We used contrastive neural networks to learn useful similarity scores between the 144 cartridge casings in the NBIDE dataset, under the common-but-unknown source paradigm. The common-but-unknown source problem is a problem archetype in forensics where the question is whether two objects share a common source (e.g. were two cartridge casings fired from the same firearm). Similarity scores are often used to interpret evidence under this paradigm. We directly compared our results to a state-of-the-art algorithm, Congruent Matching Cells (CMC). When trained on the E3 dataset of 2967 cartridge casings, contrastive learning achieved an ROC AUC of 0.892. The CMC algorithm achieved 0.867. We also conducted an ablation study where we varied the neural network architecture; specifically, the network's width or depth. The ablation study showed that contrastive network performance results are somewhat robust to the network architecture. This work was in part motivated by the use of similarity scores attained via contrastive learning for standard evidence interpretation methods such as score-based likelihood ratios.

Waymo is continuing to expand its foothold across the US, having recently started offering paid robotaxi services in more parts of the San Francisco Bay Area. Next up are Atlanta and Miami, and now the company has revealed plans to offer its driverless Waymo One service in the nation's capital in 2026. Before that can happen, though, Waymo will need to get approval from regulators. The company says it will "continue to work closely with policymakers to formalize the regulations needed to operate without a human behind the wheel in the District." DC currently requires autonomous vehicles to have a human at the wheel, ready to take control if necessary.

There might be a way to get out of this moral and scientific deadlock. Recent advances in biotechnology now provide a pathway to producing living human bodies without the neural components that allow us to think, be aware, or feel pain. Many will find this possibility disturbing, but if researchers and policymakers can find a way to pull these technologies together, we may one day be able to create "spare" bodies, both human and nonhuman. These could revolutionize medical research and drug development, greatly reducing the need for animal testing, rescuing many people from organ transplant lists, and allowing us to produce more effective drugs and treatments. A few weeks ago, when I was at the digital rights conference RightsCon in Taiwan, I watched in real time as civil society organizations from around the world, including the US, grappled with the loss of one of the biggest funders of global digital rights work: the United States government.

China and India are likely to attempt to interfere in upcoming elections, Canada's intelligence agency has warned, adding that Russia and Pakistan also pose a potential threat. The deputy director of operations for the Canadian Security Intelligence Service (CSIS) said on Tuesday that the agency is braced for efforts to meddle in the April 28 vote. Ottawa's relations with China and India in particular have been strained. Vanessa Lloyd told a media conference that such countries are increasingly using artificial intelligence (AI) to interfere in elections around the globe. China is "highly likely to use AI-enabled tools to attempt to interfere with Canada's democratic process in this current election," she said. India has the "intent and capability" to do likewise, she continued, adding that Russia and Pakistan could also potentially seek to interfere.

Maxar Intelligence demonstrates its Raptor software that can guide drones through remote regions where there is no GPS signal, like this Polar Circle demonstration. A key geospatial intelligence firm on Tuesday announced a new product that can operate drones even in areas where the GPS signal has been jammed - cutting through modern defenses in the age of unmanned vehicular warfare. The war between Russia and Ukraine presented a unique problem: each military had learned how to jam the other's GPS signals, meaning their drones would be flying blind. This prompted the latest innovation from Maxar Intelligence, a drone-guiding technology that does not rely on satellite signals from space. Now, Maxar, a global satellite imagery and geospatial intelligence provider, has the capability to counter GPS-jamming technology through its Raptor system.

As a result, some policymakers and business leaders--in Europe, in particular--are reconsidering their reliance on US-based tech and asking whether they can quickly spin up better, homegrown alternatives. This is particularly true for AI. One of the clearest examples of this is in social media. Yasmin Curzi, a Brazilian law professor who researches domestic tech policy, put it to me this way: "Since Trump's second administration, we cannot count on [American social media platforms] to do even the bare minimum anymore." Social media content moderation systems--which already use automation and are also experimenting with deploying large language models to flag problematic posts--are failing to detect gender-based violence in places as varied as India, South Africa, and Brazil.

The Self-Defense Forces' newly launched Joint Operations Command (JJOC) will play a critical role in coordinating responses with allies and partners to a broad spectrum of potential crises, the chief of a similar Australian military command established in 2004 told The Japan Times. "I see this as another milestone in an ever-evolving and strengthening relationship with Japan," Vice Adm. Justin Jones, the Australian Defence Force's (ADF) chief of joint operations, said in an exclusive interview Monday, noting that the new structure will not only enable direct communication with similar commands in partner countries but also result in greater speed and efficiency when coordinating and conducting joint operations. "Without a doubt, the new SDF command will be enormously important for contingency planning," he said.

Australian authors say they are "livid" and feel violated that their work was included in an allegedly pirated dataset of books Meta used to train its AI. In court filings in January it was alleged chief executive Mark Zuckerberg approved the use of the LibGen dataset – an online archive of books – to train the company's artificial intelligence models despite warnings from his AI executive team that it is a dataset "we know to be pirated". The Atlantic has published a searchable database where authors can type in their name to see what of their work is included in LibGen dataset. It includes books published by many Australian authors, including some by former prime ministers Malcolm Turnbull, Kevin Rudd, Julia Gillard and John Howard. Holden Sheppard, the author of Invisible Boys, a hit young adult novel that has been adapted into a series on Stan, said two of his books and two short stories were included.

Institute of Information Technology Jamk University of Applied Sciences PO Box 207, FI-40101, Jyv askyl a, Finland Abstract The progress of artificial intelligence (AI) has made sophisticated methods available for cyberattacks and red team activities. The new methods can also accelerate the execution of the attacks. This review article examines the use of AI technologies in cyber-security attacks. It also tries to describe typical targets for such attacks. We employed a scoping review methodology to analyze articles and identify AI methods, targets, and models that red teams can utilize to simulate cybercrime. From the 470 records screened, 11 were included in the review. Various cyberattack methods were identified, targeting sensitive data, systems, social media profiles, passwords, and URLs. The application of AI in cybercrime to develop versatile attack models presents an increasing threat. Furthermore, AI-based techniques in red team use can provide new ways to address these issues. Keywords: Artificial intelligence, red team, red teaming, cyberattack, cybersecurity. 1 Introduction The possibility of artificial intelligence (AI) simulating human behavior has emerged as a significant cybersecurity threat.

Representation Engineering (RepE) has emerged as a powerful paradigm for enhancing AI transparency by focusing on high-level representations rather than individual neurons or circuits. It has proven effective in improving interpretability and control, showing that representations can emerge, propagate, and shape final model outputs in large language models (LLMs). However, in Vision-Language Models (VLMs), visual input can override factual linguistic knowledge, leading to hallucinated responses that contradict reality. To address this challenge, we make the first attempt to extend RepE to VLMs, analyzing how multimodal representations are preserved and transformed. Building on our findings and drawing inspiration from successful RepE applications, we develop a theoretical framework that explains the stability of neural activity across layers using the principal eigenvector, uncovering the underlying mechanism of RepE. We empirically validate these instrinsic properties, demonstrating their broad applicability and significance. By bridging theoretical insights with empirical validation, this work transforms RepE from a descriptive tool into a structured theoretical framework, opening new directions for improving AI robustness, fairness, and transparency.