The integration of big data into criminal investigations is advancing significantly. Big data fundamentally involves the utilization of artificial intelligence technologies to analyse vast quantities of electronic information. The inherent features of big data contribute to minimizing subjectivity in investigative procedures and facilitate the evolution of criminal investigation methodologies and incident identification. However, challenges persist regarding the protection of rights and potential biases in data collection, as well as issues of subjectivity and the “black box effect” in data processing, alongside security concerns related to data storage. To address these challenges, it is essential to implement strategies such as enhancing the quality of big data, restricting the transparency of data processing methods and establishing a tiered protection framework for personal information.

Analysts often seek to compare representations in high-dimensional space, e.g., embedding vectors of the same word across groups. We show that the distance measures calculated in such cases can exhibit considerable statistical bias, that stems from uncertainty in the estimation of the elements of those vectors. This problem applies to Euclidean distance, cosine similarity, and other similar measures. After illustrating the severity of this problem for text-as-data applications, we provide and validate a bias correction for the squared Euclidean distance. This same correction also substantially reduces bias in ordinary Euclidean distance and cosine similarity estimates, but corrections for these measures are not quite unbiased and are (non-intuitively) bimodal when distances are close to zero. The estimators require obtaining the variance of the latent positions. We (will) implement the estimator in free software, and we offer recommendations for related work.

Making informed clinical decisions based on individualised outcome predictions is the cornerstone of precision psychiatry. Prediction models currently employed in psychiatry rely on algorithms that map a statistical relationship between clinical features (predictors/risk factors) and subsequent clinical outcomes. They rely on associations that overlook the underlying causal structures within the data, including the presence of latent variables, and the evolution of predictors and outcomes over time. As a result, predictions from sparse associative models from routinely collected data are rarely actionable at an individual level. To be actionable, prediction models should address these shortcomings. We provide a brief overview of a general framework for the rationale for implementing causal and actionable predictions using counterfactual explanations to advance predictive modelling studies, which has translational implications. We have included an extensive glossary of terminology used in this paper and the literature (Supplementary Box 1) and provide a concrete example to demonstrate this conceptually, and a reading list for those interested in this field (Supplementary Box 2).

An efficient compression scheme for modal flow analysis is proposed and validated on data sequences of compressible flow through a linear turbomachinery blade row. The key feature of the compression scheme is a minimal, user-defined distortion of the mutual distance of any snapshot pair in phase space. Through this imposed feature, the model reduction process preserves the temporal dynamics contained in the data sequence, while still decreasing the spatial complexity. The mathematical foundation of the scheme is the fast Johnson–Lindenstrauss transformation (FJLT) which uses randomized projections and a tree-based spectral transform to accomplish the embedding of a high-dimensional data sequence into a lower-dimensional latent space. The compression scheme is coupled to a proper orthogonal decomposition and dynamic mode decomposition analysis of flow through a linear blade row. The application to a complex flow-field sequence demonstrates the efficacy of the scheme, where compression rates of two orders of magnitude are achieved, while incurring very small relative errors in the dominant temporal dynamics. This FJLT technique should be attractive to a wide range of modal analyses of large-scale and multi-physics fluid motion.

The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

As the field of migration studies evolves in the digital age, big data analytics emerge as a potential game-changer, promising unprecedented granularity, timeliness, and dynamism in understanding migration patterns. However, the epistemic value added by this data explosion remains an open question. This paper critically appraises the claim, investigating the extent to which big data augments, rather than merely replicates, traditional data insights in migration studies. Through a rigorous literature review of empirical research, complemented by a conceptual analysis, we aim to map out the methodological shifts and intellectual advancements brought forth by big data. The potential scientific impact of this study extends into the heart of the discipline, providing critical illumination on the actual knowledge contribution of big data to migration studies. This, in turn, delivers a clarified roadmap for navigating the intersections of data science, migration research, and policymaking.

This is the first of a two-part paper. We formulate a data-driven method for constructing finite-volume discretizations of an arbitrary dynamical system's underlying Liouville/Fokker–Planck equation. A method is employed that allows for flexibility in partitioning state space, generalizes to function spaces, applies to arbitrarily long sequences of time-series data, is robust to noise and quantifies uncertainty with respect to finite sample effects. After applying the method, one is left with Markov states (cell centres) and a random matrix approximation to the generator. When used in tandem, they emulate the statistics of the underlying system. We illustrate the method on the Lorenz equations (a three-dimensional ordinary differential equation) saving a fluid dynamical application for Part 2 (Souza, J. Fluid Mech., vol. 997, 2024, A2).

This is the second part of a two-part paper. We apply the methodology of the first paper (Souza, J. Fluid Mech., vol. 997, 2024, A1) to construct a data-driven finite-volume discretization of the Liouville/Fokker–Planck equation of a high-dimensional dynamical system, i.e. the compressible Euler equations with gravity and rotation evolved on a thin spherical shell. We show that the method recovers a subset of the statistical properties of the underlying system, steady-state distributions of observables and autocorrelations of particular observables, as well as revealing the global Koopman modes of the system. We employ two different strategies for the partitioning of a high-dimensional state space, and explore their consequences.