Digital storytelling, essential in entertainment, education, and marketing, faces challenges in production scalability and flexibility. The StoryAgent framework, introduced in this paper, utilizes Large Language Models and generative tools to automate and refine digital storytelling. Employing a top-down story drafting and bottom-up asset generation approach, StoryAgent tackles key issues such as manual intervention, interactive scene orchestration, and narrative consistency. This framework enables efficient production of interactive and consistent narratives across multiple modalities, democratizing content creation and enhancing engagement. Our results demonstrate the framework's capability to produce coherent digital stories without reference videos, marking a significant advancement in automated digital storytelling.

Social media researchers at the Network Contagion Research Institute in Princeton, New Jersey, got a rude awakening early last month. They were roused by 6:30 am phone calls from a colleague warning that Reddit had started blocking the institute's Pushshift service from updating its ongoing archive of every post on the discussion platform. That was a problem for more than just NCRI, because some of Reddit's 50,000 volunteer moderators depend on Pushshift to quickly investigate problem users, and many academics rely on the service. If it went stale, mods, as Reddit calls moderators, would have to work overtime or let more trash content accumulate. Researchers studying online communities would be forced to put projects and doctoral dissertations on ice.

It had a great new idea for a movie--Elemental, based on characters from The Good Dinosaur's director Peter Sohn--but actually animating the film's titular elements was proving to be a problem. After all, it's one thing to draw a crumbling mound of sentient dirt, but how do you capture the ethereal nature of fire onscreen, and how would a corporeal body made of water even work? Can you see through it? Do the eyes just float around? While some of those questions could be answered with good old-fashioned suspension of disbelief, Pixar's animators thought the fire issue was a real conundrum, especially considering that one of their movie's leads, Ember, was actually supposed to be made of the stuff. They had tools to make a flame effect from years of previous animations, but when you actually tried to shape it into a character, the results were pretty terrifying, a cross between Studio Ghibli's Calcifer and Nicolas Cage's Ghost Rider, but somehow harsher.

Traffic signal control affects the daily life of people living in urban areas. The existing system relies on a theory- or rule-based controller in charge of altering the traffic lights based on traffic conditions. The objective is to reduce vehicle delay during unsaturated traffic conditions and maximize the vehicle throughput during congestion. However, the existing traffic signal controller cannot fulfill such objectives, and a human controller can only manage a few intersections. In view of this, recent advancements in artificial intelligence have focused on enabling alternate ways of traffic signal control. Current research on this front has explored reinforcement learning (RL) algorithms as a possible approach.

Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity constraints while others take advantage of structured similarity information among multiple data points. In this work, we approach deep metric learning from a novel perspective. We propose instance cross entropy (ICE) which measures the difference between an estimated instance-level matching distribution and its ground-truth one. ICE has three main appealing properties. Firstly, similar to categorical cross entropy (CCE), ICE has clear probabilistic interpretation and exploits structured semantic similarity information for learning supervision. Secondly, ICE is scalable to infinite training data as it learns on mini-batches iteratively and is independent of the training set size. Thirdly, motivated by our relative weight analysis, seamless sample reweighting is incorporated. It rescales samples' gradients to control the differentiation degree over training examples instead of truncating them by sample mining. In addition to its simplicity and intuitiveness, extensive experiments on three real-world benchmarks demonstrate the superiority of ICE.

They're minute disruptions in the orientations of the molecules that make up solutions of liquid crystals, said Hayley Sohn, lead author of the new study. But under the microscope, these molecular deformations -- 10 of which could fill the width of a human hair -- certainly look alive. These pseudo-particles can twirl together as a group, shift their motion on a dime and even flow around obstacles when exposed to different electric currents. "By tuning that voltage, I can have them move in different directions and make them form a nice cluster where they're all stuck together. They can branch out into a chain and then come back together," said Sohn, a graduate student in the Materials Science and Engineering Program at CU Boulder.

What makes artificial intelligence intelligent? Is it able to learn from errors or recognize, say, the letters of the alphabet in a set of random shapes like a human can? These are some of the questions developers of AI ask. What began as sluggish programs on hulking machines has taken the form of code that anyone in a particular field could test out and manipulate to suit their needs. Jae Ho Sohn, a radiologist at the University of California at San Francisco, is adapting and working with an AI algorithm to analyze thousands of positron emission tomography (PET) scans to search for early signs of Alzheimer's.

What makes artificial intelligence intelligent? Is it able to learn from errors or recognize, say, the letters of the alphabet in a set of random shapes like a human can? These are some of the questions developers of AI ask. What began as sluggish programs on hulking machines has taken the form of code that anyone in a particular field could test out and manipulate to suit their needs. Jae Ho Sohn, a radiologist at the University of California at San Francisco, is adapting and working with an AI algorithm to analyze thousands of positron emission tomography (PET) scans to search for early signs of Alzheimer's.

What makes artificial intelligence intelligent? Is it able to learn from errors or recognize, say, the letters of the alphabet in a set of random shapes like a human can? These are some of the questions developers of AI ask. What began as sluggish programs on hulking machines has taken the form of code that anyone in a particular field could test out and manipulate to suit their needs. Jae Ho Sohn, a radiologist at the University of California at San Francisco, is adapting and working with an AI algorithm to analyze thousands of positron emission tomography (PET) scans to search for early signs of Alzheimer's.

Using a common type of brain scan, researchers programmed a machine-learning algorithm to diagnose early-stage Alzheimer's disease about six years before a clinical diagnosis is made – potentially giving doctors a chance to intervene with treatment. No cure exists for Alzheimer's disease, but promising drugs have emerged in recent years that can help stem the condition's progression. However, these treatments must be administered early in the course of the disease in order to do any good. This race against the clock has inspired scientists to search for ways to diagnose the condition earlier. "One of the difficulties with Alzheimer's disease is that by the time all the clinical symptoms manifest and we can make a definitive diagnosis, too many neurons have died, making it essentially irreversible," says Jae Ho Sohn, MD, MS, a resident in the Department of Radiology and Biomedical Imaging at UC San Francisco.