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We present a series of five new broadband X-ray observations of the ultraluminous X-ray source Holmberg IX X-1, performed by $XMM$-$Newton$ and $NuSTAR$ in coordination. The first three of these show high soft X-ray fluxes but a near total collapse of the high-energy ($\gtrsim$15 keV) emission, previously seen to be surprisingly stable across all prior broadband observations of the source. The latter two show a recovery in hard X-rays, remarkably once again respecting the same stable high-energy flux exhibited by all of the archival observations. We also present a joint analysis of all broadband observations of Holmberg IX X-1 to date (encompassing 11 epochs in total) in order to investigate whether it shows the same luminosity-temperature behaviour as NGC 1313 X-1 (which also shows a stable high-energy flux), whereby the hotter disc component in the spectrum exhibits two distinct, positively-correlated tracks in the luminosity-temperature plane. Holmberg IX X-1 may show similar behaviour, but the results depend on whether the highest energy emission is assumed to be an up-scattering corona or an accretion column. The strongest evidence for this behaviour is found in the former case, while in the latter the new 'soft' epochs appear distinct from the other high-flux epochs. We discuss possible explanations for these new 'soft' spectra in the context of the expected structure of super-Eddington accretion flows around black holes and neutron stars, and highlight a potentially interesting analogy with the recent destruction and re-creation of the corona seen in the AGN 1ES 1927+654., Comment: 15 pages, 5 figures, submitted for publication in MNRAS (comments welcome)

High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and experiment to understand and explore single-phase rock salt high-entropy oxides. By leveraging a machine-learning interatomic potential, we rapidly and accurately map high-entropy composition space using our two descriptors: bond length distribution and mixing enthalpy. The single-phase stabilities for all experimentally stabilized rock salt compositions are correctly resolved, with dozens more compositions awaiting discovery.

This paper introduces the Pandemic PACT Advanced Categorisation Engine (PPACE) along with its associated dataset. PPACE is a fine-tuned model developed to automatically classify research abstracts from funded biomedical projects according to WHO-aligned research priorities. This task is crucial for monitoring research trends and identifying gaps in global health preparedness and response. Our approach builds on human-annotated projects, which are allocated one or more categories from a predefined list. A large language model is then used to generate `rationales' explaining the reasoning behind these annotations. This augmented data, comprising expert annotations and rationales, is subsequently used to fine-tune a smaller, more efficient model. Developed as part of the Pandemic PACT project, which aims to track and analyse research funding and clinical evidence for a wide range of diseases with outbreak potential, PPACE supports informed decision-making by research funders, policymakers, and independent researchers. We introduce and release both the trained model and the instruction-based dataset used for its training. Our evaluation shows that PPACE significantly outperforms its baselines. The release of PPACE and its associated dataset offers valuable resources for researchers in multilabel biomedical document classification and supports advancements in aligning biomedical research with key global health priorities.

We consider bounds on maximum nullity of a graph via transversal numbers of compatible collections of forts. Results include generalizations of theorems from symmetric to combinatorially symmetric matrices, special bases of matrix nullspaces derived from transversal sets, and examples of issues that arise when considering only minimal forts and how to avoid them. We also show an important difference between constructing symmetric and combinatorially symmetric matrices associated to a graph whose nullspaces are supported on collections of disjoint forts.

We unravel the distinct roles each cation plays in phase evolution, stability, and properties within Mg1/5Co1/5Ni1/5Cu1/5Zn1/5O high-entropy oxide (HEO) by integrating experimental findings, thermodynamic analyses, and first-principles predictions. Our approach is through sequentially removing one cation at a time from the five-component high-entropy oxide to create five four-component derivatives. Bulk synthesis experiments indicate that Mg, Ni, and Co act as rock salt phase stabilizers whereas only Mg and Ni enthalpically enhance single-phase rock salt stability in thin film growth; synthesis conditions dictate whether Co is a rock salt phase stabilizer or destabilizer. By examining the competing phases and oxidation state preferences using pseudo-binary phase diagrams and first-principles calculations, we resolve the stability differences between bulk and thin film for all compositions. We systematically explore HEO macroscopic property sensitivity to cation selection employing both predicted and measured optical spectra. This study establishes a framework for understanding high-entropy oxide synthesizability and properties on a per-cation basis that is broadly applicable to tailoring functional property design in other high-entropy materials., Comment: Saeed S. I. Almishal and Jacob T. Sivak contributed equally to this work

This study introduces a biologically-inspired model designed to examine the role of coincidence detection cells in speech segregation tasks. The model consists of three stages: a time-domain cochlear model that generates instantaneous rates of auditory nerve fibers, coincidence detection cells that amplify neural activity synchronously with speech presence, and an optimal spectro-temporal speech presence estimator. A comparative analysis between speech estimation based on the firing rates of auditory nerve fibers and those of coincidence detection cells indicates that the neural representation of coincidence cells significantly reduces noise components, resulting in a more distinguishable representation of speech in noise. The proposed framework demonstrates the potential of brainstem nuclei processing in enhancing auditory skills. Moreover, this approach can be further tested in other sensory systems in general and within the auditory system in particular.

The colliding-wind region in binary systems made of massive stars allows us to investigate various aspects of shock physics, including particle acceleration. Particle accelerators of this kind are tagged as Particle-Accelerating Colliding-Wind Binaries, and are mainly identified thanks to their synchrotron radio emission. Our objective is first to validate the idea that obtaining snapshot high-resolution radio images of massive binaries constitutes a relevant approach to unambiguously identify particle accelerators. Second, we intend to exploit these images to characterize the synchrotron emission of two specific targets, HD167971 and HD168112, known as particle accelerators. We traced the radio emission from the two targets at 1.6 GHz with the European Very Long Baseline Interferometry Network, with an angular resolution of a few milli-arcseconds. Our measurements allowed us to obtain images for both targets. For HD167971, our observation occurs close to apastron, at an orbital phase where the synchrotron emission is minimum. For HD168112, we resolved for the very first time the synchrotron emission region. The emission region appears slightly elongated, in agreement with expectation for a colliding-wind region. In both cases the measured emission is significantly stronger than the expected thermal emission from the stellar winds, lending strong support for a non-thermal nature. Our study brings a significant contribution to the still poorly addressed question of high angular resolution radio imaging of colliding-wind binaries. We show that snapshot Very Long Baseline Interferometry measurements constitute an efficient approach to investigate these objects, with promising results in terms of identification of additional particle accelerators, on top of being promising as well to reveal long period binaries., Comment: 8 pages, 1 figure, accepted for publication in A&A

Research methods and procedures are core aspects of the research process. Metadata focused on these components is critical to supporting the FAIR principles, particularly reproducibility. The research reported on in this paper presents a methodological framework for metadata documentation supporting the reproducibility of research producing Metal Organic Frameworks (MOFs). The MOF case study involved natural language processing to extract key synthesis experiment information from a corpus of research literature. Following, a classification activity was performed by domain experts to identify entity-relation pairs. Results include: 1) a research framework for metadata design, 2) a metadata schema that includes nine entities and two relationships for reporting MOF synthesis experiments, and 3) a growing database of MOF synthesis reports structured by our metadata scheme. The metadata schema is intended to support discovery and reproducibility of metal-organic framework research and the FAIR principles. The paper provides background information, identifies the research goals and objectives, research design, results, a discussion, and the conclusion., Comment: Accepted by the 17th International Conference on Metadata and Semantics Research

We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured databases and knowledge extracted from the literature. To enhance MOF-KG accessibility for domain experts, we aim to develop a natural language interface for querying the knowledge graph. We have developed a benchmark comprised of 161 complex questions involving comparison, aggregation, and complicated graph structures. Each question is rephrased in three additional variations, resulting in 644 questions and 161 KG queries. To evaluate the benchmark, we have developed a systematic approach for utilizing the LLM, ChatGPT, to translate natural language questions into formal KG queries. We also apply the approach to the well-known QALD-9 dataset, demonstrating ChatGPT's potential in addressing KGQA issues for different platforms and query languages. The benchmark and the proposed approach aim to stimulate further research and development of user-friendly and efficient interfaces for querying domain-specific materials science knowledge graphs, thereby accelerating the discovery of novel materials., Comment: In 17th International Conference on Metadata and Semantics Research, October 2023

We investigate the sparsity of null vectors of real symmetric matrices whose off-diagonal pattern of zero and nonzero entries is described by the adjacencies of a graph. We use the definition of the spark of a matrix, the smallest number of nonzero coordinates of any null vector, to define the spark of a graph as the smallest possible spark of a corresponding matrix. We study connections of graph spark to well-known concepts including minimum rank, forts, orthogonal representations, Parter and Fiedler vertices, and vertex connectivity., Comment: 20 pages, 2 figures