The inaugural boss of NASA's newly-created UFO research division has been named as a former meteorologist and liaison to the Pentagon. Mark McInerney will become the US space agency's director of research into unidentified anomalous phenomena (UAPs), more commonly known as unidentified flying objects. NASA officials initially refused to reveal McInerney's identity amid fears he would be harassed, before later backtracking on the decision. Speaking about the new UFO boss following the release of the agency's highly-anticipated study into more than 800 UAP sightings, NASA's associate administrator Nicola Fox told reporters: 'They have been working there a while now, during the study, to help be a point of contact.' But when directly asked whether she could name the official, Fox replied: 'We will not give his name out.' Alien hunter: The inaugural boss of NASA's newly-created UFO research division has been named as former meteorologist and liaison to the Pentagon Mark McInerney (pictured) NASA officials initially refused to reveal McInerney's identity amid fears he would be harassed All changed hours later, however, when NASA sent out a press release that included McInerney's name and revealed he previously worked as the agency's liaison to the Pentagon.

In 1996 Nasa and the White House made the explosive announcement that the rock contained traces of Martian bugs. The meteorite, catalogued as Allen Hills (ALH) 84001, crashed onto the frozen wastes of Antarctica 13,000 years ago and was recovered in 1984. Photographs were released showing elongated segmented objects that appeared strikingly lifelike.

On 16th July 1945 the world changed forever. The Manhattan Project's'Trinity' test, directed by Robert Oppenheimer, endowed humanity for the first time with the ability to wipe itself out: an atomic bomb had been successfully detonated 210 miles south of Los Alamos, New Mexico. On 6th August 1945 the bomb was dropped on Hiroshima and three days later, Nagasaki-- unleashing unprecedented destructive power. The end of World War II brought a fragile peace, overshadowed by this new, existential threat. While nuclear technology promised an era of abundant energy, it also launched us into a future where nuclear war could lead to the end of our civilization.

South Dakota Sen. Mike Rounds shares key takeaways from senators' closed meeting with tech titans like Elon Musk and Mark Zuckerberg on their concerns about artificial intelligence and the need for regulation. Spain has established Europe's first artificial intelligence (AI) policy task force, taking a decisive first step in determining laws around the promising but controversial technology as many governments remain uncertain about the best way forward. The Council of Ministers on Aug. 22 approved a Royal Decree to create the Spanish Agency for the Supervision of Artificial Intelligence (AESIA), a task force that will work under the guidance of the Ministry of Economic Affairs and Digital Transformation. The task force is the first of its kind in Europe, following on from the European Union's Artificial Intelligence Act, which sought to try and establish a framework for governance and oversight of the growing technology. The decree cited the "unquestionable" global impact of AI technology and the rapid advancement the technology has undergone.

Lu, who wears a black T-shirt and glasses, pulls a bag out from under his scooter seat then heads down to the beach. "Oh!" exclaims the 43-year-old once making it down to the shore. Lu bends down to pick up a worn plastic bottle that has washed up beside his foot and puts it in his bag. "Wet Chinese plastic," he says flatly. He points to the simplified Chinese characters on the packaging indicating the bottle's origin – mainland China. Lu is not at the beach to stroll among the rusty anti-landing spikes protruding from concrete blocks – a reminder of Kinmen's role as a front-line island between China and Taiwan. Nor has he come to marvel at the lights that now glitter in the dusk from the skyscrapers of the Chinese metropolis of Xiamen, less than 10km (6.2 miles) away across Xiamen Bay. Instead, he has come to the western coast of Kinmen to collect rubbish. When Lu is not working as an administrator at a local tourism office, he contributes to keeping Kinmen clean by picking up rubbish. Tides, weather and ocean currents as well as Kinmen's proximity to Xiamen and the mouth of the polluted Jiulong River in China have left the island exposed to large quantities of waste.

Control Barrier functions (CBFs) have attracted extensive attention for designing safe controllers for their deployment in real-world safety-critical systems. However, the perception of the surrounding environment is often subject to stochasticity and further distributional shift from the nominal one. In this paper, we present distributional robust CBF (DR-CBF) to achieve resilience under distributional shift while keeping the advantages of CBF, such as computational efficacy and forward invariance. To achieve this goal, we first propose a single-level convex reformulation to estimate the conditional value at risk (CVaR) of the safety constraints under distributional shift measured by a Wasserstein metric, which is by nature tri-level programming. Moreover, to construct a control barrier condition to enforce the forward invariance of the CVaR, the technique of differentiable convex programming is applied to enable differentiation through the optimization layer of CVaR estimation. We also provide an approximate variant of DR-CBF for higher-order systems. Simulation results are presented to validate the chance-constrained safety guarantee under the distributional shift in both first and second-order systems.

The deployment of autonomous agents in real-world scenarios is challenged by "unknown unknowns", i.e. novel unexpected environments not encountered during training, such as degraded signs. While existing research focuses on anomaly detection and class imbalance, it often fails to address truly novel scenarios. Our approach enhances visual perception by leveraging the Variational Prototyping Encoder (VPE) to adeptly identify and handle novel inputs, then incrementally augmenting data using neural style transfer to enrich underrepresented data. By comparing models trained solely on original datasets with those trained on a combination of original and augmented datasets, we observed a notable improvement in the performance of the latter. This underscores the critical role of data augmentation in enhancing model robustness. Our findings suggest the potential benefits of incorporating generative models for domain-specific augmentation strategies.

There is a growing interest in characterizing circular data found in biological systems. Such data are wide ranging and varied, from signal phase in neural recordings to nucleotide sequences in round genomes. Traditional clustering algorithms are often inadequate due to their limited ability to distinguish differences in the periodic component. Current clustering schemes that work in a polar coordinate system have limitations, such as being only angle-focused or lacking generality. To overcome these limitations, we propose a new analysis framework that utilizes projections onto a cylindrical coordinate system to better represent objects in a polar coordinate system. Using the mathematical properties of circular data, we show our approach always finds the correct clustering result within the reconstructed dataset, given sufficient periodic repetitions of the data. Our approach is generally applicable and adaptable and can be incorporated into most state-of-the-art clustering algorithms. We demonstrate on synthetic and real data that our method generates more appropriate and consistent clustering results compared to standard methods. In summary, our proposed analysis framework overcomes the limitations of existing polar coordinate-based clustering methods and provides a more accurate and efficient way to cluster circular data.

The capabilities of large language models (LLMs) have been successfully applied in the context of table representation learning. The recently proposed tabular language models have reported state-of-the-art results across various tasks for table interpretation. However, a closer look into the datasets commonly used for evaluation reveals an entity leakage from the train set into the test set. Motivated by this observation, we explore adversarial attacks that represent a more realistic inference setup. Adversarial attacks on text have been shown to greatly affect the performance of LLMs, but currently, there are no attacks targeting tabular language models. In this paper, we propose an evasive entity-swap attack for the column type annotation (CTA) task. Our CTA attack is the first black-box attack on tables, where we employ a similarity-based sampling strategy to generate adversarial examples. The experimental results show that the proposed attack generates up to a 70% drop in performance.

Large language models (LLMs) acquire extensive knowledge during pre-training, known as their parametric knowledge. However, in order to remain up-to-date and align with human instructions, LLMs inevitably require external knowledge during their interactions with users. This raises a crucial question: How will LLMs respond when external knowledge interferes with their parametric knowledge? To investigate this question, we propose a framework that systematically elicits LLM parametric knowledge and introduces external knowledge. Specifically, we uncover the impacts by constructing a parametric knowledge graph to reveal the different knowledge structures of LLMs, and introduce external knowledge through distractors of varying degrees, methods, positions, and formats. Our experiments on both black-box and open-source models demonstrate that LLMs tend to produce responses that deviate from their parametric knowledge, particularly when they encounter direct conflicts or confounding changes of information within detailed contexts. We also find that while LLMs are sensitive to the veracity of external knowledge, they can still be distracted by unrelated information. These findings highlight the risk of hallucination when integrating external knowledge, even indirectly, during interactions with current LLMs. All the data and results are publicly available.