N1QL runs sequentially or parallel?
In my real time app, when client socket get message it send an acknowledge message and according to that message status get updated in database. When multiple client simultaneously sends ack then simultaneous update queries get executes to update status. But I got these queries callback sequentially, second query executes only after first execution completes.


text:
Home Head Start Flexibility Important When Transporting Head Start Students During Pandemic
Head StartNews
Flexibility Important When Transporting Head Start Students During Pandemic
By Taylor Hannon
The U.S. Office of Heard Start shared best practices during a webinar attended by 1,650 people on Wednesday, with the goal of alleviating some of the concerns about once again transporting preschool students, especially those with disabilities.
Head Start regulations require that at least 10 percent of the infants, toddlers and preschool children served from low-income families have disabilities eligible for services included in the Individuals with Disabilities Education Act. Russell Newbold, the transportation services manager for Community Council of Idaho, which operates Head Start programs in the state, discussed some best practices for agencies and school districts that provide transportation to these children.
However, he reiterated that these should only be considered best practices and not requirements. The information coming from the Centers for Disease Control and Prevention and states are only recommendations, he reminded. He also advised that each district or agency that provides transportation should develop plans and seek advice from medical experts and local state and health officials in their areas.
He said when transporting students on a Type-A small school bus, students coming into contact with the driver is almost inevitable, as there is little space for students to walk when boarding the bus and proceeding down the aisle to their seats. Because of that, he said drivers can wear face masks and shields, but they shouldn't wear anything that obstructs their vision when driving.
He noted that the driver should already be on the bus when students board unless they must help a child who is in a wheelchair. In all other instances, he said having the driver outside when students are boarding is an unacceptable solution.
Another hot topic has centered on installing plexiglass or other barriers on school buses to separate the driver behind the wheel and the students. He explained that these materials haven't been crash-tested, and districts considering these barriers should first confer with their school bus dealers and manufacturers on whether they can or should be installed.
Related: Texas Advises School Districts That Barriers Are Prohibited on School Buses
He also said that younger students shouldn't be using hand sanitizer without adult supervision. While hand sanitizing is a more feasible option than providing handwashing stations on buses, he suggested that other options could include providing baby wipes to students, as hand sanitizer could be dangerous if ingested.
However, Marco Beltran, a program specialist for special populations at the Office of Head Start, added that the risks of students not using hand sanitizer are far greater.
Newbold noted that another important aspect to consider is the feasibility of temperature-checking every staff member and student, every day and before every run. He said his team purchased no-contact thermometers that staff members use to check each other for COVID-19 symptoms. He said if that if an employee is suspected of being ill, they should be instantly sent home and school districts and Head Start agencies should modify their bus routes accordingly.
For temperature-checking students, however, Newbold said changes must first be made to the location of bus stops. He recommended that bus stops should no longer be located on city streets and instead should be moved to safer locations, like parking lots or parks, where permitted. He said there needs to be enough room for social distancing at the stops and adequate space for monitors to test the students before they board.
Newbold added that because temperature-checking students could take at least an additional 60 to 90 seconds per student, school buses can't be stopped on the street blocking traffic that long. He said door-to-door services might not even be feasible, which could put additional responsibility on the parents accompany their children to and from safer pickup and dropoff locations.
Beltran advised that an effective screening tool is essential to maintaining health and safety in any operation. He advised officials to spend sufficient time researching the screening tool that would work best for them.
A Denver Public Schools transportation employee checks his coworker's temperature with a contactless thermometer.
Newbold further discussed the importance of social distancing at bus stops and communicating new procedures with parents. He noted that with social distancing properly adhered to, a district shouldn't need to suspend the entire route if only one student shows symptoms when their temperature is taken. Instead, that specific student can be removed.
But if social distancing is not adhered to, Newbold added that it will be up to local school districts and agencies if they want to continue to transport all the students on the route. Guidance regarding contact tracing of students or transportation staff after a COVID-19 diagnosis was not discussed during the webinar.
One way to accomplish social distancing is by working with city officials to affix properly spaced colored markings on sidewalks, similar to those used in grocery stores and other businesses. He advised assigning each marking to a student or family.
Chris Allen, assistant program director for the Community Council of Idaho, reminded listeners that just because a young child has a fever or displays other symptoms doesn't mean they have COVID-19. Instead, a toddler could be teething, for example. He advised talking with the parents before routes begin to determine if the child might have other physical conditions before deciding they shouldn't be transported.
Another option Newbold discussed is to keep students who are in the same class together on the same bus. That could minimize the risk of exposure to other children throughout the entire program.
Regardless, Newbold said there will need to be multiple bus runs to adhere to the social distancing guidelines. He said his operation is currently seating one child per bench, offset between the aisle and window, and loading from the rear of the bus to front, and unloading from the front to the rear. If students are from the same household, Newbold said they can be seated together.
Allen added that the children in rear-facing and forward-facing car seats, however, should always be seated at the front of the bus.
Following social distancing requirements and seating one student in each row of a 36-passenger Type A school bus would limit routes to 15 student riders, he noted, adding that no more than two students in forward-facing or rear-facing car seats should be transported at a time.
When loading the bus, Newbold advised that young children may not understand why they can't sit with their friends, and the social isolation could cause anxiety. He said districts, agencies and bus drivers could engage students by placing stickers containing the child's name on each seat or the bus floor to indicate where to sit. He added that any additional communication might help alleviate child stress levels.
Related: Texas School Districts Discuss CDC Guidelines for Transportation After Coronavirus
Related: No 'Easy Button' Available to Student Transporters in Response to Coronavirus
Related: One Aide Per School Bus Among California Guidelines for School Reopening
Related: Finding Direction
If a student needs to be seated in a child safety restraint system, Allen said that adhering to the six-foot distancing guidelines won't be possible. He said using gloves is one solution when picking up a child, but Amanda Bryans, division director at the Office of Head Start, said there is controversy over gloves. She pointed out that a driver or monitor will likely touch multiple children and potentially spread germs to each one of them. She recommended using hand sanitizer between each child interaction.
However, Bryans noted that children shouldn't get the feeling that adults are afraid of them. Instead, she said student transporters should still show the children they care for them but at the same time be more aware of proper hygiene and handwashing practices.
One thing that Beltran stressed was to not discipline or yell at students who aren't socially distancing. Instead, he advised finding new ways to redirect their actions.
Allen noted that each day provides a learning moment, and what works one day might not work the next. He advised the webinar attendees to be flexible and adjust operations as new information is released.
Previous articleCalAmp Introduces Bus Guardian: An Essential COVID-19 Student Transportation Safety Solution Featuring Contact Tracing And Hygiene Verification
Next articleTyler Technologies Launches Bus Attendance Solution

redpajama_set_name: RedPajamaCommonCrawl


A recent fire at one of its Syncrude upgrader facilities means Suncor will see less-than-optimal earnings from its oil sands operations for the second year in a row.
Much of the Canadian oil sands world was rocked by the devastating wildfires that hit the Fort McMurray region of Alberta last year. For Suncor Energy (NYSE:SU), that meant a significant drop in production and earnings on top of efforts to bring its employees' lives and operations back to normal. This quarter, Suncor experienced another fire-related incident that will likely impact its earnings for the rest of 2017.
Management doesn't think it is a huge deal, though, because it is still planning on returning a huge chunk of cash back to investors this year. Let's take a look at the company's latest results to see how the company will be able to overcome the challenges of its most recent incident and still deliver for shareholders.
DATA SOURCE: SUNCOR ENERGY EARNINGS RELEASE. *FIGURES IN MILLIONS, EXCEPT PER-SHARE DATA. ALL RESULTS WERE CONVERTED TO U.S. DOLLARS BASED ON FOREIGN EXCHANGE RATES FOR THE GIVEN QUARTERS PROVIDED BY SUNCOR IN THE EARNINGS RELEASES.
This past quarter saw a continuation of several themes that have been playing out at Suncor for a while now. The company is making big strides in improving its profitability by growing production at a rapid clip, as well as controlling costs.
These efforts were put to the test when a fire broke out at its Mildred Lake oil sands upgrader facility in Fort McMurray. The failure, which happened in mid-March, resulted in a shutdown of the facility and decreased utilization rates for Suncor's Syncrude operations from 89% in the prior quarter to just 75%. Typically, a utilization rate that low would lead to a loss as the high fixed costs associated with oil sands require high utilization rates for operations to be profitable. Thanks to management's efforts to cut costs, though, the company was able to still turn a decent profit on its Syncrude operation.
For the quarter, Suncor's oil sands operating costs per barrel were $17.14, a rather remarkable 20% decline from the first quarter in 2015. Production costs that low should make some investors reconsider the idea that oil sands are a high-cost source of oil.
Notable among Suncor's published results are its refining and marketing earnings. The large year-over-year gain was a result of two items: the sale of the Petro-Canada Lubricants business to HollyFrontier, and an $178 million gain resulting from the company's first-in, first-out accounting method. Both are one-time occurrences, so don't consider this a sustainable earnings rate for refining and marketing.
DATA SOURCE: SUNCOR ENERGY EARNINGS RELEASE. CHART BY AUTHOR.
Total production came in at 725,100 barrels of oil equivalent per day. That's a slight decrease from the prior quarter, but still a 5% increase from the prior year. Production was slightly down from the fourth quarter because of the Mildred Lake fire. Management has decided to include some basic turnaround maintenance at Mildred Lake as part of the repairs to mitigate the impact of production declines.
Management lowered its Syncrude guidance from 150,000-165,000 barrels per day to 135,000-150,000 barrels per day as a result of the outage. Those are partially offset by higher guidance for Suncor's conventional production portfolio.
First oil for its two largest projects -- Fort Hills and Hebron -- is still on track for the end of 2017.
Thanks to improving operations, reliable cash flows, and a stronger balance sheet, management has enacted a $1.5 billion share-repurchase program that it expects to complete by the end of the year.
Our Oil Sands operations and offshore assets posted impressive production results and continued low operating costs, generating industry-leading cash flow per barrel. Our Refining and Marketing segment also delivered another strong quarter, contributing to companywide total funds from operations of over [$1.5] billion.
The fire at the Mildred Lake facility looks like it will be a large blemish on what was supposed to be a stellar year for the company. Even with lower output from the Syncrude operations, management still seems to think that Suncor will produce strong enough returns to justify a sizable share repurchase program. That decision is, for the most part, a result of several major projects coming to completion by the end of the year, and management has indicated it doesn't plan on making a major oil sands investment for 10 years or more.
Luckily for Suncor, oil sands operations don't require as much capital spending to keep production levels steady, so this could be a major inflection point at which Suncor pivots from an intensive capital spending company to one that throws off lots of cash to shareholders. If that is the case, then Suncor may be worth watching over the next year or so to see what it does once Fort Hills and Hebron are complete.


PlayStation Now is streaming service from Sony, allowing PS3-playing games (currently the collection contains more than 400 names) on different platforms for a monthly fee of $ 20. On the eve of the Japanese company has announced that soon the service will be available on computers running Windows. First, the Windows-version of PS Now will be launched in some parts of Europe, and later reach and North America.
In addition, the company introduced a USB-adapter that will connect the DualShock 4 to the computer over a wireless connection and provide access to all functions of the controller, if the game application supports these functions. In addition, the new adapter can be used to play via Remote Play on PC or Mac.
DualShock 4 USB Wireless Adaptor will be available in September for $ 25, but you can do without it.


"Ekecheiria" or the Olympic Truce dates back to 9th century BC Greece. The first truce was a treaty between three kings, Iphitos of Elis, Cleosthenes of Pisa and Lycurgus of Sparta, to ensure the immunity of the sanctuary of Olympia and the region of Elis. During this truce period, athletes, artists and their families, and ordinary pilgrims could travel safely to take part in or watch the Olympic Games and then return to their respective countries. Taking account of the new political realities of which sport and the Olympic Games form a part, how was the Truce integrated into the modern Olympic Games? Find out the answer by consulting this document.


T H E S I G N E T C O L L E C T I O N is inspired by British Heritage and tradition. This Bee is hand-carved and arranged on top of a round signet ring.
M A T E R I A L S : This piece is made of sterling silver and plated with 18ct gold. All pieces are stamped with my REO Makers mark.


I just purchased a used 2014 ELR and had it detailed. The detail shop cracked the windshield, and had the windshield replaced.
and if the passenger side blade should be positioned above the driver side blade. A picture would be very helpful.


It is estimated that 11,000 people suffer spinal cord injuries (SCI) annually in the United States.
Taking preventive steps to avoid such injuries, whether at home, work or play may keep you from becoming a statistic.
Never drink and drive, and don't be a passenger in a car driven by someone who has done so. The same goes for drugs.
Buckle children into child safety seats appropriate to their age and height. Place and face the seat appropriately.
Always wear a motorcycle helmet.
Always wear the correct protective gear for your activity.
Wear a helmet when bicycling, skating, skateboarding, horseback riding, skiing, snowboarding and snowmobiling.
Wear a helmet when playing contact sports, such as football and ice hockey, and when batting and running bases in baseball.
Never hit, block or tackle with the top of your head/helmet.
Don't slide head first when stealing a base.
Replace protective gear that is damaged or too old to provide protection.
Always check the depth of the water and for debris before diving.
Don't dive in water less than nine feet deep or in above-ground pools.
Follow all rules and warning signs at water parks, swimming pools and beaches.
Never push anyone into the water.
The first time you go into a body of water, walk into it.
Put non-slip mats in bathrooms and tubs/showers.
Wear non-slip shoes in areas that may be slippery or on floors that are heavily waxed.
Because firearms are one of the leading causes, of SCI, keep your weapon in a locked cabinet, and store ammunition in a secured area.
Back to our Spinal Injury Lawyers page.


A charming covered front porch welcomes you to this beautiful new ranch-style floor plan. The open kitchen includes maple cabinets, granite countertops, a convenient center island and a walk-in pantry, and the spacious great room is perfect for entertaining. Choose from a private study or optional fourth bedroom, and relax in the lavish master suite with optional deluxe bath. Other personalization options include a gourmet kitchen and a side-entry garage.


text:
The Geography Site
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Welcome To The Democratic Republic of the Congo
Here you will find the latest information, sourced from the CIA World Factbook and other public domain references around the Internet.
All this material is in the public domain, so you may freely copy, edit and quote it in homework, projects, essays and other publications.
For more information on various countries select from the links to the right, or use the search box.
Introduction Congo, Democratic Republic of the
Established as a Belgian colony in 1908, the Republic of the Congo gained its independence in 1960, but its early years were marred by political and social instability. Col. Joseph MOBUTU seized power and declared himself president in a November 1965 coup. He subsequently changed his name - to MOBUTU Sese Seko - as well as that of the country - to Zaire. MOBUTU retained his position for 32 years through several subsequent sham elections as well as through the use of brutal force. Ethnic strife and civil war, touched off by a massive inflow of refugees in 1994 from fighting in Rwanda and Burundi, led in May 1997 to the toppling of the MOBUTU regime by a rebellion led by Laurent KABILA. He renamed the country the Democratic Republic of the Congo (DROC), but in August 1998 his regime was itself challenged by an insurrection backed by Rwanda and Uganda. Troops from Zimbabwe, Angola, Namibia, Chad, and Sudan intervened to support the Kinshasa regime. A cease-fire was signed in July 1999 by the DROC, Zimbabwe, Angola, Uganda, Namibia, Rwanda, and Congolese armed rebel groups, but sporadic fighting continued. Laurent KABILA was assassinated in January 2001 and his son Joseph KABILA was named head of state. In October 2002, the new president was successful in negotiating the withdrawal of Rwandan forces occupying eastern Congo; two months later, the Pretoria Accord was signed by all remaining warring parties to end the fighting and establish a government of national unity. A transitional government was set up in July 2003; Joseph KABILA remains as president and is joined by four vice presidents representing the former government, former rebel groups, and the political opposition.
Geography Congo, Democratic Republic of the
Central Africa, northeast of Angola
0 00 N, 25 00 E
Map references:
total: 2,345,410 sq km
land: 2,267,600 sq km
Area - comparative:
slightly less than one-fourth the size of the US
Land boundaries:
border countries: Angola 2,511 km (of which 225 km is the boundary of Angola's discontiguous Cabinda Province), Burundi 233 km, Central African Republic 1,577 km, Republic of the Congo 2,410 km, Rwanda 217 km, Sudan 628 km, Tanzania 459 km, Uganda 765 km, Zambia 1,930 km
Coastline:
Maritime claims:
territorial sea: 12 nm
exclusive economic zone: boundaries with neighbors
tropical; hot and humid in equatorial river basin; cooler and drier in southern highlands; cooler and wetter in eastern highlands; north of Equator - wet season April to October, dry season December to February; south of Equator - wet season November to March, dry season April to October
vast central basin is a low-lying plateau; mountains in east
Elevation extremes:
lowest point: Atlantic Ocean 0 m
highest point: Pic Marguerite on Mont Ngaliema (Mount Stanley) 5,110 m
Natural resources:
cobalt, copper, niobium, tantalum, petroleum, industrial and gem diamonds, gold, silver, zinc, manganese, tin, uranium, coal, hydropower, timber
Land use:
arable land: 2.96%
permanent crops: 0.52%
other: 96.52% (2001)
Irrigated land:
110 sq km (1998 est.)
Natural hazards:
periodic droughts in south; Congo River floods (seasonal); in the east, in the Great Rift Valley, there are active volcanoes
Environment - current issues:
poaching threatens wildlife populations; water pollution; deforestation; refugees responsible for significant deforestation, soil erosion, and wildlife poaching; mining of minerals (coltan - a mineral used in creating capacitors, diamonds, and gold) causing environmental damage
Environment - international agreements:
party to: Biodiversity, Climate Change, Desertification, Endangered Species, Hazardous Wastes, Law of the Sea, Marine Dumping, Ozone Layer Protection, Tropical Timber 83, Tropical Timber 94, Wetlands
signed, but not ratified: Environmental Modification
Geography - note:
straddles equator; has very narrow strip of land that controls the lower Congo River and is only outlet to South Atlantic Ocean; dense tropical rain forest in central river basin and eastern highlands
People Congo, Democratic Republic of the
note: estimates for this country explicitly take into account the effects of excess mortality due to AIDS; this can result in lower life expectancy, higher infant mortality and death rates, lower population and growth rates, and changes in the distribution of population by age and sex than would otherwise be expected (July 2005 est.)
Age structure:
0-14 years: 48.1% (male 14,513,779/female 14,396,952)
15-64 years: 49.4% (male 14,579,101/female 15,121,297)
65 years and over: 2.5% (male 597,776/female 876,099) (2005 est.)
total: 15.8 years
Population growth rate:
2.98% (2005 est.)
Birth rate:
44.38 births/1,000 population (2005 est.)
Death rate:
14.43 deaths/1,000 population (2005 est.)
Net migration rate:
-0.17 migrant(s)/1,000 population
note: fighting between the Congolese Government and Uganda- and Rwanda-backed Congolese rebels spawned a regional war in DROC in August 1998, which left 1.8 million Congolese internally displaced and caused 300,000 Congolese refugees to flee to surrounding countries (2005 est.)
Sex ratio:
at birth: 1.01 male(s)/female
under 15 years: 1.01 male(s)/female
male: 101.25 deaths/1,000 live births
Total fertility rate:
6.54 children born/woman (2005 est.)
HIV/AIDS - adult prevalence rate:
4.2% (2003 est.)
HIV/AIDS - people living with HIV/AIDS:
1.1 million (2003 est.)
HIV/AIDS - deaths:
100,000 (2003 est.)
Major infectious diseases:
degree of risk: very high
food or waterborne diseases: bacterial and protozoal diarrhea, hepatitis A, and typhoid fever
vectorborne diseases: malaria, plague, and African trypanosomiasis (sleeping sickness) are high risks in some locations
water contact disease: schistosomiasis (2004)
noun: Congolese (singular and plural)
adjective: Congolese or Congo
Ethnic groups:
over 200 African ethnic groups of which the majority are Bantu; the four largest tribes - Mongo, Luba, Kongo (all Bantu), and the Mangbetu-Azande (Hamitic) make up about 45% of the population
Religions:
Roman Catholic 50%, Protestant 20%, Kimbanguist 10%, Muslim 10%, other syncretic sects and indigenous beliefs 10%
French (official), Lingala (a lingua franca trade language), Kingwana (a dialect of Kiswahili or Swahili), Kikongo, Tshiluba
Literacy:
definition: age 15 and over can read and write French, Lingala, Kingwana, or Tshiluba
Government Congo, Democratic Republic of the
conventional long form: Democratic Republic of the Congo
conventional short form: none
local long form: Republique Democratique du Congo
local short form: none
former: Congo Free State, Belgian Congo, Congo/Leopoldville, Congo/Kinshasa, Zaire
abbreviation: DROC
dictatorship; presumably undergoing a transition to representative government
Administrative divisions:
10 provinces (provinces, singular - province) and 1 city* (ville); Bandundu, Bas-Congo, Equateur, Kasai-Occidental, Kasai-Oriental, Katanga, Kinshasa*, Maniema, Nord-Kivu, Orientale, Sud-Kivu
Independence:
30 June 1960 (from Belgium)
National holiday:
Independence Day, 30 June (1960)
new constitution adopted 17 July 2003
Legal system:
based on Belgian civil law system and tribal law; has not accepted compulsory ICJ jurisdiction
Suffrage:
18 years of age; universal and compulsory
Executive branch:
chief of state: President Joseph KABILA (since 26 January 2001); note - following the assassination of his father, Laurent Desire KABILA, on 16 January 2001, Joseph KABILA succeeded to the presidency; the president is both the chief of state and head of government
head of government: President Joseph KABILA (since 26 January 2001); note - following the assassination of his father, Laurent Desire KABILA, on 16 January 2001, Joseph KABILA succeeded to the presidency; the president is both the chief of state and head of government
cabinet: National Executive Council, appointed by the president
elections: prior to the overthrow of MOBUTU Sese Seko, the president was elected by popular vote for a seven-year term; election last held 29 July 1984 (next was scheduled to be held in May 1997); formerly, there was also a prime minister who was elected by the High Council of the Republic; note - a Transitional Government is drafting a new constitution with free elections scheduled to be held in NA 2005
election results: MOBUTU Sese Seko Kuku Ngbendu wa Za Banga reelected president in 1984 without opposition
note: Joseph KABILA succeeded his father, Laurent Desire KABILA, following the latter's assassination in January 2001, negotiations with rebel leaders led to the establishment of a transitional government in July 2003 with free elections scheduled to be held in NA 2005
Legislative branch:
a 300-member Transitional Constituent Assembly established in August 2000
elections: NA; members of the Transitional Constituent Assembly were appointed by former President Laurent Desire KABILA
Judicial branch:
Supreme Court or Cour Supreme
Political parties and leaders:
Democratic Social Christian Party or PDSC [Andre BO-BOLIKO]; Forces for Renovation for Union and Solidarity or FONUS [Joseph OLENGHANKOY]; National Congolese Lumumbist Movement or MNC [Francois LUMUMBA]; Popular Movement of the Revolution or MPR (three factions: MPR-Fait Prive [Catherine NZUZI wa Mbombo]; MPR/Vunduawe [Felix VUNDUAWE]; MPR/Mananga [MANANGA Dintoka Mpholo]); Unified Lumumbast Party or PALU [Antoine GIZENGA]; Union for Democracy and Social Progress or UDPS [Etienne TSHISEKEDI wa Mulumba]; Union of Federalists and Independent Republicans or UFERI (two factions: UFERI [Lokambo OMOKOKO]; UFERI/OR [Adolph Kishwe MAYA])
Political pressure groups and leaders:
International organization participation:
ACCT, ACP, AfDB, AU, CEPGL, FAO, G-24, G-77, IAEA, IBRD, ICAO, ICCt, ICFTU, ICRM, IDA, IFAD, IFC, IFRCS, IHO, ILO, IMF, IMO, Interpol, IOC, IOM, ISO (correspondent), ITU, MIGA, NAM, OPCW (signatory), PCA, SADC, UN, UNCTAD, UNESCO, UNHCR, UNIDO, UPU, WCL, WCO, WFTU, WHO, WIPO, WMO, WToO, WTO
Diplomatic representation in the US:
chief of mission: Ambassador Faida MITIFU
chancery: 1800 New Hampshire Avenue NW, Washington, DC 20009: note - Consular Office at 1726 M Street, NW, Wasington, DC, 20036
telephone: [1] (202) 234-7690, 7691
Diplomatic representation from the US:
chief of mission: Ambassador Aubrey HOOKS
embassy: 310 Avenue des Aviateurs, Kinshasa
mailing address: Unit 31550, APO AE 09828
telephone: [243] (88) 43608
FAX: [243] (88) 43467
Flag description:
light blue with a large yellow five-pointed star in the center and a columnar arrangement of six small yellow five-pointed stars along the hoist side
Economy Congo, Democratic Republic of the
Economy - overview:
The economy of the Democratic Republic of the Congo - a nation endowed with vast potential wealth - has declined drastically since the mid-1980s. The war, which began in August 1998, dramatically reduced national output and government revenue, increased external debt, and resulted in the deaths of perhaps 3.5 million people from war, famine, and disease. Foreign businesses curtailed operations due to uncertainty about the outcome of the conflict, lack of infrastructure, and the difficult operating environment. Conditions improved in late 2002 with the withdrawal of a large portion of the invading foreign troops. Several IMF and World Bank missions have met with the government to help it develop a coherent economic plan, and President KABILA has begun implementing reforms. Much economic activity lies outside the GDP data. Economic stability, aided by international donors, improved in 2003-04, although an uncertain legal framework, corruption, and a lack of openness in government policy continues to hamper growth. In 2005, renewed activity in the mining sector, the source of most exports, could boost Kinshasa's fiscal position and GDP growth.
GDP (purchasing power parity):
$42.74 billion (2004 est.)
GDP - real growth rate:
GDP - per capita:
purchasing power parity - $700 (2004 est.)
GDP - composition by sector:
agriculture: 55%
services: 34% (2000 est.)
Labor force:
14.51 million (1993 est.)
Labor force - by occupation:
Unemployment rate:
NA (2003 est.)
Population below poverty line:
Household income or consumption by percentage share:
lowest 10%: NA
highest 10%: NA
Inflation rate (consumer prices):
14% (2003 est.)
revenues: $269 million
expenditures: $244 million, including capital expenditures of $24 million (1996 est.)
Agriculture - products:
coffee, sugar, palm oil, rubber, tea, quinine, cassava (tapioca), palm oil, bananas, root crops, corn, fruits; wood products
mining (diamonds, copper, zinc), mineral processing, consumer products (including textiles, footwear, cigarettes, processed foods and beverages), cement, commercial ship repair
Industrial production growth rate:
Electricity - production:
6.086 billion kWh (2002)
Electricity - production by source:
fossil fuel: 1.8%
hydro: 98.2%
nuclear: 0%
other: 0% (2001)
Electricity - consumption:
Electricity - exports:
1.5 billion kWh (2002)
Electricity - imports:
8 million kWh (2002)
Oil - production:
24,000 bbl/day (2001 est.)
Oil - consumption:
Oil - exports:
Oil - imports:
Oil - proved reserves:
1.538 billion bbl (1 January 2002)
Natural gas - proved reserves:
104.8 billion cu m (1 January 2002)
$1.417 billion f.o.b. (2002 est.)
Exports - commodities:
diamonds, copper, crude oil, coffee, cobalt
Exports - partners:
Belgium 47.8%, Finland 21%, US 10.9%, China 7.6% (2004)
$933 million f.o.b. (2002 est.)
Imports - commodities:
foodstuffs, mining and other machinery, transport equipment, fuels
Imports - partners:
South Africa 18.5%, Belgium 15.5%, France 10.8%, Kenya 6.3%, US 6%, Germany 5.8% (2004)
Debt - external:
$11.6 billion (2000 est.)
Economic aid - recipient:
$195.3 million (1995)
Currency (code):
Currency code:
Exchange rates:
Congolese francs per US dollar - 401.04 (2004), 405.34 (2003), 346.49 (2002), 206.62 (2001), 21.82 (2000)
Fiscal year:
Communications Congo, Democratic Republic of the
Telephones - main lines in use:
Telephones - mobile cellular:
1 million (2003)
Telephone system:
general assessment: poor
domestic: barely adequate wire and microwave radio relay service in and between urban areas; domestic satellite system with 14 earth stations
international: country code - 243; satellite earth station - 1 Intelsat (Atlantic Ocean)
Radio broadcast stations:
AM 3, FM 11, shortwave 2 (2001)
Radios:
18.03 million (1997)
Television broadcast stations:
Televisions:
6.478 million (1997)
Internet country code:
Internet hosts:
Internet Service Providers (ISPs):
Internet users:
Transportation Congo, Democratic Republic of the
Railways:
total: 5,138 km
narrow gauge: 3,987 km 1.067-m gauge (858 km electrified); 125 km 1.000-m gauge; 1,026 km 0.600-m gauge (2004)
Highways:
total: 157,000 km (including 30 km of expressways)
paved: NA km
unpaved: NA km (1999 est.)
Waterways:
15,000 km (navigation on the Congo curtailed by fighting) (2004)
Pipelines:
gas 54 km; oil 71 km (2004)
Ports and harbors:
Banana, Boma, Bukavu, Bumba, Goma, Kalemie, Kindu, Kinshasa, Kisangani, Matadi, Mbandaka
Merchant marine:
registered in other countries: 1
230 (2004 est.)
Airports - with paved runways:
over 3,047 m: 4
2,438 to 3,047 m: 2
1,524 to 2,437 m: 16
914 to 1,523 m: 2 (2004 est.)
Airports - with unpaved runways:
914 to 1,523 m: 92
under 914 m: 97 (2004 est.)
Military Congo, Democratic Republic of the
Military branches:
Army, Navy, Air Force
Manpower available for military service:
males age 18-49: 11,052,696 (2005 est.)
Manpower fit for military service:
males age 18-49: 5,851,292 (2005 est.)
Military expenditures - dollar figure:
$93.5 million (2004)
Military expenditures - percent of GDP:
1.5% (2004)
Transnational Issues Congo, Democratic Republic of the
Disputes - international:
heads of the Great Lakes states and UN pledge to end conflict but unchecked tribal, rebel, and militia fighting continues unabated in the northeastern region of the Democratic Republic of the Congo, drawing in the neighboring states of Burundi, Rwanda and Uganda; the UN Organization Mission in the Democratic Republic of the Congo (MONUC) has maintained over 14,000 peacekeepers in the region since 1999; thousands of Ituri refugees from the Congo continue to flee the fighting primarily into Uganda; 90,000 Angolan refugees were repatriated by 2004 with the remainder in the Democratic Republic of the Congo expected to return in 2005; in 2005, DROC and Rwanda established a border verification mechanism to address accusations of Rwandan military supporting Congolese rebels and the DROC providing rebel Rwandan "Interhamwe" forces the means and bases to attack Rwandan forces; the location of the boundary in the broad Congo River with the Republic of the Congo is indefinite except in the Pool Malebo/Stanley Pool area
refugees (country of origin): 45,060 (Sudan) 100,000 (Angola) 19,552 (Burundi) 6,626 (Republic of Congo) 19,743 (Rwanda) 18,953 (Uganda)
IDPs: 2.33 million (fighting between government forces and rebels since mid-1990s; most IDPs are in eastern provinces) (2004)
Illicit drugs:
illicit producer of cannabis, mostly for domestic consumption; while rampant corruption and inadequate supervision leaves the banking system vulnerable to money laundering, the lack of a well-developed financial system limits the country's utility as a money-laundering center
Sources: The CIA World Fact Book and other public domain Internet sites
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Road Travel in Africa
Definition of a country
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Countries in the United Nations
The Global Village
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text:
Q: Can send var from javascript to use it in JSTL c:set? Is there any way that i can get a variable that i've declared in JavaScript Function to the JSTL tags  ?
Javascript
var abc = document.getElementById('asdf').value;

here is the JSP:
<input type="hidden" id="asdf" value="onetwothree" />

and i want to declare the C:set from the JavaScript
<c:set var="ex" value="${abc}" />

The purpose it to get "ex" variable with the value "onetwothree".
Sorry for the rough explanation. Thanks

A: You can't access JSTL tags from Javascript, simply because those tag exists only server side (client side they are replaced by plain HTML).
You can always send the javascript var as request parameter, then use it server side.

A: you are doing it other way. it is possible to access a variable declared in jsp from javascript using below code
<c:set var="ex" value="test" />
<script type="text/javascript">
    var abc = '${ex}';
</script>

but other way around as you mentioned above is not possible
If you are looking to modify DOM based on what is provided in input box then I would suggest to use code like below (in javascript)
var abc = document.getElementById('asdf').value;

// assuming you want to print this value in a label
document.getElementById('mylabel').innerHTML = abc;

In short if you want to do any change in DOM based on client input, do it through javascript instead of relying on JSP

redpajama_set_name: RedPajamaStackExchange


text:
Беф () — коммуна во Франции, находится в регионе Центр. Департамент — Шер. Входит в состав кантона Сансерг. Округ коммуны — Бурж.

Код INSEE коммуны — 18025.

География

Коммуна расположена приблизительно в 210 км к югу от Парижа, в 125 км юго-восточнее Орлеана, в 50 км к востоку от Буржа.

По территории коммуны протекает река Луара и проходит Боковой канал Луары.

История
Первое упоминание деревни Беф относится к 1227 году. Процветание коммуны началось в 1880-х с развитием промышленности по производству извести. XX век ознаменовался быстрым развитием цементной промышленности. Первый цементный завод был построен компанией Poliet et Chausson в 1929 году.

Население
Население коммуны на 2008 год составляло 698 человек.

Экономика
В Бефе есть цементный завод Ciments français, дочерней компании Italcementi.

В 2007 году среди 415 человек в трудоспособном возрасте (15-64 лет) 289 были экономически активными, 126 — неактивными (показатель активности — 69,6 %, в 1999 году было 67,2 %). Из 289 активных работали 246 человек (131 мужчина и 115 женщин), безработных было 43 (18 мужчин и 25 женщин). Среди 126 неактивных 34 человека были учениками или студентами, 52 — пенсионерами, 40 были неактивными по другим причинам.

Примечания

Ссылки

Коммуны департамента Шер

redpajama_set_name: RedPajamaWikipedia


The decorrelation in the azimuthal angle between the most forward and the most backward jets (Mueller-Navelet jets) is measured in data collected in pp collisions with the CMS detector at the LHC at s=7 TeV. The measurement is presented in the form of distributions of azimuthal-angle differences, Δϕ, between the Mueller-Navelet jets, the average cosines of (π − Δϕ), 2(π − Δϕ), and 3(π − Δϕ), and ratios of these cosines. The jets are required to have transverse momenta, pT, in excess of 35 GeV and rapidities, |y|, of less than 4.7. The results are presented as a function of the rapidity separation, Δy, between the Mueller-Navelet jets, reaching Δy up to 9.4 for the first time. The results are compared to predictions of various Monte Carlo event generators and to analytical predictions based on the DGLAP and BFKL parton evolution schemes.


Teachers' Day is celebrated on 5 September in India as a mark of tribute to the contributions made by the teachers and is an occasion for students to express their gratitude towards their teachers.
Students of Indian Community School Kuwait (ICSK) Senior displayed their love and gratitude to their teachers on the day with various programs organized by the School Senate, which reflected the respect and appreciation that students have for their teachers. The students presented a series of cultural programs, including a melodious duet song and an enchanting dance number that enthralled the audience.
Appreciating the efforts put by the students in organizing the event, Dr. V. Binumon, Principal and Senior Administrator said, "Respect should come from heart and not through the cards and gifts." He expressed his heartfelt thanks to the teachers. He also paid homage to Dr. Sarvepalli Radhakrishnan, the first Vice-President and second President of India. Senate Advisor, George Swamy then enlightened the students with a motivational speech.


text:
\section{Introduction}\label{sec: intro}
Temporal action understanding is at the forefront of computer vision research.
Hendricks~\etal~\cite{Hendricks_2017_ICCV} and Gao~\etal~\cite{Gao_2017_ICCV} recently introduced the task of temporally grounding language queries in videos as a generalization of the temporal action localization task, aiming to overcome the constraint of a predefined set of actions.
This novel interdisciplinary task has gained momentum within the vision and language communities for its relevance and possible applications in video retrieval~\cite{dong2019dual, Shao_2018_ECCV, yu2018joint}, video question answering~\cite{huang2020location,lei2018tvqa}, human-computer interaction~\cite{zhu2020vision}, and video storytelling~\cite{gella2018dataset}.
Enabling this fine-grained matching of language in videos can be adopted by professional video content creators during the editing process. For example, video editing often requires searching through many hours of raw, unlabelled video content for specific interesting highlights. Thus, the ability to retrieve such highlights through textual queries could provide a faster experience.

\begin{figure}[t]
    \centering
    \includegraphics[trim={0cm 0cm 0cm 0cm},width=\linewidth,clip]{figs/VLG_Intro_Grey.pdf}

    \caption{\textbf{Temporal video grounding task and multi-modality interaction schemes.} (a) A video grounding example showcasing the importance of fine-grained semantic understanding and  proper context modeling.
    (b,c,d) Approaches for multi-modal interactions. We regard moments and queries as sequences of snippets and tokens respectively.
    We employ scheme (d), which allows for fine alignment by snippet-token matching. }
    \label{fig:intro}
    \vspace{-0.3cm}
\end{figure}

\indent Natural language grounding in videos inherits challenges from temporal action localization such as context modeling and candidate moment generation \cite{alwassel_2018_detad}. Semantic context is a fundamental cue necessary to boost the performance of localization methods~\cite{Dai_2017_ICCV, Gao_2017_ICCV_TURN,Lin_2018_ECCV, TSN_ECCV_2016, Xu_2020_CVPR}. To enrich video representation~\cite{Hendricks_2017_ICCV} adopted \textit{global-context}, which is moment independent, leading to sub-optimal performance. Conversely, a moment specific \textit{local-context}, defined as a moment's temporal neighbourhood, was used in~\cite{Gao_2017_ICCV, MAC_WACV_2019, jiang2019cross, 10.1145/3240508.3240549, 10.1007/978-3-030-00767-6_32}.
In our view, \textit{non-local context} merits deeper analysis, since it has the potential to identify relevant information not restricted to the temporal neighbourhood within one data modality.
For example, in Fig.~\ref{fig:intro}(a), although ``\textit{First throw and fetch}'' is not in the temporal vicinity of ``\textit{Second throw and fetch}'', it is still semantically related with the target moment, showcasing the importance of non-local context modeling for video grounding.

Moreover and as shown by the example, the free-form nature of the language modality introduces additional challenges. A model must understand the semantic content of both videos and language queries and reason about their multi-modal interactions.
Previous studies \cite{Gao_2017_ICCV, Ge_2019_WACV, 10.1007/978-3-030-00767-6_32} employ a cross-modal processing unit designed to jointly model text and visual features through simple operations such as element-wise addition, Hadamard product, and direct concatenation of a moment's representation and the query embedding.
A high-level overview of this multi-modal interaction scheme is presented in Fig.~\ref{fig:intro}(b).
Instead, recent works such as~\cite{Mun_2020_CVPR}, only employ the Hadamard product to fuse the multi-modal information at the query-snippet level. This scheme, depicted in Fig.~\ref{fig:intro}(c), can determine different correlations between a query and each video snippet, allowing for a finer fusion with respect to Fig.~\ref{fig:intro}(b).

Motivated by the work in~\cite{Xu_2020_CVPR}, we propose to leverage the representational capability of graphs to encode snippet-snippet, token-snippet, and token-token relations as graph edge connections.
As such, we design a new architecture referred to as Video-Language Graph Matching Network (VLG-Net)
which employs Graph Convolutional Networks (GCN)~\cite{kipf2016semi}.
First, representation graphs for both video and language are constructed. The video graph models each snippet as a node and takes advantage of two sets of edges to represent both local temporal relations and non-local semantic relations between video snippets. Similarly, we construct a language graph, where each node is a token, and each edge reflects token-to-token relations, \eg syntactic dependencies~\cite{manning2014stanford, marcheggiani_titov_2017_encoding}. These modality-specific graphs are used to model local and non-local intra-modality context through graph convolutions. This sets the stage for addressing modality alignment by recasting inter-modality interactions as an algorithmic graph matching problem. Inspired by~\cite{li2019graph, xu2019cross}, we adopt a cross-graph attention-based matching mechanism to enable the mutual exchange of information between modalities, allowing for fine-grained alignment through a specialized set of learnable edges. Unlike some methods that focus on relatively coarse query-moment or query-snippet interactions, and similar to~\cite{chen2018temporally, zhang2019cross}, our method performs the matching operation at the level of snippets and tokens, as depicted in Fig.~\ref{fig:intro}(d).
With this design, we avoid the need for heuristics of context modeling while learning a successful strategy for multi-modal fusion.

\noindent\textbf{Contributions.}
\textbf{(1)} We propose VLG-Net, a new deep learning pipeline that consistently adopts graph representation for modeling modality interaction and multi-modal fusion. We address the modality fusion problem by resorting to a graph-matching approach that learns snippet-token connectivity.
\textbf{(2)} Through extensive experiments, VLG-Net demonstrates its effectiveness in capturing modality interactions by achieving performance on par or better than state-of-the-art on three standard datasets, showing significant improvements over previously published methods in TACoS~\cite{TACoS_ACL_2013} and DiDeMo~\cite{Hendricks_2017_ICCV} datasets.

\section{Related work}\label{sec: related}

\begin{figure*}[t!]
    \centering
        \includegraphics[trim={0cm 0cm 0cm 0cm},width=15cm,clip]{figs/VLG_Arch_new2.pdf}
       \caption{\textbf{VLG-Net Architecture}.
    Inputs are snippets features and tokens embeddings. The video stream comprises: 1D convolutions and $b_v$ GCNeXt operations. Correspondingly, tokens are fed to a stack of LSTM layers and $b_l$ SyntacGCN layers. A graph matching layer is adopted for cross-modal context modeling and multi-modal fusion. Masked attention pooling lists all possible moment candidates, and a Multi-Layer Perceptron (MLP) computes each moment's score to rank them as final predictions.}
    \label{fig:arch}
    \vspace{-0.3cm}
\end{figure*}

\subsection{Video Grounding}
\noindent \textbf{Moment candidates.} Previous works can be categorized into  proposal-free and proposal-based methods. Proposal-free approaches~\cite{chenhierarchical, Liu_2018_ECCV, Mun_2020_CVPR, Rodriguez_2020_WACV, chen2020learning, ABLR, Zeng_2020_CVPR} aim at directly regressing the temporal boundaries of the queried moment from the multi-modal fused feature. In contrast, proposal-based methods adopt a propose-and-rank pipeline by first generating moment proposals and then ranking them according to their similarity with the  textual query~\cite{Hendricks_2017_ICCV, chen_etal_2018_temporally, QSPN,10.1145/3240508.3240549, wang2020temporally,  2DTAN_2020_AAAI}. Similar to these approaches, VLG-Net is a proposal based approach.

\noindent \textbf{Moments in context.}
For moment context modeling, some methods~\cite{ chen_etal_2018_temporally, ghosh_etal_2019_excl} attempt to use the memory property of LSTM cells~\cite{HochSchm97} to contextualize the video features.
Alternatively, attention-based mechanisms~\cite{NIPS2017_7181} adopted in~\cite{lin2020moment,ACRN_SIGIR_18, wang2020temporally} can improve the aggregation of long-range semantic dependencies. Similar to~\cite{wang2020temporally}, we argue that visual context modeling should be dynamic and query-dependent. \cite{ 2DTAN_2020_AAAI} claims that neighbouring proposals hold valuable context and thus apply 2D convolutions (with large kernel size) to moment representations to gather context information in the latter stages of their pipeline.
Compared to~\cite{2DTAN_2020_AAAI}, we delegate context gathering to earlier stages of our pipeline and only use a Multi-Layer Perceptron (MLP) network for moment score computation, reducing the overall computation.

\noindent \textbf{Multi-modal fusion.}
Moving beyond the simple scheme adopted in~\cite{Gao_2017_ICCV},
the work of~\cite{ijcai2018-143} devises a new cross-modality interaction scheme based on circular matrices. In~\cite{chen_etal_2018_temporally, 10.1145/3240508.3240549, wang2020temporally}, frame features are concatenated with frame-guided attention-pooled features from the query.
Lu~\etal~\cite{lu_etal_2019_debug} take advantage of the QANet~\cite{YuDLZ00L18} architecture, which is based on cross-attention and convolution operations, for multi-modal fusion. Dynamic filters generated from language features are used in~\cite{Rodriguez_2020_WACV,Zhang_2019_CVPR} in order to modulate (through convolutions) the visual information based on the query content.
Recently, the Hadamard product has become a popular way to fuse/gate multi-modal information~\cite{Mun_2020_CVPR, Zeng_2020_CVPR, 2DTAN_2020_AAAI}.
In contrast to these methods, our graph matching layer specifically models local, non-local, and query-guided context, thereby exploiting the semantic neighbourhood of snippets and tokens to fuse the modalities through graph convolutions. Concurrently to our method, \cite{liu2020jointly} adopted attention based cross-modal graphs for fusing the video and language modality. However, opposed to our formulation,~\cite{liu2020jointly} lacks a formal design for the graph edges.

\subsection{Graphs and Graph Neural Networks}
\noindent \textbf{Graphs in Videos.} In various video understanding tasks, such as action recognition~\cite{chen2019graph, liu2019learning, wang2018videos} and action localization~\cite{Xu_2020_CVPR,zeng2019graph}, graphs can offer extensive representational power to data sequences. For example, a video can be represented as a space-time region graph~\cite{wang2018videos} or as a 3D point cloud in the spatial-temporal space~\cite{liu2019learning}. Moreover, Zeng~\etal~\cite{zeng2019graph} define temporal action proposals as nodes to form a graph, while Xu~\etal~\cite{Xu_2020_CVPR} consider video snippets as the graph nodes. Inspired by~\cite{Xu_2020_CVPR}, in VLG-Net, video snippets are represented as nodes in a graph and different specifically designed edges model their relationships.

\noindent \textbf{Graphs in Language.}
In natural language processing (NLP), both sequential and non-local relations are crucial. The former is usually captured by recurrent neural networks~\cite{rumelhart1985learning}, while the latter can be represented using graph neural networks~\cite{ bastings2017graph, beck2018graph, marcheggiani2017encoding, 10.1007/978-3-030-00767-6_32}.
Moreover, syntactic information has proven useful for language modeling when combined with GCNs~\cite{huang2020aligned, lin2020moment, marcheggiani_titov_2017_encoding}.
Driven by these findings, we use LSTMs and Syntactic Graph Convolution Networks (SyntacGCN) together to model and enrich the language features in the query.

\noindent \textbf{Graph Neural Networks in Graph Matching.}
Graph matching is one of the core problems in graph pattern recognition, aiming to find node correspondences between different graphs~\cite{caetano2009learning}. Given the ability of Graph Neural Networks (GNNs) to encode graph structure information, approaches leveraging GNNs have recently surfaced to address the graph matching problem~\cite{wang2019learning, xu2019cross}. For example, Li~\etal~\cite{li2019graph} propose a GNN-based graph matching network to represent graphs as feature vectors, which simplifies measuring their similarity. Following~\cite{li2019graph}, a neighborhood matching network is introduced by~\cite{wu2020neighborhood} to match graph nodes by estimating similarities of their neighborhoods. Due to their superiority in finding consistent correspondences between sets of features, graph matching methods have been widely applied in various tasks~\cite{jing2020visual, liu2020learning, wang2020cross, wu2020neighborhood, xu2019cross}.
Motivated by these works, we apply graph matching to the video grounding task by specifically employing a cross-graph attention matching mechanism.

\section{Methodology}\label{sec: method}
\subsection{Problem Formulation}
Given an untrimmed video and a language query, the video grounding task aims to localize a temporal moment in the video that matches the query. Each video-query pair has one associated ground-truth moment, defined as a temporal interval with boundary $(\tau_s, \tau_e)$. Our method scores $m$ candidate moments, where the $k$-th moment consists of start time $t_{s,k}$, end time $t_{e,k}$, and confidence score $p_k$.
The video stream is represented as a sequence of $n_v$ \textbf{snippets} $V=\{v_i\}_{i=1}^{n_v}$, where each snippet has $\epsilon$ consecutive frames. Similarly, a language query is represented by $n_l$ \textbf{tokens} $L=\{l_i\}_{i=1}^{n_l}$.
The inputs to VLG-Net are $n_v$ snippet features $X_v\in \mathbb{R}^{c_v\times n_v}$ and $n_l$ token features $X_l \in \mathbb{R}^{c_l\times n_l}$ extracted using pre-trained models, where $c_v$ and $c_l$ are the snippet and token feature dimensions. We describe the details of feature extraction in Sec.~\ref{subsec: Impl}.

\subsection{VLG-Net Architecture}
Our video grounding architecture is illustrated in Fig.~\ref{fig:arch}.
First, we feed both the video features $X_v$ and the query embeddings $X_l$ into a stack of computation blocks. On the video path, we use 1D convolutions and GCNeXt~\cite{Xu_2020_CVPR} blocks to enrich the visual representation with local and non-local intra-modality context. On the language path, we apply LSTM and SyntacGCN~\cite{huang2020aligned} to aggregate temporal and syntactic context, which models the grammatical structure of the language query. The two paths converge in the graph matching layer for cross-modal context modeling and multi-modal fusion. After the graph matching layer, we apply masked moment attention pooling to produce the representations of possible moment candidates. Finally, we use an MLP to score the query-moment pair based on their representation and post-process the score through non-maximum suppression (NMS). We report top-$\kappa$ ranked moments as the final predictions.

\subsection{Video and Language Representations}\label{subsec: GCNeXt}
Here, we detail the set of operations performed on each modality. The stack of computation blocks of each path is specifically designed to model intra-modality context to enrich the snippet and token features.

\noindent\textbf{Video Representation.} We add 1D positional encoding, as formulated in~\cite{devlin2018bert}, to each input visual feature and apply 1D convolutions to map them to a desired dimension.
The video is then cast as a graph, where each node represents a snippet and each edge represents a dependency between a snippet pair. We design two types of edges: (i)~\textit{Ordering Edges} and (ii)~\textit{Semantic Edges}. Static \textit{Ordering Edges} connect consecutive snippets and model the temporal order. Conversely, \textit{Semantic Edges} are dynamically constructed, using the k-nearest neighbors algorithm. They connect semantically similar snippets based on their current feature representations. Specifically, an ordering or semantic snippet neighborhood is determined, and its aggregated representation is computed through edge convolutions $\mathcal{F}$, similar to~\cite{wang2018dynamic}. Each edge convolution employs a split-transform-merge strategy~\cite{xie2017aggregated} to increase the diversity of transformations. These graph operations (called GCNeXt) were proposed in~\cite{Xu_2020_CVPR} to enrich video snippet representations for the purpose of temporal action localization. In our architecture, we stack $b_v$ GCNeXt blocks together and refer to the input of each block as $X_v^{(i)}$ such that
\begin{align} \label{eq:gcnext}
   &X_v^{(i+1)}=\text{GCNeXt}(X_v^{(i)})= \\\nonumber
   &\sigma\left(\mathcal{F}{(X_v^{(i)},\mathcal{A}_o^{(i)},W_o^{(i)})}+\mathcal{F}{(X_v^{(i)},\mathcal{A}_s^{(i)},W_s^{(i)})}+X_v^{(i)} \right),
\end{align}
where $X_v^{(0)}$ is the output of the convolutional layer, $\mathcal{A}_o^{(i)}$  and $\mathcal{A}_s^{(i)}$ are the adjacency matrices of~\textit{Ordering Edges} and~\textit{Semantic Edges}, respectively, and $W_{o}^{(i)}$ and $W_{s}^{(i)}$ are the trainable weights for the $i$-th GCNeXt block.
We use Rectified Linear Unit (ReLU) as the activation function $\sigma$. Refer to~\cite{Xu_2020_CVPR} for additional details about GCNeXt. The output of the last block is referred to as $X_v^{(b_v)}$, which is the input to the graph matching layer.

\noindent\textbf{Language Representation.} The query token features $X_l$ are fed through an LSTM of $b_s$ layers to capture semantic information and the sequential
context. Moreover, given that language follows a predefined set of grammatical rules, we set out to leverage syntactic information~\cite{marcheggiani_titov_2017_encoding, zhang_etal_2018_graph} to model grammatical inter-word relations. For this purpose, we adopt SyntacGCN, as shown in Fig.~\ref{fig:arch}. Syntactic graphs are preferred over fully connected graphs, since the former's sparsity property offers more robustness against noise in language~\cite{huang2020aligned}. Our SyntacGCN represents a query as a sparse directed graph, in which each output of the last LSTM layer, referred to as $X_l^{(0)}$, is viewed as a node, and each syntactic relation as an edge. The adjacency matrix $\mathcal{A}_l$ is directly constructed from the query's syntactic dependencies~\cite{manning2014stanford} and the graph convolution is formulated as:
\begin{align} \label{eq:syntacGCN}
 X_{l,j}^{(i+1)} = \sigma \left(X_{l,j}^{(i)} + \sum_{k\in \mathcal{N}(j)} {\alpha_{jk}^{(i)} \mathcal{A}_{l,jk} W_l^{(i)} X_{l,k}^{(i)} }\right),
\end{align}
where $X_{l,j}^{(i)}$ is the $j$-th token feature of previous layer's output, $\mathcal{N}(j)$ is the syntactic neighbourhood of node $j$, $W_l^{(i)}$ is the learnable weight in the $i$-th layer, and $\sigma$ is ReLU. Moreover, $\alpha_{jk}^{(i)}$ is the edge weight learned from the feature of paired nodes $X_{l,j}^{(i)}$ and $X_{l,k}^{(i)}$ , defined as:
\begin{align} \label{eq:alpha_relation}
  \alpha_{jk}^{(i)}= \mathrm{SoftMax}( \mathbf{w}_\alpha^{(i)\top} \sigma(W_\alpha^{(i)}(X_{l,j}^{(i)}||X_{l,k}^{(i)}))),
\end{align}
where $\mathbf{w}_\alpha$ and $W_\alpha$ are learnable parameters and $||$ denotes vector concatenation. We refer to the last output of the SyntacGCN as $X_l^{(b_l)}$, which will be used to match the video representation $X_v^{(b_v)}$.

\subsection{Video-Language Graph Matching}\label{subsec: Match}
The enriched video representation $X_v^{(b_v)}$ and query representation $X_l^{(b_l)}$ meet and interact in the graph matching layer. This layer models the cross-modal context and allows for multi-modal fusion.
It does so by evaluating the intra-modality correlation between each video snippet and between each query token and the inter-modality correlation between each snippet-token pair.
The process is shown in Fig.~\ref{fig:gmn}.
First, we create a video-language matching graph, where each node can be either a video snippet or a query token. We include three types of edges: (i)~\textit{Ordering Edge ($\mathcal{O}$)}, (ii)~\textit{Semantic Edge ($\mathcal{S}$)}, and (iii)~\textit{Matching Edge ($\mathcal{M}$)}.

As depicted in Fig.~\ref{fig:gmn}, we use \textit{Ordering Edge} and \textit{Semantic Edge} in the video-language matching graph (as defined in Sec.~\ref{subsec: GCNeXt}), while the \textit{Matching Edge} reflects the inter-modality relation.
\textit{Ordering Edge} models the sequential nature of both modalities. For example, if an \textit{Ordering Edge} links two tokens, the words corresponding to the two tokens are consecutive in the input query.
\textit{Semantic Edge} is used to connect graph nodes in the same modality according to their feature similarity, providing non-local dependencies over the entire graph.
Importantly, \textit{Matching Edge} is employed to explicitly model and learn the cross-modality interaction, to extract meaningful alignment information and learn an aggregation policy.
The \textit{Matching Edge} weights are referenced as $\mathcal{B}$. We use \textit{Matching Edge} to densely connect all possible snippet-token pairs, and set the edge weight proportional to the correlation between the matched node features.
Similar to \textit{Semantic Edges}, \textit{Matching Edges} are dynamic and evolve in the training process.

\begin{figure}[!t]

    \centering
    \includegraphics[trim={3.7cm 3.7cm 1.4cm 2.25cm},width=7cm,clip]{figs/VLG_Matching_new.pdf}
    \caption{\textbf{The video-language matching graph.}
    The nodes represent video snippets and query tokens.
    Ordering Edge models the sequential nature of both modalities.
    Semantic Edge connects graph nodes in the same modality according to their feature similarity.
    Matching Edge captures the cross-modality relationships.
    We apply graph convolution on the video-language graph for cross-modal context modeling and multi-modal fusion. The neighborhood is specific for the node highlighted in red.
    }
    \label{fig:gmn}

\end{figure}
To combine all three types of edges, we employ relation graph convolution~\cite{schlichtkrull2017modeling} on the constructed video-language matching graph. Eq.~\ref{eq:gmm} shows the high level representation of the convolutions in this layer. Refer to the supplementary material for the full formulation.
\begin{align} \label{eq:gmm}
   \mathbf{X}^{(GM)}\! =\! \mathcal{A}_\mathcal{O} \mathbf{X} W_\mathcal{O}\! +\! \mathcal{A}_\mathcal{S} \mathcal{B} \mathbf{X}  W_\mathcal{S}\! +\! \mathcal{A}_\mathcal{M} \Gamma \mathbf{X} W_\mathcal{M}\! +\! \mathbf{X}
\end{align}
Here, $\mathbf{X} = \{{X_{v,1}^{(b_v)},\dots,X_{v,n_v}^{(b_v)},X_{l,1}^{(b_l)},\dots,X_{l,n_l}^{(b_l)}}\}$ is the feature representation of all the nodes in the video-language matching graph.
$\mathcal{A}_{r}$ and $W_{r}$ for $r\in \{ \mathcal{O},\mathcal{S},\mathcal{M}\}$ represent the binary adjacency matrix and learnable weights for each set of edges.
$\mathcal{B}$ and $\Gamma$ scale the adjacency matrices $\mathcal{A}_\mathcal{S}$ and $\mathcal{A}_\mathcal{M}$, respectively, such that $\beta_{i,j} \in \mathcal{B}$ and $\gamma_{i,j} \in \Gamma$ are proportional to $\mathbf{x}_i^{\top} \mathbf{x}_j$,
We stack together all video and language node features to form $X_v^{(GM)}\in \mathbb{R}^{c\times n_v}$ and $X_l^{(GM)}\in \mathbb{R}^{c\times n_l}$, and we pass them to the masked moment pooling layer.

\subsection{Masked Attention Pooling}\label{subsec: Pool}
The graph matching layer returns a new video graph and a new language graph fused with information from the other modality. Then, a masked attention pooling operation is applied to the new video graph to list the relevant sub-graph representations as candidate moments. The output of this module is denoted as $Y= [\mathbf{y}_k]_{k=1}^{m}, \mathbf{y}_k\in \mathbb{R}^{c}$, where $m$ is the number of candidate moments, and $c$ is the feature dimension of each moment. For efficiency purposes, the operation is implemented as a masked attention, allowing us to process each snippet feature only once, while computing each moment's representation.

\begin{figure}[!t]

    \centering
    \includegraphics[trim={0cm 0cm 0cm 0cm},width=7cm,clip]{figs/VLG_Att_new.pdf}
    \caption{\textbf{Masked attention pooling.} Sequence of operations for the learnable cross-attention configuration. Inputs are video nodes $X_v^{(GM)}$ from the graph matching layer and the query embedding $X_l^{(att)}$ computed through self-attention pooling atop the graph matching output. The output $Y$ represents all moment candidates.}
    \label{fig:att}
    \vspace{-0.2cm}
\end{figure}

We implement three different schemes, namely: (i) learnable self-attention, (ii) cross-attention, and (iii) learnable cross-attention. In (i), we obtain the unnormalized attention weights by applying a 1D convolutional layer that maps each snippet feature to a single score. In (ii) and (iii), we compute the query representation $X_l^{(att)}$ by applying self-attention pooling atop the graph matching output $X_l^{(GM)}$. Cross-attention obtains the unnormalized weights by computing the inner product between the snippet and query feature, while learnable cross-attention concatenates each snippet feature with the query feature and uses a 1D convolutional layer to obtain the weights. Configuration (iii) is depicted in Fig.~\ref{fig:att}.  In all cases, the unnormalized weight vector has shape $w\in \mathbb{R}^{n_v \times 1}$ for each video. The vector is repeated $m$ times to obtain the matrix $W\in \mathbb{R}^{n_v \times m}$, and a fixed mask $M\in \mathbb{R}^{n_v \times m}$ is applied to it. Similar to  Songyang~\etal~\cite{2DTAN_2020_AAAI}, we generate $m$ moment candidates and apply a sparse sampling strategy to discard redundant moments. The value $m$ is dataset dependent.
The mask is generated according to the sampled moments, highlighting for each of them, which are the snippets that must be taken into account when computing the moment's pooled feature.
The attention scores are then obtained by a softmax operation. Thanks to the masking operation, snippets not related to the $n$-th moment will not be considered. Finally, the moments' features are obtained simply as a matrix multiplication $Y = X^{(GM)}  \mathrm{SoftMax}(W+M)$.

\subsection{Moment Localization}\label{subsec: Loc}
Each candidate moment representation, output of previous module, is endowed with an additive 2D positional embedding encoding the start and end timestamps $(t_{s,k},t_{e,k}$ with $k \in [1,m])$. Then each moment feature is fed to an MLP to compute the alignment confidence score $p_k$. This score predicts the Intersection-over-Union (IoU) of each moment with the ground truth of the corresponding video-query pair. For training, we supervise this process using a cross-entropy loss, shown in Eq.~\ref{eq:loss}. Similar to~\cite{2DTAN_2020_AAAI}, we assign the label $t_k=1$ if the $IoU \geq \theta_2$, $t_k=0$ if the $IoU \leq \theta_1$, and otherwise $t_k=(IoU-\theta_1)/(\theta_2-\theta_1)$.
\begin{equation}
    \mathcal{L} =\frac{1}{m}\sum_{k=1}^m t_k \log p_k + (1-t_k) \log(1-p_k),
\label{eq:loss}
\end{equation}
During inference, moment candidates are ranked based on  their predicted scores and NMS is used to discard highly overlapping moments.

\section{Experiments}\label{sec: experiments}
\subsection{Datasets}
\noindent\textbf{ActivityNet-Captions}~\cite{Krishna_2017_ICCV} is a popular  dataset initially collected for the task of dense captioning, and recently adopted for the task of moment localization with natural language queries~\cite{chen_etal_2018_temporally,lin2020moment}. The dataset is subdivided into four splits: train, val\_1,  val\_2, and test. The test set is withheld for competition purposes, while leaving the rest publicly available. Refer to  Table ~\ref{tab:datasets}
for details about the publicly available splits. Following the setting in~\cite{lin2020moment}, we use val\_1 as the validation set and val\_2 as the testing set.

\noindent\textbf{TACoS}~\cite{TACoS_ACL_2013} consists of videos selected from the MPII Cooking Composite Activities video corpus~\cite{rohrbach2012script}. It comprises 18818 video-query pairs of different cooking activities. Each video contains an average of 148 queries, some of which are annotations of short video segments.

\noindent\textbf{DiDeMo}~\cite{Hendricks_2017_ICCV} contains unedited video footage from Flickr with sentences aligned to unique moments in its 10642 videos. It contains more than 40k video-query pairs with coarse temporal annotations. Moment start and end points are aligned to five-second intervals and the maximum annotated moment length is 30 seconds.

Concerns regarding the Charades-STA~\cite{Gao_2017_ICCV} dataset discouraged us from evaluating our method on it. Refer to the supplementary material for a detailed discussion.

\subsection{Implementation}\label{subsec: Impl}
\noindent\textbf{Evaluation Metrics.}
We follow the commonly used setting in ~\cite{gao2017tall}, where the Rank@$\kappa$ for IoU=$\theta$ serves as our evaluation metric.
For example, given a video-query pair, the result is positive if any of the top-$\kappa$ predictions has IoU with the ground-truth larger or equal to $\theta$; otherwise the result is negative. We average the results across all testing samples. Following standard practice, we set $\kappa \in\{1, 5\}$ with $\theta\in\{0.3,0.5,0.7\}$ for ActivityNet Captions,
$\kappa\in\{1, 5\}$ with $\theta\in\{0.1,0.3,0.5\}$ for TACoS,  and $\kappa\in\{1, 5\}$ with $\theta\in\{0.5,0.7, 1.0\}$ for DiDeMo.

\noindent\textbf{Language and Video Features.}
After lower-case conversion and tokenization, we use the pretrained GloVe model~\cite{pennington2014glove} to obtain the initial query embedding for every token and extract the syntactic dependencies using the Stanford CoreNLP 4.0.0 parser~\cite{manning2014stanford}. The $b_s$ layers
of LSTM with 512 hidden units are used as the query encoder.  Then, the syntactic GCN encodes syntactic information of the queries and returns a new embedding with 512 dimensions. For visual features, we use pretrained C3D~\cite{tran2015learning} for ActivityNet Captions and TACoS, and VGG16~\cite{simonyan2014very} for DiDeMo,  while holding their parameters fixed during training, as they are readily available and commonly used by state-of-the-art methods. We use 1D convolutions to project the input visual features to a fixed dimension (512), and the GCNeXt blocks' hyper-parameters are set as in~\cite{Xu_2020_CVPR}.

\begin{table}[!t]
    \input{tables/table_datasets}
    \vspace{-0.5cm}
\end{table}

\noindent\textbf{Implementation details.}
We use Adam~\cite{kingma2014adam} with a StepLR scheduler~\cite{loshchilov2016sgdr}.
We adopt learning rates ranging from $10^{-3}$ to $10^{-4}$ for different datasets. The number of sampled snippets $n_v$ is set to $64$ for ActivityNet Captions, and $256$ for TACoS, and $48$ for DiDeMo. The values of ($b_v$, $b_s$, $b_l$) are equal to ($1,3,4$), ($4,5,2$), ($2,3,4$), respectively for the three datasets. In post-processing, we apply NMS with values respectively to the $m$ predictions to filter out highly overlapping moments. Values of $m$ for each dataset are: $1104$, $3101$, $505$ while the NMS thresholds are: $0.5$, $0.3$, $0.5$. We adapt BCE with logits loss to make the training process more numerically stable. The IoU thresholds ($\theta_1$, $\theta_1$) for the three datasets are: ($0.7$, $0.71$), ($0.5$, $0.7$), ($0.69$, $1.0$).

\subsection{Comparison with State-of-the-Art}
Comparisons are carried out only against methods using the same input features as VLG-Net. In the Tables, we highlight the top-1 and top-2 performance values by bold and underline, respectively.

\noindent\textbf{ActivityNet Captions (Table \ref{tab:anet}).}
VLG-Net offers the highest performance for the tight IoU=0.7. However, it falls short against~\cite{liu2020jointly} on the lousier IoU=0.5. In practical terms, the two methods are to be considered on par. Nonetheless, notice how tighter IoU translates to better retrieval quality in a real use-case scenario. In these terms, VLG-Net is to be preferred over the competitive~\cite{liu2020jointly}. Finally, notice how VLG-Net achieves a significant boost against the recently released 2D-TAN~\cite{2DTAN_2020_AAAI} and DRN~\cite{Zeng_2020_CVPR}.

\noindent\textbf{TACoS (Table~\ref{tab:tacos}).}
Our model outperforms state-of-the-art methods and achieves the highest scores for all IoU thresholds with significant improvements. In particular, VLG-Net exceeds the previous art~\cite{liu2020jointly,Zeng_2020_CVPR,2DTAN_2020_AAAI} by a large margin, ranging from $7.10$\% to $11.52$\%, across all evaluation settings, showcasing the excellent design of the architecture.

\noindent\textbf{DiDeMo (Table~\ref{tab:didemo}).  }
Our proposed technique outperforms the top-ranked methods~\cite{liu2020jointly,ACRN_SIGIR_18,10.1145/3240508.3240549} with respect to R@1 and R@5 for IoU0.5 and 0.7 with evident increases. It also reaches the highest performance in regards to R@1 IoU1.0. For R@5 IoU1.0, VLG-Net ranks second, falling short with respect to TMN~\cite{chen_etal_2018_temporally} by $1.32\%$.
For completeness, we report the performances of TGN~\cite{Liu_2018_ECCV} and TMN~\cite{chen_etal_2018_temporally}; with the caveat that their performance could not be verified as the code is not made publicly available. Hence the different colour for the corresponding rows in Table~\ref{tab:didemo}.

\begin{table}[!t]
    \input{tables/table_anet}
    \vspace{-0.2cm}
\end{table}

\subsection{Ablation Study} \label{subsec: Abl}
To motivate our design choices, we present two ablations that focus on relevant aspects of our method. The first ablation showcases the importance of context modeling. The second investigates VLG-Net's performance when other commonly adopted multi-modal fusion operations replace the graph matching module. In Table ~\ref{tab:ablation}, we report our best result (first column) and summarize all ablated variants. For simplicity, we evaluate on the TACoS dataset and specifically focus on its most challenging setups: R@1 IoU0.5 and R@5 IoU0.5. To promote fair comparison, we report each model's capacity in millions (M) of parameters. More ablations are reported in the supplementary material.

\noindent\textbf{Context Ablation.} First, we investigate the impact of different context modeling strategies and compare six variants with our VLG-Net. VLG-Net\textsubscript{NC} represents a ``No Context'' architecture, in which we replace the GCNeXt and SyntacGCN modules with fully connected layers that do not model any intra-modality context. Moreover, we switch off the \textit{Ordering} and \textit{Semantic Edges} in the graph matching module.
Although the model capacity for VLG-Net\textsubscript{NC} only drops $0.21$M ($1.6\%$), its performance degrades up to $8.82\%$  with respect to VLG-Net.
Following~\cite{Hendricks_2017_ICCV}, we devise VLG-Net\textsubscript{GM} (``Global on Moments'') and VLG-Net\textsubscript{GI} (``Global on Input'') experiments. The first one extends VLG-Net\textsubscript{NC} by concatenating each moment feature with a global video feature after the matching operation. Instead, in VLG-Net\textsubscript{GI}, we concatenate each snippet and token feature with an average pooled version of their respective raw input features.
Differently, following~\cite{Gao_2017_ICCV}, VLG-Net\textsubscript{LM} (``Local on Moments'') models local context by extending the moment's boundaries when computing the moment's features in the masked attention pooling module.
The simple context modeling adopted in these architectures allows them to improve their performance with respect to VLG-Net\textsubscript{NC} up to $2.32\%$. Nonetheless, they fall short of VLG-Net by $6.5 - 7.4 \%$.
Note that VLG-Net\textsubscript{GM} and VLG-Net\textsubscript{GI} have a larger model capacity, $0.07$M and $0.41$M, respectively, compared to VLG-Net.
VLG-Net\textsubscript{T} (``Temporal Context Only'') is as VLG-Net, where we remove each \textit{Semantic Edge} and replace the SyntacGCN layers with GCNeXt ones. In this variant, only temporal dependencies are modeled. VLG-Net\textsubscript{S} (``Semantic Context Only'') does not model temporal dependencies but just the semantic ones.
Both models' performance surpasses $30 \%$ for R@1 IoU0.5, which indicates that the introduction of GCNeXt and SyntacGCN layers can boost the performance.
Our final architecture takes advantage of both modules, achieving the best results. These ablations demonstrate that temporal and semantic context are complementary and showcase the benefits of the proposed context modeling strategy.

\begin{table}[!t]
    \input{tables/table_tacos}
\end{table}

\begin{table}[!t]
    \input{tables/table_didemo}
    \vspace{-0.2cm}
\end{table}

\begin{figure*}[!ht]
    \centering
    \includegraphics[trim={0cm 0cm 0cm 0cm},width=\linewidth,clip]{figs/VLG_Vis2_new.pdf}

    \caption{\textbf{Qualitative Results.} Examples of grounding results, we compare ground truth annotations (box) and predicted temporal endpoints (arrow). See Section~\ref{subsec: qualitative} for more details.}
    \label{fig:vis}
\end{figure*}

\begin{table*}[!ht]
    \input{tables/table_ablations}
    \vspace{-0.2cm}
\end{table*}

\noindent\textbf{Multi-modal Fusion Ablation.} To evaluate our graph matching module's capabilities for multi-modality fusion, we replace it with other commonly used operations in the literature. Following~\cite{2DTAN_2020_AAAI}, we use a Hadamard product between the video moment's features and an average pooled feature for the language query. We adopt several fully connected layers before and after the fusion to keep the model size close to ours. As compared to~\cite{2DTAN_2020_AAAI}, we adopt the learnable masked attention pooling for generating the moment's features, which allows for interactions between query tokens and video snippets before the fusion operation. We refer to this model as VLG-Net\textsubscript{HM} (``Hadamard on Moments'').
We also apply the Hadamard product at the snippet level~\cite{Mun_2020_CVPR,Rodriguez_2020_WACV,Zeng_2020_CVPR} and train VLG-Net\textsubscript{HS} (``Hadamard on Snippets''). Finally, following~\cite{chen_etal_2018_temporally,chenhierarchical,wang2020temporally}, we concatenate each snippet feature with the query feature and use linear layers for the projection in VLG-Net\textsubscript{CS} (``Concatenate on Snippets'').
We can conclude that fusing the modalities at the snippet level tends to perform better. The Hadamard operation has gained quite some traction for its good performance and absence of trainable parameters, making it efficient to compute. However, we argue that the complexity of multi-modal alignment calls for more elaborate strategies for multi-modal fusion. Our graph matching module offers a perspective in this research direction.

\subsection{Visualization} \label{subsec: qualitative}
We show several qualitative grounding results from ActivityNet Captions in Fig.~\ref{fig:vis}. Our VLG-Net can generate precise moment boundaries that match the query well in different scenarios. Worth mentioning, our method can sometimes give predictions that are more meaningful than the ground truth annotation. As shown in Fig.~\ref{fig:vis}(d), although the ground truth only aligns to the beginning of the video, the query ``\textit{Three people are riding a very colourful camel on the beach.}'' can semantically match the whole video. In this case, our VLG-Net gives a more reasonable grounding result. Additional visualizations are reported in the supplementary material.

\section{Conclusion}\label{sec: conclusions}
This paper addresses the problem of text-to-video temporal grounding, where we cast the problem as an algorithmic graph matching.
We propose Video-Language Graph Matching Network (VLG-Net) to match the video and language modalities. We represent each modality as graphs and explore four types of edges, \textit{Syntactic Edge}, \textit{Ordering Edge}, \textit{Semantic Edge}, and \textit{Matching Edge}, to encode local, non-local, and cross-modality relationships to align the video-query pair. Extensive experiments show that our VLG-Net can model inter- and intra-modality context, learn multi-modal fusion and surpass the current state-of-the-art performance on three widely used datasets.

\noindent\textbf{Acknowledgments.} This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the Visual Computing Center (VCC) funding.
\section*{Supplementary Material}
\subsection*{Formulation of Video-Language Graph Matching}
In this section, we provide a detailed overview and formulation of the video-language graph matching.
This inputs to this layer are the enriched video representation $X^{(bv)}_v$ and query representation $X^{(bl)}_l$ outputs of the single modality stack of computational blocks. The graph matching layer models the cross-modal context and allows for multi-modal fusion. To this purpose the video-language matching graph is constructed and three types of edges are designed: (i) \textit{Ordering Edge} ($\mathcal{O}$), (ii) \textit{Semantic Edge} ($\mathcal{S}$), and (iii) \textit{Matching Edge }($\mathcal{M}$).

To aggregate the information, we employ relation graph convolution~\cite{schlichtkrull2017modeling} on the constructed video-language matching graph. Eq.~\ref{eq:gmm} shows the high level representation of the convolutions in this layer.
\begin{align} \label{eq:gmm}
   \mathbf{X}^{(GM)}\! =\! \mathcal{A}_\mathcal{O} \mathbf{X} W_\mathcal{O}\! +\! \mathcal{A}_\mathcal{S} \mathcal{B} \mathbf{X}  W_\mathcal{S}\! +\! \mathcal{A}_\mathcal{M} \Gamma \mathbf{X} W_\mathcal{M}\! +\! \mathbf{X}
\end{align}
Here, $\mathbf{X} = \{{X_{v,1}^{(b_v)},\dots,X_{v,n_v}^{(b_v)},X_{l,1}^{(b_l)},\dots,X_{l,n_l}^{(b_l)}}\}$ is the feature representation of all the nodes in the video-language matching graph.
$\mathcal{A}_{r}$ and $W_{r}$ for $r\in \{ \mathcal{O},\mathcal{S},\mathcal{M}\}$ represent the binary adjacency matrix and learnable weights for each set of edges.
Specifically, $\mathcal{B}$ and $\Gamma$ scale the adjacency matrices $\mathcal{A}_\mathcal{S}$ and $\mathcal{A}_\mathcal{M}$. Both $\beta_{i,j}\in \mathcal{B}$ and $\gamma_{i,j} \in \Gamma$ are proportional to $\mathbf{x}_i^{\top} \mathbf{x}_j$,
\begin{align}
      \beta_{i,j}= \frac{\exp{[\mathbf{x}_i^{\top} \mathbf{x}_j}]}
      {\sum_{\mathcal{A}_\mathcal{S}(k,j)=1} \exp{[\mathbf{x}_k^{\top} \mathbf{x}_j}]}, \\
      \gamma_{i,j}= \frac{\exp{[\mathbf{x}_i^{\top} \mathbf{x}_j}]}
      {\sum_{\mathcal{A}_\mathcal{M}(k,j)=1} \exp{[\mathbf{x}_k^{\top} \mathbf{x}_j}]}.
\end{align}

In practise, to implement GPU-memory efficient graph convolution operation, we replace the time-consuming matrix multiplication by indexing operation of tensors.
Thus, the semantic and matching edge convolution can be present as
\begin{align} \label{eq:gmm2}
   \mathcal{A}_\mathcal{S} \mathcal{B} \mathbf{X}  W_\mathcal{S} = \sum_{j\in \mathcal{N}_i^\mathcal{S}} ( \hat{W}_\mathcal{S}^T [\beta_j\mathbf{x}_{j}||\mathbf{x}_{i}]), \\
   \mathcal{A}_\mathcal{M} \Gamma \mathbf{X} W_\mathcal{M} = \sum_{j\in \mathcal{N}_i^\mathcal{M}} ( \hat{W}_\mathcal{M}^T [\gamma_j\mathbf{x}_{j}||\mathbf{x}_{i}]),
\end{align}
where $\mathcal{N}_i^\mathcal{*}$ is the neighbourhood of node $i$ connected by edge with type $*$, $* \in \{\mathcal{S},\mathcal{M}\}$. The $||$ sign means concatenation of features. $\hat{W}_\mathcal{S}, \hat{W}_\mathcal{M}$ are learnable weights.

Moreover, as shown by A.2 of G-TAD\cite{Xu_2020_CVPR}, our ordering edge convolution, can be efficiently computed as a 1D convolution with kernel size 3.
\begin{align} \label{eq:gmm3}
   \mathcal{A}_\mathcal{O} \mathbf{X} W_\mathcal{O} = Conv1D[X]
\end{align}
Therefore, we can equivalently formulate Eq.~\ref{eq:gmm} as:
\begin{align} \label{eq:gmm_final}
\begin{split}
   \mathbf{X}^{(GM)} & =  Conv1D[X] \\
   & + \sum_{j\in \mathcal{N}_i^\mathcal{S}} ( \hat{W}_\mathcal{S}^T [\beta_j\mathbf{x}_{j}||\mathbf{x}_{i}]) \\
   & + \sum_{j\in \mathcal{N}_i^\mathcal{M}} ( \hat{W}_\mathcal{M}^T [\gamma_j\mathbf{x}_{j}||\mathbf{x}_{i}]) \\
   & + \mathbf{X}
\end{split}
\end{align}

\subsection*{Graph matching edges ablation}
We ablate the contribution of the three different types of edges designed for the graph matching module.
We report in Table~\ref{tab:ablation2}  the performance of VLG-Net for the TACoS dataset when each edge is removed from the architecture.
As previously stated, the \textit{Ordering Edges} or \textit{Semantic Edges} are responsible for aggregating contextual information within the graph matching module. When removed, they lead to noticeable degradation of the performance of $2.15 \%$ and $3.77 \%$, respectively.
Conversely, as expected, when the \textit{Matching Edges} are removed, the performances are severely impaired. We assist in a drop of $27.34 \%$, showcasing the high relevance of the matching operation.
Note that, the removal of the \textit{Matching Edges} prevents the fusion between the modalities. Nonetheless, the two modalities still interact in the Masked Attention Pooling module through the learnable cross-attention pooling method. However, this limited interaction cannot bridge the complex semantic information between modalities.
The ablation showcases the importance of designing effective operation for multi-modal fusion to achieve high performance on the grounding task.
Nonetheless, we can conclude that all edges are relevant and necessary to obtain state-of-the-art performance.

\begin{table}[!h]
    \input{tables/table_ablation2}
\end{table}

\subsection*{Visualization graph matching attention}

In Fig.~\ref{fig:supp_att_vis}, we plot the \textit{Matching Edge} weights (before $\mathrm{SoftMax}$) for two video-query pairs,
where the \textit{Matching Edge} weights are used to measure the similarity between video snippets and language tokens.
In graph convolutions, a \textit{Matching Edge} propagates more information if its weight is high, and vice versa.

\begin{figure}[!ht]
\captionsetup[subfigure]{labelformat=empty}
\begin{center}

    \subfloat[(a) Query: ``She is holding an accordian as she talks.'']{\includegraphics[width=0.75\linewidth,clip]{figs/VLG_Supp_Vis_a.pdf} \label{fig:supp_att_vis_a}}

    \subfloat[\centering (b) Query: ``Three people are riding a\linebreak very colorful camel on the beach.'']{\includegraphics[width=0.75\linewidth,clip]{figs/VLG_Supp_Vis_b.pdf} \label{fig:supp_att_vis_b}}

    \vspace{.1cm}
    \caption{\textbf{Visualization of graph matching attention.} We visualize the \textit{Matching Edge} of the graph matching layer. Correspondence between video snippets and query tokens can be evaluated through the heat-map.
    }
    \label{fig:supp_att_vis}
\end{center}
\end{figure}

In Fig.~\ref{fig:supp_att_vis_a}, we show the grounding result for a 2 minutes accordian tutorial, with associated query: ``She is holding an accordian as she talks''. It can be observed from the blue-yellow heat-map that high scores are assigned to the words ``holding'', ``accordian'', and ``talks'', which are the most discriminative tokens for the query localization.
Below the heat-map, we visualize the snippets of the video. The unrelated snippets (first and last) are associated with low scores. Conversely, more relevant snippets (central ones) have higher \textit{Matching Edge} weights. This entails that the algorithm is successfully correlating important language cues with relevant video cues when performing the graph matching operation.

Similarly, Fig.~\ref{fig:supp_att_vis_b} shows the result for a 22 second camel riding video, for which the associated query is: ``Three people are riding a very colorful camel on the beach.'' The heat-map highlights the keywords: ``riding'', ``colorful camel'', and ``beach'', which are relatively more informative in the query sentence. Interestingly, the word ``riding'' is always associated with high attention weights, and a visual inspection confirms that the action happens throughout the whole video. This showcases that our VLG-Net can successfully learn semantic video-language matching.
If we focus on the first two snippets of Fig.~\ref{fig:supp_att_vis_b}, we can see that both have associated high scores with the word ``riding''. However, given the smaller field of view of the first frame, only the second frame contains a more distinguishable camel. In fact, for this particular frame, we observe a high weight score for the words ``colorful'' and ``camel''. Moreover, the context of ``beach'' can be learned from all the last three snippets.

\begin{figure}[!ht]
\captionsetup[subfigure]{labelformat=empty}
\begin{center}

    \subfloat[(a) Learnable self-attention.]{ \includegraphics[trim={0cm 0cm 0cm 0cm},width=0.75\linewidth,clip]{figs/VLG_Att_Self.pdf} \label{fig:att_self}}\\

    \subfloat[(b) Cross-attention.]{\includegraphics[trim={0cm 0cm 0cm 0cm},width=0.87\linewidth,clip]{figs/VLG_Att_Cross.pdf} \label{fig:att_cross}}

    \subfloat[(c) Learnable cross-attention.]{\includegraphics[trim={0cm 0cm 0cm 0cm}, width=0.87\linewidth, clip ]{figs/VLG_Att_Learnable_Cross.pdf} \label{fig:att_learnable_cross}}

    \vspace{.1cm}
     \caption{\textbf{Masked attention pooling.} Inputs are video nodes $X_v^{(GM)}$ from the graph matching layer and the query embedding $X_l^{(att)}$ computed through self-attention pooling atop the graph matching output. The output $Y$ represents all moment candidates.}
    \label{fig:att}

\end{center}
\end{figure}

\subsection*{Ablation of Masked Attention Pooling}
As presented in the main paper, three different implementations of attention for moment pooling operation have been tested. They differ for inputs and operations to achieve the attention scores. Learnable self-attention (Fig.~\ref{fig:att_self}), only relies on the fused features of video and language modalities, which are the output of the graph matching layer, while the cross-attention and learnable cross-attention configurations (Fig.~\ref{fig:att_cross} and~\ref{fig:att_learnable_cross}) also involve a global sentence representation $X_l^{(att)}$ in the process. (See Sec. 3.5 of the paper for more details.)
We compare the performances of the three different implementations in Tab~\ref{tab:abl_att}.

Following the ablation settings in our main paper, we focus on R@1 IoU0.5 and R@5 IoU0.5 for TACoS dataset.
We find that the cross-attention setup leads to the lowest performance. Conversely the learnable cross-attention configuration instead, obtains the best performance. Therefore we adopt this configuration as default in the main paper.

Interestingly we notice that the learnable self-attention setup can achieve relatively high performance. This can be motivated by the intuition that our graph matching layer can effectively fuse the video and language modalities, and by relying on those enriched features only, can we obtain a good representation of the moment's feature. However, involving a global language representation for guiding the moment creation from the enriched snippets features has been shown to yield the best results.

\begin{table}[!t]
    \input{tables/table_ablation_att}

\end{table}

\begin{table}[!b]
    \input{tables/table_datasets2}
\end{table}

\subsection*{Charades-STA}

\begin{table}[!t]
    \input{tables/table_datasets3}
    \vspace{-0.4cm}
\end{table}

Based on the results obtained from Activitynet-Caption, TACoS, and DiDeMo, our method can theoretically achieve state-of-the-art performance in the Charades-STA dataset. However, we choose not to evaluate VLG-Net on this dataset because of the following observations.

(1) This dataset is characterized by the \textbf{smallest vocabulary size} and \textbf{shortest language annotation} with respect all others datasets (see Tab.~\ref{tab:datasets2} and Tab.~\ref{tab:datasets3})
For example, its vocabulary contains $43\%$ less unique words with respect to TACoS~\cite{TACoS_ACL_2013}, $83\%$ with respect to DiDeMo~\cite{Hendricks_2017_ICCV}, and $92\%$ with respect to Activity-Captions~\cite{Krishna_2017_ICCV}. This fact can potentially hamper the development of successful methods and reduce the applicability to a real-world scenario where \textbf{users might use a richer vocabulary} when querying for moments.
Given the great importance of the language for the task at hand, it's diversity in terms of unique tokens' number, and sentence lengths are important factors. This suggests that Charades-STA is less favourable for evaluating the video-language grounding task.

(2) Charades-STA has the \textbf{smallest number of video-query pairs} (16124) with respect to all other datasets (See Tab~\ref{tab:datasets2}).
As deep learning methods benefit from a large amount of annotated data, the reduced number of training/testing samples makes the dataset less suited for deep-learning approaches.

(3) Most importantly, Charades-STA \textbf{lacks an official validation split.}
In machine learning applications, the validation set is mandatory for hyper-parameters search, while the test set is adopted for evaluating the generalization capabilities of a given method to previously unseen data. Given the absence of a validation set, nor a widely accepted procedure for selecting the best models during the development phase, some might use the test set for tuning the hyper-parameters, therefore, harming the measurement of generalization performance.
The goal of research is to develop tailor-made solutions for specific problems rather than finding the hyper-parameters that can fit the test set best.
A conservative researcher could attempt at using the training set (or part of it) as a synthetic validation split. However, this could lead the model to overfit on the specific set of samples. Other methods could be potentially applied (\eg cross-validation), yet no previous work mentioned the adoption of such techniques.

For all these reasons we can conclude that, despite the popularity of Charades-STA as benchmark for the language grounding in video task, we decide not to evaluate our method on it.

redpajama_set_name: RedPajamaArXiv


The first peppy song 'Vaddi Sharaban' from the upcoming film De De Pyaar De has been released. The film stars Ajay Devgan, Tabu and Rakul Preet Singh in pivotal roles.
The trailer of the upcoming film De De Pyaar De starring Ajay Devgan received an amazing response from the audiences. Helmed by Akiv Ali, the film also stars Tabu and Rakul Preet Singh. Now, the makers have finally released the first song of the movie titled as 'Vaddi Sharaban'. This song features Ajay and Rakul dancing at a friend's wedding brunch. It also shows a few glimpses of their heart touching romantic sequences. The song is choreographed by Bosco Caesar and has been shot at the Leeds Castle, lake and garden. It has been written by Kumaar and has been sung beautifully by Sunidhi Chauhan and Navraj Hans. In the song, Rakul Preet looks beautiful in a pink saree, while Ajay looks dapper in a blue blazer.
De De Pyaar De is a Rom-Com, in which a 50-year-old guy (Ajay) falls in love with a 26-year-old girl (Rakul Preet) and then enters the man's ex-wife (Tabu). It will be a comic fun, filled with emotions. The film is set to hit the theatres on May 17. It also stars Jimmy Sheirgill, Alok Nath and Jaaved Jaaferi in major roles.


I have no clue how to get into the bunker in green fairies' moor. I finished the game some time ago. What am I missing?
You need the Bandit's Key. You get that as a reward for doing the Hanged Bandit's quest: For the Hanged Bandit, Emain Macha Fort -1st Floor, bring him a rope. And here is where he is.


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