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An understanding of the CO2 + H2O hydration reaction is crucial for modeling the effects of ocean acidification, for enabling novel carbon storage solutions, and as a model process in the geosciences. While the mechanism of this reaction has been investigated extensively in the condensed phase, its mechanism at the air-water interface remains elusive, leaving uncertain the contribution that surface-adsorbed CO2 makes to the overall acidification reaction. In this study, we employ state-of-the-art machine-learned potentials to provide a molecular-level understanding of CO2 hydration at the air-water interface. We show that reaction at the interface follows a surface-mediated `In and Out' mechanism: CO2 diffuses into the aqueous surface layer, reacts to form carbonic acid, and is subsequently expelled from solution. We show that this surface layer provides a bulk-like solvation environment, engendering similar modes of reactivity and near-identical free energy profiles for the bulk and interfacial processes. Our study unveils a new, unconventional reaction mechanism that underscores the dynamic nature of the molecular reaction site at the air-water interface. The similarity between bulk and interfacial profiles shows that CO2 hydration is equally as feasible under these two solvation environments and that acidification rates are likely enhanced by this additional surface contribution., Comment: 15 pages, 4 figures

Altermagnetism has surfaced as a novel magnetic phase, bridging the properties of ferro- and anti-ferromagnetism. The momentum-dependent spin-splitting observed in these materials reflects their unique symmetry characteristics which also establish the conditions for chiral magnons to emerge. Here we provide the first direct experimental evidence for a chiral magnon in the altermagnetic candidate MnTe, revealed by circular-dichroism resonant inelastic X-ray scattering (CD-RIXS). This mode which we term chiral altermagnon exhibits a distinct momentum dependence consistent with the proposed altermagnetic $g-$wave symmetry of MnTe. Our results reveal a new class of magnetic excitations, demonstrating how altermagnetic order shapes spin dynamics and paves the way for advances in spintronic and quantum technologies.

The experimental determination of the magnitude and momentum dependence of electron-phonon coupling (EPC) is an outstanding problem in condensed matter physics. Resonant inelastic x-ray scattering (RIXS) has been previously employed to determine the EPC, since the intensity of phonon peaks in RIXS spectra has been directly related to the underlying EPC strength. In order to assess the limits of validity of such a relation, we compare experimental results and theoretical predictions for several high-T$_c$ superconducting cuprates. Namely, we investigate the intensity of the bond-stretching phonon mode in CaCuO$_2$, La$_2$CuO$_{4+\delta}$, La$_{1.84}$Sr$_{0.16}$CuO$_4$ and YBa$_2$Cu$_3$O$_{7-\delta}$ along the high symmetry ($\zeta$,0), ($\zeta$,$\zeta$) directions and as a function of the azimuthal angle $\phi$ at fixed modulus of the in-plane momentum $\mathbf{q_\parallel}$. Using two different theoretical approaches for the description of the RIXS scattering from phonons, we find that the $\mathbf{q_\parallel}$-dependence of the RIXS intensity can be largely ascribed to the symmetry of the phonon mode, and that satisfactory prediction of the experimental results cannot be obtained without including realistic details of the electronic structure in the calculations. Regardless of the theoretical model, RIXS provides a reliable momentum dependence of EPC in cuprates and can be used to test advanced theoretical predictions., Comment: 15 pages, 7 figures

Many-body excitons in CrSBr are attracting intense interest in view of their highly anisotropic magneto-optical coupling and their potential for novel optical interfaces within spintronic and magnonic devices. Characterizing the orbital character and propagation of these electronic excitations is crucial for understanding and controlling their behavior; however, this information is challenging to access. Ultra-high resolution resonant inelastic x-ray scattering is a momentum-resolved technique that can address these crucial questions. We present measurements collected at the Cr $L_3$-edge which show a rich spectrum of excitations with a variety of spin-orbital characters. While most of these excitations appear to be localized, the dispersion of the lowest energy dark exciton indicates that it is able to propagate along both the $a$ and $b$ directions within the planes of the crystal. This two-dimensional character is surprising as it contrasts with electrical conductivity and the behavior of the bright exciton, both of which are strongly one-dimensional. The discovery of this propagating dark exciton highlights an unusual coexistence of one- and two-dimensional electronic behaviors in CrSBr., Comment: 5 pages not including references and supplementary information

Language models (LMs) built upon deep neural networks (DNNs) have recently demonstrated breakthrough effectiveness in software engineering tasks such as code generation, completion, and repair. This has paved the way for the emergence of LM-based code optimization techniques, which are crucial for enhancing the performance of existing programs, such as accelerating program execution time. However, a comprehensive survey dedicated to this specific application has been lacking. To fill this gap, we present a systematic literature review of over 50 primary studies, identifying emerging trends and addressing 11 specialized questions. Our findings reveal five critical open challenges, such as balancing model complexity with practical usability, cross-language/performance generalizability, and building trust in AI-driven solutions. Furthermore, we provide eight future research directions to facilitate more efficient, robust, and reliable LM-based code optimization. Thereby, this study aims to provide actionable insights and foundational references for both researchers and practitioners in this rapidly evolving field., Comment: Submitted to the ACM Computing Surveys (CSUR)

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources., Comment: 26 pages, 5 figures

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language models (pLM) training on hundreds of graphical processing units (GPUs). We introduce the BioNeMo Framework to facilitate the training of computational biology and chemistry AI models across hundreds of GPUs. Its modular design allows the integration of individual components, such as data loaders, into existing workflows and is open to community contributions. We detail technical features of the BioNeMo Framework through use cases such as pLM pre-training and fine-tuning. On 256 NVIDIA A100s, BioNeMo Framework trains a three billion parameter BERT-based pLM on over one trillion tokens in 4.2 days. The BioNeMo Framework is open-source and free for everyone to use.

Optically pumped magnetometers (OPMs) are compact and lightweight sensors that can measure magnetic fields generated by current flow in neuronal assemblies in the brain. Such sensors enable construction of magnetoencephalography (MEG) instrumentation, with significant advantages over conventional MEG devices including adaptability to head size, enhanced movement tolerance, lower complexity and improved data quality. However, realising the potential of OPMs depends on our ability to perform system calibration, which means finding sensor locations, orientations, and the relationship between the sensor output and magnetic field (termed sensor gain). Such calibration is complex in OPMMEG since, for example, OPM placement can change from subject to subject (unlike in conventional MEG where sensor locations or orientations are fixed). Here, we present two methods for calibration, both based on generating well-characterised magnetic fields across a sensor array. Our first device (the HALO) is a head mounted system that generates dipole like fields from a set of coils. Our second (the matrix coil (MC)) generates fields using coils embedded in the walls of a magnetically shielded room. Our results show that both methods offer an accurate means to calibrate an OPM array (e.g. sensor locations within 2 mm of the ground truth) and that the calibrations produced by the two methods agree strongly with each other. When applied to data from human MEG experiments, both methods offer improved signal to noise ratio after beamforming suggesting that they give calibration parameters closer to the ground truth than factory settings and presumed physical sensor coordinates and orientations. Both techniques are practical and easy to integrate into real world MEG applications. This advances the field significantly closer to the routine use of OPMs for MEG recording., Comment: 36 pages, 10 figures

Driven-dissipative nonlinear systems exhibit rich critical behavior, related to bifurcation, bistability and switching, which underlie key phenomena in areas ranging from physics, chemistry and biology to social sciences and economics. The importance of rare fluctuations leading to a dramatic jump between two very distinct states, such as survival and extinction in population dynamics, success and bankruptcy in economics and the occurrence of earthquakes or of epileptic seizures, have been already established. In the quantum domain, switching is of importance in both chemical reactions and the devices used in quantum state detection and amplification. In particular, the simplest driven single oscillator model serves as an insightful starting point. Here we describe switching induced by quantum fluctuations and illustrate that an instanton approach within Keldysh field theory can provide a deep insight into such phenomena. We provide a practical recipe to compute the switching rates semi-analytically, which agrees remarkably well with exact solutions across a wide domain of drive amplitudes spanning many orders of magnitude. Being set up in the framework of Keldysh coherent states path integrals, our approach opens the possibility of studying quantum activation in many-body systems where other approaches are inapplicable.

Complex oxides are well known to develop oxygen ordering patterns with well defined periodicities, but their interplay with electronic correlations remains largely unexplored. Here, we report resonant and non-resonant x-ray diffraction data indicating a four-unit-cell periodic superstructure related to oxygen vacancy ordering in La$_{2-x}$Ca$_{x}$CuO$_{4}$ films with doping levels from the underdoped ($x = 0.15$) to the extremely overdoped ($x = 0.50$) regime. Whereas the lattice superstructure is temperature independent up to 300 K, a strongly temperature dependent electronic charge density wave (CDW) is observed in the underdoped and slightly overdoped regimes ($x\leq0.20$). The periodicity, in-plane and out-of-plane correlation lengths of the CDW are locked in by the lattice superstructure. Our results highlight the necessity to consider lattice and electronic energetics on equal footing in the high-temperature oxygen-disordered phase to explain oxygen ordering phenomena in complex oxides., Comment: 4 figures, supplement included

Both magnetism and spin-orbit coupling in systems with broken inversion symmetry lift the spin degeneracy of electronic bands, but the consequences of interplay between these mechanisms remain poorly understood. Here, we show that ultrathin transition ferromagnet-heavy metal bilayers exhibit anomalous temperature- and electric bias-dependent behaviors in the vicinity of the Curie temperature, inconsistent with the usual Weiss magnetism. Characterization by several complementary techniques and analysis of the dependence on composition reveal that these effects originate from interfacial spin-orbit interaction, which results in the emergence of a state with distinct magnetic and magnetoelectronic properties that can be described as Rashba magnetism. Our findings open a new route for the characterization and control of spin-orbit phenomena in heterostructures enabling the development of efficient spin-orbitronic devices., Comment: 6 pages, 4 figures, comments are welcome

In vector tomography (VT), the aim is to reconstruct an unknown multi-dimensional vector field using line integral data. In the case of a 2-dimensional VT, two types of line integral data are usually required. These data correspond to integration of the parallel and perpendicular projection of the vector field along integration lines. VT methods are non-invasive, non-intrusive and offer more information on the field than classical point measurements; they are typically used to reconstruct divergence-free (or source-free) velocity and flow fields. In this paper, we show that VT can also be used for the reconstruction of fields with non-zero divergence. In particular, we study electric fields generated by dipole sources in bounded domains which arise, for example, in electroencephalography (EEG) source imaging. To the best of our knowledge, VT has not previously been used to reconstruct such fields. We explain in detail the theoretical background, the derivation of the electric field inverse problem and the numerical approximation of the line integrals. We show that fields with non-zero divergence can be reconstructed from the longitudinal measurements with the help of two sparsity constraints that are constructed from the transverse measurements and the vector Laplace operator. As a comparison to EEG source imaging, we note that VT does not require mathematical modelling of the sources. By numerical simulations, we show that the pattern of the electric field can be correctly estimated using VT and the location of the source activity can be determined accurately from the reconstructed magnitudes of the field., Comment: Application in EEG Source Imaging

In situ imageomics leverages machine learning techniques to infer biological traits from images collected in the field, or in situ, to study individuals organisms, groups of wildlife, and whole ecosystems. Such datasets provide real-time social and environmental context to inferred biological traits, which can enable new, data-driven conservation and ecosystem management. The development of machine learning techniques to extract biological traits from images are impeded by the volume and quality data required to train these models. Autonomous, unmanned aerial vehicles (UAVs), are well suited to collect in situ imageomics data as they can traverse remote terrain quickly to collect large volumes of data with greater consistency and reliability compared to manually piloted UAV missions. However, little guidance exists on optimizing autonomous UAV missions for the purposes of remote sensing for conservation and biodiversity monitoring. The UAV video dataset curated by KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos required three weeks to collect, a time-consuming and expensive endeavor. Our analysis of KABR revealed that a third of the videos gathered were unusable for the purposes of inferring wildlife behavior. We analyzed the flight telemetry data from portions of UAV videos that were usable for inferring wildlife behavior, and demonstrate how these insights can be integrated into an autonomous remote sensing system to track wildlife in real time. Our autonomous remote sensing system optimizes the UAV's actions to increase the yield of usable data, and matches the flight path of an expert pilot with an 87% accuracy rate, representing an 18.2% improvement in accuracy over previously proposed methods.

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO$_2$-H$_2$O interface, in particular, has been the subject of a number of studies on account of its importance for the carbon life cycle as well as carbon capture and sequestration schemes. Despite this attention, there remain a number of open questions on the nature of the CO$_2$-H$_2$O interface, particularly concerning the interfacial tension and phase behavior of CO$_2$ at the interface. In this paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing the benefits of machine-learned potentials and enhanced statistical sampling methods, we present an ab initio-level description of the CO$_2$-H$_2$O interface. Interfacial tensions are predicted from 1-500 bar and found to be in close agreement with experiment at the pressures for which experimental data is available. Structural analyses indicate the build-up of an adsorbed, saturated CO$_2$ film forming at low pressure (20 bar) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. CO$_2$ monolayer build-up coincides with a reduced structuring of water molecules close to the interface. This study highlights the predictive nature of machine-learned potentials for complex macroscopic properties of biphasic interfaces, and the mechanistic insight obtained into carbon dioxide aggregation at the water interface is of high relevance for geoscience, climate research, and materials science., Comment: 14 pages, 10 figures

We use Resonant Inelastic X-ray Scattering (RIXS) to compare the doping dependence of magnetic excitations of an Infinite-Layer nickelate to those of a prototypical superconducting cuprate. The polarization analysis of RIXS spectra establishes the dominant spin-flip nature of the mid-infrared peak in both cases. Hole doping leads to opposite behavior of the magnetic energy in the two materials. By fitting the data with an original Hubbard-based model for dynamic susceptibility, we find that t is comparable in the two materials while U is about twice larger in the nickelate. This finding accounts for the smaller magnetic bandwidth of nickelates and for its decrease upon doping., Comment: 7 pages with bibliography, 4 figures

In the last three decades, the Steered Response Power (SRP) method has been widely used for the task of Sound Source Localization (SSL), due to its satisfactory localization performance on moderately reverberant and noisy scenarios. Many works have analyzed and extended the original SRP method to reduce its computational cost, to allow it to locate multiple sources, or to improve its performance in adverse environments. In this work, we review over 200 papers on the SRP method and its variants, with emphasis on the SRP-PHAT method. We also present eXtensible-SRP, or X-SRP, a generalized and modularized version of the SRP algorithm which allows the reviewed extensions to be implemented. We provide a Python implementation of the algorithm which includes selected extensions from the literature.

Heterostructures from complex oxides allow one to combine various electronic and magnetic orders as to induce new quantum states. A prominent example is the coupling between superconducting and magnetic orders in multilayers from high-Tc cuprates and manganites. A key role is played here by the interfacial CuO2 layer whose distinct properties remain to be fully understood. Here, we study with resonant inelastic X-ray scattering (RIXS) the magnon excitations of this interfacial CuO2 layer. In particular, we show that the underlying antiferromagnetic exchange interaction at the interface is strongly suppressed to J ~ 70 meV, as compared to J ~ 130 meV for the CuO2 layers away from the interface. Moreover, we observe an anomalous momentum dependence of the intensity of the interfacial magnon mode and show that it suggests that the antiferromagnetic order is accompanied by a particular kind of orbital order that yields a so-called altermagnetic state. Such a two-dimensional altermagnet has recently been predicted to enable new spintronic applications and superconducting proximity effects.

We show that chimpanzee behaviour understanding from camera traps can be enhanced by providing visual architectures with access to an embedding of text descriptions that detail species behaviours. In particular, we present a vision-language model which employs multi-modal decoding of visual features extracted directly from camera trap videos to process query tokens representing behaviours and output class predictions. Query tokens are initialised using a standardised ethogram of chimpanzee behaviour, rather than using random or name-based initialisations. In addition, the effect of initialising query tokens using a masked language model fine-tuned on a text corpus of known behavioural patterns is explored. We evaluate our system on the PanAf500 and PanAf20K datasets and demonstrate the performance benefits of our multi-modal decoding approach and query initialisation strategy on multi-class and multi-label recognition tasks, respectively. Results and ablations corroborate performance improvements. We achieve state-of-the-art performance over vision and vision-language models in top-1 accuracy (+6.34%) on PanAf500 and overall (+1.1%) and tail-class (+2.26%) mean average precision on PanAf20K. We share complete source code and network weights for full reproducibility of results and easy utilisation.

The intimate correlation between the local atomic arrangement and electronic states in Li-ion battery cathode materials plays a crucial role in determining their electrochemical properties, including capacity, cycling stability, and rate capability. Despite almost 30 years of research efforts on high performance cathodes based on Ni rich layered oxides, there is still no consensus on LiNiO2 local atomic and electronic structure. Ni sites could be either Jahn-Teller distorted or bond disproportionated and the role of Ni and oxygen in the charge compensation mechanism remains unclear. In this study, we compare the local and electronic structure of LiNiO2 and NaNiO2, a long-range Jahn-Teller system, using a novel approach which aims at correlating the results from bulk spectroscopy techniques, particularly under operando conditions, obtained on standard samples to ensure sample interoperability and enhance the reliability and robustness of our results. Despite being a site-selective and local technique, XAS is unable to discriminate between the proposed scenarios, as confirmed also by theoretical calculations. On the contrary, Raman spectroscopy show local structural differences between monoclinic distorted NaNiO2 and rhombohedral LiNiO2. Additionally, HAXPES confirms the presence of multiple formal oxidation states for Ni, and RIXS data provides evidence of 3d8 states, confirming the negative charge transfer character of Ni and some degree of bond disproportionation in LiNiO2. Regarding the charge compensation mechanism, XRS and RIXS support the participation of oxygen holes in the redox activity, while Raman spectroscopy does not detect molecular oxygen. By combing several high-fidelity spectroscopy datasets, this study shows the value of correlative characterization workflows to provide insights into complex structural-electrochemical relationships.

This paper details the first application of a software tagging algorithm to reduce radon-induced backgrounds in liquid noble element time projection chambers, such as XENON1T and XENONnT. The convection velocity field in XENON1T was mapped out using $^{222}\text{Rn}$ and $^{218}\text{Po}$ events, and the root-mean-square convection speed was measured to be $0.30 \pm 0.01$ cm/s. Given this velocity field, $^{214}\text{Pb}$ background events can be tagged when they are followed by $^{214}\text{Bi}$ and $^{214}\text{Po}$ decays, or preceded by $^{218}\text{Po}$ decays. This was achieved by evolving a point cloud in the direction of a measured convection velocity field, and searching for $^{214}\text{Bi}$ and $^{214}\text{Po}$ decays or $^{218}\text{Po}$ decays within a volume defined by the point cloud. In XENON1T, this tagging system achieved a $^{214}\text{Pb}$ background reduction of $6.2^{+0.4}_{-0.9}\%$ with an exposure loss of $1.8\pm 0.2 \%$, despite the timescales of convection being smaller than the relevant decay times. We show that the performance can be improved in XENONnT, and that the performance of such a software-tagging approach can be expected to be further improved in a diffusion-limited scenario. Finally, a similar method might be useful to tag the cosmogenic $^{137}\text{Xe}$ background, which is relevant to the search for neutrinoless double-beta decay., Comment: 17 pages, 19 figures

Steered Response Power (SRP) is a widely used method for the task of sound source localization using microphone arrays, showing satisfactory localization performance on many practical scenarios. However, its performance is diminished under highly reverberant environments. Although Deep Neural Networks (DNNs) have been previously proposed to overcome this limitation, most are trained for a specific number of microphones with fixed spatial coordinates. This restricts their practical application on scenarios frequently observed in wireless acoustic sensor networks, where each application has an ad-hoc microphone topology. We propose Neural-SRP, a DNN which combines the flexibility of SRP with the performance gains of DNNs. We train our network using simulated data and transfer learning, and evaluate our approach on recorded and simulated data. Results verify that Neural-SRP's localization performance significantly outperforms the baselines., Comment: Presented at Asilomar Conference on Signals, Systems, and Computers

Studies have shown that in noisy acoustic environments, providing binaural signals to the user of an assistive listening device may improve speech intelligibility and spatial awareness. This paper presents a binaural speech enhancement method using a complex convolutional neural network with an encoder-decoder architecture and a complex multi-head attention transformer. The model is trained to estimate individual complex ratio masks in the time-frequency domain for the left and right-ear channels of binaural hearing devices. The model is trained using a novel loss function that incorporates the preservation of spatial information along with speech intelligibility improvement and noise reduction. Simulation results for acoustic scenarios with a single target speaker and isotropic noise of various types show that the proposed method improves the estimated binaural speech intelligibility and preserves the binaural cues better in comparison with several baseline algorithms., Comment: Accepted to ICASSP 2024

Strongly-correlated transition-metal oxides are widely known for their various exotic phenomena. This is exemplified by rare-earth nickelates such as LaNiO$_{3}$, which possess intimate interconnections between their electronic, spin, and lattice degrees of freedom. Their properties can be further enhanced by pairing them in hybrid heterostructures, which can lead to hidden phases and emergent phenomena. An important example is the LaNiO$_{3}$/LaTiO$_{3}$ superlattice, where an interlayer electron transfer has been observed from LaTiO$_{3}$ into LaNiO$_{3}$ leading to a high-spin state. However, macroscopic emergence of magnetic order associated with this high-spin state has so far not been observed. Here, by using muon spin rotation, x-ray absorption, and resonant inelastic x-ray scattering, we present direct evidence of an emergent antiferromagnetic order with high magnon energy and exchange interactions at the LaNiO$_{3}$/LaTiO$_{3}$ interface. As the magnetism is purely interfacial, a single LaNiO$_{3}$/LaTiO$_{3}$ interface can essentially behave as an atomically thin strongly-correlated quasi-two-dimensional antiferromagnet, potentially allowing its technological utilisation in advanced spintronic devices. Furthermore, its strong quasi-two-dimensional magnetic correlations, orbitally-polarized planar ligand holes, and layered superlattice design make its electronic, magnetic, and lattice configurations resemble the precursor states of superconducting cuprates and nickelates, but with an $S \rightarrow 1$ spin state instead., Comment: 41 pages, 14 figures

The multi-staged XENON program at INFN Laboratori Nazionali del Gran Sasso aims to detect dark matter with two-phase liquid xenon time projection chambers of increasing size and sensitivity. The XENONnT experiment is the latest detector in the program, planned to be an upgrade of its predecessor XENON1T. It features an active target of 5.9 tonnes of cryogenic liquid xenon (8.5 tonnes total mass in cryostat). The experiment is expected to extend the sensitivity to WIMP dark matter by more than an order of magnitude compared to XENON1T, thanks to the larger active mass and the significantly reduced background, improved by novel systems such as a radon removal plant and a neutron veto. This article describes the XENONnT experiment and its sub-systems in detail and reports on the detector performance during the first science run., Comment: 32 pages, 19 figures

The effects of confinement on electron transport in GaN have been studied via an ensemble Monte-Carlo code. Excellent agreement is obtained with experimental data from the literature up to moderate fields. In agreement with experimental results, negative-differential-conductivity is not observed in velocity-field curves. The reasons for this are discussed in detail. The dynamics of the non-equilibrium confined electron-LO phonon system is studied via a bulk phonon spectrum. In contrast to the bulk electron case hot or non-equilibrium phonons do not play a significant role in determining the transport properties. This is explained via the energy and momentum conservation rules for the polar optical phonon scattering. In addition, impact ionization is shown to be insignificant for the applied fields considered., Comment: 5 pages, 6 figures

Given a semigroup $S$, for each Green's relation $\mathcal{K}\in\{\mathcal{L},\mathcal{R},\mathcal{J},\mathcal{H}\}$ on $S,$ the $\mathcal{K}$-height of $S,$ denoted by $H_{\mathcal{K}}(S),$ is the height of the poset of $\mathcal{K}$-classes of $S.$ More precisely, if there is a finite bound on the sizes of chains of $\mathcal{K}$-classes of $S,$ then $H_{\mathcal{K}}(S)$ is defined as the maximum size of such a chain; otherwise, we say that $S$ has infinite $\mathcal{K}$-height. We discuss the relationships between these four $\mathcal{K}$-heights. The main results concern the class of stable semigroups, which includes all finite semigroups. In particular, we prove that a stable semigroup has finite $\mathcal{L}$-height if and only if it has finite $\mathcal{R}$-height if and only if it has finite $\mathcal{J}$-height. In fact, for a stable semigroup $S,$ if $H_{\mathcal{L}}(S)=n$ then $H_{\mathcal{R}}(S)\leq2^n-1$ and $H_{\mathcal{J}}(S)\leq2^n-1,$ and we exhibit a family of examples to prove that these bounds are sharp. Furthermore, we prove that if $2\leq H_{\mathcal{L}}(S)<\infty$ and $2\leq H_{\mathcal{R}}(S)<\infty,$ then $H_{\mathcal{J}}(S)\leq H_{\mathcal{L}}(S)+H_{\mathcal{R}}(S)-2.$ We also show that for each $n\in\mathbb{N}$ there exists a semigroup $S$ such that $H_{\mathcal{L}}(S)=H_{\mathcal{R}}(S)=2^n+n-3$ and $H_{\mathcal{J}}(S)=2^{n+1}-4.$ By way of contrast, we prove that for a regular semigroup the $\mathcal{L}$-, $\mathcal{R}$- and $\mathcal{H}$-heights coincide with each other, and are greater or equal to the $\mathcal{J}$-height. Moreover, in a stable, regular semigroup the $\mathcal{L}$-, $\mathcal{R}$-, $\mathcal{H}$- and $\mathcal{J}$-heights are all equal.

Van der Waals materials provide a versatile toolbox for the emergence of new quantum phenomena and the fabrication of functional heterostructures. Among them, the trihalide VI3 stands out for its unique magnetic and structural landscape. Here we investigate the spin and orbital magnetic degrees of freedom in the layered ferromagnet VI3 by means of temperature-dependent x-ray absorption spectroscopy and x-ray magnetic circular and linear dichroism. We detect localized electronic states and reduced magnetic dimensionality, due to electronic correlations. We furthermore provide experimental evidence of (a) an unquenched orbital magnetic moment (up to 0.66(7)) in the ferromagnetic state, and (b) an instability of the orbital moment in proximity of the spin reorientation transition. Our results support a coherent picture where electronic correlations give rise to a strong magnetic anisotropy and a large orbital moment, and establish VI3 as a prime candidate for the study of orbital quantum effects.

We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across ~20,000 camera trap videos of chimpanzees and gorillas collected at 14 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts., Comment: Accepted at IJCV

A monoid $S$ is right coherent if every finitely generated subact of every finitely presented right $S$-act itself has a finite presentation; it is weakly right coherent if every finitely generated right ideal of $S$ has a finite presentation. We show that full and partial transformation monoids, symmetric inverse monoids and partition monoids over an infinite set are all weakly right coherent, but that none of them is right coherent. Left coherency and weak left coherency are defined dually, and the corresponding results hold for these properties. In order to prove the non-coherency results, we give a presentation of an inverse semigroup which does not embed into any left or right coherent monoid., Comment: 10 pages