Bills cruise past Lions to christen new home behind Josh Allen's five-touchdown performance Patriots' TreVeyon Henderson explains siding with family over DCF in custody dispute involving gender identity Colin Kaepernick accuses NFL of'colluding' against Macklemore amid'Free Palestine' controversy Virginia Governor Abigail Spanberger quietly rolls back Youngkin's plans to protect girls' sports Human vs. ChatGPT NFL Week 2 betting picks: AI looks to rebound after disastrous 0-5 debut NBA star Kyrie Irving jumps into NFL drama, offers to pay player's fine for Gaza-themed eye black NFL's highest-paid TE George Kittle reveals favorite purchase: A 50-foot flagpole for a massive American flag Bill Belichick would like you to know that his 25-year-old girlfriend isn't running things at UNC Bijan Robinson believes Texas' comeback over Ohio State can change entire season trajectory Barstool chief Dave Portnoy blasts Macklemore's shady past, strongly supports Patriots owner Robert Kraft Lane Kiffin's return to Ole Miss comes with betrayal, lawsuits and memories of him trying to burn it all down Paige Spiranac and her'cans' go to bat for Sydney Sweeney, Ole Miss-LSU gets nasty & UF alumni speak out Iran's AI-powered cyber threat sparks alarm over US infrastructure Raman would'catastrophically' push LA over the edge if elected: Former Trump campaign official'Gutfeld!': Tirade at 30K feet left him taped to a seat We have'no idea' about long-term effects of Chinese pesticides at Cali pot grows: Ex-DEA chief Gutfeld: If you're concerned about AI, fear the humans - not the device OutKick Sports Steve Sarkisian says he should have shown'more patience' after running from Holly Rowe interview The Texas Longhorns coach addressed the viral post-game moment ahead of the team's next contest against UTSA Craig breaks down why Arch Manning shouldn't have to apologize for laughimg at the Holly Rowe/Steve Sarkisian AI video Texas Longhorns head coach Steve Sarkisian went viral following his team's miraculous 24-23 comeback against Ohio State this past Saturday after running off from a postgame interview with ESPN's Holly Rowe. Sarkisian screamed, That's called Texas fight! Hook'em! before running off back to the locker room to celebrate the victory with his team . At the time, Rowe hadn't even asked her first question to him postgame, and she seemed quite upset as she darted out of camera view after Sarkisian ran away.

Jordon Hudson dusts herself off and resumes her WAG duties, Belichick vs Sydney Sweeney & 'Gainesville Ripper' Mets are'trash' because they're not listening to former NJ Gov Chris Christie, Stephen A Smith says Orioles pitcher Shane Baz drilled with 105.1 mph line drive in scary scene NFL's battle with drinking and driving continues as Keenan Allen formally charged in Indianapolis Anthony Molina and Matthew Liberatore's struggles make over 8 runs the play for Giants-Cardinals Shota Imanaga's home run struggles and Wrigley wind make over 7.5 the play for Braves-Cubs Jaxson Dart's girlfriend Marissa Ayers shines in Week 1 win, a Giants-Cowboys fan fight & Ole Miss tension MLB's most electric slugger just had one of the slowest, most disrespectful walk-off home runs of all time James Franklin makes Virginia Tech press conference extremely awkward with bizarre'Hey Michael' moment Texans' Azeez Al-Shaair to be fined once again by NFL for wearing Pro-Palestinian personal message on eyeblack Emily Compagno: FBI made America safer, but we were treated to a'congressional tantrum Trump Butler rally shooting could be sign of a'larger conspiracy': Former deputy assistant AG Sheriff decries Democrat's'disgusting display' at House hearing on sanctuary laws Sheriff decries Democrat's'disgusting display' at House hearing on sanctuary laws Sheriff calls Democrat's questioning at House hearing a'disgusting display' It's urgent for Congress to step in and protect college sports: Sen Ted Cruz Mike Pence warns this Trump move would send a'deafening' message to Putin'COMPLETE WASTE OF MONEY': Real estate celebrity calls out $200,000 housing mistake Rand Paul slams Congress over AI regulation: 'I wouldn't put Congress in charge of a McDonald's' Rand Paul slams Congress over AI regulation: 'I wouldn't put Congress in charge of a McDonald's' Ed Sheeran responds to Macklemore backlash amid'Free Palestine' controversy OutKick Sports Reporter for The Athletic fumes over Arch Manning, silent on Josh Jacobs' alleged violence against woman OutKick highlights a coverage disparity between Manning's meme controversy and Jacobs' felony strangulation charge The hyper-sensitive outrage machine immediately went into overdrive, but Dan Dakich is calling out the media crusade for what it really is: completely off the rails. In this episode of Don't @ Me, Dan breaks down the ridiculous fake outrage surrounding the Texas quarterback and explains why claiming Arch condones violence against women over a dumb internet video is total nonsense. On Wednesday, The Athletic posted an op-ed fuming over Arch Manning and Steve Sarkisian. Did Arch Manning, Steve Sarkisian and cackling reporters act this way? It can't be real, Candace Buckner wrote in a headline.

'Is AI keeping us from getting the social support that we need?' asks Dr Dan Wolfson. 'I created my mom and talk to her': AI ghosts and deathslop are changing the way we mourn'Is AI keeping us from getting the social support that we need?' asks Dr Dan Wolfson. The rise of AI-generated tributes and bots for'talking' to the dead has experts concerned more people will grieve in isolation T ributes rolled in after the death of Dolly Parton last month. Jack White performed Jolene at a London gig. Kesha sang Old Flames (Can't Hold a Candle to You), a single her mother wrote for the country music titan. Even Pitbull stopped his Madison Square Garden show to memorialize Parton and all the other "powerful women" in the crowd.

The pre-trained language model (PrLM) demonstrates domination in downstream natural language processing tasks, in which multilingual PrLM takes advantage of language universality to alleviate the issue of limited resources for low-resource languages. Despite its successes, the performance of multilingual PrLM is still unsatisfactory, when multilingual PrLMs only focus on plain text and ignore obvious universal linguistic structure clues. Existing PrLMs have shown that monolingual linguistic structure knowledge may bring about better performance. Thus we propose a novel multilingual PrLM that supports both explicit universal dependency parsing and implicit language modeling. Syntax in terms of universal dependency parse serves as not only pre-training objective but also learned representation in our model, which brings unprecedented PrLM interpretability and convenience in downstream task use. Our model outperforms two popular multilingual PrLM, multilingual-BERT and XLM-R, on cross-lingual natural language understanding (NLU) benchmarks and linguistic structure parsing datasets, demonstrating the effectiveness and stronger cross-lingual modeling capabilities of our approach.

Program translation isanimportant tool tomigrate legacycode inone language into an ecosystem built in a different language. In this work, we are the first to employ deep neural networks toward tackling this problem.

Last December, flames engulfed Ohio's Overfield Tavern Museum. Now archaeologists get to dig beneath the floorboards. A 50 cent coin from 1817 (left) and an 1846 Liberty head one-cent coin (right) are just two of the numerous objects uncovered in a dig in Troy, Ohio. Breakthroughs, discoveries, and DIY tips sent every weekday. Out of the ashes of a devastating fire, archeologists are uncovering exciting insights into Ohio history.

We expand the second language (L2) Korean Universal Dependencies (UD) treebank with 5,454 manually annotated sentences. The annotation guidelines are also revised to better align with the UD framework. Using this enhanced treebank, we fine-tune three Korean language models and evaluate their performance on in-domain and out-of-domain L2-Korean datasets. The results show that fine-tuning significantly improves their performance across various metrics, thus highlighting the importance of using well-tailored L2 datasets for fine-tuning first-language-based, general-purpose language models for the morphosyntactic analysis of L2 data.

Shallow water equations (SWEs) are the backbone of most hydrodynamics models for flood prediction, river engineering, and many other water resources applications. The estimation of flow resistance, i.e., the Manning's roughness coefficient $n$, is crucial for ensuring model accuracy, and has been previously determined using empirical formulas or tables. To better account for temporal and spatial variability in channel roughness, inverse modeling of $n$ using observed flow data is more reliable and adaptable; however, it is challenging when using traditional SWE solvers. Based on the concept of universal differential equation (UDE), which combines physics-based differential equations with neural networks (NNs), we developed a universal SWEs (USWEs) solver, Hydrograd, for hybrid hydrodynamics modeling. It can do accurate forward simulations, support automatic differentiation (AD) for gradient-based sensitivity analysis and parameter inversion, and perform scientific machine learning for physics discovery. In this work, we first validated the accuracy of its forward modeling, then applied a real-world case to demonstrate the ability of USWEs to capture model sensitivity (gradients) and perform inverse modeling of Manning's $n$. Furthermore, we used a NN to learn a universal relationship between $n$, hydraulic parameters, and flow in a real river channel. Unlike inverse modeling using surrogate models, Hydrograd uses a two-dimensional SWEs solver as its physics backbone, which eliminates the need for data-intensive pretraining and resolves the generalization problem when applied to out-of-sample scenarios. This differentiable modeling approach, with seamless integration with NNs, provides a new pathway for solving complex inverse problems and discovering new physics in hydrodynamics.

We introduce MorphNLI, a modular step-by-step approach to natural language inference (NLI). When classifying the premise-hypothesis pairs into {entailment, contradiction, neutral}, we use a language model to generate the necessary edits to incrementally transform (i.e., morph) the premise into the hypothesis. Then, using an off-the-shelf NLI model we track how the entailment progresses with these atomic changes, aggregating these intermediate labels into a final output. We demonstrate the advantages of our proposed method particularly in realistic cross-domain settings, where our method always outperforms strong baselines with improvements up to 12.6% (relative). Further, our proposed approach is explainable as the atomic edits can be used to understand the overall NLI label.

We propose a novel architecture for graph-based dependency parsing that explicitly constructs vectors, from which both arcs and labels are scored. Our method addresses key limitations of the standard two-pipeline approach by unifying arc scoring and labeling into a single network, reducing scalability issues caused by the information bottleneck and lack of parameter sharing. Additionally, our architecture overcomes limited arc interactions with transformer layers to efficiently simulate higher-order dependencies. Experiments on PTB and UD show that our model outperforms state-of-the-art parsers in both accuracy and efficiency.