Environment Animals Wildlife Sharks Megalodon set to become Maryland's state shark The Bay State is now home to the first state shark in the country. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The bill now goes to the governor's desk. Breakthroughs, discoveries, and DIY tips sent six days a week. The megalodon () is another step closer to becoming the first state shark in the United States.

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AOC hit by shockingly crude sex insult by White House after she mocked'TINY' Stephen Miller Biden ordered CIA cover-up of his'corrupt' business ties to Ukraine, astonishing secret files show NYC girls aged 12 and 13 meet tragic end after going subway surfing across Williamsburg Bridge at 3.10am ERIC TRUMP: The darkest day in my dad's marriage to Melania... before the ugly truth was exposed More girls are starting their periods younger than ever before - scientists think they've finally found what's causing it Taylor Swift reveals truth behind raunchy song about Travis Kelce's manhood Meghan is accused of'giggling as model stumbles on the catwalk': More Paris Fashion Week disasters emerge, including awkward moment with Kristin Scott Thomas The TRUTH to the doting mother who slaughtered her children and husband told by those she'd been quietly tormenting for years The troubled background of delivery man stabbed by Mark Sanchez... as he launches million-dollar lawsuit and sparks civil war at Fox 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' I haven't heard that name in so long' Ominous warning for humanity as birds suddenly adopt'unsettling' behavior And a humiliating lifeline: Backroom secrets of Taylor Swift and Blake Lively... after hit new song Bottled water contains dangerous levels of microplastics that lodge in vital organs and raise cancer risk', scientists warn Sea level rise could plunge 100 MILLION buildings underwater, warn scientists - so, is your home at risk? Rising sea levels could plunge more than 100 million buildings underwater by 2100, scientists have warned. The experts in Canada estimated how many buildings in Africa, Southeast Asia and Central and South America would be flooded by different sea level changes. Their assessment found that sea level rises of just 1.6 feet (0.5 metres) would flood three million buildings in the global south alone.

Image generation models are revolutionizing many domains, and urban analysis and design is no exception. While such models are widely adopted, there is a limited literature exploring their geographic knowledge, along with the biases they embed. In this work, we generated 150 synthetic images for each state in the USA and related capitals using FLUX 1 and Stable Diffusion 3.5, two state-of-the-art models for image generation. We embed each image using DINO-v2 ViT-S/14 and the Fréchet Inception Distances to measure the similarity between the generated images. We found that while these models have implicitly learned aspects of USA geography, if we prompt the models to generate an image for "United States" instead of specific cities or states, the models exhibit a strong representative bias toward metropolis-like areas, excluding rural states and smaller cities. {\color{black} In addition, we found that models systematically exhibit some entity-disambiguation issues with European-sounding names like Frankfort or Devon.

--Programmers have long ignored warnings, especially those generated by static analysis tools, due to the potential for false-positives. In some cases, warnings may be indicative of larger issues, but programmers may not understand how a seemingly unimportant warning can grow into a vulnerability. Because these messages tend to be long and confusing, programmers tend to ignore them if they do not cause readily identifiable issues. Large language models can simplify these warnings, explain the gravity of important warnings, and suggest potential fixes to increase developer compliance with fixing warnings. The views expressed in this article are those of the author(s) and do not reflect the official policy or position of the U.S. Naval Academy, Department of the Navy, the Department of Defense, or the U.S. Government. Warning messages generated by compilers and static analysis tools [1] have historically been overlooked and ignored [2].

Statistical topic modeling is widely used in political science to study text. Researchers examine documents of varying lengths, from tweets to speeches. There is ongoing debate on how document length affects the interpretability of topic models. We investigate the effects of aggregating short documents into larger ones based on natural units that partition the corpus. In our study, we analyze one million tweets by U.S. state legislators from April 2016 to September 2020. We find that for documents aggregated at the account level, topics are more associated with individual states than when using individual tweets. This finding is replicated with Wikipedia pages aggregated by birth cities, showing how document definitions can impact topic modeling results.

The Unlearning Sensitive Content from Large Language Models task aims to remove targeted datapoints from trained models while minimally affecting their general knowledge. In our work, we leverage parameter-efficient, gradient-based unlearning using low-rank (LoRA) adaptation and layer-focused fine-tuning. To further enhance unlearning effectiveness, we employ data chunking, splitting forget data into disjoint partitions and merging them with cyclically sampled retain samples at a pre-defined ratio. Our task-agnostic method achieves an outstanding forget-retain balance, ranking first on leaderboards and significantly outperforming baselines and competing systems.

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

The idea of entire cities being plunged underwater might sound like the plot of the latest science fiction blockbuster. But it could become a reality in just 75 years, according to a terrifying new study. Scientists from Nanyang Technological University (NTU), Singapore, have predicted that global sea levels could rise by a staggering 6.2 feet (1.9 metres) by 2100 if carbon dioxide (CO2) emissions continue to increase. 'The high-end projection of 1.9 metres underscores the need for decision-makers to plan for critical infrastructure accordingly,' said Dr Benjamin Grandey, lead author of the study. If global sea levels were to rise by 6.2ft (1.9 metres), towns and cities around the world could be plunged underwater - including several in the UK.