Verifying the fully kinematic nature of the cosmic microwave background (CMB) dipole is of fundamental importance in cosmology. In the standard cosmological model with the Friedman-Lemaitre-Robertson-Walker (FLRW) metric from the inflationary expansion the CMB dipole should be entirely kinematic. Any non-kinematic CMB dipole component would thus reflect the preinflationary structure of spacetime probing the extent of the FLRW applicability. Cosmic backgrounds from galaxies after the matter-radiation decoupling, should have kinematic dipole component identical in velocity with the CMB kinematic dipole. Comparing the two can lead to isolating the CMB non-kinematic dipole. It was recently proposed that such measurement can be done using the near-IR cosmic infrared background (CIB) measured with the currently operating Euclid telescope, and later with Roman. The proposed method reconstructs the resolved CIB, the Integrated Galaxy Light (IGL), from Euclid's Wide Survey and probes its dipole, with a kinematic component amplified over that of the CMB by the Compton-Getting effect. The amplification coupled with the extensive galaxy samples forming the IGL would determine the CIB dipole with an overwhelming signal/noise, isolating its direction to sub-degree accuracy. We develop details of the method for Euclid's Wide Survey in 4 bands spanning 0.6 to 2 mic. We isolate the systematic and other uncertainties and present methodologies to minimize them, after confining the sample to the magnitude range with negligible IGL/CIB dipole from galaxy clustering. These include the required star-galaxy separation, accounting for the extinction correction dipole using the method newly developed here achieving total separation, accounting for the Earth's orbital motion and other systematic effects. (Abridged)

Supermassive black hole binary systems (SMBHBs) are thought to emit the recently discovered nHz gravitational wave background; however, not a single individual nHz source has been confirmed to date. Long-term radio-monitoring at the Owens Valley Radio Observatory has revealed two potential SMBHB candidates: blazars PKS 2131-021 and PKS J0805-0111. These sources show periodic flux density variations across the electromagnetic spectrum, signaling the presence of a good clock. To explain the emission, we propose a generalizable jet model, where a mildly relativistic wind creates an outward-moving helical channel, along which the ultra-relativistic jet propagates. The observed flux variation from the jet is mostly due to aberration. The emission at lower frequency arises at larger radius and its variation is consequently delayed, as observed. Our model reproduces the main observable features of both sources and can be applied to other sources as they are discovered. We make predictions for radio polarization, direct imaging, and emission line variation, which can be tested with forthcoming observations. Our results motivate future numerical simulations of jetted SMBHB systems and have implications for the fueling, structure, and evolution of blazar jets.

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care.

A galaxy's mid-IR spectrum encodes key information on its radiation field, star formation, and dust properties. Characterizing this spectrum therefore offers strong constraints on a galaxy's activity. This project describes a diagnostic tool for identifying main-sequence (MS) star-forming galaxies (SFGs) in the local Universe using IR dust emission features that are characteristic of galaxy activity. A physically-motivated sample of mock galaxy spectra has been generated to simulate the IR emission of SFGs. Using this sample, we developed a diagnostic tool for identifying MS SFGs based on machine learning methods. Custom photometric bands were defined to target dust emission features, including polycyclic aromatic hydrocarbons (PAHs) and the dust continuum. Three bands were chosen to trace PAH features at 6.2 {\mu}m, 7.7 {\mu}m, 8.6 {\mu}m, and 11.3 {\mu}m, along with an additional band to probe the radiation field strength responsible for heating the dust. This diagnostic was subsequently applied to observed galaxies to evaluate its effectiveness in real-world applications. Our diagnostic achieves high performance, with an accuracy of 90.9% on MS SFGs (observed sample of SFGs). Additionally, it shows low contamination, with only 16.2% of AGN galaxies being misidentified as SF. Combining observational data with stellar population synthesis models enables the creation of physically-motivated samples of SFGs that match the spectral properties of real galaxies. By positioning custom photometric bands targeting key dust features, our diagnostic can extract valuable information without the need to measure emission lines. Although PAHs are sensitive indicators of star formation and interstellar medium radiation hardness, PAH emission alone is insufficient for identifying MS SFGs. Finally, we developed a physically-motivated spectral library of MS SFGs spanning from UV to FIR wavelengths.

Semantic future prediction is important for autonomous systems navigating dynamic environments. This paper introduces FUTURIST, a method for multimodal future semantic prediction that uses a unified and efficient visual sequence transformer architecture. Our approach incorporates a multimodal masked visual modeling objective and a novel masking mechanism designed for multimodal training. This allows the model to effectively integrate visible information from various modalities, improving prediction accuracy. Additionally, we propose a VAE-free hierarchical tokenization process, which reduces computational complexity, streamlines the training pipeline, and enables end-to-end training with high-resolution, multimodal inputs. We validate FUTURIST on the Cityscapes dataset, demonstrating state-of-the-art performance in future semantic segmentation for both short- and mid-term forecasting. Project page and code at this https URL .

Extracting structured computational representations of historical events from narrative text remains computationally expensive when constructed manually. While RDF/OWL reasoners enable graph-based reasoning, they are limited to fragments of first-order logic, preventing deeper temporal and semantic analysis. This paper addresses both challenges by developing automatic historical event extraction models using multiple LLMs (GPT-4, Claude, Llama 3.2) with three enhancement strategies: pure base generation, knowledge graph enhancement, and Retrieval-Augmented Generation (RAG). We conducted comprehensive evaluations using historical texts from Thucydides. Our findings reveal that enhancement strategies optimize different performance dimensions rather than providing universal improvements. For coverage and historical breadth, base generation achieves optimal performance with Claude and GPT-4 extracting comprehensive events. However, for precision, RAG enhancement improves coordinate accuracy and metadata completeness. Model architecture fundamentally determines enhancement sensitivity: larger models demonstrate robust baseline performance with incremental RAG improvements, while Llama 3.2 shows extreme variance from competitive performance to complete failure. We then developed an automated translation pipeline converting extracted RDF representations into Coq proof assistant specifications, enabling higher-order reasoning beyond RDF capabilities including multi-step causal verification, temporal arithmetic with BC dates, and formal proofs about historical causation. The Coq formalization validates that RAG-discovered event types represent legitimate domain-specific semantic structures rather than ontological violations.

Agentic systems built on large language models (LLMs) offer promising capabilities for automating complex workflows in healthcare AI. We introduce mAIstro, an open-source, autonomous multi-agentic framework for end-to-end development and deployment of medical AI models. The system orchestrates exploratory data analysis, radiomic feature extraction, image segmentation, classification, and regression through a natural language interface, requiring no coding from the user. Built on a modular architecture, mAIstro supports both open- and closed-source LLMs, and was evaluated using a large and diverse set of prompts across 16 open-source datasets, covering a wide range of imaging modalities, anatomical regions, and data types. The agents successfully executed all tasks, producing interpretable outputs and validated models. This work presents the first agentic framework capable of unifying data analysis, AI model development, and inference across varied healthcare applications, offering a reproducible and extensible foundation for clinical and research AI integration. The code is available at: this https URL

Causal Discovery plays a pivotal role in revealing relationships among observed variables, particularly in the temporal setup. While the majority of CD methods rely on synthetic data for evaluation, and recently for training, these fall short in accurately mirroring real-world scenarios; an effect even more evident in temporal data. Generation techniques depending on simplified assumptions on causal structure, effects and time, limit the quality and diversity of the simulated data. In this work, we introduce Temporal Causal-based Simulation (TCS), a robust framework for generating realistic time-series data and their associated temporal causal graphs. The approach is structured in three phases: estimating the true lagged causal structure of the data, approximating the functional dependencies between variables and learning the noise distribution of the corresponding causal model, each part of which can be explicitly tailored based on data assumptions and characteristics. Through an extensive evaluation process, we highlight that single detection methods for generated data discrimination prove inadequate, accentuating it as a multifaceted challenge. For this, we detail a Min-max optimization phase that draws on AutoML techniques. Our contributions include a flexible, model-agnostic pipeline for generating realistic temporal causal data, a thorough evaluation setup which enhances the validity of the generated datasets and insights into the challenges posed by realistic data generation. Through experiments involving not only real but also semi-synthetic and purely synthetic datasets, we demonstrate that while sampling realistic causal data remains a complex task, our method enriches the domain of generating sensible causal-based temporal data.

We present specialized Large Language Models for theoretical High-Energy Physics, obtained as 20 fine-tuned variants of the 8-billion parameter Llama-3.1 model. Each variant was trained on arXiv abstracts (through August 2024) from different combinations of hep-th, hep-ph and gr-qc. For a comparative study, we also trained models on datasets that contained abstracts from disparate fields such as the q-bio and cs categories. All models were fine-tuned using two distinct Low-Rank Adaptation fine-tuning approaches and varying dataset sizes, and outperformed the base model on hep-th abstract completion tasks. We compare performance against leading commercial LLMs (ChatGPT, Claude, Gemini, DeepSeek) and derive insights for further developing specialized language models for High-Energy Theoretical Physics.

We investigate the problem of Object State Classification (OSC) as a zero-shot learning problem. Specifically, we propose the first Object-agnostic State Classification (OaSC) method that infers the state of a certain object without relying on the knowledge or the estimation of the object class. In that direction, we capitalize on Knowledge Graphs (KGs) for structuring and organizing knowledge, which, in combination with visual information, enable the inference of the states of objects in object/state pairs that have not been encountered in the method's training set. A series of experiments investigate the performance of the proposed method in various settings, against several hypotheses and in comparison with state of the art approaches for object attribute classification. The experimental results demonstrate that the knowledge of an object class is not decisive for the prediction of its state. Moreover, the proposed OaSC method outperforms existing methods in all datasets and benchmarks by a great margin.

Cosmic microwave background (CMB) photons are deflected by large-scale structure through gravitational lensing. This secondary effect introduces higher-order correlations in CMB anisotropies, which are used to reconstruct lensing deflections. This allows mapping of the integrated matter distribution along the line of sight, probing the growth of structure, and recovering an undistorted view of the last-scattering surface. Gravitational lensing has been measured by previous CMB experiments, with $\textit{Planck}$'s $42\,\sigma$ detection being the current best full-sky lensing map. We present an enhanced $\textit{LiteBIRD}$ lensing map by extending the CMB multipole range and including the minimum-variance estimation, leading to a $49$ to $58\,\sigma$ detection over $80\,\%$ of the sky, depending on the final complexity of polarized Galactic emission. The combination of $\textit{Planck}$ and $\textit{LiteBIRD}$ will be the best full-sky lensing map in the 2030s, providing a $72$ to $78\,\sigma$ detection over $80\,\%$ of the sky, almost doubling $\textit{Planck}$'s sensitivity. Finally, we explore different applications of the lensing map, including cosmological parameter estimation using a lensing-only likelihood and internal delensing, showing that the combination of both experiments leads to improved constraints. The combination of $\textit{Planck}$ + $\textit{LiteBIRD}$ will improve the $S_8$ constraint by a factor of 2 compared to $\textit{Planck}$, and $\textit{Planck}$ + $\textit{LiteBIRD}$ internal delensing will improve $\textit{LiteBIRD}$'s tensor-to-scalar ratio constraint by $6\,\%$. We have tested the robustness of our results against foreground models of different complexity, showing that improvements remains even for the most complex foregrounds.

Domain walls in antiferromagnets under a spin-polarized current present rich dynamics that is not observed in ferromagnets, and it is tunable by the current polarization. Precessional dynamics is obtained for perpendicular spin polarization, in agreement with expectations in older works. Propagating walls are obtained for an in-plane polarization. We obtain the velocity as a function of current by a perturbation method for low velocities, and the wall profile is found to lack a definite parity. For high velocities, the main features of the wall profile are obtained by a direct solution of an equation that is valid in a limiting case. We discuss the magnetization of the dynamical walls and find that this can become large, providing a potential method for observations. Oscillatory motion of domain walls is obtained for spin polarization that has both perpendicular and in-plane components, and an analytical description is given.

The star-forming main sequence (SFMS) is a tight relation observed between stellar masses and star formation rates (SFR) in a population of galaxies. This relation is observed at different redshifts, in various morphological, and environmental domains, and is key to understanding the underlying relations between a galaxy budget of cold gas and its stellar content. Euclid Quick Data Release 1 (Q1) gives us the opportunity to investigate this fundamental relation in galaxy formation and evolution. We complement the Euclid release with public IRAC observations of the Euclid Deep Fields, improving the quality of recovered photometric redshifts, stellar masses, and SFRs, as is shown both with simulations and a comparison with available spectroscopic redshifts. From Q1 data alone, we recover more than $\sim 30\,\mathrm{k}$ galaxies with $\log_{10} (M_\ast/M_\odot) > 11$, giving a precise constraint of the SFMS at the high-mass end. We investigated the SFMS, in a redshift interval between $0.2$ and $3.0$, comparing our results with the existing literature and fitting them with a parameterisation taking into account the presence of a bending of the relation at the high-mass end, depending on the bending mass, $M_0$. We find good agreement with previous results in terms of $M_0$ values, and an increasing trend for the relation scatter at higher stellar masses. We also investigate the distribution of physical (e.g. dust absorption, $A_V$, and formation age) and morphological properties (e.g., Sérsic index and radius) in the SFR--stellar mass plane, and their relation with the SFMS. These results highlight the potential of Euclid in studying the fundamental scaling relations that regulate galaxy formation and evolution in anticipation of the forthcoming Data Release 1.