The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane preparedness and response strategies vastly rely on the accuracy and timeliness of the predicted households' evacuation decisions, current studies featuring psychological-driven linear models leave some significant limitations in practice. Hence, the present study proposes a new methodology for predicting households' evacuation decisions constructed by easily accessible demographic and resource-related predictors compared to current models with a high reliance on psychological factors. Meanwhile, an enhanced logistic regression (ELR) model that could automatically account for nonlinearities (i.e., univariate and bivariate threshold effects) by an interpretable machine learning approach is developed to secure the accuracy of the results. Specifically, low-depth decision trees are selected for nonlinearity detection to identify the critical thresholds, build a transparent model structure, and solidify the robustness. Then, an empirical dataset collected after Hurricanes Katrina and Rita is hired to examine the practicability of the new methodology. The results indicate that the enhanced logistic regression (ELR) model has the most convincing performance in explaining the variation of the households' evacuation decision in model fit and prediction capability compared to previous linear models. It suggests that the proposed methodology could provide a new tool and framework for the emergency management authorities to improve the estimation of evacuation traffic demands in a timely and accurate manner.

While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. Project site with code: https://react-lm.github.io

When robots are deployed in the field for environmental monitoring they typically execute pre-programmed motions, such as lawnmower paths, instead of adaptive methods, such as informative path planning. One reason for this is that adaptive methods are dependent on parameter choices that are both critical to set correctly and difficult for the non-specialist to choose. Here, we show how to automatically configure a planner for informative path planning by training a reinforcement learning agent to select planner parameters at each iteration of informative path planning. We demonstrate our method with 37 instances of 3 distinct environments, and compare it against pure (end-to-end) reinforcement learning techniques, as well as approaches that do not use a learned model to change the planner parameters. Our method shows a 9.53% mean improvement in the cumulative reward across diverse environments when compared to end-to-end learning based methods; we also demonstrate via a field experiment how it can be readily used to facilitate high performance deployment of an information gathering robot.

From Cornwall and Wales to Essex, Blackpool and the Norfolk coast, Britain has experienced a flurry of earthquakes in the past month. The biggest – a 3.8 magnitude tremor that struck Wales on February 24 – sparked panic as locals reported their beds started to move and walls shook. One resident in the small Welsh town of Abertillery not far from the epicentre said the quake was so noticeable'it felt like the roof was falling off'. The Welsh quake was preceded by several more including a 1.5 magnitude quake in Cornwall and a 3.8 magnitude event off the coast of Great Yarmouth. Here's all you need to know about the British tremors – including whether recent tectonic activity suggests a'big one' is soon to hit parts of the country.

Generative AI such as ChatGPT have been carrying on interactive online conversations meant to ... [ ] manipulate humans, raising serious concerns, We've all dealt with those manipulative personalities that try to convince us that up is down and aim to gaslight us into the most unsettling of conditions. Their rhetoric can be overtly powerful and overwhelming. You can't decide what to do. Should you merely cave in and hope that the verbal tirade will end? But if you are played into doing something untoward, acquiescing might be quite endangering. Trying to verbally fight back is bound to be ugly and can devolve into even worse circumstances. It can be a no-win situation, that's for sure. The manipulator wants and demands that things go their way. For them, the only win possible is that you completely capitulate to their professed bidding. They will incessantly verbally pound away with their claims of pure logic and try to make it appear as though they are occupying the high ground. You are made to seem inconsequential and incapable. Any number of verbal tactics will be launched at you, over and over again. Repetition and steamrolling are the insidious tools of those maddening manipulators. Turns out that we not only need to be on the watch for humans that are manipulators, but we now also need to be wary of Artificial Intelligence (AI) that does likewise. AI can be a masterful manipulator of humans. When it comes to AI, there is the hoped-for AI For Good, while in the same breath, we are faced with AI For Bad. I've previously covered in my columns that AI is considered to have a dual-use capacity, see my analysis at the link here. Seems that if we can make AI that can generate amazingly fluent and upbeat essays, the same capacity can be readily switched over to produce tremendously wrongful bouts of fluently overbearing manipulations. This is especially impactful when experienced in an interactive conversational dialogue with the AI. All of this happens via a type of AI known as Generative AI.

Kyiv, Ukraine – "UFOs" have rained on Russia in recent days – some dangerously close to the capital Moscow and President Vladimir Putin's hometown. Russian officials and media, using that term – "unidentified foreign objects" – seem unnerved and are accusing Ukraine of drone attacks. Ukraine on Wednesday denied targeting Russia, suggesting attempts at domestic assaults – which Moscow did not accept. With a dash of black humour, presidential adviser Mykhailo Podolyak tweeted that a sense of "panic and collapse" was growing in Russia, "manifested by increasing domestic attacks of unidentified flying objects on infrastructure sites". Throughout the war, Ukrainian leaders and top brass have routinely refused any responsibility for attacks on Russian soil – and often resort to ridiculing disorganised Russian servicemen.

The transfer learning paradigm of model pre-training and subsequent fine-tuning produces high-accuracy models. While most studies recommend scaling the pre-training size to benefit most from transfer learning, a question remains: what data and method should be used for pre-training? We investigate the impact of pre-training data distribution on the few-shot and full fine-tuning performance using 3 pre-training methods (supervised, contrastive language-image and image-image), 7 pre-training datasets, and 9 downstream datasets. Through extensive controlled experiments, we find that the choice of the pre-training data source is essential for the few-shot transfer, but its role decreases as more data is made available for fine-tuning. Additionally, we explore the role of data curation and examine the trade-offs between label noise and the size of the pre-training dataset. We find that using 2000X more pre-training data from LAION can match the performance of supervised ImageNet pre-training. Furthermore, we investigate the effect of pre-training methods, comparing language-image contrastive vs. image-image contrastive, and find that the latter leads to better downstream accuracy

Taipei, Taiwan – At opposite ends of Southeast Asia, researchers Pornampai Narenpitak and Heri Kuswanto are both working on the same problem: Is it possible to mimic the cooling effects of volcanic eruptions to halt global warming? Using computer modelling and analysis, Narenpitak and Kuswanto are separately studying whether shooting large quantities of sulphur dioxide into the earth's stratosphere could have a similar effect on global temperatures as the eruption of Indonesia's Mount Tambora in 1815. The eruption, the most powerful in recorded history, spewed an estimated 150 cubic kilometres (150,000 gigalitres) of exploded rock and ash into the air, causing global temperatures to fall as much as 3 degrees Celsius (5.4 degrees Fahrenheit) in what became known as the "year without a summer". Stratospheric aerosol injection is among a number of nascent – and controversial – technologies in the field of solar geoengineering (SRM) that have been touted as potential solutions to mitigating the effects of climate change. Other proposed strategies include brightening marine clouds to reflect the sun or breaking up cirrus clouds that capture heat.

Dictionaries are one of the oldest and most used linguistic resources. Building them is a complex task that, to the best of our knowledge, has yet to be explored with generative Large Language Models (LLMs). We introduce the "Spanish Built Factual Freectianary" (Spanish-BFF) as the first Spanish AI-generated dictionary. This first-of-its-kind free dictionary uses GPT-3. We also define future steps we aim to follow to improve this initial commitment to the field, such as more additional languages.

Due to the success of the pre-trained language model (PLM), existing PLM-based summarization models show their powerful generative capability. However, these models are trained on general-purpose summarization datasets, leading to generated summaries failing to satisfy the needs of different readers. To generate summaries with topics, many efforts have been made on topic-focused summarization. However, these works generate a summary only guided by a prompt comprising topic words. Despite their success, these methods still ignore the disturbance of sentences with non-relevant topics and only conduct cross-interaction between tokens by attention module. To address this issue, we propose a topic-arc recognition objective and topic-selective graph network. First, the topic-arc recognition objective is used to model training, which endows the capability to discriminate topics for the model. Moreover, the topic-selective graph network can conduct topic-guided cross-interaction on sentences based on the results of topic-arc recognition. In the experiments, we conduct extensive evaluations on NEWTS and COVIDET datasets. Results show that our methods achieve state-of-the-art performance.