PLAID (Physics-Learning AI Datamodel) is a Python library and data format for representing, storing, and sharing physics simulation datasets for machine learning.Unlike domain-specific formats, PLAID accommodates time-dependent, multi-resolution simulations and heterogeneous meshes.The library provides a high-level API to easily load, inspect, and save data.Beyond basic I/O, PLAID adopts a backend-oriented storage design supporting several backends (cgns, hf_datasets, and zarr), and integrates with the Hugging Face Hub to push and pull datasets.It also ships command-line tools to check, serve, and interactively visualize datasets.In short, PLAID couples a flexible on-disk standard with a software toolkit to manipulate physics data, addressing the needs of ML researchers in fluid dynamics, structural mechanics, and related fields in a generic fashion.Full documentation, examples and tutorials are available at plaid-lib.readthedocs.io.
Rydberg atomic radio-frequency (rf) sensors are an emerging technology platform that relies on vaporous atoms, interrogated with laser beams and nearly ionized, to receive rf signals. Rydberg rf sensors have a number of interesting fundamental distinctions from traditional receiver technologies, such as those based on metallic antennas, since they are governed by the quantum physics of atom-light interactions. As Rydberg sensors quickly advance from laboratory experiments into fieldable devices, there is a need for a general software modeling tool that fully encompasses the internal physics of the sensor. The Rydberg Interactive Quantum Module (RydIQule) is a Python package designed to fill this need. The initial public release of RydIQule in late 2023 built the core functionality described above. Here we outline RydIQule's version 2 release which expands on its capabilities to more accurately model real-world atoms.
Combining complementary imaging modalities is critical to build reliable 3D coronary models: intravascular imaging gives sub-millimetre resolution but limited whole-vessel context, while CCTA supplies 3D geometry but suffers from limited spatial resolution and artefacts (e.g., blooming). Prior work demonstrated intravascular/CCTA fusion, yet no open, flexible toolkit is tailored for multi-state analysis (rest/stress, pre-/post-stenting) while offering deterministic behaviour, high performance, and easy pipeline integration. multimodars addresses this gap with deterministic alignment algorithms, a compact NumPy-centred data model, and an optimised Rust backend suitable for scalable, reproducible experiments. The package accepts CSV/NumPy inputs including data formats produced by the AIVUS-CAA software
Marketing Mix Modeling (MMM) estimates the impact of marketing activities on business outcomes such as sales or revenue. Traditional MMM approaches rely on linear regression or Bayesian hierarchical models that assume channel independence and struggle to capture temporal dynamics and non-linear saturation. DeepCausalMMM addresses these limitations by combining deep learning, causal inference, and marketing science. It uses Gated Recurrent Units (GRUs) to learn temporal patterns (adstock, lag) while learning statistical dependencies between channels through Directed Acyclic Graph (DAG) structure with upper triangular constraints. It implements Hill equation saturation curves for diminishing returns and budget optimization. Key features: (1) data-driven hyperparameters learned from data with defaults, (2) linear mean scaling of the dependent variable, (3) configurable attribution priors with dynamic loss scaling, (4) multi-region modeling with shared and region-specific parameters, (5) robust methods including Huber loss, (6) response curve analysis.
Understanding the architecture of the human heart requires analysis of its microstructural organization across scales. With the advent of high-resolution imaging techniques such as synchrotron-based tomography, it has become possible to visualize entire hearts at micron-scale resolution. However, translating these large, complex volumetric datasets into interpretable, quantitative descriptors of cardiac organization remains a major challenge. Here we present cardiotensor, an open-source Python package designed to quantify 3D cardiomyocyte orientation in whole- or partial-heart imaging datasets. It provides efficient, scalable implementations of structure tensor analysis, enabling extraction of directional metrics such as helical angle (HA), intrusion angle (IA), and fractional anisotropy (FA). The package supports datasets reaching teravoxel-scale and is optimized for high-performance computing environments, including parallel and chunk-based processing pipelines. In addition, cardiotensor includes tractography functionality to reconstruct continuous cardiomyocyte trajectories. This enables multi-scale myoaggregate visualization down to the myocyte level, depending on resolution. These capabilities enable detailed structural mapping of cardiac tissue, supporting the assessment of anatomical continuity and regional organization.
