Existing benchmarks for evaluating long-context language models (LCLMs) primarily focus on long-context recall, requiring models to produce short responses based on a few critical snippets while processing thousands of irrelevant tokens. We introduce LongProc (Long Procedural Generation), a new benchmark that requires both the integration of highly dispersed information and long-form generation. LongProc consists of six diverse procedural generation tasks, such as extracting structured information from HTML pages into a TSV format and executing complex search procedures to create travel plans. These tasks challenge LCLMs by testing their ability to follow detailed procedural instructions, synthesize and reason over dispersed information, and generate structured, long-form outputs (up to 8K tokens). Furthermore, as these tasks adhere to deterministic procedures and yield structured outputs, they enable reliable rule-based evaluation. We evaluate 17 LCLMs on LongProc across three difficulty levels, with maximum numbers of output tokens set at 500, 2K, and 8K. Notably, while all tested models claim a context window size above 32K tokens, open-weight models typically falter on 2K-token tasks, and closed-source models like GPT-4o show significant degradation on 8K-token tasks. Further analysis reveals that LCLMs struggle to maintain long-range coherence in long-form generations. These findings highlight critical limitations in current LCLMs and suggest substantial room for improvement. Data and code available at: https://princeton-pli.github.io/LongProc

Online social media platforms, such as Twitter, provide valuable information during disaster events. Existing tweet disaster summarization approaches provide a summary of these events to aid government agencies, humanitarian organizations, etc., to ensure effective disaster response. In the literature, there are two types of approaches for disaster summarization, namely, supervised and unsupervised approaches. Although supervised approaches are typically more effective, they necessitate a sizable number of disaster event summaries for testing and training. However, there is a lack of good number of disaster summary datasets for training and evaluation. This motivates us to add more datasets to make supervised learning approaches more efficient. In this paper, we present ADSumm, which adds annotated ground-truth summaries for eight disaster events which consist of both natural and man-made disaster events belonging to seven different countries. Our experimental analysis shows that the newly added datasets improve the performance of the supervised summarization approaches by 8-28% in terms of ROUGE-N F1-score. Moreover, in newly annotated dataset, we have added a category label for each input tweet which helps to ensure good coverage from different categories in summary. Additionally, we have added two other features relevance label and key-phrase, which provide information about the quality of a tweet and explanation about the inclusion of the tweet into summary, respectively. For ground-truth summary creation, we provide the annotation procedure adapted in detail, which has not been described in existing literature. Experimental analysis shows the quality of ground-truth summary is very good with Coverage, Relevance and Diversity.

Over the past decades, the increase in both frequency and intensity of large-scale wildfires due to climate change has emerged as a significant natural threat. The pressing need to design resilient landscapes capable of withstanding such disasters has become paramount, requiring the development of advanced decision-support tools. Existing methodologies, including Mixed Integer Programming, Stochastic Optimization, and Network Theory, have proven effective but are hindered by computational demands, limiting their applicability. In response to this challenge, we propose using artificial intelligence techniques, specifically Deep Reinforcement Learning, to address the complex problem of firebreak placement in the landscape. We employ value-function based approaches like Deep Q-Learning, Double Deep Q-Learning, and Dueling Double Deep Q-Learning. Utilizing the Cell2Fire fire spread simulator combined with Convolutional Neural Networks, we have successfully implemented a computational agent capable of learning firebreak locations within a forest environment, achieving good results. Furthermore, we incorporate a pre-training loop, initially teaching our agent to mimic a heuristic-based algorithm and observe that it consistently exceeds the performance of these solutions. Our findings underscore the immense potential of Deep Reinforcement Learning for operational research challenges, especially in fire prevention. Our approach demonstrates convergence with highly favorable results in problem instances as large as 40 x 40 cells, marking a significant milestone in applying Reinforcement Learning to this critical issue. To the best of our knowledge, this study represents a pioneering effort in using Reinforcement Learning to address the aforementioned problem, offering promising perspectives in fire prevention and landscape management

It's hard to find something pithy to say about 2023, a year of dissonant extremes, when wildfires devoured Canadian forests, Twitter withered into X, the Titan submersible imploded into infamy, Silicon Valley's power players rejoiced over the rise of generative AI, scientists cheered Crispr treatment breakthroughs, peace activists became terrorist-attack victims, and the world despaired over the thousands of children killed in Gaza. It is, frequently, a painful one. Appropriate, then, that this was a year for unwieldy, searching, big-swing books. Doorstoppers and sagas rose to the moment, providing insight into an increasingly inscrutable world even when they couldn't provide comfort. As always, this is an idiosyncratic, incomplete, and subjective list, the result of one person's avid but disorganized reading schedule.

In this paper, we explore the use of large language models to assess human interpretations of real world events. To do so, we use a language model trained prior to 2020 to artificially generate news articles concerning COVID-19 given the headlines of actual articles written during the pandemic. We then compare stylistic qualities of our artificially generated corpus with a news corpus, in this case 5,082 articles produced by CBC News between January 23 and May 5, 2020. We find our artificially generated articles exhibits a considerably more negative attitude towards COVID and a significantly lower reliance on geopolitical framing. Our methods and results hold importance for researchers seeking to simulate large scale cultural processes via recent breakthroughs in text generation.

INTRODUCTION Adaptive planning is ideally suited for the deep uncertainties presented by climate change. While there is a robust scholarship on the theory and methods of adaptive planning, this has largely neglected how adaptive planning is affected by existing planning institutions and how to move forward within the constraints of traditional planning organizations. This study asks: How do existing traditional planning institutions support adaptive planning? We explore this for flood planning in the Lower Rio Grande Valley of Texas, United States. We draw on county hazard plan and regional flood plan documents as well as transcripts of regional flood planning meetings to explore the emergent topics of these institutional outputs. Using Natural Language Processing to analyze this large amount of text, we find that hazard plans and discussions developing these plans are largely lacking an adaptive approach. KEYWORDS adaptive planning; uncertainty; flood plan; Rio Grande Valley INTRODUCTION Planning for natural hazard risk reduction in the context climate change involves decision making under conditions of interacting, multiple uncertainties. Some of these are "deep uncertainties" connected to long time horizons, nonlinear changes in climates and ecosystems, and inability to reliably quantify the rate and magnitude of climate changes (Babovic & Mijic, 2018; Bosomworth & Gaillard, 2019). Other uncertainties are associated with the ambiguities and unpredictability of socioeconomic systems, including population growth, land use change, social conflict, and the whims of political will (Babovic & Mijic 2019; Buurman & Babovic, 2014). In the face of these uncertainties, a new paradigm of decision making has emerged that emphasizes the development of adaptive plans and policies (Hassnoot et al., 2013; Walker et al., 2013). Traditional planning approaches typically generate a static optimal plan to reduce vulnerability to a single'most likely' future or to respond a wide range of plausible future scenarios (Haasnoot et al., 2013; Manocha & Babovic, 2018). Because the future is largely unknowable, static optimal plans are likely to fail and adaptations are made adhoc to adjust to emerging risk conditions (Haasnoot et al., 2013).

A leading technology provider and data-driven consulting organisation, and the Schulich School of Business at York University have announced a partnership to create a predictive analytics model that identifies and forecasts supply and demand of necessary resources in a disaster-related emergency. The model evaluates existing wildfire data points and feeds into an ad hoc trading platform which key stakeholders can use to option the right amount of services and supplies in the most cost-effective manner. The project aims to bring together local governments, insurers and medical supplies providers to collaborate and plan proactively for optimal disaster management. Available in June 2020, the platform is the first in a series of analytics tools that the Schulich School of Business and Exigent will develop to deliver on their core focus: turning data into actionable business intelligence and community-centric analytics products. The collaboration is part of the Masters in Business Analytics Program (MBAN) at Schulich.

In this tutorial, you will learn how to automatically detect natural disasters (earthquakes, floods, wildfires, cyclones/hurricanes) with up to 95% accuracy using Keras, Computer Vision, and Deep Learning. I remember the first time I ever experienced a natural disaster -- I was just a kid in kindergarten, no more than 6-7 years old. We were outside for recess, playing on the jungle gym, running around like the wild animals that young children are. Rain was in the forecast. My mother had given me a coat to wear outside, but I was hot and unconformable -- the humidity made the cotton/polyester blend stick to my skin.

In this profile series, we interview AI innovators on the front-lines - those who have dedicated their life's work to improving the human condition through technology advancements. He is also a founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damian's background is in research where he focuses on large- scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. Damian talks about his realization in deep learning and shares why integrating his work with deep learning is an important part to help prevent future natural disasters. What has your journey been like in deep learning?

The wildfire in Alberta, Canada, seems to be diminishing and residents should be able to return to the city of Fort McMurray over the next two weeks. The fire had appeared to be out of control just a few days ago but thanks to favourable weather conditions appears under control. The weather has played a huge part, but what about technology? AI, drones and satellites have all been used. Dr Guillermo Rein, from Imperial College, London and Editor-in-Chief of the journal Fire Technology explains how tech is now incorporated in fire management.