loss function is the mean of the five cross-entropy losses. A linear bottleneck layer in the network is the final representation. System \textbf{[C1]} is trained on MFCCs; \textbf{[C2]} transforms the input features using unsupervised linear VTLN. The three systems in \textbf{Ansari et al.} \cite{ansari-ae} combine two sets of features, also trained on all five languages. The first set of features is always a high-dimensional hidden layer from an autoencoder trained on the MFCC frames. The second set of features is a hidden layer from a network trained to predict unsupervised frame labels. These labels are obtained by training a Gaussian mixture model on the speech frames. In system \textbf{[A1]}, the cluster labels are used as targets for the DNN trained with the MFCCs as input. System \textbf{[A2]} instead uses time-smoothed frame labels as decoded by a large Gaussian-mixture-HMM (one state for each of the initial cluster labels), in which states have a high self-transition probability. System \textbf{[A3]} does the same but trains the DNN with the autoencoder features as inputs. In systems \textbf{[A1]} and \textbf{[A2]}, the DNN features are combined with the autoencoder features in the dissimilarity function for the ABX task: the dissimilarity is a weighted average between the cosine DTW distance using the autoencoder features, and the cosine DTW distance using the other set of features (the weights are optimized on the development languages and not reported). An additional system is described in \cite{ansari-no-ae}, system \textbf{[A4]}, which consists of the DNN features from system \textbf{[A2]}.

The third strategy is to use spoken term discovery to improve the acoustic features, following \cite{thiolliere,renshaw}. The system of \textbf{Yuan et al.} \cite{yuan} generates bottle-neck features in the same way as \cite{chen}, but then applies the STD system of \cite{aren} to discover pairs of matched acoustic motifs, which are then used to further train the features. Specifically, following \cite{renshaw}, a stacked autoencoder is trained on DTW-aligned frame pairs from instances of motifs paired by the STD system. System \textbf{[Y1]} uses pairs obtained by applying the STD system (only) to the English training data. System \textbf{[Y2]} (not reported in \cite{chen}) obtained pairs from all corpora of all five languages.
System \textbf{[YS]} is a supervised comparison, using transcribed pairs from the Switchboard corpus rather than STD.

The strategy of \textbf{Shibata et al.} \cite{shibata} is to use supervised training on out-of-domain languages. In \textbf{[S1]}, the features are bottleneck features from a neural network acoustic model trained supervised on Japanese as part of an HMM. In \textbf{[S2]}, an end-to-end convolutional network plus bidirectional LSTM is trained using corpora from ten languages, and features from a hidden layer are combined with the bottleneck Japanese features. This second scheme can only be considered out of domain in a weak sense: new corpora are used, but the ten additional languages include English, Mandarin, and German.

\subsection{Track 2}

The baseline of track two was computed using \cite{aren}, which does pair-matching using locally sensitive hashing applied to PLP features and then groups pairs using graph clustering. The parameters of the STD stayed the same across all languages, except that the DTW threshhold was increased for Mandarin (to 0.90, from 0.88) in order to obtain a NED value similar to that of other languages. The topline was an exhaustive-parsing word segmentation model based on the textual transcriptions (a unigram grammar trained  in the adaptor grammar framework: \cite{adaptorgrammar}).

System \textbf{[K]}, from \textbf{Kamper et al.} \cite{kamper} uses $k$-means to discover recurring acoustic patterns, jointly optimized with an exhaustive segmentation. The variable-length acoustic chunks of speech yielded by a segmentation are reduced to fixed dimension by down-sampling in the time dimension for clustering by $k$-means. The systems of \textbf{Garc\'ia-Granada et al.} \cite{garcia} use a supervised ASR system for Hungarian to decode the speech. Chunks of (transcribed) speech match if they have the same transcription. These matches are then filtered: a representation of the speech is obtained by training an autoencoder, and only pairs with DTW sufficiently low in this representational space (below a threshhold) are retained (system \textbf{[G1]}). In system \textbf{[G2]}, pairs are tolerated that have a one-phone difference in their transcriptions.

\section{Results}
\label{sec:results}

The results of track one are summarized in Table \ref{tab:track1res}.
The clear winner among the unsupervised models is \textbf{[H]}. While the paper focuses on the role of learned feature transformations---and demonstrates that they improve the evaluation---these are unlikely to be solely responsible for the very good performance of the system. The baseline system described in the paper, with no transformations applied, consisting of posteriorgrams from a frame-wise DPGMM clustering, still performs extremely well. Although it was not evaluated on the surprise languages, its average ABX score on the development languages (9.7) is still better that of the second-ranked system \textbf{[C2]} (10.7), from \textbf{Chen et al.} The systems presented by \textbf{Heck et al.} all have in common monolingual training, direct evaluation of GMM posteriorgrams, and the fact that they use PLP features as input, rather than MFCCs.

The systems of \textbf{Chen et al.} (as well as \textbf{Yuan et al.}) use an initial stage of monolingual training, followed by multilingual training. Among the other systems in \cite{chen} (not submitted for final evaluation) are some with fully monolingual training, which serve to partly evaluate the effect of multilingual training: monolingual training fares better, within language. However, as the numerical scores are not presented, it is not possible to determine whether the use of monolingual training could account for the good scores of \textbf{Heck et al.} What is clear is that multilingual training still works reasonably well---all the remaining systems, except for the lower performing system \textbf{[P]}, use multilingual training, and show substantial improvements over the baseline. The setting for these systems corresponds to the situation of an infant raised in a multilingual environment.

The STD strategy for improving subword features fares relatively poorly; a limitation of the \textbf{Yuan et al.} systems is that they use an architecture (autoencoders) that was previously reported to be suboptimal for improving acoustic features \cite{renshaw}. A Siamese network \cite{thiolliere} might improve the result.

The results of track two are in Table \ref{tab:track2res}.
As in the previous challenge, the baseline provided by the system of \cite{aren} has a high NED (very good matching), but has a low coverage (few matches). Given that system \textbf{[K]} demands an exhaustive parse, it is guaranteed to have full coverage, which risks poorer matches. It is thus notable that system \textbf{[K]} has a NED on the development languages comparable to the partly supervised systems \textbf{[G1]} and \textbf{[G2]}. On the other hand, grouping by supervised transcription clearly works better than $k$-means clustering; while the groups have low precision (many erroneously grouped items) for both kinds of systems, \textbf{[G1]}/ \textbf{[G2]} have much higher grouping recall (more of the true group members among the items grouped together) than system \textbf{[K]}. The quality of segmentation, in terms of tokens and of boundaries, is, on the other hand, much better in system \textbf{[K]}, presumably as a result of increased coverage.

\section{Conclusion}
\label{sec:conclusion}

The results of the 2017 Zero Resource Speech Challenge build very clearly on the previous challenge. The strategies most successful in the previous challenge for track one, bottom-up clustering and training based on pairs of matching words discovered using spoken term discovery---are built upon, and a new strategy, multilingual training, is introduced. This strategy, although not completely foreseen by the organizers, would be similar to the situation of a multilingual infant. This new approach is used in conjunction with additional tools, including further training using neural networks and the application of time series models.  Interestingly, simple bottom-up clustering still seems to work best in the unsupervised case.  The fact that supervised labels trained fully out of domain seem to yield good results in both track one and track two (on Japanese, in the case of \textbf{[S1]}, and on Hungarian in the case of \textbf{[G1]} and \textbf{[G2]}) indicates that the approach of having systems with strong prior knowledge about human speech is still a benchmark to be beaten in the unsupervised case. (The excellent results of \textbf{[S2]} may yet be partly attributable to the use of supervised training in German, which was one of the surprise languages.) Further work is needed to systematically evaluate the effect of each of these different manipulations, however, and to directly compare these new approaches with those from the previous challenge.

Potential future lines of inquiry building on the new track two systems might include exploring using these systems to feed subword modelling, especially in combination with some of the new subword modelling strategies used here, in light of the successful use of STD to improve subword models in the previous challenge, and the reasonable improvement over baseline shown in this challenge. In the other direction, the major unsupervised improvements to the speech features demonstrated in track one suggest strong room for improvement in unsupervised spoken term discovery systems like \textbf{[K]}.

The 2017 ZeroSpeech Challenge remains open for submissions exploring these, or any other new strategies for discovering phoneme- and word-like units without access to linguistic resources.

\bibliographystyle{IEEEbib}
\clearpage

redpajama_set_name: RedPajamaArXiv


Rafael de la Fuente Joins ABC's "When We Rise"
You might recall that sometime in 2015, I interviewed Empire's own Rafael de la Fuente for JUST ADD COLOR. Now, a year later, I'm excited to say that de la Fuente is going to be on a brand new project coming to ABC, When We Rise.
When We Rise, coming to the network in 2017, will be directed by Gus Van Sant (Good Will Hunting, Milk) and Dee Rees (Pariah, Bessie) and written by Dustin Lance Black (Milk, Pedro, J. Edgar). For those who hated the film Stonewall (and with good reason), When We Rise could be your saving grace, since it's focusing on the American gay rights movement after the Stonewall Riots. De la Fuente will play Ricardo, who is described as "a character who ends up in a long-term relationship with Cleve [Guy Pearce]". Mary Louise Parker is also a part of the project; other cast members will be announced in the coming months.
What do you think about When We Rise? Give your opinions in the comments section below!


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For more information about MBA (Grenoble) at Grenoble Graduate School of Business, please visit the webpage using the button above.
Classes are taught in English. Year 1 of the program consists of coursework. In Year 2 of the program, students complete a final management project of their choosing while pursuing internships or full-time work. The Grenoble MBA is delivered on a full-time basis at GGSB's Grenoble campus and London partner campus, and on a part-time basis at partner campuses in Moscow and Tbilisi. Students may choose to follow one of 8 90-hour specializations offered each year.
Program applicants must: - Have a Bachelor's degree or equivalent- Have 3 years of full-time work experience- Send an application file (CV, Diploma, English test, etc.)- An interview and/or GMAT scores may be required on a per-case basis.


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Gender, Race & Place in Minnesota
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Dialogues Archive
Gender Policy Dialogues
When Abortion is Illegal
The Supreme Court's June decision in Dobbs v. Jackson Women's Health Organization reversed the legal precedent set by Roe v. Wade, which established the constitutional right to have an abortion, just one year short of Roe's 50th anniversary. The Dobbs decision comes off after decades of concerted antiabortion activism and state-level policymaking that had already substantially curtailed abortion access. With Dobbs, antiabortion activists struck a blow to the fight for gender equality. Abortion is just one aspect of a broader continuum of rights, resources, and demands that constitute reproductive justice. The Dobbs decision has nonetheless rendered the legal scaffolding for the exercise of reproductive justice fragile.
As the justices who authored the dissenting opinion cautioned, the Dobbs decision raises the possibility that state lawmakers will criminalize seeking, obtaining, performing, or sharing information about abortion. Indeed, in the wake of Dobbs, we have seen these predictions materialize as numerous states have already severely restricted the right to abortion and others plan to follow suit.
To understand the future of abortion in the United States, we can learn from those who have studied abortion in contexts and historical moments in which it was illegal or criminalized. For this Gender Policy Report Dialogue, we invited scholars from fields such as history, legal studies, sociology, and gender, women's and sexuality studies, to examine abortion in contexts of criminalization. These essays provide cross-historical and transnational perspectives on how people have secured and performed illegal abortions, built grassroots and faith-based networks to ensure abortion access, and decriminalized or otherwise undermined legal restrictions on abortion.
Looking to histories of grassroots feminist and faith-based reproductive rights activism brings into view tools, tactics, and theories, even theologies, we might bring to unfinished struggles. Examining diverse geographic and national contexts in which abortion has recently been decriminalized, challenges fantasies that construct the U.S. as the vanguard of gender equality. These scholars remind us that whether abortion is illegal, people will continue to need and help each other obtain abortions.
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The Religious Network that Made Abortion Safe When it was Illegal
Gillian Frank: The CCS grew out of longstanding practices of clergy covertly helping abortion seekers obtain (il)legal abortions. Pastoral counseling placed clergy and their congregants in quiet conflict with the laws governing abortion,which included restrictions in sharing information about abortifacients and abortion providers
Minimizing Harm: Feminist Legal Activism and Abortion
Fiona de Londras: Having grown up in Ireland when abortion was almost completely prohibited, I have some sense of the challenges that pregnant people in the United States will soon face in seeking to bring their pregnancies safely to an end. In Ireland we learned that when abortion is criminalized, feminists must simultaneously attempt to minimize harm through formal legal activism and engage in extra-legal activism to ensure access to abortion
Menstrual Extraction was Feminist Managed Early Abortion
Hannah Dudley-Shotwell: Shortly before Roe legalized abortion in 1973, feminist self-help activists developed menstrual extraction. Many feminists believed that by working in groups to learn about their own bodies, (cis)women could control the provision of much of their own healthcare.
Medication Abortion Access Strategies: Lessons from Latin America
Julia McReynolds-Pérez One place to find clues to what 21st century illegal abortion might look like is Latin America. In recent decades, Latin America has been the site of extensive abortion activism that uses new technologies to expand access to safe abortion even under the most restrictive regimes.
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