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(Image credit: OnePlus)
CES may not be the biggest launchpad for phones, but try telling that to OnePlus. The Chinese phone maker is using CES 2022 to introduce us to its latest flagship, the OnePlus 10 Pro.
We got our first official look at the OnePlus 10 Pro this evening, and while there's a certain similarity to past OnePlus flagships, we'd note the camera array is pretty distinctive on this year's phone. The Hasselblad branding is back on the camera array, too, so OnePlus' partnership with the Swedish lens specialist is still in effect. That's good because the OnePlus-Hasselblad team-up led to a vastly improved camera in the OnePlus 9 series.
We've also gotten our first hint as to where the OnePlus 10 Pro will ship first. It's arriving in China on January 11. No word on other markets, but we're hopeful that a U.S. launch is in the cards sooner rather than later.
(Image credit: Targus)
The new Targus Cypress Hero Backpack will help you get your stuff back if you misplace the bag. That's because it has Apple Find My technology baked in. Thanks to the built-in location tracker, owners can find the backpack using the Find My app should it go missing.
Another cool perk: if your iPhone is lost you can press a button in the backpack to ping your phone. Targus says that the Cypress Hero comes with a replaceable battery that's chargeable via USB.
The Targus Cypress Hero will be available for $149 in spring/summer 2022.
Products that can track health data figure to garner a lot of attention at CES, and EarlySense is hoping that includes its Insight+ sensing system for gathering health data while you sleep. As demonstrated in the above video, Insight+ using a sensing pad that slips under a mattress. From there, it's able to not only measure sleep patterns, but vital signs and patient movement.
Plenty of home sensors promise similar tracking for devices that consumers directly interact with, but EarlySense targets Insight+ at doctors who want patient data that doesn't require any interaction from those patients. Right now, EarlySense is making Insight+ available for clinical research purposes; the technology should be broadly available later this year.
(Image credit: Udelv)
It's not CES until someone breaks out a self-driving car, so Udelv's announcement of the Transporter truly heralds the launch of CES 2022.
Transporter isn't a car, really — it's a delivery vehicle. But it's a self-driving, cabless delivery vehicle that can carry up to 2,000 pounds of cargo and make 80 stops per run. Detach the pod from the vehicle, and you can now haul 5,000 pounds of cargo.
The idea is that Transporter can haul goods from merchants directly to shoppers, with a Udelv app making it easier to retrieve the goods you ordered. A video helps explain it all for us.
The first Transporters aren't expected to be commercially deployed until 2023, with Udelv hoping to have 50,000 units of the Transporter making their way down streets near you by 2028.
(Image credit: Sengled)
Sengled has a lot of smart lighting announcements for CES 2022 — everything from Wi-Fi outdoor string lights designed for outdoor spaces like patios to video-sync TV light strips that adjust to the on-screen action on your TV. But the announcement that really caught our eye involves a smart light bulb from Sengled that offers health-tracking capabilities.
Specifically, Sengled's Health Monitoring Light features the company's A19 bulbs that are capable of tracking sleep as well as other biometric measurements such as your heart rate, body temperature and other vital signs. The bulbs are equipped with a Wi-Fi/Bluetooth dual chip for connectivity. Set up a second A19 bulb, and you create a Bluetooth-based mesh network that can figure out if someone in the room has fallen and needs help.
The product's already picked up a CES 2022 innovation award, though it will be some time before we see the Health Monitoring Light out in the real world. The product is in the early stages of development, and Sengled isn't expecting to launch it until the last three months of the year.
(Image credit: Xfinity)
Not many consumers have Wi-Fi 6 routers yet, and with good reason — there's not a lot of devices that support this newer, speedier version of Wi-Fi. Comcast is looking to future-proof its Xfinity customers with its new xFi Advanced Gateway. According to the company, the new gateway will offer up to three times the bandwidth as its current products, thanks to the addition of a new low-latency 6 GHz band that should reduce interference.
In addition, the gateway will serve as a smart home hub, and support both the Zigbee and Matter standards. It will also work with Xfinity extenders and Pods, which create a mesh network in your home. The new gateway will be available in the coming months, and will cost $14 per month — the same as its current rates.
(Image credit: Eve)
While Matter may change things, when it comes to the best HomeKit-compatible smart home devices — namely, outdoor security cameras. Eve's new Outdoor Cam ($250, available in April) is just the third such device, and only the second that works with HomeKit Secure Video. This 1080p camera has a 157-degree field of view, two-way communication, and infrared motion detection and night vision.
The Eve Outdoor Cam needs to be hardwired, and all video is stored in the cloud; it can distinguish between people, animals, packages, and vehicles, but you need an iCloud+ account (starting at 99 cents per month for 50GB of storage) in order to use these features.
(Image credit: L'Oreal)
Coloring your hair can often be a messy process, but L'Oreal hopes to change that with the new Colorsonic. This handheld device uses an innovative process to mix hair color and apply it evenly.
After you select your hair color from among 40 shades on the Colorsonic website, the kit is shipped directly to your home. Then you load the cartridge into the device for on-demand color mixing and use the brush to apply the color. A oscillating nozzle moves 300 times a minute to cover hair quickly.
We don't have a price yet, but L'Oreal plans to launch the Colorsonic in early 2023.
(Image credit: Consumer Technology Association)
What will be some of the biggest trends at CES — and, to a larger extent — in 2022? We talked to several analysts to find out what they had to say about a number of topics, from electric vehicles to smart home to the future of work in CES 2022: 5 trends we're expecting as the show goes on.
We also expect that there will be a lot of TV news coming out of CES; check out our TV trends to watch in 2022 to get a preview of the tech that could be announced at the show.
(Image credit: LG)
Peloton better look out if LG ever makes this concept exercise bike, which features three large vertically mounted LG OLED panels, which would give a rider a real sense of immersion when spinning.
LG's exercise bike wasn't the only concept the company showed off; there was also the LG Media Display, the mother of all recliners with a built-in OLED TV and Cinematic Sound OLED (CSO) technology.
For more details and photos, check out our full story: LG's immersive exercise bike with flexible OLED panels looks amazing
(Image credit: Acer)
Acer has announced three new Chromebooks aimed at students:
The Acer Chromebook Spin 513 ($599, available in June) has an aluminum case, MIL-STD 810H durability, a 13.5-inch VertiView display with a 3:2 aspect ratio, up to 10 hours of battery life, and is powered by an octa-core MediaTek Kompanio 1380 processor. In addition, it has Wi-Fi 6, a backlit keyboard, and a USB Type-C port. As its name suggests, its display can spin to be used as a regular laptop or tablet.
The Acer Chromebook 315 ($299, available in January) has a 15.6-inch anti-glare FHD display with an optional touchscreen, numeric keypad, and an HDR webcam with flare-reducing technology. It uses an Intel processor, lasts up to 10 hours on a charge, and has two USB Type-C ports and a microSD card reader.
The Acer Chromebook 314 ($299, available in June), also has MIL-STD 810H durability and will run up to 10 hours on a charge. It also uses Intel processors, has a 14-inch FHD IPS touchscreen display, Wi-Fi 6, and two USB Type-C ports.
(Image credit: Arlo)
Recognized as a 2022 CES Innovations Award honoree, Arlo has unveiled its new Arlo Security System, which is a DIY security monitoring solution. It boats a "first-of-its-kind" all-in-one multi-sensor that's capable of eight different sensing functions. And it comes with a security hub with integrated keypad.
The multi-sensor can recognize motion, door/window openings and tilt, water leaks, light/temperature changes and T3 and T4 smoke/CO alarm patterns. Arlo says the 1.1-inch wide multi-sensor can be placed anywhere without hard wiring.
The hub offers a siren, motion and smoke alarm sensor, and with NFC support you can quickly arm and disarm the system with a tap of your phone. Plus, the Arlo Security System works with existing Arlo security cameras.
There's no price or availability info yet but stay tuned.
(Image credit: Kohler )
Many of us like the idea of taking a relaxing bath, but it's all too easy to get distracted while the tub is filling. Enter PerfectFill from Kohler, which is being announced at CES 2022.
PerfectFill is a smart drain, digital/app controller and bath filler. It's designed to draw a bath to your preferred temperature and depth, and you can do it either via a voice command or the Kohler Konnect app. That way you don't have to keep an eye on the batch as it fills.
Kohler says PerfectFill will be available in May 2022, and pricing starts at $2,700.
(Image credit: PanelSemi)
CES 2002 isn't just about the products you can buy soon. It's about innovations that will be paving the way for the gadgets of tomorrow. PanelSemi, a Taiwan-based panel semiconductor provider, is unveiling what it calls the "world's first flexible Active-Matrix Mini LED Display.
Primary is a 55-inch display that weighs only 1 kilogram and ultra thin at only 1mm thick. And it's flexible with a 360-degree viewing angle. The company says that it uses 9-inch tiles that make it easy to assemble, and you get four hours of use out of a 10K-mAh power bank.
PanelSemi says the the display can transform into concave, convex, s-curve, cylinder and rollable form. Use cases for now include commercial advertising, robotics and subways, as well as boardroom applications.
(Image credit: Baracoda)
A lot of us are using thermometers these days as the world tries to fight off the omicron variant, and Baracoda has arrived with something different at CES 2022. The BCool is a thermometer that never needs batteries, as it charges with just a few shakes via patented sensor technology. After that you just press the button and hold or slide it slowly across the forehead.
The temperature readings can then be sent to the BCool app, and you can add notes for household members. The BCool is made of recycled plastic and is waterproof for easy cleaning. We don't know a price yet, but the company is targeting a release date for Q4 2022.
(Image credit: Jabra)
A new pair of Jabra earbuds is usually a cause for celebration, and the Elite 4 Active announced at CES 2022 looks like being another great addition to the company's enviable lineup.
It certainly looks like an upgrade in some regards over the excellent Elite Active 75t, our current pick as the best wireless earbuds, despite costing a fair bit less — not least because it has Active Noise Cancellation, rather than the software-based passive version.
IP57 water resistance is another bonus, but the real star could be the price — just £119 in the U.K., which is £60 less than the RRP of the Elite Active 75t. No release date or price has yet been announced for the U.S., but expect to see it cost around the $120-$130 mark if and when it does hit these shores.
(Image credit: BBalance)
Until now, I never knew I needed a smart bath mat. But it turns out I do — and it's made by BBalance.
The BBalance smart bath mat is a weight scale, balance and stability reader, and posture evaluator in one, all topped with a foot-friendly, removable, washable, soft cotton finish.
Obviously it also syncs with an app (for iOS or Android), so you can track your weight, posture and more, and it even has footprint recognition — because of course it does. No word yet on whether it also dresses you afterwards, but that can only be a matter of time.
The BBalance smart bath mat is available to pre-order now, with deliveries expected in the spring. But you'll need deep pockets — it'll cost $449 as standard, though a current sale brings that down to $349.
(Image credit: Samsung)
One of the coolest products revealed in Samsung's 2022 TV lineup wasn't a TV at all, but rather its New Eco Remote.
This can charge via radio waves from your router — yes, really — or, as with last year's model, via the light from your living room's light fixtures. Plus it has a USB-C port as a backup. It'll also come in a white version this year, to better match some of the Lifestyle TV offerings, and will ship with all Samsung 2022 TVs.
(Image credit: InWith)
CES is always equal parts consumer tech you can actually buy soon and futuristic tech that looks amazing but that's not necessarily coming to the market for a while yet.
InWith's smart contact lens falls firmly into the latter camp — although I very much hope it does make its way into my eyes before long. Billed as "potentially the most advanced platform for viewing the coming Metaverse," it will apparently offer "tunable vision" via mobile device and "augmented vision" — which sounds suitably Cyberpunk-esque to excite me.
InWith says it's planning to achieve FDA Breakthrough Clearance this year and to put the lenses on the market soon after, so I'll be watching with interest (through my sadly non-smart contact lenses).
We were fortunate to get a sneak peek at several new products ahead of their CES 2022 reveal, and the Alienware Concept Nyx was one of our favorites.
This is best thought of as a cloud gaming service like Xbox Game Pass, Google Stadia or Nvidia GeForce Now — but one that streams locally via a central server in your home.
We were impressed by it in our Alienware Concept Nyx demo, with the Concept Nyx's ability to stream two different games to one TV simultaneously a particular highlight. We'll be interested to see how this one develops over time.
If we had to choose just one highlight from Samsung's 2022 range, it would have to be the first MicroLED TV that could fit in your home.
Admittedly, at 89 inches the new Samsung MicroLED TV is hardly small, but it'll be a lot easier to find space for than last year's 99-inch and 110-inch models, let alone the first 146-inch version of The Wall TV from 2019.
Size-aside, the MicroLED TV promises to be an exciting addition to Samsung's 2022 lineup. It has a million micro-sized LEDs, bringing a 10% wider color offering, and should also have an improved audio performance thanks to Dolby Atmos support and Object Tracking Sound Pro.
Samsung has already been busy, showing off its entire 2022 TV lineup.
There are far too many highlights to list here, but the 2022 range covers everything from new 8K and 4K Neo QLED sets to a new McroLED TV, refreshed versions of The Frame lifestyle TV and updates to the Samsung smart TV platform. Even Samsung's Eco Remote is getting a refresh.
Get all the details in our guide to the Samsung 2022 TV lineup.
Good morning, and welcome to Tom's Guide's CES 2022 live blog!
The show starts in earnest today, with the first of two media days and with plenty of products due to be revealed later.
The two big events on the horizon are the CES 2022 Tech Trends to Watch (4-4.45 p.m. PST) and CES Unveiled (5-8.30 p.m. PST) conferences. The latter, in particular, tends to showcase exciting products from various startups as well as some established brands.
OnePlus 10 Pro rumored release date, price, specs and more
Samsung Galaxy Buds 2 vs. OnePlus Buds Pro: Which ANC earbuds win?
Best Smartphone Cameras in August: Apple, Samsung, Xiaomi, OnePlus — We've tested them all!
These are the Best Android Phones in India in September: OnePlus, Xiaomi, iQOO, Realme, and more!
Categories Lite Tags Chrome Post navigation
Hands off: Moen's new smart faucet doesn't even need a handle
This is the OnePlus 10 Pro, and it launches soon in China

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Troop 21215, a new initiative from Girl Scouts of Central Maryland, held its first meeting last weekend at 1st Mariner Arena in downtown Baltimore. Troop 21215 was launched with the aim of providing girls who do not have Girl Scout troops in their neighborhoods the chance to have the Girl Scout troop experience.
Girls who attended participated in a flag ceremony, sang songs, snacked on Girl Scout cookies, and stayed to watch the Baltimore Blast game. The meeting was a great success and fun for all.
While the girls, parents and staff who participated had a wonderful time, there is still a great need for volunteers to lead troops, help run events and programs. There are opportunities for everyone, no matter how limited your time commitment is. Visit www.gscm.org/volunteeropportunities.html to learn more about how you can help.


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IDW Publishing Prepares for the launch of a brand-new G.I. Joe!
by generalsjoes | Jul 29, 2019 | IDW | 0 comments
Yes, I'm a little late to the table on this one, what with new G.I. Joe writer Paul Allor sharing plenty of #GIJonday images on his Twitter account, but for anyone who isn't already aware, IDW Publishing is launching a brand new G.I. Joe title this September!
Looking at things from the perspective of COBRA ruling the world, this new series sees the G.I. Joe team taking to the streets, fighting back against the power of COBRA Commander and the ruling class. So far, the designs look like a lot of fun, taking some obvious cues from the vintage looks, but adding a nice little twist to things.
The ashcan has also been released, which has revealed the first few pages of the comic. It's available for Pre-Order now through Diamond and your favorite comic stores, so show some support for G.I. Joe… by the sounds of things, they're up against a wall!
Check out some of the images below, and follow Paul Allor on Twitter for some of the latest reveals.
Five-Page Preview (Courtesy of Westfield Comics)
Images shared via #GIJonday


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As I neared the end, I slowed down because I knew what was coming and yet didn't want to believe that it happened.
Doris Kearns Goodwin's biography of Lincoln, Team of Rivals, describes not just Lincoln's life but also the lives of his political rivals, who would eventually be part of his cabinet to form the "team of rivals".
A lot of Goodwin's book describes the events of the American Civil War, which I wrote about after I read Battle Cry of Freedom.
Coming from a humble background, Lincoln lived in the log cabin he was born in. His mother died young. For several months, whilst his father went in search for a new wife, the young Lincoln and his sister lived alone. Lincoln liked reading. His father returned with his new wife, and she encouraged Lincoln to read more. He read classics such as Shakespeare, Byron, Bunyan — memorising extracts from the writings. Much of his learning came through reading alone rather than formal education. Lincoln was not just a bookworm: he was athletic, excelling at running, climbing and wrestling.
Lincoln paid little attention to his appearance. He was tall and scruffy. With his stovepipe hat, he stood over two metres (seven feet). On early dates with women, he tried to hide his gangly arms and legs. In his early professional life, people were often bemused when they saw him, not getting a positive impression. One person noted on first meeting Lincoln for a legal case that he had "neither coat nor vest" and didn't seem a lawyer of sufficient standing for the case he was looking to recruit him for. However, Lincoln's charm, openness, good humour, stories and guilelessness usually won people over.
Lincoln was a compromiser. He didn't make enemies gratuitously; and he bore no grudges against those who disliked or wronged him. The lawyer Edwin Stanton, for example, showed initial contempt for Lincoln, the "long armed ape", as he once called him. Nonetheless, when Lincoln first saw Stanton in action, he found Stanton's legal arguments "a revelation". Lincoln resolved to go home "to study law". Years later, when Lincoln was forming his first cabinet, he knew that Stanton's clarity of thought and organisational skills were just what he needed and offered him the "most powerful civilian post within his gift" — the post of secretary of war. As secretary of war, Stanton came to respect and love Lincoln.
When Lincoln stood for leader of the Republican Party, he was not the favourite. (At the time, the Republicans were more liberal and the Democrats more conservative — the opposite of now.) Edward Bates and Salmon B Chase were more likely to win than Lincoln, but the clear favourite was William H Seward. These rivals were keener abolitionists. Lincoln, himself in favour of abolishing slavery, realised that the country was divided. Simply to abolish slavery with half the country objecting would result in chaos. This caution made him in some ways the least objectionable.
After winning the nomination for the Republican Party, Lincoln went on to win the presidential race. In picking his cabinet, he chose his three rivals, who all considered themselves more suitable for the role of president. When he was picking his cabinet, someone told him that Chase considered himself more intelligent, more capable than Lincoln. Instead of taking offence, Lincoln asked the person to find a whole cabinet of people who considered themselves more capable than him!
Until his death, Lincoln never stopped being generous to his rivals. Even Chase, who conspired to stand against Lincoln in the second presidential election, was treated kindly. Lincoln refused to remove him from the cabinet. Eventually Lincoln did accept Chase's second resignation request, but he bore no ill-will. When it was time to pick the Chief Justice of the Supreme Court, Lincoln offered the job to Chase. (Chase, who's ambition to become president knew no bounds, even after Lincoln died, would never gain the highest office.)
Lincoln believed that having a cabinet of people who agreed with him was no foundation for good decision making. As Seward later remarked, "a Cabinet which should agree at once on every such question would be no better or safer than one counsellor." Disagreement and alternative views were ultimately beneficial. The cabinet members didn't disappoint Lincoln — he may have got more than he bargained for! Despite that, Lincoln had a singular ability to transcend personal vendetta, humiliation, or bitterness. By gathering his cabinet in this way, Lincoln had given them the opportunity to use their talents to the fullest and to share the labour and glory of the struggle that would reunite and transform their country and secure their own places in posterity.
The Civil War was beset with defeats. Lincoln was not helped by some poor generals, such as the forever procrastinating General George B McClellan. Even when it became obvious that McClellan must be dismissed, Lincoln managed through consensus. Lincoln eventually found in Ulysses S Grant and William T Sherman two generals who would be worthy adversaries of the formidable Confederacy general — Robert E Lee. When victory for the North became obvious, Lincoln instructed Grant not to humiliate the Confederates: there would be no revenge against the politicians who instigated the war or the armies who fought it.
Lincoln's relationship to the abolition of slavery is complicated. He did want to abolish it, but he wanted to preserve the Union. That was his priority. His middle way frustrated people on both sides of the debate: for the radicals he was too conservative and for the slave-keeping states he was a perennial threat to their way of life.
Almost three years into the war, Lincoln surprised many people when he issued the Emancipation Proclamation, which declared "that all persons held as slaves" within the rebellious states "are, and henceforward shall be free." Again, forever the compromiser, Lincoln didn't apply this to everyone. It didn't apply to the border states, who supported the Union, for fear of alienating them. These states kept their slaves. It also didn't apply to the Southern secessionist states who'd come under Northern control. But the Proclamation was historic: it changed the emphasis of the War from keeping the Union to giving freedom to slaves. Soon after Lincoln's death, the Thirteenth Amendment to the American Constitution was passed, banning slavery.
Lincoln was a renowned speaker and storyteller. He had a never-ending treasure chest of stories to entertain people with. He laughed easily and joked frequently, sometimes to the annoyance of colleagues, who thought some subjects were no laughing matter. Perhaps his most well-known speech is the one that begins:
and ends about 200 words later:
that the nation, shall have a new birth of freedom, and that government of the people, by the people, for the people, shall not perish from the earth.
The Gettysburg address was as powerful as it was short. It set a direction for America's future.
Lincoln's assassin, the actor John Wilkes Booth, might be described today as a white supremacist. He could not bear the idea of Lincoln, who'd recently won his second term, ruling America forever, like an unopposed king. Such was Lincoln's popularity that this was a distinct possibility in the absence of presidential term limits.
Booth and his three conspirators planned to kill Lincoln, Seward (the Secretary of State) and Vice President Johnson. As Lincoln and his wife rode to Ford's Theatre on 10th Street, their plan was under way. Johnson's assassin got cold feet, but Lewis Powell advanced towards Seward with venom. He stabbed Seward and his son (and others). Powell left a bloodbath but miraculously Seward and his son survived. The strain of the ordeal, however, eventually claimed his wife and daughter.
After Lincoln and his wife, Mary, sat in the theatre, Booth presented his calling card to the footman and entered the Lincolns' box. Once inside, he raised his pistol, pointed to the back of the President's head and fired from a few inches away. Booth fled whilst Mary shouted, "They have shot the President!" Lincoln was soon moved to the Petersen boarding house across the street. Such was his vitality that he held on longer than anyone expected. On 15 April 1865 at 7.22am, Abraham Lincoln was pronounced dead. Stanton's concise tribute from his deathbed was poignant: "Now he belongs to the ages." But Stanton, like many, was inconsolable. Stanton loved Lincoln and whenever Lincoln's name was mentioned "he would break down and weep bitterly."
Seward, who himself was fighting for his life, had not been told of the President's death. His doctors feared he would not survive the shock. However, a few days after Lincoln's death, Seward saw the War Department flag at half-past. "He gazed awhile, then turning to his attendant, said 'The President is dead'." The attendant tried to deny it, but Seward knew with grim certainty. He knew that if Lincoln had been alive, he would have been the first to visit him. Seward lay back on the bed, "the great tears coursing down his gashed cheeks, and the dreadful truth sinking into his mind." Seward had become Lincoln's friend and closest cabinet member. Seward, despite having his lifelong ambition to be president thwarted, was the first to recognise the greater man.
Sometime after Lincoln's death, Leo Tolstoy, the writer, wrote that "he was bigger than his country — bigger than all the Presidents together." He concluded, "We are still too near to his greatness, but after a few centuries more our posterity will find him considerably bigger than we do. His genius is still too strong and too powerful for the common understanding, just as the sun is too hot when its light beams directly on us."
The mid-1800s were a dangerous time for the United States. There was a real danger of the country splitting into two. Looking back, we can see a precarious, circuitous, tortuous path that, if traversed, could prevent that future. It would require unique skills. Lincoln, it seems, was put on Earth to travel that path. No one else could have done it. Lincoln embraced the challenge that many could not see. With perspicacity, humility, fairness, kindness, and good humour, he lived his life "with malice toward none; with charity for all". He was loved, even by the armies who fought the wretched war that claimed so many lives. Lincoln paid his own price for keeping his conviction alive: that America was one nation, indivisible, "conceived in Liberty, and dedicated to the proposition that all men are created equal." His task accomplished, the universe reclaimed him. It's a bargain he would have accepted.
[Much of the above, with or without quotes, is sourced from Team of Rivals. I've stitched it together, and rearranged it, to tell a story. But I have only skimmed the surface of the 750-page book. For more on my writing style, see the FAQ.]
Posted on 4 Jan 2023 Author PrafulCategories CultureTags USA
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Sandy Bottom Par-3 and Mini-Golf is OPEN for the 2019 season!
Mini-Golf will remain open weekly, Friday thru Sunday until Memorial Day. Beginning May 29th, Mini-Golf will be open seven days a week.
$5.00 Daily 12:00 - 10:00 p.m.
Join us on fridays from 5:00-8:00 p.m. at Sandy Bottom Mini-Golf for theme night! there will be music, activities, and giveaways! Wear a theme appropriate costume and get a FREE ice cream!
Nurses & Teachers Join us for a Pajama Party!
their pajamas for a late night golf experience.
Milk and cookies will be offered to all that attend.
Sandy Bottom Park is home to Bridgewater's par three, nine hole golf course, and an 18 hole miniature golf course. The park facilities also include a practice green, restrooms, several picnic tables, two gazebos, and ample parking. The par-3 course is open daily from 6 am to dark. The miniature golf course is open Friday thru Sunday from 12 p.m. to 10 p.m.
We also have season passes available for purchase for non-town residents. The cost is $150 for par-3 only or you can purchase a pass for both par-3 and mini-golf for $175. Passes must be purchased (cash or check only) from the Bridgewater Community Center at 201 Green Street.
For more information, contact (540) 908-4212.
From Interstate 81: Take exit 240 (Bridgewater exit). Follow State Route 257 into Bridgewater for approximately 3 miles. Turn left onto College View Drive (this street is just before two large prominent brick pillars which mark the entrance to Bridgewater College campus). Go one block and turn left onto East College Street. Follow East College Street until it ends at the entrance sign to Sandy Bottom Park. Turn left and continue past the town's maintenance shop into the parking area.
From Main Street (Route 42): Turn onto East College Street and follow until it ends at the entrance sign to Sandy Bottom Park. Turn left and continue past the town's maintenance shop into the parking area.


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Home Extras International Sundar Pichai Named CEO Of Alphabet Inc
Sundar Pichai Named CEO Of Alphabet Inc
Sundar Pichai, the Chief Executive Officer (CEO) of Google, will now take over as the CEO of Alphabet Inc., the parent company of Google.
The announcement came after Larry Page, the CEO of Alphabet, stepped down from his post. Larry Page was the CEO of the Company and Sergey Brin was the President.
On the 3rd of December, Mr. Page and Mr. Brin wrote a blog post which read, "While it has been a tremendous privilege to be deeply involved in the day to day management of the company for so long, we believe it's time to assume the role of proud parents offering advice and love, but not daily nagging!"
Larry Page and Sergey Brin are the co founders of Alphabet, the multinational conglomerate parent company of Google. Alphabet emerged in 2015 as Google was restructured and owns more than 12 companies which include Waymo, healthcare software company Verily, Malta, Makani, X Development, Calico and others.
Larry Page and Sergey Brin said, "With Alphabet now well established, and Google and the Other Bets operating effectively as independent companies, it's the natural time to simplify our management structure. We've never been ones to hold on to management roles when we think there's a better way to run the company. And Alphabet and Google no longer need two CEOs and a President. Going forward, Sundar will be the CEO of both Google and Alphabet."
Later, Mr. Page and Mr. Brin wrote a letter to employees of Google and said, "I want to be clear that this transition won't affect the Alphabet structure or the work we do day to day. I will continue to be very focused on Google and the deep work we're doing to push the boundaries of computing and build a more helpful Google for everyone. At the same time, I'm excited about (the) Alphabet and its long term focus on tackling big challenges through technology."
Sundar Pichai would now lead both the companies, Google and Alphabet, as their CEO, whereas Larry Page and Sergey Brin would be part of the Board of Directors.
After the announcement, the shares of Alphabet Inc., rose by 0.64 % to 1,303 dollars.
Stay tuned for further updates.
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Fall head over heels during a special 'Small Stage Series' performance from The Go-Go's
Don't miss an upcoming Small Stage Series show that's all you ever wanted when The Go-Go's take the stage at L.A.'s Whiskey a Go Go on December 14. This concert - along with shows from Ed Sheeran and Old Dominion - will close out the 2021 Small Stage Series season, featuring performances with premier artists spanning music genres and styles, and comedy, held in small iconic venues. The Go-Go's exclusive Small Stage Series performance will air on December 17 at 5pm ET via satellite on 1st Wave (Ch. 33) and on December 17 at 6pm ET the SXM App via Andy Cohen's Kiki Lounge (Ch. 312). Both channels will rebroadcast the full performance throughout the weekend.
These shows follow 16 live exclusive performances that SiriusXM and Pandora's Small Stage Series has presented as concerts and events have come alive again. The series launched on August 19 with Dave Matthews from Stephen Talkhouse in Amagansett, NY, and Kane Brown from his tour rehearsal space in Nashville. These performances were followed by an array of exclusive shows from music's biggest and buzziest artists on stage in various cities across the country. The multi-genre-spanning lineup included Alicia Keys, Brandi Carlile, Coldplay, Dave Matthews, Glass Animals, H.E.R., J Balvin, J. Cole, Jason Aldean, Kane Brown, Kenny Chesney, Nathaniel Rateliff & The Night Sweats, Shaggy and Twenty One Pilots.
2021 Rock & Roll Hall of Fame inductees The Go-Go's will bring their live show back to a stage where it all began at L.A.'s Whiskey a Go Go. Rooted in music history as groundbreakers and pop phenomenon, the group was born out of the L.A. punk scene and became the first all-female band writing their own material and playing their own instruments to top the U.S. Billboard album chart with their multi-platinum debut, Beauty and the Beat. Featuring the hits "Our Lips Are Sealed" (named one of Rolling Stone's Top 100 Pop Singles) and "We Got The Beat," the album stayed at No. 1 for six consecutive weeks and also earned them a GRAMMY Award nomination for Best New Artist. Their 1982 follow up, Vacation, hit Top 10 on the Billboard 200 and featured the Top 10 title track "Vacation." Now, The Go-Go's - Charlotte Caffey (lead guitar, keyboards and vocals), Belinda Carlisle (lead vocals), Gina Schock (drums), Kathy Valentine (bass and vocals) and Jane Wiedlin (guitar and vocals) - come full circle with this special performance as they take the stage at a significant venue in the band's early history. The band will deliver a spirited and memorable performance featuring their biggest hits and new music.
For information on how to attend SiriusXM and Pandora's Small Stage Series, visit: siriusxm.com/smallstage.
To maintain the highest public health standards and/or requirements, SiriusXM will adhere to health and safety protocols for each venue, and all state and local health mandates to protect attendees, staff and artists.
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To Be a True Submission Artist Requires Dedication to Mastering Movement and Positioning.
Here at 10th Planet San Diego, we have some of the best instructors in the world teaching progressive and innovative techniques that are on the cutting edge of no-gi grappling. Our classes are filled with both fundamental and advanced technical instruction. We break down every sequence, position or transition, so everyone from high level black belts to even the newest practitioner will grow in their training.
We not only have a dedicated strength and conditioning program, but with Bill Crawford leading the training, we have the experience behind it to guide you in your goals. No matter if you are conditioning for competition in any of the combat or martial arts or if you are training in general and looking for the best way to lose weight and get stronger, strength and conditioning classes are not exclusionary to anybody. You can push yourself as far as you want to here.
Our mixed martial arts program, led by Manolo Hernandez, builds on the strength of the 10th Planet jiu jitsu classes and incorporates a strong striking game to help you develop your own style and approach. Whether your goals are to compete against the world's best, train in the martial arts, learn self defense, or get in shape and learn something new, come see where your desire and talent can take you.
Our kickboxing training involves high-intensity body weight and plyometric exercises, designed for improved conditioning, explosiveness, agility, and flexibility. Classes with UFC veteran Liz Carmouche integrate combos of punches, kicks, knees, and elbows to cover a full range of motion and techniques.
Our youth classes in jiu jitsu and striking are designed to make sure every kid has fun and is comfortable while learning the discipline of each sport. Each of our youth instructors focus on the basics, and as the kids progress, they learn how to be competitive and develop their own skill set.


Outdoor Furniture Orlando picture above is part of the post in Outdoor Furniture Orlando gallery. Related with Outdoor Furniture category.
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Educational toys enable children to learn as they play. Educational toys help a child acquire new skills and improve their current ones all the while having fun. Educational toys are great at this - in encouraging learning in a fun environment. Educational toys are not only fun but practical. Of course, there are important factors that a parent needs to consider in getting the best educational toys for their children.


text:
Caldera House is the Height of Luxury Comfort
October 18, 2018 Words by Marina Zoukova
Photo: Courtesy of Douglas Friedman
Skiing is a favorite luxury past time, and while we're past the golden age of the jetset in St. Moritz, the newly opened Caldera House in Jackson Hole is revamping the fashion of the winter sport at their new elevated ski lodge.
Memberships for the Caldera House ski club are over $100,000 to start, which sets it apart from the spaces in the area. As it only has around 100 members, it's a fairly exclusive establishment, thus attracting guests who seek the best slopes and lodging, combined with exquisite personal service.
The renowned Los-Angeles based design studio, Commune, known for its esteemed work in architecture and various branches of design oversaw the elegant Caldera House. Combining the styles of Alpine skiing through the elements of Switzerland's 50s and Gstaad in the 60s, as well as the Italian intricacies of Cortina, Commune's branding of the Caldera House logo – a downhill skier – is quintessential to the style of the rest of the ski chalet.
Photo: Courtesy of Douglas Frienman
Roman Alonso, one of the two principals of Commune (alongside Steven Johanknecht) said the project came about after the client had visited some of the projects Commune designed for Ace Hotel, and appreciated the way that they each maintained their own personality connected to their locations. "They were looking for design that was sophisticated and alpine in feel but connected in a nuanced way to Jackson Hole," says Alonso. "They wanted the spaces to feel unique for the location but somehow remain integrated to the surroundings, elevated and yet casual." This brief would then turn into a minimalist, Scandinavian-inspired space focused on the texture and overall feel of a traditional ski chalet, brought to life by CLB Architects.
The resort was inspired by Charlotte Perriand's Meribel Ski Resort in the Tarentaise Valley of the French Alps, Carlo Mollino's Casa del Sole, as well as elements of mid-century modern Scandinavian design. The chalet incorporates ideas from the works of George Nakashima (American architect, woodworker, and one of the fathers of the American craft movement), Jean Prouvé (French metal worker, architect, and designer), as well as the work of local furniture and interior designer Thomas Molesworth. Other inspirations include 1950's Sun Valley ski bums, and scenes from films such as Belle du Jour and Charade. Caldera House also features the craft of numerous contemporary artists and experts, such as Lindsey Adelman, BDDW, Nakashima Workshop, Axel Vervoordt, Michael Boyd, Caste and Heath Ceramics, among many others.
The inside is not only cozy, but was designed to exhibit a "new type of luxury, rooted in comfort." The surfaces are complete with natural finishes of the highest quality, including claro walnut, American oak, travertine, and unlacquered metals that "will age well with time and patina beautifully," Alonso explains. A separate layer of comfort was also added to the furnishings and fabrics by adding cashmere, shearling, mohair, and handwoven fabrics. "Everything had to feel special and luxurious but not fussy or precious….it's a fine line," says Alonso. "Nothing pretentious."
"Caldera House offers a mix of European sophistication with the comfort and casual attitude of the West," Alonso concludes.
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Miguel by Erreà is the ideal jacket to wear in your free time and as team uniform. Designed for men and perfect to wear before and after a match, it is characterised by a sporty cut and an athletic look and is extremely comfortable to wear. Its strong point is represented by the vertical and horizontal coloured inlaids at the front: they will allow you to have the UEA colours with you at all times. This jacket is perfect for your free time and, thanks to the waistband and the wide-ribbed cuffs, it provides an impeccable fit. Its technical fabric and the internal mesh will protect you from the cold and wind. Materials: Micro-soft.
This is a lightweight fabric with a non stretch finish.
The track pant to best go with this jacket is Errea Austin.


Full digital multifunction controller-Main Controller RX-M2703XP-Restmoment Digital Audio Device Co., Ltd.
The system controller is the core component of conference system, which is the important intercommunication factor between units and PC management software modules. It controls all the conference functions via the navigation keyboard and 2.8 inch LCD screen on the front panel.
Ø Restmoment original conference technique, compliant to IEC60914.
Ø Adopt digital audio transmission technique, close to CD sound quality.
Ø 2.8 Inch LCD, Chinese and English menu, could order any language.
Ø Built-in input/out volume adjustment.
Ø Built-in 8X8 video matrix switcher, it can manage multipath video signs easily.
Ø Built-in 8X4 overall view video switching, time and order can setup freely.
Ø Original U disk and SD card recording function, can record the whole meeting in real-time.
Ø Users can control the starting and ending of recording, even change the setup of recording.
Ø MP3 recorder can record audio signal from external device automatically as soon as it coming.
Ø Exchanging function between MP3 recording and SD card. Users can change SD card during speaking. And it will not loose the content or audio.
Ø After recording, user can choose listening or replay.
Ø It has monitoring earphone jack on front panel.
Ø System can support 80pcs chairman units simultaneously and appoint one as executive chairman.
l FIFO mode: the last opened MIC covers the first opened MIC.
l Apply mode: executive chairman can approve or reject the speaking.
Ø 6 channels output ports, which can connect 180pcs units.
Ø Working with extension controller, the whole system can be extended to 4094pcs units.
Ø Turning off speaking unit, camera will track previous unit. Turning off all the speaking units, it will return to overall view.
Ø Protocols sharing, it can compatible with all the popular cameras. As the units positions being exchanged, camera always keep the correct tracking position by edit ID automatically.
Ø Cooperating with conference management software, the system can have more functions with easier operation.
3. LCD screen, can adjust the brightness and contrast.
4. Switch, with electrifying indicator.
7. Recording key: Press to record meeting; Press again to stop recording.
6. 8P DIN interface(EXTEND):interface for extension controller, connect to EXTEND IN on extension controller.
7. Video signal output interface (VIDEO OUT):Connect to TV or other external screen via 8 channels.
8. Video signal input interface (VIDEO IN):8 channels input, connecting to camera.
9. Cannon balance audio output interface (BALANCE OUT): Balance audio output.
10. RCA Audio interface(LINK OUT):Audio output interface of system controller, can connect peripheral amplifying system or recording system.


text:
\section{Introduction}
\label{s:introduction}
\input{01-introduction}

\section{Annotation Design}
\label{s:annotation}
\input{03-annotation}

\section{Qualitative Analysis}
\label{s:qualitative}
\input{04-qualitative}

\section{Experiments and Results}
\label{s:experiments}
\input{05-experiments}

\subsection{Adaptation to Unseen Languages}
\label{s:extension}
\input{06-extension}

\section{Related Work}
\label{s:related}
\input{02-rw}

\section{Conclusion and Future Work}
\label{s:conclusion}
\input{07-conclusion}

\section*{Acknowledgements}
This work is supported by the ERC Consolidator Grant LEXICAL (no 648909). EMP, IV, and AK are also funded through the Google Faculty Research Award 2018 for Natural Language Processing. GG is supported by the Eliteprogramm of the Baden-W\"{u}rttemberg Stiftung (AGREE Grant). Heartfelt thanks to Fangyu Liu, Ulla Meeri Petti, and Yi Zhu for their invaluable help.

\subsection{Inter-translator agreement}
The original inter-annotator agreement (Cohen's $\kappa$) on the choice of correct alternative reported for the English COPA \cite{Roemmele:2011aaai} was 0.965. The 26 questions on which there was disagreement were subsequently replaced with new questions to reach perfect agreement (i.e., two validators agree with the question author). In our task, translators performed labeling prior to translation, deciding on the correct alternative for the English premise and preserving the correctness of the same alternative in translation. We measure inter-translator agreement using the Fleiss' $\kappa$ statistic, used to estimate inter-rater reliability between three or more raters beyond that expected by chance \cite{fleiss1971measuring}. Table \ref{tab:iaa} shows the results for the validation and test sets, and the observed agreement (i.e., average pairwise percent agreement not corrected for chance). The scores ($0.921$) reveal very high agreement between translators (\citet{landis1977measurement} define $\kappa\geq0.81$ as almost perfect agreement). From the 11 sets of annotation labels we obtain the majority labels (i.e., 6+ translators agree). We observe perfect agreement between our majority labels and the English COPA. To examine the patterns of agreement and variation across languages, we compute the percentage of annotated labels which agree with the majority label for each language individually, reported in Table \ref{tab:overlap}. We find very high agreement across 11 languages ($\geq96.0\%$). Although high overlap on correct alternative choice is expected, considering that the translators reason over the same English sentences (and proficiency in English was an eligibility criterion), the \emph{near}-perfect scores indicate there is a degree of ambiguity in the phrasing of the questions, leaving room for divergent interpretations. Indeed, even the perfect agreement reported on English COPA should be considered with some caution, as the validation procedure included just two validators per question, sharing both native language and culture. In contrast, discrepancies in label choices in our work stem not only from the actual semantic ambiguity of the original English question, but also reflect the translators' different cultural frames of reference and patterns of association. On average, 2.10\% of labels in the validation set and 2.12\% of labels in the test set do not match the original labels in COPA. Rather than simply discarding it as noise, we believe inter-translator disagreement constitutes valuable signal and enforcing a single ground truth masks a natural phenomenon intrinsic to semantic interpretation \cite{aroyo2015truth}. In the next section we scrutinise the low-agreement instances to investigate the main factors at play in producing divergent label assignments. Moreover, to accurately represent ambiguity of the small number of disagreement labels, in the final datasets we explicitly tag the questions with observed mismatch between the language-specific and the majority label. We recommend excluding the tagged instances from model evaluation, but encourage incorporating them in future analyses focused on cross-lingual variation and patterns of ambiguity.
\fi

\iffalse
\begin{table}[t]
    \centering
    \def0.93{1.0}

    \begin{tabularx}{1.0\linewidth}{l XXX}
    \toprule
        {} & {$N$} & {$\kappa$} & {\%}\\
        \cmidrule(lr){2-4}
        {\textit{val}} & {100} & {0.921} & {0.961}\\
        {\textit{test}} & {500} & {0.921} & {0.961} \\

        \bottomrule

    \end{tabularx}%
    \caption{Inter-translator agreement between 10 translators on the correct alternative choice in terms of Fleiss $\kappa$ \cite{fleiss1971measuring}\ignore{where $\kappa \geq 0.81$  is almost perfect agreement \cite{landis1977measurement}}, for the test and validation sets of $N$ questions. \% denotes average pairwise percent agreement, i.e., the observed agreement. Each decision is a binary choice where only one alternative is correct. The translators are asked to select the correct alternative based on the original premise and two alternatives provided in English and preserve that choice in their translation.}

    \label{tab:iaa}
\end{table}
\fi
\subsection{Translation challenges}
As highlighted in \S\ref{s:annotation}, our guidelines anticipated that the adopted translation approach may entail language-specific challenges, e.g., the lack of equivalent concepts or the grammatical expression of tense and aspect. We now analyse the main design challenges and the adopted solutions.

\vspace{1.4mm}
\noindent \textbf{Cultural Context.}
The scenarios included in English COPA were authored by American English speakers with a particular cultural background. It is therefore inevitable that some concepts, intended as commonplace, sound unusual or even completely foreign in the target language. Examples include: (i) concrete referents with no language-specific term available (e.g., \textit{bowling ball}, \textit{hamburger}, \textit{lottery}); (ii) systems of social norms absent in the target culture, e.g., traffic regulations (e.g., \textit{parallel parking}, \textit{parking meter}); (iii) social, political, and cultural institutions and related terminology (e.g., e.g.,
\textit{mortgage}, \textit{lobbyist}, \textit{gavel}); (iv) idiomatic expressions (e.g., \textit{put the caller \underline{on hold}}).
\iffalse
\begin{itemize}
\item concrete referents rare in the target speaker community with no language-specific term available, e.g., \textit{bowling ball}, \textit{hamburger}, \textit{lottery};
\item systems of social norms absent in the target culture, e.g., traffic law and parking enforcement regulations (e.g., \textit{parallel parking}, \textit{parking meter});
\item social, political, and cultural institutions which are not found in the target culture, and the related terminology, e.g.,
\textit{mortgage}, \textit{lobbyist}, \textit{gavel};
\item idiomatic expressions, e.g., \textit{put the caller \underline{on hold}}, \textit{take (some) time apart};
\end{itemize}

As per translation guidelines, the translators tackled such cases by resorting to:
\begin{enumerate}
\item paraphrasing, e.g., in Swahili, the concept of `parallel parking' in \textit{He parallel parked on the street} is paraphrased as `He parked \underline{parallel to the street}': \textit{Aliegesha \underline{kisambamba kwenye barabara}};
\item substitutions with similar concepts, e.g., `faucet' is replaced with `pipe' in Tamil (\phraseC{}, \textit{kuḻāy}) and Haitian Creole (\textit{tiyo});
\item loan words, phonetically transcribed, e.g., in Tamil: \phraseA{} (\textit{\underline{pauliṅ} pan\-tu}, `\underline{bowling} ball'), \phraseB{} (\textit{cōppu}, `soap');

\end{enumerate}
\fi

In such cases, the translators were advised to resort (in the following order of preference) to (i) paraphrasing; (ii) substitutions with similar concepts, e.g., `faucet' is replaced with `pipe' in Tamil (\phraseC{}, \textit{kuḻāy}) and Haitian Creole (\textit{tiyo}); or (iii) phonetically transcribed loan words, e.g., in Tamil: \phraseA{} (\textit{\underline{pauliṅ} pan\-tu}, `\underline{bowling} ball'), \phraseB{} (\textit{cōppu}, `soap').

\vspace{1.4mm}
\noindent \textbf{Grammatical Tense.}
The temporal contiguity between two events and their duration is crucial in establishing their causal relationship \citep{enfieldmacro}. A number of languages in our sample (i.e., \textsc{th}, \textsc{vi}, \textsc{id}, \textsc{zh}) do not have the grammatical category of tense and express temporality by means of aspect, mood or lexical items and expressions referring to time (e.g., adverbs), or rely entirely on pragmatic context to provide sufficient information for the interpretation of the utterance. Even if aspectual viewpoint markers exist, they are optional, e.g., the perfective marker \begin{CJK}{UTF8}{min}了\end{CJK} (\textit{le}) in \textsc{zh}. To ensure naturalness of the translated sentences and faithfully represent the properties of the so-called tenseless languages, we favoured the unmarked variants, with the temporal relations established by the situational context. For example, compare: (a) \begin{CJK}{UTF8}{gbsn}我想节约能源。\end{CJK}, \textit{Wǒ xiǎng jiéyuē néngyuán.}, `I want(ed) to conserve energy.' (no perfective marker), and (b) \begin{CJK}{UTF8}{gbsn}学生拼错了这个词。\end{CJK}, \textit{Xuéshēng pīn cuò\textbf{le} zhège cí.}, `The student misspelled the word.' (with completed action marker).

\iffalse
\begin{table*}[!t]
    \centering
    \def0.93{0.91}
    {\footnotesize
    \begin{tabularx}{1.0\textwidth}{c ccYY}
\toprule
\textsc{id} & \textsc{premise} & \textsc{question} & \textsc{choice 1} & \textsc{choice 2} \\
\cmidrule(lr){2-5}
52 & \textit{The detective revealed an anomaly in the case.}	& R(esult) &	\textit{He finalized his theory.} & \textit{He scrapped his theory.} \\
92 & \textit{The man received a parking ticket.} & C(ause) & \textit{He parallel parked on the street.} & \textit{The parking meter expired.} \\
\bottomrule
    \end{tabularx}}%
    \vspace{-1.5mm}
    \caption{Examples in the development set with highest disagreement.}
    \label{tab:maxdisagree}
    \vspace{-1.5mm}
\end{table*}
\fi

\vspace{1.4mm}
\noindent \textbf{Label Discrepancies.}
The analysis of inter-translator agreement in \S\ref{s:annotation} revealed a small number of COPA scenarios with discrepancies in annotations across languages. To better understand the source of such disagreements, we identified all the validation set instances on which one or more translators diverged from the majority label.\footnote{Overall, there were 10 validation set questions with 1 translator out of 11 in disagreement, 5 questions with 2, and 1 question with 3.
} We identified two cases where the translator's experience and cultural frame of reference played a role (as attested in translator feedback), which required, for instance, understanding of the procedures and structure of U.S. court trials (e.g., \textit{The judge pounded the gavel.} \textsc{cause}: (a) \textit{The courtroom broke into uproar.} (b) \textit{The jury announced its verdict.}).

Most disagreement cases (87.5\%), however, seem to be culturally independent and concern genuinely ambiguous cases (e.g.\ \textit{The detective revealed an anomaly in the case.} \textsc{result}: (a) \textit{He finalized his theory.} (b) \textit{He scrapped his theory.}). To verify this in a monolingual setting, we conducted a follow-up experiment where 4 Italian native speakers labeled the translated validation and test instances. The Fleiss' $\kappa$ agreement scores were $0.926$ (validation) and $0.917$ (test).
This corroborates
our decision to override a single translator's label with the majority label without altering the translation.

\subsection{Baselines}
\label{ssec:mcclas}
We evaluate baselines in several combinations of experimental setups based on: \textbf{1)} different methods for cross-lingual transfer, either based on model transfer or machine translation; \textbf{2)} different multilingual pretrained encoders; \textbf{3)} different sources of training and validation data.

\vspace{1.4mm}
\noindent \textbf{Cross-lingual Transfer Methods.}
We consider two high-level methods for cross-lingual transfer \citep{tiedemann-2015-cross,Ponti:2019cl}: \textbf{1)} \textit{multilingual model transfer} (MuMoTr), whereby a Transformer-based encoder is pretrained on multiple languages in an unsupervised fashion, and subsequently trained on English annotated data for multiple-choice classification, therefore enabling zero-shot generalisation to the other languages. \textbf{2)} \textit{translate test} (TrTe), whereby target test data\footnote{As shown by \citet{Conneau:2018emnlp}, translating the test data from the target language to English is more cost-effective than translating English training data into the target language.} are translated into English via Google Translate. This includes all languages except for \textsc{qu}, for which the service is not available.

\vspace{1.4mm}
\noindent \textbf{Multilingual Encoders.}
For model transfer, we evaluate the following state-of-the-art pretrained multilingual encoders: \textbf{1)} multilingual BERT (MBERT) \cite{Devlin:2019bert} and XLM-on-RoBERTa \cite{conneau2019unsupervised}, both the Base (XLM-R) and Large (XLM-R-L) variants, in the standard \textit{fine-tuning regime} (i.e., their parameters are fine-tuned together with the task classifier's parameters), and \textbf{2)} multilingual Universal Sentence Encoder (USE) \cite{yang2019multilingual} in the \textit{feature-based regime} (i.e., its parameters are fixed during the task classifier's training). Both MBERT and XLM-R include all XCOPA languages in their pretraining data spanning $\sim$100 languages, except Haitian Creole and Quechua. Multilingual USE was trained on 16 languages, covering \textsc{it}, \textsc{th}, \textsc{tr}, and \textsc{zh} from the XCOPA language sample.

\vspace{1.4mm}
\noindent \textbf{Data Sources.} The only direct in-domain data available for training is the original English COPA training set covering 400 instances.
Due to data scarcity, we probe the usefulness of an intermediate training stage \citep{phang2018sentence,glavavs2020supervised} on larger multiple--choice English commonsense reasoning datasets, such as \textsc{SocialIQa} \cite[SIQA;][]{Sap:2019siqa}, before fine-tuning the model on COPA. The SIQA dataset is in a distant domain (commonsense reasoning about social interactions) with open-format prompts and three answer choices. However, it comes with a much larger training set, consisting of 33K instances. Therefore, it can provide useful learning signal also for causal commonsense reasoning in XCOPA. Moreover, we consider two different model selection regimes for hyper-parameter tuning and early stopping, namely (i) using the \textsc{En} COPA validation set or (ii) target language XCOPA validation set. Table~\ref{tab:ftsetups} lists all experimental setups.

\vspace{1.4mm}
\noindent \textbf{Neural Architecture.}
As multiple--choice selection tasks differ in the number of choices (e.g., there are 2 possible answers in COPA, whereas there are 3 in SIQA), a classifier with a fixed number of classes is not a good fit for this scenario. We thus follow \citet{Sap:2019siqa} and couple the (pretrained) encoder with a feed-forward net which produces a single scalar score for each possible answer. The scores for individual choices are then concatenated and passed to the softmax function.

As an input to the encoder, we couple each of the answer choices with the concatenation of the premise and the prompt. We feed this as a ``sentence pair'' input to MBERT and XLM-R, or as a single ``sentence'' to USE.\footnote{For MBERT and XLM-R, we insert the standard special tokens. For example, for the last example from Table~\ref{tab:causeeffect} and Choice 1, the input for MBERT would be as follows: ``\textit{[CLS] My eyes became red and puffy. What was the cause? [SEP] I was sobbing. [SEP]}''.}

Let $c_i$ be the $i$-th answer choice of an instance of multiple-choice dataset (i.e., $i \in \{1, 2\}$ in XCOPA and $i \in \{1, 2, 3\}$ in SIQA) and let $\mathbf{x}_i \in \mathbb{R}^H$ (with $H$ as the vector size of the encoder)\footnote{For MBERT Base and XLM-R Base, $H = 768$; for XLM-R Large, $H=1,024$; for multilingual USE Large, $H = 512$.} be the encoding of its corresponding input consisting of the premise, prompt and the answer itself, as explained above.\footnote{For MBERT and XLM-R $\mathbf{x}_i$ is the transformed representation of the sequence start token. For USE, $\mathbf{x}_i$ is the average of contextualised vectors of all tokens.} The predicted score $\hat{y}_i$ for the answer $c_i$ is then obtained with the following feed-forward net: $\hat{y}_i = \mathbf{W}_o \tanh \left(\mathbf{W}_h \mathbf{x}_i + \mathbf{b}_h\right)$,
with $\mathbf{W}_h \in \mathbb{R}^{H \times H}$, $\mathbf{b}_h \in \mathbb{R}^{H}$ and $\mathbf{W}_o \in \mathbb{R}^{1 \times H}$ as parameters. We obtain the score $\hat{y}_i$ for each answer $c_i$ and concatenate them into a prediction vector to which we apply softmax normalisation: $\hat{\mathbf{y}} = \mathrm{softmax}([\hat{y}_1, \dots, \hat{y}_N])$, where $N$ is the number of answers in the multiple-choice classification dataset. We minimise the cross-entropy loss function via stochastic gradient descent.

\setlength{\tabcolsep}{7pt}
\begin{table}[t]
    \centering
    \def0.93{1.0}
    {\footnotesize
    \begin{tabularx}{0.48\textwidth}{l YY YY}
    \toprule
    & \multicolumn{2}{c}{Train dataset} & \multicolumn{2}{c}{Valid dataset} \\
    \cmidrule(lr){2-3} \cmidrule(lr){4-5}
    Setup & SIQA & COPA & \textsc{en} & target \\
    \cmidrule(lr){2-3} \cmidrule(lr){4-5}
    CO-ZS & & $\checkmark$ & $\checkmark$ & \\
    CO-TLV & & $\checkmark$ & & $\checkmark$ \\
    SI-ZS & $\checkmark$ & & $\checkmark$ & \\
    SI+CO-ZS & $\checkmark$ & $\checkmark$ & $\checkmark$ & \\
    SI+CO-TLV & $\checkmark$ & $\checkmark$ & & $\checkmark$ \\

    \bottomrule
    \end{tabularx}
}%
    \vspace{2mm}
    \caption{Different experimental setups for data sources. CO=COPA; SI=SIQA; ZS=Zero-Shot; TLV=Target Language Validation (Set).}
    \label{tab:ftsetups}
    \vspace{1mm}
\end{table}

\subsection{Results and Discussion}
\label{ssec:resdisc}

\setlength{\tabcolsep}{5pt}
\begin{table}[t]
    \centering
    {\footnotesize
    \begin{tabularx}{\linewidth}{c l c c c}
    \toprule
\textbf{Setup} & \textbf{Model} & All & {\scriptsize \shortstack{MBERT $\cap$ \\ XCOPA}} & {\scriptsize \shortstack{USE $\cap$ \\ XCOPA}} \\ \midrule
\multirow{3}{*}{CO-ZS} & XLM-R & 55.6 & 56.9 & 55.4 \\
& XLM-R-L & 52.4 & 52.5 & 52.1\\
& MBERT & 54.1 & 54.4 & 55.7 \\
& USE & 54.7 & 56.0 & 58.1 \\ \midrule
\multirow{3}{*}{CO-TLV} & XLM-R & 55.1 & 56.4 & 55.2\\
& XLM-R-L & 51.6 & 51.7 & 52.1 \\
& MBERT & 54.2 & 54.5 & 55.8 \\
& USE & 54.8 & 55.4 & 59.0 \\ \midrule
\multirow{3}{*}{SI-ZS} & XLM-R & 60.1 & 62.3 & 62.9 \\
& XLM-R-L & 68.4 & 72.1 & 72.9 \\
& MBERT & 54.7 & 55.6 & 56.4 \\
& USE & 55.0 & 56.4 & 60.1 \\ \midrule
\multirow{3}{*}{\shortstack{SI+CO-ZS}} & XLM-R & 59.0 & 60.7 & 61.9 \\
& XLM-R-L & 67.3 & 70.8 & 71.8 \\
& MBERT & 55.8 & 56.8 & 57.9 \\
& USE & 54.1 & 54.9 & 58.9 \\ \midrule
\multirow{3}{*}{\shortstack{SI+CO-TLV}} & XLM-R & 60.7 & 63.5 & 63.6 \\
& XLM-R-L & \textbf{69.1} & \textbf{72.8} & \textbf{74.6} \\
& MBERT & 54.4 & 54.8 & 54.2 \\
& USE & 54.3 & 55.2 & 59.1 \\
\bottomrule
    \end{tabularx}
}
    \vspace{2mm}
    \caption{Summary of XCOPA results. \textbf{All}: average over all 11 XCOPA languages; \textbf{MBERT\,$\cap$\,XCOPA}: average over 9 XCOPA languages (without \textsc{ht} and \textsc{qu}) included in MBERT and XLM-R pretraining; \textbf{USE\,$\cap$\,XCOPA}: average over 4 XCOPA languages (\textsc{it}, \textsc{th}, \textsc{tr}, and \textsc{zh}), included in the USE pretraining.}
    \label{tab:results}
    \vspace{1mm}
\end{table}

We first present the results for model transfer based on multilingual pretrained encoders. Table~\ref{tab:results} shows the aggregate accuracy of MBERT, XLM-Rs and USE over 11 XCOPA languages for each of the previously described training setups from Table~\ref{tab:ftsetups}. We first compare our cross-lingual average XCOPA results in the best setup with the English COPA performance of the monolingual English BERT (Base) reported by \newcite{Sap:2019siqa}, namely 63 accuracy in COPA-only fine-tuning (+7\%) and 80 after sequential SIQA\,+\,COPA fine-tuning (+17\%). This contributes to recent suspicions \cite{cao2020multilingual,Hu:2020arxiv} that massively multilingual pretrained transformers do not offer a completely satisfactory solution for language transfer.
\begin{figure*}[!t]
    \centering
    \includegraphics[width=0.99\linewidth]{Images/siqa_copa-tld.pdf}
    \vspace{-3.5mm}
    \caption{Per-language XCOPA results for XLM-R, MBERT, and USE in the SIQA\,+\,COPA-TLV setup. Striped bars correspond to language-model pairs where the language was not included in model pretraining.}
    \label{fig:lang_res}
    \vspace{-1.5mm}
\end{figure*}

\vspace{1.4mm}
\noindent \textbf{Multilingual Encoders.} XLM-R (both Base and Large) outperforms MBERT and USE in all setups, but the gains are pronounced only where the models were first fine-tuned on SIQA (SI-ZS, SI+CO-ZS, and SI+CO-TLV). USE outperforms MBERT often, which is especially surprising in the few-shot learning setup of COPA-only, as it contradicts the received wisdom that a larger amount of trainable parameters guarantees higher sample efficiency \citep{kaplan2020scaling}.
What is more, USE in some setups even surpasses MBERT for some of the languages (e.g., \textsc{id}, \textsc{ta}, \textsc{sw}) on which MBERT was pretrained and USE was not (cf.\,the scores in the MBERT\,$\cap$\,XCOPA column). We speculate that this is due to a combination of two effects: (1) the infamous ``curse of multilinguality'' \cite{conneau2019unsupervised} is much more pronounced for MBERT (which is pretrained on 104 languages) than for USE, pretrained on only 16 languages; and (2) there are subword-level similarities between XCOPA target languages and the 16 languages used in USE pretraining.

Unsurprisingly, XLM-R Large substantially outperforms its Base counterpart in all setups with SIQA training. Due to almost 3 times more parameters (355M vs.\,125M), XLM-R-L stores more language-specific information during pretraining. The large parameter space, however, also causes XLM-R-L to under-perform XLM-R in COPA-only setups (CO-ZS and CO-TLV), with the small COPA fine-tuning dataset.

\vspace{1.4mm}
\noindent \textbf{Data Sources.}
Training models only on SIQA yields performance that is comparable (and for MBERT and USE often better) to performance we obtain with additional COPA training (setups SI\,+\,CO-ZS and SI\,+\,CO-TLV). While this is in part due to the limited size of the COPA training set, it confirms the assumption that SIQA and COPA are compatible tasks. We also note that only slight gains are achieved by hyper-parameter tuning on the target language validation set (TLV).

\vspace{1.4mm}
\noindent \textbf{Per-Language Performance.}
In Figure~\ref{fig:lang_res}, we report the language-specific performances for the best setup, SIQA\,+\,COPA-TLV, while we provide detailed results for all other setups in the Appendix.
As expected, all models fluctuate around random-level accuracy on the two out-of-sample languages, \textsc{ht} and \textsc{qu}.
Surprisingly, we also observe that for some languages (\textsc{id}, \textsc{vi}, \textsc{zh}) performance of transfer from English is slightly higher than the actual performance in English, without transfer. Moreover, the scores are often better for languages typologically distant from English than for closer ones (e.g., \textsc{th}, \textsc{vi}, \textsc{zh} vs. \textsc{it}). We speculate that this is due to the fact that languages such as \textsc{zh} and \textsc{th} are better represented in the pretrained models due to their unique scripts, which are shared with few other languages and therefore prevent lexical interferences.

\vspace{1.4mm}
\noindent \textbf{Cross-Lingual Transfer Methods.} Finally, in Table~\ref{tab:adv_res} (bottom) we compare the best setup for multilingual model transfer (XLM-R-L encoder with SIQA\,+\,COPA-TLV) with the method based on machine translation of the test set into English. In particular, for machine translation we consider both the massively multilingual (XLM-R-L) and the monolingual (RoBERTa Large, R-L) versions of the best encoder found in previous experiments.\footnote{As a caveat, note that these results are not perfectly comparable as Google Translate was trained on different (possibly more abundant) data, in addition to parallel texts.} No clear pattern emerges as the same model, XLM-R-L, is superior in 5 languages under the translation-based setup, and in 4 languages for the multilingual model transfer setup. A boost in accuracy, however, is especially evident for \textsc{sw}, with a gain of 13.8 points (+25\%). On the other hand, the translation-based setup largely outpaces multilingual model transfer when paired with a monolingual encoder, R-L. This baseline surpasses the others in all languages except for \textsc{th}. This brings us to the conclusion that it is not the cross-lingual transfer method in itself, but rather the avoidance of the `curse of multilinguality,' that makes translation-based cross-lingual transfer superior.

\subsection{Adversarial Variants}
\label{ssec:advvar}
After detecting an effective baseline, we `stress test' its robustness to prove that true causal reasoning is needed to solve our task. Recently, \citet{niven-kao-2019-probing} demonstrated that state-of-the-art pretrained encoders tend to rely on spurious correlations in natural language understanding datasets. Inspired by their work, we create two `adversarial' variants of XCOPA (and SIQA) where part of the input is masked. In the first variant (NoP), we hide premises; in the second (NoQ), we hide prompts.

The results of the best configuration for multilingual model transfer are reported in Table~\ref{tab:adv_res} (top). Clearly, without premises the performance is not much better than chance (accuracy of 50.0). Without prompts (\textsc{cause} and \textsc{effect} for XCOPA), performance is higher but still lags behind the original variant with full inputs. Hence, we may conclude that solving this task requires causal reasoning over all components of the event dynamics.

\setlength{\tabcolsep}{6.5pt}
\begin{table*}[t]
    \centering
    {\footnotesize
    \begin{tabularx}{\textwidth}{l l l c c c c c c c c c c c}
    \toprule
\textbf{Setup} & \textbf{Variant} & \textbf{Model} & EN & ET & HT & ID & IT & SW & TA & TH & TR & VI & ZH \\ \midrule
MuMoTr & NoP & XLM-R-L & 63.0 & 56.2 & - & 62.4 & 56.6 & 56.0 & 60.2 & 60.6 & 58.8 & 60.2 & 63.2 \\
MuMoTr & NoQ & XLM-R-L & 76.2 & 63.8 & - & 73.4 & 72.8 & 59.0 & 64.4 & 70.2 & 69.2 & 69.8 & 70.8 \\

\hline
MuMoTr & Full & XLM-R-L & 84.2 & 71.4 & - & 79.8 & 72.6 & 59.2 & 73.0 & \textbf{72.8} & 74.4 & 73.8 & 78.6 \\
TrTe & Full & XLM-R-L & 84.2 & 76.8 & 70.0 & 79.6 & 74.6 & 74.0 & 74.0 & 71.4 & 72.8 & 77.8 & 78.4 \\
TrTe & Full & R-L & \textbf{88.4} & \textbf{81.0} & \textbf{73.8} & \textbf{82.2} & \textbf{77.8} & \textbf{74.2} & \textbf{79.6} & 71.4 & \textbf{79.6} & \textbf{81.0} & \textbf{86.0} \\

\bottomrule
    \end{tabularx}
}
    \caption{Detailed per-language XCOPA results of the best cross-lingual transfer setups (bottom). Performance with two adversarial variants of the dataset where premises and prompts are hidden, respectively (top).}
    \label{tab:adv_res}
    \vspace{-1em}
\end{table*}

\section{Detailed Translation Guidelines}
\label{ss:dataguide}
Translation of the English COPA validation and test set instances into each of the 11 languages was carried out by a single translator per language, meeting the following eligibility criteria: (i) a native speaker of the target language, (ii) fluent in English, (iii) with minimum undergraduate education level. Each translator was presented with translation guidelines and a spreadsheet accessible online, containing one English premise-hypothesis triple per line, followed by an empty line where target translations were entered. The task consisted in (a) identifying the correct alternative for the English premise and (b) translating the premise and both alternatives into the target language, preserving the causal relations present in the original (see \S\ref{s:qualitative} for discussion of ambiguous and problematic cases). Each translator worked independently (using any external resources, such as English-target language dictionaries, if needed) and completed the task in its entirety, producing 100 validation and 500 test instance translations, and a label for each. To ensure the output preserves the lexical, temporal, and causal relations present in the original triples, the guidelines instructed to:

\begin{enumerate}[label=\roman*., itemsep=0pt, topsep=0pt]
\item maintain the original correspondence relations between lexical items, i.e., if the same English word appeared both in the premise and in the alternative choices (Premise: \textit{The friends decided to share the \underline{hamburger}.}; A1: \textit{They cut the \underline{hamburger} in half.}; A2: \textit{They ordered fries with the \underline{hamburger}.}), it was mapped into the same target-language equivalent in all three translated sentences;
\item ensure that the original chronology and temporal extension of events is preserved through appropriate choice of verbal tense and aspect in the target language, e.g., maintaining the distinction between perfective and imperfective aspect (Premise: \textit{My eyes became red and puffy.} [\textsc{perf}], A1: \textit{I was sobbing.} [\textsc{imperf}], A2: \textit{I was laughing.} [\textsc{imperf}]; See also \S \ref{s:qualitative} and Appendix \ref{ss:gramtense} for discussion of the challenges posed by tenseless languages);

\item in case of English words with no exact translations in the target language or referring to concepts absent from the target language culture (e.g., \textit{peach}), the following solutions were to be adopted, in order of preference: (1) using a common loanword from another language, provided it is understood by the general population of target-language speakers; (2) using a periphrasis to describe the same concept (e.g., \textit{a juicy fruit}); (3) substituting the original concept with a similar one that is more familiar to the target language speaker community (e.g., \textit{santol}), provided that it can play a similar role in the causal relations captured by the original premise--prompt--answers triple.
\end{enumerate}

\noindent
The translators were encouraged to split the workload into multiple sessions with breaks in between. Additionally, translators were encouraged to provide feedback, commenting on translation challenges and solutions, which we discuss in \S\ref{s:qualitative}.

\iffalse
\begin{table}[t]
    \centering
    \begin{tabularx}{1.0\columnwidth}{l|XXXXXXXXXXX}
    \toprule
& \textsc{et} & \textsc{ht} & \textsc{id} & \textsc{it} & \textsc{sw} & \textsc{ta} & \textsc{th} & \textsc{tr} & \textsc{vi} & \textsc{zh}\\
\hline
Time & ? & 20 & 15 & ? & 28 & 20 & ? & ? & 17 & ?\\
\bottomrule
    \end{tabularx}
    \caption{Time spent [hrs] on translation per language. \alert{ Add missing durations.}}
    \label{tab:time}
\end{table}
\fi

\begin{figure*}[t]
    \centering
    \includegraphics[width=0.9\textwidth]{entropies}
    \vspace{-2.5mm}
    \caption{Heatmap of the entropy of the distributions of WALS features (x axis) in language samples from famous cross-lingual datasets outlined in \S\ref{s:related} (y axis).}
    \label{fig:entropiesbyfeat}
    \vspace{-1.5mm}
\end{figure*}
\setlength{\tabcolsep}{6.5pt}
\begin{table*}[t]
    \centering
    {\footnotesize
    \begin{tabularx}{\textwidth}{c l c c c c c c c c c c c c}
    \toprule
\textbf{Setup} & \textbf{Model} & EN & ET & HT & ID & IT & QU & SW & TA & TH & TR & VI & ZH \\ \midrule
\multirow{4}{*}{CO-ZS} & XLM-R & 57.6 & 59.8 & 49.4 & 58 & 56 & 50.7 & 57.2 & 56.6 & 52.8 & 56.2 & 58.5 & 56.6 \\
& XLM-R-L & 53 & 49.6 & 55.8 & 53 & 52.4 & 48 & 54 & 51.4 & 51.8 & 51 & 56 & 53 \\
& MBERT & 62 & 50.6 & 51.4 & 55 & 53.8 & 54.7 & 53.6 & 52 & 53.2 & 56.8 & 55.4 & 59  \\
& USE & 63 & 53.8 & 49.4 & 57.6 & 60 & 48.3 & 52.2 & 53 & 57.2 & 55 & 54.8 & 60.2 \\ \midrule
\multirow{4}{*}{CO-TLV} & XLM-R & 57.6 & 57.8 & 48.6 & 60.8 & 54.4 & 49.5 & 55.4 & 55.8 & 54.2 & 54.8 & 57.6 & 57.2 \\
& XLM-R-L & 53 & 49.4 & 47.8 & 51.4 & 53.6 & 54.2 & 50 & 47.8 & 53 & 50.6 & 58.2 & 51 \\
& MBERT & 62 & 52 & 52.6 & 58.2 & 55 & 52.7 & 53 & 52 & 52.4 & 53.8 & 52.6 & 61.8 \\
& USE & 63 & 49.4 & 49.6 & 57.6 & 62 & 54 & 50.8 & 53.6 & 58.6 & 56.2 & 51.4 & 59.2 \\ \midrule
\multirow{4}{*}{SI-ZS} & XLM-R & 68 & 59.4 & 49.2 & 67.2 & 63.6 & 51 & 57.6 & 58.8 & 61.6 & 60.4 & 65.8 & 66 \\
& XLM-R-L & 85 & 70.4 & 53.4 & 79.4 & 72.8 & 50.2 & 60.8 & 71 & 69.4 & 71.2 & 76 & 78.2 \\
& MBERT & 62.2 & 55.2 & 51.4 & 57 & 57 & 50.2 & 51 & 52.2 & 51 & 53.2 & 59.2 & 64.4 \\
& USE & 62.6 & 51.6 & 46.8 & 60.2 & 61.8 & 50.5 & 52.4 & 48.8 & 60.8 & 54.6 & 54.8 & 63 \\ \midrule
\multirow{4}{*}{\shortstack{SI+CO-ZS}} & XLM-R & 66.8 & 58 & 51.4 & 65 & 60.2 & 51.2 & 52 & 58.4 & 62 & 56.6 & 65.6 & 68.8 \\
& XLM-R-L & 84.2 & 68.8 & 52.8 & 79.8 & 72.4 & 50.7 & 59.4 & 68.2 & 67.2 & 71.2 & 73.8 & 76.2 \\
& MBERT & 63.2 & 52.2 & 54 & 59.4 & 57.2 & 48 & 56 & 54.6 & 51.2 & 57.4 & 58 & 65.6 \\
& USE & 63.8 & 51.2 & 48.4 & 57.6 & 61.8 & 52 & 51.8 & 47 & 58 & 55.6 & 51 & 60.2 \\ \midrule
\multirow{4}{*}{\shortstack{SI+CO-TLV}} & XLM-R & 66.8 & 59.4 & 50 & 71 & 61.6 & 46 & 58.8 & 60 & 63.2 & 62.2 & 67.6 & 67.4 \\
& XLM-R-L & 84.2 & 71.4 & 52.8 & 79.8 & 72.6 & 52 & 59.2 & 73 & 72.8 & 74.4 & 73.8 & 78.6 \\
& MBERT & 63.2 & 52.2 & 51.8 & 58.2 & 57.2 & 53 & 51 & 57.2 & 52.6 & 54.6 & 57.8 & 52.4 \\
& USE & 63.8 & 51.8 & 47.8 & 56.6 & 61.6 & 52.2 & 52.4 & 47 & 59.8 & 54.4 & 52.8 & 60.6 \\
\bottomrule
    \end{tabularx}
}
    \caption{Detailed per-language XCOPA results for multilingual model transfer. None of the encoders was exposed to \textsc{ht} and \textsc{qu} during pretraining. USE was exposed only to \textsc{it}, \textsc{th}, \textsc{tr}, and \textsc{zh}.}
    \label{tab:lang_res}
    \vspace{-1mm}
\end{table*}
\setlength{\tabcolsep}{5pt}
\begin{table*}[!t]
\def0.93{0.93}
\centering
{\footnotesize
\begin{tabularx}{\textwidth}{l c l l X}
\toprule
{\bf Name} & {\bf Languages} & \textbf{Vocab} & {\bf Params} & {\bf URL} \\ \midrule
mBERT & 104 & 119K & 125M & {\url{https://huggingface.co/bert-base-multilingual-cased}} \\
XLM-R & 100 & 250K & 125M & {\url{https://huggingface.co/xlm-roberta-base}} \\
XLM-R-L & 100 & 250K & 355M & {\url{https://huggingface.co/xlm-roberta-large}} \\
\bottomrule
\end{tabularx}
}
\vspace{-1.5mm}
\caption{Pretrained transformers used in our study.}
\label{tbl:models}
\end{table*}

\section{Why is Grammatical Tense Problematic for XCOPA?}
\label{ss:gramtense}
The scenarios included in COPA refer to events that took place in the past and are formulated in what can be described as a narrative register: one of the sources from which question topics were drawn was a corpus of personal stories published online \cite{gordon2009identifying}. This is grammatically rendered exclusively by means of past simple (preterite) or past continuous (imperfect) verb forms. Temporal anteriority of a hypothesis sentence with respect to the premise is not grammatically marked (e.g., with a past perfect verb form) and can only be deduced based on the prompt (``\textit{What was the \textsc{cause} of this?}''). The preterite-imperfect contrast used in English to distinguish background states (imperfective) from the main event (perfective) (e.g., \textit{I was expecting company.} \textsc{imperf} vs. \textit{I tidied up my house.} \textsc{perf}) is not universally applicable and different languages employ different discourse grounding strategies \cite{hopper1979aspect}, which has interesting implications for the multilingual extension of COPA to XCOPA.

In the languages with grammatical tense, different strategies are employed to capture the perfective--imperfective distinction, which is prominent in COPA. For example, in Haitian Creole, the simple past marker \textit{te} is used to indicate a bounded event in the past, while the continuous aspect is signaled with an \textit{ap} marker. Italian additionally distinguishes between two perfective past tenses, expressed by means of a simple and compound past (\textit{vidi} - \textit{ho visto}, `I saw'). The opposition is between completed actions whose effects are detached from the present and those with persisting effects on the present. Both contrast with the imperfect, which emphasises the event's extension or repetition in time. Given that the opposition is a matter of the speaker's perspective on events and partially based on deixis (remote versus proximate past), the translator opted for the most natural choice given a specific context/situation.

\section{Hyper-Parameter Search}
For MBERT and XLM-R we searched the following hyper-parameter grid in both SIQA and COPA training: learning rate $\in \{5\cdot10^{-6}, 10^{-5}, 3\cdot 10^{-5}\}$, dropout rate (applied to the output layer of the transformer and the hidden layer of the feed-forward scoring net) $\in \{0, 0.1\}$, and batch size $\in \{4, 8\}$. For USE, we searched over different values for the learning rate, $\{10^{-3},10^{-4},10^{-5}\}$. We evaluate the performance on the respective development set every $500$ updates for SIQA and every $10$ updates for COPA and stop the training if there is no improvement after $10$ consecutive evaluations. In all setups, we optimise the parameters with the Adam algorithm \cite{kingma2015adam} with $\epsilon = 10^{-8}$, no weight decay, and no warm-up. We clip the norms of gradients in single updates to $1$.

\section{Full Results (Per Language)}
\label{ss:resultsperlang}
Table~\ref{tab:lang_res} contains the detailed per-language results of multilingual model transfer for all XCOPA languages and all five of our evaluation setups (CO-ZS, CO-TLV, SI-ZS, SI+CO-ZS, SI+CO-TLV).

\section{Code and Dependencies}

Our code is built on top of the HuggingFace Transformers framework\footnote{\url{github.com/huggingface/transformers}} and USE\footnote{\url{tfhub.dev/google/universal-sentence-encoder-multilingual-large/3}} and is available at \url{github.com/cambridgeltl/xcopa}. Table \ref{tbl:models} details the pretrained models that we exploited in this work. Besides these, our code relies only on standard Python's scientific computing libraries (e.g., \texttt{numpy}).

redpajama_set_name: RedPajamaArXiv


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During the autumn the pike will be nestled amongst the weed which is perfect camouflage ready to attack. During the winter the pike will be in the much deeper water, around say 10 to 15 feet and so you will need to adjust your tactics accordingly based on the time of the year and the conditions.
When fishing on rivers look for alcoves and inlays where the pike will be sitting and waiting for passing prey. I have caught a number of decent size pike by placing a dead bait a few yards down the river from when I am fishing in one of these hotspots. More often than not you will find pike in these areas an in actual fact any sort of feature you can find is worth sticking a bait on.
If you are still struggling to locate the pike, have a look at the surface of the water and try to spot any signs of bream or roach. If you can find these silver fish jumping and rolling you will be close to the pike. Also, keep an eye out for birds that feed on small fish, they are effectively hunting for the same food as the pike and again if you can find signs of this the pike will be present nearby.
If you can invest the time, pre-baiting is a simple and highly effective method to get the pike into an area you plan on fishing and also getting a taste for the type of bait you are planning on attracting the pike with. Head down to your fishing spot a couple of time before you actually go fishing and place a few deadbeats in some interesting spots, ideally you will have found some useful features where you think the pike could be held up.
If you put together all of these location tips I have given you, you will increase your chances of fishing where the pike are located and will give yourself the best possible chances of landing a fish or two.
There are a number of different baits to use when fishing for pike.
I started with dead baits and on the day trying to catch live baits with a simple floater rod and reel that I could attach to my rig. I would always check the rules of the venue you are fishing that they allow for this and if you are in any doubt, avoid catching live baits to use for pike fishing.
Dead baits such as frozen mackerel and herring work very well in the winter with mackerel being my choice of best bait for pike fishing. These baits have great attractors that the pike go crazy for, such as their high oil content and smell.
Fish, in general, are more lethargic due to the water temperatures and the pike are far less likely to want to exert their energy to chase a lure during the colder months. I have caught many pike in the coldest of conditions and have quite often gone out when it is snowing and have more often than not landed several decent size pike.
You can pick up frozen pike bait from your local tackle shop or online. I would always suggest you support your local tackle shop if you can as their knowledge of all things fishing related in invaluable. If you do any questions, the guys down your local tackle shop are more than willing to help out.
Fishing using lures for pike is great fun as you are always working and doing something. This approach is far more suited for the warmer months where the pike has plenty of energy and will hunt and attack a lure. During these warmer periods the pike's prey is moving around the lake much more and so the pike could be in a number of different locations. Using lures you are able to cover a wide expanse of the water and this is exactly what you want when the pike are moving often.
Live baits are very similar to a dead bait in that the bait is in a fixed position but has that extra attraction of movement and distress that the pike is naturally drawn to. Catching live baits and attaching them to a rig is not for everyone but is a great method that can grab you a bite or two if dead baits and lures are not working on that particular day.
I will say this again, please always check where you are fishing allows for live baits to be caught and taken from the water.
The most important piece of pike fishing tackle you must have is a wire-trace of at least 28lbs breaking strain. If you use any other kind of line the pike's razor-sharp teeth with cut through this instantly.
Line types such as monofilament, fluorocarbon or braid are not going to work for the end tackle. You can and should still use these types for your mainline. I usually have at least 15 lbs mainline which is the minimum I would fish with for pike.
As for hooks, you will need to use trebles. Trebles, as the name suggests, have three hook points which allow you to hook on a dead bait and still have a couple of hook points to grab onto the pike's mouth. I suggest you use semi barbless where only one of the three hook points is barbed.
I would use this barbed hook point for the deadbeat and the remaining two for the pike. It can be tricky to unhook a treble from a pikes mouth and this approach will make things easier.
There are a couple of simple pike rigs that I use that I do think are the best pike fishing rigs.
The first is a basic floater setup. This consists of a float fixed into position using a bead, this position will depend on the depth you are fishing at. You will then attach the wire-trace and bait to the end of the mainline. Cast the rig into the water and see how the float sits and adjust the position on the line until the float sits just upright in the water.
This works well if you are fishing with live baits as this rig allows for some movement which is one of the main reasons to use live baits, giving that extra attraction to lure a pike.
The other rig you can use is a free-running ledger. This simple pike rig is similar to the float setup just without the float. The rig and bait will sit on the bottom complete with a free-running weight. There is no float to indicate a bite so you need to feel for resistance to detect if a pike has taken the bait. This is a very effective pike rig.
If you are a carp angler you will be able to use your carp fishing rod, reel and mainline for pike fishing. All you need to do is swap over your carp rig for the more beefed up pike rig. These carp setups will allow you to cast and land pike in the majority of scenarios. You do not need to purchase dedicated pike tackle if you have carp fishing equipment.
If you do not have carp fishing equipment I would suggest a rod of around 3lbs test curve which will give you enough power in the rod to cast and land pike.
For a reel, I would look at a free spools type of reel from Shimano who make excellent high-quality products. I would suggest a spool size of 6000 - 10000 the larger size allowing for more line to be fed onto the reel.
If you are planning on doing some carp fishing in the summer months I would suggest you buy some equipment that can cover both types of fishing. My recommendations would be as follows.
As soon as you see any signs of a bite you need to be ready to strike. Wind in any slack line and hit it hard. I really do mean this, strike hard to ensure you get a decent hook hold in the pike's mouth which is very strong and tough.
Some pike anglers like to wait and let the pike swallow the bait which I am totally against, this can be dangerous for the fish and make removing the rig very difficult. I have seen first hand this happen and the angler spent far too long removing the rig. I would try to avoid this and as I said previously strike as soon as you see any signs of a bite and strike hard.
Pike look incredibly vicious but need to be treated in a safe and delicate manner. They are in fact very fragile fish that must be handled with care at all times. Ideally, you will have the fish back in the water ASAP. You will need a good size padded unhooking mat, a pair of 8-inch forceps and a pair of cutters in case you are faced with difficulty when removing the treble hooks.
When you land a pike you need to place the fish on the unhooking mat, grab the fish under the gill plate and gently open the fishes mouth, with your other hand use the forceps to remove the treble hooks using a simple twist and pull motion.
Pike have very sharp teeth and so you must always be alert when handling the fish and remember to keep your fingers well clear.
If you can go pike fishing with an experienced angler they can guide you through this process which I think is very important to ensure you are doing this correctly. It can be notoriously difficult to remove the hooks from a pikes mouth.
When returning the pike to the water ensure the fish has enough energy to swim away with confidence. If this is not the case, hold the pike in the water and allow oxygen to enter the gills which will revive the pike ready to be safely returned and to fight another day.