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HUMAN WRONGS WATCH
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'You Have Not Seen Anything Yet,' Thunberg Warns Ahead of Davos 18/01/2020
Who Turned Up the Temperature? Climate Change, Heatwaves and Wildfires 18/01/2020
Brazil: Bolsonaro Leads 2019 Record for Attacks on the Press 18/01/2020
Urgent Need for 'Immediate' Solutions to Combat Drug-Resistant Infections, warns World Health Organization 18/01/2020
Libya: 'Dire and Untenable' Situation for Tens of Thousands of Children in Unrelenting Conflict 18/01/2020
Amazon Deforestation Is Expected to Accelerate in 2020 17/01/2020
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Time to Phase Out Mercury-Added Products Has Arrived
Geneva, 30 November 2019 (UN Environment)* — Parties to the Minamata Convention decided to move forward with their agreement in phasing out the use of products which contain mercury and to promote alternatives at the Third Conference of the Parties, that closed today in Geneva after a one-week meeting from 26 to 29 November 2019.
Pixabay | Photo posted here from UN Environment.
Delegates representing 113 parties decided to undertake the review regarding the reduction on the use of mercury in manufactured products.
Parties also agreed on how to use custom codes for mercury-added products that would permit to gather reliable information and therefore facilitate trade, including the banning of products containing mercury.
By 2020 the manufacture, import and export of mercury-added products is no longer allowed
Parties agreed on a framework to monitor the effectiveness of the Convention in order to strengthen its implementation
The Third meeting of the Conference of the Parties to the Minamata Convention on Mercury took place from 25 to 29 November in Geneva, Switzerland
Batteries, switches, fluorescent lamps, cosmetics, pesticides, barometers and thermometers containing mercury have their days numbered. By 2020 the manufacture, import and export of these products is no longer allowed.
The good news is that a wide range of safe and high-functioning alternatives to mercury-added products have been developed. Thus, it's just a matter of time before mercury-free alternatives fully replace their more toxic counterparts.
As it was emphasized during the opening session on Monday, the road map for effectiveness evaluation agreed at the second meeting of the Parties resulted now in a framework, which will help in defining how effective the Convention is by 2023.
"This COP has a key role to play in establishing the framework for the first evaluation of the effectiveness of the Convention, set for 2023. Strengthening legal frameworks and institutional capacity is also a basic requirement for the implementation of the Convention", said Inger Andersen, the UN Environment Executive Director.
In the same line, the Executive Secretary of the Minamata Convention, Rossana Silva Repetto, admitted that "it is our common wish that the results of such evaluation reflect that our convention is proving to be effective.
For this to happen, we need to work on the implementation of the convention at all levels in order to yield the fruits that indicate, in the data that will be collected, that we are on the right path towards attaining the objective that you, yourselves established for the Minamata Convention".
Meanwhile, Parties have an obligation to submit their first national reports by December 2019 on the measures they have taken to implement a number of provisions of the Convention, on the effectiveness of such measures and on possible challenges in meeting the objectives of the Convention, as was agreed at the first meeting.
Other key decisions adopted at the Minamata COP3 include the programme of work and budget for the biennium 2020-2021, terms of reference for the Implementation and Compliance Committee, guidance on the management of contaminated sites, releases and waste thresholds.
Parties reiterated their wish to continue to enhance cooperation with international organizations in areas of relevance to the Convention, towards the achievement of the 2030 Agenda for Sustainable Development Goals.
Donors announced voluntary contributions to the Specific International Programme to support capacity building and technical assistance, which is one of the components of the financial mechanism of the convention, being the Global Environmental Facility (GEF) the other one.
COP3 has also been the opportunity for consolidating the substantial progress made so far at the previous COPs. At the first and second meetings, the COP took decisions that were key for the ongoing implementation of the Convention. Several guidance documents were adopted in relation to trade, best available techniques and best environmental practices in relation to emissions, and artisanal and small-scale gold mining (ASGM) National Action Plans (NAPs).
The events at the COP included a special session on mercury science highlighting the linkage between policy and science.
Among the other 26 side events broader issues were discussed such as the linkages between chemicals management and biodiversity, artisanal and small-scale gold mining, trade, contaminated sites, chemicals and waste management beyond 2020, and global community's efforts to protect human health and the environment from the negative effects of mercury.
Since the convention entered into force in 2017, meetings of the conferences of the Parties to the Minamata Convention on Mercury have been held during a one-week period every year in late September (COP1) or November (COP2 and COP3) at the seat of the Secretariat in Geneva, Switzerland.
From now, next COPs will be convened every two years. 2021 will be the turn of Indonesia to host the fourth Conference of the Parties to the Minamata Convention in Bali, to keep making mercury history.
About the Minamata Convention
The Minamata Convention on Mercury is the most recent global agreement on environment and health, adopted in 2013. It is named after the bay in Japan where, in the mid-20th century, mercury-tainted industrial wastewater poisoned thousands of people, leading to severe health damage that became known as the "Minamata disease". Since it entered into force on 16 August 2017, 113 Parties have been working together to control the mercury supply and trade, reduce the use, emission and release of mercury, raise public awareness, and build the necessary institutional capacity.
About the UN Environment Programme
UNEP is the leading global voice on the environment. It provides leadership and encourages partnership in caring for the environment by inspiring, informing and enabling nations and peoples to improve their quality of life without compromising that of future generations.
*SOURCE: UN Environment. Go to ORIGINAL.
Find out more about the Minamata Convention COP3
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2019 Human Wrongs Watch
Posted on 03/12/2019 at 09:44 in Africa, Asia, Latin America & Caribbean, Market Lords, Middle East, Mother Earth, Others-USA-Europe-etc., The Peoples	| RSS feed | Reply	| Trackback URL
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Jason Roth
Brandan J. Davies
Lauren Fields
A Step by Step Guide to a Personal Injury Case
Product and Premise Liability
Frequently Asked Personal Injury Questions
A Step by Step Guide to a Criminal Case
Information on Specific Crimes
Frequently Asked Criminal Defense Questions
A Step by Step Guide To Divorce
Child Custody & Support FAQs
Experienced Legal Counsel You Can Trust
What Happens If The Complaint Fails To Have All The Elements Of The Crime Listed?
Sometimes the black letter law passed by the legislature is unclear. The legislature can't anticipate every possible fact scenario when they pass a law, so it lay to the courts to interpret the law and give guidance to what it means. This interpretation is called case law. When the court decides a certain meeting to the law it essentially answers a legal question. Lawyers and other courts then can rely on that ruling when they have a similar issue in their case. The following case answers the question above.
State v. Shirley, 89 P.3d 649 (Kan. 2004).
This case addresses the following issue:
This case explores what happens if the complaint fails to have all of the elements of the crime listed. In the present case, the court found that a complaint which omits one or more essential elements of the crimes it attempts to charge is jurisdictionally and fatally defective, and a conviction based on such a complaint must be reversed. Id. at 652.
The defendant was charged with a host of offenses, the most important being conspiracy to manufacture methamphetamine. Id. at 651. Prior to being arrested, the defendant had picked up a girl from jail and drove her to her mother's house. Id. at 650. On the way to the mother's house, the defendant stopped at several convenience stores and picked up plastic jugs, lithium batteries, gas, and Sudafed. Id. After picking up all of the materials, the defendant discussed manufacturing methamphetamine with the girl. Id. After reaching the mother's house, the defendant refused to leave and the girl called the sheriff. Id. The sheriff arrived on site and arrested the defendant for possessing a firearm with a criminal record. Id. Additionally, after receiving consent, the sheriff found the methamphetamine items. Id. In the end, the jury found the defendant guilty of conspiracy to manufacture methamphetamine. Id.
The defendant argued that the complaint charging him with conspiracy to manufacture methamphetamine was jurisdictionally defective and, therefore, his conviction should have been reversed. Id. at 651. Further, the defendant's specific argument was that the complaint failed to allege specific facts that amounted to an overt act in furtherance of the conspiracy. Id.
The court looked to State v. Hall, 793 P.2d 737 (1990) to determine what standard would be used in examining the complaint. Id at 652. Hall determined that a challenge that was raised before the district court would use the pre-Hall standard and would focus on the technical considerations of the complaint and whether the complaint set out the essential elements of the crime. Id. If the complaint did not set out the essential elements, it was considered fatally defective and the conviction would be reversed. Id. On the other hand, if the challenge were raised for the first time on appeal, the post-Hall standard would be used. Id. The post-Hall standard outlined that the defendant must show that the claimed defect in the complaint (1) prejudiced the defendant's preparation of a defense, (2) impaired the defendant's ability to plead the conviction in any subsequent prosecution, or (3) limited the defendant's substantial rights to a fair trial. Id.
The court determined that the pre-Hall standard would be used because the defendant initially challenged the complaint at the district court level. Id. Thereafter, the court looked to the State's definition of conspiracy and found that it included, ". . . no person may be convicted of a conspiracy unless an overt act in furtherance of such conspiracy is alleged and prove to have been committed by such person or by a conspirator." Id. With this said, the court had to determine whether the State's complaint alleged an overt act in furtherance of the conspiracy. Id. The complaint in question stated that the defendant committed "an overt act in furtherance of the conspiracy" but did not articulate what that overt act actually was. Id. at 654.
In order to determine whether the complaint met the pre-Hall standard, the court looked to old case law and a Kansas statute. Id. at 652-53. Both cases the court examined mirrored the defendant's case and both cases resulted in defective complaints. Id. In addition, the Kansas statute explained that the purpose for requiring a recital of the essential facts in a complaint was to ensure that the accused was informed of the charges against him or her so that he or she may prepare a defense. Id. at 653-54. Further, the court noted that the test for sufficiency ought to be whether it was fair to require the defendant to defend on the basis of the charge as stated in the particular complaint. Id. at 654. In sum, the court found that the complaint was fatally defective because it did not set out all of the essential elements of the crime. Id.
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Last month four new starters joined Rollem in Tankersley, Barnsley, the business has seen growth in 2017 particularly in its European markets and is anticipating an increased turnover this year.
Rollem International will be exhibiting at Print 17 on booth 845 from Sunday 10th to Thursday 14th September 2017 at McCormick Place in Chicago, Il, USA.
Rollem will be exhibiting at The Print Show 2017 on stand A03b from Wednesday 11th to Friday 13th October 2017 at the International Centre in Telford, UK. Rollem will be demonstrating an offline business card print finishing system with conveyor.


Free Shipping Included! The Display Guys 16x16 Framed Masonite Cut-Out Wall Décor, Art Decor, Home Office Decor, Decorative Fun Design. No Glass. (Anchor Design) by The Display Guys at Home Decor Outlet Store. Hurry! Limited time offer. Offer valid only while supplies last.
READY TO HANG. Excellent Home Décor has a Sawtooth Hanger that is already screwed on the back of the Frame for easy and ready to hang on the wall for home or office decor.
Outstanding Art Décor that will enhance any room in your home, office, business place or apartment to give a remarkable feel and vibe.
Bring Appeal and Color to your wall space, alter the mood by filling out the gaps and bring excitement to any room. Wonderful decorative and fun design!


OTTAWA, Ontario – March 17, 2015. Nominations for the 2015 Electric Vehicle Dealership Awards are open, and the Canadian Electricity Association (CEA) and Plug'n Drive are encouraging dealerships in Canada selling electric vehicles (EVs) to self-nominate for these one-of-a-kind awards.
The 2015 Electric Vehicle Dealership Awards Program includes three award categories for leadership in EV sales: Leading Battery Electric Vehicle (BEV) Dealership Award, Leading Plug-In Hybrid Electric Vehicle (PHEV) Dealership Award, and the EV Dealership Inspiration Award. Dealerships in Canada selling EVs are eligible to win as many as two of three 2015 awards.
In addition to the award, winners will also be featured on partner and sponsor websites and social media channels, which include the Canadian Electricity Association, Plug'n Drive, Electric Mobility Canada, and the Canadian Automobile Association.
"CAA is pleased to celebrate dealerships that are driving innovation and educating Canadians about the benefits of choosing an electric vehicle," said Ian Jack, Managing Director of Communications and Government Relations at the Canadian Automobile Association (CAA).
The 2015 nomination package is available at www.electricvehicleawards.ca. Nominations close on April 17, 2015.
The 2015 EV Dealership Award winners will be announced at the EV2015VÉ Electric Vehicles Conference and Trade Show in Halifax May 25-27, 2015. EV2015VÉ is Canada's biggest electric vehicle event held annually by Electric Mobility Canada, the hosting sponsor of the Awards program.
Click here to download the PDF of this News Release.


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2022 CBODN Conference
Volunteer - Long Term Impact
Volunteer - Limited Time
Coaching CoP
DC Area OD Practitioners CoP
LOGIN MEMBERS
DC Area OD Practitioners CoP: How Do Coincidences Show Up In Our Work?
Engage with one another, develop knowledge and skills, share expertise, and contribute to our community.
Zoom link and password will be included in the registration confirmation email
$10 for non-members – $10.00
March 2, 2023 7:00 PM - 8:30 PM Eastern
How Do Coincidences Show Up In Our Work?
For many of us, we have coincidences in our lives. They include unexpected occurrences that may be meaningful. In addition to our own personal lives, they may show up in our working with clients, and coincidences our clients may have that affect the success of their organizations. Before the session, think about important coincidences you've seen in your work, and in the session you will have an opportunity to learn the importance of them and share with colleagues experiences that you and your clients have had and what you've learned from them.
Bio: Eric Reisman
Eric is a retired high school special educator of 34 years. His focus has always been on preparing young people with disabilities for employment, and figuring out their path for what happens after high school. Eric has been fascinated by the concept of conversation starters and finding common ground with others. He is the founder and creator of The Gentle Man, a quest to find out how we make it more attractive for boys to show their compassionate side. More about him is at www.ericreisman.com.
Chesapeake Bay Organization Development Network (CBODN) - is the premier DC Metro area network and community of practice of Organization Development (OD) practitioners. Founded in Chesapeake Bay, MD, our name still pays homage to our founders, and we are grateful for their brilliant minds and vital contributions to the field of OD.
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Serving you worldwide! Haven't heard of us yet and want to change this? Please contact us! We look forward to hearing from you. We would be delighted to meet you in person at one of our shoe order centres (SOC) or dazzle you with our products at one of the many tradeshows at which we exhibit.


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100 Years of Grand: The Grand Canyon Centennial Project
A digital archive celebrating the Centennial of the Grand Canyon National Park. A collaborative project of the Arizona State University Library, Cline Library, Northern Arizona University and the Grand Canyon National Park. Additional materials are available via the project website.
1 Hayden, Carl T.
1 Kimball, George W.
Control of Public Lands (1912-1928)
Babbitt Brothers Trading Company
1 Buggeln, Martin, 1867-1939
1 Grand Canyon National Park (Ariz.)
1 Grazing
1 Hayden, Carl Trumbull, 1877-1972
1 Pasture, Right of
Tertiary History of the Grand Cañon District with Atlas
History of the Grand Canyon National Park
Stage Coach Trip to the Grand Canyon, 1895, by Josephine Frankenberg of Tempe
Letter from A. E. Demaray, National Park Service to Carl Hayden
Letter from Carl Hayden to J. R. Eakin, Grand Canyon National Park
Sorted by: Date Added (Latest)
Letter from George W. Kimball, Tusayan National Forest, to Carl Hayden
Letter from George W. Kimball to Carl Hayden with an enclosed map detailing the cattle and sheep allotments on the portion of the Tusyan Forest next to the Grand Canyon. Listed are names of permit holders in both stock and allotment that are most likely to trespass into the park. The names are: W. F. Griffin, W. W. Bass, H. R. Lauzon, Swanner and Griffin, Bankhead and Henderson, Martin Buggeln, Babbitt Brothers, Sanford Rowe, and P. D. Berry.
Kimball, George W., Hayden, Carl T.


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Cairo Contenuto disponibile in Italiano
Egypt Elections: Tawadros II, "a stable country is a guarantee of stability for the world"
26 marzo 2018 @ 9:59
(From our correspondent) "A stable Egypt is a guarantee of stability for the world". Which is why the presidential elections in the country on 26-28 March are of crucial importance. Pope Tawadros II, Coptic Orthodox Patriarch, sees the vote as a path to peace and prosperity for the country. There is tension in Egypt. Two days before the presidential elections, a new attack has shaken the life of the country. In Alexandria, a car bomb exploded near the convoy that was carrying the local security chief. Security is being stepped up, and the army is everywhere. Along the busy streets of Cairo, the images of the current head of state, Abdel-Fattah al-Sisi, who has been in office since 8 June 2014, are everywhere. He is running for a second 4-year term, and has practically no rivals.
"Presidential elections are always a crucial opportunity for Egyptians to express their love for the homeland", the Patriarch of the Coptic Orthodox, who account for 10% of the 93-million population, tells SIR news agency. In an interview to be published in the next few days, Pope Tawadros II also stresses the importance of "going to the polls". And goes on to say: "The current President is very popular and people would very much like to re-elect him to give him the chance to complete two crucial battles for the history of modern Egypt. The first is the fight against terrorism and violence. And Egypt is fighting this war for the whole world. The second battle is for development and progress, and this is also a battle that Egypt is fighting for the entire world because a stable Egypt is a guarantee of stability for the world".
Pope Tawadros II
19 gennaio 2021 18 gennaio 2021 16 gennaio 2021 15 gennaio 2021 14 gennaio 2021 13 gennaio 2021 12 gennaio 2021 11 gennaio 2021 9 gennaio 2021 8 gennaio 2021 7 gennaio 2021 6 gennaio 2021 5 gennaio 2021 4 gennaio 2021 2 gennaio 2021 1 gennaio 2021 31 dicembre 2020 30 dicembre 2020 29 dicembre 2020 28 dicembre 2020 27 dicembre 2020 26 dicembre 2020 25 dicembre 2020 24 dicembre 2020 23 dicembre 2020 22 dicembre 2020 21 dicembre 2020 19 dicembre 2020 18 dicembre 2020 17 dicembre 2020 26 marzo 2018
Abuse Ireland
Ireland: Bishop of Dromore's resignation Mgr. Eamon Martin (President of the Bishops' Conference), "the Church can never become complacent concerning the safeguarding of children"
Diocese of Stockholm Sweden
Sweden: the Catholic Church buys a place of worship in Motala for a new parish church
Pre-Synod Germany
Germany: Andonie (Catholic Youth Federation), "the Church needs unity in diversity"
Mourning Russia
Pope Francis: Telegram of condolences for Russia fire
Terrorism France
Pope Francis: telegram of condolences for attacks in France, condemning "blind violence"
Pope Francis: accepts resignation of mgr. McAreavey, bishop of Dromore, recently criticised for paedophilia
Tragedy Siberia
Siberia, fire in shopping centre: Patriarch Kirill, "prayers for all innocent victims"
Tragedy Russia
Siberia, shopping mall fire: 53 dead. "Remembrance service in all Churches of the region" tomorrow
France: remembering the hero policeman. Father Jean-Baptiste, "I could not marry him because he was unconscious"
Cairo Cairo


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Wake traditions in Ireland and Church opposition to wake and associated practices
On the topic
Foreigners often see Ireland as a deeply Catholic country. Yet when dealing with their traditional customs, the Irish can prove to be very disobedient to the orders of the Church. One of the best examples is probably that of the 'merry wake': the Church tried for hundreds of years to forbid this very special kind of funeral vigil or at least some of the practices associated with it, but all these attempts were unsuccessful, so deeply established was the habit in people's lives.
The rural Irish custom called nosanna
The death old person
Mocking civil and religious authorities
Thomas Crofton Crocker and his view of the custom
The amount of money spent on wakes and funerals and the clergy
Games and music: Offending the Church
Enforcing the Statutes issued during Synods
[...] During the same Synod, a Statute was issued that 'ordered all who attended Catholic wakes to abstain from excess in drink and food, from merry-making, from games and from illegal mispractices which had been introduced to lead people astray' and also from keening as we have already mentioned that is to say from all the principal features of a merry wake of the time. Enforcing the very large amount of Statutes issued during Synods and other episcopal meetings for at least five centuries was a 'burden . [...]
[...] it was not unusual 'to see even the tombstone in readiness Mr and Mrs Hall, who visited Ireland in the nineteenth century, wrote that "the most anxious thoughts of the Irish peasant through life revert[ed] to death and he [would] endure extreme poverty in order that he [might] scrape together the means of obtaining the wake and a 'decent funeral'."[7] This amount of money spent on wakes and funerals was harshly criticized by the Church as early as 1614. Seán Ó Súilleabháin relates that in this year the Synod of Armagh 'deplored the large amount of money being spent on festivities at wakes and funerals, as well as on dark mourning clothes. [...]
[...] Amsterdam: Rodopi pp. 26-29. N. Witoszek and P. Sheeran, Talking to the Dead: A Study of Irish Funerary Traditions, Amsterdam: Rodopi p G. Ó Crualaoich, "The 'Merry Wake'". Irish Popular Culture 1650-1850. Eds. J. S. Donnelly Jr., Kerby A. Miller. Dublin: Irish Academic Press p S. Ó Súilleabháin, Irish Wake Amusements. Dublin: The Mercier Press (Irish) (English translation). G. Ó Crualaoich, "The 'Merry Wake'", p T. [...]
[...] Hall, Anna Maria and Samuel. Ireland: Its Scenery, Character etc. London Vol. p Cited in Witoszek and Sheeran, Talking to the Dead, p (see below). Ó Crualaoich, Gearóid. "The 'Merry Wake'". Irish Popular Culture 1650-1850. Eds. J. S. Donnelly Jr., Kerby A. Miller. Dublin: Irish Academic Press Ó Súilleabháin, Seán. Irish Wake Amusements. Dublin: The Mercier Press (Irish) (English translation by the author). Witoszek, Nina and Pat Sheeran. Talking to the Dead: A Study of Irish Funerary Traditions. [...]
On the topic : Wake traditions in Ireland and Church opposition to wake and associated practices
Wake traditions in Ireland and Church opposition to wake and associated practices - published: 29/09/2010
Essay From 4 pages - Sociology
Foreigners often see Ireland as a deeply Catholic country. Yet when dealing with their traditional customs, the Irish can prove to be very disobedient to the orders of the Church. One of the best examples is probably that of the 'merry wake': the Church tried for hundreds of years to forbid this very special kind of funeral vigil or at least...
Is capitalism a positive or negative force in International Relations?
The double personality in Edgar Allan Poes tales: William Wilson and The Cask of Amontillado
"High, Popular and "Low" cultures and everyday life
Critical thinking questions - The media and its influence
Critical thinking questions - Social problem
Idea of progress - What is the purpose of science? Is scientific progress always something positive for humanity?
Myths and heroes - To what extent can the choices that people make, condition and influence their lives and have an impact on the world they live in?
Places and forms of power - What power and determination do young people have to promote change in our societies and in the world of today?


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Babel, Caledonian Park, presented by World Stages London, Wildworks, and Battersea Arts Centre, Sunday 6 May, 2012
I got lost on my way to Babel. I'm a visitor to London and didn't have the opportunity to seek out Caledonian Park prior to Sunday night's performance. I had GoogleMaps leading me astray; I'm not sure what did it for the production. Urged along park paths dotted with men and women dressed in white berets and trench coats (who I deemed angels) and lined with people doing their own thing- reading books in trees, making salad, doing yoga, sleeping in real beds, was fascinatingly surreal. That surrealism vanished as soon as we entered Caledonian Park's main field. The central clearing was dotted with tents. There was music, provided by both a band and a choir. After purchasing a spiced cider from the bar tent, I found myself being invited into a "circle of love" in a round, raised tent. I was promised games and stories, but instead found myself the object of other audience members' curiosity; who was this American girl drinking cider on the floor? Leaving the Love Tent, I discovered a knit skyline of London, a fire artist, and a dancing Southeast Asian hermaphrodite. Every so often, an angel would speak, encouraging us to "build our new city." It was utterly thrilling and bizarre, almost like living in an environmental production of Angels in America. I was half expecting someone to tell me to "look up."
When night fell, the "dramatic" portion of the evening began. Where the prologue was one of the most interesting and different art instillations I've ever seen, the story of the play-part of Babel was simplistic, under-defined, and ended too patly to be of any real interest. There was a political message folded in there, something about the Occupy Movement and the current eviction of low-income East Londoners in advance of the Olympics, but the politics wasn't carried far enough. We were left with more questions than answers. Why, for example, were the people so dedicated to living in the shadow of the tower? Why weren't they allowed to stay? What were the security guards protecting? What were the angels? Aside from a tower and a bunch of people in native costume (most of whom spoke English), what did this have to do with the Tower of Babel story?
Like my journey to Caledonian Park, Babel had a few wrong turns. While I ultimately found myself in the right place, I'm not sure Babel did- though I have faith that it could, with further development on the plot of the piece.


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Butchayyapeta
Villages in Butchayyapeta taluk, Visakhapatanam district
List of villages in Butchayyapeta taluka or tehesil is listed below. Please click on a village link to view every detail of the village. Butchayyapeta is under the Visakhapatanam district in the state of Andhra Pradesh in India.
Butchayyapeta Butchayyapeta is a City in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapatnam ...
Chinappannapalem
Chinappannapalem Chinappannapalem is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapa...
Chintapaka
Chintapaka Chintapaka is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapatnam li...
Chittiyyapalem
Chittiyyapalem Chittiyyapalem is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapatnam...
Dibbidi
Dibbidi Dibbidi is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapatnam 547 K...
Gantikorlam
Gantikorlam Gantikorlam is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapatnam ...
Gunnempudi
Gunnempudi Gunnempudi is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapatnam li...
K.p. Agrahram
K.p Agrahram K.p Agrahram is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vi...
Kandipudi
Kandipudi Kandipudi is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapatnam 5...
Karaka Karaka is a Village in Butchayyapeta Mandal in Visakhapatanam District of Andhra Pradesh State, India It belongs to Andhra region It is located 45 KM towards west from District head quarters Vishakhapatnam 547 KM ...
Bibinagar | S.rayavaram | Tangutur | T.sundupalle | Sarubujjili | Shyampet | Hanamkonda | Narva | T.narasapuram | Dichpalle | Santhakavati | Lakkavarapukota | Dakkili | Gantyada | Konakanamittla | Duvvur | Palakoderu | Cumbum | Mydukur | Vemula Pally | Ozili | Indukurpet | Varni | Payakaraopeta | Makkuva | Vallur | Nanganur | Narasannapeta | Janakavaram Ponguluru | Kodad | Rajampet | Madnur | Gandeed | Pedacherlo Palle | Rambilli | Penugonda | Achutapuram | Chintalapudi | Tallur | Nidamarru |


text:
Creating requests with the Self-Service Portal in On Demand Group Management
Thanks for your interesting Quest on Demand, your go-to SAS dashboard for tackling Microsoft challenges in a hybrid world. I'm Shawny Reiner, senior strategic systems consultant at Quest. In this short video tour, I'm going to explain what On Demand Group Management is and show you On Demand Group Management features. If you've taken any of the other tours, you know that ODGM is a SAS based group management tool for on-prem only, hybrid, and Azure only groups. It allows for controlled self-service creation, management, and attestation, as well as provide the administrator with a single pane for the whole hybrid environment. The Self-Service Portal is designed with the end user in mind. And creating a Group Action Request is an important function of self-service group management that ODGM makes easy and consistent for the end user. The user does not need to know or care where the group resides-- on-prem, hybrid, or Azure only-- in order to request an action, such as to join or leave it. The Gallery is a list of available groups that the user can select to join. And if the user has the appropriate permissions granted, they can choose to add a group from here as well. And of course that creation, is governed by the action permissions, policies, and approval workflows defined by admins. From the Groups page, the user can search for a group using the many available filters. They can also look at the details and descriptions of a group. And they can choose to join from here. From the New Request menu options, every available request action is listed. And again, what they can do and how they do it is governed by the group action permissions, policies, and approval workflows defined by the admin. Clicking any of the titles and then selecting an appropriate group, such as this on prem-only group, will walk the user through the selections needed. And then they simply submit the request, which will then show up on their My Activities My Requests page. With On Demand Group Management Self-Service Portal it's easy for users to self service their group ownership, management, and attestation needs, regardless of the group type or where it resides. So that's the quick tour. Ready to get started? Check out www.Quest.com/On-Demand, and navigate to On Demand Group Management to learn more. Be sure to check out the other available videos.
Managing groups through the Admin Portal in On Demand Group Management
Learn how to manage groups through the Admin Portal in On Demand Group Management.
Hybrid setup for On Demand Group Management
First steps when installing On Demand Group Management
First steps when installing On Demand Group Management.
Using the My Activities page in the Self-Service Portal of On Demand Group Management
Using the My Profile page in the Self-Service Portal of On Demand Group Management
Learn how to use the My Profile page in the Self-Service Portal of On Demand Group Management.
Managing approval workflows in the Admin Portal in On Demand Group Management
Learn how to manage approval workflows in the Admin Portal in On Demand Group Management.
Managing policies in the Admin Portal in On Demand Group Management
Learn how to manage policies in the Admin Portal in On Demand Group Management.
Using the Activities page in the Admin Portal in On Demand Group Management
Learn how to use the Activities page in the Admin Portal in On Demand Group Management.
Using the Admin dashboard in On Demand Group Management
Learn how to use the Admin dashboard in On Demand Group Management.
On Demand Group Management
Regain peace of mind and control of Azure AD and Office 365 groups
Online testen


text:
Oecanthus rileyi är en insektsart som beskrevs av Baker, C.F. 1905. Oecanthus rileyi ingår i släktet Oecanthus och familjen syrsor. Inga underarter finns listade i Catalogue of Life.

Källor

Externa länkar

Syrsor
rileyi

redpajama_set_name: RedPajamaWikipedia


text:
require 'rails/generators/migration'

module ActiveRecord
  module Generators # :nodoc:
    module Migration
      extend ActiveSupport::Concern
      include Rails::Generators::Migration

      module ClassMethods
        # Implement the required interface for Rails::Generators::Migration.
        def next_migration_number(dirname)
          next_migration_number = current_migration_number(dirname) + 1
          ActiveRecord::Migration.next_migration_number(next_migration_number)
        end
      end
    end
  end
end

redpajama_set_name: RedPajamaGithub


text:
Home / Books / Used Books / Shore Ecology of the Gulf of Mexico (Used)
Shore Ecology of the Gulf of Mexico (Used)
SKU: TSHA-USEDBOOK_0181 Categories: Books, Used Books
Shore Ecology of the Gulf of Mexico
By Joseph C. Britton
To the casual visitor, the Gulf of Mexico shores offer mainly sun, sand, and sea. Even the standard field guides, focused on one group of animals or plants, barely hint at the wealth and diversity of habitats and species along Gulf shores. Shore Ecology of the Gulf of Mexico, using a "whole habitat" approach, breaks new ground in describing all the conspicuous vascular plants, algae, birds, mammals, mollusks, crustaceans, and other invertebrates for each marine habitat. The area covered begins west of the Mississippi delta in Louisiana and follows the shores west and south to the Yucatan Peninsula in Mexico.
Transitions between habitats also receive detailed treatment. The authors discuss changes in flora and fauna that result from differences in climate, shore geology, and patterns of precipitation in the succeeding habitats along the Gulf rim. They include discussion of more than 1,000 species of plants and animals, both on shore and in the near-shore subtidal zone, to give a virtually complete picture of western Gulf coast ecosystems. Excellent line drawings and photographs of over 800 species complement the text.
For marine scientists, students, and knowledgeable beachcombers, this is a thorough source on Gulf coast marine life.
TSHA Press
Chronicles of the Big Bend: A Photographic Memoir of Life on the Border
A Cowman's Wife
Dallas: A History of "Big D"
Sale! $8.95 – $10.46 Select options


Vintage Solid Silver Hallmarked Christening Cup Birmingham 1964, Made by the renowned silversmiths Angora Silver Co. A very fine example of this type of Christening cup exceptional weight and quality. We have fully restored this cup to near new condition. Size Height 3 inches Diameter 2.75 inches and weighs a very hefty 4oz or 122 grams. We can engrave a name and date around the cup.


I hardly understand it . Can any of you guide me a way?
Unfortunately Wikipedia isn't going to help much. The topic is large enough that you'll get an overview, but not much more than that.
2) I don't know if you are interested in Math or Physics but I'd recommend a good Math Physics text. (I prefer Arfkin but here's the Barnes and Noble page.) If all you want is the Spherical Harmonics you shouldn't have to pay the hundred or so dollars to get a good one. The Physics approach won't give you the full theory, though it won't be too bad, but you can always pick up on that as soon as you get a feel for it.
What is "orthogonal expansions"? When I searched a bit about it, references about "furrier" came into the scene. I know very little about "furrier". I think I am not getting the whole picture at all. Can you be a bit more specific for me?
About the books you referred, this is a whole collection in a site. Can you recommend one for me?
Last edited by skipjack; November 9th, 2016 at 01:50 AM.
Was that "Fourier" rather than "furrier"?


text:
NEW DOs
La Ronciere's 200ha vineyard in Licantén DO in Colchagua Valley
Signalling the strength of its ambitions, in May four new Denomination of Origins (DOs) were signed into law, marking a major step in the industry's ability to communicate its multitude of microclimates beyond its politically fixed geographical boundaries. What made the addition of three of these DOs significant was that they were not part of a political municipality. Up until now, any DO had to be tied to one to come into being.
Now, a prospective DO must only be close to a locality of the same name and/or a vineyard of international acclaim, setting a new precedent. The three new DOs include Lo Abarca in the San Antonio Valley, and Apalta and Los Lingues, both in the Colchagua Valley. An application put forward for Quebrada Seca, a microclimate in the Limarí Valley, known for its production of Chardonnay, was not permitted to become a DO because it is not associated with a defined locality. Nevertheless, the relaxation of its rules signifies the Chilean authority's recognition of individual, smaller regions, known by producers for their unique climactic characteristics, but previously ineligible….


text:
Q: RMAN Config History I know this seems kind of a strange request, but does RMAN have any history of the changes to config parameters?    Seems like one of the team members has been changing the config parameters to a number of instances, and of course no one is claiming responsibility nor communicating with the team on why they are changing the parameters.
Is there any history on the changes?  Or, if we wanted to put some before update triggers in place, can that be done?

A: If you save the rman backup log for every backup, then before backup - rman will push all current RMAN settings (identical to rman> show all) to that file, then backup result.
Just open daily rman log backup and compare, at least you will get the date of (some) rman settings has changed.
RMAN just keep notion of original (by # default), but don't mention when it was changed!

redpajama_set_name: RedPajamaStackExchange


text:
Dispute between partners over building sparks business lawsuit
On Behalf of Codispoti & Associates, P.C. | Nov 2, 2020 | Real Estate Law
Dealing with real estate issues in New York is complicated at any time, but when outside factors are weighing it down, it can lead to a unique set of challenges that could escalate into a full-blown dispute that needs legal intervention. With the current health situation, legal disagreements are increasingly common. One concern that frequently arises centers around control and finances. This can impact people who were partners and find themselves on opposite sides of the situation. For these cases, having experienced legal advice from a firm that understands business law and real estate may be essential.
Partners battle over control of Manhattan building
A luxury building is at the center of a dispute between two partners in the real estate business. One partner is accusing the other of taking control of the building as it is struggling financially. The plaintiff is the CEO of his own company. He accuses his partner, who runs his own company, of taking over a bank account with $20 million that was held jointly. He also says information was censored and access to the records and personnel was restricted. The defendant had developed the building more than three decades ago. It is a combination commercial and residential structure and has a parking garage.
The dispute began in 2019 before the health crisis began. As the pandemic worsened, the urgency of the case rose. There is a significant number of vacancies at 35%. The complainant says it needs financial help and restructuring. The sides have made conflicting statements as to their role in overseeing the building. The defendant is reportedly in ill health and the complainant is accused of trying to take over the business in an underhanded way. In the case, the goal is to stop the defendant from making decisions without consulting with the partner. The books would also need to be available to analyze the financial circumstances. Decision-making and the future of the building are also factors in the case. The partners own five other Manhattan properties together.
Experienced legal help is imperative with real estate lawsuits
While commercial real estate is considered a lucrative business, there are often unavoidable challenges that must be addressed. In some cases, negotiation can settle the matter and partners can cobble together a workable agreement to continue a partnership. In others, it becomes contentious. To analyze the issues that are in dispute and seek a resolution in or out of court, having legal assistance that understands real estate and business law can be crucial.


Stoves ST SEB602PY Black built-in oven review - Which?
This multifunctional oven from Stoves offers a myriad of cooking modes, so you should be able to find the perfect technique for all your dishes. The SEB602PY Black could also please those who hate cleaning, as it has a pyrolytic cleaning program that simply burns away splashes and spills. Extras like this are all well and good, but an oven's main task is cooking - so did this one impress our experts in the test lab? Read our review to find out how well it cooks.


text:
\section*{Acknowledgments}
This research is supported by the Sea-NExT Joint Lab, Singapore MOE AcRF T2, National Natural Science Foundation of China (U19A2079) and National Key Research and Development Program of China (2020AAA0106000). Our thanks also go to all the anonymous reviewers for their valuable suggestions.

\bibliographystyle{acl_natbib}

\section{Introduction}
Language understanding~\cite{ke2020sentilare} is a central theme of artificial intelligence~\cite{chomsky2002syntactic}, which empowers a wide spectral of applications such as sentiment evaluation~\cite{feldman2013techniques}, commonsense inference~\cite{snliemnlp2015}. The models are trained on labeled data to recognize the textual patterns closely correlated to different labels. Owing to the extraordinary representational capacity of deep neural networks, the models can well recognize the pattern and make prediction accordingly~\cite{jacob2019bert}.
However, the cognitive ability of these data-driven models is still far from human beings due to lacking \textit{counterfactual thinking}~\cite{pearl2019seven}.

Counterfactual thinking is a high-level cognitive ability beyond pattern recognition~\cite{pearl2019seven}.
In addition to observing the patterns within factual samples, counterfactual thinking calls for comparing the fact with imaginations, so as to make better decision.
For instance,
given a factual sample ``\textit{What do lawyers do when they die? Lie still.}'', the intuitive evaluation of its sentiment based on the textual patterns will recognize ``\textit{Lie still}'' as an objective description of body posture which is \textit{neutral}. By scrutinizing that the ``still'' could be intentionally postposed, we can imagine a counterfactual sample ``\textit{What do lawyers do when they die? \underline{Still lie}.}'' and uncover the negative sarcastic pun, whose sentiment is more accurate.

Recent work~\cite{kaushik2019cfdata,zeng2020counterfactual} shows that incorporating counterfactual samples into model training improves the generalization ability. However, these methods follow the standard machine learning paradigm that uses the same procedure (\emph{e.g., } a forward propagation) to make prediction in the testing phase. That is, making decision for testing samples according to their relative positions to the model decision boundary. The indiscriminate procedure focuses on the textual patterns occurred in the testing sample and treats all testing samples equally, which easily fails on hard samples (\emph{cf. } Figure~\ref{fig:conf-acc}).
On the contrary, humans can discriminate hard samples and ponder the decision with a rational system~\cite{daniel2017thinking}, which imagines counterfactual and adjusts the decision.

The key to bridge this gap lies in imitating the counterfactual thinking ability of humans, \emph{i.e., } learning a decision making procedure to serve for the testing phase. That is a procedure of: 1) constructing counterfactual samples for a target factual sample; 2) calling the trained language understanding model to make prediction for the counterfactual samples; and 3) comparing the counterfactual and factual samples to retrospect the model prediction.
However, the procedure is non-trivial to achieve for two reasons: 1) the space of counterfactual sample is huge since any variant from the target factual sample can be a counterfactual sample. It is thus challenging to search for suitable counterfactual samples that can facilitate the decision making. 2) The mechanism of how we retrospect the decision is still unclear, making it hard to be imitated.

Towards the target, we propose a \textit{Counterfactual Reasoning Model} (CRM), which is a two-phase procedure consisting a \textit{generation module} and a \textit{retrospection module}. In particular, given a factual sample in the testing phase, the generation module constructs representative counterfactual samples by imagining \textit{what would the content be if the label of the sample is $y$}. To imitate the unknown retrospection mechanism of humans, we build the retrospection module as a carefully designed deep neural network that separately compares the latent representation and the prediction of the factual and counterfactual samples. The proposed CRM forms a general paradigm that can be applied to most existing language understanding models without constraint on the format of the language understanding task. We select two language understanding tasks: SA and NLI, and test CRM on three representative models for each task. Extensive experiments on benchmark datasets validate the effectiveness of CRM, which achieves performance gains ranging from 5.1\% to 15.6\%.

The main contributions are as follow:
\begin{itemize}[leftmargin=*]
    \item We propose the \textit{Counterfactual Reasoning Model} to enlighten the language understanding model with counterfactual thinking.
    \item We devise a generation module and a retrospection module that are task and model agnostic.
    \item We conduct extensive experiments, which validate the rationality and effectiveness of the proposed method.
\end{itemize}
\section{Pilot Study}\label{sec:pilot}
Decisions are usually accompanied by confidence, a feeling of being wrong or right~\cite{boldt2019confidence}. From the perspective of model confidence, we investigate the performance of language understanding models across different testing samples. We estimate the model confidence on a sample as the widely used \textit{Maximum Class Probability} (MCP)~\cite{corbiere2019addressing}, which is the probability over the predicted class. A lower value of MCP means less confidence and ``hard'' sample. According to the value of MCP, we rank the testing samples in ascending order and split them into ten groups, \emph{i.e., } confidence level from 1 to 10.

Figure~\ref{fig:conf-acc} shows the performance of representative models over samples at different model confidence levels on the SA and NLI tasks (see Section~\ref{ssec:setting} for model and dataset descriptions). From the figures, we can observe a clear increasing trend of classification accuracy as the confidence level increases from 1 to 10 in all cases. In other words, these models fail to predict accurately for the hard samples. It is thus essential to enhance the standard inference with a more precise decision making procedure.
\begin{figure}[tbp]
	\centering
	\mbox{
	    \hspace{-0.15in}
		\subfigure[Sentiment analysis]{
			\label{fig:conf-acc-sa}
			\includegraphics[width=0.24\textwidth]{figures/sentiment_base_model.png}
		}
		\hspace{-0.15in}
		\subfigure[Natural language inference]{
			\label{fig:conf-acc-nli}
			\includegraphics[width=0.24\textwidth]{figures/nli_base_model.png}
		}
	}
	\vspace{-0.4cm}
	\caption{Prediction performance of the language understanding models over testing samples at different confidence levels.
	}
	\vspace{-0.3cm}
	\label{fig:conf-acc}
\end{figure}

\section{Methodology}
In this section, we first formulate the task of learning a decision making procedure for the testing phase (Section~\ref{ssec:formulation}), followed by introducing the proposed CRM (Section~\ref{ssec:crp}) and the paradigm of building language understanding solutions with CRM (Section~\ref{ssec:paradigm}).

\subsection{Problem Formulation}\label{ssec:formulation}
As discussed in the previous work~\cite{wu2020corefqa,li2020unified,li2019entity}, language understanding tasks can be abstracted as a classification problem where the input is a text and the target is to make decision across a set of candidates of interests.
We follow the problem setting with consideration of counterfactual samples~\cite{kaushik2019cfdata,liang2020col}, where the training data are twofold: 1) \textit{factual samples} $\mathcal{T} = \left\{({\bm{x}}, {{y}})\right\}$ where ${{y}} \in [1, C]$ denotes the class or the target decision of the text; ${\bm{x}} \in \mathbb{R}^{D}$ is the latent representation of the text, which encodes the textual contents\footnote{The input is indeed the plain text which is projected to a latent representation by an encoder (\emph{e.g., } a Transformer~\cite{jacob2019bert}) in the cutting edge solutions. We omit the encoder for briefness since focusing on the decision making.}.
2) \textit{counterfactual samples} $\mathcal{T}^* = \left\{({\bm{x}}^*_c, c) | ({\bm{x}}, {{y}}) \in \mathcal{T}, c \in [1, C]\&c \neq {{y}} \right\}$ where $({\bm{x}}^*_c, c)$ is a counterfactual sample in class $c$ corresponds to the factual sample $({\bm{x}}, {{y}})$\footnote{Given the labeled factual sample, counterfactual samples can be constructed either manually~\cite{kaushik2019cfdata} or automatically~\cite{chen2020counterfactual} by conducting minimum changes on ${\bm{x}}$ to swap its label from ${{y}}$ to $c$}.
We assume that a classification model (\emph{e.g., } BERT~\cite{jacob2019bert}) has been trained over the labeled data. Formally,
\begin{equation}
{\small
\begin{aligned}\label{eq:obj-model}
    \hat{\bm{\theta}} = \min_{\bm{\theta}} \sum_{\left({\bm{x}}, {{y}}\right) \in \mathcal{T} / \mathcal{T}^*} l({{y}}, f({\bm{x}} | \bm{\theta})) + \alpha \|\bm{\theta}\|,
\end{aligned}
}%
\end{equation}
where $\hat{\bm{\theta}}$ is the learned parameters of the model $f(\cdot)$
; $l(\cdot)$ is a classification loss such as cross-entropy~\cite{kullback1997information}, and $\alpha$ is a hyper-parameter to adjust the regularization.

The target is to build a decision making procedure to perform counterfactual reasoning when serving for the testing phase. Given a testing sample $\bm{x}$, the core is a policy of generating counterfactual samples and retrospecting the decision, which is formulated as:
\begin{equation}
{\small
\begin{aligned}
    \bm{y} =
    h\left(
        \bm{x}, \{\bm{x}^*\}
        | {\bm{\eta}}, \hat{\bm{\theta}}
    \right),~
    \{\bm{x}^*\} =
    g\left(
        \bm{x} \big| {\bm{\omega}}
    \right), \notag
\end{aligned}
}%
\end{equation}
$\bm{y} \in \mathbb{R}^C$ denotes the final prediction for the testing sample $\bm{x}$, which is a distribution over the classes; $\bm{x}^*$ is one of the generated counterfactual samples for $\bm{x}$. The generation module $g(\cdot)$ parameterized by $\bm{\omega}$ is expected to construct a set of representative counterfactual samples for the target factual sample, which provide signals for the retrospection module $h(\cdot)$ parameterized by $\bm{\eta}$ to retrospect the prediction $f\big(\bm{x} | \hat{\bm{\theta}}\big)$ given by the trained classification model. In particular, $h(\cdot)$ and $g(\cdot)$ will be learned from the factual and counterfactual training samples, respectively.

\subsection{Counterfactual Reasoning Model}\label{ssec:crp}
Figure~\ref{fig:framework} illustrates the process of CRM where the arrows in grey color represent the standard inference of trained classification model, and arrows in red color represent the retrospection with consideration of counterfactual samples.

\subsubsection{Retrospection Module}
We devise the retrospection module with one key consideration---distilling signals for making final decision by comparing both the latent representation and the prediction of the counterfactual samples with the factual sample. To achieve the target, we devise three key building blocks for  retrospection, which successively perform \textit{representation comparison}, \textit{prediction comparison}, and \textit{fusion} . In particular, the module first compares the representation of each counterfactual sample with the factual sample; then compares their predictions accordingly; and fuses the comparison across the counterfactual samples.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.45\textwidth]{figures/framework.png}
	\vspace{-0.3cm}
	\caption{Illustration of the proposed CRM.
	}
	\vspace{-0.5cm}
	\label{fig:framework}
\end{figure}
\paragraph{Representation comparison.}
Given a pair of counterfactual sample $\bm{x}^*$ and factual sample $\bm{x}$, we believe the signals meaningful for making final decision lie in the difference of the samples and how the difference affects the classification. To distill such signals, we devise the representation comparison block as $\bm{y}_{\Delta} = f(\bm{x} - \bm{x}^* | \hat{\bm{\theta}})$,
where $\bm{y}_{\Delta} \in \mathcal{R}^C$ denotes the prediction of the representation difference $\bm{x} - \bm{x}^*$ given by the trained classification model.
Note that we leverage the trained model to enlighten how the content difference affects the classification since the model is trained to capture the connection between the textual patterns and the classes.
It should be noted that we use a duplicate of the trained classification model for the representation comparison. That is to say, the training of the retrospection module will not affect the classification model.

\paragraph{Prediction comparison.} To retrospect the prediction $f(\bm{x} | \hat{\bm{\theta}})$, we devise a prediction comparison block to compare the predictions of each counterfactual and factual sample pair and distill patterns from $f(\bm{x} | \hat{\bm{\theta}})$, $f(\bm{x}^* | \hat{\bm{\theta}})$, and $\bm{y}_{\Delta}$. Inspired by the success of convolutional neural network (CNN) in capture local-region patterns, the block is devised as a CNN, which is formulated as:
\begin{equation}
{\small
\begin{aligned}\label{eq:hp}
    \bm{y}^* =
    \text{CNN}\left(
        f(\bm{x} | \hat{\bm{\theta}}), f(\bm{x}^* | \hat{\bm{\theta}}), \bm{y}_{\Delta}
    \right),
\end{aligned}
}%
\end{equation}
where $\bm{y}^*$ denotes the retrospected prediction when comparing to $\bm{x}^*$. In particular, a \textit{stack layer} first stacks the three predictions as a matrix, which serves as an ``image'' to facilitate ``observing'' patterns. Formally,
$\bm{Y} = \left[f(\bm{x} | \hat{\bm{\theta}}), f(\bm{x}^* | \hat{\bm{\theta}}), \bm{y}_{\Delta}\right]$
where $\bm{Y} \in \mathbb{R}^{C \times 3}$. $\bm{Y}$ is then fed into an 1D convolution layer to capture the intra-class patterns across the predictions, which is formulated as:
\begin{equation}
{\small
\begin{aligned}\label{eq:hpcnn}
    \bm{H} = \sigma(\bm{Y} * \bm{F}),~\bm{H}_{ij} = \sigma( \bm{Y}_{:i} \bm{F}_j),
\end{aligned}
}%
\end{equation}
where $\bm{F} \in \mathbb{R}^{3 \times K}$ denotes the filters in the convolution layer, and $\sigma(\cdot)$ is an activation function such as \textit{GELU}~\cite{hendrycks2016gaussian}. $\bm{Y}_{:i}$ and $\bm{F}_j$ represent the $i$-th row of $\bm{Y}$ and the $j$-th column of $\bm{F}$, respectively. The filter $\bm{F}_j$ can learn rules for conducting retrospection. For instance, a filter $[1, -1, 0]$ means deducting the prediction of the counterfactual sample from that of the factual sample. The output $\bm{H} \in \mathbb{R}^{C \times K}$ is then flattened as a vector and fed into a fully-connected (FC) layer to capture the inter-class patterns. Formally,
\begin{equation}\label{eq:hpfc}\small
    \bm{y}^* = \bm{W} flatten(\bm{H}) + \bm{b},
\end{equation}
where $\bm{W}$ and $\bm{b}$ are model parameters.

\paragraph{Fusion.} The target is to fuse the retrospected predictions $\{\bm{y}^*\}$ into a final decision $\bm{y}$. Inspired by the success of pooling function in reading out patterns, we devise the block as $\bm{y} = pooling(\{\bm{y}^*\})$. As the fusion is performed after the pairwise comparison, we term it as \textit{late fusion}.

\paragraph{Training.} We update the parameters of the retrospection module by minimizing the classification loss over the factual training samples, which is:
\begin{equation}
{\small
\begin{aligned}\label{eq:obj-retrospection}
    \hat{\bm{\eta}} = \min_{\bm{\eta}}
    & \sum_{\left({\bm{x}}, {{y}}\right) \in \mathcal{T}}

    l({y}, \bm{y}) + \lambda \|\bm{\eta}\|.
\end{aligned}
}%
\end{equation}
where $\lambda$ denotes the hyper-parameter to adjust the weight of the regularization term.

It should be noted that no existing research has uncovered the specific mechanism of retrospection in our brain, \emph{i.e., } the order of comparison and fusion is unclear. As such,
we further devise two fusion strategies: \textit{middle fusion} and \textit{early fusion}, which performs fusion within the CNN, \emph{i.e., } during comparison, and before the CNN, respectively.

\noindent $\bullet$ \textit{Middle fusion} performs aggregation between the convolution layer and the FC layer. This fusion first calculates the latent comparison signals $\bm{H}$ for each pair of counterfactual and factual samples according to Equation~\ref{eq:hpcnn}. The aggregated signals $pooling(\{\bm{H}\})$ are then fed into the FC layer (Equation~\ref{eq:hpfc}) to obtain the final decision $\bm{y}$.

\noindent $\bullet$ \textit{Early fusion} aggregates the counterfactual samples before performing comparison, which is formulated as $\tilde{\bm{x}}^* = pooling(\{\bm{x}^*\})$. In this way, the retrospection module is formulated as: $\bm{y} = \text{CNN}\left(f(\bm{x} | \hat{\bm{\theta}}), f(\tilde{\bm{x}}^* | \hat{\bm{\theta}}), f(\tilde{\bm{x}}^* - \bm{x} | \hat{\bm{\theta}}) \right).$

For all the three fusion methods, we can use either regular pooling function without parameter or parameterized pooling function~\cite{ying2018hierarchical} to enhance the expressiveness of the retrospection module. In our experiments, using a simple mean pooling achieves a performance that is comparable to the parameterized one in most cases (\emph{cf. } Table~\ref{tab:fusion}).

\subsubsection{Generation Module}
The target is to construct counterfactual samples that are informative for retrospecting the decision on the target factual sample $\bm{x}$. As the task involves making decision among
$C$ candidate classes, we believe that the key to generate representative counterfactual samples lies in imagining \textit{``what would the content be if the sample belongs to class $c$''}, \emph{i.e., } generating $C$ counterfactual samples $\{\bm{x}^*_c\}$. With the $C$ classes as the targets, the searching space of samples can also be largely narrowed down. Toward this end, we devise the generation module with two main considerations: 1) decomposing the factual sample $\bm{x}$ to distill contents irrelevant to the label of the sample $\bm{u} = d(\bm{x} | \bm{\omega})$; 2) injecting class $c$ into $\bm{u}$ to form the counterfactual sample $\bm{x}^*_c$.

\paragraph{Decomposition.} To distill $\bm{u}$, we need to recognize the connection between the content of the factual sample and each class. We thus account for class representations in the decomposition function. To align the sample space of the generation module with the retrospection module $h(\cdot)$ and the classification model $f(\cdot)$, we extract the parameters from the prediction layer of the trained classification model as the class representations. In particular, we extract the mapping matrix $\bm{W} \in \mathbb{R}^{C \times D}$ where the $c$-th row corresponds to class $c$. Note that we assume that the prediction layer has the same dimensionality as the latent representation, which is a common setting in most cutting edge language understanding models. The decomposition function is devised as a CNN to capture both the intra-dimension and inter-dimension connections between the factual sample and the classes.

\noindent $\bullet$ \textit{Stack layer.} The stack layer stacks the factual sample, class representations, and the element-wise product between sample and each class, which is formulated as:
$\bm{X} = [\bm{x}, \bm{W}^T, \bm{x} \odot \bm{W}^T]$.
$\bm{x} \odot \bm{W}^T \in \mathbb{R}^{D \times C}$ shed lights on how closely each dimension of $\bm{x}$ connect to each class, where large absolute value indicates closer connections.

\noindent $\bullet$ \textit{Convolution layer.} This layer uses 1D horizontal filters to learn patterns of deducting class relevant contents from the factual sample, which is formulated as $\bm{h} = pooling(\sigma(\bm{X} * \bm{F}^g))$.
$\bm{F}^g \in \mathbb{R}^{(2C + 1) \times L}$ denotes the filters where $L$ is the total number of filters. The output $\bm{h} \in \mathbb{R}^D$ is a hidden representation.

\noindent $\bullet$ \textit{FC layers.} We use two FC layers to capture the inter-dimension connections. Formally, $\bm{u} = \bm{W}^2 \sigma(\bm{W}^1 \bm{h} + \bm{b}^1) + \bm{b}^2$,
where $\bm{W}^2 \in \mathcal{R}^{D \times M}$, $\bm{W}^1 \in \mathcal{R}^{M \times D}$, $\bm{b}^2 \in \mathcal{R}^{D}$, and $\bm{b}^1 \in \mathcal{R}^{M}$ are learnable parameters. $M$ is a hyper-parameter to adjust the complexity of the decomposition function. Note that we can stack more layers to enhance the expressiveness of the function, whereas using two layers according to the universal approximation theorem~\cite{hornik1991approximation}.

We learn the parameters of the decomposition function from the counterfactual training samples by optimizing the following objective:
\begin{equation}
{\small
\begin{aligned}\label{eq:obj-generator}
    \min_{\bm{\omega}} \sum_{\left({\bm{x}}^*_c, c\right) \in \mathcal{T}^*}
    &r\big(
        {\bm{u}}^*_c, \tilde{\bm{u}}_c
    \big)
    +
    \gamma
    l\big(
        c, f({\bm{x}}^*_c - \bm{u}^*_c | \hat{\bm{\theta}})
    \big) \\
    &
    +
    r\big(
        {\bm{u}}, \tilde{\bm{u}}_c
    \big)
    +
    \gamma
    l\big(
        {y}, f({\bm{x}} - {\bm{u}} | \hat{\bm{\theta}})
    \big),
\end{aligned}
}%
\end{equation}
where ${\bm{u}}^*_c = d({\bm{x}}^*_c | \bm{\omega})$ and ${\bm{u}} = d({\bm{x}} | \bm{\omega})$ are the decomposition results of the counterfactual sample ${\bm{x}}^*_c$ and the corresponding factual sample ${\bm{x}}$; $\tilde{\bm{u}}_c = \frac{1}{2}({\bm{x}} + {\bm{x}}^*_c)$ denotes the target value of the decomposition. The two terms $r(\cdot)$ and $l(\cdot)$ are Euclidean distance~\cite{dattorro2010convex} and classification loss. By minimizing the two terms, we encourage the decomposition result: 1) to be close to the target value $\tilde{\bm{u}}_c$; and 2) if being deducted from the original sample (\emph{e.g., }, ${\bm{x}} - {\bm{u}}$), the classification cannot be influenced. $\gamma$ is a hyper-parameter to balance the two terms.

The rationality of setting $\tilde{\bm{u}}_c = \frac{1}{2}({\bm{x}} + {\bm{x}}^*_c)$ as the target class irrelevant content of ${\bm{x}}$ and ${\bm{x}}^*_c$ comes from the parallelogram law~\cite{nash2003generalized}. Note that this pair of samples belong to two different classes where a decision boundary (a hyperplane) lies between the two classes ${y}$ and $c$. Considering that the sample ${\bm{x}}$ corresponds to a vector in the hidden space, we can decompose the vector into two components that are orthogonal and parallel to the decision boundary, \emph{i.e., } ${\bm{x}}^*_c = {\bm{o}}^*_c + {\bm{p}}^*_c$ and ${\bm{x}} = {\bm{o}} + {\bm{p}}$. Since the two samples belong to different classes, their orthogonal components are in opposite directions and their addition will only retain the parallel components, which are irrelevant to judging the class between ${y}$ and $c$\footnote{Note that we normalize all samples to be unit vectors in the decomposition function. Moreover, inspired by~\cite{parascandolo2018learning}, we train a decomposition function for each class, \emph{i.e., } class-specific parameters $\hat{\bm{\omega}}_c$}.

\paragraph{Injection.} Accordingly, given a testing sample $\bm{x}$, we can inject the orthogonal components towards class $c$ via $\bm{x}^*_c = 2 * d(\bm{x} | \hat{\bm{\omega}}_c) - \bm{x}$, which is the imagined content of the sample if it belongs to class $c$. In this way, for each testing sample, we conduct the injection over all the classes and construct $C$ counterfactual samples $\{\bm{x}^*_c\}$, which are then used in the retrospection module\footnote{The generation module consists of $C$ decomposition functions $d(\bm{x} | \hat{\bm{\omega}}_c)$ and the non-parametric injection function.}.

\subsection{Learning Paradigm with CRM}\label{ssec:paradigm}
The existing work~\cite{kaushik2019cfdata,zeng2020counterfactual} for language understanding typically follows the standard learning paradigm, \emph{i.e., } training a classification model over labeled data. Applying the proposed CRM indeed forms a new learning paradigm for constructing language understanding solutions. Algorithm~\ref{algo:paradigm-crm} illustrates the procedure of the new paradigm.

\begin{algorithm}[h]\small
	\caption{Learning paradigm with CRM}
	\label{algo:paradigm-crm}
	\begin{algorithmic}[1]
		\Require Training data $\mathcal{T}$, $\mathcal{T}^*$.

	    /* Training */

	    \State Optimize Equation~\ref{eq:obj-model};  \algorithmiccomment{Classification model training}

	    \State Optimize Equation~\ref{eq:obj-generator}; \algorithmiccomment{Generation module training}

	    \State Optimize Equation~\ref{eq:obj-retrospection}; \algorithmiccomment{Retrospection module training}

	    \State Return $\hat{\bm{\theta}}$, ${\hat{\bm{\omega}}_c}$, and $\hat{\bm{\eta}}$.

		/* Testing */
		\State Calculate $f(\bm{x} | \hat{\bm{\theta}})$; \algorithmiccomment{Classification model inference}

		\For{$c = 1 \to C$}
		\State $\bm{x}^*_c = 2 * g(\bm{x} | \hat{\bm{\omega}}_c) - \bm{x}$; \algorithmiccomment{Generation}
		\EndFor

		\State Calculate $h(\bm{x}, \{\bm{x}^*_c\}|\hat{\bm{\eta}}, \hat{\bm{\theta}})$; \algorithmiccomment{Retrospection}
	\end{algorithmic}
\end{algorithm}
\setlength{\textfloatsep}{0.1cm}
\section{Experiments}\label{sec:exp}
We conduct experiments on two representative language understanding tasks, SA and NLI, to answer the following research questions:

\noindent $\bullet$ \textbf{RQ1:} To what extent counterfacutal reasoning improves language understanding?

\noindent $\bullet$  \textbf{RQ2:} How does the design of the retrospection module affect the proposed CRM?

\noindent $\bullet$  \textbf{RQ3:} How effective are the counterfactual samples generated by the proposed generation module?

\subsection{Experiment Settings}\label{ssec:setting}

\textbf{Datasets.} We adopt the same datasets in~\cite{kaushik2019cfdata} for both tasks. The SA data are reviews from IMDb, which are labeled as either positive or negative. For each factual review, the dataset contains a manually constructed counterfactual sample where the crowd workers are asked to manipulate the text to reverse the label with the constraint of no gratuitous change. NLI is a three-way classification task with two sentences as inputs and the target of detecting their relation within entailment, contradiction, and neutral. For each factual sample, four counterfactual samples are given, which are constructed by editing either the first or the second sentence with target relations different to the label of the factual sample.

\textbf{Classification models.} Owing to the extraordinary representational capacity of language model, fine-tuning pre-trained language model has become the emergent technique for solving language understanding tasks~\cite{jacob2019bert}. We select the widely used RoBERTa-base\footnote{\url{https://huggingface.co/roberta-base}.} and RoBERTa-large\footnote{\url{https://huggingface.co/roberta-large}.} for the consideration of the robustness of the RoBERTa~\cite{liu2019roberta} and our limited computation resources. For SA, we also test the classical Multi-Layer Perceptron (MLP)~\cite{teney2020+gs} with \textit{tf-idf} text features~\cite{schutze2008introduction} as inputs. For NLI, we further test RoBERTa-large-nli\footnote{\url{https://huggingface.co/roberta-large-mnli}.}, which has been fine-tuned on the large-scale MultiNLI dataset~\cite{williams2018broad}.

\textbf{Baselines.} As the proposed CRM leverages counterfactual samples, we compare CRM with three representative methods using counterfactual samples in language understanding tasks: 1) \textbf{+CF}~\cite{kaushik2019cfdata}, which uses counterfactual samples as data augmentation for model training; 2) \textbf{+GS}~\cite{teney2020+gs}, which compares the factual and counterfactual samples in model training through regularizing their gradients; and 3) \textbf{+CL}~\cite{liang2020col}, which compares the factual and counterfactual samples through a contrastive loss. Moreover, we report the performance of the testing model under \textit{Normal Training}, \emph{i.e., } training over factual samples only.

\textbf{Implementation.} We implement the proposed CRM with PyTorch 1.7.0 based on Hugging Face Transformer\footnote{\url{https://github.com/huggingface/transformers}.}, which is released at: \url{https://github.com/fulifeng/Counterfactual_Reasoning_Model}. In all cases, we follow the setting of +CF for training the classification model, which is a standard fine-tuning in~\cite{liu2019roberta}. We then use \textit{adam}~\cite{kingma2014adam} with learning rate of 0.001 to optimize the retrospection module and the generation module. For the retrospection module, we set the number of filters in the convolution layer $K$ as $10$, the weight for regularization $\lambda$ as $0$. As to the generation module, we set the number of convolution filters as $10$, the size of the hidden layer $M$ as $256$, and the weight for balancing Euclidean distance and classification loss $\gamma$ as $15$. We report the average classification accuracy over 5 different runs. For each repeat, we train the model with 20 epochs and select the model with the best performance on the validation set.

\begin{table*}[tbhp]
\centering
    \begin{tabular}{l|c|ccc|c|c}
    \hline
    \multicolumn{7}{c}{\cellcolor[HTML]{C0C0C0}Sentiment Classification} \\ \hline
    Backbone & Normal Training & +CF & +GS & +CL & +CRM & \textit{RI} \\ \hline
    MLP & 86.9$\pm$0.5 & 85.3$\pm$0.3 & 84.6$\pm$0.4 & - & \textbf{98.6$\pm$0.2} & 15.6\% \\
    RoBERTa-base & 93.2$\pm$0.6  & 92.3$\pm$0.7 & 92.2$\pm$0.9 & 91.8$\pm$1.1 & \textbf{97.5$\pm$0.3} & 5.7\% \\
    RoBERTa-large & 93.6$\pm$0.6 & 93.4$\pm$0.4 & 93.1$\pm$0.5 & 94.1$\pm$0.4 & \textbf{98.2$\pm$0.3} & 5.1\% \\ \hline
    \multicolumn{7}{c}{\cellcolor[HTML]{C0C0C0}Natural Language Inference} \\ \hline
    Backbone & Normal Training & +CF & +GS & +CL & +CRM & \textit{RI} \\ \hline
    RoBERTa-base & 83.5$\pm$0.8 & 83.4$\pm$0.9 & 83.8$\pm$1.7 & 84.1$\pm$1.1 & \textbf{91.5$\pm$1.6} & 9.7\% \\
    RoBERTa-large & 87.9$\pm$1.7 & 85.8$\pm$1.2 & 86.2$\pm$1.2 & 86.5$\pm$1.6 & \textbf{93.8$\pm$1.9} & 9.3\% \\
    RoBERTa-large-nli & 89.4$\pm$0.7 & 88.2$\pm$1.0 & 87.2$\pm$1.4 & 88.2$\pm$1.0 & \textbf{94.4$\pm$1.2} & 7.1\% \\ \hline
    \end{tabular}%
\vspace{-0.2cm}
\caption{Performance of the proposed CRM (Early Fusion) and baselines on the SA and NLI tasks. RI means the relative performance improvement achieved by +CRM over the classification model without CRM, \emph{i.e., } +CF.}
\vspace{-0.4cm}
\label{tab:perf_comp}
\end{table*}

\subsection{Performance Comparison (RQ1)}
We first use the handcrafted counterfactual samples to demonstrate the effectiveness of counterfactual reasoning in the inference stage of language understanding model, which can be seen as using a golden standard generation module to provide counterfactual samples for the retrospection module. Note that we do not use the label of counterfactual samples in the testing set. Table~\ref{tab:perf_comp} shows the performance of the compared methods on the two tasks. From the table, we observe that:
\begin{itemize}[parsep=0pt, leftmargin=*]
    \item +CRM largely outperforms all the baseline methods in all cases. As compared to +CF, the same classification model without CRM in the testing phase, +CRM achieves relative performance improvement up to 15.6\%. The performance gain is attributed to the retrospection module, which justifies the rationality and effectiveness of incorporating counterfactual thinking into the inference stage of language understanding model. In other words, by comparing the factual sample with its counterfactual samples, the retrospection module indeed makes more accurate decisions.

    \item On the SA task, a huge gap (85.3 $\leftrightarrow$ 93.4) lies in the performance of the shallow model MLP and the deep RoBERTa-base/RoBERTa-large. When applying +CRM, MLP achieves a performance that is comparable to the deep models. The result indicates that counterfactual reasoning can compensate for the disadvantages caused by the insufficient model representational capacity. In addition, the result reflects that CRM brings cognitive ability beyond recognizing textual patterns. If the retrospection module only facilitates capturing the correlation between textual patterns and classes, such simple model cannot bridge the huge gap of representational capacity between MLP and RoBERTa-large.
    \item The performance of baseline methods are comparable to each other in most cases, \emph{i.e., } incorporating counterfactual samples into model training does not necessarily improve the testing performance on factual samples. This result is consistent with~\cite{kaushik2019cfdata}, which is reasonable since these methods are devised for enhancing the generalization ability, especially for the out-of-distribution testing samples, which can sacrifice the performance on normal testing samples. Besides, the result indicates that training with counterfactual samples is insufficient for achieving counterfactual thinking, which reflects the rationality of enhancing the inference paradigm with a decision making procedure.
\end{itemize}

\begin{figure}[h]
	\centering
	\vspace{-0.3cm}
	\mbox{
	    \hspace{-0.15in}
		\subfigure[Sentiment analysis]{
			\label{fig:conf-comp-sa}
			\includegraphics[width=0.24\textwidth]{figures/sentiment-conf-comp.png}
		}
		\hspace{-0.15in}
		\subfigure[Natural language inference]{
			\label{fig:conf-comp-nli}
			\includegraphics[width=0.24\textwidth]{figures/nli-conf-comp.png}
		}
	}
	\vspace{-0.4cm}
	\caption{Prediction performance of +CF and +CRM over testing samples at different confidence levels.
	}
	\vspace{-0.1cm}
	\label{fig:conf-comp}
\end{figure}
\paragraph{Performance on hard samples.} Furthermore, we investigate whether the proposed CRM facilitate dealing with hard samples. Recall that we split the testing samples into 10 groups according to the confidence of the classification model, \emph{i.e., } +CF (\emph{cf. } Section~\ref{sec:pilot}). We perform group-wise comparison between +CF and +CRM. Figure~\ref{fig:conf-comp} shows the performance of all the classification models with +CF and +CRM. From the figures, 1) we observe that the performance of +CRM is stable across different confidence levels, whereas the performance of the classification model shows a clear decreasing trend as the confidence level decreases from 10 to 1. The result indicates that the retrospection module is insensitive to the confidence of the classification model. 2) In all cases, +CRM achieves the largest performance gain at the first group with confidence level of 1, \emph{i.e., } the hardest group to the classification model. For instance, the improvement reaches 85.7\% on the RoBERTa-base model for the NLI task. The large improvements further justifies the effectiveness of the retrospection module, \emph{i.e., } comparing the prediction of factual samples to counterfactual samples indeed facilitates dealing with hard samples.

\begin{table}[htbp]
\centering
    \begin{tabular}{l|c|c}
    \hline
    \multicolumn{3}{c}{\cellcolor[HTML]{C0C0C0}Sentiment Classification} \\ \hline
    Backbone & Implicit & +CRM \\ \hline
    MLP & 79.3$\pm$0.2 & \textbf{98.6$\pm$0.2} \\
    RoBERTa-base & 94.7$\pm$0.6 & \textbf{97.5$\pm$0.3} \\
    RoBERTa-large & 98.0$\pm$0.4 & 98.2$\pm$0.3 \\ \hline
    \multicolumn{3}{c}{\cellcolor[HTML]{C0C0C0}Natural Language Inference} \\ \hline
    Backbone & Implicit & +CRM \\ \hline
    RoBERTa-base & 81.9$\pm$3.5 & \textbf{91.5$\pm$1.6} \\
    RoBERTa-large & 87.4$\pm$2.2 & \textbf{93.8$\pm$1.9} \\
    RoBERTa-large-nli & 88.8$\pm$1.6 & \textbf{94.4$\pm$1.2} \\ \hline
    \end{tabular}%
\vspace{-0.2cm}
\caption{Performance comparison of implicit modeling (end-to-end model) and explicit modeling (CRM) of counterfactual thinking.}
\vspace{-0.4cm}
\label{tab:implicit_VS_CRM}
\end{table}
\paragraph{CRM \textit{V.S.} implicit modeling.} According to the uniform approximation theorem~\cite{hornik1991approximation}, the CRM can also be approximated by a deep neural network. We thus investigate whether counterfactual thinking can be learned in an implicit manner. In particular, we evaluate a model that takes both the factual sample and counterfactual samples as inputs to make prediction for the factual one. Table~\ref{tab:implicit_VS_CRM} shows the performance, where we have the following observations: 1) The implicit modeling performs much worse than the proposed CRM in most cases, which justifies the effectiveness of the retrospection module and the rationality of modeling comparison explicitly. 2) On the NLI task, RoBERTa-base+CRM outperforms RoBERTa-large (implicit), which means that the superior performance of CRM is not because of the additional model parameters introduced by the retrospection module, but the explicit comparison between factual and counterfactual samples.

\subsection{In-depth Analysis}
\begin{table*}[]
\centering
\begin{tabular}{l|c|cc|cc|cc}
\hline
Backbone           & +CF & EF & \textit{RI} & LF & \textit{RI} & MF & \textit{RI} \\ \hline \hline
RoBERTa-base       & 83.4$\pm$0.9 & 91.5$\pm$1.6 & 9.7\% &      \textbf{92.8$\pm$1.8} & 11.3\% &         89.6$\pm$2.0 & 7.4\% \\
RoBERTa-large      & 85.8$\pm$1.2 & 93.8$\pm$1.9 & 9.3\% &      \textbf{95.3$\pm$0.7} & 11.1\% &     93.4$\pm$1.7 & 8.9\% \\
RoBERTa-large-nli & 88.2$\pm$1.0 & 94.4$\pm$1.2 & 7.1\% &      93.8$\pm$0.4 & 6.4\% &      \textbf{94.7$\pm$1.3} & 7.4\% \\ \hline
\end{tabular}
\vspace{-0.2cm}
\caption{Performance of the proposed CRM based on early fusion (EF), late fusion (LF), or middle fusion (MF) on the NLI task. RI represents the relative performance improvement over the +CF method.}
\vspace{-0.2cm}
\label{tab:fusion}
\end{table*}
\paragraph{Effects of retrospection module design (RQ2).}
Note that the order of comparison and fusion in the retrospection mechanism of us humans is still unclear. We investigate how the fusion strategies influence the effectiveness of the proposed CRM. Table~\ref{tab:fusion} shows the performance of CRM based on the early fusion (EF), late fusion (LF), and middle fusion (MF) on the NLI task. We omit the comparison on the SA task since the dataset only has one counterfactual sample for the target factual sample. For both EF and LF, we use the mean pooling as the pooling function. As to MF, we use a pooling function that is equipped with self-attention~\cite{vaswani2017attention}. The reasons of this setting are twofold: 1) using mean pooling will make LF and MF equivalent since the FC layer in the retrospection module is a linear mapping. Note that LF performs pooling after the FC layer, while the pooling function of MF is just before the FC layer. 2) The comparison between the LF and MF can thus shed light on whether parameterized pooling function can benefit the retrospection.

From the table, we can observe that, in most cases, CRM based on different fusion strategies achieve performance comparable to each other. It indicates that the retrospection is insensitive to the order of fusion and the comparison between counterfactual and factual samples. Considering that MF with mean pooling is equivalent to LF, we can see that the benefit of parameterized pooling function is limited. In particular, MF only performs better than LF on one of the three testing models.

\paragraph{Effects of generation module (RQ3).}
We then investigate whether the proposed generation module constructs useful counterfactual samples for retrospection. We train and test the retrospection module (using EF) with the generated samples on RoBERTa-large on the SA task. We omit the experiments of other settings for saving computation resources. In this way, the model achieves an accuracy of 94.5 which is better than +CF (93.4) but worse than +CRM with manually constructed counterfactual samples (98.2) (\emph{cf. } Table~\ref{tab:perf_comp}). The result indicates that the generated samples indeed facilitate the retrospection while the generation quality can be further improved. Moreover, on the testing samples at confidence level of 1, using the generated samples achieves an accuracy of 81.3 which is much better than +CF (70.8) (\emph{cf. } Figure~\ref{fig:conf-comp}). The generated samples indeed benefit the decision making over hard testing samples.
\section{Related Work}
\textbf{Counterfactual sample.} Constructing counterfactual samples has become an emergent data augmentation technique in natural language processing, which has been used in a wide spectral of language understanding tasks, including SA~\cite{kaushik2019cfdata,yang2020generating}, NLI~\cite{kaushik2019cfdata}, named entity recognition~\cite{zeng2020counterfactual} question answering~\cite{chen2020counterfactual}, dialogue system~\cite{zhu2020counterfactual}, vision-language navigation~\cite{fu2020counterfactual}. Beyond data augmentation under the standard supervised learning paradigm, a line of research explores to incorporate counterfactual samples into other learning paradigms such as
adversarial training~\cite{zhu2020counterfactual,fu2020counterfactual,teney2020+gs} and contrastive learning~\cite{liang2020col}. This work lies in an orthogonal direction that incorporates counterfactual samples into the decision making procedure of model inference.

\textbf{Counterfactual inference.} A line of research attempts to enable deep neural networks with counterfactual thinking by incorporating counterfactual inference~\cite{yue2021counterfactual,wang2020click,niu2020counterfactual,tang2020long,feng2020should}. These methods perform counterfactual inference over the model predictions according to a pre-defined causal graph. Due to the requirement of causal graph, such methods are hard to be generalized to different tasks. Our method does not suffer from such limitation since working on the counterfactual samples which can be generated without a comprehensive causal graph.

\textbf{Hard sample.} A wide spectral of machine learning techniques are related to dealing with the hard samples in language understanding. For instance, adversarial training~\cite{khashabi2020more} enhances the model robustness against perturbations and attacks, which are hard samples for normally trained models. Debiased training~\cite{tu2020empirical,utama2020mind} eliminates the spurious correlation or bias in training data to enhance the generalization ability and deal with out-of-distribution samples. In addition to the training phase, a few inference techniques might improve the model performance on hard samples, including posterior regularization~\cite{srivastava2018zero} and causal inference~\cite{yu2020counterfactual,niu2020counterfactual}. However, both techniques require domain knowledge such as prior or causal graph tailored for specific applications. On the contrary, this work provides a general paradigm that can be used for most language understanding tasks.
\section{Conclusion}
In this work, we pointed out the issue of standard inference of existing language understanding models. We proposed a Counterfactual Reasoning Model which empowers the trained model with a high-level cognitive ability, counterfactual thinking. By applying the proposed CRM, we formed a new paradigm for building language understanding solutions. We conducted extensive experiments, which validate the effectiveness of our proposal, especially in dealing with hard samples.

This work opens up a new research direction about the decision making procedure in testing phase. In the future, we will explore sequential decision procedure to resolve the constraint on the number of constructed counterfactual samples. In addition, we will investigate generation module for language understanding with unsupervised generative techniques~\cite{sauer2021counterfactual}.
\section{Reproduction}\label{appsec:hyper_parameter}
In this section, we elaborate the details for reproducing the experiment results.

\subsection{Hyper-parameters of baselines}
We fine-tuned the BERT-based models from the pre-trained Chinese RoBERTa with Whole Word Masking implemented by Huggingface Transformers \cite{Wolf2019HuggingFacesTS} and \cite{cui-2020-revisiting}. For ColBERT, we adopt the original code on Github \footnote{\url{https://github.com/stanford-futuredata/ColBERT}.}. The optimizers for all models are AdamW \cite{loshchilov2017decoupled} implemented by Huggingface Transformers.
The hyper-parameters for all compared methods are in Table \ref{tab::param_bsln}.
``lr'', ``bsize'', ``accum'' stands for learning rate, batch size, and the number of gradient accumulation steps.
In addition,
``scheduler'' indicates the usage of the linear scheduler which linearly increases the learning rate from 0 during warmup steps and linearly decreases the learning rate to 0 until trained max steps.

\begin{table}[h]
\centering
\resizebox{0.48\textwidth}{!}{
\begin{tabular}{lcccccc}
\multicolumn{7}{c}{\cellcolor[HTML]{C0C0C0}Metal} \\
Method & lr & bsize & accum & scheduler & warmup steps & max step \\ \hline \hline
Cla\_M & 1e-5 & 32 & 2 & no & - & - \\
Cla\_S & 1e-4 & 256 & 4 & yes & 150 &  1500 \\ \hline
ColBERT & 1e-4 & 512 & 8 & yes & 100 & 1000 \\
Ret & 1e-4 & 256 & 2 & yes& 150 & 1500 \\
Ret\_QE & 1e-4 & 512 & 8 & yes & 150 & 1500 \\ \hline
\multicolumn{7}{c}{\cellcolor[HTML]{C0C0C0}Agriculture} \\
Method & lr & bsize & accum & scheduler & warmup steps & max step \\ \hline \hline
Cla\_M & 1e-5 & 32 & 2 & no & - & - \\
Cla\_S & 1e-4 & 256 & 2 & yes & 100 & 1000 \\ \hline
ColBERT & 1e-5 & 64 & 2 & yes & 100 & 1000 \\
Ret & 1e-4 & 256 & 2 & yes & 200 & 2000 \\
Ret\_QE & 1e-4 & 128 & 2 & yes & 300 & 3000 \\ \hline
\multicolumn{7}{c}{\cellcolor[HTML]{C0C0C0}Chemical} \\
Method & lr & bsize & accum & scheduler & warmup steps & max step \\ \hline \hline
Cla\_M & 1e-5 & 32 & 2 & no & - & - \\
Cla\_S & 1e-5 & 128 & 2 & yes & 100 & 1000 \\ \hline
ColBERT & 1e-4 & 128 & 4 & yes & 100 & 1000 \\
Ret & 1e-4 & 256 & 2 & yes & 150 & 1500 \\
Ret\_QE & 1e-4& 128 & 2 & yes & 100 & 1000 \\ \hline
\end{tabular}
}
\caption{Hyper-parameters for document classification and document retrieval methods.}
\label{tab::param_bsln}
\end{table}

As can be seen from the table, we did not use scheduler for Cla\_M since Cla\_M is trained very fast and can easily reach good performance.
It should be noted that the training data of Cla\_M is the annotated positive news and the original news from the analyst report, which are labeled by their corresponding queries. Table \ref{tab::clam_acc} shows the classification accuracy of Cla\_M \emph{w.r.t. } the classification on the testing period, which validates the ability of Cla\_M to differentiate news influencing different assets. In addition, due to the consideration of query expansion, Ret\_QE has two additional hyper-parameters where the number of candidate terms for query expansion and the number for expanded terms are set as 30 and 15, respectively.

\begin{table}[htbp]
\centering
\resizebox{0.3\textwidth}{!}{
\begin{tabular}{lccc}
   & Metal & Agriculture & Chemical \\ \hline \hline
Acc & 0.97 & 0.936 & 0.954\\ \hline
\end{tabular}
}
\caption{Classification accuracy of Cla\_M on the testing set.}
\label{tab::clam_acc}
\end{table}

Furthermore, the proposed hybrid method has an additional hyper-parameter $\lambda$, which adjust the contribution of the classification model. Table \ref{tab::lambda} shows the selected value of $\lambda$ for hybrid models of each dataset.
\begin{table}[htbp]
\centering
\resizebox{0.2\textwidth}{!}{
\begin{tabular}{lcc}
   & Ret\_QE & Ret\\ \hline \hline
Metal & 3 & 0.5 \\
Agriculture & 0.5 & 0.5  \\
Chemical & 0.5& 1 \\ \hline
\end{tabular}
}
\caption{$\lambda$ for hybrid models.}
\label{tab::lambda}
\end{table}

Lastly, the All\_Data in the study of transfer learning is trained and validated on the aggregation of three datasets. That is to say, the training (validation) set of the three datasets are aggregated to be the training (validation) set. Upon All\_Data, further fine-tuning on separate datasets becomes All\_Data\_FT. Table~\ref{tab::transfer} shows the hyper-parameters of the fine-tuning procedure for these methods.

\begin{table}[htbp]
\centering
\resizebox{0.4\textwidth}{!}{
\begin{tabular}{lccc}
   &lr & bsize & accum\\ \hline \hline
ALL\_DATA & 1e-5 & 256 & 4 \\
ALL\_DATA\_FT(Metal) & 1e-5 & 256 & 4  \\
ALL\_DATA\_FT(Agriculture) & 1e-5 & 256 & 4  \\
ALL\_DATA\_FT(Chemical) & 1e-6 & 128 & 2 \\ \hline
\end{tabular}
}
\caption{Hyper-parameters for the study of transfer learning.}
\label{tab::transfer}
\end{table}

It should be noted that the value of hyper-parameters are selected according to the best Recall@10 (\emph{i.e., } Rec10) on validation set, since our goal is to provide as much positive news by only looking at the top few news. It should be noted that, when two combinations tie on Rec10, we select the one with higher MAP.

redpajama_set_name: RedPajamaArXiv