Constructing confidence intervals (CIs) for the average treatment effect (ATE) from patient records is crucial to assess the effectiveness and safety of drugs. However, patient records typically come from different hospitals, thus raising the question of how multiple observational datasets can be effectively combined for this purpose. In our paper, we propose a new method that estimates the ATE from multiple observational datasets and provides valid CIs. Our method makes little assumptions about the observational datasets and is thus widely applicable in medical practice. The key idea of our method is that we leverage predictionpowered inferences and thereby essentially'shrink' the CIs so that we offer more precise uncertainty quantification as compared to naïve approaches. We further prove the unbiasedness of our method and the validity of our CIs. We confirm our theoretical results through various numerical experiments. Finally, we provide an extension of our method for constructing CIs from combinations of experimental and observational datasets. Estimating the average treatment effect (ATE) together with confidence intervals (CIs) is relevant in many fields, such as medicine, where the ATE is used to assess the effectiveness and safety of drugs (Glass et al., 2013; Feuerriegel et al., 2024). Nowadays, there is a growing interest in using observational datasets for this purpose, for example, electronic health records (EHRs) and clinical registries (Johnson et al., 2016; Corrigan-Curay et al., 2018; Hong, 2021). Importantly, such observational datasets typically originate from different hospitals, different health providers, or even different countries (Colnet et al., 2024), thus raising the question of how to construct CIs for ATE estimation from multiple observational datasets. Motivating example: During the COVID-19 pandemic, the effectiveness and safety of potential drugs and vaccines were often assessed from electronic health records that originated from different hospitals to rapidly generate new evidence with treatment guidelines (Tacconelli et al., 2022). For example, one study (Wong et al., 2024) estimated the effect of nirmatrelvir/ritonavir (also known under the commercial name "paxlovid") in patients with COVID-19 diagnosis on 28-day all-cause hospitalizations from data obtained through a retrospective, multi-center study.

Hydroelectric power generation is a critical component of the global energy matrix, particularly in countries like Brazil, where it represents the majority of the energy supply. However, its strong dependence on river discharges, which are inherently uncertain due to climate variability, poses significant challenges. River discharges are linked to precipitation patterns, making the development of accurate probabilistic forecasting models crucial for improving operational planning in systems heavily reliant on this resource. Traditionally, statistical models have been used to represent river discharges in energy optimization. Yet, these models are increasingly unable to produce realistic scenarios due to structural shifts in climate behavior. Changes in precipitation patterns have altered discharge dynamics, which traditional approaches struggle to capture. Machine learning methods, while effective as universal predictors for time series, often focus solely on historical data, ignoring key external factors such as meteorological and climatic conditions. Furthermore, these methods typically lack a probabilistic framework, which is vital for representing the inherent variability of hydrological processes. The limited availability of historical discharge data further complicates the application of large-scale deep learning models to this domain. To address these challenges, we propose a framework based on a modified recurrent neural network architecture. This model generates parameterized probability distributions conditioned on projections from global circulation models, effectively accounting for the stochastic nature of river discharges. Additionally, the architecture incorporates enhancements to improve its generalization capabilities. We validate this framework within the Brazilian Interconnected System, using projections from the SEAS5-ECMWF system as conditional variables.

Network data often represent multiple types of relations, which can also denote exchanged quantities, and are typically encompassed in a weighted multiplex. Such data frequently exhibit clustering structures, however, traditional clustering methods are not well-suited for multiplex networks. Additionally, standard methods treat edge weights in their raw form, potentially biasing clustering towards a node's total weight capacity rather than reflecting cluster-related interaction patterns. To address this, we propose transforming edge weights into a compositional format, enabling the analysis of connection strengths in relative terms and removing the impact of nodes' total weights. We introduce a multiplex Dirichlet stochastic block model designed for multiplex networks with compositional layers. This model accounts for sparse compositional networks and enables joint clustering across different types of interactions. We validate the model through a simulation study and apply it to the international export data from the Food and Agriculture Organization of the United Nations.

We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are given as input, models for dynamics and estimation algorithms that incorporate long-term temporal dependencies are largely absent from existing studies. In this paper, we introduce a latent state to allow time-dependent modeling and formulate this problem as a dynamics estimation problem in latent states. We face multiple technical challenges, including (1) modeling latent non-linear dynamics and (2) solving circular dependencies caused by the presence of latent states. To tackle these challenging problems, we propose a new method, Latent Non-Linear equation modeling (LaNoLem), that can model a latent non-linear dynamical system and a novel alternating minimization algorithm for effectively estimating latent states and model parameters. In addition, we introduce criteria to control model complexity without human intervention. Compared with the state-of-the-art model, LaNoLem achieves competitive performance for estimating dynamics while outperforming other methods in prediction.

This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility,Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.

The development and deployment of chatbot technology, while spanning decades and employing different techniques, require innovative frameworks to understand and interrogate their functionality and implications. A mere technocentric account of the evolution of chatbot technology does not fully illuminate how conversational systems are embedded in societal dynamics. This study presents a structured examination of chatbots across three societal dimensions, highlighting their roles as objects of scientific research, commercial instruments, and agents of intimate interaction. Through furnishing a dimensional framework for the evolution of conversational systems, from laboratories to marketplaces to private lives, this article contributes to the wider scholarly inquiry of chatbot technology and its impact in lived human experiences and dynamics.

Attention heads are one of the building blocks of large language models (LLMs). Prior work on investigating their operation mostly focused on analyzing their behavior during inference for specific circuits or tasks. In this work, we seek a comprehensive mapping of the operations they implement in a model. We propose MAPS (Mapping Attention head ParameterS), an efficient framework that infers the functionality of attention heads from their parameters, without any model training or inference. We showcase the utility of MAPS for answering two types of questions: (a) given a predefined operation, mapping how strongly heads across the model implement it, and (b) given an attention head, inferring its salient functionality. Evaluating MAPS on 20 operations across 6 popular LLMs shows its estimations correlate with the head's outputs during inference and are causally linked to the model's predictions. Moreover, its mappings reveal attention heads of certain operations that were overlooked in previous studies, and valuable insights on function universality and architecture biases in LLMs. Next, we present an automatic pipeline and analysis that leverage MAPS to characterize the salient operations of a given head. Our pipeline produces plausible operation descriptions for most heads, as assessed by human judgment, while revealing diverse operations.

In recent years, Geospatial Artificial Intelligence (GeoAI) has gained traction in the most relevant research works and industrial applications, while also becoming involved in various fields of use. This paper offers a comprehensive review of GeoAI as a synergistic concept applying Artificial Intelligence (AI) methods and models to geospatial data. A preliminary study is carried out, identifying the methodology of the work, the research motivations, the issues and the directions to be tracked, followed by exploring how GeoAI can be used in various interesting fields of application, such as precision agriculture, environmental monitoring, disaster management and urban planning. Next, a statistical and semantic analysis is carried out, followed by a clear and precise presentation of the challenges facing GeoAI. Then, a concrete exploration of the future prospects is provided, based on several informations gathered during the census. To sum up, this paper provides a complete overview of the correlation between AI and the geospatial domain, while mentioning the researches conducted in this context, and emphasizing the close relationship linking GeoAI with other advanced concepts such as geographic information systems (GIS) and large-scale geospatial data, known as big geodata. This will enable researchers and scientific community to assess the state of progress in this promising field, and will help other interested parties to gain a better understanding of the issues involved.

It was some time in the early 2000s and Asif Kapadia, already a successful film director, a wunderkind whose first feature in 2001, The Warrior, won the Bafta for outstanding British film, was travelling back from New York. I'm in a limo being taken to the airport. And I was taking photos of Manhattan because I was driving over Brooklyn Bridge and it's just all so cinematic and I became subconsciously aware of the driver watching me in the rear view mirror. "I get to the airport and I'm in the Virgin lounge when my name is called out. And I thought: 'Have I left a bag or something?' But then five or six people come: homeland security. And they stop me in the lounge in front of everyone, the only person of colour in there, and empty out my bag, and they say: 'Someone's reported you.' And it's like: 'Who are you? An itinerary of his trip and its purpose proved his credentials and he was eventually allowed to go and boarded his flight. But for nearly a decade afterwards, he found himself on a "watch list". "I would get stopped and interviewed two times before I got on a plane, pulled out in a room.

Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. The cornerstone benefits of LLM include processing textual data from technical manuals, instructions, academic papers, and user queries based on the knowledge provided. However, deploying LLM-generated code in robotic systems without safety verification poses significant risks. This paper outlines a safety layer that verifies the code generated by ChatGPT before executing it to control a drone in a simulated environment. The safety layer consists of a fine-tuned GPT-4o model using Few-Shot learning, supported by knowledge graph prompting (KGP). Our approach improves the safety and compliance of robotic actions, ensuring that they adhere to the regulations of drone operations.