Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modeling emotional trajectories in stories has thus attracted considerable scholarly interest. However, as most existing works have been limited to unsupervised dictionary-based approaches, there is no benchmark for this task. We address this gap by introducing continuous valence and arousal labels for an existing dataset of children's stories originally annotated with discrete emotion categories. We collect additional annotations for this data and map the categorical labels to the continuous valence and arousal space. For predicting the thus obtained emotionality signals, we fine-tune a DeBERTa model and improve upon this baseline via a weakly supervised learning approach. The best configuration achieves a Concordance Correlation Coefficient (CCC) of $.8221$ for valence and $.7125$ for arousal on the test set, demonstrating the efficacy of our proposed approach. A detailed analysis shows the extent to which the results vary depending on factors such as the author, the individual story, or the section within the story. In addition, we uncover the weaknesses of our approach by investigating examples that prove to be difficult to predict.

If you're looking to understand the philosophy that underpins Silicon Valley's latest gold rush, look no further than OpenAI's Scarlett Johansson debacle. The story, according to Johansson's lawyers, goes like this: Nine months ago, OpenAI CEO Sam Altman approached the actor with a request to license her voice for a new digital assistant; Johansson declined. She alleges that just two days before the company's keynote event last week, in which that assistant was revealed as part of a new system called GPT-4o, Altman reached out to Johansson's team, urging the actor to reconsider. Johansson and Altman allegedly never spoke, and Johansson allegedly never granted OpenAI permission to use her voice. Nevertheless, the company debuted Sky two days later--a program with a voice many believed was alarmingly similar to Johansson's.

By the time I sat down with Christopher Nolan in his posh hotel suite not far from the White House, I guessed that he was tired of Washington, D.C. The day before, he'd toured the Oval Office and had lunch on Capitol Hill. Later that night, I'd watched him receive an award from the Federation for American Scientists, an organization that counts Robert Oppenheimer, the subject of Nolan's most recent film, among its founders. He'd endured a joke, repeated too many times by Senate Majority Leader Chuck Schumer, about the subject of his next film--"It's another biopic: Schumer." The award was sitting on an end table next to Nolan, who was dressed in brown slacks, a gray vest, and a navy suit jacket--his Anglo-formality undimmed by decades spent living in Los Angeles. "It's heavy, and glass, and good for self-defense," he said of the award, while filling his teacup.

Artists outraged by artificial intelligence that copies in seconds the styles they have sacrificed years to develop are waging battle online and in court. Fury erupted in the art community last year with the release of generative artificial intelligence (AI) programs that can convincingly carry out commands such as drawing a dog like cartoonist Sarah Andersen would, or a nymph the way illustrator Karla Ortiz might do. Such style-swiping AI works are cranked out without the original artist's consent, credit or compensation--the three C's at the heart of a fight to change all that. In January, artists including Andersen and Ortiz filed a class-action lawsuit against DreamUp, Midjourney and Stable Diffusion, three image-generating AI models programmed with art found online. Andersen told AFP she felt "violated" when first she saw an AI drawing that copied the style of her "Fangs" comic book work.

MSNBC guest Kurt Andersen on Tuesday attacked Mike Pence for not supporting the indictment of Donald Trump. He called the ex-VP a "p---y." An MSNBC host and his guest piled on Mike Pence on Tuesday, suggesting the former vice president was a "p---y" for not supporting the indictment of Donald Trump. Host John Heilemann mocked Pence as boring and dull, suggesting he had the personality of the "squarest person you knew growing up." But it was guest and author Kurt Andersen who made things personal.

Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modelling emotional trajectories in stories has thus attracted considerable scholarly interest. However, as most existing works have been limited to unsupervised dictionary-based approaches, there is no labelled benchmark for this task. We address this gap by introducing continuous valence and arousal annotations for an existing dataset of children's stories annotated with discrete emotion categories. We collect additional annotations for this data and map the originally categorical labels to the valence and arousal space. Leveraging recent advances in Natural Language Processing, we propose a set of novel Transformer-based methods for predicting valence and arousal signals over the course of written stories. We explore several strategies for fine-tuning a pretrained ELECTRA model and study the benefits of considering a sentence's context when inferring its emotionality. Moreover, we experiment with additional LSTM and Transformer layers. The best configuration achieves a Concordance Correlation Coefficient (CCC) of .7338 for valence and .6302 for arousal on the test set, demonstrating the suitability of our proposed approach. Our code and additional annotations are made available at https://github.com/lc0197/emotion_modelling_stories.

For years, fears about the disruptive potential of automation and artificial intelligence have centered on repetitive labor: Perhaps machines could replace humans who do secretarial work, accounting, burger-flipping. Doctors, software engineers, authors--any job that requires creative intelligence--seemed safe. But the past few months have turned those narratives on their head. A wave of artificial-intelligence programs, collectively dubbed "generative AI," have shown remarkable aptitude at using the English language, competition-level coding, creating stunning images from simple prompts, and perhaps even helping discover new drugs. In a year that has seen numerous tech hype bubbles burst or deflate, these applications suggest that Silicon Valley still has the power to, in subtle and shocking ways, rewire the world.

Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph analysis applications. Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achieve provable clustering qualities. To overcome these limitations, we devise a powerful \textit{peeling}-based graph clustering framework \textit{PCon}. We show that many existing solutions can be reduced to our framework. Namely, they first define a score function for each vertex, then iteratively remove the vertex with the smallest score. Finally, they output the result with the smallest conductance during the peeling process. Based on our framework, we propose two novel algorithms \textit{PCon\_core} and \emph{PCon\_de} with linear time and space complexity, which can efficiently and effectively identify clusters from massive graphs with more than a few billion edges. Surprisingly, we prove that \emph{PCon\_de} can identify clusters with near-constant approximation ratio, resulting in an important theoretical improvement over the well-known quadratic Cheeger bound. Empirical results on real-life and synthetic datasets show that our algorithms can achieve 5$\sim$42 times speedup with a high clustering accuracy, while using 1.4$\sim$7.8 times less memory than the baseline algorithms.

"Many computer games are expensive and require a lot of data and power. We need games that require little computing power to train algorithms in industrial environments," says Per-Arne Andersen, assistant professor at the University of Agder's (UiA's) Department of ICT. He recently earned his PhD with a thesis on how artificial intelligence in computer games can function well even if there is not much computing power. Andersen has developed artificial intelligence algorithms that can be used in systems where frequent decisions have to be made. Here, computer games are widely used to train artificial intelligence in game environments that are modelled on complicated industrial environments.

The technology of the '90s, including innovations in 3D graphics and affordable storage in the form of CD-ROMs, opened doors for a new generation of video game innovators. One of them was Kenji Eno. Eno's games became known for their singular creativity, though they never managed to land major commercial success. But that was all part of what kept Eno going and inspired his fervent work ethic and indie-first mindset. "Eno's work serves as a lesson in overcoming hardship," says John Andersen, a writer and video game historian.