Accounting forms a significant part of information on any enterprise and plays the most important role in different levels of enterprise' management as well as in country's. Accounting information is used by various information users, whose decisions may influence various spheres: from performance of enterprises to proceeding the state policy. Its role increased recently under conditions of modern global and competitive market economy, and will be increasing in the future. Importance of accounting information usage highlights the need of appropriate formation of such information. And accounting regulation plays the main role in the process of formation and presentation of accounting information as it determines requirements for accounting methodology, information performed in the single set of financial statements or consolidated financial statements. The purpose of the research is to evaluate evolution of accounting regulation for the period 1990 – 2017 and distinguish accounting regulation evolution phases and factors, which influenced development of the phases. Six countries, which are members of the European Union – the Republic of Croatia, the Czech Republic, the Republic of Estonia, the Republic of Latvia, the Republic of Lithuania and the Slovak Republic - were chosen for the evaluation of accounting evolution. For the research were used legislation analysis, systematisation, inductive and deductive, comparison and summary of information methods. Research results show, that evolution of accounting regulation in analysed countries may be distinguished in four general phases. The period of evolution started at the beginning of nineties (1990–1992) when the countries transformed from a central planned economy to market economy, this phase was followed by other phases (1993–2001–2005), when laws on accounting and additional legislation were issued. Accounting regulation evolution phase for alignment accounting legislation with Fourth and Seventh EU Directives started from 2002/2006 and the phase of the compliance with requirements of Directive 2013/34/EU of the European Parliament and of the Council of 26 June 2013, which started in all analysed countries in 2016, except Slovakia – in 2014, completes the accounting regulation evolution period till present.
Alver, J., Alver, L. 2008. Development of Accounting and Implementation of international Financial Reporting Standards in Estonia, in McGee, R. W. (Ed.) Accounting Reform in Transition and Developing Economies. New York: Springer, 101–113.
Alver, J., Alver, L., Mackevicius, J., Paupa, V., Bailey, D. 2001. Baltic States. In Alexander, D; Acher, S (Eds) European Accounting Guide, New York: Harcourt Brace Professional Publishing, 1481–1504.
Brūna I., Millere I., Šneidere R. Accounting Policy and Developmental Issue in Latvia. Rachunkowość W Zarządzaniu Jednostkami Gospodarczymi. Szczecin, 2006, 37. – 43.p.
Decree No. 281, 283/77 411/2000 of 10 November 2000 laying down details of the accounting procedures for accounting units under the basis of cash receipts and disbursements.
Decree No. 281/71 701/95 of 21 December 1995 laying down the content of financial statements for entrepreneurs.
Decree No. 281/89 759/2001 of 14 November 2001 laying down the framework for the chart of accounts and details of the accounting procedures for entrepreneurs.
Decree No. 281/97 417/2001 of 14 December 2001 laying down the content of financial statements for entrepreneurs.
Decree No. 312/2014 Coll. on conditions for compiling accounting statements for the Czech Republic (the consolidation degree of the state).
Decree No. 325/2015 Coll. implementing some provisions of Act No. 563/1991 Coll., on Accounting, as amended, for accounting units using the single-entry accounting.
Decree No. 410/2009 Coll. implementing some provisions of Act No. 563/1991 Coll., on Accounting, as amended, for selected accounting units (state administration).
Decree No. 500/2002 Coll. implementing some provisions of Act No. 563/1991 Coll., on Accounting, as amended, for accounting units that are entrepreneurs using the double-entry accounting system.
Decree No. 501/2002 Coll. implementing some provisions of Act No. 563/1991 Coll., on Accounting, as amended, for accounting units that are banks and other financial institutions.
Decree No. 502/2002 Coll. implementing some provisions of Act No .563/1991 Coll., on Accounting, as amended, for accounting units that are insurance companies.
Decree No. 503/2002 Coll. implementing some provisions of Act No. 563/1991 Coll., on Accounting, as amended, for health insurance companies.
Decree No. 504/2002 Coll. implementing some provisions of Act No. 563/1991 Sb., on Accounting, as amended, for accounting units whose objects clause is not business, providing they use the double-entry accounting system.
Decree No. 506/2002 Coll. implementing some provisions of Act No. 563/1991 Coll., on Accounting, as amended, for the following accounting units: National Property Fund of the Czech Republic and National Land Fund of the Czech Republic.
Decree No. 507/2002 Coll. implementing some provisions of Act No. 563/1991 Coll., on Accounting, as amended, for accounting units using the single-entry system.
Decree No. V/1- 31 388/1992 of 29 December 1992 laying down the structure and content of items of the balance sheet and the income statement for entrepreneurs.
Decree No. V/1-31 370/1992 of 23 December 1992 laying down details of the accounting procedures for sole traders carrying out business activities or other activities generating income under the basis of cash receipts and disbursements.
Decree No. V/20 100/1992 of 15 July 1992 laying down the framework for the chart of accounts and details of the accounting procedures for entrepreneurs.
Decree of the Ministry of Finance of the Czech Republic No. 23/1990 Coll., on Accounting.
Decree of the Ministry of Finance of the Slovak Republic No. 23054/2002-92 of 16 December 2002 laying down details of the accounting procedures and the framework for the chart of accounts for entrepreneurs maintaining accounts under the system of double entry bookkeeping, as amended.
Decree of the Ministry of Finance of the Slovak Republic No. MF/15464/2013-74 of 11 December 2013 laying down details of the structure, description and content of items of individual financial statements for micro accounting entities, as amended.
Decree of the Ministry of Finance of the Slovak Republic No. MF/23377/2014-74 of 3 December 2014 laying down details of the structure, description and content of items of individual financial statements for large accounting entities and public interest entities, as amended.
Decree of the Ministry of Finance of the Slovak Republic No. MF/23378/2014-74 of 3 December 2014 laying down details of the structure, description and content of items of individual financial statements for small accounting entities, as amended.
Farkaš, R. 2013. Konsolidovaná účtovná závierka v Slovenskej republike [Consolidated Financial Statements in the Slovak Republic]. Bratislava: Iura Edition, p. 309.
Gulin, D., Spajić, F., Spremić, I., Tadijančević, S., Vašiček, V., Žager, K., Žager, L., Računovodstvo [Accounting], 2003. Zagreb: Hrvatska zajednica računovođa i financijskih djelatnika.
Hladika, M., Mokošová, D., Molín, J. 2017. Comparison of national accounting framework-the case of Croatia, Slovakia and Czechia. In Economy of Eastern Croatia-vision and growth. International scientific symposium. Economy of Eastern Croatia-vision and growth : 6th International scientific symposium: Osijek, 25-27 May 2017, Croatia. - Osijek: Sveučilište Josipa Jurja Strossmayera u Osijeku, Ekonomski fakultet u Osijeku.
Hladika, M., Perspektiva primjene modela obračunske osnove i Međunarodnih računovodstvenih standarda za javni sektor u proračunsko računovodstvo u Republici Hrvatskoj [The perspectives of the application of the accrual basis of accounting and the International public sector accounting standards to government accounting in Croatia], 2013, doktorski rad. Zagreb: Ekonomski fakultet.
Huleš J., Jeřábek A, 2001. Act on Accounting valid from 1 January 2002. Bilance, Prague 2001.
Kordošová, A. 2008. Základné zmeny v účtovníctve rozpočtových organizácií v kontexte IPSAS [Basic Changes in Accounting of Budgetary Organisations in accordance with IPSAS]. In Teória a prax v účtovníctve a v audítorstve-jej miesto v prostredí uplatňujúcom prvky vedomostnej ekonomiky: zborník príspevkov zo štvrtého vedeckého seminára k výsledkom vedeckovýskumnej činnosti Katedry účtovníctva a audítorstva. - Bratislava: Vydavateľstvo EKONÓM.
Lakis, V., Subačienė, R. 2014. Transformation of Accounting in Lithuania since 1990, Zeszyty teoretyczne rachunkowosci [Theoretical Journal of Accounting], tom 78 (134).Warsaw, SKwP, 177-196.
Mackevičius, J. 2005. Nepriklausomos Lietuvos buhalterinės apskaitos raida ir problemos (1990 – 2005 m.) [Development and Problems of Accounting of Independent Lithuania]. Apskaitos ir audito sistemų integracija į Europos Sąjungos erdvę. Nauji iššūkiai ir galimybės [Accounting and Audit System Integration into European Union Area. New Challenges and Opportunities]. Research papers. Vilnius: Lithuanian chamber of auditors.
Mackevičius, J., Subačienė, R. 2016a. Lietuvos buhalterinės apskaitos Sistema: praeitis, dabartis, perspektyvos [Lithuanian accounting system: past, present and prospects]. Vilnius: Akademinė leidyba.
Mackevičius, J., Zverovich, S., Kazlauskienė, L. 2011. Financial Reporting in Lithuania, Harmonization of SME's Financial Reporting in Emerging CEE Countries, 1 part. WSEAS Press, 138-168.
Millere I. Implementing the legislative requirements in Latvian accounting. Vilnius, Accounting & Audit System Integration into European Union Area, new challenges and opportunities. The Papers of International Conference (6-7th October 2005), 2005. – 152.-158.p.
Millere I. Latvijas grāmatvedības vēsturiskās attīstības tendences. Rīga: Latvijas Universitāte, Latvijas Universitātes raksti, 689.sējums, 2005. – 201.-212.pp.
Mokošová, D., Subačienė, R, Hladika, M., Molín, J. 2017. Impact of changes in accounting regulation on sanctions for its violation in selected countries. In Financial Management of Firms and Financial Institutions 2017. 11th International Scientific conference: Ostrava. (print version in proceeding).
Pacter, P. 2016. Estonia, IFRS as global standards: A pocket guide. London: IFRS Foundation.
Parišová, R. 2010. Podnikové kombinácie podľa IFRS [Business Combination in accordance with IFRS]. Bratislava: Iura Edition, p. 173.
Pravilnik o financijskom izvještavanju u proračunskom računovodstvu, 2015, Official Gazette, No. 3/15, 93/15, 135/15, 2/17, 28/17. [Ordinance for financial reporting in public sector accounting].
Pravilnik o proračunskom računovodstvu i računskom planu, 2014, Official Gazette, No. 124/14, 57/15, 115/15, 87/16. [Ordinance for public sector accounting and chart of accounts].
Pravilnik o strukturi i sadržaju godišnjih financijskih izvještaja, 2008, Official Gazette, No. 38/08, 12/09, 130/10. [Ordinance on the structure and content of annual financial statements].
Pravilnik o strukturi i sadržaju godišnjih financijskih izvještaja, 2016, Official Gazette, No. 95/16. [Ordinance on the structure and content of annual financial statements].
Regulation of the Cabinet of Ministers of Latvia No 201 Regulations on Obligatory Applicable Latvian Accounting Standards.18.10.2015.
Skálová, J. 2017. Taxes in Accounting. Wolters Kluwer, Prague 2001.
Šlosár, R. 2008. Dejiny účtovníctva na Slovensku [History of Accounting in Slovakia]. Bratislava: Iura Edition, p. 234.
Subačienė, R., Mačerinskienė, A., Budrionytė, R. 2012. Apskaitos standartų harmonizavimas [Harmonization of Accouting Standards] "Accounting, audit, analysis: science in the context of innovation and globalization" research papers of international scientific conference 2012, Vilnius: Vilniaus universiteto leidykla, 713 – 722.
Zakon o proračunu, 1994, Official Gazette, No. 92/94. [Budget Law].
Zakon o proračunu, 2003, Official Gazette, No. 96/03. [Budget Law].
Zakon o proračunu, 2008, Official Gazette, No. 87/08, 136/12, 15/15. [Budget Law].
Zakon o računovodstvu, 1991, Official Gazette, No. 53/91, 33/92. [Law on Accounting].
Zakon o računovodstvu, 1992, Official Gazette, No. 90/92. [Law on Accounting].
Zakon o računovodstvu, 2005, Official Gazette, No. 146/05. [Law on Accounting].
Zakon o računovodstvu, 2007, Official Gazette, No. 109/07, 54/13. [Law on Accounting].
Zakon o računovodstvu, 2015, Official Gazette, No. 78/15, 134/15, 120/16. [Law on Accounting].
Zakon o trgovačkim društvima, 1993, Official Gazette, No. 111/93, 34/99, 121/99, 52/00, 118/03, 107/07, 146/08, 137/09, 125/11, 152/11, 111/12, 68/13, 110/15. [Company Law].
Бухгалтерский учет в зарубежных странах [Accountancy in foreign countries]. 2007. под. редакцией Ф.Ф. Бутынца. Житомир, ЖГТУ.


text:
Home Page | Planned Exhibits | Research Support | Want to Help? | Why a Museum in Cyberspace? | Updates
Carter's Quarter Barbed Wire Cherokee Removal FT Civil War Coulter Dolls
County Officials Death Certificates Early Chenille Early Doctors Early Newspapers
Fort Mountain Free Negroes 1870 Gateways Historical County Lines Historical Markers
History of Murray Korean War Landmarks Lost Lists Memoirs of a Slave
Methodist Church Murray Artists Murray Cemeteries Murray Characters Murray Census 1834
Murray Families Murray Heritage Book Murray High School Murray History 1911 Murray Memories
Murray Post Offices Murray Quilts Murray Schools Old News Stories Photographs
Planned Displays Poems Prized Possessions Road to Dalton 1950 Rolling Stores
Roseville Pottery School Valentines Stained Glass Time Capsules Vann House
Vann Slaves Veterans Memorial Vietnam War Vintage ADs War Dead
Wood Vases World War I World War II Wright Hotel
Herman McDaniel
(Founder and owner of the Murray County Museum WebSite)
April 17, 1938 ~ November 22, 2022
Herman �Mac� McDaniel passed away peacefully at his home in Myrtle Beach , South Carolina on November 22, 2022.
He was preceded in death by his mother Amie Long McDaniel, brothers Eugene, Carlton, and Elmer, and sisters Juanita and Wanda.
Herman left Chatsworth his senior year in high school and was appointed as a Congressional Page in Washington DC. After graduation, he served in the US Army.
Herman lived his adult life in the nation�s capital making great strides in the early stages of computer programming. He was the creator of the virtual museum about the history of Murray County. He was also part of the committee that helped in the creation of Murray County Veteran�s Park.
Herman is survived by numerous nieces and nephews as well as great nieces and nephews.
Services to celebrate the life of Herman McDaniel will be Saturday, November 26th at 4 pm at Jones Funeral Home. Family will receive friends from 2-4 pm. There will be no further updates to the Murray County Museum WebSite
Murray County Museum
Murray County, Georgia's Heritage in Cyberspace
This unusual museum, devoted to life in Murray County, Georgia, first appeared on the Internet February 15, 2005, with no advance publicity or notice.
This is a brief report of what has happened since.
Many of the originally stated objectives have been met earlier than anticipated. This is especially true in the area of providing serious historic materials relating to Murray County:
With the permission of the authors and the Whitfield-Murray Historical Society, we have posted the text of the first 500 pages of Murray County Heritage, published in 1987.
Charles Shriner's History of Murray County, published in 1911 is also now available in this museum.
The full text of the memoirs of a Murray County slave, Levi Branham, published in 1929 as My Life and Travels also is now available in the museum.
All three of these works are computer searchable, usually performing searches in less than one second.
Serious historic researchers can also access through our Gateways section, the computer files of Georgia State Archives.
Our own Chronological History of Murray County has been posted and will continue to be corrected, revised, and updated as time permits and new material becomes available. Another newly created report lists post offices that have served Murray County citizens since the first white settlers arrived in the area. Historical lists of importance now available in the museum include:
Georgia Census of Murray County 1834
Murray County's Elected Officials (1832-1986)
Early Murray Newspapers
Early Murray Doctors
A section of the museum titled "Landmarks Lost" contains dozens of pictures of structures, once important, that no longer exist. When time permits, there are numerous structures to be added to this section.
Most people who grew up in Murray County attended Murray County High School. This section is being developed as time and materials permit. Eventually we hope to have coverage of the faculty and students for every school year since the school opened in 1934. Coverage of some 25 years has been completed.
Major museum displays include:
historical roadside markers of Murray County (not complete)
Murray cemeteries
Coulter dolls
a Roseville pottery collection
vintage Murray advertisements from 1920s, 1930s, 1940s, 1950s
a barbed wire collection
a collection of wooden vases
school valentines from 1940s;
chenille products from the 1930s-1970s
scores of Murray County quilts
works of art by Murray County artists
The museum section titled "Prized Possessions" displays museum-type items owned by Murray County people. It includes antique weapons, furniture, tools, business equipment, Indian artifacts, telephones, works of art, toys, dolls, needlework, and a comprehensive, privately owned collection of washday items: wash pots, wash tubs, rub-boards, irons, ironing boards, washing machines, water buckets, and dryers.
The museum's Gateways section provides free and easy access to online editions of nearly 1,000 English Language newspapers around the world, as well as online displays of many museums and art galleries in several countries.
To understand what the website is about, first-time visitors should click on and read the following topics at the top of this page: Why a Museum in Cyberspace?
Planned Exhibits?
Three notes of interest:
The man who is doing all of the computer aspects of creating and managing this website has never even been in Murray County!
The guy who envisioned the site, then decided to actually create it, although a Murray resident in his youth and a graduate of Murray County High School, has not lived in Murray County for the past 45 years.
The computer from which this website operates is not even in Georgia—but in a state far distance removed.
Please take a few minutes to explore some of the topics on this site, then click some of its links to other sites to discover worlds not previously available to those living in Murray County!
Visitors returning to the museum can quickly find what has been added recently. Simply click on "Updates" at the top of each page. We maintain a chronological listing of what has been changed.
PLEASE TELL OTHERS ABOUT THIS MUSEUM!
www.murraycountymuseum.com
Three notes of intere
� Copyrighted 2005 - 2023 Murray County Museum - All Rights Reserved

redpajama_set_name: RedPajamaCommonCrawl


It's Labor Day, which I hope means most of you are off work today. Yay to long weekends! I must say, it's been a bit hot here in LA lately, so baking anything has been a little bit on the "ugh" side when you live in a place with no air conditioning. Normally, it's fine because we get a decent breeze coming off the ocean a few miles away, but it's been especially humid this summer so... bluck!
Nonetheless, last night I was determined to make something to share with all of you, and my boyfriend made a request for oatmeal cookies. Now, if I'd had my druthers, I would probably have made some homemade brownies, but he is one of those crazy people who doesn't like chocolate. I know, right? Also, I didn't have enough chocolate to make the kind I wanted to make, so poo.
Anyhow, these soft oatmeal cookies are chewy and have a little hint of cinnamon. The BF is not the biggest fan of the cinnamon addition, but I kind of like it myself. It gives it a little extra bit of flavor without shouting at you that there's a bunch of cinnamon. To each their own! If you don't like cinnamon, then you can go ahead and omit it from the below recipe.
If you would like your cookies to not spread as much, go ahead and stick the dough in the fridge for a couple hours. I was not in the mood to wait around for refrigerated dough, so these ended up being a little bit flatter than I normally like. BUT! They still taste really good and are just as chewy and soft, so it's no big deal.
For those who aren't chocolate fans, but still want a classic cookie, look no further than these soft cookies stuffed with oatmeal.
Preheat oven to 350°F. Line your cookie sheet with parchment paper or a silpat.
Cream together the butter and two sugars until combined (in a large bowl). Add in the eggs and vanilla, and beat until smooth. Next, stir in the flour, baking soda, baking powder and ground cinnamon, if your using it.
Finally, add in the oats and stir until combined. You can also put in any desired add-ins at this stage as well (i.e. raisins).
Scoop dough onto your cookie sheet and bake for 12-15 minutes depending on the size of your cookies. They will be light brown in color. Remove from the oven and let sit on the cookie sheet for 5 minutes before moving them to wire racks to cool completely.
I did not take photos of the whole process this time around, as I was baking them at night and you know those night pictures never turn out very well thanks to no natural light. So I hope you enjoy the pictures of just the final product this time around!


text:
Mish Alone: A Necromantic Scene
Mish's experience of the world had been reduced to darkness, rustling, and pain: so much pain that he couldn't even tell where it came from anymore. He had been upset about the warlock burn on his stomach before, but that seemed ridiculous now. He couldn't even locate that pain underneath the spreading new pain. This new pain started in his eyes, he knew, or rather, in his sockets. Mish tried not to think too much about the fact that he was never getting his eyes back. That he would be blind forever.
Lords of the Sea, his sight was gone forever…his eyes were gone forever—
No. No, he needed to find shelter, medicine, help. What had happened to him was already done, and dwelling on it would only make him feel worse.
He truly had nothing to lose anymore. He had thought the same thing when following that old woman—Garnet, or whatever her name had been—and he had been truly mistaken. But now, he couldn't think of anything else he could lose. Anything anyone else could do to him would only make death come faster for him, something that part of him wanted.
But there was another part, strong and scared, that would do anything to stay alive. No matter who he had to kill or what horrors he had to face. Even if it meant living as the slave of a warlock, though he truly hoped it wouldn't come to that.
He didn't have the slightest idea where he was going. The warlocks had purposefully abandoned him, to die on his own, to "crawl in darkness." And the Mooric and Highlandic people said Ponticusans were cruel. Peh. Though, warlocks couldn't really be taken as a representative of any culture. The warlocks of an area usually took on something of the culture through accidental appropriation, but Mish had to remember that they weren't from the area.
They weren't from any area that Mish knew of. They had been entrenched in the world for so long, and all anyone knew of them was that they arrived one day hundreds of years ago, and they would not be gotten rid of: even now, with them having fallen out of power in Ponticus and it seemed at least partially in the Moors and Highlands as well. They spoke a different language, had different customs, and possessed powers unseen in any other culture that Mish knew about. There weren't any warlock apprentices in Ponticus. There weren't any children who discovered they could summon fire. Warlocks were something outside that had moved in and taken over, an invasive species.
I need to get home. The thought came strongly and suddenly, and Mish was quite startled by it. He did want to get home, but that wasn't the most important task ahead of him. First, he needed somewhere to get cleaned up and make sure he wasn't going to die from his blinding, or from the burn that had incapacitated him in the first place. As he thought about "home," he didn't necessarily think of Ponticus, though as he thought longer about getting home, he did really want to leave this cruel and godless country. He needed to find shelter, food, water, medicine though it was a long shot.
He noticed something in the back of his head: almost like a thought, but it didn't taste like one of his own thoughts. Mish stopped his useless walking for a moment and held onto a tree branch for balance as he examined this odd feeling. It was growing, big and black and angry. His heart started to beat more quickly. He wanted to run, but how can you run from something without a body? Maybe he was sensing someone nearby, as the warriors in the tradition of Daun and Mok could do. But he couldn't tell where this invisible enemy was, so running in any direction would be useless. He might run right into it.
Mish stayed still and strained to hear any sound that would give away the location of this enemy. There was no sound. Just the deafening static in his own head, steadily growing. It grew and grew, and it brought with it an expanding darkness like a curtain closing.
This wasn't an enemy he couldn't see; this was something going on in his own head. He tried to fight it back, but he didn't know what it was or how to stop it, and eventually, he lost contact with everything that told him he was awake. His conscious thought cut off suddenly, like a songbird shot from a tree.
A/N: Silly Mish, you're not alone. You have Garnet now. Though, to be honest, you'd be better off alone.
Fiction, Necromantic
fantasy, fiction, ghosts, mish, necromancers, necromantic, paranormal, supernatural, warlocks
Enter Summer: A Countersink Scene
I'll Save You a Seat Next to Me Down Below: A Frankenstein and Co. Scene


The world famous Three Gorge Dam is built on the Yangtze River, as the biggest project in the world. With an overall length of more than 6,300 kilometers, the Yangtze River, next only to the Amazon and the Nile, is the third biggest river in the world and is a cradle that breeds the ancient civilization of the Chinese nation.
Three Gorges Dam is located in the area of Sandouping Town, Yichang city, Hubei Province; it is the largest water conservancy project in China, and also in the world, mainly comprising three parts: dam, hydropower station and navigation buildings. After the implementation of the Three Gorges Dam, the normal pool level of the reservoir is at 175m, and the total storage capacity is 39.3 billion cubic meters; the total acreage of the reservoir is 1084 sq.km. This spans over 600 km, and whose average width is 1.1km. The dam is 181 meters in height. Its construction investment comes up to 203.9 billion RMB, equals to 24.65 billion U.S. dollars. The installed power generation capacity is expected to be 18.2 million kilo watts.
The whole project will take 17 years to finish, divided into 3 stages. because of the future water rise, people living along the Yangtze River in Chongqing area and Hubei area, as many as 1.13 million, are supposed to be moved to other unaffected places, leaving their hometowns where they've been living for generations; the money used in the project mainly comes from three ways: the Three Gorge project special fund, Gezhouba Hydropower Station benefit and the Three Gorges hydropower station benefit. It's expecting its completion in 2009.
After the implementation of the TGP, its normal pool level can provide the flood control capacity of 22.15 billion cubic meters, which can control the biggest flood in 100 years; with the help of the flood diversion structure, it can protect the both banks of the Yangtze River from suffering the destructive disaster in 1000 years.
With the dam built on the three gorges, the river water will generate electricity for the water and electricity station of the three Gorges, the profits are very considerable. Designed annual generate electricity is 84.6 billion kw.h. It's number one in the world. it plays an important role in the development of the economy and reduction of the environmental pollution.
The 600km long water way from Yichang to Chongqing will obviously be improved, making it impossible for 10,000 tons of fleet to sail directly upstream to Chongqing. The river boating is improved from 10 million to 50 million tons per year; the transportation cost decreases 35-37 percent. After the adjustment to the reservoir, navigation conditions upstream and downstream the Yangtze River has been greatly improved in low water seasons.
The TGP changes quickly with each passing day. In order to develop the tourism, ChinaThree Gorges Project Corporation built the Tanzi Ling (Jar-hill) for the visitors to see the panorama of the gorges at the highest point of the building site. In the area of the dam, a environmental garden was built. Including the natural scenic spot and the old architecture Yellow Ox Temple, all of these shows that three gorges has the beauty of science and culture, also making TGP highlight in China's water Conservancy.
the essence route of visiting is: the dam site, a garden like place now where you might have a close look to the dam itself and enjoy moisture and beautiful scenery, sometimes if encountering water discharging, a magnificent view with large amount of discharged water out of the dam spill gates; the dam museum, where you might be provided with more detailed and background information about the dam; Jar-hill, the highest point of the whole dam area, by standing on top you might have a bird's eye view of the project.
Books of the project are very informative thus highly recommended. They're in fixed prices and issued by the dam organization, so quality guaranteed.
AS the biggest and most important national project in China, it owns key significance both in military and politics. So the buses should go through strict checking points when visiting the Three Gorge Dam area, and get specially issued permission paper before visiting.
Wheelchairs are accessible in the whole area. Restroom facilities are available at every site.


text:
De kleine buidelmol of kakarratul (Notoryctes caurinus) is een buidelmol die voorkomt in zandgebieden langs de noordkust van West-Australië.

Beschrijving
Deze soort is lange tijd tot de gewone buidelmol (N. typhlops) gerekend, maar aangezien de vegetatie en bodem en het klimaat van de West-Australische kust sterk verschilt van de situatie in de binnenlanden waar de gewone buidelmol voorkomt, is het waarschijnlijk dat de kleine buidelmol een aparte soort is. Deze soort weegt rond de 34 g. De lichaamstemperatuur bedraagt 22 tot 31°C.

Dier uit het Australaziatisch gebied
Buidelmollen
Endemisch dier uit West-Australië
IUCN-status niet bedreigd

redpajama_set_name: RedPajamaWikipedia


The North of Vietnam has many tourist attractions for you to explore and relax. If you can not know how to find the right one for you so please refer to the suggested places through this article.
At the end of May, early June, when the sun is blazing the sky, it is also the golden rice paddy throughout Tam Coc - Bich Dong. If you are asking poetic, casual and peaceful contexts in movie and music videos, the answer is largely Tam Coc - Bich Dong. This is also the place to inspire the image series, wedding photos and a lot of beautiful virtual photos.
The climate is the most precious and wonderful gift that nature has given to Moc Chau. The cold mist in the thick fog along with the magnificent landscape, the green tea hills or the beautiful villages along the road is a unique beauty that makes visitors come to enjoy the full experience. Great stay when booking Moc Chau hotel and explore this beautiful land.
Cat Ba is considered one of the most attractive northern tourist destination when it welcomes thousands of tourists to bathe each year. A place of harmony between the beauty of the forest and the sea. Travel to Cat Ba visitors here will have just discovered the forest with abundant rich flora and fauna, just bathed in beautiful beaches with cool blue water.
Recognized as a World Heritage Site with thousands of islands and historical relics. Halong Bay is known as a lively, charming and beautiful painting. The beauty of the blue water in which Ha Long Bay has captivated the hearts of tourists from abroad.
You can enjoy the scenery of Ha Long Bay from many different angles such as a walk around the bay by kayak. Especially explore the cave with full of sediment, shale art design, watch the bay from above. the plane, the most abundant sight or the most visited or simply visit the local fishing village to know about the casual life of a fisherman. It's the best way to discover the full corner of Halong Bay by overnight cruise. But there are many of cruise in Halong bay, so here is the the best Halong bay cruise recommended.
Coming to Ba Vi National Park, tourists will not only be in harmony with the natural beauty of the mountains, admire many kinds of rare plants only in this place but also participate in activities such as climbing mountain, camp fire, sport ... Beside that Ba Vi National Park also owns beautiful streams, cactus greenhouses and ancient churches in the mountains so that you can admire as well as enjoy shooting image.
Scenic scenery, only about 140km from Hanoi, easy to travel by car, motorbike, many services resort from homestay to guesthouse, hotel, resort, Mai Chau is the destination to meet the demand Calendar for the bush family to go to the resort. Depending on the type of homestay or resort you only spend about 100,000 to 2, 3 million / night.
At Mai Chau Hoa Binh, you can walk and visit the Lac, Pom Coong, Van, Nhot village. Bike to rice fields enjoy the natural scenery. Also, you can watch at night dance and fire camp. If you want to explore, you can go to the afternoon, Mo Luong Cave, or a little further Ba Khan area. Don't forget our Mai Chau tour recommendation here.
Mai Chau has many specialties. Such as rice, grilled chicken, roasted pork skewers, grilled spring fish, sticky rice. However, there are not many restaurants here, so you can order rice at your guest house or homestay.
Hope with the article above will help you how to find the best place to visit North of Vietnam on June.


Notre Dame Football Podcast - Dome and Domer: Last season still lingering?
Please never stop Dome & Domer, you guys provide an original podcast with personalities and game insights that are worthwhile, intriguing, and a joy to listen to.
Would love to hear Travers and Ed on same podcast. Irish win by 10 this week. Still don't care for Kelly but will like him a lot better if he get the W on Saturday.


text:
Thames Valley Police
Five police officers served misconduct notices over search for murdered Jayden Parkinson
The Independent Police Complaints Commission is also looking into complaints from Jayden's mother officers did not react appropriately when dealing with Jayden in the months leading to her disappearance
Natasha Adkins
11:26, 1 AUG 2014
Five Thames Valley Police officers have been served with misconduct notices over the investigation over the murder of Jayden Parkinson
Five police officers have been served with misconduct notices in relation to their handling of the search for murdered teenager Jayden Parkinson.
The Independent Police Complaints Commission (IPCC) is also looking into complaints from Jayden's mother that Thames Valley Police did not react appropriately when dealing with Jayden in the months before she disappeared.
Ben Blakeley, 22, of Christchurch Road, in the university area of Reading, was found guilty of murdering the pregnant 17-year-old at Oxford Crown Court last Thursday and sentenced to life imprisonment with a minimum term of 20 years.
The IPCC is focussed on the force's actions between December 4, when Jayden was reported missing, and December 12, 2013, when Blakeley was arrested for murder.
The independent police investigators have confirmed Jayden was considered to be a medium risk missing person for the first week of her disappearance.
The investigation is also examining the force's response to specific concerns about Blakeley and alleged threats made against Jayden, members of her family or other individuals in 2013.
IPCC Associate Commissioner Guido Liguori said: "Jayden's family continue to go through an incredibly difficult time, and I again offer my condolences to them.
"Now that the murder trial has concluded, we can provide a clearer picture of our investigation and the progress that has been made in recent months in parallel with Thames Valley Police's criminal investigation.
"As well as examining the individual actions of officers and staff in response to Jayden being reporting missing and whether these were appropriate, our investigation has also been looking at the force's policies and systems around missing person reports.
"There is still work to do and it is vital that we establish as full a picture as we can as to how the force responded to Jayden being reported missing, and the contact officers and staff had with her and Ben Blakeley in the months before her murder."
Reading Crown Court


Allows block/unblock calls on the go ..
The rules screen allowing users to add any contact or a phone number that shall be rejected automatically. As soon as a rule is added it is set as active, note the green light, later if you would like to allow incoming calls from this number tap the light and that particular rule would become inactive.
2.1.	Add Rule Adding a rule is simple. Select "more" -> "new" and you can add a contact from your phone book or you can add a phone number directly that shall be blocked.
2.2.	Edit Rule On the main screen you will find an Edit button, it will allow you to edit a selected rule. You can only edit one rule at a time.
2.3.	Activate/Deactivate Rule A rule when added is set as Active, to deactivate it you can tap the green button on the rule and the button shall become red hence its deactivated now.
2.4.	Delete Rule You may delete a single rule or a marked selection of rules from more-> Delete.


We're convinced that with joint endeavours, the business between us will bring us mutual benefits. We are able to guarantee you products high quality and competitive value for Wooden Creative Document Clutter Book Shelf , Creative Household Shelves , Firm Wooden Storage Four Corner Cover , work together and create a brilliant future!
"""Sincerity, Innovation, Rigorousness, and Efficiency"" may be the persistent conception of our organization for that long-term to establish jointly with customers for mutual reciprocity and mutual gain for Wooden Creative Document Clutter Book Shelf , Creative Household Shelves , Firm Wooden Storage Four Corner Cover , In order to meet more market demands and long-term development, we shall own a large capacity of producing. Of course we are going to continue improving the service system to meet the requirements of customers bringing health happiness and beauty to everyone.


If you have been trying to find the best exotic car glass repair in Reisterstown, call 410-576-6860, and we will get back to you with a quote as quickly as possible. Clearview Auto Glass and Tint is an auto glass and tint firm built on delivering convenience to you at a low cost.
For exotic car glass repair work, there is no better provider than Clearview Auto Glass and Tint. We provide auto glass repair, upholstery, tint services, sunroof repair, and services to vehicle owners in Reisterstown and throughout the Baltimore area.


Raj Mahal India Ale – Off the Rail Brewing Co. (Vancouver, BC, Canada) | Justintoxicated.
Visual: Pours translucent golden orange with no real head, just a bit of a spotty island, a few singular slow streams.
Summary: If this one won the CBA 2016 Gold Medal, then all the others must have been really lackluster. This is just a bit above average; the lemongrass is barely there and the kaffir lime leaf only seems to keep it at the same level as opposed to kicking it up a notch.


text:
//
//  Created by Nextpeer development team.
//  Copyright (c) 2014 Nextpeer, Inc. All rights reserved.
//

#ifndef __NextpeerRand__
#define __NextpeerRand__

#include "mtrand.h"

namespace nextpeer {
    class Rand
    {
    public:
        static void seed(unsigned long s);
        static unsigned long generate();
        static unsigned long generate(unsigned long min, unsigned long max);
    protected:
        static MTRand_int32 mGenerator;
    };
}

#endif /* defined(__NextpeerRand__) */

redpajama_set_name: RedPajamaGithub


As we lumber along in life, sometimes we encounter opportunities to reflect upon what motivates us. There can be many types/layers of motivation and taking inventory of them can be useful in becoming more focused upon what comes naturally to us: Because, it is easier to do what comes naturally. Right?
Myself, I like puzzles. Spatial puzzles are the easiest. Rubik's cube, I didn't dig so much after staying up most of a night thinking I was so close to solving it (I wasn't). Some puzzles are easily solved and others are long-term projects that require years to see the consequences of choices made. This is why winemaking suits my personality: I like some things to move slowly so I can ponder them. By contrast, I do some other things to satisfy needs for the immediate and on-the-fly creative moment. Live music and jamming is a perfect example of that. An inappropriate note once in a while is a fleeting thing that has little consequence in either the larger scheme or the momentum of being in that moment. However, winemaking has lasting, long-term consequences to inappropriate notes – you just can't have them. They stay with the wine for its duration like typographical errors in a musical score that would eternally mar every symphony performance. Winemaking is a puzzle to solve over a long period of time.


text:
Software Preservation and Attribution in Astronomy
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# Supporting Astronomy's Software Authors The components that supplement this project contain documents related to the below efforts. ---- ### Credit Lost: Two Decades of Software Citation in Astronomy A case study analyzing historical software citation practices in Astronomy. - [ApJS Manuscript PDF](https://mfr.osf.io/render?url=https://osf.io/dukjy/?direct%26mode=render%26action=download%26mode=render) Code associated with this case study is archived on Zenodo. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3530867.svg)](https://doi.org/10.5281/zenodo.3530867) ---- ### Copyright Guide for Scientific Software December 2019 A brief copyright guide on scientific software. This guide covers how to make it easier for others to use your software and how to build on the software that others have written. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3581326.svg)](https://doi.org/10.5281/zenodo.3581326) This guide is an affiliated effort of the [Software Preservation Network](https://www.softwarepreservationnetwork.org/). ---- ### Citing Astronomy Software: Inline Text Examples July 2019 [AstroBetter post](https://www.astrobetter.com/blog/2019/07/01/citing-astronomy-software-inline-text-examples/) with a [companion overleaf document](https://www.overleaf.com/read/cnqjkkmvgpnj) to help astronomers receive credit for their code and give credit to others. Archival copy of this blog post: [perma.cc/DS73-5BZE](https://perma.cc/DS73-5BZE) ---- ### Software Citation Implementation Challenges May 2019 An update on software citation compiled by the [FORCE11 Software Citation Implementation Working Group](https://www.force11.org/group/software-citation-implementation-working-group): Katz, D. S., Bouquin, D., Chue Hong, N. P., Hausman, J., Jones, C., Chivvis, D., … Zhang, Q. (2019). Software Citation Implementation Challenges. [arXiv:1905.08674](https://arxiv.org/abs/1905.08674) ---- ### Curating Jupyter Notebooks: A Primer March 2019 Guide is meant to inform curatorial practices around Jupyter Notebooks and support the development of resources to ensure long-term availability of software in curated archival repositories. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2591580.svg)](https://doi.org/10.5281/zenodo.2591580) This guide is part of a collection of primers published by the [Data Curation Network](https://sites.psu.edu/dcnworkshops/primers/). ---- ### Software Citation in Astronomy: A White Paper March 2018 This [white paper](https://github.com/CfA-Library/Cite_Astro_Software/blob/master/whitepaper_2018.md) summarizes issues impacting software citation implementation in astronomy as discussed at [AAS 231](https://aas.org/meetings/aas231). [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1473408.svg)](https://doi.org/10.5281/zenodo.1473408) The white paper was also reposted by [Astronomy Computing Today](https://astrocompute.wordpress.com/2018/03/05/software-citation-implementation-in-astronomy/) and [Generation R](https://doi.org/10.25815/3H8N-G736).
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text:
Super Lawyers → Lawyer Directory → Family Law Attorneys → New York → Westbury → Alissa Van Horn
Alissa Van Horn
Top Rated Family Law Attorney in Westbury, NY
Van Horn & Friedman, PC
| 1025 Old Country Road, Suite 407
Visit: http://www.vanhornfriedman.com
Education: Widener University School of Law
Alissa L. Van Horn, Esq., serves as the managing partner of Van Horn & Friedman, P.C., a full-service family and matrimonial law firm, with its primary office in Westbury, New York. Ms. Van Horn has extensive experience in all family law-related cases, including divorce, adoption, custody, child support, orders of protection, foster care litigation, name changes, prenuptial, postnuptial, and separation agreements.
After five years of practicing in boutique law firms in New York City, Alissa opened the doors to her solo practice in 2010. She spent five years vigorously working to expand the firm, and ultimately, in 2015, Alissa was joined by her colleague and friend, Meredith Friedman, Esq. and together they formed Van Horn & Friedman, P.C. Always putting the interests of her clients first, Ms. Van Horn implements negotiation strategies to achieve the optimal results for her clients when a trial is not in their best interests. As a pioneer in her field, she works tirelessly on behalf of her clients to obtain the best possible results.
Alissa is a member of the New York State Bar Association, Family Law Chapter, the Suffolk County Bar Association, the Nassau County Bar Association, a former member of the Queens County Women's Bar Association and the New York City Bar Association.
Alissa is licensed to practice law in New York, Connecticut and the United States Supreme Court. Alissa has a juris doctor from Widener University School of Law, and obtained her undergraduate degree in Public Administration from St. Joseph's University in Philadelphia, PA.
In 2017, Alissa received Alissa received the Long Island Power Women in Business Award and the Long Island Business News Leadership in Law Award and has been selected as an honoree at the Long Island Business News 40 Under 40 Awards in 2018 and the Top 50 Women In Business Award in 2019. Alissa is also a 5-time recipient of the Rising Star Award and the Top Female Attorneys Award for NYC Metro by Super Lawyers Magazine. Alissa received the "10 Best" Award by the American Institute for Family Law Attorneys, the Top 10 Under 40 Award by the National Academy of Family Law Attorneys, the Top 100 Attorneys Award and the Top 40 under 40 Award for NYC by the National Advocates in the area of Divorce and Family Law, the Outstanding Women in the Law Award for 2016 by Hofstra Law School's Center for Children and Families, and was featured by LI Pulse Magazine as a Top Legal Eagle, and in 2020 received the Attorney of the Year designation of Business and Professional Leaders.
Van Horn & Friedman, P.C. was selected as a Top 10 Law Firm in New York in the area of Family Law 3 years in a row.
Alissa began teaching CLE's in 2015 on family and matrimonial matters for 5 CLE companies nationwide. Alissa was a Guest Speaker at Legaltech's LegalWeek in 2018 on the topic of starting and succeeding in a small law firm. Additionally, Alissa has presented on topics related to divorce and Family Law by the New York State Society of CPA's and New York Presbyterian Hospital. Throughout the year, Alissa offers free seminars to residents of Long Island on procedural and substantive matters they face in Family and Supreme Courts. Alissa is also a member of Pinktie.org, networking for a cause, and supports many local charities including the American Kidney Fund, Kids Clubhouse and Big Brothers Big Sisters of Long Island, LIAFF.
100%Family Law
Adoption, Alimony & Spousal Support, Child Support, Custody & Visitation, Dissolution, Divorce, Domestic Violence, Juvenile Law, Name Change, Paternity, Prenuptial Agreements, Marital Property, Mediation & Collaborative Law, Same Sex Family Law, Father's Rights
Rising Stars: 2015 - 2019
To: Alissa Van Horn
Super Lawyers: Potential Client Inquiry
If you send a lawyer or law firm email through this service, your email will not create an attorney-client relationship and will not necessarily be treated as privileged or confidential. You should not send sensitive or confidential information via this email service. The lawyer or law firm to whom you are writing may not choose to accept you as a client. Moreover, as the Internet is not necessarily a secure environment it is possible that your email sent via the Internet might be intercepted and read by third parties.
Additional Sources of Information About Alissa Van Horn
About Alissa Van Horn
Admitted: 2006, New York
Professional Webpage: http://vanhornfriedman.com/van-horn.php
Honors/Awards:
Recipient for 3 years; Recognizing Excellence in Family and Divorce Law , Lawyer of Distinction , Lawyers of Distinction , 2020
2016 Award, 10 Best Family Law Attorneys, American Academy of Family Law Attorneys, 2016
Recipient, Attorney of the Year, Top Business and Professionals, 2020
Recipient honoring the Top 50 Women in Business on Long Island , Top 50 Women in Business, Long Island Business News, 2019
Recipient, Top 10 Under 40, National Academy for Family Law Attorneys, 2017
Recipient of the Long Island Business News Leadership In Law Award, Leadership in Law, Long Island Business News, 2017
Recipient, Best Family Law Firms, American Institute of Family Law Attorneys, 2017
Recipient, Outstanding Women in the Law, Hofstra University, 2016
Recipient of the Top 40 Under 40 Award , Top 40 Under 40, Long Island Business News, 2018
Recipient for 5 years, Top Women Attorneys for the NY Metro Area, SuperLawyers, 2019
Recipient for 5 years, Rising Star, Super Lawyers, 2019
Recipient of the Long Island Power Women In Business Award, Long Island's Power Women In Business, Schnepps Communications, 2017
Bar/Professional Activity:
Founded VHF Legal Seminars, Inc., 2020
CLE Instructor for Clear Law Institute , 2020
Suffolk County Bar Association, 2020
Matrimonial and Adoption Committees of the Nassau County Bar Association, 2020
Nassau County Bar Association, Member, 2020
Queens Chapter of the Women's Bar Association of the State of New York, 2020
New York State Bar Association, Member, 2020
NACLE - Woman Faculty of the Year , 2019
CLE Instructor at LawLine , 2020
United States Supreme Court Admission , 2016
CLE Instructor for NACLE, 2020
CLE Instructor for Marino CLE, 2020
Pro bono/Community Service:
Provide Pro Bono Legal Services to various local LI groups, including organizing lectures, 2020
Business Consultant - Pro Bono; assist other professionals in setting up their businesses and mentoring individuals starting up new businesses, 2020
Serve on the Business Advisory Board of the North Shore Child & Family Guidance Center, 2014
Pinktie1000 Member; Pinktie.org, 2020
Scholarly Lectures/Writings:
Lecturer for Marino CLE for 5 years, Lecturer, Attorney , Marino CLE, Legal, 2020
Guest Speaker for students interested in pursuing matrimonial and family law , Guest Speaker , Attorney , Touro Law School , Legal; Educaitonal, 2017
Presented on the Topic of Starting and Maintaining a Successful Small Law Firm, Presenter, Attorney , LegalTech's Legal Week, Legal; Technical, 2018
Lecturer, Lecturer, Attorney , Clear Law Institute, Legal, 2019
Lecturer for 4 years, Lecturer, Attorney , LawLine , Legal, 2020
Lecturer for NACLE for 3 years, Lecturer, Attorney , NACLE, Legal, 2020
Educational Background:
Bachelor's in Science for Public Administration from Saint Joseph's University in Philadelphia, PA, 2002
Juris Doctor, Widener University School of Law, 2005
Website - www.vanhornfriedman.com
Facebook - www.facebook.com/vhfpc/
LinkedIn - www.linkedin.com/in/aliss...
Office Location for Alissa Van Horn
1025 Old Country Road
Alissa Van Horn:


text:
\section{Introduction}
\section{Sleep stage scoring}
\label{sec:sleep}
Studies of sleep assist doctors to diagnose sleep disorders and provide the baseline for appropriate follow up. Clinical sleep study design is based on polysomnography (PSG) in which several  biological signals are acquired while the patient is asleep, including electroencephalography (EEG) for monitoring brain activity, electrooculogram (EOG) for eye movements and electromyogram (EMG) to measure muscle tone. The measurements are used to classify sleep score, i.e., classify the sleep stages the patient goes through during the study and assess the existence of any dysfunction. With the increasing accessibility of EEG signals (e.g., using permanent implanted electrodes or semi-permanent measures through sensors placed, e.g., in the ear \cite{Mikkelsen2015}) there is a growing interest in sleep quantification based on EEG alone. The EEG signal often presents significant bursts of rhythmic  components, In particular the frequency band `alpha' (8-12 Hz) is prominent. The American Academy of Sleep Medicine (AASM) recommends segmentation of sleep in five stages \cite{Berry2017}:
\begin{itemize}\compresslist
\item W (wakefulness): alpha  (8-12 Hz) rhythm is present, high-amplitude muscle contractions and movement artefacts on EMG and eye blinking on EOG, which can also be appreciated in low frequency EEG (0.5-2 Hz).
\item N1 (Non-REM 1): alpha (8-12 Hz) rhythm is attenuated and replaced by mixed frequency theta signal (4-7 Hz), decrease in muscle tone and slow eye movements.
\item N2 (Non-REM 2): presents K-complexes (negative peak followed by a positive complex and a final negative voltage) in the $<$1.5 Hz range and sleep spindles (burst of oscillatory waves) at sigma (12-15 Hz) band.
\item N3 (Non-REM 3): slow wave activity exists (0.5-3 Hz), eye movements are unusual and EMG tone is low.
\item R (REM): existence of rapid eye movements clearly visible in EOG, relatively low-amplitude and mixed-frequency activity in EEG, and presents the lowest muscle tone on EMG.
\end{itemize}
Manually scoring sleep stages is a tedious task requiring sleep experts to \emph{visually} inspect PSG data recorded during the whole sleep study. Thus, there has been considerable effort over the past years to develop machine learning (ML) methods for automatic sleep scoring. For a recent survey on the available literature refer to \cite{Aboalayon2016}. Recent research based on fully-connected artificial neural networks (ANN) with a variety of hand-crafted features extracted from the EEG signal includes \cite{Ronzhina2012, Ebrahimi2008}. More recently,  recurrent neural network variants have been used to capture long-term dependencies or stage transition rules \cite{Hsu2013,Supratak2017}.

Here our aim is to discuss and test the hypothesis that sleep stage classification can be assisted by \emph{transfer learning} (e.g., support the classifier training process by use of related data). We will take advantage of the fact that sleep scoring is a {\it visual} classification problem, hence, the strong progress in developing artificial visual perception using convolutive neural networks (CNNs, \cite{LeCun1989,Krizhevsky2012,Simonyan2014,He2016}). In particular, time-frequency spectrogram images are created from windowed EEG signals and fed to a CNN pre-trained on a visual object recognition task, allowing the use of this powerful model for sleep stage classification in EEG data, which would otherwise lead to overfitting due to limited availability of data.

Despite the quite extensive research efforts in sleep scoring, there exist several challenges related to evaluation and comparison: one is the large variety of datasets (most outside the public domain) used to evaluate the methods; another frequent obstacle is the design of experiments and evaluation procedures, specifically proper cross-validation procedures. Some use a single train/test split, where no cross-validation is performed; others completely lack an independent test set, hence have no unbiased performance measure. Finally, others neglect the important dependency structures in the data when cross-validating, e.g., by completely random sampling. Random sampling ignores the strong dependence between data from the same subject or even obtained on the same night.

In this paper we have selected two recent results for comparison of performance. They both have been cross-validated in a proper sense and provide relevant information for reproducibility. In \cite{Tsinalis2016a}, authors conduct a time-frequency analysis of the EEG signal to extract relevant features to feed an ensemble of stacked sparse autoencoders. In a later investigation \cite{Tsinalis2016b}, authors construct an end-to-end ANN by combining a CNN architecture using raw EEG signals with a 2D stack of frequency-specific activity over time. Both studies obtain state-of-the-art results.

ANNs have often been criticized to work as black boxes: input data are fed into the one end and output values are obtained at the other end. Understanding the complex internal transformations that ANNs perform to the data is crucial for both domain practitioners, who need to comprehend the ANNs functioning in order to rely on them, and ML engineers, who require a deep knowledge of the system for correcting errors and improving the models. Therefore, different efforts have been made to provide a set of visualisation techniques to aid on this matter \cite{Simonyan2013,Zeiler2014}.

Our contributions in this paper are the following: we provide a framework for the automatic analysis of single sensor EEG sleep stage scoring, which is both highly accurate and visually interpretable by first using multitaper spectral estimation to generate colour image spectrograms, containing natural image-like features (blobs, vertices, etc.), emphasizing sleep patterns for the experts to analyse; secondly, given the natural image look of our inputs, we employ and refine the super-accurate models trained on natural images, which have been proven to excel on natural image visualisation, to classify the different sleep stages; and finally, we make use of sensitivity analysis to map the most influential features in our network back to the input space, providing highly interpretable images about the network's functioning. In particular, we employ a highly used publicly available database to classify the $5$ sleep stages mentioned before with an average accuracy of $86\%$ on an independent test set, but more importantly, we report the time-frequencies of interest for our network to decide on each sleep stage.

\section{Transfer learning with convolutional neural networks}
\label{sec:deep_nets}
CNNs were originally introduced in the late 80's \cite{LeCun1989} as biologically inspired models to perform image recognition. However, a major breakthrough in the field occurred in 2012, when the deep CNN \emph{AlexNet} \cite{Krizhevsky2012}, won the \emph{ImageNet Large-Scale Visual Recognition Challenge} (ILSVRC) by a big margin to all other competing models. Since then, CNNs have become the workhorse for visualisation tasks and ILSVRC the preferred benchmark to test their performance. AlexNet consisted of five convolutional layers, max-pooling layers and three fully-connected layers. In 2013, ZFNet \cite{Zeiler2014} won the competition with a similar architecture to AlexNet but better optimised hyperparameters; authors also provided an approach to visualise the working of the CNN. GoogLeNet \cite{Szegedy2015} won the competition in 2014 with a CNN composed of 22 layers; many of which contained an \emph{inception module} with several convolutions and pooling in parallel, as well as replacing fully-connected layers by average-pooling. Nevertheless, a highly successful model this year was the VGGNet \cite{Simonyan2014}, thanks to its simplicity: it is a 16-layer network composed exclusively of $3 \times 3$ convolutions, $2 \times 2$ max-pooling and 3 fully-connected layers. In 2015, the challenge was won by ResNet \cite{He2016}, a 152-layer CNN that uses \emph{residual blocks} in which convolutions and activations do not compute a transformation function of the feature maps but rather the deviation from the identity function.

One big challenge when training such flexible models is to avoid overfitting: all previously mentioned CNNs use large amounts of training data and apply data augmentation and dropout techniques to address this issue. Still, there exist many domains in which acquiring input data is very costly (e.g., medical sciences) and training a deep neural network de novo is simply not feasible.

Therefore \emph{transfer learning} is of interest. In the case of CNNs trained on natural images there seems to be a consistent behaviour in which lower layers of the network hierarchy learn general features similar to Gabor filters and colour blobs, while higher layers capture more domain specific representations. This observation has been studied in \cite{Yosinski2014}, where the layers' generality-specificity trade-offs have been quantitatively evaluated. Authors also analysed the decrease of transferability performance as the target task differs from the original one. In a different study \cite{Razavian2014}, authors use the features learned in an object recognition task to extract features from a varying set of visual problems and datasets, obtaining superior results than task-specific state-of-the-art systems. Finally, \cite{Donahue2013} used transfer learning in CNNs to extract features from unrelated tasks presenting insufficient data, an approach allowing the use of these flexible models.

\section{Methods}
\label{sec:model}
\subsection{Image creation}
\label{ssec:image_creation}
EEG data is represented as time-frequency images. Spectral estimation is based on Fourier analysis, which assumes a series of properties on the data such as infinite signals, continuity, periodicity and stationarity. However, none of these assumptions are typically fulfilled in EEG data, therefore in practise the spectrogram becomes a highly biased estimator. A common strategy to reduce the spectrogram's bias is by convolving the raw signal with a window function (called taper) before performing spectral estimation. Another important drawback of the spectrogram is that it produces estimates with high variance across all frequencies, which are even increased with the use of tapers. A method called \emph{multitaper spectral estimation} \cite{Thomson1982} can be used to reduce both bias and variance by applying multiple taper functions to the raw signal and averaging their results.
This technique has recently been proven to be highly successful in analysing neurophysiological dynamics of sleep using EEG \cite{Prerau2016}.
There exists a set of hyperparameters that highly influence the results of the multitaper spectral estimation: they are the window size $\omega$ in seconds, the window stepsize $\sigma$ in seconds, the minimum frequency resolution that can be resolved $f$ in Hz, the time-half-bandwidth product, usually defined as $W=\omega f/2$ and the number of tapers used, often set according to the heuristic $L = \lfloor2W \rfloor- 1$, where $\lfloor x \rfloor$ is a function that rounds $x$ down to the closest integer \cite{Prerau2016}.
After estimating the multitaper spectrogram, we convert its values to a logarithmic scale $x = \log(x) + 1$ and split it into equally-length bins of size $s$, called epochs. Then, we convert each epoched spectrogram to an RGB colour matrix (i.e., an image) by applying our preferred colourmap (i.e., a lookup table that translates real values in the $[0,1]$ interval to RGB colours).
\subsection{Network architecture}
\label{ssec:network_architecture}
The CNN of choice to analyse the images created according to the previous block is the VGGNet \cite{Simonyan2014}, due to its simplicity and flexibility. It is composed of $16$ weighted layers:
\[
\mathrm{ccm_{64} ccm_{128} cccm_{256} cccm_{512}cccm_{512}fcr_{4096}fcr_{4096}fcs_{1000}},
\]
\normalsize
where $\mathrm{c}$ means a $3 \times 3$ convolutional filter of stride $1$ using a ReLU activation function, $\mathrm{m}$ stands for $2 \times 2$ max-pooling layer with a stride of $2$, $\mathrm{fcr}$ and $\mathrm{fcs}$ correspond to fully-connected layers with ReLU and soft-max activations, respectively; sub-indexed values represent the number of channels in each block. \emph{Transfer learning} is employed by using weight values in all convolutional layers that have been previously trained on ILSVRC-2014 data provided by the authors in \url{http://www.robots.ox.ac.uk/~vgg/research/very_deep}. Fully-connected layers are initialised from scratch using Xavier's initialisation \cite{Glorot2010} and trained using dropout. The number of final outputs is set according to the task we are tackling.
\subsection{Network visualisation}
\label{ssec:network_visualisation}
In the current study, we resort to sensitivity analysis \cite{Rasmussen2011, Hashem1992, Zurada1994} as a visualisation tool to better understand the decisions made by our network. More precisely, let $\mathcal{D}=\{\mathbf{x}_n, t_n\}_{n=1}^N$ be a dataset of $P$-dimensional input vectors $\mathbf{x}$ (i.e., spectral images in our work) and corresponding class labels $t \in \{1,\dots, C\}$, the built ANN acts as a function approximator, such that $\hat{t} = f(\mathbf{x})$. We can estimate the relative importance that our network places to every input feature $j$ (i.e., RGB colour channel in a pixel, in our context) to discriminate among the existing classes as:
\begin{equation}
\label{eq:sensitivity}
\hat{s}^{(j)} = \frac{1}{N}\sum_{n=1}^N \left| \frac{\partial {\cal L}(f(\mathbf{x}),t)}{\partial \mathbf{x}^{(j)}} \right|_{\mathbf{x}=\mathbf{x}_n}
\end{equation}
where ${\cal L}$ is the loss function of choice and $| x |$ is the absolute value of $x$. \emph{Sensitivity maps} are created by disposing $\hat{s}^{(j)}$ in the corresponding RGB colour matrix forming an image. The fact that most of the current frameworks supplying ANN building capabilities are provided with automatic differentiation procedures reduces the calculation of sensitivity maps to a simple function call.

\section{Empirical evaluation}
\label{sec:evaluation}
\subsection{Experimental setup}
\label{ssec:sleep_experiments}
\begin{table*}[tb]
\small
    \centering
  \begin{tabular}{l l | r r r r r | r r r r r | c  c  c  c}

    & &\multicolumn{5}{c}{\bf{Predicted (aggregate)}} & \multicolumn{5}{c}{\bf{Normalised pred. (in \%)}} &\multicolumn{4}{c}{\bf{Per-class metrics (in \%)}}\\

    & & W & N1 & N2 & N3 & R & W & N1 & N2 & N3 & R &  Pre. & Sen. & F1 & Acc.\\

    \hline
     \multirow{ 5}{*}{\bf{VGG-FE}} & W & 3529 & 579 & 97& 46 & 258 & 78 & 13 & 2 & 1 & 6 &  93 & 78 & 85 & 86 \\
     &N1 & 458 & 1219 & 353 & 29 & 703  & 17 & 44 & 13 & 1 & 25&  85 & 44 & 58 & 68 \\
     &N2 & 346 & 1215 & 13118 & 1676 & 1222 & 2 & 7 & 75 & 10 & 7 &  91 & 75 & 82 & 84 \\
     &N3 & 80 & 31 & 461 & 5003 & 16 & 1 &  1 & 8 & 89 & 0 & 97 &  89 & 93 & 93 \\
     &R & 219 & 781 & 470 & 6 & 6235 & 3 & 10 & 6 & 0 & 81 &  89 & 81 & 85 & 86 \\
     \hline
     \multirow{ 5}{*}{\bf{VGG-FT}} & W & 3505 & 671 & 52 & 39 & 242 & 78 & 15 & 1 & 1 & 5 & 96 & 78 & 86 & 87 \\
     & N1 & 301 & 1553 & 334 & 19 & 555  & 11 & 56 & 12 & 1 & 20 & 89 & 56 & 69 & 75 \\
     & N2 & 192 & 985 & 13884 & 1411 & 1105 & 1 & 6 & 79 & 8 & 6  & 92 & 79 & 85 & 86 \\
     & N3 & 73 & 24 & 462 & 5015 & 17  & 1 & 0 & 8 & 90 & 0 & 97 & 90 & 93 & 94 \\
     & R & 82 & 563 & 378 & 14 & 6674  & 1 & 7 & 5 & 0 & 87 & 92 & 87 & 89 & 89 \\
     \hline

  \end{tabular}
  \caption{Raw confusion matrix, normalised confusion matrix and per-class metrics for both VGG-FE and VGG-FT.}
  \label{tab:confusion_matrix}

\end{table*}

We use EEG sleep recordings from the Sleep-EDF Database \cite{Kemp2000} in the PhysioNet repository \cite{Goldberger2000}. In particular, a subset of data from a study of age effects on sleep in healthy subjects, containing two whole-night EEG recordings (approximately 20 hours) from Fpz-Cz and Pz-Oz channels sampled at 100 Hz and corresponding hypnograms (expert annotations of sleep stages) from 20 subjects (10 males and 10 females) between 25-34 years old (second night of subject 13 was not provided). Sleeping time was retrieved from each recording as the interval between annotated lights off and lights on times or from 15 minutes before/after the first/last scored sleep epoch, if these annotations were not provided. Class labels were obtained from the hypnograms at every $30$ s.

Images were created for Fpz-Cz sensor as explained in Section~\ref{ssec:image_creation}, setting $\omega=3.0$ s., $f=2$ Hz, $W=3$ and $L=5$ tapers, with the purpose to capture the sleeping dynamics at the microevent time scale while maintaining a somewhat fine resolution \cite{Prerau2016}. The window stepsize was set to $\sigma=0.67$ s. in order to match the final image resolution (prefixed to $224 \times 224$ pixels by the pre-trained VGGNet). Bin size was set to $s=150$ s., corresponding to the current epoch plus the two previous and two posterior epochs, as it has been shown to improve overall accuracy by better classifying N1-N2, N1-R and N2-R transition stages \cite{Tsinalis2016a}. Spectrogram log values were thresholded to the $[0, 1]$ interval before applying the `Jet' colourmap to generate the images.

Following \cite{Tsinalis2016a, Tsinalis2016b}, we used a leave-one-subject-out sampling schema to partition the data into train and test datasets, further splitting the training dataset by randomly selecting $4$ subjects for validation and the remaining $15$ for actual training. The mentioned works address the skewed performance favouring the most represented classes during training in such imbalanced datasets using different approaches: first study employs class-balanced random sampling with an ensemble of classifiers, while the second one uses a different class-balanced batch at each stochastic gradient descent (SGD) epoch. We virtually use both strategies by randomly balancing all classes in each SGD epoch and obtaining ensemble behaviour thanks to the dropout layers \cite{Srivastava2014}.

CNNs were trained by optimising the categorical cross-entropy between predicted values and class labels using \emph{adam} \cite{Kingma2014} SGD on mini batches of 250 training examples with a learning rate of $10^{-5}$, and decay rate of first and second moments set to $0.9$ and $0.999$, respectively. The validation set was employed to choose the hyper-parameters and its loss as a stopping criterion to avoid overfitting.

\subsection{Results}
\label{ssec:sleep_results}
We evaluated our method in two different scenarios: first, we used the VGGNet as a feature extractor (VGG-FE), where all convolutional layers were kept fixed and only the last $3$ fully-connected layers of the network were trained from scratch to the specific EEG sleep scoring problem. In the second scenario, all weights in the network were updated, obtaining a fine-tuned network (VGG-FT). Preliminary results including a randomly initialised network consistently showed suboptimal classification accuracy, being it more pronounced the smaller the training set was. Convergence time for this network was on average 3 times slower than VGG-FT.

We report the aggregated confusion matrix for both scenarios over all test sets (Table~\ref{tab:confusion_matrix}, left), out of which we compute all different scoring performance metrics. To do so, we first class-balance the confusion matrix (Table~\ref{tab:confusion_matrix}, middle) and split the results into $5$ binary one-vs-all classification, which are again renormalised in order to calculate the per-class \emph{precision, sensitivity, F1-score and accuracy} (Table~\ref{tab:confusion_matrix}, right). Comparison with existing literature is presented in Table~\ref{tab:literature_comparison}, where the 95\% confidence interval for each measure is computed using bootstrapping. In each of the 1000 bootstrap iterations, 20 confusion matrices out of the 20 subjects are sampled (with replacement) and their values aggregated. Then, each evaluation metric is calculated. The 1000 bootstrap samples per evaluation metric are ordered and the values at 26th, 500th and 975th positions are reported.

As shown in Table~\ref{tab:confusion_matrix}, top block, the most correctly sleep stage classified by VGG-FE is N3 with $89\%$ of epochs properly assigned. It is followed by R (81\%), W (78\%) and N2 (75\%). The most difficult stage to classify is N1, with $44\%$ of epochs correctly assigned. The highest misclassification rate for this stage is for assigning epochs to R stage (25\%), followed by W (17\%) and finally N2 (13\%); a behaviour that is consistent with the literature \cite{Tsinalis2016a,Tsinalis2016b}. These results seem to indicate that, while N1-N2 transitions have been acceptably captured by our method, N1-R clearly misses some important information. If we turn our attention towards per-class metrics (Table ~\ref{tab:confusion_matrix}, top-right), we can observe that their precisions range between $89-97\%$, except for the N1, which reaches a not inconsiderable $85\%$. However, sensitivities are scoring significantly lower, their values lying in the $75-89\%$ interval, with the exception of N1, which presents a score of $44\%$, highly impacting the accuracy and F1-score for this class.

\begin{figure*}[t]
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{spectrogramW.pdf}}
  \centerline{(a) Wakefulness}\medskip
\end{minipage}
\hfill
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{spectrogramN1.pdf}}
  \centerline{(b) Non-REM 1}\medskip
\end{minipage}
\hfill
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{spectrogramN2.pdf}}
  \centerline{(c) Non-REM 2}\medskip
\end{minipage}
\hfill
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{spectrogramN3.pdf}}
  \centerline{(d) Non-REM 3}\medskip
\end{minipage}
\hfill
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{spectrogramR.pdf}}
  \centerline{(e) REM}\medskip
\end{minipage}
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{smW.pdf}}

\end{minipage}
\hfill
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{smN1.pdf}}

\end{minipage}
\hfill
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{smN2.pdf}}
\end{minipage}
\hfill
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{smN3.pdf}}

\end{minipage}
\hfill
\begin{minipage}[b]{0.19\linewidth}
  \centering
  \centerline{\includegraphics[width=\linewidth]{smR.pdf}}

\end{minipage}
\vspace{-3mm}
\caption{Sensitivity analysis for subject 7. Top row shows characteristic sleep stage spectra; bottom row presents per-class sensitivity maps.}
\label{fig:class_sm}
\end{figure*}

Table~\ref{tab:confusion_matrix}, bottom block, shows the results obtained by VGG-FT. The improvement of this network with respect to VGG-FE is homogeneous across classes, the most prominent one being the increase of sensitivity for N1 class by $12\%$. This is achieved by reducing the misclassification between N1-W by $6\%$, N1-R by $5\%$ and N1-N2 by $1\%$. It is followed by an increase of sensitivity in R ($6\%$) and N2 ($4\%$), and precision in N1 ($4\%$), W ($3\%$) and R ($3\%$).

\begin{table}[htb]
\small
    \centering
  \begin{tabular}{c | c  c  c  c }

    Study & Precision & Sensitivity & F$_1$-score & Accuracy\\

    \hline
     \cite{Tsinalis2016a} & 92--\textbf{93}--94 & 75--\textbf{78}--80 & 82--\textbf{84}--86 & 84--\textbf{86}--88 \\
     \cite{Tsinalis2016b} & 90--\textbf{91}--92 & 71--\textbf{74}--76 & 79--\textbf{81}--83 & 80--\textbf{82}--84 \\

     VGG-FE & 90--\textbf{91}--93 & 70--\textbf{73}--77 & 78--\textbf{81}--83 & 81--\textbf{83}--85  \\
     VGG-FT & 92--\textbf{93}--94 & 75--\textbf{78}--81 & 82--\textbf{84}--86 & 84--\textbf{86}--88  \\
     \hline

  \end{tabular}
  \caption{Comparison to existing literature. Mean values in bold within the corresponding 95\% confidence interval.}
  \label{tab:literature_comparison}
     \vspace{-2mm}
\end{table}

In Table~\ref{tab:literature_comparison}, we compare the performance of our method to the existing literature. As can be seen, both VGG-FE and VGG-FT exhibit state-of-the-art results, VGG-FT aligning with the best model to date \cite{Tsinalis2016a}.

\subsection{Visualisation}
\label{ssec:sleep_visualisation}
To better understand the internal representation of the network, we selected our best performing subject (i.e., subject 7) and calculated the sensitivity maps of each class independently. Fig.~\ref{fig:class_sm}, top row, depicts the most accurately classified spectrogram of each sleep stage, as characteristic examples of inputs to the classification system. Notice that the current epoch for classification is at the centre of the spectrogram (time between $0$ and $30$ s.), the two previous epochs span between $[-60,0]$ s. and the two posterior epochs are placed within the $[30,90]$ s. interval. Fig.~\ref{fig:class_sm}, bottom row, shows the per-class sensitivity map, each generated according to Eq.~\ref{eq:sensitivity} using only instances of subject 7 for this specific class, summing over the RGB colour channels, followed by 0-1 normalisation and converted back to RGB image using the `Jet' colour map. Now, we analyse them according to the sleep stage definitions in Section~\ref{sec:sleep}: Fig.~\ref{fig:class_sm}-a presents high sensitivity in the alpha band (8-12 Hz), characteristic of W stage, mainly centred at the current epoch and the immediately following one, maybe in order to be able to identify a transition stage. Sensitivity to low frequency activity corresponding to eye blinks is also evident. N1 stage (Fig.~\ref{fig:class_sm}-b) seems to show slightly decreased sensitivity in the alpha band as compared to W and rather higher sensitivity to theta (4-7 Hz) in the preceding epochs. Interestingly, Fig.~\ref{fig:class_sm}-c exhibits increased sensitivity around the upper sigma band (12-15 Hz) for the current epoch and low frequencies ($<$1.5 Hz) across the whole image, which might correspond to the network identifying spindles and K-complexes, respectively, which are characteristic features of the N2 stage. Slow wave activity (0.5-3 Hz) seems to be present in the N3 sensitivity map (Fig.~\ref{fig:class_sm}-d), with highest impact in the current and succeeding epochs. Finally, wide power band spanning from approximately 0.5 to 9 Hz is present in Fig.~\ref{fig:class_sm}-e, probably accounting for the mixed-frequency signal distinctive of R stage.

\section{Conclusions}
\label{sec:conclusions}
We have demonstrated that classification of sleep stages can be effectively framed as a visual task by first creating natural colour like images using multitaper spectral estimation and then applying recent achievements in the object recognition field to obtain state-of-the-art classification accuracy. Moreover, this approach greatly enhances the interaction with the domain expert by providing interpretable patterns to make sense of as well as a framework based on sensitivity analysis to easily inspect the network's reasoning. We think that the tools presented here can transcend EEG sleep scoring and be applied to other tasks within EEG analysis or, more generally, to other biological domains (e.g., EMG) where time-frequency signals are recorded. Further improvement of the method includes better hyperparameter optimisation when generating the spectral images. A thorough study of the obtained VGGNet layers might also be of interest to gain a deeper understanding of the internal structure of the network.

\begin{spacing}{0.965}
\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


text:
package auto.parse.processor;

import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.SortedSet;
import java.util.TreeSet;

import javax.lang.model.element.Element;
import javax.lang.model.element.PackageElement;
import javax.lang.model.element.TypeElement;
import javax.lang.model.type.ArrayType;
import javax.lang.model.type.DeclaredType;
import javax.lang.model.type.TypeMirror;
import javax.lang.model.type.TypeVariable;
import javax.lang.model.type.TypeVisitor;
import javax.lang.model.type.WildcardType;
import javax.lang.model.util.SimpleTypeVisitor6;
import javax.lang.model.util.Types;

/**
 * Takes a set of types and a package and determines which of those types can be imported, and how
 * to spell any of the types in the set given those imports.
 *
 * @author emcmanus@google.com (Éamonn McManus)
 */
final class TypeSimplifier {
  /**
   * The spelling that should be used to refer to a given class, and an indication of whether it
   * should be imported.
   */
  private static class Spelling {
    final String spelling;
    final boolean importIt;

    Spelling(String spelling, boolean importIt) {
      this.spelling = spelling;
      this.importIt = importIt;
    }
  }

  private final Types typeUtil;
  private final Map<String, Spelling> imports;

  TypeSimplifier(Types typeUtil, String packageName, Set<TypeMirror> types) {
    this.typeUtil = typeUtil;
    Set<TypeMirror> referenced = referencedClassTypes(typeUtil, types);
    this.imports = findImports(typeUtil, packageName, referenced);
  }

  /**
   * Returns the set of types to import. We import every type that is neither in java.lang nor in
   * the package containing the AutoParse class, provided that the result refers to the type
   * unambiguously. For example, if there is a property of type java.util.Map.Entry then we will
   * import java.util.Map.Entry and refer to the property as Entry. We could also import just
   * java.util.Map in this case and refer to Map.Entry, but currently we never do that.
   */
  SortedSet<String> typesToImport() {
    SortedSet<String> typesToImport = new TreeSet<String>();
    for (Map.Entry<String, Spelling> entry : imports.entrySet()) {
      if (entry.getValue().importIt) {
        typesToImport.add(entry.getKey());
      }
    }
    return typesToImport;
  }

  /**
   * Returns a string that can be used to refer to the given type given the imports defined by
   * {@link #typesToImport}.
   */
  String simplify(TypeMirror type) {
    return type.accept(new ToStringTypeVisitor(), new StringBuilder()).toString();
  }

  /**
   * Visitor that produces a string representation of a type for use in generated code.
   * The visitor takes into account the imports defined by {@link #typesToImport} and will use
   * the short names of those types.
   *
   *
A simpler alternative would be just to use TypeMirror.toString() and regular expressions to
   * pick apart the type references and replace fully-qualified types where possible. That depends
   * on unspecified behaviour of TypeMirror.toString(), though, and is vulnerable to formatting
   * quirks such as the way it omits the comma in
   * {@code java.util.Map<java.lang.String, java.lang.String>}.
   */
  private class ToStringTypeVisitor extends SimpleTypeVisitor6<StringBuilder, StringBuilder> {
    @Override protected StringBuilder defaultAction(TypeMirror type, StringBuilder sb) {
      return sb.append(type);
    }

    @Override public StringBuilder visitArray(ArrayType type, StringBuilder sb) {
      return visit(type.getComponentType(), sb).append("[]");
    }

    @Override public StringBuilder visitDeclared(DeclaredType type, StringBuilder sb) {
      TypeElement typeElement = (TypeElement) typeUtil.asElement(type);
      String typeString = typeElement.getQualifiedName().toString();
      if (imports.containsKey(typeString)) {
        sb.append(imports.get(typeString).spelling);
      } else {
        sb.append(typeString);
      }
      List<? extends TypeMirror> arguments = type.getTypeArguments();
      if (!arguments.isEmpty()) {
        sb.append("<");
        String sep = "";
        for (TypeMirror argument : arguments) {
          sb.append(sep);
          sep = ", ";
          visit(argument, sb);
        }
        sb.append(">");
      }
      return sb;
    }

    @Override public StringBuilder visitWildcard(WildcardType type, StringBuilder sb) {
      sb.append("?");
      TypeMirror extendsBound = type.getExtendsBound();
      TypeMirror superBound = type.getSuperBound();
      if (superBound != null) {
        sb.append(" super ");
        visit(superBound, sb);
      } else if (extendsBound != null) {
        sb.append(" extends ");
        visit(extendsBound, sb);
      }
      return sb;
    }
  }

  /**
   * Returns the name of the package that the given type is in. If the type is in the default
   * (unnamed) package then the name is the empty string.
   */
  static String packageNameOf(TypeElement type) {
    while (true) {
      Element enclosing = type.getEnclosingElement();
      if (enclosing instanceof PackageElement) {
        return ((PackageElement) enclosing).getQualifiedName().toString();
      }
      type = (TypeElement) enclosing;
    }
  }

  /**
   * Given a set of referenced types, works out which of them should be imported and what the
   * resulting spelling of each one is.
   *
   *
This method operates on a {@code Set<TypeMirror>} rather than just a {@code Set<String>}
   * because it is not strictly possible to determine what part of a fully-qualified type name is
   * the package and what part is the top-level class. For example, {@code java.util.Map.Entry} is
   * a class called {@code Map.Entry} in a package called {@code java.util} assuming Java
   * conventions are being followed, but it could theoretically also be a class called {@code Entry}
   * in a package called {@code java.util.Map}. Since we are operating as part of the compiler, our
   * goal should be complete correctness, and the only way to achieve that is to operate on the real
   * representations of types.
   *
   * @param packageName The name of the package where the class containing these references is
   *     defined. Other classes within the same package do not need to be imported.
   * @param referenced The complete set of declared types (classes and interfaces) that will be
   *     referenced in the generated code.
   * @return a map where the keys are fully-qualified types and the corresponding values indicate
   *     whether the type should be imported, and how the type should be spelled in the source code.
   */
  private static Map<String, Spelling> findImports(
      Types typeUtil, String packageName, Set<TypeMirror> referenced) {
    Map<String, Spelling> imports = new HashMap<String, Spelling>();
    Set<String> ambiguous = ambiguousNames(typeUtil, referenced);
    for (TypeMirror type : referenced) {
      TypeElement typeElement = (TypeElement) typeUtil.asElement(type);
      String fullName = typeElement.getQualifiedName().toString();
      String simpleName = typeElement.getSimpleName().toString();
      String pkg = packageNameOf(typeElement);
      boolean importIt;
      String spelling;
      if (ambiguous.contains(simpleName)) {
        importIt = false;
        spelling = fullName;
      } else if (pkg.equals(packageName) || pkg.equals("java.lang")) {
        importIt = false;
        spelling = fullName.substring(pkg.isEmpty() ? 0 : pkg.length() + 1);
      } else {
        importIt = true;
        spelling = simpleName;
      }
      imports.put(fullName, new Spelling(spelling, importIt));
    }
    return imports;
  }

  /**
   * Finds all declared types (classes and interfaces) that are referenced in the given
   * {@code Set<TypeMirror>}. This includes classes and interfaces that appear directly in the set,
   * but also ones that appear in type parameters and the like. For example, if the set contains
   * {@code java.util.List<? extends java.lang.Number>} then both {@code java.util.List} and
   * {@code java.lang.Number} will be in the resulting set.
   */
  private static Set<TypeMirror> referencedClassTypes(Types typeUtil, Set<TypeMirror> types) {
    Set<TypeMirror> referenced = new HashSet<TypeMirror>();
    TypeVisitor<Void, Void> typeVisitor = new ReferencedClassTypeVisitor(typeUtil, referenced);
    for (TypeMirror type : types) {
      type.accept(typeVisitor, null);
    }
    return referenced;
  }

  private static class ReferencedClassTypeVisitor extends SimpleTypeVisitor6<Void, Void> {
    private final Types typeUtil;
    private final Set<TypeMirror> referenced;

    ReferencedClassTypeVisitor(Types typeUtil, Set<TypeMirror> referenced) {
      this.typeUtil = typeUtil;
      this.referenced = referenced;
    }

    @Override
    public Void visitArray(ArrayType t, Void p) {
      return visit(t.getComponentType(), p);
    }

    @Override
    public Void visitDeclared(DeclaredType t, Void p) {
      boolean added = referenced.add(typeUtil.erasure(t));
      if (added) {
        for (TypeMirror param : t.getTypeArguments()) {
          visit(param, p);
        }
      }
      return null;
    }

    @Override
    public Void visitTypeVariable(TypeVariable t, Void p) {
      visit(t.getLowerBound(), p);
      return visit(t.getUpperBound(), p);
    }

    @Override
    public Void visitWildcard(WildcardType t, Void p) {
      for (TypeMirror bound : new TypeMirror[] {t.getSuperBound(), t.getExtendsBound()}) {
        if (bound != null) {
          visit(bound, p);
        }
      }
      return null;
    }
  }

  private static Set<String> ambiguousNames(Types typeUtil, Set<TypeMirror> types) {
    Set<String> ambiguous = new HashSet<String>();
    Set<String> simpleNames = new HashSet<String>();
    for (TypeMirror type : types) {
      String simpleName = typeUtil.asElement(type).getSimpleName().toString();
      if (!simpleNames.add(simpleName)) {
        ambiguous.add(simpleName);
      }
    }
    return ambiguous;
  }
}


MINNEAPOLIS - Learning techniques to help manage stress may help people with epilepsy reduce how often they have seizures, according to a study published in the February 14, 2018, online issue of Neurology®, the medical journal of the American Academy of Neurology.
"Despite all the advances we have made with new drugs for epilepsy, at least one-third of people continue to have seizures, so new options are greatly needed," said study author Sheryl R. Haut, MD, of Montefiore Medical Center and the Albert Einstein College of Medicine in the Bronx, NY, and member of the American Academy of Neurology. "Since stress is the most common seizure trigger reported by patients, research into reducing stress could be valuable."
Half of the participants learned the progressive muscle relaxation technique, a stress reduction method where each muscle set is tensed and relaxed, along with breathing techniques. The other participants were the control group--they took part in a technique called focused attention. They did similar movements as the other group, but without the muscle relaxation, plus other tasks focusing on attention, such as writing down their activities from the day before. The study was conducted in a blinded fashion so that participants and evaluators were not aware of treatment group assignment.
Before the study, the researchers had hypothesized that the people doing the muscle relaxing exercises would show more benefits from the study than the people doing the focused attention exercises, but instead they found that both groups showed a benefit--and the amount of benefit was the same.
"It's possible that the control group received some of the benefits of treatment in the same way as the 'active' group, since they both met with a psychologist and every day monitored their mood, stress levels and other factors, so they may have been better able to recognize symptoms and respond to stress," said Haut. "Either way, the study showed that using stress-reducing techniques can be beneficial for people with difficult-to-treat epilepsy, which is good news."
The study was supported by Shor Foundation for Epilepsy Research and the Epilepsy Foundation.