Breakthroughs, discoveries, and DIY tips sent every weekday. Judging by newly released photos and video, the crew aboard Ocean Exploration Trust's Nautilus research vessel had an extremely productive summer trip to the South Pacific. Over 22 days, the team completed detailed archaeological surveys of more than a dozen shipwrecks sunk amid the Solomon Islands campaign during World War II. In addition to imaging four of them for the first time, experts guided remotely operated vehicles (ROVs) towards the rediscovery of two long-lost vessels:the separated bow from the USS New Orleans as well as the Imperial Japanese Naval destroyer Teruzuki. Although researchers originally spotted some of these shipwrecks more than 34 years ago, Ocean Exploration Trust president Robert Ballard explained that the most recent trip to Iron Bottom Sound provided opportunities to document their finds using a new generation of technology including high-definition survey cameras, underwater vehicles, and imaging tools aboard the EV Nautilus.

The Swedish prime minister, Ulf Kristersson, has come under fire after admitting that he regularly consults AI tools for a second opinion in his role running the country. Kristersson, whose Moderate party leads Sweden's centre-right coalition government, said he used tools including ChatGPT and the French service LeChat. His colleagues also used AI in their daily work, he said. Kristersson told the Swedish business newspaper Dagens industri: "I use it myself quite often. And should we think the complete opposite? Tech experts, however, have raised concerns about politicians using AI tools in such a way, and the Aftonbladet newspaper accused Kristersson in a editorial of having "fallen for the oligarchs' AI psychosis". "You have to be very careful," Simone Fischer-Hübner, a computer science researcher at Karlstad University, told Aftonbladet, warning against using ChatGPT to work with sensitive information. Kristersson's spokesperson, Tom Samuelsson, later said the prime minister did not take risks in his use of AI. "Naturally it is not security sensitive information that ends up there.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. This time, we hear from Shaghayegh (Shirley) Shajarian and learn about her research applying generative AI to computer networks. I am a third-year PhD student in the Computer Science department at North Carolina A&T State University, working under Dr Sajad Khorsandroo and Dr Mahmoud Abdelsalam. I am part of the Autonomous Cybersecurity and Resilience Lab, where my research focuses on applying generative AI to computer networks. I am developing AI-driven agents that assist with some network operations, such as log analysis, troubleshooting, and documentation.

When Ukrainian President Volodymyr Zelenskyy said at the end of last month that Ukraine needs 6 billion to fund the production of interceptor drones, setting a target of 1,000 a day, he had his reasons. Having already reshaped the battlefield by doing work once reserved for long-range missiles, field artillery and human intelligence, drones are now fighting Russian drones -- a boon for Ukraine's dwindling stock of air defense missile systems. In the last two months, just one Ukrainian charity supplying aerial interceptor drones says its devices have downed around 1,500 of the drones that Russia has been sending to reconnoiter the battlefield or to bomb Ukraine's towns and cities.

Three people were killed in a Russian attack on the Stepnohirsk community in Ukraine's Zaporizhia region, the local military administration said on Telegram. Russia launched 405 attacks on 10 settlements in the region in the past day, the administration said on Monday. Russian drone attacks killed three people in the Chuhuiv district of Ukraine's Kharkiv region, the regional prosecutor's office said. The victims included a man killed when Russian drones caused a fire in his home in the village of Losivka, and a man and a woman who were riding a motorcycle when they were killed. The prosecutor's office said it was investigating the motorcycle attack as a possible war crime.

To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains an elusive task. In this paper, we make progress on this task by utilizing instrumental variables (IVs). IVs are a classic tool for mitigating bias from unobserved confounders when estimating causal effects. While IV methods have been extended to non-separable structural models at the population level, existing approaches to counterfactual prediction typically assume additive noise in the outcome. In this paper, we show that under standard IV assumptions, along with the assumptions that latent noises in treatment and outcome are strictly monotonic and jointly Gaussian, the treatment-outcome relationship becomes uniquely identifiable from observed data. This enables counterfactual inference even in non-separable models. We implement our approach by training a normalizing flow to maximize the likelihood of the observed data, demonstrating accurate recovery of the underlying outcome function. We call our method Flow IV .

Advanced intelligent automation becomes an important feature to deal with the increased complexity in managing wireless networks. This paper proposes a novel automation approach of intent-based network for Radio Access Networks (RANs) management by leveraging Large Language Models (LLMs). The proposed method enhances intent translation, autonomously interpreting high-level objectives, reasoning over complex network states, and generating precise configurations of the RAN by integrating LLMs within an agentic architecture. We propose a structured prompt engineering technique and demonstrate that the network can automatically improve its energy efficiency by dynamically optimizing critical RAN parameters through a closed-loop mechanism. It showcases the potential to enable robust resource management in RAN by adapting strategies based on real-time feedback via LLM-orchestrated agentic systems.

Large trucks substantially contribute to work zone-related crashes, primarily due to their large size and blind spots. When approaching a work zone, large trucks often need to merge into an adjacent lane because of lane closures caused by construction activities. This study aims to enhance the safety of large truck merging maneuvers in work zones by evaluating the risk associated with merging conflicts and establishing a decision-making strategy for merging based on this risk assessment. To predict the risk of large trucks merging into a mixed traffic stream within a work zone, a Long Short-Term Memory (LSTM) neural network is employed. For a large truck intending to merge, it is critical that the immediate downstream vehicle in the target lane maintains a minimum safe gap to facilitate a safe merging process. Once a conflict-free merging opportunity is predicted, large trucks are instructed to merge in response to the lane closure. Our LSTM-based conflict prediction method is compared against baseline approaches, which include probabilistic risk-based merging, 50th percentile gap-based merging, and 85th percentile gap-based merging strategies. The results demonstrate that our method yields a lower conflict risk, as indicated by reduced Time Exposed Time-to-Collision (TET) and Time Integrated Time-to-Collision (TIT) values relative to the baseline models. Furthermore, the findings indicate that large trucks that use our method can perform early merging while still in motion, as opposed to coming to a complete stop at the end of the current lane prior to closure, which is commonly observed with the baseline approaches.

Counterfactual explanations study what should have changed in order to get an alternative result, enabling end-users to understand machine learning mechanisms with counterexamples. Actionability is defined as the ability to transform the original case to be explained into a counterfactual one. We develop a method for actionable counterfactual explanations that, unlike predecessors, does not directly leverage training data. Rather, data is only used to learn a density estimator, creating a search landscape in which to apply path planning algorithms to solve the problem and masking the endogenous data, which can be sensitive or private. We put special focus on estimating the data density using Bayesian networks, demonstrating how their enhanced interpretability is useful in high-stakes scenarios in which fairness is raising concern. Using a synthetic benchmark comprised of 15 datasets, our proposal finds more actionable and simpler counterfactuals than the current state-of-the-art algorithms. We also test our algorithm with a real-world Environmental Protection Agency dataset, facilitating a more efficient and equitable study of policies to improve the quality of life in United States of America counties. Our proposal captures the interaction of variables, ensuring equity in decisions, as policies to improve certain domains of study (air, water quality, etc.) can be detrimental in others. In particular, the sociodemographic domain is often involved, where we find important variables related to the ongoing housing crisis that can potentially have a severe negative impact on communities.

Computer perception (CP) technologies (digital phenotyping, affective computing and related passive sensing approaches) offer unprecedented opportunities to personalize healthcare, but provoke concerns about privacy, bias and the erosion of empathic, relationship-centered practice. A comprehensive understanding of perceived risks, benefits, and implementation challenges from those who design, deploy and experience these tools in real-world settings remains elusive. This study provides the first evidence-based account of key stakeholder perspectives on the relational, technical, and governance challenges raised by the integration of CP technologies into patient care. We conducted in-depth, semi-structured interviews with 102 stakeholders: adolescent patients and their caregivers, frontline clinicians, technology developers, and ethics, legal, policy or philosophy scholars. Transcripts underwent thematic analysis by a multidisciplinary team; reliability was enhanced through double coding and consensus adjudication. Stakeholders articulated seven interlocking concern domains: (1) trustworthiness and data integrity; (2) patient-specific relevance; (3) utility and workflow integration; (4) regulation and governance; (5) privacy and data protection; (6) direct and indirect patient harms; and (7) philosophical critiques of reductionism. To operationalize humanistic safeguards, we propose "personalized roadmaps": co-designed plans that predetermine which metrics will be monitored, how and when feedback is shared, thresholds for clinical action, and procedures for reconciling discrepancies between algorithmic inferences and lived experience. By translating these insights into personalized roadmaps, we offer a practical framework for developers, clinicians and policymakers seeking to harness continuous behavioral data while preserving the humanistic core of care.