The new designs honor the Constitution, Civil War, and more. Breakthroughs, discoveries, and DIY tips sent every weekday. While we've said goodbye to both the year 2025 and the penny, five new United States quarters will be finding their way into your pocket soon enough. The designs of each new quarter will honor the country's 250th anniversary (aka its semiquincentennial). According to a press release from the U.S. Mint, the coins "commemorate 250 years of American Liberty by reflecting our country's founding principles and honoring our Nation's history."

A BBC investigation has uncovered claims that essay cheating remains widespread at UK universities despite the introduction of a law designed to stop it. Since April 2022, it has been illegal to provide essays for students in post-16 education in England. But so far there have been no prosecutions. The BBC has spoken to a former lecturer who describes essay cheating as an open secret and to a businessman who claims to have made millions from selling model answer essays to university students. Universities UK, which represents 141 institutions, said there were severe penalties for students caught submitting work that was not their own.

Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-depth preliminary analysis, we observe two important characteristics of high-order dynamics in real-world networks: high-order relations tend to (O1) have a structural and temporal influence on other relations in a short term and (O2) periodically re-appear in a long term. In this paper, we propose LINCOLN, a method for Learning hIgh-order dyNamiCs Of reaL-world Networks, that employs (1) bi-interactional hyperedge encoding for short-term patterns, (2) periodic time injection and (3) intermediate node representation for long-term patterns. Via extensive experiments, we show that LINCOLN outperforms nine state-of-the-art methods in the dynamic hyperedge prediction task.

Why is untangling two small knots more difficult than unravelling one big one? Surprisingly, mathematicians have found that larger and seemingly more complex knots created by joining two simpler ones together can sometimes be easier to undo, invalidating a conjecture posed almost 90 years ago. "We were looking for a counterexample without really having an expectation of finding one, because this conjecture had been around so long," says Mark Brittenham at the University of Nebraska at Lincoln. "In the back of our heads, we were thinking that the conjecture was likely to be true. It was very unexpected and very surprising. Mathematicians like Brittenham study knots by treating them as tangled loops with joined ends. One of the most important concepts in knot theory is that each knot has an unknotting number, which is the number of times you would have to sever the string, move another piece of the loop through the gap and then re-join the ends before you reached a circle with no crossings at all – known as the "unknot". Calculating unknotting numbers can be a very computationally intensive task, and there are still knots with as few as 10 crossings that have no solution. Because of this, it can be helpful to break knots down into two or more simpler knots to analyse them, with those that can't be split any further known as prime knots, analogous to prime numbers. But a long-standing mystery is whether the unknotting numbers of the two knots added together would give you the unknotting number of the larger knot. Intuitively, it might make sense that a combined knot would be at least as hard to undo as the sum of its constituent parts, and in 1937, it was conjectured that undoing the combined knot could never be easier. The latest on what's new in science and why it matters each day. Now, Brittenham and Susan Hermiller, also at the University of Nebraska at Lincoln, have shown that there are cases when this isn't true. "The conjecture's been around for 88 years and as people continue not to find anything wrong with it, people get more hopeful that it's true," says Hermiller. "First, we found one, and then quickly we found infinitely many pairs of knots for whom the connected sum had unknotting numbers that were strictly less than the sum of the unknotting numbers of the two pieces." "We've shown that we don't understand unknotting numbers nearly as well as we thought we did," says Brittenham. "There could be – even for knots that aren't connected sums – more efficient ways than we ever imagined for unknotting them.

Abstractive summarization for long-form narrative texts such as movie scripts is challenging due to the computational and memory constraints of current language models. A movie script typically comprises a large number of scenes; however, only a fraction of these scenes are salient, i.e., important for understanding the overall narrative. The salience of a scene can be operationalized by considering it as salient if it is mentioned in the summary. Automatically identifying salient scenes is difficult due to the lack of suitable datasets. In this work, we introduce a scene saliency dataset that consists of human-annotated salient scenes for 100 movies. We propose a two-stage abstractive summarization approach which first identifies the salient scenes in script and then generates a summary using only those scenes. Using QA-based evaluation, we show that our model outperforms previous state-of-the-art summarization methods and reflects the information content of a movie more accurately than a model that takes the whole movie script as input.

The properties of a cluster of multiple back-propagation (BP) networks are examined and compared to the performance of a single BP net(cid:173) work. The underlying idea is that a synergistic effect within the cluster improves the perfonnance and fault tolerance. Five networks were ini(cid:173) tially trained to perfonn the same input-output mapping. Following training, a cluster was created by computing an average of the outputs generated by the individual networks. The output of the cluster can be used as the desired output during training by feeding it back to the indi(cid:173) vidual networks.

When we talk about the risks of using AI, we usually think of scenes from movies like A Space Odyssey or The Terminator, where robots turn against humans. While the consequences are significant, they're far less dramatic than the dystopian future often portrayed in movies. The risks are much more nuanced and subtle, and many people fail to recognize them until it is too late. Recently, AI-assisted writing has been hailed as a way to create content faster, but hardly anyone talks about the risks involved. When companies like Open AI commercialized AI text generation, it also intensified the risk landscape.

It doesn't take much to get ChatGPT to make a factual mistake. My son is doing a report on US presidents, so I figured I'd help him out by looking up a few biographies. Garry Wills famously wrote "Lincoln at Gettysburg," and Lincoln himself wrote the Emancipation Proclamation, of course, but it's not a bad start. Then I tried something harder, asking instead about the much more obscure William Henry Harrison, and it gamely provided a list, nearly all of which was wrong. Numbers 4 and 5 are correct; the rest don't exist or are not authored by those people.

It doesn't take much to get ChatGPT to make a factual mistake. My son is doing a report on U.S. presidents, so I figured I'd help him out by looking up a few biographies. Garry Wills famously wrote "Lincoln at Gettysburg," and Lincoln himself wrote the Emancipation Proclamation, of course, but it's not a bad start. Then I tried something harder, asking instead about the much more obscure William Henry Harrison, and it gamely provided a list, nearly all of which was wrong. Books about Harrison, fewer than half of which are correct.

Attempts to ban generative AI such as ChatGPT are not all they are cracked up to be. To ban, or not to ban, that is the question. I would guess that if Shakespeare were around nowadays, he might have said something like that about the recent efforts to ban the use of a type of AI known as Generative AI, which is especially exemplified and popularized due to an AI app called ChatGPT. Some high-profile entities have been attempting to ban the use of ChatGPT. For example, the New York City (NYC) Department of Education recently announced that they were proceeding to block access to ChatGPT on its various networks and connected devices. The reported rationale for the ban consisted of indications that this AI app and the overall use of generative AI seemingly portend negative consequences for student learning. Students that opt to use ChatGPT are said to be undercutting the development of their crucial critical-thinking skills and undermining the growth of their problem-solving abilities. On top of those rather stoutly worrisome qualms, there is the undisputed fact that such AI can produce inaccurate outputs that contain errors and other factual maladies. The dangerous icing on the cake is the imagined possibility that the outputs could potentially be used in an unsafe manner by students that unknowingly rely upon said falsehoods. No such documented harms have yet surfaced that I've seen, so we'll need to just take at face value that this could potentially happen (I have discussed the range of possibilities in my postings; for example, some have posited that generative AI essays could tell someone to take medicines that they should not be taking or provide mental health advice that ought to be proffered by human mental health professionals, etc.).