What is the US critical minerals stockpile? Has the Trump administration overplayed its spin? United States aviation authorities have announced that the airspace over El Paso, Texas, has been reopened after initially closing the airspace due to an alleged drone incursion from a Mexican cartel. Wednesday's announcement walked back an earlier statement from the Federal Aviation Administration (FAA), abruptly pausing air traffic over the southern border city for 10 days. By late morning, though, the FAA announced that flights would resume in and out of the area as normal, prompting questions about the legitimacy of the drone claims. "The temporary closure of airspace over El Paso has been lifted.

Misinformation continues to pose a significant challenge in today's information ecosystem, profoundly shaping public perception and behavior. Among its various manifestations, Out-of-Context (OOC) misinformation is particularly obscure, as it distorts meaning by pairing authentic images with misleading textual narratives. Existing methods for detecting OOC misinformation predominantly rely on coarse-grained similarity metrics between image-text pairs, which often fail to capture subtle inconsistencies or provide meaningful explainability. While multi-modal large language models (MLLMs) demonstrate remarkable capabilities in visual reasoning and explanation generation, they have not yet demonstrated the capacity to address complex, fine-grained, and cross-modal distinctions necessary for robust OOC detection. To overcome these limitations, we introduce EXCLAIM, a retrieval-based framework designed to leverage external knowledge through multi-granularity index of multi-modal events and entities. Our approach integrates multi-granularity contextual analysis with a multi-agent reasoning architecture to systematically evaluate the consistency and integrity of multi-modal news content. Comprehensive experiments validate the effectiveness and resilience of EXCLAIM, demonstrating its ability to detect OOC misinformation with 4.3% higher accuracy compared to state-of-the-art approaches, while offering explainable and actionable insights.

Human evaluation of generated language through pairwise preference judgments is pervasive. However, under common scenarios, such as when generations from a model pair are very similar, or when stochastic decoding results in large variations in generations, it results in inconsistent preference ratings. We address these challenges by introducing a meta-evaluation measure, separability, which estimates how suitable a test instance is for pairwise preference evaluation. For a candidate test instance, separability samples multiple generations from a pair of models, and measures how distinguishable the two sets of generations are. Our experiments show that instances with high separability values yield more consistent preference ratings from both human- and auto-raters. Further, the distribution of separability allows insights into which test benchmarks are more valuable for comparing models. Finally, we incorporate separability into ELO ratings, accounting for how suitable each test instance might be for reliably ranking LLMs. Overall, separability has implications for consistent, efficient and robust preference evaluation of LLMs with both human- and auto-raters.

Misinformation is a prevalent societal issue due to its potential high risks. Out-of-context (OOC) misinformation, where authentic images are repurposed with false text, is one of the easiest and most effective ways to mislead audiences. Current methods focus on assessing image-text consistency but lack convincing explanations for their judgments, which is essential for debunking misinformation. While Multimodal Large Language Models (MLLMs) have rich knowledge and innate capability for visual reasoning and explanation generation, they still lack sophistication in understanding and discovering the subtle crossmodal differences. In this paper, we introduce SNIFFER, a novel multimodal large language model specifically engineered for OOC misinformation detection and explanation. SNIFFER employs two-stage instruction tuning on InstructBLIP. The first stage refines the model's concept alignment of generic objects with news-domain entities and the second stage leverages language-only GPT-4 generated OOC-specific instruction data to fine-tune the model's discriminatory powers. Enhanced by external tools and retrieval, SNIFFER not only detects inconsistencies between text and image but also utilizes external knowledge for contextual verification. Our experiments show that SNIFFER surpasses the original MLLM by over 40% and outperforms state-of-the-art methods in detection accuracy. SNIFFER also provides accurate and persuasive explanations as validated by quantitative and human evaluations.

Recent Reference-Based image super-resolution (RefSR) has improved SOTA deep methods introducing attention mechanisms to enhance low-resolution images by transferring high-resolution textures from a reference high-resolution image. The main idea is to search for matches between patches using LR and Reference image pair in a feature space and merge them using deep architectures. However, existing methods lack the accurate search of textures. They divide images into as many patches as possible, resulting in inefficient memory usage, and cannot manage large images. Herein, we propose a deep search with a more efficient memory usage that reduces significantly the number of image patches and finds the $k$ most relevant texture match for each low-resolution patch over the high-resolution reference patches, resulting in an accurate texture match. We enhance the Super Resolution result adding gradient density information using a simple residual architecture showing competitive metrics results: PSNR and SSMI.

Last year, the United States Department of Homeland Security advertised the impending "deployment" on the US-Mexico border of "robot dogs". According to a celebratory feature article published on the department's website, the goal of the programme was to "force-multiply" the presence of US Customs and Border Protection (CBP) as well as to "reduce human exposure to life-threatening hazards". In case there was any doubt as to which human lives were of concern, the article specified: "The American Southwest is a region that blends a harsh landscape, temperature extremes and various other non-environmental threats that can create dangerous obstacles for those who patrol the border." There is no denying that the US-Mexico border is an inhospitable place; just ask the countless refuge seekers who have died trying to navigate it, thanks in large part to ongoing US efforts to effectively criminalise the very right to asylum. And the terrain is becoming ever more hostile with the mad dash to run the entire world on artificial intelligence, border "security" operations to boot. The proliferation of AI-reliant surveillance technology has increasingly forced undocumented people into ever more dangerous territory, where "non-environmental threats" will apparently now also include canine robots.

Adversarial data poisoning is an effective attack against machine learning and threatens model integrity by introducing poisoned data into the training dataset. So far, it has been studied mostly for classification, even though regression learning is used in many mission critical systems (such as dosage of medication, control of cyber-physical systems and managing power supply). Therefore, in the present research, we aim to evaluate all aspects of data poisoning attacks on regression learning, exceeding previous work both in terms of breadth and depth. We present realistic scenarios in which data poisoning attacks threaten production systems and introduce a novel black-box attack, which is then applied to a real-word medical use-case. As a result, we observe that the mean squared error (MSE) of the regressor increases to 150 percent due to inserting only two percent of poison samples. Finally, we present a new defense strategy against the novel and previous attacks and evaluate it thoroughly on 26 datasets. As a result of the conducted experiments, we conclude that the proposed defence strategy effectively mitigates the considered attacks.

On the street outside Joseph Baca's home in El Paso, Texas, there is a traffic light that always seems to be red. Whether the intersection is clear, the traffic waits. He knows that, like most traffic lights in El Paso, this one has a camera. Why, he often said to his family, couldn't the camera be used to monitor the road and control the signal? That question eventually led to the development of an idea that could save not only time but also, potentially, lives.

Prime Minister Narendra Modi has said India is facing a "long battle" ahead in its efforts to defeat the pandemic as the country set a new record for daily coronavirus infections. United States President Donald Trump has said the US is "terminating" its relationship with the World Health Organization (WHO), saying the agency has not made coronavirus reforms. The WHO and 37 countries launched the COVID-19 Technology Access Pool, an alliance aimed at making coronavirus vaccines, tests, treatments and other technologies available to all countries. More than 5.9 million cases of coronavirus have been confirmed around the world, according to data from Johns Hopkins University. Some 365,000 people have died, while more than 2.4 million have recovered.

Valentine's Day is a time to get close to the ones you love. And, just as importantly, not to get close to scammers. The loving feeling that abounds in February, and the sadness it provokes in many single people, are being exploited by fraudsters who use it to steal people's money and infect their computers. Hundreds of millions of fake emails are being sent out that appear as if they are coming from admirers. But if people follow them up they'll just be subject to scams and frauds, or being sent viruses. Bride Amornrat Ruamsin (L), 27, who is a transgender, holds up her five-month-old daughter with her groom Pitchaya Kachainrum (R), 16, during their wedding ceremony organised by a local TV show, in Bangkok, Thailand, February 9, 2018. The ceremony is not legally-binding as Pitchaya in under 17, the legal age for marriage in Thailand.