Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.

Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.

Operator learning has emerged as a promising tool for accelerating the solution of partial differential equations (PDEs). The Deep Operator Networks (DeepONets) represent a pioneering framework in this area: the "vanilla" DeepONet is valued for its simplicity and efficiency, while the modified DeepONet achieves higher accuracy at the cost of increased training time. In this work, we propose a series of Transformer-inspired DeepONet variants that introduce bidirectional cross-conditioning between the branch and trunk networks in DeepONet. Query-point information is injected into the branch network and input-function information into the trunk network, enabling dynamic dependencies while preserving the simplicity and efficiency of the "vanilla" DeepONet in a non-intrusive manner. Experiments on four PDE benchmarks -- advection, diffusion-reaction, Burgers', and Korteweg-de Vries equations -- show that for each case, there exists a variant that matches or surpasses the accuracy of the modified DeepONet while offering improved training efficiency. Moreover, the best-performing variant for each equation aligns naturally with the equation's underlying characteristics, suggesting that the effectiveness of cross-conditioning depends on the characteristics of the equation and its underlying physics. To ensure robustness, we validate the effectiveness of our variants through a range of rigorous statistical analyses, among them the Wilcoxon Two One-Sided Test, Glass's Delta, and Spearman's rank correlation.

Building damage identification shortly after a disaster is crucial for guiding emergency response and recovery efforts. Although optical satellite imagery is commonly used for disaster mapping, its effectiveness is often hampered by cloud cover or the absence of pre-event acquisitions. To overcome these challenges, we introduce a novel multimodal deep learning (DL) framework for detecting building damage using single-date very high resolution (VHR) Synthetic Aperture Radar (SAR) imagery from the Italian Space Agency (ASI) COSMO SkyMed (CSK) constellation, complemented by auxiliary geospatial data. Our method integrates SAR image patches, OpenStreetMap (OSM) building footprints, digital surface model (DSM) data, and structural and exposure attributes from the Global Earthquake Model (GEM) to improve detection accuracy and contextual interpretation. Unlike existing approaches that depend on pre and post event imagery, our model utilizes only post event data, facilitating rapid deployment in critical scenarios. The framework effectiveness is demonstrated using a new dataset from the 2023 earthquake in Turkey, covering multiple cities with diverse urban settings. Results highlight that incorporating geospatial features significantly enhances detection performance and generalizability to previously unseen areas. By combining SAR imagery with detailed vulnerability and exposure information, our approach provides reliable and rapid building damage assessments without the dependency from available pre-event data. Moreover, the automated and scalable data generation process ensures the framework's applicability across diverse disaster-affected regions, underscoring its potential to support effective disaster management and recovery efforts. Code and data will be made available upon acceptance of the paper.

Neural networks are very effective machine learning tools that prove extremely valuable in unwinding the best representation of the data at hand. To improve the ability of neural networks to automatically perform the tasks assigned, innovative architectures have been proposed and thoroughly tested. Employed architectures have been customarily developed by human experts, with manual, time-consuming, and error-prone processes. To go beyond manual design, novel algorithmic strategies for automated discovery of optimal neural architectures have been developed. Consequently, architecture engineering has become a relevant field of active research [1,2]. Neural Architecture Search (NAS), the process that seeks to optimize network architecture, has been successfully applied on tasks as image classification [3,4], object detection [3], or semantic segmentation [5], yielding remarkable performance, as compared to manually designed benchmarks. According to [1], NAS is a subfield of Automated Machine Learning (AutoML) [6], the process that aims at automating the steps propaedeutic to applying machine learning to real-world problems. It also shows a notable overlap with hyperparameter optimization, a critical process in machine learning that involves selecting the optimal set of hyperparameters for a learning algorithm.

This study examines ChatGPT-4's capability to replicate linguistic strategies used in political discourse, focusing on its potential for manipulative language generation. As large language models become increasingly popular for text generation, concerns have grown regarding their role in spreading fake news and propaganda. This research compares real political speeches with those generated by ChatGPT, emphasizing presuppositions (a rhetorical device that subtly influences audiences by packaging some content as already known at the moment of utterance, thus swaying opinions without explicit argumentation). Using a corpus-based pragmatic analysis, this study assesses how well ChatGPT can mimic these persuasive strategies. The findings reveal that although ChatGPT-generated texts contain many manipulative presuppositions, key differences emerge in their frequency, form, and function compared with those of politicians. For instance, ChatGPT often relies on change-of-state verbs used in fixed phrases, whereas politicians use presupposition triggers in more varied and creative ways. Such differences, however, are challenging to detect with the naked eye, underscoring the potential risks posed by large language models in political and public discourse.Using a corpus-based pragmatic analysis, this study assesses how well ChatGPT can mimic these persuasive strategies. The findings reveal that although ChatGPT-generated texts contain many manipulative presuppositions, key differences emerge in their frequency, form, and function compared with those of politicians. For instance, ChatGPT often relies on change-of-state verbs used in fixed phrases, whereas politicians use presupposition triggers in more varied and creative ways. Such differences, however, are challenging to detect with the naked eye, underscoring the potential risks posed by large language models in political and public discourse.

The growing popularity of Answer Set Programming (ASP; [13]) in both academia and industry necessitates the development of user-friendly graphical interfaces to cater to end users. This is especially critical for interactive applications where users engage in iterative feedback loops with ASP systems. Examples include timetabling or product configuration tools. This leads to challenges in frontend development and requires skills in areas beyond ASP development. In addition, custom solutions have a limited reach, as they cannot be easily adapted. Clinguin addresses this challenge and streamlines User Interface (UI) development for ASP developers by letting them build interactive prototypes directly in ASP, eliminating the need for separate frontend languages. To this end, clinguin uses a few dedicated predicates to define UIs and the treatment of user-triggered events.

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, heterogeneous quantum-probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability.

Classical planning asks for a sequence of operators reaching a given goal. While the most common case is to compute a plan, many scenarios require more than that. However, quantitative reasoning on the plan space remains mostly unexplored. A fundamental problem is to count plans, which relates to the conditional probability on the plan space. Indeed, qualitative and quantitative approaches are well-established in various other areas of automated reasoning. We present the first study to quantitative and qualitative reasoning on the plan space. In particular, we focus on polynomially bounded plans. On the theoretical side, we study its complexity, which gives rise to rich reasoning modes. Since counting is hard in general, we introduce the easier notion of facets, which enables understanding the significance of operators. On the practical side, we implement quantitative reasoning for planning. Thereby, we transform a planning task into a propositional formula and use knowledge compilation to count different plans. This framework scales well to large plan spaces, while enabling rich reasoning capabilities such as learning pruning functions and explainable planning.

We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.