text:
Here's Everything We Know About The Deadly Russian Submarine Fire (Updated)
Reports and rumors are flying about the accident, which may have been aboard the shadowy spy sub Losharik, as Putin clears his schedule.
BY JOSEPH TREVITHICK
Hard details about the accident are still limited, but Russia has revealed that 14 sailors died in a fire on board an unspecified deep-diving "research" submarine yesterday, making it the deadliest Russian submarine accident in more than a decade. Unconfirmed reports say that the incident occurred on board the Project 10831 Losharik, one of the Russian Navy's secretive "special projects" spy subs, a submersible it had launched, or its larger modified Project 667BDRM Delfin-class mothership submarine, the BS-64 Podmoskovye. This is still very much a developing story, so all of this information could be subject to change.
The incident occurred on July 1, 2019, within Russian territorial waters, according to a statement from the Russian Ministry of Defense. The statement does not name the submarine, but says it was carrying out "bathymetric measurements," or underwater mapping, at the time. Russia says the blaze is now extinguished and the submarine is in port at Severomorsk, home of the Russian Navy's Northern Fleet's headquarters, but there is no detailed timeline of how long the submarine was on fire or how long it took to get it back pier-side.
"On July 1, a fire broke out in the Russian territorial waters on a deep-water research apparatus designed to study the bottom of the ocean … in the interests of the Russian Navy, during bathymetric measurements," a machine translation of the Ministry of Defense statement reads. "14 submariners died as a result of poisoning by combustion products," it added, indicating they had perished as a result of smoke inhalation, rather than from the fire itself.
The Russian business daily RBC, citing an anonymous source, subsequently reported that fire had occurred on board a submersible that the nuclear-powered Losharik had deployed.
RUSSIAN INTERNET – A reported picture of Losharik.
Russian newspaper Novaya Gazeta separately reported that the fire had been on board Losharik itself and initially reported that the entire crew of 25 had died, though they subsequently changed this to the official tally of 14 fatalities. The updated Novaya Gazeta story also said that the submarine in question had the hull number AS-31, rather than the Losharik's reported hull number of AS-12.
Yet another report from Open Media, again citing unnamed sources, says that the submarine in question was actually BS-64, which reportedly serves as the mothership for Losharik. The small nuclear-powered submarine is supposed to ride closer to the mission area underneath the larger BS-64.
The official description of the submarine is "deep-water research apparatus" could point to a smaller deep-diving submersible, which the Russian Navy and other Russian government agencies do operate for intelligence gathering and special operations missions, scientific research, and deep-sea rescue purposes. However, these vessels typically have crews that are much smaller than 14 individuals. For instance, Russia's AS-28 deep-sea rescue vehicle (DSRV) has a crew of only five, though it can carry up to 20 additional individuals during a rescue mission.
Furthermore, the Russian Ministry of Defense statement also did not suggest that everyone on board the submarine had died, pointing to an even higher total crew compliment. It's also not clear is Losharik can carry something as large as the AS-28, to begin with.
GEORGIY ZIMAREV/SPUTNIK VIA AP – The Russian Navy's AS-28 deep-sea rescue vehicle.
In addition, the Russians also describe their special mission submarines, ranging from the deep-diving, nuclear-powered, yet relatively small submarine Losharik to the new Belgorod, presently the world's longest submarine, as "research" boats. That a mothership submarine carries Losharik ventrally during extended-range missions may be part of the confusion, as well. There is limited information about any of these submarines.
Losharik can reportedly dive to depths as deep as 3,300 feet and its primary mission is to investigate and manipulate objects of interest on the seabed. This could include recovery of sensitive objects for intelligence analysis or to prevent other countries from doing the same, the deployment of sensors or other equipment, or the tapping or cutting of seabed cables supporting military and civilian communications and other networks. The midget submarine is such a concern that U.S. Northern Command mentioned it by name in a 2016 briefing as an example of the kind of "dark targets" that required the development of new underwater detection and monitoring capabilities. You can read more about this submarine here, here.
NORTHCOM – A 2016 briefing slide from US Northern Command regarding desired future sub-surface maritime surveillance capabilities that mentions Losharik by name.
If Losharik or Podmoskovye suffered the fire, one of the immediate questions is whether or not their nuclear reactors were ever in danger and what their statuses are at present. Russian President Vladimir Putin has reportedly canceled his entire schedule for the day and is set to meet with Defense Minister Sergei Shoigu, very likely about the accident.
There were also reports that Vice President Mike Pence had canceled an event in New Hampshire and raced back to Washington, D.C., but this spokesperson subsequently said he had never left the city. White House officials have since further stressed that this change in plans was not related to health issues or matters of national security. Pease Air National Guard Base in New Hampshire, where Pence had originally been scheduled to arrive, was also dealing with a reported active shooter situation this morning, though this now appears to have been a false alarm.
There are separate reports that European Union officials had called an emergency meeting of their "security council," but this entity does not exist. This does not necessarily mean European officials are not holding an emergency gathering, but that remains unconfirmed and there is no clear evidence that it is related to Russia's submarine accident.
1) Putin cancels event
2) Pence returns to DC
3) EU calls emergency meeting??
4)Ted Cruz is boycotting Nikes
5) Marianne Williamson vanishes into thin air
Something's going on
— Dominic Holden (@dominicholden) July 2, 2019
Deleted tweet about all this weird stuff happening at the same time (Pence, Putin, European officials) because there is no European Security Council meeting since it does not exist.
Believed I read that detail on a verified acct but it is not the case.
— Jennifer Bendery (@jbendery) July 2, 2019
Regardless, this new incident is already the deadliest Russian submarine mishap since 2008, when the fire suppression system on the Russian Project 971 Akula-class submarine Nerpa went off accidentally, killing 20 submariners and injuring 41 more. Russia subsequently leased Nerpa to India, where it remains in service as the INS Chakra.
But this is just one in a string of significant Russian submarine accidents since the end of the Cold War. Most notably, in 2000, a pair of explosions sunk the Russian Navy's Project 949A Oscar-class submarine Kursk and led to the deaths of all 118 personnel on board. The attempted rescue operation was also an embarrassing debacle for the Russian government and its then-new President Vladimir Putin.
Fires have not been uncommon, either. In 2011, the Project 667BDRM Delfin-class ballistic missile submarine Ekaterinburg burst into flames in drydock in Murmansk. In 2018, then Russian Deputy Prime Minister Dmitry Rogozin said that the sub had been carrying a full load of 16 R-29RM Shtil missiles, each with four nuclear warheads, at the time, something observers had previously dismissed as a possibility given that it would have been an obvious and serious safety hazard.
In January 2018, there was also an apparent fire on board a Kilo-class submarine at Russia's submarine base in Vladivostok in the country's Far East region, where the Russian Navy's Pacific Fleet is headquartered. At the time, officials insisted this was a drill, but this seemed highly unlikely. It remains unknown what the exact source of that fire was or the extent of the damage.
The War Zone will be following this still-developing story closely and will be sure to update this post as new information becomes available.
UPDATE: 2:20pm EST—
Russian President Vladimir Putin has now met with Defense Minister Sergei Shoigu about the accident. "It's a great loss," Putin reportedly said.
Among the 14 dead were seven Russian Navy Captains of the First Rank, roughly equivalent to the U.S. Navy Captain rank, according to state-run media outlet Sputnik. Two of these individuals had also been decorated as Hero of the Russian Federation, the country's highest honorary title.
First public statement by Putin about the deadly incident on the Northern Fleet: 7 officers out of 14 dead were "captains 1st rank" (Russian equivalent to NATO OF-5/US O-6 captain rank), 2 were Heroes of Russia. Crème de la crème of the Russian Navy (9/x) https://t.co/VXxiXp2UoK
— Ilya.A—Petya.A's bro (@ain92ru) July 2, 2019
Russian officials have still not named the submarine that suffered the accident, but they have announced there will be an investigation.
Russia's Defense Ministry has denied telling Norwegian officials that the cause of the accident onboard the still unnamed submarine was a "gas explosion," according to Interfax. Per Strand, a director at the Norwegian Radiation and Nuclear Safety Authority, had previously told Reuters that this was the answer Russian officials given to his agency in response to queries about the accident and whether there had been any release of radiation as a result.
"We have made checks and we are not monitoring too high radiation levels in the area," Strand had added.
The Kremlin has released a full English readout of Putin's meeting with Shoigu, in which the Russian President describes the submarine that suffered the fire as an "unusual vessel." This would seem to further point to the boat in question being Losharik or another one of Russia's special projects submarines.
https://twitter.com/JosephHDempsey?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1146192039702355969&ref_url=https%3A%2F%2Fwww.thedrive.com%2Fthe-war-zone%2F28804%2Fheres-everything-we-know-about-the-deadly-russian-submarine-fire
Here's Everything We Know About The Deadly Russian Submarine Fire (Updated) – The Drive
Author: The Millennium Report
Author The Millennium ReportPosted on July 3, 2019 Categories Millenium Report
Previous Previous post: Did a sub-protected Russian depp submersible just get into a firefight with a U.S. submarine?
Next Next post: Johnstone: Jingoistic Military Fetishization Is As American As Bald Eagle McNuggets


Perhaps it's no coincidence that the most mysterious man in Los Angeles lives in one of the city's most famous houses. James Goldstein has a face you don't forget, whether you spot him courtside at a Lakers game, front row at the Saint Laurent show, or driving through the Hollywood Hills in his vintage convertible Rolls Royce (the only car he's ever owned since he moved to LA in the 1960s). He has shoulder-length silver hair that frames a face like an old rock star's and a swagger to match; he dresses in head-to-toe couture and is never without one of his many trademark leather and snakeskin hats (and rarely without a hot young thing on his arm).
Many legends float around Los Angeles about Mr. Goldstein. People speculate about his age, his former lovers, and mostly about how he made his money. But there is one thing that is not in question and that is his house: it is one of the most spectacular in Los Angeles.
Set on four acres of tropically landscaped land in Beverly Crest, the house is a jewel in the crown of American Organic Architecture. Built in 1963 by architect John Lautner, the house is so seamlessly integrated into the natural surroundings it is not always obvious if you're inside or out, upstairs or down. Natural light flows through floor to ceiling windows and the 750 drinking glass skylights placed there during construction by the original owner, Paul Sheats, who grabbed his kids and climbed onto the roof where they spent a day stuffing drinking glasses into holes in the ceiling. The desired effect was achieved: the living room has an under-a-shade-tree feel to it. The light comes through the ceiling as if it were filtered through leafy branches. With stunning views out over Los Angeles, windows in the side of the pool that look down into the den (originally Helen Sheats' art studio where she could work and watch the kids swim at the same time), a near-total absence of 90-degree angles, and an incomprehensible volume of poured-in-place concrete, there is nothing about the house that is unimpressive.
Lautner, who originally built the house for the Sheats family, was called back to it in 1972 when Mr. Goldstein bought it in some disrepair. For the next 20 years, the two worked together on what Goldstein calls "perfecting" the house by remodeling and adding onto it. Most of the more magical elements of the house were added in these twenty years of collaboration between the eccentric Goldstein and the space-age-thinking Lautner. From built-in concrete furniture to roof and wall panels that slide open at the touch of a button, the house is at once a nod to the ultramodern era in which it was built and proof of the staying power of good design. It's no wonder that Goldstein, though immensely wealthy and despite his frequent trips to Europe, doesn't own another home. You just can't do better than this house.
Now after four decades of almost daily construction, the house has expanded to include a floating tennis court which sits above "Club James," a fully-functioning private club which hosted Rihanna's 27th birthday party earlier this year attended by Naomi Campbell, Mick Jagger, Beyonce and Jay Z. A James Turrell "skyspace" installation is hidden at the bottom of the hill. The concrete room called Above Horizon designed by Turrell, features a sunken leather bed in the center of the room and panels on the ceiling and wall that open to reveal the sky. Carefully calibrated light and music play while guests recline on the bed and watch the sky. Goldstein says there are two shows per day, both 40 minutes long, one at sunrise and one at sunset. The room throws off your senses; sound and light are different in here and a whole event is built around the sun, twice a day.
Goldstein has never been married and with no family to speak of, the house has become more than a home: it's like his child. The house gets his attention and his resources in return for immeasurable bragging rights and the ability to throw one hell of a party.


text:
Q: How to get IDs of selected cells in VTK python I work on Programmable Filter which should change values of selected cells.
How can I get IDs of selected cells to apply filter on them?
from paraview import vtk
pdi = self.GetPolyDataInput()
numCells = pdi.GetNumberOfCells()
for i in range(numCells): # <- ONLY FOR SELECTED CELLS?
    pdi.GetCellData().GetArray('force').SetValue(i, -1)

A: Currently unsuported from a python programmable filter. you could however first use an ExtractSelection filter.

redpajama_set_name: RedPajamaStackExchange


Descripcion: MARCO de FOTO PATRULLA CANINA from the above 1200x858 resolutions which is part of the Cumpleaños FelizInfantiles directory. Download this image for free in HD resolution the choice "download button" below. If you do not find the exact resolution you are looking for, then go for a native or higher resolution.


text:
A Gift That Keeps On Giving
January 3, 2014 • Cambodia
A small investment in pigs multiplies and completely changes a family's life in rural Cambodia
Wayne Hester and his wife, Tricia, moved to Phnom Penh, Cambodia, with their three boys in 2011. Wayne serves as an Education Technical Adviser with Samaritan's Purse Cambodia.
Teaching our boys the meaning of Christmas and to be givers has been a priority for Tricia and me. We've also wanted to teach our boys to be wise in their giving and to look for ways to invest in people that have a long-term effect.
One of the great tools we've used is a Christmas gift catalog that serves people in need, like the one Samaritan's Purse sends out every year. The idea is that you can purchase a gift for someone in need. But these aren't ordinary gifts; they are really an investment in people's future.
Pig projects have been changing the lives of people in rural Cambodia for years.
We would always decide what to give as a family. Our boys, being animal lovers, always chose to give the gift of chickens or pigs to a family in a developing country. We'd talk about how the chickens or pigs weren't a one-time gift but something that would continue to give if the person who received it learned how to turn it into a small business.
We've done things like this before, but we were never able to see firsthand what the real outcome was in people's lives. Recently, I had the opportunity to visit a family in Kampong Chhnang province who received one of these gifts in 2012. I was completely shocked by what I saw.
How Pigs Can Change a Life
This family was a recipient of pigs through the Seeds of Hope here in Cambodia. Working with Samaritan's Purse, I've learned families receive much more than just the animals. They receive training in animal husbandry, assistance with constructing a proper pigpen, and even training on marketing their pigs once they are ready to sell, increasing the price they receive.
Khiem Moeun and her family were living with a number of challenges. She had eight family members living in a one-room thatch and bamboo home. Her three teenage children not only had to live in this cramped house with five other family members, but they also had a difficult time obtaining an education and staying healthy.
Moeun and her family received help with starting a vegetable garden, and raising chickens and pigs. Moeun soon became successful at raising and marketing her animals. She was an entrepreneur who simply needed a little help getting started. She soon realized she was a natural.
Khiem Moeun's small house held eight family members. Her children didn't have transportation to get to school, and they were often sick from living in such close quarters.
Moeun started out by raising four pigs. She then sold those, saved the profits, and raised four more.
Four pigs turned to eight and eight turned to 16. Soon she had sold the 16 and decided it was time to recognize a dream she had for her family. The original small investment in four little piglets had quickly turned into enough savings to build a new home for her and her family. They went from a small thatched home to a large wooden home that is big enough for the whole family and is much more secure.
However, she didn't stop there. She continued to increase the number of pigs she was raising and saved the profits to purchase a motorbike for the family. Now her teenagers can travel to school.
With the combined income and fresh food from her chickens, vegetables, and pigs, the family's life has been completely changed. What was once a desperate situation is now a healthy, happy home where everyone has big dreams for the future!
While I was in the village, her teenage children helped lead the young people in a song and dance performance at a small village church. As we talked with members of the village, the chief told us this family was not the only success story. He said that as a result of the Seeds of Hope project in his village, many families have now seen increased income, domestic violence cases have dropped significantly, and mothers and fathers have stopped having to migrate to other provinces or countries to work for income. This has led to healthier, stable families throughout the village.
Our boys now know the full impact a simple gift of pigs can have on a family in need.
Dairy Project Feeds into School Meal Program
Farmers in Bolivia are making yogurt for local school children to enjoy during breakfast. ▶
God's Blessing Through Beekeeping
By learning how to raise bees, people in Liberia are rebuilding their lives and incomes... ▶
Families Growing in God's Word Through Agriculture
We are teaching families in Bolivia how to plant and tend fruit and vegetable gardens,... ▶
Families engaged in subsistence farming often live in chronic poverty and malnutrition, lacking the skills to get the most out of their small patches of land. Others have seen their livelihoods wiped out by drought, disaster, or war. Through our Animals, Agriculture, and Livelihoods programs, Samaritan's Purse has helped to provide thousands of impoverished farmers, herders, and fishermen around the world with the resources and training they need to feed their families and even obtain a modest income that can be used for education, medical care, or other needs.

redpajama_set_name: RedPajamaCommonCrawl


Talking Technology was a weekly 15-minute segment, aired as part of the 'Up All Night' programme. I was responsible for researching technology-led content for the slot and filing ideas to the programme producer. These ideas formed the basis of a two-way discussion with the main presenter, typically Rhod Sharp, which was recorded as-live from the station's studios at BBC Television Centre. Around this time I was regularly called upon to talk on radio and television as a 'technical expert'. Appearances included LBC, BBC News, BBC Radio Four and independent local radio stations across the UK.
At Kix FM I was presenter.
I was responsible for page design, layout and subbing on the daily Shields Gazette newspaper and repurposing content for use online. I also designed pages for other publications in the Johnston Press / North East Press portfolio and deputised for the Chief Sub Editor, regularly producing the front and inside pages to live deadline. Even as a native North-Easterner, one of the more challenging aspects of my role was subbing a weekly column, resurrected from the newspaper archive, which was presented in very broad Geordie dialect. I also produced a range of features content for use in print and online.


How much does an Office Manager make in Guelph, ON?
The average salary for an Office Manager is $23.06 per hour in Guelph, ON, which meets the national average. Salary estimates are based on 8 salaries submitted anonymously to Indeed by Office Manager employees, users, and collected from past and present job advertisements on Indeed in the past 36 months. The typical tenure for an Office Manager is 2-4 years.


text:
Q: htaccess rewriterule with order in url I'm trying to put together a rewrite rule for an Apache server.  It should take 'order/###' and change it to 'order.php?id=###'.  For some reason it's rewriting as 'order.php/###'.  If I change it to anything other than 'order' my rule works fine.
Anyone know what's going on?
My .htaccess file looks like this:
RewriteEngine on

## tighten host
RewriteCond %{HTTP_HOST} !^mydomain\.com$ [NC]
RewriteRule .? http://mydomain.com%{REQUEST_URI} [R=301,L]

## Dynamic Pages
RewriteRule ^order/([0-9]+)/?$ order.php?code=$1 [L,NC]

## Static Page Redirects
RewriteRule ^prices$ /prices.php [L,NC]
RewriteRule ^examples$ /examples.php [L,NC]

I have no access to the httpd main server config file on the live server.
I'm using a wamp server for local development.


text:
Home > News > As Black Belt tops state in high gas prices, local leaders warn tax repeal could doom Highway 69 expansion
As Black Belt tops state in high gas prices, local leaders warn tax repeal could doom Highway 69 expansion
In recent days, gas prices have soared nationwide and Alabama has not been immune to the hike. Everyday consumers and local governments alike have been on edge with petroleum prices now exceeding an average of $4 per gallon.
AAA Alabama rated Perry County among the counties with the highest increase in gas prices. At $4.29 a gallon on average, Perry ranks second to Macon County where gas stands at an average $4.31 a gallon. Neighboring Dallas County ranks third in the tier at $4.28, one cent behind Perry County.
Both government officials and citizens have been looking to legislators in Montgomery for relief from the gas tax, a move many states have enacted following recent setbacks in petroleum flow. Alabama's 28 cent tax is midrange among other states per gallon. Nationwide, gas tax rates average at 26.16 cents per gallon. The federal gas tax currently stands at 18.4 cents. However, petroleum prices have been fluctuating, with AAA averaging Alabama's gas prices at $4.14 a gallon on Monday, a one cent decrease from Sunday. Last month, gas prices in Alabama averaged $3.23, increasing to $3.91 last week. This is juxtaposed with the national average, currently at $4.32 per gallon.
So far, legislators have not explicitly indicated whether or not the suspension of the gas tax is on the table for this legislative session. It has been three years since state legislators approved a 10 cent rise in petroleum over a three-year phase, with the final two cents implemented this past October. The first fuel tax increase in the state since 1992, it was noted for its bipartisan support and heralded by Governor Kay Ivey.
Gov. Ivey, however, has not pointed to any specific solutions to provide relief to consumers yet. Senate Minority Leader Bobby Singleton, D-Greensboro, indicated support for a suspension of the tax by not an overhaul of the tax itself.
"There's a western corridor that's going to be built over some of this money that's going to be very much needed. All of that comes from gas tax money. And so I'm not for repealing the gas tax. However, if members of the administration want to entertain giving taxpayers a break at the tank for a period of time, then I'll be willing to entertain those conversations," Singleton told AL.com.
The West Alabama Corridor, a recently proposed four-lane highway from Mobile to Tuscaloosa, is a direct product of the gas tax and its intentions to improve state infrastructure. Namely, roads and bridges have been mentioned in the same breath as the gas tax in terms of potential improvements stemming from the tax hike. Tied to the Rebuild Alabama Act, adjustments to the gas tax were to be made on an annual basis based on reported highway construction costs. Adjustments, though, are limited to one cent per gallon and are not set to take effect until October 2023.
"Repealing the gas tax only takes away the ability to build strong infrastructure in the state, and right now we have a lot of road and bridge work that's going on across the state," Singleton added.
Water issues still plague Marion residents: W. Cornelia St. residents 'can't brush their teeth'
Perry County Board of Education Superintendent leads charge to ease teacher certification requirements
Following back-to-back homicides, Marion institutes a curfew for citizens
NBC will air second annual HBCU Pigskin Showdown game from Marion in December
MMI starts work on new dining hall, first new construction on campus in 30 years
Uniontown council votes down proposal to halt digging in city


text:
ABOUT VRINDA L SETH
Standing proud at five feet tall, Vrinda L Seth is a freelance editorial stylist and a fashion director based in Singapore.Vrinda is known for her strong fashion sense, charisma and instinct for style with broad based experience in editorial fashion styling, campaigns, events, catwalks and editorial makeovers.
Surely her personality has a lot to do with her success, but her styling work is driven by contemporary, feminine aesthetics that aims to balance modernity with simple fusion styles yet bold. Her keen eye for creative details can be found in some of her shoots done as her makeover campaign project series ,whereby ordinary girls from different walks of life are given a total head to toe makeover and they look as good as any professional models.
Vrinda's work has also been featured in Pump Magazine (Los Angeles), Shuba Magazine (New York), Lush Magazine (Singapore) and has done numerous freelance shoots, fashion show styling with some of the best in the industry.
ABOUT VL
Started out in San Francisco, now based in Singapore, VL is a fashion styling and creative production service agency. A niche platform that aims to offer best resources with styling, beauty and fashion visual image solutions. A "one-stop shop" approach, assisting clients from initial conceptualisation and budgeting stages, to the final execution of styling services for individuals, corporates, designers, events, photoshoots, fashion shows and creative direction seeking keen eye to details.
VL's focus is to sell an avant garde image and lifestyle on a collaborative foundation with emerging and existing talents. Providing the best creative service in the field of fashion, beauty, photography, art, entertainment and events.
Freelance fashion stylist available for work and collaborations, for all inquiries drop me an email or just to say HI.
E: vrilekhi@gmail.com
VL STYLIST


Kendall Ford of Eugene is pleased to be currently offering this 2019 Ford F-150 XL with 2,744mi. This is a well kept ONE-OWNER Ford F-150 XL with a fu… ll CARFAX history report. This 4WD-equipped vehicle will handle majestically on any terrain and in any weather condition your may find yourself in. The benefits of driving an Four Wheel Drive vehicle, such as this FordF-150 XL, include superior acceleration, improved steering, and increased traction and stability. This Ford F-150 is beautiful and the interior is clean with virtually no flaws. This incredibly rare Ford F-150 XL is a work of art and is the type of vehicle a true automotive aficionado will appreciate for a lifetime.


I tried to send texts from my Pc but uts a pain!
How fo you list just cell phones for contacts?
Almoat all of my contacts have 2 or more numbers. airdroid lists them all wjth no way to tell which is whi h.
I only want to see contacts with cellphone numbers. Cant text a fax machine.
How do you display cellphone numbers only?
Thanks for your feedback. But sorry that we don't quite understand you. Would you please give us an example?
I think what Gfch means that when you go to send a message and type a name in the "To:" box, it brings up a list of phone numbers, but there is no indication what type of phone number each is; So unless you know the numbers offhand, you don't have any way to know if a number is their mobile number, home, fax, etc.


text:
Hulu subscribers will be able to stream the February 7th Democratic debate
Thank you, Disney
By Makena Kelly@kellymakena Feb 6, 2020, 1:13pm EST
Share All sharing options for: Hulu subscribers will be able to stream the February 7th Democratic debate
Photo by ROBYN BECK/AFP via Getty Images
Friday, February 7th, marks the eighth Democratic debate, and Disney-owned ABC News has graciously partnered with Disney-owned Hulu to stream the event live for subscribers.
Streaming services like Netflix, Hulu, and Amazon Prime Video have largely been left out of the pool of options US voters have to watch candidates discuss pressing policy issues ahead of the 2020 election. But lucky for Hulu, its owner, Disney, also owns the news network that's hosting the upcoming debate.
Hulu's live stream of the event will begin at 7PM ET, and subscribers will also have access to ABC News' pre-show, the debate itself, and the network's post-show coverage. The 2020 Democratic debates have always been available on cable-replacement services like Hulu with Live TV, and subscribers will be able to watch them there just as they were able to before if they want.
"As the only streaming service with both live and on-demand plans, Hulu is uniquely positioned to deliver live events to our on-demand subscribers," the streaming service said in a statement.
Friday's debate will take place at St. Anselm College in Manchester, New Hampshire, only a few short days ahead of the state's primary. The Democratic National Committee also partnered with Apple News to bring live television coverage of the debate to its app and on Apple TV. Earlier this week, Apple News launched its own 2020 election coverage hub with curated news by the app's editors.
Other 2020 Democratic debates have been live-streamed, too, but typically on apps owned by the networks hosting them. Every CNN debate, for example, has been offered on the CNNgo app.
Next Up In Policy


Celia Berkowitz, 64, Fayette Boulevard, died Monday at her home. A life resident of Syracuse, she was a member of Temple Adath Yeshurun. She is the widow of Sidney Berkowitz. Surviving are three sons, Robert A., Steven H. of DeWitt, and Neil D. of Cleveland, Ohio; a brother, George Hodes of Coral Springs, Fla.; two sisters, Ida Youngerman of Fredonia, and Lenore Kroot of Long Island; and five grandchildren. Services will be at 11 a.m; Wednesday at the Birnbaum Funeral Chapel with Rabbi Charles S. Sherman and Cantor Harold Lerner, officiating. Burial will be in Adath Yeshurun Cemetery. Friends may call from 10 to 11 a.m. Wednesday. Contributions may be made to St. Josephs Hospital Kidney Dialysis Unit, or the American Red Cross.


text:
Driving Under the Influence Frequently Asked Questions
What are the penalties for a DUI in South Carolina?
A summary of the penalties* for a DUI in Charleston, South Carolina are as follows:
Anyone convicted of a DUI in South Carolina, whether it is a first offense or subsequent conviction, faces some amount of jail time. First offenders have a minimum sentence of 48 hours up to a maximum of 90 days in jail. The penalty for a second offense ranges from a minimum of five days to three years. Third and subsequent offenses carry jail terms ranging from 60 days to five years. As you can see, penalties for DUI in South Carolina are quite harsh – another reason it is highly recommended to consult with a DUI lawyer in Charleston as soon as possible.
In addition to jail time, those convicted of DUI face fines which increase in severity for multiple offenses. First-time offenders will pay a fine of $400 – $1000, while the maximum fine for third and subsequent offenses is $10,000. DUI convictions in South Carolina also come with the possibility of license suspension from 6 months to two years or a permanent forfeiture after the fourth offense. Your DUI lawyer in Charleston can help you to reduce these fines and jail times.
For a first offender who tests above the legal limit, but blows below a critical value of 0.15, you may be able to serve your time, pay your fine, and go about your life. However, those who are second offenders or those who blow 0.15 or higher on a breathalyzer will need to install a mandatory ignition interlock device for a period of no less than six months. This is a relatively recent addition to South Carolina DUI law, known as Emma's Law, after Emma Longstreet, a six-year-old girl, was killed by a drunk driver.
Driving Under the Influence in Charleston, South Carolina
According to state law in South Carolina, it is illegal to operate a vehicle while impaired by drugs or alcohol. Impairment is defined as a state in which a driver's faculties are appreciably and materially impaired. In terms of measurements, for adults over the age of 21 with a standard (non-commercial) driver's license, the legal limit for BAC is 0.08. If you are tested and have a BAC at this level or higher, then it will be inferred that you were driving while under the influence of alcohol. For those who test at least 0.05, but less than 0.08, this result may be considered along with other evidence in order to infer that you were impaired.
Implied Consent in Charleston, SC
In the state of South Carolina, anyone operating a vehicle inside the state has legally given their implied consent for the testing of their breath, blood, or urine for drugs or alcohol. While you may refuse this testing, it will result in a six-month mandatory license suspension. If you have previously been convicted of DUI or a similar crime, this suspension may be extended to nine months.
Driving with Unlawful Alcohol Concentration, or DUAC, in Charleston, SC
As mentioned, the legal limit for BAC in South Carolina is 0.08. Unlike a DUI, in which it must be inferred that a driver's ability to operate a vehicle was impaired by this alcohol content, a charge of DUAC requires no evidence of impairment. Simply possessing a BAC above the legal limit can result in a DUAC charge. This charge sometimes appears alongside a DUI charge or can be an alternative, depending upon the details of your case. Contact a DUI attorney in Charleston, SC for more information.
Zero Tolerance for DUI Under 21
For drivers in South Carolina who are under 21, the legal limit is a significantly reduced BAC of 0.02. Anyone who tests at this level or higher while under 21 will face an automatic three-month license suspension under the state's zero tolerance laws. If a driver who is under the age of 21 refuses testing, the same six-month suspension applies. These suspension times may all be extended in the case of prior convictions.
Facing DUI or DUAC Charges in Charleston, SC? Contact an Experienced DUI Lawyer Today
If you are facing DUI charges in Charleston, South Carolina, contact a DUI attorney to begin working on an aggressive defense. Even if you have tested above the legal limit on a breathalyzer, this is not a guarantee of guilt or a conviction. South Carolina's DUI laws are complex, and a DUI lawyer can help you understand their implications for your case, your rights, your responsibilities, and your requirements to get your license back, obtain the lightest possible sentence, and get on with your life after making a serious – but common – mistake.
Contact Us Today for a Free Consultation
"Would you like to speak a DUI Attorney? Please submit the online contact form and one of our attorneys will call you as soon as possible.
If you would prefer, we are available, and you can call us now at 208-914-6763.
Write Your Question Here.


text:
Duel Rock
Cub Tor, Minor Tor
The elusive Duel Rock also known as Cub Tor and Minor Tor, the place where William Crossing describes the legend of Roody the Smuggler. The third photo is courtesy of Sylvia Coates and the last is of a small outcrop above and to the north-west of the tor.
William Crossing tells little of the tor itself, even fails to name it. But in 'Gems in a Granite Setting' he does place it close to a curious cut hexagonal stone known as the 'threepenny bit' on the private road that goes between Hecklake and Vixen Tor Cottage; "Near by, on the opposite side of the road, is a tor, which though its size may not render it striking, is nevertheless so well placed as to add in no small degree to the picturesqueness of the spot. It was here that one Roody, who was in league with a band of smugglers, decapitated a sheep with a billhook, when leaping from the rocks to escape from his pursuers."
The name of Duel Rock is introduced by Sylvia Coates in her letter to Tim Jenkinson of December 1998 but she also calls it Cub Tor and Minor Tor. There is no mention of where Duel originates from. It could be something to do with the smuggling tale. However, the name 'Duel Rock' seems to have been known in the 1940s when landowners the Lee family lived in the area. Sylvia also told Tim about a cavern within the rocks which could hide a person hence the tale of Roody is given some credence.
Private (seek permission)
Sylvia Coates
Tim Jenkinson: Lesser Known Tors and Rocks of Dartmoor
William Crossing: Gems in a Granite Setting
https://www.torsofdartmoor.co.uk/tor-page.php?tor=duel-rock


ANKARA, Turkey (AP) — A Turkish newspaper says authorities decided to expel one of its journalists — an Azerbaijani national — after he posted anti-government tweets.
Today's Zaman newspaper said Mahir Zeynalov left Turkey for Azerbaijan on Friday before the government took steps to enforce a deportation order. He was nevertheless escorted by police at the airport.
The decision comes at a time of heightened concerns over media freedoms in Turkey. This week, Parliament approved measures, not yet signed into law by the president, to tighten government controls over the Internet.
Editor-in-Chief Bulent Kenes said Zeynalov was under investigation for allegedly insulting officials in tweets critical of the government's handling of a police corruption probe.
The newspaper is affiliated with an Islamic movement which the government accuses of instigating the corruption probe.


Chromogranin A (CgA), UniProt # P10645, is a 439 amino acid member of the granin family of secretory proteins. Found in vesicles of neuroendocrine cells, CgA is considered a valuable marker for neuroendocrine tumors. CgA is a precursor to a number of biologically active peptides including vasostatin I, parastatin, and pancreastatin.


A local DUI checkpoint will be done in memory of the late Andrea Bailes and to note the passing of "Andrea and Willy's Law" which was recently signed into law and into effect.
POINT PLEASANT — DUI checkpoints are common but this Friday's checkpoint in Point Pleasant conducted by the Mason County Sheriff's Department will have an uncommon element.
According to Chief Deputy Dave Downing of the sheriff's department, the checkpoint will be done in memory of the late Andrea Bailes and to note the passing of "Andrea and Willy's Law" which recently was signed into law and into effect.
Attending the checkpoint, alongside deputies, will be Andrea's mother, Deana Spaulding as well as some of Andrea's friends and family who have created a local, grassroots movement to educate the public on the dangers of drunk driving. In addition, members of SADD (Students Against Destructive Decisions) will also be at the checkpoint. SADD has several members at local high schools in Mason County.
As previously reported, Andrea is the law's namesake, along with Willy Shuman, a graduate of George Washington High School. Bailes lost her life Nov. 18, 2011, at the age of 14 in a drunk driving crash. The crash was caused by a drunk driver who had eight previous convictions. Shuman, a passenger in a vehicle, was killed in a drunk driving crash just days before heading to the U.S. Air Force Academy.
The new law establishes tough penalties for driving under the influence of alcohol, controlled substances or drugs.
The checkpoint is financed with support from the Governor's Highway Safety Program. Officers will briefly stop all traffic at the checkpoint and distribute informational brochures as well as check drivers for signs of intoxication. All efforts will be made to ensure the flow of traffic remains constant and the inconvenience to motorists is minimized.
For additional information contact checkpoint coordinators Sheriff Greg Powers and Cpl. J.R. Gilley at the Mason County Sheriff's Department.
Andrea's friends and family recently attended a special ceremony at the Capitol where Gov. Earl Ray Tomblin signed the bill into law.


Elmira ranges are designed and manufactured in North America.
Choose from traditional cast electric elements, high-speed radiant elements under ceramic smoothtops, high-efficiency sealed gas burners – or a combination of gas and smoothtop. Prefer a gas top and electric oven? You'll love our "dual fuel" options. Design your dream antique kitchen on Elmira Stove Work's website below.


These beautiful baby bibs are available in many colours and patterns, each designed to wear with our award winning tops and leggings. Makes a great gift when bough together.
Each bib is two sided with a different design or colour on each so you can wear which ever suits! The poppers have two sizes to insure a good fit. Dribble never looked so good.


Our actuarial science program is designed to prepare students for careers in the highly regarded actuarial field; the mathematical backbone of the insurance and pension industries. Actuaries are the leading professionals in finding ways to manage risk. It takes a combination of strong analytical skills, business knowledge and understanding of human behavior to manage today's complex risks facing our society. Actuaries solve a variety of problems related to risk, such as determining premiums for insurance companies, helping companies establish their retirement plans, developing life insurance products and assisting banks in managing their assets and liabilities and developing ways to manage their financial risk.
Very few universities of our size have an actuarial program, and ours is growing rapidly. When you major in actuarial science at Ashland University, we'll prepare you for the first two professional actuarial examinations on probability and financial mathematics.
Unlike programs in large universities where you are no more than a face in a crowd, At Ashland, you will actively participate in class and get to know each of your professors well and benefit from their knowledge. Your professors have earned multiple awards for excellence in teaching -- testimony to their exceptional classroom skills and the strong mentoring relationships they develop with students.
You will in all likeliehood graduate having taken and passed the first two professional actuarial science exams. As such, you will be highly attractive to potential employers and command a favorable salary.
Ashland University's Actuarial Program is one of only 4 private institutions from Ohio, and one of only 13 overall from Ohio, with a Universities and Colleges with Actuarial Programs (UCAP) designation. UCAP website.


text:
Gottlieb, Bernhard
GOTTLIEB, BERNHARD (1885–1950), dental scientist. Born in Kuty, Slovakia, Gottlieb trained in Vienna, where he did research in diseases of the teeth, specializing in the cause of caries. He was the first to describe the epithelial tissue which joins the tooth surface to the gum. During World War i he served as a dental surgeon on the Russian-Romanian front. In 1921, he started to lecture at the University of Vienna, and was a pioneer in experimental animal studies which drew the attention of researchers in this field in Europe and the U.S. In 1938, under Nazi rule, Gottlieb was dismissed from his post at the university. With the help of some non-Jewish admirers he was able to leave Austria. Gottlieb was a keen talmudist, and identified with the cause and interests of a Jewish state in Ereẓ Israel. He went to Palestine where he spent two years teaching at the Hebrew University and helped to set up dental clinics. In 1940, Gottlieb emigrated to the United States, where he was visiting professor at the Kellogg Foundation Institute at Ann Arbor, Michigan.
[Jacob Yardeni]
"Gottlieb, Bernhard ." Encyclopaedia Judaica. . Encyclopedia.com. 13 Jan. 2021 <https://www.encyclopedia.com>.
"Gottlieb, Bernhard ." Encyclopaedia Judaica. . Encyclopedia.com. (January 13, 2021). https://www.encyclopedia.com/religion/encyclopedias-almanacs-transcripts-and-maps/gottlieb-bernhard
"Gottlieb, Bernhard ." Encyclopaedia Judaica. . Retrieved January 13, 2021 from Encyclopedia.com: https://www.encyclopedia.com/religion/encyclopedias-almanacs-transcripts-and-maps/gottlieb-bernhard
Vladimir Jabotinsky , JABOTINSKY, VLADIMIR JABOTINSKY, VLADIMIR (Ze'ev ; 1880–1940), Zionist activist, soldier, orator, writer and poet; founder of the *Jewish Legion duri… New Haven , NEW HAVEN , U.S. port city in Connecticut. New Haven has a Jewish population of 24,300 (2001) out of a general population of about 124,000. It was se… Salo Wittmayer Baron , Salo Wittmayer Baron Salo Wittmayer Baron (1895-1989), an Austrian-born American scholar and educator, was the foremost Jewish historian of the 20th… Frederick Chapman Robbins , Weller, Thomas (1915- ) American physician Thomas Weller was corecipient, with John F. Enders and Frederick Robbins, of the Nobel Prize in physiology… Henrietta Szold , Szold, Henrietta SZOLD, HENRIETTA SZOLD, HENRIETTA (1860–1945), founder of *Hadassah, the Women's Zionist Organization of America, and organizational… Jacob Henry Schiff , SCHIFF, JACOB HENRY (1847–1920), U.S. financier and philanthropist. Born in Frankfurt, Germany, he was the descendant of a distinguished rabbinical f…
NOY, DOV
Jewish Studies: Jewish Studies since 1919
Zweig, Ronald W. 1949-
Schechter, Solomon (1847–1915), Jewish Scholar
Endelman, Todd M.
RUDERMAN, DAVID B.
Gottlieb, Beatrice
Gottlieb, Arthur
Gottlieb, Annie
Gottlieb, Andrew R. 1952–
Gottlieb, Alma
Gottlieb, (Maria) Anna
Gottlieb Wilhelm Daimler
Gotti, Vincenzo Lodovico
Gotti, Victoria 1963–
Gotti, John (1940—)
Gotti, Irv 1971–
Gottheil, Richard James Horatio
Gottheil, Gustav
Gottfried, Theodore Mark 1928-
Gottfried, Robert R(ichard)
Gottfried, Paul Edward 1941–
Gottfried, Martin 1933-
Gottfried, Gesina Margaretha (d. 1828)
Gottfried Wilhelm von Leibniz
Gottfried Wendelin
Gottfried Kirch
Gottfried Achenwall
Gottlieb, Daphne
Gottlieb, Edward
Gottlieb, Ephraim
Gottlieb, Erika (Simon)
Gottlieb, František
Gottlieb, Freema (Peninah)
Gottlieb, Gilbert
Gottlieb, Heinrich
Gottlieb, Hinko
Gottlieb, Hirsch Leib
Gottlieb, Jack
Gottlieb, Jacob
Gottlieb, Jedidiah ben Israel
Gottlieb, Lisa 1971-
Gottlieb, Lori 1967(?)-
Gottlieb, Marvin R. 1939–
Gottlieb, Maurycy
Gottlieb, Roger S. 1946-
Gottlieb, Sherry Gershon
Gottlieb, Stephen E.
Gottlieb, Steven 1946-
Gottlieb, William
Gottlieb, William P. 1917-2006
Gottlieb, Yehoshua
Gottlober, Abraham Baer


text:
\section{Conclusion}\label{sec:conclusion}
In this paper, we proposed, CAPSAR, a capsule network based model for improving aspect-level sentiment analysis.
The network is piled up hierarchical capsule layers equipped with a shared-weight routing algorithm to capture key features for predicting sentiment polarities.
Meanwhile, the instantiation parameters of sentiment capsules are used to reconstruct the aspect representation, and the reconstruction loss is taken as a part of the training objective. As a consequence, CAPSAR could further capture the coherent patterns between sentiment and aspect information and is able to detect potential aspect terms by parsing the sentiment capsules when these aspect terms are unseen.
Experimental results on three real-world benchmarks demonstrate the superiority of the proposed model.

\section{Experiments}\label{sec:expt}
In this section, we verify the effectiveness of CAPSAR.
Firstly, we verify the ability of CAPSAR on perceiving the potential aspect terms when they are unknown.
Secondly, we investigate the performance of CAPSAR on standard ATSA tasks, where the aspect terms are always known for test sentences. Finally, we demonstrate the detailed differences of compared methods by case studies.
\subsection{Datasets}
Three widely used benchmark datasets are adopted in the experiment whose statistics are shown in Table~\ref{tab:statistics}. \textbf{Restaurant} and \textbf{Laptop} are from SemEval2014 Task 4\footnote{\url{http://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools}}, which contain reviews from Restaurant and Laptop domains, respectively.
We delete a tiny amount of data with conflict labels which follows previous works~\cite{wang2016attention,tang2016aspect}.
\textbf{Twitter} is collected by~\cite{dong2014adaptive} containing twitter posts. Though these three benchmarks are not large-scale datasets, they are most popular and fair test beds for recent methods.

\begin{table}[!htbp]
    \centering
    \scriptsize
        \begin{tabular}{ccccc}
        \toprule
        \textbf{Dataset} &       & \textbf{Neg.} & \textbf{Neu.} & \textbf{Pos.} \\
        \midrule
        \multirow{2}[1]{*}{\textbf{Restaurant}} & \textbf{Train} & 807   & 637   & 2164 \\
        & \textbf{Test} & 196   & 196   & 728 \\
        \hline
        \multirow{2}[0]{*}{\textbf{Laptop}} & \textbf{Train} & 870   & 464   & 994 \\
        & \textbf{Test} & 128   & 169   & 341 \\
       \hline
        \multirow{2}[1]{*}{\textbf{Twitter}} & \textbf{Train} & 1562  & 3124  & 1562 \\
        & \textbf{Test} & 173   & 346   & 173 \\
        \bottomrule
    \end{tabular}%
    \vspace{-2mm}
    \caption{Statistics of datasets.}
      \label{tab:statistics}\vspace{-5mm}
\end{table}%

\begin{table*}[!htbp]\scriptsize
    \centering
    \begin{tabular}{cccccccc}
        \toprule
        & &  \multicolumn{2}{c}{\textbf{Restaurant}} & \multicolumn{2}{c}{\textbf{Laptop}} & \multicolumn{2}{c}{\textbf{Twitter}} \\ \cline{3-8}
        & \textbf{Models} & \textbf{Accuracy} & \textbf{Macro-F1} & \textbf{Accuracy} & \textbf{Macro-F1} & \textbf{Accuracy} & \textbf{Macro-F1} \\
        \midrule

        \multirow{8}{*}{\textbf{Baselines}}
        &{ATAE-LSTM} & {0.7720} &  {NA} & {0.6870} & {NA} & {NA} & {NA} \\
        &{TD-LSTM} & {0.7560} & {NA} & {0.6810} & {NA} & {0.6662$^{*}$} & {0.6401$^{*}$} \\
        &{IAN} & {0.7860} &{NA} &{0.7210} &{NA} &{NA} &{NA}\\
        &{MemNet(3)} & {0.8032} &{NA} &{0.7237} &{NA} &{0.6850$^{*}$} &{0.6691$^{*}$} \\
        &{RAM(3)} & {0.8023} & {0.7080} & {0.7449} & {0.7135} & {0.6936} & {0.6730} \\

        &{MGAN} & 0.8125 & 0.7194 & 0.7539  & \textbf{0.7247} &  0.7254 & 0.7081 \\
        &{ANTM} & {0.8143} & {0.7120} & {0.7491} & {0.7142} & {0.7011} & {0.6814} \\

        \midrule
        \multirow{3}{*}{\textbf{Ablation Test}}
        &{CAPSAR w/o R} & 0.8185 & 0.7216 &0.7484 & {0.7039} & 0.7255 & 0.7067 \\
        &{CAPSAR w/o H} &0.8188  &0.7226  &0.7461 & {0.7054}  &0.7298  &0.7080\\
        &{CAPSAR} & \textbf{0.8286}& \textbf{0.7432} & {\textbf{0.7593}}  &{0.7221}& {\textbf{0.7368}} & {\textbf{0.7231}} \\

        \midrule
        \multirow{2}{*}{\textbf{Combine BERT}}
        &{BERT} & 0.8476 & 0.7713&0.7787 &0.7371 & 0.7537 & 0.7383 \\

        &{CAPSAR-BERT} & \textbf{0.8594} & \textbf{0.7867} &\textbf{0.7874} &\textbf{0.7479} & \textbf{0.7630} & \textbf{0.7536} \\

        \bottomrule
    \end{tabular}%
    \vspace{-2mm}
    \caption{The average accuracy and F1-score on standard ATSA tasks. The results with `*' are retrieved from the papers of RAM, and other results of baselines are rerieved from corresponding papers.}

    \label{tab:results}%
    \vspace{-3mm}
\end{table*}%

\subsection{Compared Methods}
We compare our method with several SOTA approaches.

\textbf{ATAE-LSTM}~\cite{wang2016attention} appends aspect embedding with each input word embeddings.
\textbf{TD-LSTM}~\cite{tang2016effective}
employs two LSTMs to model contexts of the targets and performs predictions based on the concatenated context representations.
\textbf{IAN}~\cite{ma2017interactive} interactively learns the context and target representation.
\textbf{MemNet}~\cite{tang2016aspect} feds target word embedding to multi-hop memory to learn better representations.
\textbf{RAM}~\cite{chen2017recurrent}
uses recurrent attention to capture key information on a customized memory.
\textbf{MGAN~\cite{fan2018multi}} equips a multi-grained attention to address the aspect has multiple words or larger context.
\textbf{ANTM~\cite{mao2019aspect}} adopts attentive neural turing machines to learn dependable correlation of aspects to context.
\textbf{CAPSAR} and \textbf{CAPSAR-BERT} are models proposed in this paper.
\textbf{BERT}~\cite{devlin2018bert} is also compared to show the improvement of CAPSAR-BERT.

\subsection{Experimental Settings}

In our experiments, we implement our method by Keras 2.2.4.
The word embedding is initialized by Glove 42B~\cite{pennington2014glove} with dimension of 300.
The max length for each sentence is set to 75.
The batch size of training is 64 for 80 epochs, and Adam~\cite{kingma2014adam} with default setting is taken as the optimizer.
For sequence encoder, we adopt the Bi-GRU with the dropout rate of 0.5 for CAPSAR. The pre-trained BERT of the dimension of 768 is used in CAPSAR-BERT. For hyper-parameters in Eq.~\ref{eq:location}, $\alpha$, $\beta$, and $\gamma$ are set to be 3, 10 and 1.
For the capsule layers, there are 450 primary capsules with dimensions of 50, 30 intermediate capsules with dimensions of 150 and 3 sentiment capsules with dimensions of 300.
The default routing number is 3.
For hyper-parameters in the loss function~(cf. Eq.~\ref{eq:loss}), we set $m^+$, $m^-$, $\lambda$ to be 1.0, 0.1 and 0.003 respectively.
Evaluation metrics we adopt are Accuracy and Macro-F1, and the latter is widely used for recent ATSA tasks since it is more appropriate for datasets with unbalanced classes.

\subsection{Results on Standard ATSA}
\subsubsection{Main Results}
Table~\ref{tab:results} demonstrates the performances of compared methods over the three datasets on the standard ASTA tasks.
On such setting, every aspect term is known to all the models, and each model predicts the corresponding polarity for a given aspect term. Here we only consider the longest sentiment capsule is active.
All the reported values of our methods are the average of 5 runs to eliminate the fluctuates with different random initialization, and the performance of baselines are retrieved from their papers for fair comparisons. The best performances are demonstrated in bold face.
From the table, we observe CAPSAR has clear advantages over baselines on all datasets. Our model can outperform all the baselines by a large-margin on both evaluate measures except the F1 on Laptop dataset.
Meanwhile, we also observe that CAPSAR-BERT further improves the performance of BERT. It demonstrates the advantages by combining CAPSAR with advanced pre-trained model.

\begin{table*}[!htbp]
    \centering
    \scriptsize
    \begin{tabular}{p{20em}cccc}
        \toprule
        \multicolumn{1}{c}{\textbf{Sentence}} & \textbf{CAPSAR} & \textbf{ANTM} & \textbf{MGAN} & \textbf{RAM} \\
        \midrule
        1. The \textcolor[rgb]{0,0,1}{[{\bfseries chocolate raspberry cake}]$\rm_{Pos}$ is heavenly} - not too sweet , but full of \textcolor[rgb]{1,0,0}{[{\bfseries flavor}]$\rm_{Pos}$.} & (\textcolor[rgb]{0,0,1}{Pos},\textcolor[rgb]{1,0,0}{Pos}) & (\textcolor[rgb]{0,0,1}{Pos},\textcolor[rgb]{1,0,0}{Neg}\ding{55}) & (\textcolor[rgb]{0,0,1}{Neg}\ding{55},\textcolor[rgb]{1,0,0}{Pos}) & (\textcolor[rgb]{0,0,1}{Pos},\textcolor[rgb]{1,0,0}{Neg}\ding{55}) \\
        \midrule
        2. Not only was the sushi fresh , they also served other \textcolor[rgb]{0,0,1}{[{\bfseries entrees}]$\rm_{Neu}$} allowed each guest something to choose from and \textcolor[rgb]{1,0,0}{we all left happy $($try the [{\bfseries duck}]$\rm_{Pos}$!} & (\textcolor[rgb]{0,0,1}{Neu},\textcolor[rgb]{1,0,0}{Pos}) & (\textcolor[rgb]{0,0,1}{Pos}\ding{55},\textcolor[rgb]{1,0,0}{Pos}) & (\textcolor[rgb]{0,0,1}{Pos}\ding{55},\textcolor[rgb]{1,0,0}{Pos}) & (\textcolor[rgb]{0,0,1}{Pos}\ding{55},\textcolor[rgb]{1,0,0}{Pos}) \\
        \midrule

        3. The \textcolor[rgb]{0,0,1}{[{\bfseries baterry}]$\rm_{Pos}$} is very \textcolor[rgb]{0,0,1}{longer} . & \textcolor[rgb]{ .000,  .000,  1}{Pos} & \textcolor[rgb]{ .0,  .0,  1}{Neg}\ding{55} & \textcolor[rgb]{  .000,  .000,  1}{Pos} & \textcolor[rgb]{ .0,  .0,  1}{Neg}\ding{55} \\
        \midrule

        4. \textcolor[rgb]{0,0,1}{[{\bfseries Startup times}]$\rm_{Neg}$} are incredibly \textcolor[rgb]{0,0,1}{long} : over two minutes.  & \textcolor[rgb]{ .0,  .0,  1}{Neg} & \textcolor[rgb]{ .0,  .0,  1}{Neg} & \textcolor[rgb]{ .0,  .0,  1}{Pos}\ding{55} & \textcolor[rgb]{ .0,  .0,  1}{Pos}\ding{55} \\
        \midrule
        5. However I chose two day \textcolor[rgb]{0,0,1}{[{\bfseries shipping}]$\rm_{Neg}$} and it took over a week to arrive. & \textcolor[rgb]{ .0,  .0,  1}{Neu}\ding{55}& \textcolor[rgb]{ .0,  .0,  1}{Neu}\ding{55}& \textcolor[rgb]{ .0,  .0,  1}{Pos}\ding{55} & \textcolor[rgb]{ .0,  .0,  1}{Pos}\ding{55} \\

        \bottomrule
    \end{tabular}%
\vspace{-2mm}
    \caption{Prediction examples of some of the compared methods. The abbreviations Pos, Neu and Neg in the table represent positive, neutral and negative. \ding{55} indicates incorrect prediction.}
\label{tab:case}%
\vspace{-2mm}
\end{table*}%

\subsubsection{Ablation Test}
Next we perform ablation test to show the effectiveness of  component of CAPSAR. We remove the aspect reconstruction and intermediate capsule layer, respectively, and derive two degrade models, namely \textbf{CAPSAR w/o R} and \textbf{CAPSAR w/o H}. Their performance on the three datasets are exhibited in Table~\ref{tab:results}, and we can observe the degraded model gives a clear weaker performance than CAPSAR, without the sentiment-aspect reconstruction.
We conjecture such a regularizer might be able to learn the interactive patterns among the aspects with the complex sentimental expressions. Meanwhile, we also observe the advantage of the intermediate capsule layer, which illustrates the hierarchical capsule layers may be stronger in learning aspect-level sentiment features.

\subsubsection{Case Studies}

Then we illustrate several case studies on the results of ATSA task in Table~\ref{tab:case}. The predictions of CAPSAR, ANTM, MGAN and RAM are exhibited. We directly run their available models on test set to get the results. The input aspect terms are placed in the brackets with their true polarity labels as subscripts.
``Pos'', ``Neu'' and ``Neg'' in the table represent positive, neutral and negative, respectively.
Different targets are demonstrated by different colors, such as blue and red, etc.
The context which may support the sentiment of targets is manually annotated and dyed with the corresponding color.

For instance, in the first sentence, one target is ``\emph{chocolate raspberry cake}'' of which the sentiment is positive and the context ``\emph{is heavenly}'' supports this sentiment.
In this case, the results of CAPSAR, ANTM, and RAM are correct. For the second aspect ``\emph{flavor}'', our method and MGAN predict correctly.
We conjecture the reason is the sentence contains turns in its expression, which may confuse the existing neural network models.
Similar observation is achieved on the second sentence in which only our method can predict different sentiments for distinct aspects

Next we discuss a target-sensitive case which is shown by the third and the fourth sentences in the same table. The word ``long'' in the exhibited two sentences indicates entirely opposite sentiment polarities, since the expressed sentiment also depends on the considered aspects. These cases are challenge for algorithms to identify the sentiment of each sentence correctly. Among the demonstrated methods, only our approach can predict them all successfully. ANTM, as a strong competitor, can predict one of them correctly. The others fail to give any correct prediction. We do not claim our CAPSAR can perfectly address the target-sensitive cases, however, the results give an initial encouraging potential. We argue that it is the sentiment-aspect reconstruction in our model which makes aspect and its corresponding sentiment become more coupled, and it might be one of the essential reasons why our model achieves a salient improvement on ATSA task. How to specifically explore capsule networks for target-sensitive sentiment analysis is out of the scope of this paper.

\begin{table}[!tphb]\scriptsize
    \vspace{-0.2cm}
    \setlength{\belowcaptionskip}{-0.2cm}
    \centering
    \begin{tabular}{p{1cm}<{\centering}p{1cm}<{\centering}p{1cm}<{\centering}p{1cm}<{\centering}p{1cm}<{\centering}p{1cm}<{\centering}}
        \toprule
        \multirow{2}{*}{\textbf{Datasets}} & \textbf{Avg. Aspect}&\textbf{Avg. SenLen}&\textbf{Pre.@1}& \textbf{Rec.@5}&\textbf{mAP}    \\
        \midrule
        \textbf{Resturant} & 2.76 &16.25&0.8233 & 0.7884&0.7139\\
        \textbf{Laptop} &  2.54 &15.79&0.6408&0.7557&0.6173\\
        \bottomrule
    \end{tabular}%
    \caption{The average Precision@1, Recall@5 and mAP on aspect term detection. The column "Avg. Aspect" and "Avg.SenLen" indicate average number of words on aspect terms in each sentence and average length of each sentence on the test set,  respectively.}
    \label{tab:aspectdetection}%
    \vspace{-2mm}
\end{table}%

For error analysis, we find that all the listed models cannot predict correctly on the last sentence of Table~\ref{tab:case}.
By looking closer to this sentence, we recognize that the sentiment polarity of this sentence comes from implicit semantics instead of its explicit opinion words.
It indicates implicit semantics inference behind sentences is still a major challenge of neural network models, even exploiting capsule networks.

\subsection{Results on Aspect Term Detection}\label{sec:reconstruction}

Next, we investigate whether CAPSAR can detect potential aspect terms when they are unknown during the test. To this end, we use the trained model to predict every test sentence on Restaurant and Laptop datasets but we intentionally conceal the information about the aspect terms in the input. In another word, the model is only fed the test sentence without any other additional input.
Then we de-capsulize the sentiment capsule whose length is longer than $0.5$ and compute normalized dot-product between its reconstructed vector and every word embedding in the test sentence.
These dot-products can be regarded as the probabilities representing the possibility of the word to be part of an aspect term.

A sentence may simultaneously contain multiple sentiments~(c.f. the $2$nd sentence in Table~\ref{tab:case}), which derives more than one sentiment capsule whose length surpasses $0.5$. As a result, we detect the potential aspect terms for every active sentiment capsule, respectively, in our evaluation.
There could be more than one aspect terms for each sentiment category in the same sentence~(c.f. the $1$st sentence in Table~\ref{tab:case}).
Hence we compute Precision@$k$, Recall@$k$ and mean Average Precision~(mAP) to comprehensively verify the effectiveness of CAPSAR on aspect term detection.

\begin{figure}[!htbp]
    \vspace{-0.2cm}
    \setlength{\belowcaptionskip}{-0.2cm}
    \centering
    \includegraphics[height=2.88cm,width=8.5cm]{one_sentiment.png}\vspace{-2mm}
    \caption{The visualization of aspect term detection when single sentiment capsule is active. The real aspect terms are marked in bold face in the sub-figure~(b).}
    \label{fig:aspectdetectcase1}
\end{figure}

\begin{figure}[!htbp]
    \vspace{-0.2cm}
    \setlength{\belowcaptionskip}{-0.2cm}
    \centering
    \includegraphics[height=2.88cm,width=8.5cm]{two_sentiments.png}\vspace{-2mm}
    \caption{The visualization of aspect term detection when multiple sentiment capsules are active. The real aspect terms are marked in bold face in the sub-figure~(b), respectively. }\label{fig:aspectdetectioncase2}
\end{figure}

We retrain our CAPSAR five times and use the trained model to detect aspect terms on the test set.
Table~\ref{tab:aspectdetection} shows the average results. From the table we observe that CAPSAR shows an encouraging ability to extract aspect terms even though they are unknown in new sentences. Meanwhile, the model achieves better performance on Restaurant dataset. We conjecture the reason is the Laptop dataset has more complicated aspect terms such as ``Windows 7''.

Finally, we visualize two test sentences in this evaluation shown as Figure~\ref{fig:aspectdetectcase1} and \ref{fig:aspectdetectioncase2}. Figure~\ref{fig:aspectdetectcase1} demonstrates a case that only one sentiment capsule is active. The sentiment capsule length is exhibited in Fig.~\ref{fig:aspectdetectcase1}(a), and the corresponding dot-products are demonstrated as Fig.~\ref{fig:aspectdetectcase1}~(b). The darker the color in Fig.~\ref{fig:aspectdetectcase1}~(b), the higher value the dot-product is, which means the corresponding word is more likely to be a part of an aspect term. From this figure, we observe that the two real aspect terms hold much higher weights compared with the other words, which derives a correct detection.
We can also obtain similar observations from the case shown as Figure~\ref{fig:aspectdetectioncase2} in which two sentiment capsules are active. In this case, there are two aspect terms, namely ``food'' and ``service''.
From the sentence, we observe that this review has a strong negative sentiment towards on ``service'' while has a neutral attitude to ``food''. After we de-capsulize the active sentiment capsules, we find both ``service'' and ``food'' are perceived by the negative capsule because of the overall negative sentiment of the whole sentence. While in the neutral capsule, only the corresponding aspect term ``food'' is highlighted.

\section{Introduction}

This short example shows a contrived example on how to format the authors' information for {\it IJCAI--PRICAI--20 Proceedings}.

\section{Author names}

Each author name must be followed by:
\begin{itemize}
    \item A newline {\tt \textbackslash{}\textbackslash{}} command for the last author.
    \item An {\tt \textbackslash{}And} command for the second to last author.
    \item An {\tt \textbackslash{}and} command for the other authors.
\end{itemize}

\section{Affiliations}

After all authors, start the affiliations section by using the {\tt \textbackslash{}affiliations} command.
Each affiliation must be terminated by a newline {\tt \textbackslash{}\textbackslash{}} command. Make sure that you include the newline on the last affiliation too.

\section{Mapping authors to affiliations}

If some scenarios, the affiliation of each author is clear without any further indication (\emph{e.g.}, all authors share the same affiliation, all authors have a single and different affiliation). In these situations you don't need to do anything special.

In more complex scenarios you will have to clearly indicate the affiliation(s) for each author. This is done by using numeric math superscripts {\tt \$\{\^{}$i,j, \ldots$\}\$}. You must use numbers, not symbols, because those are reserved for footnotes in this section (should you need them). Check the authors definition in this example for reference.

\section{Emails}

This section is optional, and can be omitted entirely if you prefer. If you want to include e-mails, you should either include all authors' e-mails or just the contact author(s)' ones.

Start the e-mails section with the {\tt \textbackslash{}emails} command. After that, write all emails you want to include separated by a comma and a space, following the same order used for the authors (\emph{i.e.}, the first e-mail should correspond to the first author, the second e-mail to the second author and so on).

You may ``contract" consecutive e-mails on the same domain as shown in this example (write the users' part within curly brackets, followed by the domain name). Only e-mails of the exact same domain may be contracted. For instance, you cannot contract ``person@example.com" and ``other@test.example.com" because the domains are different.

\end{document}

\section*{Acknowledgments}

The preparation of these instructions and the \LaTeX{} and Bib\TeX{}
files that implement them was supported by Schlumberger Palo Alto
Research, AT\&T Bell Laboratories, and Morgan Kaufmann Publishers.
Preparation of the Microsoft Word file was supported by IJCAI.  An
early version of this document was created by Shirley Jowell and Peter
F. Patel-Schneider.  It was subsequently modified by Jennifer
Ballentine and Thomas Dean, Bernhard Nebel, Daniel Pagenstecher,
Kurt Steinkraus, Toby Walsh and Carles Sierra. The current version
has been prepared by Marc Pujol-Gonzalez and Francisco Cruz-Mencia.
\fi

\bibliographystyle{named}
\small{
\section{Introduction}\label{sec:intro}

Aspect-level sentiment analysis is an essential building blocks of sentiment analysis~\cite{liu2012sentiment}.
It aims at extracting and summarizing the sentiment polarities of given aspects of entities, i.e. \emph{targets}, from customers' comments. Two subtasks are explored in this field, namely Aspect-Term level Sentiment Analysis~(ATSA) and Aspect-Category level Sentiment Analysis~(ACSA).
The purpose of ATSA is to predict the sentiment polarity with respect to given targets appearing in the text.
For example, consider the sentence ``\emph{The camera of iPhone XI is delicate, but it is extremely expensive.}'', ATSA may ask the sentiment polarity towards the given target ``\emph{camera}''.
Meanwhile, ACSA attempts to predict the sentiment tendency regarding a given target chosen from predefined categories, which may not explicitly appear in the comments.
Take the same sentence as an example, ACSA asks the sentiment towards the aspect ``\emph{Price}'' and derives a negative answer.
In this paper, we aim at addressing the ATSA task.

Conventional approaches~\cite{hu2004mining,ding2008holistic,kiritchenko2014nrc} incorporated linguistic knowledge, such as sentiment-lexicon, syntactic parser, and negation words, etc., and tedious feature engineering into the models to facilitate the prediction accuracy.
Recently, supervised deep neural networks, e.g. Recurrent Neural Network~(RNN)~\cite{tang2016effective,zhang2016gated}, Convolution Neural Network~(CNN)~\cite{xue2018aspect,li2018transformation} and attention mechanism~\cite{tang2016aspect,wang2016attention,ma2017interactive,chen2017recurrent,huang2018aspect,fan2018multi,wang2018learning,luo2019unsupervised,mao2019aspect} have shown remarkable successes without cumbersome feature designing.
These models are able to effectively screen unrelated text spans and detect the sentiment context about the given target.

Despite these efforts, there is still a major deficiency in previous deep neural network based studies. Specifically, fully labeled aspect terms and their locations in sentence are explicitly required in both training and test process for recent methods, which may derive them degrade to sentence-level prediction and would fail for the test data without such annotations.
To acquire aspect terms on predicted sentences, automatic aspect term detection may lead to error accumulation~\cite{wang2019www}, and manually identifying is inefficient even infeasible.
To support more authentic applications, it calls for an approach that is able to predict potential aspect-related sentiments based on the sentence and what it has learned from the training set.

To this end, we propose a capsule network-based approach to remedy the above problem.
Compared with previous studies, our method is able to explore featured sentiments so as to answer the question: ``\emph{What are the protagonists of the predicted sentiment polarity?}''
We access this goal by leveraging the capsule network\footnote{Here we refer to the capsule network proposed by~\cite{sabour2017dynamic}. Though the models in~\cite{wang2018www} and \cite{wang2019www} also called capsule network in their papers, they are basically built upon RNN and attention mechanisms with distinct concepts and implementations.}~\cite{sabour2017dynamic},
which has achieved promising results in computer vision~\cite{sabour2017dynamic,hinton2018matrix}, natural language processing~\cite{zhao2018investigating,zhang2018multi,zhang2018attention,Jiang2019dataset,du2019capsule} and recommendation tasks~\cite{Li2019sigir}, etc.

The core idea of capsule network is the unit named \emph{capsule}, which consists of a group of neurons in which its activity vector can represent the instantiation parameters of a specific type of entity.
The length of activity vector denotes the probability that the entity exists and its orientation can encode the properties of the entity.
Inspired by that, we propose CAPSAR~(\textbf{CAP}sule network with \textbf{S}entiment-\textbf{A}spect \textbf{R}econstruction) framework by leveraging capsules to denote sentiment categories and enforce the potential aspect information as the corresponding properties.

Specifically, during the training process, the sentence is first encoded with given aspects through a location proximity distillation.
Then the encoded sentence representations are fed to hierarchical capsule layers and the final capsule layer represents all the concerned sentiment categories.
To capture coherent patterns among aspects and sentimental expressions, the sentiment capsules are encouraged to encode the information about the aspect terms. We implement such procedure by reconstructing the aspect with the sentiment capsules.
The reconstruction loss is taken as an additional regularization during the training.
During the test phase, if the annotated aspect term is unseen by the model, CAPSAR can also make prediction and the potential aspect terms in the sentence could be detected by de-capsulizing the sentiment capsules.
We evaluate the proposed methods on three widely used benchmarks. The results show the model has potential in unearthing aspect terms for new sentences, and it can also surpass SOTA baselines in standard aspect-term level sentiment analysis tasks.

\section{The CAPSAR Model}\label{sec:model}

\subsection{Model Overview}

The overall architecture of CAPSAR is shown in Figure~\ref{fig:model}.
It starts from an embedding representation of words. In particular, we represent the $i$-th sentence in a dataset $\mathbb{D}$ with $m$ sentences as $\{w_1^{(i)},w_2^{(i)},...,w_{n_i}^{(i)}\}$, where $i\in [1,...,m]$, $n_i$ is the sentence length, and $w_{.}^{(i)}\in W$ denotes a word where $W$ is the set of vocabulary.
The embedding layer encodes each word $w_{t}^{(i)}$ into a real-value word vector $x_t^{(i)} \in \mathbb{R}^{D_x}$ from a matrix $M\in\mathbb{R}^{|W|\times D_x}$, where $|W|$ is the vocabulary size and $D_x$ is the dimension of word vectors.
The sentence is encoded by a sequence encoder to construct a sentence representation. Next, the output of the sequence encoder is fed to 3-layer capsules. The up-most capsule layer contains $C$ \emph{sentiment capsules}, where $C$ is the number of sentiment categories. The capsules layers are communicated with a simple yet effective \emph{sharing-weight} routing algorithm.

\begin{figure}[!htbp]
    \centering
    \includegraphics[height=7.5cm, width=8.2cm]{capsar-framework.pdf}
    \caption{The network architecture of CAPSAR. }
    \label{fig:model}
\end{figure}

During training, one objective of our model is to maximize the length of sentiment capsules corresponding to the ground truth since it indicates the likelihood of potential sentiments.
Meanwhile, these active vectors of the sentiment capsules are used to model the connections between the considered aspect terms and its corresponding sentiment via an aspect reconstruction. The distance between the reconstructed aspect representation produced by sentiment capsules and the given aspect embedding\footnote{The aspect embedding is calculated by the average of the word embeddings that form the aspect term.} is regarded as an additional regularization. In this manner, we encourage the sentiment capsules to learn the aspect information as their active vectors' orientations.
In the test process, a sentiment capsule will be ``active'' if its length is above a user-specific threshold, e.g. $0.5$. All others will then be ``inactive''. The sentiment prediction of a test sentence will be determined by the sentiment category associated with the active sentiment capsules.

\subsection{Sequence Encoder}

In our model, we adopt Bi-GRU as the sequence encoder for simplicity.
For $i$-th sentence at step $t$, the corresponding hidden state $h_t^{(i)}$ are updated as follows.

\begin{equation}\vspace{-0.5mm}
h_{t}^{(i)}=\begin{bmatrix}\overrightarrow{h}_{t}^{(i)}\\\overleftarrow{h}_{t}^{(i)}\end{bmatrix}
=\begin{bmatrix}\overrightarrow{\mbox{GRU}}(x_{t}^{(i)})\\\overleftarrow{\mbox{GRU}}(x_{t}^{(i)})\end{bmatrix},\quad t=1,\dots,n_i,
\end{equation}

where $h_{t}^{(i)}$ concatenates hidden states of the $t$-th word in the $i$-th sentence from both directions.
Note that more advanced encoders such as LSTM~\cite{hochreiter1997long} or BERT~\cite{devlin2018bert}, can also be utilized as the sequence encoder. We will introduce how to combine CAPSAR with BERT in the following part.

\subsection{Location Proximity with Given Aspect}

In order to highlight potential opinion words that are closer to given aspect terms, we adopt a location proximity strategy, which is observed effective in~\cite{chen2017recurrent,li2018transformation}.
Specifically, we calculate relevance $l_t^{(i)}$ between the $t$-word and the aspect\footnote{$t$ is possibly larger than $n_i$ because of sentence padding.}.

\begin{equation}\label{eq:location}\small
l_t^{(i)}=\left\{
\begin{array}{lcl}
1+\max(0, \alpha+n_i/\beta-|\gamma*(k-t)|) & & {t \leq n_i}\\
0 & & {t>n_i}
\end{array} \right.
\end{equation}
where $k$ is the index of the first aspect word, $n_i$ is the sentence length, $\alpha$, $\beta$ and $\gamma$ are pre-specified constants.

We use $l$ to help the sequence encoder locate possible key words w.r.t the given aspect.
\begin{equation}\label{eq:hhat}\small
\hat{h}_t^{(i)}=h_t^{(i)}*l_t^{(i)}, t\in[1, n_i], i\in[1,m]
\end{equation}

Based on Eq.~\ref{eq:location} and \ref{eq:hhat}, the salience of words that are distant to the aspect terms will be declined.
Note that such location proximity could be optional in the test process when the annotated aspect terms are unavailable.

\subsection{Capsule Layers with Sharing-weight Routing}

The capsule layers of CAPSAR consist of a primary capsule layer, an intermediate capsule layer and a sentiment capsule layer.
The primary capsule layer contains a group of neurons which are constructed by the hidden vectors of the sequence encoder. Specifically, we simply perform convolutional operation over $h_{n_i}^{(i)}$ for $i$-th sentence and take its output to formulate the primary capsules. As a result, the primary capsules may contain the sentence coupled with aspect representations.

Next, the primary capsules are transformed into the intermediate layer and the subsequent sentiment capsule layer via a \emph{sharing-weight} routing mechanisms. Unlike the conventional dynamic routing algorithm in~\cite{sabour2017dynamic}, our routing algorithm simultaneously keeps local-proximity information and significantly reduces training parameters.

The sharing-weight routing algorithm shares the weights between different children and the same parent.
Specifically, we denote the output vector of a capsule $i$ at level $L$ and the total input vector of a capsule $j$ at level $L+1$ as ${p}_i\in\mathbb{R}^{D_{L}}$ and ${\tilde{q}}_j\in\mathbb{R}^{D_{L+1}}$, respectively.
We use a unified transformation weight matrix ${W}_j \in \mathbb{R}^{D_{L+1}\times D_{L}}$ for the capsule $j$ at level $L+1$ to compute the prediction vectors, i.e. ${\hat{p}}_{j|i}\in\mathbb{R}^{D_{L+1}}$, for every possible child capsule $i$ at level $L$.
As a result, the total input ${\tilde{q}}_j$ of capsule $j$ is updated as
\begin{equation}\label{eq:newpi}\small
{\tilde{q}}_j = \sum_{i}c_{ij}\hat{p}_{j|i}, \ \ {\hat{p}}_{j|i}={W}_{j}{p}_i
\end{equation}
where $c_{ij}$ denotes coupling coefficients between capsule $i$ and $j$ and is initialized with equal probability.
During the iterative dynamic routing process, $c_{ij}$ is updated to $q_j\cdot\hat{p}_{j|i}$, where $q_j$ is the output vector of capsule $j$ computed by the \texttt{squash} function.
\begin{equation}\small
q_j = \frac{\vert\vert \tilde{q}_j \vert\vert^2}{1 + \vert\vert \tilde{q}_j \vert\vert^2}\frac{\tilde{q}_j}{\vert\vert \tilde{q}_j \vert\vert}
\end{equation}

Compared with the conventional dynamic routing algorithm, the sharing-weight routing algorithm clearly reduces the number of parameters and saves computational cost.
For example, if two consecutive capsule layers have $M$ and $N$ capsules and the dimensions are $D_{L}$ and $D_{L+1}$ respectively, then the number of parameters to be learned in this layer will be reduced by $(M-1) \times N \times D_{L} \times D_{L+1}$ compared with the original routing algorithm in~\cite{sabour2017dynamic}.

\subsection{Model Training with Aspect Reconstruction}
The training objective of this model is two-fold.
On one hand, we aim to maximize the length of the correct sentiment capsules since it indicates the probability that the corresponding sentiment exists.
To this end, we use a margin loss for every given sentence $i$
\begin{equation}\small
\begin{aligned}
L_1^{(i)} &= v_\mathit{{mask}}^{(i)}\max(0,m^+-\vert\vert v_{\mathit{prob}}^{(i)}\vert\vert)^2\\
&+(1-v_{\mathit{mask}}^{(i)})\max(0,\vert\vert v_{\mathit{prob}}^{(i)}\vert\vert-m^-)^2
\end{aligned}
\end{equation}

Here $v_{\mathit{prob}}^{(i)}=(||q_{1}^{(i)}||,\cdots,||q_{C}^{(i)}||)$ where $q_{j}^{(i)}$ denotes the output vector of sentiment capsule $j$ for the sentence $i$.
Each element in such $v_{\mathit{prob}}^{(i)}$ indicates the existence probability of the corresponding sentiment in sentence $i$; $v_\mathit{{mask}}^{(i)}$ is the mask for sentence $i$; $m^+$ and $m^-$ are hyper-parameters.

On the other hand, we attempt to encourage the sentiment capsules to capture interactive patterns between the aspect and their corresponding sentiments.
To this end, we utilize the output vectors of all the sentiment capsules $q_j$($j\in [1, C]$) to participate in reconstructing the representation of aspect terms.
Specifically, suppose $v_{\mathit{mask}}$ is a one-hot mask\footnote{The dimension of $v_{mask}$ is $C$.} whose element representing the ground truth sentiment is 1, and the rest values are 0.
Then we derive two vectors, namely $v_{\mathit{recon1}}$ and $v_{\mathit{recon2}}$, through this mask and the sentiment capsules.
First, we mask out all but the output vector of the correct sentiment capsule by $v_{\mathit{mask}}$.
Then $v_{\mathit{recon1}}$ is derived by transforming the masked output vector through a fully-connect layer.
$v_{\mathit{recon2}}$ can be derived in a similar manner where $1-v_{\mathit{mask}}$ is used as the mask.
We force both $v_{\mathit{recon1}}$ and $v_{\mathit{recon2}}$ have the same dimension of word embedding, i.e., $v_{\mathit{recon1}}$, $v_{\mathit{recon2}}$ $\in \mathbb{R}^{D_{x}}$, and they are contributed to the aspect reconstruction during the training.

Suppose a given aspect embedding\footnote{If there are more than one aspect in a same sentence, every aspect will be separately trained.} of the sentence $i$ is denoted as $v_{\mathit{asp}}^{(i)}$, another training objective in our CAPSAR is to minimize the distance between $v_{\mathit{asp}}$ and $v_{\mathit{recon1}}$, and to maximize that between $v_{\mathit{asp}}$ and $v_{\mathit{recon2}}$.
\begin{equation}\small
L_2^{(i)} = -{v_{\mathit{asp}}^{(i)}}\frac{v_{recon1}^{(i)}}{\vert\vert v_{recon1}^{(i)}\vert\vert}+ {v_{\mathit{asp}}^{(i)}}\frac{v_{recon2}^{(i)}}{\vert\vert v_{recon2}^{(i)}\vert\vert}
\end{equation}

Finally, the overall loss is the combination of $L_1^{(i)}$ and $L_2^{(i)}$, and a hyper-parameter $\lambda$ is used to adjust the weight of $L_2$.
\begin{equation}\label{eq:loss}\small
Loss = \sum_{i}(L_1^{(i)} + \lambda L_2^{(i)})
\end{equation}\vspace{-3mm}

For prediction, a sentence and an optional aspect in the sentence are fed to the network and the polarity attached to the sentiment capsule with the largest length will be assigned.

\subsection{Combining CAPSAR with BERT}
Our CAPSAR is meanwhile easily extended that utilizes the features learnt from large-scale pre-trained encoders, e.g. BERT~\cite{devlin2018bert}. An upgraded model,namely CAPSAR-BERT, is achieved by replacing the sequence encoder with BERT in CAPSAR. The other structures are kept the same. In this manner, the strength of BERT and the proposed structures could be combined.

\section{Related Work}\label{sec:survey}

\noindent\textbf{Sentiment analysis based on neural network.}
Neural network approaches have achieved promising results on both document level~\cite{luo2018beyond,Yang2016HierarchicalAN,DBLP:conf/ijcai/WangWZY17} and sentence level~\cite{socher2011semi} sentiment classification tasks without expensive feature engineering.
\cite{wang2018www} firstly adopted capsules into document-level sentiment analysis, but their capsule is still based on RNN and attentions, which is different with the capsule designs in~\cite{sabour2017dynamic}.

\noindent\textbf{Aspect level sentiment classification.}
Aspect level sentiment classification is an emerging essential research topic in the field of sentiment analysis.
The purpose is to infer the polarity with respect to aspect phrase or predefined aspect categories within the text.
\cite{tang2016effective,zhang2016gated} used multiple RNN layers to jointly model the relations between target terms and their left and right context. Attention-based methods was brought to this field by many researches~\cite{tang2016aspect,wang2016attention,ma2017interactive,chen2017recurrent,huang2018aspect,fan2018multi} and have achieved promising results.
However, the final representation may still fail to capture the accurate sentiment due to target-sensitive problem~\cite{wang2018target} or because of the noise in data.
Recently, \cite{wang2019www} proposed to use capsules to perform aspect-category level sentiment analysis. However as their previous work~\cite{wang2018www}, the basic capsule module is based on attention mechanisms, which is entirely different with ours.

redpajama_set_name: RedPajamaArXiv


text:
Home News Perth & Kinross
More than 60 staff and pupils in isolation after Kinross High School teacher tests positive for Covid-19
By Jamie Buchan
November 11 2020, 10.24am Updated: November 11 2020, 1.15pm
More than 60 pupils and staff at Kinross High School have been told to self-isolate after a teacher tested positive for coronavirus.
Council chiefs confirmed a total of 56 students, eight employees and one contract worker are staying home after the single case was detected.
A S4 pupil has also tested positive at Blairgowrie High School, forcing 13 classmates into self-isolation.
And there were confirmed cases amongst two children at Dunning Primary School, meaning 24 pupils and three staff need to isolate. The school has a roll of under 120.
The figures were released by Perth and Kinross Council as the region prepares to enter Tier 3.
Fears for 'widespread job losses' as Perth and Kinross coronavirus restrictions are levelled up
The tougher restriction level means that – from Friday – students in S4 and S6 and their teachers will be subject to Scottish Government guidance that they should wear masks during lessons, as well as in corridors and other communal areas of school buildings.
In Perthshire, S1 to S3 pupils have also been requested to consider wearing face coverings in class.
Pressed on the effectiveness of safety measures in schools on the BBC's Good Morning Scotland programme on Wednesday, Deputy First Minister and Perthshire North MSP John Swinney said: "There is a challenging situation in our schools.
"Our teaching staff have done an incredible job in making sure that full-time education has been available for young people in Scotland since August 11.
"We are generally seeing attendance by pupils at around about 90%. The average in Scotland last year was 93% over the whole of the year, so obviously there is a degree of reduction because of Covid.
"In terms of staff attendance, generally we are seeing a very high level with about two-to-three per cent staff absences, varying in different parts of the country."
He said: "When you look into the particular outbreaks that take place that affect school pupils or staff, we tend to find those are cases where there has been an external community transmission. There is very little evidence of in-school transmissions.
"The fundamental point is that our schools are very safe. The mitigation measures that we have insisted upon being in place are being followed, and must be followed in all circumstances, to make sure our schools are safe for staff and pupils."
Last week, it emerged that around 500 pupils had been forced to self-isolated as a result of Covid-19 cases in Perth and Kinross schools.
Conservative councillor Caroline Shiers, convener of the lifelong learning committee said: "I absolutely understand what an anxious time this is for families and for staff in our schools at this time.
"I know that we have been taking all possible steps to minimise the risk of transmission of the virus since the time of schools reopening in August. All appropriate health and safety and hygiene measures are in place and all children and young people and staff are expected to stick to these."
She said: "Staff in Education and Children's Services have worked in partnership with NHS Tayside to ensure that appropriate contact tracing is undertaken when any positive cases are identified.
"Everyone should stay off school or work and get tested if they experience any COVID-19 symptoms, such a cough, fever or loss of taste or smell, even if they are mild."
Kinross-shire councillor Willie Robertson (Lib Dem) added: "Teachers are very much on the front line in his pandemic and throughout Perth and Kinross and Scotland, many pupils and teaching staff are having to self-isolate because of outbreaks.
"I hope that all those who have contracted Covid-19 recover quickly and no lasting harm is caused."
State school teachers 'report higher staff absences than private school peers'
Covid-19 absences in schools: Latest figures as thousands of pupils miss school due to virus
Around 500 former teachers sign up to tackle staff shortages in schools
Number of pupils and staff absent from England's schools rises after Christmas
Blairgowrie High School
dunning primary school
kinross high school


Imua Family Services along with Title Sponsor BLUESMITHS Crafted Waterwear announced the date of the 7th Annual BLUESMITHS Paddle Imua race on Saturday, May 5, 2018.
A physical embodiment of the Hawaiian word Imua ('to move forward') Bluesmiths Paddle Imua is a multi-discipline paddling race with proceeds directly benefiting Imua Family Services' Camp Imua program, a week long recreational camp for children with developmental disabilities, a Maui summer tradition for over 40 years.
Registration will be open on Jan. 1, 2018 at www.paddleimua.com. All racers at registration are given the opportunity to magnify their impact by crowd fundraising from friends and family in support of their participation. Last year an additional $1,500 was added to the fundraiser event by Paddle Imua racers.
BLUESMITHS Paddle Imua brings together hundreds of ocean athletes from around the world to support this worthy cause that provides an opportunity for children with developmental challenges or disabilities to enjoy a free summer camp experience. Paddle Imua's goal is to raise $50,000 in 2018 which will play a monumental role in keeping Camp Imua's tradition alive by providing over 30% of funds necessary to operate the program.
BLUESMTIHS Paddle Imua follows the renowned Maliko Downwind Run and concludes at Hawaiian Canoe Club in Kahului. It is the fastest growing multiple discipline ocean paddle "fun race" open to men and women of all ages in Stand-up Paddle (SUP), OC1, OC2, OC6, prone paddleboard and surf ski categories.
In 2017, BLUESMITHS Paddle Imua added experimental Hydrofoil SUP and Laser Sailboat categories to the race and expect to see this competition increase in 2018. Check-in will open at 10 a.m. at Maliko Gulch, and the race will begin promptly at 1 p.m. Trophies will be given to the top three finishers in each division.
BLUESMITHS Paddle Imua also includes a post-race celebration called the 'Ohana Festival, which is hosted at Hawaiian Canoe Club. This free event is open to the public where they can enjoy entertainment provided by Old Lahaina Lūʻau, tasty food from a selection of local food trucks, relays, games, prizes, and more. Race registrants will receive lunch, a commemorative 7th anniversary canteen, event tee, and access to the beer garden (ages 21+). The first 150 people to register will also receive a special Mahalo Bag filled with various gifts from sponsors.
Current event sponsors include: BLUESMITHS, Maui Jim, Aloha Mixed Plate, Hawaiian Canoe Club, Deep Relief//Peak Performance and Old Lahaina Lūʻau. For more info about becoming a sponsor or making a donation in support of Paddle Imua, visit www.paddleimua.com.