Graph neural networks (GNNs) have recently been shown to be vulnerable to adversarial attacks, where slight perturbations in the graph structure can lead to erroneous predictions. However, current robust models for defending against such attacks inherit the transductive limitations of graph convolutional networks (GCNs). As a result, they are constrained by fixed structures and do not naturally generalize to unseen nodes. Here, we discover that transductive GCNs inherently possess a distillable robustness, achieved through a wave-induced resonance process. Based on this, we foster this resonance to facilitate inductive and robust learning. Specifically, we first prove that the signal formed by GCN-driven message passing (MP) is equivalent to the edge-based Laplacian wave, where, within a wave system, resonance can naturally emerge between the signal and its transmitting medium. This resonance provides inherent resistance to malicious perturbations inflicted on the signal system. We then prove that merely three MP iterations within GCNs can induce signal resonance between nodes and edges, manifesting as a coupling between nodes and their distillable surrounding local subgraph. Consequently, we present Graph Resonance-fostering Network (GRN) to foster this resonance via learning node representations from their distilled resonating subgraphs. By capturing the edge-transmitted signals within this subgraph and integrating them with the node signal, GRN embeds these combined signals into the central node's representation. This node-wise embedding approach allows for generalization to unseen nodes. We validate our theoretical findings with experiments, and demonstrate that GRN generalizes robustness to unseen nodes, whilst maintaining state-of-the-art classification accuracy on perturbed graphs.

Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just classic Agent-Based Models (ABM)s where the agents talk to one another. Rather, GABMs are constructed using an LLM to apply common sense to situations, act "reasonably", recall common semantic knowledge, produce API calls to control digital technologies like apps, and communicate both within the simulation and to researchers viewing it from the outside. Here we present Concordia, a library to facilitate constructing and working with GABMs. Concordia makes it easy to construct language-mediated simulations of physically- or digitally-grounded environments. Concordia agents produce their behavior using a flexible component system which mediates between two fundamental operations: LLM calls and associative memory retrieval. A special agent called the Game Master (GM), which was inspired by tabletop role-playing games, is responsible for simulating the environment where the agents interact. Agents take actions by describing what they want to do in natural language. The GM then translates their actions into appropriate implementations. In a simulated physical world, the GM checks the physical plausibility of agent actions and describes their effects. In digital environments simulating technologies such as apps and services, the GM may handle API calls to integrate with external tools such as general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar, Email, Search, etc.). Concordia was designed to support a wide array of applications both in scientific research and for evaluating performance of real digital services by simulating users and/or generating synthetic data.

Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare. However, if maximizing welfare is the ultimate goal, prediction is only a small piece of the puzzle. There are various other policy levers a social planner might pursue in order to improve bottom-line outcomes, such as expanding access to available goods, or increasing the effect sizes of interventions. Given this broad range of design decisions, a basic question to ask is: What is the relative value of prediction in algorithmic decision making? How do the improvements in welfare arising from better predictions compare to those of other policy levers? The goal of our work is to initiate the formal study of these questions. Our main results are theoretical in nature. We identify simple, sharp conditions determining the relative value of prediction vis-\`a-vis expanding access, within several statistical models that are popular amongst quantitative social scientists. Furthermore, we illustrate how these theoretical insights may be used to guide the design of algorithmic decision making systems in practice.

On the evening of January 6, 2021, Brad Parscale texted Donald Trump adviser Katrina Pierson about the insurrectionist assault on the US Capitol that had finally been quashed by police. "This is about [T]rump pushing for uncertainty in our country," wrote Parscale, who ran digital and data operations for Trump's 2016 campaign and managed his 2020 reelection effort before being replaced. This week I feel guilty for helping him win." "You did what you felt right at the time and therefore it was right," Pierson replied. "Yeah," Parscale answered, "but a woman is dead." The conversation continued, with Pierson texting, "You do realize this was going to happen." Parscale responded that Trump's rhetoric had "killed someone." Pierson countered, "It wasn't the rhetoric." Parscale was obviously blaming Trump for the storming of the Capitol and the death of Trump supporter Ashli Babbitt. In these private texts--which were not made public until mid-2022 during the House investigation of January ...

The tech company aims to expedite the nuclear regulatory process using AI

When you think of Big Tech these days, Yahoo is probably not top of mind. But for Chinese dissident Xu Wanping, the company still looms large--and has for nearly two decades. In 2005, Xu was arrested for signing online petitions relating to anti-Japanese protests. He didn't use his real name, but he did use his Yahoo email address. Yahoo China violated its users' trust--providing information on certain email accounts to Chinese law enforcement, which in turn allowed the government to identify and arrest some users.

Yemen's Houthi movement said on Tuesday they struck a Norwegian oil and chemical tanker with a rocket in its latest operation to protest against Israel's bombardment of Gaza. The Iran-aligned group targeted the ship after its crew "rejected all warning calls," Houthi military spokesperson Yehia Sareea said in a televised statement. He vowed that the Houthis would continue blocking ships heading to Israeli ports until Israel allows the entry of food and medical aid into the Gaza Strip - more than 1,000 miles from the Houthi seat of power in Sanaa. NETANYAHU TELLS BIDEN ISRAEL WILL ACT MILITARILY AGAINST YEMEN'S HOUTHIS IF US WON'T: REPORT The attack on the tanker Strinda took place about 60 nautical miles north of the Bab al-Mandab Strait. The ship, a Norwegian-owned-and-operated vessel called Strinda, was struck on Monday night as it traveled near the Bab al-Mandab strait, a sea lane through which much of the world's oil is shipped.

Democrat Shamaine Daniels is running for Congress, eyeing a seat held by Trump-aligned Republican Rep. Scott Perry, who played a key role challenging the 2020 election results. Daniels, who lost to Perry by less than 10 points last year, hopes a new weapon will help her underdog candidacy: Ashley, an artificial intelligence campaign volunteer. Ashley is not your typical robocaller; none of her responses are canned or pre-recorded. Her creators, who intend to mainly work with Democratic campaigns and candidates, say she is the first political phone banker powered by generative AI technology similar to OpenAI's ChatGPT. She is capable of having an infinite number of customized one-on-one conversations at the same time.

The U.S. Department of Defense has warned that China's artificial intelligence (AI) initiatives have seen heavy integration with the People's Liberation Army (PLA), raising concerns of a possible AI arms race. "The size, scope and sophistication of Chinese military modernization programs is breathtaking," James Anderson, who served as the deputy undersecretary of defense during the Trump administration, told Fox News Digital. "The report makes clear that Beijing remains hellbent on developing a world-class military force, despite its recent economic slowdown." The annual Pentagon report on the Military and Security Developments involving the People's Republic of China argues in the preface that China remains "the" pacing challenge for the Department of Defense as Beijing seeks "national rejuvenation" by 2049 – the centenary anniversary for the Chinese Communist Party (CCP). Chief among the various avenues the party has pursued to achieve this goal stands the "multi-domain precision warfare" concept, which seeks to incorporate advances in big data and AI to "rapidly identify key vulnerabilities in the U.S. operational system and then combine joint forces across domains to launch precision strikes," according to the report. The concept would help China develop "additional subordinate operational concepts" with an eye toward refining China's capabilities to fight and win "future wars."

This dissertation will combine new tools and methodologies to answer pressing questions regarding inundation area and hurricane events in complex, heterogeneous changing environments. In addition to remote sensing approaches, citizen science and machine learning are both emerging fields that harness advancing technology to answer environmental management and disaster response questions. Freshwater lakes supply a large amount of inland water resources to sustain local and regional developments. However, some lake systems depend upon great fluctuation in water surface area.