Search

Untreated periodontitis causes inflammation within the supporting tissue of the teeth and can ultimately lead to tooth loss. Modeling periodontal outcomes is beneficial as they are difficult and time consuming to measure, but disparities in representation between demographic groups must be considered. There may not be enough participants to build group specific models and it can be ineffective, and even dangerous, to apply a model to participants in an underrepresented group if demographic differences were not considered during training. We propose an extension to RECaST Bayesian transfer learning framework. Our method jointly models multivariate outcomes, exhibiting significant improvement over the previous univariate RECaST method. Further, we introduce an online approach to model sequential data sets. Negative transfer is mitigated to ensure that the information shared from the other demographic groups does not negatively impact the modeling of the underrepresented participants. The Bayesian framework naturally provides uncertainty quantification on predictions. Especially important in medical applications, our method does not share data between domains. We demonstrate the effectiveness of our method in both predictive performance and uncertainty quantification on simulated data and on a database of dental records from the HealthPartners Institute., Comment: 30 pages, 1 figure, 17 tables

Wheeled rovers have been the primary choice for lunar exploration due to their speed and efficiency. However, deeper areas, such as lunar caves and craters, require the mobility of legged robots. To do so, appropriate end effectors must be designed to enable climbing and walking on the granular surface of the Moon. This paper investigates the behavior of an underactuated soft gripper on deformable granular material when a legged robot is walking in soft soil. A modular test bench and a simulation model were developed to observe the gripper sinkage behavior under load. The gripper uses tendon-driven fingers to match its target shape and grasp on the target surface using multiple micro-spines. The sinkage of the gripper in silica sand was measured by comparing the axial displacement of the gripper with the nominal load of the robot mass. Multiple experiments were performed to observe the sinkage of the gripper over a range of slope angles. A simulation model accounting for the degrees of compliance of the gripper fingers was created using Altair MotionSolve software and coupled to Altair EDEM to compute the gripper interaction with particles utilizing the discrete element method. After validation of the model, complementary simulations using Lunar gravity and a regolith particle model were performed. The results show that a satisfactory gripper model with accurate freedom of motion can be created in simulation using the Altair simulation packages and expected sinkage under load in a particle-filled environment can be estimated using this model. By computing the sinkage of the end effector of legged robots, the results can be directly integrated into the motion control algorithm and improve the accuracy of mobility in a granular material environment., Comment: Proceedings of the 21st International and 12th Asia-Pacific Regional Conference of the ISTVS

Although wheeled robots have been predominant for planetary exploration, their geometry limits their capabilities when traveling over steep slopes, through rocky terrains, and in microgravity. Legged robots equipped with grippers are a viable alternative to overcome these obstacles. This paper proposes a gripping system that can provide legged space-explorer robots a reliable anchor on uneven rocky terrain. This gripper provides the benefits of soft gripping technology by using segmented tendon-driven fingers to adapt to the target shape, and creates a strong adhesion to rocky surfaces with the help of microspines. The gripping performances are showcased, and multiple experiments demonstrate the impact of the pulling angle, target shape, spine configuration, and actuation power on the performances. The results show that the proposed gripper can be a suitable solution for advanced space exploration, including climbing, lunar caves, or exploration of the surface of asteroids., Comment: The 27th issue of the International Conference Series on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR)

Frustrated magnets can elude the paradigm of conventional symmetry breaking and instead exhibit signatures of emergent symmetries at low temperatures. Such symmetries arise from "accidental" degeneracies within the ground state manifold and have been explored in a number of disparate models, in both two and three dimensions. Here we report the systematic construction of a family of classical spin models that, for a wide variety of lattice geometries with triangular motifs in one, two and three spatial dimensions, such as the kagome or hyperkagome lattices, exhibit an emergent, continuous U(1) symmetry. This is particularly surprising because the underlying Hamiltonian actually has very little symmetry - a bond-directional, off-diagonal exchange model inspired by the microscopics of spin-orbit entangled materials (the $\Gamma^{\prime}$-model). The construction thus allows for a systematic study of the interplay between the emergent continuous U(1) symmetry and the underlying discrete Hamiltonian symmetries in different lattices across different spatial dimensions. We discuss the impact of thermal and quantum fluctuations in lifting the accidental ground state degeneracy via the thermal and quantum order-by-disorder mechanisms, and how spatial dimensionality and lattice symmetries play a crucial role in shaping the physics of the model. Complementary Monte Carlo simulations, for representative one-, two-, and three-dimensional lattice geometries, provide a complete account of the thermodynamics and confirm our analytical expectations., Comment: 14 pages, 14 figures

Transpiration cooling is an active thermal protection system of increasing interest in aerospace applications wherein a coolant is effused through a porous wall into a hot external flow. The present work focuses on the interaction between the high-temperature turbulent boundary layer and the pressure-driven coolant flow through the porous wall. Coupling functions were obtained from pore-network simulations to characterize the flow through the porous medium. These were then coupled to direct numerical simulations of a turbulent boundary layer over a massively-cooled flat plate. Two different types of coupling function were used: linear expressions, which do not account for flow interactions between neighbouring pores, and shallow convolutional neural networks (CNN) which incorporate spatial correlations. All coupled cases demonstrated a significant variation in blowing due to the streamwise variation in mean pressure associated with the onset of coolant injection. This trend was reflected in the cooling effectiveness, and was mitigated in the CNN-coupled cases due to the incorporation of lateral flow between neighbouring pores. The distribution of turbulent kinetic energy (TKE) in the coupled cases was also modified by the coupling due to the competing effects of near-wall turbulence attenuation and increased shear due to increasing blowing ratio. Finally, the coupling was shown to impact the power spectral density of the pressure fluctuations at the wall within the transpiration region, attenuating the largest scales of the turbulence whilst leaving the smaller scales relatively unaffected.

This paper examines the dual-use challenges of foundation models and the consequent risks they pose for international security. As artificial intelligence (AI) models are increasingly tested and deployed across both civilian and military sectors, distinguishing between these uses becomes more complex, potentially leading to misunderstandings and unintended escalations among states. The broad capabilities of foundation models lower the cost of repurposing civilian models for military uses, making it difficult to discern another state's intentions behind developing and deploying these models. As military capabilities are increasingly augmented by AI, this discernment is crucial in evaluating the extent to which a state poses a military threat. Consequently, the ability to distinguish between military and civilian applications of these models is key to averting potential military escalations. The paper analyzes this issue through four critical factors in the development cycle of foundation models: model inputs, capabilities, system use cases, and system deployment. This framework helps elucidate the points at which ambiguity between civilian and military applications may arise, leading to potential misperceptions. Using the Intermediate-Range Nuclear Forces (INF) Treaty as a case study, this paper proposes several strategies to mitigate the associated risks. These include establishing red lines for military competition, enhancing information-sharing protocols, employing foundation models to promote international transparency, and imposing constraints on specific weapon platforms. By managing dual-use risks effectively, these strategies aim to minimize potential escalations and address the trade-offs accompanying increasingly general AI models.

Achieving localized light emission from monolayer two-dimensional (2D) transition metal dichalcogenides (TMDs) embedded in the matrix of another TMD has been theoretically proposed but not experimentally proven. In this study, we used cathodoluminescence performed in a scanning transmission electron microscope to unambiguously resolve localized light emission from 2D monolayer MoSe2 nanodots of varying sizes embedded in monolayer WSe2 matrix. We observed that the light emission strongly depends on the nanodot size wherein the emission is dominated by MoSe2 excitons in dots larger than 85 nm, and by MoSe2/WSe2 interface excitons below 50 nm. Interestingly, at extremely small dot sizes (< 10 nm), the electron energy levels in the nanodot become quantized, as demonstrated by a striking blue-shift in interface exciton emission, thus inducing quantum confined luminescence. These results establish controllable light emission from spatially confined 2D nanodots, which holds potential to be generalized to other 2D systems towards future nanophotonic applications., Comment: 40 pages total, 5 figures in main text and 12 figures in Supporting Information, submitted to Nature Materials as of 06/14/2024

Counterspeech -- speech that opposes hate speech -- has gained significant attention recently as a strategy to reduce hate on social media. While previous studies suggest that counterspeech can somewhat reduce hate speech, little is known about its effects on participation in online hate communities, nor which counterspeech tactics reduce harmful behavior. We begin to address these gaps by identifying 25 large hate communities ("subreddits") within Reddit and analyzing the effect of counterspeech on newcomers within these communities. We first construct a new public dataset of carefully annotated counterspeech and non-counterspeech comments within these subreddits. We use this dataset to train a state-of-the-art counterspeech detection model. Next, we use matching to evaluate the causal effects of hostile and non-hostile counterspeech on the engagement of newcomers in hate subreddits. We find that, while non-hostile counterspeech is ineffective at keeping users from fully disengaging from these hate subreddits, a single hostile counterspeech comment substantially reduces both future likelihood of engagement. While offering nuance to the understanding of counterspeech efficacy, these results a) leave unanswered the question of whether hostile counterspeech dissuades newcomers from participation in online hate writ large, or merely drives them into less-moderated and more extreme hate communities, and b) raises ethical considerations about hostile counterspeech, which is both comparatively common and might exacerbate rather than mitigate the net level of antagonism in society. These findings underscore the importance of future work to improve counterspeech tactics and minimize unintended harm., Comment: 19 pages, 11 figures. arXiv admin note: text overlap with arXiv:2303.13641

Many online hate groups exist to disparage others based on race, gender identity, sex, or other characteristics. The accessibility of these communities allows users to join multiple types of hate groups (e.g., a racist community and a misogynistic community), raising the question of whether users who join additional types of hate communities could be further radicalized compared to users who stay in one type of hate group. However, little is known about the dynamics of joining multiple types of hate groups, nor the effect of these groups on peripatetic users. We develop a new method to classify hate subreddits and the identities they disparage, then apply it to understand better how users come to join different types of hate subreddits. The hate classification technique utilizes human-validated deep learning models to extract the protected identities attacked, if any, across 168 subreddits. We find distinct clusters of subreddits targeting various identities, such as racist subreddits, xenophobic subreddits, and transphobic subreddits. We show that when users become active in their first hate subreddit, they have a high likelihood of becoming active in additional hate subreddits of a different category. We also find that users who join additional hate subreddits, especially those of a different category develop a wider hate group lexicon. These results then lead us to train a deep learning model that, as we demonstrate, usefully predicts the hate categories in which users will become active based on post text replied to and written. The accuracy of this model may be partly driven by peripatetic users often using the language of hate subreddits they eventually join. Overall, these results highlight the unique risks associated with hate communities on a social media platform, as discussion of alternative targets of hate may lead users to target more protected identities., Comment: 18 pages, 13 figures. Accepted to the Proceedings of the International AAAI Conference on Web and Social Media 2025

Local moments with a spin $S>1/2$ can exhibit a rich variety of elementary quasiparticle excitations, such as quadrupolar excitations, that go beyond the dipolar magnons of conventional spin-$1/2$ systems. However, the experimental observation of such quadrupolar excitations is often challenging due to the dipolar selection rules of many linear response probes, rendering them invisible. Here we show that non-linear spectroscopy, in the form of two-dimensional coherent spectroscopy (2DCS), can be used to reveal quadrupolar excitations. Considering a family of spin-1 Heisenberg ferromagnets with single-ion easy-axis anisotropy as an example, we explicitly calculate their 2DCS signature by combining exact diagonalization and generalized spin wave theory. We further demonstrate that 2DCS can provide access to the quadrupolar weight of an excitation, analogous to how linear response provides access to the dipolar weight. Our work highlights the potential of non-linear spectroscopy as a powerful tool to diagnose multipolar excitations in quantum magnets., Comment: 13 pages, 8 figures

Inelastic neutron scattering (INS) has traditionally been one of the primary methods for investigating quantum magnets, particularly in identifying a continuum of excitations as a hallmark of spin fractionalization in quantum spin liquids (QSLs). However, INS faces severe limitations due to its inability to distinguish between such QSL signatures and similar excitation continua arising from highly frustrated magnetic orders with large unit cells or classical spin liquids. In contrast, two-dimensional coherent spectroscopy (2DCS) has emerged as a powerful tool to probe nonlinear excitation dynamics, offering insights into the underlying mechanisms behind these broad spectral features. In this paper, we utilize classical molecular dynamics (MD) techniques to explore the 2DCS responses of frustrated magnets with dominant Kitaev interactions. Comparing the classical and quantum versions of the pure Kitaev model our results indicate both clear similarities, in the form of sharp line features, and clear distinctions, in the locations of these features and in selection rules. Moreover, in the extended $K\Gamma\Gamma'$ model, we show that the 2DCS response of the Kitaev spin liquid is completely distinct from that of large unit cell magnetic orders, despite both generating a broad continuum in INS. Additionally, we demonstrate the extreme sensitivity of classical 2DCS to thermal fluctuations and discuss the potential significance of quantum coherence in experimental settings. Overall, our work illustrates the potential of 2DCS in resolving the complex physics underlying ambiguous spin excitation continua, thereby enhancing our understanding of the dynamics in these frustrated systems., Comment: 10 pages, 5+1 figures

Structural and magnetic properties of Mn5(SixGe1-x)3 thin films were investigated. Ferromagnetic Mn5Ge3 and anti-ferromagnetic Mn5Si3 thin films have been synthesized and characterized as these compounds exhibit interesting features for the development of spintronics. Here, Mn5(SixGe1-x)3 thin films were grown on Ge(111) substrates by co-deposition using molecular beam epitaxy. Crystalline thin films can be produced with controlled Si concentrations ranging from 0 to 1. The thin films were relaxed by dislocations at the interface with the substrate. A lattice parameter variation was observed as the Si content increased, which is comparable to previous works done in bulk. Reflection highenergy electron diffraction diagrams and X-ray diffraction profiles showed that lattice parameters a and c are shrinking and that the surface roughness and crystallinity degrade as the Si amount increases. Magnetometric measurements revealed a ferromagnetic behavior for all Si concentrations. The measured average ferromagnetic moment per manganese atom decreased from 2.33 to 0.05 {\mu}B/Mn atom. No ferro to anti-ferromagnetic transition was observed contrary to the bulk Mn5(SixGe1-x)3 compound.

GaSe is an important member of the post-transition metal chalcogenide family and is an emerging two-dimensional (2D) semiconductor material. Because it is a van der Waals material, it can be fabricated into atomic-scale ultrathin films, making it suitable for the preparation of compact, heterostructure devices. In addition, GaSe possesses unusual optical and electronic properties, such as a shift from an indirect-bandgap single-layer film to a direct-bandgap bulk material, rare intrinsic p-type conduction, and nonlinear optical behaviors. These properties make GaSe an appealing candidate for the fabrication of field-effect transistors, photodetectors, and photovoltaics. However, the wafer-scale production of pure GaSe single crystal thin films remains challenging. This study develops an approach for the direct growth of nanometer-thick GaSe films on GaAs substrates using molecular beam epitaxy. It yields smooth thin GaSe films with the rare {\gamma}'-polymorph. We analyze the formation mechanism of {\gamma}'-GaSe using density functional theory and speculate that it is stabilized by Ga vacancies since the formation enthalpy of {\gamma}'-GaSe tends to become lower than that of other polymorphs when the Ga vacancy concentration increases. Finally, we investigate the growth conditions of GaSe, providing valuable insights for exploring 2D/3D quasi-van der Waals epitaxial growth.

We present local and nonlocal electrical transport measurements of the correlated insulator FeSb$_2$. By employing wiring configurations that delineate between bulk- and surface-dominated conduction, we reveal the formation of a metallic surface state in FeSb$_2$ for temperatures $\lessapprox 5$~K. This result is corroborated by an angular rotation study of this material's magnetotransport, which also shows signatures of the transition from bulk- to surface-dominated conduction over the same temperature interval as the local/nonlocal transport divergence. Notable similarities with the topological Kondo insulator candidate SmB$_6$ are discussed.

The phase landscape of UTe$_2$ features a remarkable diversity of superconducting phases under applied pressure and magnetic field. Recent quantum oscillation studies at ambient pressure have revealed the quasi-2D Fermi surface of this material. However, the pressure-dependence of the Fermi surface remains an open question. Here we track the evolution of the UTe$_2$ Fermi surface as a function of pressure up to 19.5 kbar by measuring quantum interference oscillations. We find that in sufficient magnetic field to suppress both superconductivity at low pressures and incommensurate antiferromagnetism at higher pressures, the quasi-2D Fermi surface found at ambient pressure smoothly connects to that at 19.5 kbar, with no signs of a reconstruction over this pressure interval. The warping of the cylindrical Fermi sheets continuously increases with pressure, which is consistent with increased $f$-orbital contribution at the Fermi level, up to and beyond the critical pressure at which superconductivity is truncated. These findings highlight the value of high pressure quantum interference measurements as a new probe of the electronic structure in heavy fermion materials.

Quantum critical phenomena are widely studied across various materials families, from high temperature superconductors to magnetic insulators. They occur when a thermodynamic phase transition is suppressed to zero temperature as a function of some tuning parameter such as pressure or magnetic field. This generally yields a point of instability - a so-called quantum critical point - at which the phase transition is driven exclusively by quantum mechanical fluctuations. Here we show that the heavy fermion metamagnet UTe$_2$ possesses a quantum phase transition at extreme magnetic field strengths of over 70 T. Surprisingly, rather than terminating at one singular point, we find that the phase boundary is sensitive to magnetic field components in each of the three cartesian axes of magnetic field space. This results in the three-dimensional transition surface being bounded by a continuous ring of quantum critical points, the locus of which forms an extended line of quantum criticality - a novel form of quantum critical phase boundary. Within this quantum critical line sits an intensely field-resilient superconducting state in a striking toroidal shape, persisting to fields over 70 T. We model our data by a phenomenological free energy expansion, and show how a three-dimensional quantum critical phase boundary - rather than a more conventional singular point of instability - anchors the remarkable high magnetic field phase landscape of UTe$_2$.

Order-by-disorder, whereby fluctuations lift an accidental classical ground state degeneracy to stabilize a subset of ordered states, is a recurrent and prominent theme in the field of frustrated magnetism where magnetic moments, or spins, are subject to competing interactions. Thus far, such a phenomenon has been discussed in systems where the quantum ground state is not a product state. In this circumstance, both thermal and quantum fluctuations act to lift the accidental classical degeneracy, begging the question whether one mechanism of order-by-disorder is possible without the other. In this paper, we present results exposing a novel route to order-by-disorder, without quantum zero-point fluctuations, in a family of ferromagnetic Heisenberg materials with the Dzyaloshinskii-Moriya (DM) interaction as the leading perturbation. We prove that any colinear ferromagnetic state is an exact eigenstate, even in the presence of the anisotropic DM interaction, while thermal fluctuations give rise to a preference in the magnetization direction. Here, we present in detail an example of the pyrochlore Heisenberg ferromagnet with DM interactions. Using both linear spin-wave theory and classical Monte Carlo simulations, we find that the magnetization can spontaneously rotate at intermediate temperature(s) within the colinear ferromagnetic phase when the DM interaction is large. Additionally, we find that in the large DM regime, the ferromagnetic ground state becomes unstable within the framework of non-linear spin-wave theory. Our results show that thermal order-by-disorder is possible even in the absence of quantum zero-point fluctuations., Comment: 15 pages, 8 figures

A promising route towards the realization of chiral spin liquids is the quantum melting of classically noncoplanar spin states via quantum fluctuations. In the classical realm, such noncoplanar orders can effectively be stabilized by interactions beyond nearest neighbors. Motivated by the recent synthesis of materials with a maple-leaf lattice geometry, we study the effect of cross-plaquette couplings on elementary Heisenberg antiferromagnets for this geometry (as well as their ferromagnetic counterparts). We find a rich spectrum of noncoplanar states, including a novel icosahedral order as well as incommensurate spin spirals, using large-scale Monte Carlo simulations in combination with a semi-analytical analysis. To inspect the potential quantum melting of these states, we analyze the quantum $S = 1/2$ variant of these models using pseudo-fermion functional renormalization group (pf-FRG) simulations. Notably, we indeed find extended parameter regimes lacking long-range magnetic order -- in regions classically occupied by noncoplanar orders -- which we putatively identify with the possible formation of chiral quantum spin liquids., Comment: 22 pages, 20 figures

This paper introduces a user-friendly platform developed by the University of Kentucky Center for Applied AI, designed to make large, customized language models (LLMs) more accessible. By capitalizing on recent advancements in multi-LoRA inference, the system efficiently accommodates custom adapters for a diverse range of users and projects. The paper outlines the system's architecture and key features, encompassing dataset curation, model training, secure inference, and text-based feature extraction. We illustrate the establishment of a tenant-aware computational network using agent-based methods, securely utilizing islands of isolated resources as a unified system. The platform strives to deliver secure LLM services, emphasizing process and data isolation, end-to-end encryption, and role-based resource authentication. This contribution aligns with the overarching goal of enabling simplified access to cutting-edge AI models and technology in support of scientific discovery., Comment: 10 pages 5 figures, 1 table

Two-dimensional coherent spectroscopy (2DCS) with terahertz radiation offers a promising new avenue for the exploration of many-body phenomena in quantum magnets. This includes the potential diagnosis of fractionalized excitations, for which linear response often struggles due to the indistinguishability of a continuum of fractional excitations from that caused by disorders or impurities. However, the interpretation of the complex results produced by 2DCS remains a challenge, and a general prediction of the spectral characteristics of different types of excitations has not yet been established. In this paper, we develop a numerical approach based on exact diagonalization (ED) to push our understanding of 2DCS towards different scenarios. We first validate our approach by comparing numerical ED and exact analytical results for the spectroscopic signatures of spinons in one-dimensional transverse field Ising model and develop how to deal with the inherently small system sizes in ED calculations. Augmenting the model by a longitudinal field, we demonstrate significant changes to the 2DCS spectrum upon the field-induced spinon pair confinement, which can be rationalized in our ED calculations and from a "two-kink" model (in the absence of integrability). One advantage of our ED approach is its possible extension to finite temperatures, which we explore using thermally pure quantum states and demonstrate to change the intensity and spectroscopical patterns of 2DCS when going beyond the integrable model. Our numerically exact results provide a benchmark for future experiments and theoretical studies relying on approximation methods, and pave the way for the exploration of fractionalized excitations in quantum magnets., Comment: 17 pages, 14 figures for main text

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled Enabling Global Image Data Sharing in the Life Sciences, which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data. In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location., Comment: This manuscript is published with a closely related companion entitled, Enabling Global Image Data Sharing in the Life Sciences, which can be found at the following link, arXiv:2401.13023 [q-bio.OT]

GaAs(111)B is a semiconductor substrate widely used in research and commercial fields due to its low cost, mature synthesis technology, and excellent properties for manufacturing electronic devices. It is not only used to grow three-dimensional (3D) strongly-bonded materials, but has also been used as a substrate for layered, van der Waals (vdW)-bonded chalcogenide film growth. However, GaAs(111)B wafers cannot be directly used for growing epitaxial vdW chalcogenide films for two reasons: (1) the GaAs surface has a substantial number of dangling bonds that need to be passivated for vdW layers growth; (2) the substrate surface is covered with a thin epi-ready oxide layer which must be removed before film growth. In this paper, we optimize the method for deoxidizing GaAs(111)B substrates under a Se overpressure and successfully create a smooth, deoxidized, and passivated substrate for subsequent growth of vdW chalcogenide materials. We demonstrate the benefits of this method for the growth of vdW chalcogenide thin films using GaSe as a representative of vdW chalcogenides. In addition, we find that severely aged substrates have difficulty maintaining a smooth surface during the deoxidation and passivation process and cause GaSe crystals to nucleate in random shapes and orientations. We describe a method using water droplet testing to determine the age of the substrate. Finally, X-ray photoelectron spectroscopy (XPS) characterization reveals that the natural aging of GaAs(111)B in the air results in an increase in surface oxides, Ga2O3 and As2O3, while exposure to ultraviolet (UV)-ozone not only enhances the contents of these two oxides but also generates a new oxide, As2O5. Our research contributes to expanding the compatibility of GaAs(111)B with diverse growth materials and the production of high-quality heterostructure devices.