Based on the 10.9K articles from top 40 news providers of South Korea, this paper analyzed the media framing of Itaewon Halloween Crowd Crush during the first 72 hours after the incident. By adopting word-vector embedding and clustering, we figured out that conservative media focused on political parties' responses and the suspect's identity while the liberal media covered the responsibility of the government and possible unequal spillover effect on the low-income industry workers. Although the social tragedy was not directly connected to institutional politics, the media clearly exhibited political bias in the coverage process.

Earth System Models (ESMs) are essential tools for understanding the impact of human actions on Earth's climate. One key application of these models is studying extreme weather events, such as heat waves or dry spells, which have significant socioeconomic and environmental consequences. However, the computational demands of running a sufficient number of simulations to analyze the risks are often prohibitive. In this paper we demonstrate that diffusion models -- a class of generative deep learning models -- can effectively emulate the spatio-temporal trends of ESMs under previously unseen climate scenarios, while only requiring a small fraction of the computational resources. We present a diffusion model that is conditioned on monthly averages of temperature or precipitation on a $96 \times 96$ global grid, and produces daily values that are both realistic and consistent with those averages. Our results show that the output from our diffusion model closely matches the spatio-temporal behavior of the ESM it emulates in terms of the frequency of phenomena such as heat waves, dry spells, or rainfall intensity.

As the availability, size and complexity of data have increased in recent years, machine learning (ML) techniques have become popular for modeling. Predictions resulting from applying ML models are often used for inference, decision-making, and downstream applications. A crucial yet often overlooked aspect of ML is uncertainty quantification, which can significantly impact how predictions from models are used and interpreted. Extreme Gradient Boosting (XGBoost) is one of the most popular ML methods given its simple implementation, fast computation, and sequential learning, which make its predictions highly accurate compared to other methods. However, techniques for uncertainty determination in ML models such as XGBoost have not yet been universally agreed among its varying applications. We propose enhancements to XGBoost whereby a modified quantile regression is used as the objective function to estimate uncertainty (QXGBoost). Specifically, we included the Huber norm in the quantile regression model to construct a differentiable approximation to the quantile regression error function. This key step allows XGBoost, which uses a gradient-based optimization algorithm, to make probabilistic predictions efficiently. QXGBoost was applied to create 90\% prediction intervals for one simulated dataset and one real-world environmental dataset of measured traffic noise. Our proposed method had comparable or better performance than the uncertainty estimates generated for regular and quantile light gradient boosting. For both the simulated and traffic noise datasets, the overall performance of the prediction intervals from QXGBoost were better than other models based on coverage width-based criterion.

Finding saddle points of dynamical systems is an important problem in practical applications such as the study of rare events of molecular systems. Gentlest ascent dynamics (GAD) is one of a number of algorithms in existence that attempt to find saddle points in dynamical systems. It works by deriving a new dynamical system in which saddle points of the original system become stable equilibria. GAD has been recently generalized to the study of dynamical systems on manifolds (differential algebraic equations) described by equality constraints and given in an extrinsic formulation. In this paper, we present an extension of GAD to manifolds defined by point-clouds, formulated using the intrinsic viewpoint. These point-clouds are adaptively sampled during an iterative process that drives the system from the initial conformation (typically in the neighborhood of a stable equilibrium) to a saddle point. Our method requires the reactant (initial conformation), does not require the explicit constraint equations to be specified, and is purely data-driven.

Non-AutoRegressive (NAR) text generation models have drawn much attention because of their significantly faster decoding speed and good generation quality in machine translation. However, in a wider range of text generation tasks, existing NAR models lack proper pre-training, making them still far behind the pre-trained autoregressive models. In this paper, we propose Pre-trained Directed Acyclic Transformer (PreDAT) and a novel pre-training task to promote prediction consistency in NAR generation. Experiments on five text generation tasks show that our PreDAT remarkably outperforms existing pre-trained NAR models (+4.2 scores on average) and even achieves better results than pre-trained autoregressive baselines in n-gram-based metrics, along with 17 times speedup in throughput. Further analysis shows that PreDAT benefits from the unbiased prediction order that alleviates the error accumulation problem in autoregressive generation, which provides new insights into the advantages of NAR generation.

Dukes and Jackson, both with No Left Turn in Education, said parents should be concerned about how AI is being used in schools, and what information it may gather on students. Educators at over 120 districts across the country are implementing a pervasive school curriculum that has been denounced by opponents as an effort to manipulate children's values and beliefs and replace parents as the primary moral authority in their child's lives, with many critics specifically pointing to similarities with programs from the Centers for Disease Control and Prevention (CDC) as a major point of contention. The School Superintendent's Association (AASA), with the help of superintendents, board members and school administrators, is implementing the Learning 2025 program, which calls for an equity-focused, "holistic redesign" of the United States' public education system by 2025, in districts across the country The parents' advocacy group, No Left Turn in Education (NLTE), is sounding the alarm about the curriculum's alleged ties to the CDC, especially since Learning 2025 outlines its plans as a solution to the fallout of the COVID-19 pandemic. Learning 2025 frequently references the idea of a "Whole Child" educational framework to promote the notion that school districts should focus on a collective, whole community vision that is strikingly similar to the Whole School, Whole Community, Whole Child (WSCC) educational framework devised by the CDC. Both programs place a strong emphasis on students' and teachers' social and emotional health, including employee wellness programs, as well as psychological and social services like school-based health and counseling centers.

Employees of the US Immigration and Customs Enforcement agency (ICE) abused law enforcement databases to snoop on their romantic partners, neighbors, and business associates, WIRED exclusively revealed this week. New data obtained through record requests show that hundreds of ICE staffers and contractors have faced investigations since 2016 for attempting to access medical, biometric, and location data without permission. The revelations raise further questions about the protections ICE places on people's sensitive information. Security researchers at ESET found old enterprise routers are filled with company secrets. After purchasing and analyzing old routers, the firm found many contained login details for company VPNs, hashed root administrator passwords, and details of who the previous owners were.

Whether you love him or hate him, Elon Musk is the mastermind behind some of the most ingenuous technology projects of the modern era. The billionaire entrepreneur is the boss of carmaker Tesla, private space firm SpaceX and brain-computer interface startup Neuralink, among other projects. But Musk – who routinely tops the list as the world's richest person – became more infamous than ever when he bought Twitter last autumn. Here, MailOnline takes a look at all the companies Musk has invested in, from Zip2 back in the 1990s to his new artificial intelligence venture. Musk has vowed to create his own'trustworthy and reliable' AI chatbot called'TruthGPT', as a more'truthful alternative' to ChatGPT. Musk has previously tweeted that we need'TruthGPT' - a chatbot that would not censor its replies Zip2, Musk's first enterprise, was founded in California along with his brother Kimbal and their friend, the late Greg Kouri, back in 1995.

Panic rippled through the cybersecurity world in early December 2021 as word spread about a newly discovered vulnerability in a piece of open source software used by millions. A string of code called Log4J, which instructs programs written in Java to create a record of program activity, would allow attackers to insert malicious code into programs. The flaw led to risks in software used by government agencies, Web service providers such as Amazon Web Services and Apple iCloud, and even video games such as Minecraft. In fact, within days of the first announcement, attackers used the flaw to get into the computer of the Suffolk County, NY, clerk's office. Over the next few months, they stole files and passwords, installed malware and crypto-currency mining software, and gained access to other county networks, including the health and sheriff's departments.

The recent blizzard of warnings about artificial intelligence and how it is transforming learning, upending legal, financial and organizational functions, and reshaping social and cultural interaction, have mostly left out the role it is already playing in governance. Governments in the US at every level are attempting the transition from a programmatic model of service delivery to a citizen-focused model. Los Angeles, the US's second largest city, is a pioneer in the field, unveiling technologies to help streamline bureaucratic functions from police recruitment to paying parking tickets to filling potholes or locating resources at the library. For now, AI advances are limited to automation. When ChatGPT was asked recently about how it might change how people deal with government, it responded that "the next generation of AI, which includes ChatGPT, has the potential to revolutionize the way governments interact with their citizens."