夏威夷大学

Asteroseismic studies of red giants have primarily relied on two global parameters: the large frequency separation (Delta nu) and the frequency of maximum power (nu(max)). Meanwhile, the p-mode phase shift (epsilon) and small frequency separations (delta nu(01), delta nu(02)), which offer additional constraints on stellar interiors, remain underexplored due to measurement challenges. Here, we develop an automated pipeline based on collapsed & eacute;chelle diagrams and apply it to similar to 16 000 Kepler red giants, jointly measuring Delta nu, epsilon, delta nu(01),and delta nu(02) and assembling the largest homogeneous catalogue of these quantities to date, together with updated Delta nu values and formal internal uncertainties. Using this catalogue, we quantify evolutionary trends across the red-giant branch and core-helium-burning phase. We find that delta nu(02)/Delta nu stays nearly constant for RGB stars and, for core-helium-burning stars, organizes into two sequences that are systematically offset but partially overlap, broadly separating stars in the red-clump and secondary-clump regimes. We also trace the mass- and metallicity-dependent helium-flash transition. Meanwhile, epsilon follows a single Delta nu-epsilon relation common to both evolutionary phases. Comparisons with stellar-evolution models reveal systematic offsets in epsilon and delta nu(01), which we interpret as signatures of near-surface and outer-envelope modelling deficiencies. These comparisons further suggest that dipole-mode small separations are sensitive to mode-dependent surface terms in evolved stars. Overall, our results demonstrate that epsilon and the small separations provide important diagnostics of core structure, convective-boundary mixing, and helium ignition that are complementary to those provided by Delta nu and nu(max) alone. The resulting catalogue offers a reference for testing and calibrating future stellar-evolution models.

Physical reservoir computing (PRC) performance is known to strongly depend on its nonlinear dynamics which complicates identifying appropriate metrics for design purposes. Here, we evaluate nonlinear structural dynamics using mutual information to understand the PRC's ability to track a target signal. The information processing ability of a mechanical PRC is first elucidated by introducing a simple three node causal structure that fuses information between discrete sensors and the PRC's dynamic states. The three node directed acyclic graph (DAG) is motivated by the Monty Hall problem, where the PRC serves as the new information similar to increasing odds of winning the car in the game show Let's Make a Deal. This concept is applied to PRCs to understand how information is processed through a nonlinear structure to increase the odds of knowing the state of the environment. For practical aerodynamic state estimation applications, we take pressure measurements from experimental supersonic cavity flow as the input to the PRC. It is computationally shown that mutual information provides a good measure to design nonlinearities into a PRC.

ABSTRACT The economic cost of invasive species continues to rise as more species are introduced globally every year. One of the most effective ways to reduce these costs is to implement robust early detection and rapid response (EDRR) programs, in which invasive populations are detected at low enough abundances that control efforts are effective and cost‐efficient. Environmental DNA (eDNA) surveys have become increasingly common in aquatic invasive species EDRR programs, but they have not been explored for use in terrestrial ecosystem invasions. Here, we investigate the deployment of eDNA surveys to detect invasive coconut rhinoceros beetles (Oryctes rhinoceros; CRB) on the island of Oʻahu, Hawaiʻi, USA. CRB impose substantial economic and cultural impacts across Pacific Island nations and territories, with the potential to spread globally. We designed a sensitive, species‐specific molecular assay targeting a 64‐bp segment of the COI region. We tested four field sampling methods at CRB‐positive sites, with roller and mulch‐rinse samples successfully capturing CRB eDNA. We estimated these two methods had per‐sample detection probabilities of 0.47 and 0.54, respectively, in sites with known CRB presence. We then evaluated how these methods performed at low‐ or unknown‐CRB‐abundance sites. The overall per‐sample probability of detection dropped to 0.09 and 0.06 for the roller and mulch methods, respectively, due to low eDNA concentrations in samples. Finally, we compared eDNA detection probability with co‐located pheromone‐baited traps to assess the performance of eDNA against a standard early‐detection protocol. We found that roller eDNA sampling exhibited twice the per‐sample detection probability of traps, with mulch sampling showing comparable patterns, though with greater uncertainty. Our results indicate that eDNA surveys for CRB can be a feasible and useful addition to EDRR programs in Hawaiʻi, and perhaps other sites where CRB are likely to be introduced.

Conventional recommender systems and Large Language Model (LLM)-based recommender systems each have their strengths and weaknesses. While conventional recommendation methods excel at mining collaborative information and modeling sequential behavior, they struggle with data sparsity and the long-tail problem. LLM, on the other hand, is proficient at utilizing rich textual contexts but faces challenges in mining collaborative or sequential information. Despite their individual successes, there is a significant gap in leveraging their ensemble potential to enhance recommendation performance. In this paper, we introduce a general and model-agnostic framework known as Large language models with mutual augmentation and adaptive aggregation for Recommendation (Llama4Rec), aiming to bridge this gap via explicitly ensemble LLM and conventional recommendation model for more effective recommendation. We propose data augmentation and prompt augmentation strategies tailored to enhance the conventional recommendation model and LLM respectively. An adaptive aggregation module is adopted to combine the predictions of both kinds of models to refine the final recommendation results. Empirical studies on three datasets validate the superiority of Llama4Rec, demonstrating significant improvements in recommendation performance.

We present the COSMOS Spectroscopic Redshift Compilation encompassing ∼20 yr of spectroscopic redshifts within a 10 deg ^2 area centered on the 2 deg ^2 COSMOS legacy field. This compilation contains 487,666 redshifts of 266,284 unique objects from 138 individual programs up to z ∼ 8 with median stellar mass ∼10 ^8.4 –10 ^10 M _⊙ (redshift dependent). Rest-frame NUVrJ colors and star formation rate–stellar mass correlations show that the compilation primarily contains low-to-intermediate-mass star-forming and massive, quiescent galaxies at z < 1.25 and mostly low-mass bursty star-forming galaxies at z > 2. Sources in the compilation cover a diverse range of environments, including protoclusters such as “Hyperion.” The full compilation is 50% spectroscopically complete by i ∼ 23.4 mag and K _s ∼ 21.6 mag; however, this is redshift dependent. Spatially, the compilation is >50% (>30%) complete within the central (outer) region limited to i < 24 mag and K _s < 22.5 mag, separately. We demonstrate how the compilation can be used to validate photometric redshifts and investigate calibration metrics. By training self-organizing maps on COSMOS2020/Classic and projecting the compilation onto it, we find key subpopulations currently lacking spectroscopic coverage, including z < 1 intermediate-mass quiescent and low-/intermediate-mass bursty star-forming galaxies, z ∼ 2 massive quiescent galaxies, and z > 3 massive star-forming galaxies. This highlights how combining self-organizing maps with our compilation can provide guidance for future spectroscopic observations to get a complete spectroscopic view of galaxy populations. Lastly, the compilation will undergo periodic data releases incorporating new spectroscopic redshifts and providing a lasting legacy resource for the community.