In Ari Aster's movies, the price of understanding how the world really works is your sanity, if not your life. His first three movies--Hereditary, Midsommar, and Beau Is Afraid--center on characters whose feeling that there's something sinister going on beneath the surface of their existence is eventually proved to be correct, but it's as if their bodies aren't equipped to contain that knowledge. One way or another, their minds are gone. The people in Aster's polarizing fourth movie, Eddington, a Western-inflected psychodrama set during the early days of the COVID-19 pandemic, don't get off so easy. The stress test of a rapidly spreading virus with no known treatment exposes innumerable cracks in society's facade: the gap between remote workers and people forced to risk their lives in order to earn a living; between people who breathe a sigh of relief when they see a police car approaching and people who have to be sure to keep their hands in plain sight.

"Eddington" is a slog, but a slog with ambitions--and its director and screenwriter, Ari Aster, is savvy enough to cultivate an air of mystery about what those ambitions are. His earlier chillers, "Hereditary" (2018) and "Midsommar" (2019), had their labyrinthine ambiguities, too, but they also had propulsive craft and cunning, plus a resolute commitment to scaring us stupid. Then came the ungainly "Beau Is Afraid" (2023), a cavalcade of Oedipal neuroses both showy and coy, in which Aster didn't seem to lose focus so much as sacrifice it on the altar of auteurism. With "Eddington," his high-minded unravelling continues. No longer a horror wunderkind, Aster, at thirty-nine, yearns to be an impish anatomist of the body politic.

Supporting decision-making has long been a central vision in the field of spatio-temporal intelligence. While prior work has improved the timeliness and accuracy of spatio-temporal forecasting, converting these forecasts into actionable strategies remains a key challenge. A main limitation is the decoupling of the prediction and the downstream decision phases, which can significantly degrade the downstream efficiency. For example, in emergency response, the priority is successful resource allocation and intervention, not just incident prediction. To this end, it is essential to propose an Adaptive Spatio-Temporal Early Decision model (ASTER) that reforms the forecasting paradigm from event anticipation to actionable decision support. This framework ensures that information is directly used for decision-making, thereby maximizing overall effectiveness. Specifically, ASTER introduces a new Resource-aware Spatio-Temporal interaction module (RaST) that adaptively captures long- and short-term dependencies under dynamic resource conditions, producing context-aware spatiotemporal representations. To directly generate actionable decisions, we further design a Preference-oriented decision agent (Poda) based on multi-objective reinforcement learning, which transforms predictive signals into resource-efficient intervention strategies by deriving optimal actions under specific preferences and dynamic constraints. Experimental results on four benchmark datasets demonstrate the state-of-the-art performance of ASTER in improving both early prediction accuracy and resource allocation outcomes across six downstream metrics.

The application of natural language processing models to PDF documents is pivotal for various business applications yet the challenge of training models for this purpose persists in businesses due to specific hurdles. These include the complexity of working with PDF formats that necessitate parsing text and layout information for curating training data and the lack of privacy-preserving annotation tools. This paper introduces DOCMASTER, a unified platform designed for annotating PDF documents, model training, and inference, tailored to document question-answering. The annotation interface enables users to input questions and highlight text spans within the PDF file as answers, saving layout information and text spans accordingly. Furthermore, DOCMASTER supports both state-of-the-art layout-aware and text models for comprehensive training purposes. Importantly, as annotations, training, and inference occur on-device, it also safeguards privacy. The platform has been instrumental in driving several research prototypes concerning document analysis such as the AI assistant utilized by University of California San Diego's (UCSD) International Services and Engagement Office (ISEO) for processing a substantial volume of PDF documents.

The popularity of automatic speech recognition (ASR) systems nowadays leads to an increasing need for improving their accessibility. Handling stuttering speech is an important feature for accessible ASR systems. To improve the accessibility of ASR systems for stutterers, we need to expose and analyze the failures of ASR systems on stuttering speech. The speech datasets recorded from stutterers are not diverse enough to expose most of the failures. Furthermore, these datasets lack ground truth information about the non-stuttered text, rendering them unsuitable as comprehensive test suites. Therefore, a methodology for generating stuttering speech as test inputs to test and analyze the performance of ASR systems is needed. However, generating valid test inputs in this scenario is challenging. The reason is that although the generated test inputs should mimic how stutterers speak, they should also be diverse enough to trigger more failures. To address the challenge, we propose ASTER, a technique for automatically testing the accessibility of ASR systems. ASTER can generate valid test cases by injecting five different types of stuttering. The generated test cases can both simulate realistic stuttering speech and expose failures in ASR systems. Moreover, ASTER can further enhance the quality of the test cases with a multi-objective optimization-based seed updating algorithm. We implemented ASTER as a framework and evaluated it on four open-source ASR models and three commercial ASR systems. We conduct a comprehensive evaluation of ASTER and find that it significantly increases the word error rate, match error rate, and word information loss in the evaluated ASR systems. Additionally, our user study demonstrates that the generated stuttering audio is indistinguishable from real-world stuttering audio clips.

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. The self-organized emergence of macroscopic patterns from microscopic interactions between self-propelled particles can be widely observed nature. Although hydrodynamic theories help us better understand the physical basis of this phenomenon, identifying a sufficient set of local interactions that shape, regulate, and sustain self-organized structures in active particle systems remains challenging. We investigate a classic hydrodynamic model of self-propelled particles that produces a wide variety of patterns, like asters and moving density bands. Our data-driven analysis shows that propagating bands are formed by local alignment interactions driven by density gradients, while steady-state asters are shaped by a mechanism of splay-induced negative compressibility arising from strong particle interactions. Our method also reveals analogous physical principles of pattern formation in a system where the speed of the particle is influenced by local density. This demonstrates the ability of our method to reveal physical commonalities across models. The physical mechanisms inferred from the data are in excellent agreement with analytical scaling arguments and experimental observations.

In this article, Beau is a-spoiled. In an Ari Aster movie, the best thing that can happen is losing your head. Not literally, of course, although the Midsommar auteur is notoriously fond of literally cutting his characters off at the neck. In 2019, he said that "head trauma will always have a place in my movies," and his latest, Beau Is Afraid, holds true to that promise. Early on, just after Beau Wasserman (Joaquin Phoenix) cancels a planned visit to his mother, she is decapitated by a falling chandelier. But alongside the characters who get their skulls crushed and faces smashed are ones who desperately need a respite from the buzzing of their brains--who would give anything if they could, even for a minute, just stop thinking. Toni Colette's character in Hereditary comes from family with a long history of mental illness--a mother with dissociative identity disorder, a father with psychotic depression, a brother with schizophrenia--and is plagued by the feeling that she and her family are the object of a sinister conspiracy.

For die-hards, no horror movie can be too scary. But for you, a wimp, the wrong one can leave you miserable. Never fear, scaredies, because Slate's Scaredy Scale is here to help. We've put together a highly scientific and mostly spoiler-free system for rating new horror movies, comparing them with classics along a 10-point scale. And because not everyone is scared by the same things--some viewers can't stand jump scares, while others are haunted by more psychological terrors or can't stomach arterial spurts--it breaks down each movie's scares across three criteria: suspense, spookiness, and gore.

Lending Document AI is a Document Understanding solution that allows for classification and parsing of documents commonly used in the mortgage lending industry. The data in these unstructured files is then converted into a structured format, which can be stored in a database or used for analysis and calculations. You can read more about the product in the announcement blog post. This sample application creates an automated pipeline where the user can bulk upload a collection of PDFs, the Lending Document Splitter & Classifier will classify each document and send each PDF to the appropriate specialized parser to extract the data, which can then be used to calculate an individual tax return and fill out a 1040 Form. Let's explore how this application works.

We'll leave the actual model creation and optimization processes to the experts: BigQuery ML and AutoML Tables. Even better, we'll train two different models and select the one that performs better with our dataset. Before we dive into the pipeline, let's take a quick look at the tools we'll rely on for model development: BigQuery ML (BQML) lets you create and execute machine learning models in BigQuery using standard SQL queries while leveraging BigQuery's petabyte scale. BigQuery ML democratizes machine learning by letting SQL practitioners build models using existing SQL tools and skills. AutoML Tables is even more hands-off.