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Motivated by a problem in Generative Specification-Producing AIs (SPAIs), we focus on a problem of deep clustering of three- dimensional shapes specified by point clouds. After reviewing deep clustering, we propose a novel approach involving angle based clustering and semisupervised contrastive penalties. The proposed approach is evaluated on the ModelNet dataset and compared against an unsupervised approach leveraging autoencoding. 

Networks of nanoelectromechanical (NEMS) resonators are useful analogs for a variety of many-body systems and enable applications in sensing, phononics, and mechanical information processing. A challenge toward realizing practical NEMS networks is the ability to characterize the constituent resonator building blocks and their coupling. Here, we spatially map graphene NEMS networks and introduce an efficient algebraic formalism to quantify the site-specific elasticity, mass, damping, and coupling parameters of network clusters. In a departure from multiple regression, our algebraic analysis uses minimal measurements to fully characterize the network parameters without a priori parameter estimates or iterative computation. We apply this suite of techniques to single-resonator and coupled-pair clusters and find excellent agreement with expected parameter values and broader spectral response. Our approach provides a nonregressive framework for accurately characterizing a range of classical and quantum resonator systems, offering a versatile modeling tool applicable across multiple disciplines and advancing the development of programmable NEMS networks. 

Generative AI in music (GAIM) technologies are rapidly transforming music production, yet little is known about how working musicians perceive and respond to these changes. This study presents findings from in-depth interviews with 43 musicians, spanning diverse genres, professional roles and experience with music technology. Our analysis, informed by a reflexive thematic analysis approach, suggests complex tensions between perceived benefits and risks of GAIM adoption. Key themes were generated around tensions between (i) fear of reduced job opportunities for professional musicians and appreciation of the potential of AI to make individual musicians more independent and productive; (ii) fear about the exploitation of artists’ work and benefits of open music exchanges; (iii) fear that AI will exacerbate inequities and recognition of AI’s potential to increase access to music production. Our findings highlight the need for careful consideration of justice and fairness in GAIM development and deployment, suggesting that different types of GAIM use (from assistant to replacement) carry distinct ethical implications. This work provides a foundation for understanding how GAIMs can be integrated into music production while respecting artists’ rights and creative agency. 

This meta-analysis studied the development of ability stereotypes that could limit girls’ and women’s participation in science, technology, engineering, and mathematics (STEM) fields, as well as contribute to boys’ underachievement in reading and writing. We integrated findings from 98 studies measuring children’s gender stereotypes about STEM and verbal abilities. The data comprised 145,204 children (ages 4–17) from 33 nations across more than 40 years (1977–2020). Preregistered analyses showed why prior researchers have reached diverging conclusions about the onset, change, and extent of these stereotypes in childhood and adolescence. Contrary to some prior conclusions, math stereotypes favoring male ability were minimal on average (0.11 SDs from gender neutrality). Stereotypes were instead far stronger for computer science, engineering, and physics (0.51 SDs), which favored male ability by age 6. Girls increasingly endorsed pro-male STEM stereotypes with age. Pro-female verbal ability stereotypes were also substantial (0.46 SDs), emerging by age 8 and becoming more female-biased with age. Additionally, STEM stereotypes were weaker for Black than White U.S. participants, as predicted. Unexpectedly, however, boys’ STEM stereotypes declined before age 13 but increased thereafter, revealing an asymmetric development across STEM versus verbal domains. We integrated developmental intergroup theory and social role theory to explain this asymmetry, considering both cognitive and sociocultural processes. The early emergence of verbal stereotypes and certain STEM stereotypes (e.g., engineering) means that they have ample time to affect later downstream outcomes such as domainspecific confidence and interests. 

The scattering and absorption of light within biological tissue severely limits the penetration depth of optical imaging techniques. Recently, it has been found that water-soluble, strongly absorbing dye molecules, such as tartrazine, can achievein vivotissue transparency by increasing the refractive index of aqueous components in tissue, as predicted by the Lorentz oscillator model and Kramers–Kronig relations. In this study, we topically applied absorbing dye molecules to the abdominal skin of pigmented and nonpigmented mice to enhance the penetration depth of optical coherence tomography (OCT) and photoacoustic microscopy (PAM). In both types of mice, the penetration depth of OCT was significantly improved using tartrazine and 4-aminoantipyrine. As predicted by the Kramers–Kronig relations and absorption spectra of the dyes, mice treated with 4-aminoantipyrine showed significantly improved penetration depth compared to mice treated with tartrazine for the PAM system with 532 nm excitation. These findings further demonstrate the use of absorbing dye molecules for achieving tissue transparency to enhance the penetration depth of depth-resolved optical imaging modalities in skin, thus accelerating the translation of these technologies in clinical areas, such as dermatology. 

Understanding local hydraulic conditions is imperative to coastal harmful algal bloom (HAB) monitoring. The research summarized herein describes how the locations and tidal phases selected for coastal hazard sampling can influence measurement results used to guide management decisions for HABs. Our study was conducted in Frenchman Bay, Maine, known for its complex deglaciated coastline, strong tidal influence, and shellfishing activities that are susceptible to problematic HABs such as those produced by some species (spp.) of the diatom genus Pseudo-nitzschia. In-situ measurements of current velocity, density, and turbulence collected over a semidiurnal tidal cycle and a companion numerical model simulation of the study area provide concurrent evidence of two adjacent counter-rotating sub-mesoscale eddies (2–4 km diameter) that persist in the depth-averaged residual circulation. The eddies are generated in the wake of several islands in an area with abrupt bathymetric gradients, both legacy conditions partly derived from deglaciation ∼15 kya. Increased concentrations of Pseudo-nitzschia spp. measured during the semidiurnal survey follow a trend of elevated turbulent dissipation rates near the water surface, indicating that surface sampling alone might not adequately indicate species abundance. Additional measurements of Pseudo-nitzschia spp. from two years of weekly sampling in the region show that algal cell abundance is highest where residual eddies form. These findings provide incentive to examine current practices of HAB monitoring and management by linking coastal geomorphology to hydraulic conditions influencing HAB sampling outcomes, coastal morphometric features to material accumulation hotspots, and millennial time scales to modern hydraulic conditions. 

Abstract While the quantity, quality, and variety of movement data has increased, methods that jointly allow for population- and species-level movement parameters to be estimated are still needed. We present a formal data integration approach to combine individual-level movement and population-level distribution data. We show how formal data integration can be used to improve precision of individual and population level movement parameters and allow additional population level metrics (e.g., connectivity) to be formally quantified.We describe three components needed for an Integrated Movement Model (IMM): a model for individual movement, a model for among-individual heterogeneity, and a model to quantify changes in species distribution. We outline a general IMM framework and develop and apply a specific stochastic differential equation model to a case study of telemetry and species distribution data for golden eagles in western North American during spring migration.We estimate eagle movements during spring migration from data collected between 2011 and 2019. Individual heterogeneity in migration behavior was modeled for two sub-populations, individuals that make significant northward migrations and those that remained in the southern Rocky Mountain region through the summer. As is the case with most tracking studies, the sample population of individual telemetered birds is not representative of the population, and underrepresents the proportion of long-distance migrants in. The IMM was able to provide a more biological accurate subpopulation structure by jointly estimating the structure using the species distribution data. In addition, the integrated approach a) improves accuracy of other estimated movement parameters, b) allows us to estimate the proportion of migratory and non-migratory birds in a given location and time, and c) estimate future spatio-temporal distributions of birds given a wintering location, which provide estimates of seasonal connectivity and migratory routes.We demonstrate how IMMs can be successfully used to address the challenge of estimating accurate population level movement parameters. Our approach can be generalized to a broad range of available movement models and data types, allowing us to significantly improve our knowledge of migration ecology across taxonomic groups, and address population and continental level information needs for conservation and management. 

A<sc>bstract</sc> PELICAN is a novel permutation equivariant and Lorentz invariant or covariant aggregator network designed to overcome common limitations found in architectures applied to particle physics problems. Compared to many approaches that use non-specialized architectures that neglect underlying physics principles and require very large numbers of parameters, PELICAN employs a fundamentally symmetry group-based architecture that demonstrates benefits in terms of reduced complexity, increased interpretability, and raw performance. We present a comprehensive study of the PELICAN algorithm architecture in the context of both tagging (classification) and reconstructing (regression) Lorentz-boosted top quarks, including the difficult task of specifically identifying and measuring theW-boson inside the dense environment of the Lorentz-boosted top-quark hadronic final state. We also extend the application of PELICAN to the tasks of identifying quark-initiated vs. gluon-initiated jets, and a multi-class identification across five separate target categories of jets. When tested on the standard task of Lorentz-boosted top-quark tagging, PELICAN outperforms existing competitors with much lower model complexity and high sample efficiency. On the less common and more complex task of 4-momentum regression, PELICAN also outperforms hand-crafted, non-machine learning algorithms. We discuss the implications of symmetry-restricted architectures for the wider field of machine learning for physics.