There have been several instances of unidentified drones flying over military bases recently. At least 10 such drones had been seen flying above Manching Air Base near the city of Ingolstadt on Sunday evening, German police said. Last month, there were sightings at Manching and nearby Neuburg an der Donau. Drones were also spotted at the US air base at Ramstein and at an industrial zone near it in the North Sea. In her statement, Interior Minister Faeser said "espionage or sabotage are regularly considered as a possible reason".

Russia appeared to ready itself for talks on the future of Ukraine with United States President-elect Donald Trump ahead of his swearing-in on Monday. "No special conditions are needed for this. What is required is the mutual intent and political will to have a dialogue," said Russian President Vladimir Putin's spokesman Dmitry Peskov on Saturday. But Russia expressed its parameters very quickly. Putin aide Nikolai Patrushev told Russian news outlet KP that a Ukraine settlement should be reached by the US and Russia, without Ukraine and without the European Union.

DJI has lifted its geofence that prevents users in the US from flying over restricted areas like nuclear power plants, airports and wildfires, the company wrote in a blog post on Monday. As of January 13th, areas previously called "restricted zones" or no-fly zones will be shown as "enhanced warning zones" that correspond to designated Federal Aviation Administration (FAA) areas. DJI's Fly app will display a warning about those areas but will no longer stop users from flying inside them, the company said. In the article, DJI wrote that the "in-app alerts will notify operators flying near FAA designated controlled airspace, placing control back in the hands of the drone operators, in line with regulatory principles of the operator bearing final responsibility." It added that technologies like Remote ID [introduced after DJI implemented geofencing] gives authorities "the tools needed to enforce existing rules," DJI's global policy chief Adam Welsh told The Verge.

A Palestinian teenager and three brothers were among at least six people killed in an Israeli air attack on the Jenin refugee camp in the occupied West Bank, according to reports. The Palestinian news agency Wafa said that an Israeli drone fired three missiles at a group of people near a traffic roundabout in the camp on Tuesday evening, killing six people, including a 15-year-old boy, and injuring several others. Five other victims of the attack were aged between 23 and 34, and included three brothers, Wafa reports. Earlier this month, an Israeli drone strike on the occupied West Bank's Tammun town killed two Palestinian children and a 23-year-old from the same family. Al Jazeera's Hamdah Salhut said the drone strike on the Jenin camp comes amid intense Israeli military raids on local communities and the killing by Israeli forces of almost 800 Palestinians in the occupied West Bank since October 7, 2023 – as well as the arrest of several thousand others.

Artificial intelligence (AI) has transformed the geoscience community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. This success has led to the development of geoscience foundation models (FMs), which promise to accomplish multiple tasks within a workflow or replace the workflow altogether. However, lack of robust evaluation frameworks, even for traditional DLMs, leaves the geoscience community ill prepared for the inevitable adoption of FMs. We address this gap by designing an evaluation framework that jointly incorporates three crucial aspects to current DLMs and future FMs: performance uncertainty, learning efficiency, and overlapping training-test data splits. To target the three aspects, we meticulously construct the training, validation, and test splits using clustering methods tailored to geoscience data and enact an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. The framework's ability to guard against misleading declarations of model superiority is demonstrated through evaluation of PhaseNet, a popular seismic phase picking DLM, under 3 training approaches. Furthermore, we show how the performance gains due to overlapping training-test data can lead to biased FM evaluation. Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance at varying budgets of training data.

This paper critically examines the evolving ethical and regulatory challenges posed by the integration of artificial intelligence (AI) in cybersecurity. We trace the historical development of AI regulation, highlighting major milestones from theoretical discussions in the 1940s to the implementation of recent global frameworks such as the European Union AI Act. The current regulatory landscape is analyzed, emphasizing risk-based approaches, sector-specific regulations, and the tension between fostering innovation and mitigating risks. Ethical concerns such as bias, transparency, accountability, privacy, and human oversight are explored in depth, along with their implications for AI-driven cybersecurity systems. Furthermore, we propose strategies for promoting AI literacy and public engagement, essential for shaping a future regulatory framework. Our findings underscore the need for a unified, globally harmonized regulatory approach that addresses the unique risks of AI in cybersecurity. We conclude by identifying future research opportunities and recommending pathways for collaboration between policymakers, industry leaders, and researchers to ensure the responsible deployment of AI technologies in cybersecurity.

As artificial intelligence (AI) systems become increasingly integral to critical infrastructure and global operations, the need for a unified, trustworthy governance framework is more urgent that ever. This paper proposes a novel approach to AI governance, utilizing blockchain and distributed ledger technologies (DLT) to establish a decentralized, globally recognized framework that ensures security, privacy, and trustworthiness of AI systems across borders. The paper presents specific implementation scenarios within the financial sector, outlines a phased deployment timeline over the next decade, and addresses potential challenges with solutions grounded in current research. By synthesizing advancements in blockchain, AI ethics, and cybersecurity, this paper offers a comprehensive roadmap for a decentralized AI governance framework capable of adapting to the complex and evolving landscape of global AI regulation.

The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, evaluations of risks associated with AI-driven systems in the public sector remain insufficiently explored. Building upon an established taxonomy of AI risks derived from diverse government policies and corporate guidelines, we investigate the critical risks posed by generative AI in the public sector while extending the scope to account for its multimodal capabilities. In addition, we propose a Systematic dAta generatIon Framework for evaluating the risks of generative AI (SAIF). SAIF involves four key stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types. It ensures the systematic and consistent generation of prompt data, facilitating a comprehensive evaluation while providing a solid foundation for mitigating the risks. Furthermore, SAIF is designed to accommodate emerging jailbreak methods and evolving prompt types, thereby enabling effective responses to unforeseen risk scenarios. We believe that this study can play a crucial role in fostering the safe and responsible integration of generative AI into the public sector.

Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications. The swarm is governed by a central swarm controller responsible for distributing different search and tracking tasks among the cooperating UAVs. Each UAV agent is then controlled by a collection of cooperative sub-agents, whose behaviors have been trained using different deep reinforcement learning models, tailored for the different task types proposed by the swarm controller. More specifically, proximal policy optimization (PPO) algorithms were used to train the agents' behavior. In addition, several metrics to assess the performance of the swarm in this application were defined. The results obtained through simulation show that our system searches the operation area effectively, acquires the targets in a reasonable time, and is capable of tracking them continuously and consistently.

In the context of voting with ranked ballots, an important class of voting rules is the class of margin-based rules (also called pairwise rules). A voting rule is margin-based if whenever two elections generate the same head-to-head margins of victory or loss between candidates, then the voting rule yields the same outcome in both elections. Although this is a mathematically natural invariance property to consider, whether it should be regarded as a normative axiom on voting rules is less clear. In this paper, we address this question for voting rules with any kind of output, whether a set of candidates, a ranking, a probability distribution, etc. We prove that a voting rule is margin-based if and only if it satisfies some axioms with clearer normative content. A key axiom is what we call Preferential Equality, stating that if two voters both rank a candidate $x$ immediately above a candidate $y$, then either voter switching to rank $y$ immediately above $x$ will have the same effect on the election outcome as if the other voter made the switch, so each voter's preference for $y$ over $x$ is treated equally.