Abstract--COVID-19 is a respiratory disease that caused a global pandemic in 2019. It is highly infectious and has the following symptoms: fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. These symptoms vary in severity; some people with many risk factors have been known to have lengthy hospital stays or die from the disease. In this paper, we analyze patients' electronic health records (EHR) to predict the severity of their COVID-19 infection using the length of stay (LOS) as our measurement of severity. This is an imbalanced classification problem, as many people have a shorter LOS rather than a longer one. T o combat this problem, we synthetically create alternate oversampled training data sets. Once we have this oversampled data, we run it through an Artificial Neural Network (ANN), which during training has its hyperparameters tuned by using bayesian optimization. We select the model with the best F1 score and then evaluate it and discuss it. COVID-19 is defined by the Centers for Disease Control and Prevention (CDC) as "a respiratory disease caused by SARS-CoV -2, a coronavirus discovered in 2019. The virus spreads mainly from person to person through respiratory droplets produced when an infected person coughs, sneezes, or talks" [1]. Furthermore, they add, "For people who have symptoms, illness can range from mild to severe. Adults 65 years and older and people of any age with underlying medical conditions are at higher risk for severe illness" [1].In 2019 this novel coronavirus was first detected. The highly infectious nature of this disease, combined with the respiratory nature of the infection, caused a pandemic. Along with being highly contagious, COVID-19 also has an extensive range of symptoms such as fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea [2]. Along with a long list of symptoms, COVID-19 has many risk factors, which may increase the severity of the infection.

AI Relationships Are on the Rise. Secret chatbot flings are creating new legal challenges for married couples when it comes to infidelity. Rebecca Palmer isn't a psychic, but as a divorce attorney she can often see what's coming next. For many people today, as AI saturates every aspect of life --from work to therapy--the allure of an AI romance is tantalizing. Chatbots are dependable, can provide emotional support, and, for the most part, will never pick a fight with you.

Ominous warning for humanity as birds suddenly adopt'unsettling' behavior Meghan is accused of'giggling as model stumbles on the catwalk': More Paris Fashion Week disasters emerge, including awkward moment with Kristin Scott Thomas More girls are starting their periods younger than ever before - scientists think they've finally found what's causing it The TRUTH to the doting mother who slaughtered her children and husband told by those she'd been quietly tormenting for years Insiders confirm what everyone suspects about Taylor Swift and Blake Lively... the private apology... and how any future friendship hangs on one humiliating condition Outrage as Baltimore's Dem mayor spends $164k of taxpayer cash on ultra-luxurious new SUV I have no sympathy for them - but this disturbing new trend isn't the answer: JANA HOCKING Taylor Swift reveals truth behind raunchy song about Travis Kelce's manhood Revealed: Which slimming jab REALLY works best. The doctors' ultimate expert guide on which to pick, how to save money, beat every side effect... and what you need to know about the'golden dose' Functioning alcoholics hide in plain sight... so are YOU one? Trump brands NFL's Bad Bunny Super Bowl halftime show selection'absolutely ridiculous' The troubled background of delivery man stabbed by Mark Sanchez... as he launches million-dollar lawsuit and sparks civil war at Fox Millions of Americans are facing a dangerous freeze warning on Tuesday as temperatures drop below freezing across multiple states. Sub-freezing temperatures as low as 22 to 30 F are expected in parts of Wisconsin, Minnesota, North Dakota, South Dakota, Michigan, Colorado, Wyoming and Idaho . The National Weather Service (NWS) issued the warning for tonight into Wednesday morning, ending between 8 and 10am local time, depending on the state and county .

Wisconsin's Paris Solar-Battery Park in Kenosha County can power about 130,000 homes for up to four hours by capturing excess energy from solar panels.

Misinformation evolves as it spreads, shifting in language, framing, and moral emphasis to adapt to new audiences. However, current misinformation detection approaches implicitly assume that misinformation is static. We introduce MPCG, a multi-round, persona-conditioned framework that simulates how claims are iteratively reinterpreted by agents with distinct ideological perspectives. Our approach uses an uncensored large language model (LLM) to generate persona-specific claims across multiple rounds, conditioning each generation on outputs from the previous round, enabling the study of misinformation evolution. We evaluate the generated claims through human and LLM-based annotations, cognitive effort metrics (readability, perplexity), emotion evocation metrics (sentiment analysis, morality), clustering, feasibility, and downstream classification. Results show strong agreement between human and GPT-4o-mini annotations, with higher divergence in fluency judgments. Generated claims require greater cognitive effort than the original claims and consistently reflect persona-aligned emotional and moral framing. Clustering and cosine similarity analyses confirm semantic drift across rounds while preserving topical coherence. Feasibility results show a 77% feasibility rate, confirming suitability for downstream tasks. Classification results reveal that commonly used misinformation detectors experience macro-F1 performance drops of up to 49.7%. The code is available at https://github.com/bcjr1997/MPCG

Science Archaeology Wisconsin'ghost ship' uncovered after 139 years It took citizen scientists only two hours to find the F.J. King's final resting place. Breakthroughs, discoveries, and DIY tips sent every weekday. A group of Wisconsin maritime historians and citizen scientists uncovered a Lake Michigan shipwreck "hidden in plain sight" for nearly 140 years. The team uncovered the waterlogged wreckage of the three-masted wooden schooner in the waters off Bailey's Harbor, Wisconsin. On September 15, 1886, the 144-foot left Escanaba, Michigan, bound for Chicago with 600 tons of iron ore onboard.

Last night, X's "For You" algorithm offered me up what felt like a dispatch from an alternate universe. It was a post from Elon Musk, originally published hours earlier. "This is the first time humans have been in orbit around the poles of the Earth!" he wrote. Underneath his post was a video shared by SpaceX--footage of craggy ice caps, taken by the company's Dragon spacecraft during a private mission. Taken on its own, the video is genuinely captivating.

President Biden will travel to Wisconsin on Wednesday to announce the creation of an artificial intelligence data center, highlighting one of his administration's biggest economic accomplishments in a crucial battleground state -- and pointing up a significant failure by his immediate predecessor and 2024 challenger. At a technical college in Racine, Mr. Biden will announce that Microsoft will invest 3.3 billion to build the center, which the tech giant estimates will create 2,300 union construction jobs and 2,000 permanent jobs, according to the White House. The project is part of Mr. Biden's "Investing in America" agenda, which has focused on bringing billions of private-sector dollars into manufacturing and industries such as clean energy and artificial intelligence. In his fourth trip to Wisconsin this year, Mr. Biden will continue an aggressive campaign to paint a contrast between him and former President Donald J. Trump, the presumptive Republican nominee, who is in the fourth week of his criminal trial in connection with payments to a pornographic film star. While in Wisconsin, Mr. Biden will also attend a campaign event, where he will speak to Black voters about the stakes in the election.

Recently, Donald Trump fans in Florida and Michigan have been auto-generating and spreading around faked "pictures" of Trump surrounded by crowds of Black supporters--and earning significant traction for doing so. Coming at a time when President Joe Biden is worried about losing the Black voters who came out for his 2020 election, the Trump images have become a whole new subgenre of A.I. sludge. And no one in any position of power appears to know what to do about it. Last month, BBC Panorama reported on the proliferation of these deceitful likenesses. The first example displayed Trump at a Christmas party with his arm around a couple of Black women, one of whom is seen wearing a Pen & Pixel–style tank; another shows him sitting on a house porch with six young Black men, smiling with his hands clasped.

Early warning systems (EWS) are predictive tools at the center of recent efforts to improve graduation rates in public schools across the United States. These systems assist in targeting interventions to individual students by predicting which students are at risk of dropping out. Despite significant investments in their widespread adoption, there remain large gaps in our understanding of the efficacy of EWS, and the role of statistical risk scores in education. In this work, we draw on nearly a decade's worth of data from a system used throughout Wisconsin to provide the first large-scale evaluation of the long-term impact of EWS on graduation outcomes. We present empirical evidence that the prediction system accurately sorts students by their dropout risk. We also find that it may have caused a single-digit percentage increase in graduation rates, though our empirical analyses cannot reliably rule out that there has been no positive treatment effect. Going beyond a retrospective evaluation of DEWS, we draw attention to a central question at the heart of the use of EWS: Are individual risk scores necessary for effectively targeting interventions? We propose a simple mechanism that only uses information about students' environments -- such as their schools, and districts -- and argue that this mechanism can target interventions just as efficiently as the individual risk score-based mechanism. Our argument holds even if individual predictions are highly accurate and effective interventions exist. In addition to motivating this simple targeting mechanism, our work provides a novel empirical backbone for the robust qualitative understanding among education researchers that dropout is structurally determined. Combined, our insights call into question the marginal value of individual predictions in settings where outcomes are driven by high levels of inequality.