RFK Jr taunts Donald Trump as he shares pointed'Thanksgiving dinner' photo with the president, Elon Musk and Don Jr Fans hail Cece Winans' 'best ever' rendition of the national anthem on Thanksgiving and beg the NFL to get her to the Super Bowl I've seen it too many times - I have to speak up: KENNEDY Trump plunged into security scandal over Afghan shooter's asylum - after president blamed Biden Bryan Kohberger becomes nightmare prison diva... as he throws huge tantrum over BANANAS behind bars My wife was blindsided when I asked for a divorce. There was no foul play or'other woman' but this is why I did it... and the six subtle signs your partner is planning on leaving you too: RICHARD WARNER My book on the Kennedys was used as a'mistress manual' by Olivia Nuzzi... then this wannabe Carolyn Bessette had the nerve to hound me with these outrageous texts: MAUREEN CALLAHAN Americans are finally realizing why we don't eat turkey eggs Plastic surgeon reveals secrets of Tom Brady's changing face, including'unnatural' procedure... and truth about Ozempic use Lilibet's locks steal the show! Meghan's daughter is every inch the little Princess with her fiery red locks in a neat plait at Thanksgiving outing Kimberly Guilfoyle leaves little to the imagination in a figure-hugging sheer lace gown for Thanksgiving dinner in Athens in her role as US Ambassador - after admitting she's'husband hunting' Hollywood stars who REFUSE to celebrate Thanksgiving over animal cruelty and its'blood-soaked' history A strong winter storm is set to hit parts of the US Midwest and Great Lakes region this weekend, threatening flight delays and road safety for millions after the holiday . Winter Storm Watches are now in effect across Illinois, Wisconsin, Iowa, Missouri, Indiana, Michigan, Nebraska, South Dakota and Minnesota, impacting around 50 million Americans. Forecasters warned of potentially heavy snowfall, with accumulations of six to 12 inches or more possible in many areas, especially north of Interstate 70 and along and south of Interstate 90.

During WWII, the U.S. government censored the weather Even baseball rain delays went unexplained. A World War II poster, created for the War Production Board around 1942-1943, declares "Weather is a weapon." Breakthroughs, discoveries, and DIY tips sent every weekday. The call went out from WREC's studios in downtown Memphis at 6:57 p.m. Central War Time: Doctors and nurses were urgently needed in communities south and west of the city. That was all the information the station was allowed to provide, despite the ongoing threat.

Crop mapping involves identifying and classifying crop types using spatial data, primarily derived from remote sensing imagery. This study presents the first comprehensive review of large-scale, pixel-wise crop mapping workflows, encompassing both conventional supervised methods and emerging transfer learning approaches. To identify the optimal time-series generation approaches and supervised crop mapping models, we conducted systematic experiments, comparing six widely adopted satellite image-based preprocessing methods, alongside eleven supervised pixel-wise classification models. Additionally, we assessed the synergistic impact of varied training sample sizes and variable combinations. Moreover, we identified optimal transfer learning techniques for different magnitudes of domain shift. The evaluation of optimal methods was conducted across five diverse agricultural sites. Landsat 8 served as the primary satellite data source. Labels come from CDL trusted pixels and field surveys. Our findings reveal three key insights. First, fine-scale interval preprocessing paired with Transformer models consistently delivered optimal performance for both supervised and transferable workflows. RF offered rapid training and competitive performance in conventional supervised learning and direct transfer to similar domains. Second, transfer learning techniques enhanced workflow adaptability, with UDA being effective for homogeneous crop classes while fine-tuning remains robust across diverse scenarios. Finally, workflow choice depends heavily on the availability of labeled samples. With a sufficient sample size, supervised training typically delivers more accurate and generalizable results. Below a certain threshold, transfer learning that matches the level of domain shift is a viable alternative to achieve crop mapping. All code is publicly available to encourage reproducibility practice.

Recently, decoder-only pre-trained large language models (LLMs), with several tens of billion parameters, have significantly impacted a wide range of natural language processing (NLP) tasks. While encoder-only or encoder-decoder pre-trained language models have already proved to be effective in discourse parsing, the extent to which LLMs can perform this task remains an open research question. Therefore, this paper explores how beneficial such LLMs are for Rhetorical Structure Theory (RST) discourse parsing. Here, the parsing process for both fundamental top-down and bottom-up strategies is converted into prompts, which LLMs can work with. We employ Llama 2 and fine-tune it with QLoRA, which has fewer parameters that can be tuned. Experimental results on three benchmark datasets, RST-DT, Instr-DT, and the GUM corpus, demonstrate that Llama 2 with 70 billion parameters in the bottom-up strategy obtained state-of-the-art (SOTA) results with significant differences. Furthermore, our parsers demonstrated generalizability when evaluated on RST-DT, showing that, in spite of being trained with the GUM corpus, it obtained similar performances to those of existing parsers trained with RST-DT.

To promote and further develop RST-style discourse parsing models, we need a strong baseline that can be regarded as a reference for reporting reliable experimental results. This paper explores a strong baseline by integrating existing simple parsing strategies, top-down and bottom-up, with various transformer-based pre-trained language models. The experimental results obtained from two benchmark datasets demonstrate that the parsing performance strongly relies on the pretrained language models rather than the parsing strategies. In particular, the bottom-up parser achieves large performance gains compared to the current best parser when employing DeBERTa. We further reveal that language models with a span-masking scheme especially boost the parsing performance through our analysis within intra- and multi-sentential parsing, and nuclearity prediction.