Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. However, research in applied history suggests that people have difficulty finding appropriate analogies. And previous studies in the AI community have also overlooked historical analogies. To fill this gap, in this paper, we focus on the historical analogy acquisition task, which aims to acquire analogous historical events for a given event. We explore retrieval and generation methods for acquiring historical analogies based on different large language models (LLMs). Furthermore, we propose a self-reflection method to mitigate hallucinations and stereotypes when LLMs generate historical analogies. Through human evaluations and our specially designed automatic multi-dimensional assessment, we find that LLMs generally have a good potential for historical analogies. And the performance of the models can be further improved by using our self-reflection method.

The sophistication and diversity of contemporary cyberattacks have rendered the use of proxies, gateways, firewalls, and encrypted tunnels as a standalone defensive strategy inadequate. Consequently, the proactive identification of data anomalies has emerged as a prominent area of research within the field of data security. The majority of extant studies concentrate on sample equilibrium data, with the consequence that the detection effect is not optimal in the context of unbalanced data. In this study, the unsupervised learning method is employed to identify anomalies in dynamic data flows. Initially, multi-dimensional features are extracted from real-time data, and a clustering algorithm is utilised to analyse the patterns of the data. This enables the potential outliers to be automatically identified. By clustering similar data, the model is able to detect data behaviour that deviates significantly from normal traffic without the need for labelled data. The results of the experiments demonstrate that the proposed method exhibits high accuracy in the detection of anomalies across a range of scenarios. Notably, it demonstrates robust and adaptable performance, particularly in the context of unbalanced data.

The price elasticity of demand can be estimated from observational data using instrumental variables (IV). However, naive IV estimators may be inconsistent in settings with autocorrelated time series. We argue that causal time graphs can simplify IV identification and help select consistent estimators. To do so, we propose to first model the equilibrium condition by an unobserved confounder, deriving a directed acyclic graph (DAG) while maintaining the assumption of a simultaneous determination of prices and quantities. We then exploit recent advances in graphical inference to derive valid IV estimators, including estimators that achieve consistency by simultaneously estimating nuisance effects. We further argue that observing significant differences between the estimates of presumably valid estimators can help to reject false model assumptions, thereby improving our understanding of underlying economic dynamics. We apply this approach to the German electricity market, estimating the price elasticity of demand on simulated and real-world data. The findings underscore the importance of accounting for structural autocorrelation in IV-based analysis.

This article was originally featured on MIT Press Reader. This article is adapted from Marcus Carter and Ben Egliston's book "Fantasies of Virtual Reality." The political and cultural theorist Paul Virilio famously wrote that we live in a state of permanent (or "pure") war. By this, he meant that there is an increasing "perversion" of any clear-cut distinction between civilian and military institutions and, by extension, civilian and military life. According to Virilio, after the Second World War, Western economies and societies were permanently reorganized to support military power.

In 2013, US robotics company Boston Dynamics revealed its new robot, Atlas. Unveiled at the Darpa Robotics Challenge, the 6ft 2in humanoid could walk on uneven ground, jump off boxes, and even climb stairs. It was like a vision frequently depicted in fiction: a robot designed to operate like us, able to take on all manner of everyday tasks. It seemed like the dawn of something. Robots were going to do all of our boring and arduous chores, and step up as elderly care workers to boot.

From the hunt for the philosopher's stone to the snake oil salesmen of the Wild West, the history of medicine has had more than its fair share of fraudulent'cure-alls'. So, when I first heard of a device that claimed to cure everything from depression to my rapidly deteriorating attention span, I was understandably sceptical. To make things even stranger, this potential wonder-cure isn't a pill, powder, or trendy new supplement. Instead, the Nurosym is a 599 gadget that claims to rewire your nervous system - by zapping your ear. MailOnline's Wiliam Hunter bravely tested it out - and, as strange as it all might sound, he's almost ready to believe the hype.

The U.S. Commerce Department is planning to reveal proposed rules as soon as Monday that would ban Chinese- and Russian-made hardware and software for connected vehicles, people familiar with the matter said. The Commerce Department has been meeting with industry experts in recent months looking to address security concerns raised by a new generation of so-called smart cars. The move would include bans on use and testing of Chinese and Russian technology for automated driving systems and vehicle communications systems, the people said. While the bans mostly focus on software, the proposed rules will include some hardware, they said.

Materials design is a critical driver of innovation, yet overlooking the technological, economic, and environmental risks inherent in materials and their supply chains can lead to unsustainable and risk-prone solutions. To address this, we present a novel risk-aware design approach that integrates Supply-Chain Aware Design Strategies into the materials development process. This approach leverages existing language models and text analysis to develop a specialized model for predicting materials feedstock supply risk indices. To efficiently navigate the multi-objective, multi-constraint design space, we employ Batch Bayesian Optimization (BBO), enabling the identification of Pareto-optimal high entropy alloys (HEAs) that balance performance objectives with minimized supply risk. A case study using the MoNbTiVW system demonstrates the efficacy of our approach in four scenarios, highlighting the significant impact of incorporating supply risk into the design process. By optimizing for both performance and supply risk, we ensure that the developed alloys are not only high-performing but also sustainable and economically viable. This integrated approach represents a critical step towards a future where materials discovery and design seamlessly consider sustainability, supply chain dynamics, and comprehensive life cycle analysis.

The resurgence and rapid advancement of Generative Artificial Intelligence (GenAI) in 2023 has catalyzed transformative shifts across numerous industry sectors, including urban transportation and logistics. This study investigates the evaluation of Large Language Models (LLMs), specifically GPT-4 and Phi-3-mini, to enhance transportation planning. The study assesses the performance and spatial comprehension of these models through a transportation-informed evaluation framework that includes general geospatial skills, general transportation domain skills, and real-world transportation problem-solving. Utilizing a mixed-methods approach, the research encompasses an evaluation of the LLMs' general Geographic Information System (GIS) skills, general transportation domain knowledge as well as abilities to support human decision-making in the real-world transportation planning scenarios of congestion pricing. Results indicate that GPT-4 demonstrates superior accuracy and reliability across various GIS and transportation-specific tasks compared to Phi-3-mini, highlighting its potential as a robust tool for transportation planners. Nonetheless, Phi-3-mini exhibits competence in specific analytical scenarios, suggesting its utility in resource-constrained environments. The findings underscore the transformative potential of GenAI technologies in urban transportation planning. Future work could explore the application of newer LLMs and the impact of Retrieval-Augmented Generation (RAG) techniques, on a broader set of real-world transportation planning and operations challenges, to deepen the integration of advanced AI models in transportation management practices.

Social networks form a valuable source of world knowledge, where influential entities correspond to popular accounts. Unlike factual knowledge bases (KBs), which maintain a semantic ontology, structured semantic information is not available on social media. In this work, we consider a social KB of roughly 200K popular Twitter accounts, which denotes entities of interest. We elicit semantic information about those entities. In particular, we associate them with a fine-grained set of 136 semantic types, e.g., determine whether a given entity account belongs to a politician, or a musical artist. In the lack of explicit type information in Twitter, we obtain semantic labels for a subset of the accounts via alignment with the KBs of DBpedia and Wikidata. Given the labeled dataset, we finetune a transformer-based text encoder to generate semantic embeddings of the entities based on the contents of their accounts. We then exploit this evidence alongside network-based embeddings to predict the entities semantic types. In our experiments, we show high type prediction performance on the labeled dataset. Consequently, we apply our type classification model to all of the entity accounts in the social KB. Our analysis of the results offers insights about the global semantics of the Twitter sphere. We discuss downstream applications that should benefit from semantic type information and the semantic embeddings of social entities generated in this work. In particular, we demonstrate enhanced performance on the key task of entity similarity assessment using this information.