People have a right to know who their neighbors are, especially when it's ICE. On Tuesday, WIRED published details of ICE's planned expansion into more than 150 office spaces across the United States, including 54 specific addresses. ICE has designs on every major US city. It plans to not only occupy existing government spaces but share hallways and elevator bays with medical offices and small businesses. It will be down the street from daycares and within walking distance of churches and treatment centers.

The Aedes aegypti mosquito that can carry dengue, yellow fever, and Zika was thought to be too reliant on a hot and wet climate to survive in the Mountain West. But now, a population is thriving in Western Colorado. Hannah Livesay, biologist at the Grand River Mosquito Control District, points out the characteristic white markings of an Aedes aegypti mosquito shown under a microscope at her lab in Grand Junction, Colo. It can carry life-threatening diseases. It's difficult to find and hard to kill.

More'Frankenstein' rabbits are appearing across the US, sparking fears of a wider outbreak. Originally spotted in Colorado, these bizarre rabbits, with tentacle-like growths sprouting from their faces, have now been reported in Minnesota, Nebraska, and South Dakota. The animals are infected with cottontail rabbit papilloma virus (CRPV), also known as Shope papilloma virus, which can be spread through mosquito and tick bites. While humans are unlikely to contract CRPV, Dr Omer Awan of the University of Maryland School of Medicine cautioned that people could still face risks from other diseases carried by ticks or mosquitoes that have fed on infected rabbits. 'You're not going to get CRPV, and you likely won't show symptoms of it,' Dr Awan told the Daily Mail.

The bizarre virus turning harmless rabbits into terrifying, tentacle-faced creatures has been spotted by more Americans, sparking fears that a wildlife crisis is emerging. The'Frankenstein' rabbits recently made headlines in Colorado, as locals reported seeing the infected animals wandering through neighborhoods. However, the sightings have not been isolated that state. Residents in Minnesota and Nebraska have shared more images and stories of these deformed rabbits popping up. The rabbits are infected with the cottontail papilloma virus (CRPV), also known as Shope papilloma virus, which causes horn- or tentacle-like tumors to grow around the animals' heads and faces.

A mysterious virus has left ordinary rabbits in the US with shocking deformities, including faces full of horns and tentacles. The mutated rabbits have been spotted multiple times in Colorado, specifically in the city of Fort Collins. The sightings date back to 2024, when a Fort Collins resident shared a picture online, showing the creature's entire head covered in black, tentacle-like protrusions. It's believed the horns are due to a virus that causes cancerous growths and has no known cure. Colorado Parks and Wildlife (CPW) has urged anyone who sees rabbits in the wild with these growths to stay away and not touch them.

Reasoning abilities of LLMs have been a key focus in recent years. One challenging reasoning domain with interesting nuances is legal reasoning, which requires careful application of rules, and precedents while balancing deductive and analogical reasoning, and conflicts between rules. Although there have been a few works on using LLMs for legal reasoning, their focus has been on overall accuracy. In this paper, we dig deeper to do a step-by-step analysis and figure out where they commit errors. We use the college-level Multiple Choice Question-Answering (MCQA) task from the \textit{Civil Procedure} dataset and propose a new error taxonomy derived from initial manual analysis of reasoning chains with respect to several LLMs, including two objective measures: soundness and correctness scores. We then develop an LLM-based automated evaluation framework to identify reasoning errors and evaluate the performance of LLMs. The computation of soundness and correctness on the dataset using the auto-evaluator framework reveals several interesting insights. Furthermore, we show that incorporating the error taxonomy as feedback in popular prompting techniques marginally increases LLM performance. Our work will also serve as an evaluation framework that can be used in detailed error analysis of reasoning chains for logic-intensive complex tasks.

This paper presents a risk analysis of flowlines in the oil and gas sector using Geographic Information Systems (GIS) and machine learning (ML). Flowlines, vital conduits transporting oil, gas, and water from wellheads to surface facilities, often face under-assessment compared to transmission pipelines. This study addresses this gap using advanced tools to predict and mitigate failures, improving environmental safety and reducing human exposure. Extensive datasets from the Colorado Energy and Carbon Management Commission (ECMC) were processed through spatial matching, feature engineering, and geometric extraction to build robust predictive models. Various ML algorithms, including logistic regression, support vector machines, gradient boosting decision trees, and K-Means clustering, were used to assess and classify risks, with ensemble classifiers showing superior accuracy, especially when paired with Principal Component Analysis (PCA) for dimensionality reduction. Finally, a thorough data analysis highlighted spatial and operational factors influencing risks, identifying high-risk zones for focused monitoring. Overall, the study demonstrates the transformative potential of integrating GIS and ML in flowline risk management, proposing a data-driven approach that emphasizes the need for accurate data and refined models to improve safety in petroleum extraction.

Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has often been limited by fixed, single-objective definitions of fairness, built into algorithms or optimization criteria that are applied to a single fairness dimension or, at most, applied identically across dimensions. These narrow conceptualizations limit the ability to adapt fairness-aware solutions to the wide range of stakeholder needs and fairness definitions that arise in practice. Our work approaches recommendation fairness from the standpoint of computational social choice, using a multi-agent framework. In this paper, we explore the properties of different social choice mechanisms and demonstrate the successful integration of multiple, heterogeneous fairness definitions across multiple data sets.

The first attempts to regulate artificial intelligence programs that play a hidden role in hiring, housing and medical decisions for millions of Americans are facing pressure from all sides and floundering in statehouses nationwide. Only one of seven bills aimed at preventing AI's penchant to discriminate when making consequential decisions -- including who gets hired, money for a home or medical care -- has passed. Colorado Gov. Jared Polis hesitantly signed the bill on Friday. Colorado's bill and those that faltered in Washington, Connecticut and elsewhere faced battles on many fronts, including between civil rights groups and the tech industry, and lawmakers wary of wading into a technology few yet understand and governors worried about being the odd-state-out and spooking AI startups. Polis signed Colorado's bill "with reservations," saying in an statement he was wary of regulations dousing AI innovation.

Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly focused on a symmetric problem setting where both input instances and label features are short-text in nature. Short-text XMC with label features has found numerous applications in areas such as query-to-ad-phrase matching in search ads, title-based product recommendation, prediction of related searches. In this paper, we propose Gandalf, a novel approach which makes use of a label co-occurrence graph to leverage label features as additional data points to supplement the training distribution. By exploiting the characteristics of the short-text XMC problem, it leverages the label features to construct valid training instances, and uses the label graph for generating the corresponding soft-label targets, hence effectively capturing the label-label correlations. Surprisingly, models trained on these new training instances, although being less than half of the original dataset, can outperform models trained on the original dataset, particularly on the PSP@k metric for tail labels. With this insight, we aim to train existing XMC algorithms on both, the original and new training instances, leading to an average 5% relative improvements for 6 state-of-the-art algorithms across 4 benchmark datasets consisting of up to 1.3M labels. Gandalf can be applied in a plug-and-play manner to various methods and thus forwards the state-of-the-art in the domain, without incurring any additional computational overheads.