Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications or exhaustive parameter scans only simple approximations are currently fast enough. We address this lack of fast simulators using neural PDE surrogates, data-driven neural network-based surrogate models trained using solutions generated with a classical numerical method. The surrogate approximates a time-stepping operator that evolves the full spatial solution of a reference physics-based model over time. We use DIV1D, a 1D dynamic model of the divertor plasma, as reference model to generate data. DIV1D's domain covers a 1D heat flux tube from the X-point (upstream) to the target. We simulate a realistic TCV divertor plasma with dynamics induced by upstream density ramps and provide an exploratory outlook towards fast transients. State-of-the-art neural PDE surrogates are evaluated in a common framework and extended for properties of the DIV1D data. We evaluate (1) the speed-accuracy trade-off; (2) recreating non-linear behavior; (3) data efficiency; and (4) parameter inter- and extrapolation. Once trained, neural PDE surrogates can faithfully approximate DIV1D's divertor plasma dynamics at sub real-time computation speeds: In the proposed configuration, 2ms of plasma dynamics can be computed in $\approx$0.63ms of wall-clock time, several orders of magnitude faster than DIV1D.

Human languages vary widely in how they encode information within circumscribed semantic domains (e.g., time, space, color, human body parts and activities), but little is known about the global structure of semantic information and nothing about its relation to human communication. We first show that across a sample of ~1,000 languages, there is broad variation in how densely languages encode information into their words. Second, we show that this language information density is associated with a denser configuration of semantic information. Finally, we trace the relationship between language information density and patterns of communication, showing that informationally denser languages tend toward (1) faster communication, but (2) conceptually narrower conversations within which topics of conversation are discussed at greater depth. These results highlight an important source of variation across the human communicative channel, revealing that the structure of language shapes the nature and texture of human engagement, with consequences for human behavior across levels of society.

The United States Justice Department (DOJ) is being asked to investigate whether a gunshot-detection system widely in use across the US is being selectively deployed to justify the over-policing of mainly Black neighborhoods, as critics of the technology claim. Attorneys for the nonprofit Electronic Privacy Information Center--a leading US-based civil liberties group--argue that "substantial evidence" suggests American cities are disproportionately deploying an acoustic tool known as ShotSpotter in majority-minority neighborhoods. Citing past studies, EPIC alleges that data derived from these sensors has encouraged some police departments to spend more and more time patrolling areas where the fewest number of white residents live--an allegation disputed by SoundThinking, the system's manufacturer. In a letter today to Merrick Garland, the US attorney general, attorneys for EPIC call for an investigation into whether cities using ShotSpotter are running afoul of the Civil Rights Act--namely, Title VI, which forbids racial discrimination by anyone who receives federal funds. "State and local police departments around the country have used federal financial assistance to facilitate the purchase of a slew of surveillance and automated decision-making technologies, including ShotSpotter," EPIC says.

Amazon has an artificial intelligence problem. Namely, that its "everything store" is filled with books authored by bots. Several AI detection startups say they have a straightforward solution to help customers: Tell them when a book is AI-generated. It's unclear exactly how many AI-generated books are currently for sale on the platform, which is both the largest bookseller in the world and the purveyor of the most popular self-publishing system in Kindle Direct Publishing. Certain genres, like travel guides and foraging handbooks, have been hit noticeably hard with a glut of low-quality titles.

As Republican presidential candidates flocked to Southern California for a debate and the state GOP convention this week, President Biden was busy in the San Francisco Bay Area collecting campaign checks and painting the election as a choice between MAGA chaos and functioning government. During three fundraising events in some of the swankiest neighborhoods of Silicon Valley and San Francisco on Tuesday and Wednesday, Biden touted his administration's accomplishments on climate and infrastructure, the United States' support for Ukraine and opposition to Russian President Vladimir Putin and his appointment of the first Black woman to the Supreme Court. As Wednesday's GOP presidential debate at the Reagan library made clear, the Republican Party has moved a long way from Reaganism. Despite that progress for the Democratic Party, Biden said he was running for reelection because "democracy is still at stake" in next year's election, a likely rematch with former President Trump, who has surged ahead in Republican primary polls. "Donald Trump and the MAGA Republicans are determined to destroy this democracy," Biden said during a private fundraising event Tuesday evening at the Atherton mansion of Democratic donors and philanthropists Liz Simons and Mark Heising in San Francisco.

With housing prices exploding all over the country, it's getting harder and harder to find a good deal on property. Mashvisor pools up-to-date data from resources like the MLS, Zillow, Rentometer, Airbnb, and the US Census Bureau to give you the most accurate, current real estate market data available in a single place. All data is stored and analyzed using AI and machine learning, turning raw data into actionable analytics for you and your business. Mashvisor offers comprehensive coverage for 95% of US markets, both large and small. All you have to do is search and you can find the best long-term and short-term rental markets in the US, filter based on your desires, and identify high-potential properties for your most important criteria, or to better understand the neighborhood you're looking to buy in.

Uncertainty is prevalent in engineering design, statistical learning, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to address uncertainty by formulating and solving conservative optimization models expressed using measures of risk and related concepts. We survey the rapid development of risk measures over the last quarter century. From their beginning in financial engineering, we recount the spread to nearly all areas of engineering and applied mathematics. Solidly rooted in convex analysis, risk measures furnish a general framework for handling uncertainty with significant computational and theoretical advantages. We describe the key facts, list several concrete algorithms, and provide an extensive list of references for further reading. The survey recalls connections with utility theory and distributionally robust optimization, points to emerging applications areas such as fair machine learning, and defines measures of reliability.

The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross-scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State-of-the-art bias correction methods only address errors in the simulated frequency distributions locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a post-processing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state-of-the-art, CMIP6-class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold-standard bias-adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.

Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks. However, the typical lack of interpretability of unsupervised representation learning has become a limiting factor with regard to recent transparent-AI regulations. In this paper, we study graph representation learning and we show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis. We also introduce additional metrics addressing the lack of formalism and metrics in the understudied area of unsupervised-representation learning interpretability. Our results are supported by an experimental study applied to both graph-level and node-level tasks and show that interpretable embeddings provide state-of-the-art performance on subsequent downstream tasks.

Large language models (LLMs) have demonstrated strong capabilities in various aspects. However, when applying them to the highly specialized, safe-critical legal domain, it is unclear how much legal knowledge they possess and whether they can reliably perform legal-related tasks. To address this gap, we propose a comprehensive evaluation benchmark LawBench. LawBench has been meticulously crafted to have precise assessment of the LLMs' legal capabilities from three cognitive levels: (1) Legal knowledge memorization: whether LLMs can memorize needed legal concepts, articles and facts; (2) Legal knowledge understanding: whether LLMs can comprehend entities, events and relationships within legal text; (3) Legal knowledge applying: whether LLMs can properly utilize their legal knowledge and make necessary reasoning steps to solve realistic legal tasks. LawBench contains 20 diverse tasks covering 5 task types: single-label classification (SLC), multi-label classification (MLC), regression, extraction and generation. We perform extensive evaluations of 51 LLMs on LawBench, including 20 multilingual LLMs, 22 Chinese-oriented LLMs and 9 legal specific LLMs. The results show that GPT-4 remains the best-performing LLM in the legal domain, surpassing the others by a significant margin. While fine-tuning LLMs on legal specific text brings certain improvements, we are still a long way from obtaining usable and reliable LLMs in legal tasks. All data, model predictions and evaluation code are released in https://github.com/open-compass/LawBench/. We hope this benchmark provides in-depth understanding of the LLMs' domain-specified capabilities and speed up the development of LLMs in the legal domain.