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Low- and intermediate-mass stars evolve through the asymptotic giant branch (AGB), when an efficient mass-loss process removes a significant fraction of their initial mass. A substantial increase in the mass-loss rate at the end of the AGB is observed for at least some stars for unknown reasons. This creates post-AGB objects that are completely enshrouded in thick dusty envelopes and might be associated with binary interactions. We observed the $J=2-1$ line of $^{13}$CO, C$^{17}$O, and C$^{18}$O with the Atacama Large Millimeter / submillimeter Array (ALMA) towards six obscured post-AGB stars (four C-rich and two O-rich sources) to constrain the properties of their circumstellar envelopes, recent mass-loss histories, and initial mass of the central stars. Based on the inferred $^{17}$O/$^{18}$O isotopic ratios, we find all stars to have relatively low initial masses ($< 2~M_\odot$) contrary to suggestions in the literature of higher masses for some sources. We infer a mass for HD~187885 $\sim 1.15~M_\odot$, which is relatively low for a carbon star. For all but one source (GLMP~950), we observe kinematic components with velocities $\gtrsim 30$~km~s$^{-1}$, which are faster than typical AGB wind expansion velocities. For most sources, these higher-velocity outflows display point-symmetric morphologies. The case of Hen~3-1475 is particularly spectacular, with the high-velocity molecular outflow interleaved with the high-velocity outflow of ionised gas observed at optical wavelengths. Based on the size of the emission regions of the slow components of the outflows, we derive typical kinematic ages associated with the C$^{18}$O~$J=2-1$ emission $\lesssim 1500$~years and obtain relatively high associated mass-loss rates ($\gtrsim10^{-4}~M_\odot~{\rm yr}^{-1}$). The sources with known spectral types are found to have evolved faster than expected based on stellar evolutionary models., Comment: Accepted for publication in Astronomy & Astrophysics

Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. We demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.

The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast materials databases. In this study, we developed an approach based on deep learning (DL) to predict new superconducting materials. We have synthesized a compound derived from our DL network and confirmed its superconducting properties in agreement with our prediction. Our approach is also compared to previous work based on random forests (RFs). In particular, RFs require knowledge of the chem-ical properties of the compound, while our neural net inputs depend solely on the chemical composition. With the help of hints from our network, we discover a new ternary compound $\textrm{Mo}_{20}\textrm{Re}_{6}\textrm{Si}_{4}$, which becomes superconducting below 5.4 K. We further discuss the existing limitations and challenges associated with using AI to predict and, along with potential future research directions., Comment: 14 pages + 2 appendices + references. EPJ submission

Experiments on quantum materials have uncovered many interesting quantum phases ranging from superconductivity to a variety of topological quantum matter including the recently observed fractional quantum anomalous Hall insulators. The findings have come in parallel with the development of approaches to probe the rich excitations inherent in such systems. In contrast to observing electrically charged excitations, the detection of charge-neutral electronic excitations in condensed matter remains difficult, though they are essential to understanding a large class of strongly correlated phases. Low-energy neutral excitations are especially important in characterizing unconventional phases featuring electron fractionalization, such as quantum spin liquids, spin ices, and insulators with neutral Fermi surfaces. In this perspective, we discuss searches for neutral fermionic, bosonic, or anyonic excitations in unconventional insulators, highlighting theoretical and experimental progress in probing excitonic insulators, new quantum spin liquid candidates and emergent correlated insulators based on two-dimensional layered crystals and moir\'e materials. We outline the promises and challenges in probing and utilizing quantum insulators, and discuss exciting new opportunities for future advancements offered by ideas rooted in next-generation quantum materials, devices, and experimental schemes., Comment: 23 pages, 4 figures; A Perspective/Review Article published in Nature

Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aeronautical industries have recently developed a roadmap for concepts of design assurance and integration of neural network-related technologies in the aeronautical sector. This paper aims to contribute to this paradigm of AI-based certification in the context of supervised learning, by outlining a complete validation pipeline that integrates deep learning, optimization and statistical methods. This pipeline is composed by a directed graphical model of ten steps. Each of these steps is addressed by a merging key concepts from different contributing disciplines (from machine learning or optimization to statistics) and adapting them to an industrial scenario, as well as by developing computationally efficient algorithmic solutions. We illustrate the application of this pipeline in a realistic supervised problem arising in aerostructural design: predicting the likelikood of different stress-related failure modes during different airflight maneuvers based on a (large) set of features characterising the aircraft internal loads and geometric parameters., Comment: 38 pages, 17 figures

The synthesis of inorganic crystalline materials is essential for modern technology, especially in quantum materials development. However, designing efficient synthesis workflows remains a significant challenge due to the precise experimental conditions and extensive trial and error. Here, we present a framework using large language models (LLMs) to predict synthesis pathways for inorganic materials, including quantum materials. Our framework contains three models: LHS2RHS, predicting products from reactants; RHS2LHS, predicting reactants from products; and TGT2CEQ, generating full chemical equations for target compounds. Fine-tuned on a text-mined synthesis database, our model raises accuracy from under 40% with pretrained models, to under 80% using conventional fine-tuning, and further to around 90% with our proposed generalized Tanimoto similarity, while maintaining robust to additional synthesis steps. Our model further demonstrates comparable performance across materials with varying degrees of quantumness quantified using quantum weight, indicating that LLMs offer a powerful tool to predict balanced chemical equations for quantum materials discovery., Comment: 66 pages total, 6 main figures + 3 supplementary figures

La$_3$Ni$_2$O$_7$ exists in two polymorphs: an unconventional structure with alternating layers of single- and triple-layered nickel-oxygen octahedra, and a classical double-layered Ruddlesden-Popper phase. In this study, we report the growth of single crystals of classical double-layered La$_3$Ni$_2$O$_7$ using the floating zone method. Structural characterization under pressures up to 15.4 GPa reveals a gradual transition from orthorhombic to tetragonal symmetry near 12 GPa. Additionally, we present pressure and field-dependent electrical resistance measurements under pressures as high as 27.4 GPa, from which we construct a phase diagram., Comment: 13+5 pages, 4+2 figures

3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to distinct application scenarios. These assumptions limit their use when acquisition conditions, such as the subject's distance from the camera or the camera's characteristics, are different than expected, as typically happens in video surveillance. Additionally, 3DFR algorithms follow various strategies to address the reconstruction of a 3D shape from 2D data, such as statistical model fitting, photometric stereo, or deep learning. In the present study, we explore the application of three 3DFR algorithms representative of the SOTA, employing each one as the template set generator for a face verification system. The scores provided by each system are combined by score-level fusion. We show that the complementarity induced by different 3DFR algorithms improves performance when tests are conducted at never-seen-before distances from the camera and camera characteristics (cross-distance and cross-camera settings), thus encouraging further investigations on multiple 3DFR-based approaches., Comment: Accepted at T-CAP - Towards a Complete Analysis of People: Fine-grained Understanding for Real-World Applications, workshop in conjunction with the 18th European Conference on Computer Vision ECCV 2024

Ensuring content compliance with community guidelines is crucial for maintaining healthy online social environments. However, traditional human-based compliance checking struggles with scaling due to the increasing volume of user-generated content and a limited number of moderators. Recent advancements in Natural Language Understanding demonstrated by Large Language Models unlock new opportunities for automated content compliance verification. This work evaluates six AI-agents built on Open-LLMs for automated rule compliance checking in Decentralized Social Networks, a challenging environment due to heterogeneous community scopes and rules. Analyzing over 50,000 posts from hundreds of Mastodon servers, we find that AI-agents effectively detect non-compliant content, grasp linguistic subtleties, and adapt to diverse community contexts. Most agents also show high inter-rater reliability and consistency in score justification and suggestions for compliance. Human-based evaluation with domain experts confirmed the agents' reliability and usefulness, rendering them promising tools for semi-automated or human-in-the-loop content moderation systems.

Non-magnetic FeNb$_3$Se$_{10}$ has been demonstrated to be an insulator at ambient pressure through both theoretical calculations and experimental measurements and it does not host topological surface states. Here we show that on the application of pressure, FeNb$_3$Se$_{10}$ transitions to a metallic state at around 3.0 GPa. With a further increase in pressure, its resistivity becomes independent of both temperature and pressure. Its crystal structure is maintained to at least 4.4 GPa., Comment: 20 pages, 5 figures

The lifetime of superconducting qubits is limited by dielectric loss, and a major source of dielectric loss is the native oxide present at the surface of the superconducting metal. Specifically, tantalum-based superconducting qubits have been demonstrated with record lifetimes, but a major source of loss is the presence of two-level systems (TLSs) in the surface tantalum oxide. Here, we demonstrate a strategy for avoiding oxide formation by encapsulating the tantalum with noble metals that do not form native oxide. By depositing a few nanometers of Au or AuPd alloy before breaking vacuum, we completely suppress tantalum oxide formation. Microwave loss measurements of superconducting resonators reveal that the noble metal is proximitized, with a superconducting gap over 80% of the bare tantalum at thicknesses where the oxide is fully suppressed. We find that losses in resonators fabricated by subtractive etching are dominated by oxides on the sidewalls, suggesting total surface encapsulation by additive fabrication as a promising strategy for eliminating surface oxide TLS loss in superconducting qubits.

We consider a class of non-integrable 2D Ising models obtained by perturbing the nearest-neighbor model via a weak, finite range potential which preserves translation and spin-flip symmetry, and we study its critical theory in the half-plane. We prove that the leading order long-distance behavior of the correlation functions for spins on the boundary is the same as for the nearest-neighbor model, up to an analytic multiplicative renormalization constant. In particular, the scaling limit is the Pfaffian of an explicit matrix. The proof is based on an exact representation of the generating function of correlations in terms of a Grassmann integral and on a multiscale analysis thereof, generalizing previous results to include observables located on the boundary., Comment: 59 pages, 9 figures

The layered honeycomb material BaCo$_2$(AsO$_4$)$_2$ (BCAO) is of topical interest because its magnetic state is related to that of the Kitaev magnet $\alpha$-RuCl$_3$. Using thermal transport to probe how magnetic excitations interact with phonons in the magnetically disordered regime, we have uncovered an unusually large enhancement of the thermal conductivity $\kappa_{xx}$ in an in-plane magnetic field ${\bf H}$. Just above the N\'{e}el temperature $T_{\rm N}$, a field of 13 T increases $\kappa_{xx}$ by a factor $\sim 211$, much larger than reported previously in any magnetic insulator. Interestingly, $\kappa_{xx}(H,T)$ exhibits a scaling behavior in the entire magnetically disordered region that surrounds the ordered zigzag state. The ratio $\Delta \kappa_{xx}(H,T)/\kappa_{xx}(13,T)$, measured throughout the disordered region, collapses to a one-parameter scaling function ${\rm exp}(-1/gx)$ (where $x = \mu_{\rm B}B/k_{\rm B}T$ and $g$ is a constant)., Comment: 8 pages, 4 figures

In the Kitaev honeycomb model, spins coupled by strongly-frustrated anisotropic interactions do not order at low temperature but instead form a quantum spin liquid with spin fractionalization into Majorana fermions and static fluxes. The realization of such a model in crystalline materials could lead to major breakthroughs in understanding entangled quantum states, however achieving this in practice is a very challenging task. The recently synthesized honeycomb material RuI$_3$ shows no long-range magnetic order down to the lowest probed temperatures and has been theoretically proposed as a quantum spin liquid candidate material on the verge of an insulator to metal transition. Here we report a comprehensive study of the magnetic anisotropy in un-twinned single crystals via torque magnetometry and detect clear signatures of strongly anisotropic and frustrated magnetic interactions. We attribute the development of sawtooth and six-fold torque signal to strongly anisotropic, bond-dependent magnetic interactions by comparing to theoretical calculations. As a function of magnetic field strength at low temperatures, torque shows an unusual non-parabolic dependence suggestive of a proximity to a field-induced transition. Thus, RuI$_3$, without signatures of long-range magnetic order, displays key hallmarks of an exciting new candidate for extended Kitaev magnetism with enhanced quantum fluctuations., Comment: 8 pages, 4 figures

Kitaev materials often order magnetically at low temperatures due to the presence of non-Kitaev interactions. Torque magnetometry is a very sensitive technique for probing the magnetic anisotropy, which is critical in understanding the magnetic ground state. In this work, we report detailed single-crystal torque measurements in the proposed Kitaev candidate honeycomb magnet $\alpha$-RuBr$_3$, which displays zigzag order below 34 K. Based on angular-dependent torque studies in magnetic fields up to 16 T rotated in the plane normal to the honeycomb layers, we find an easy-plane anisotropy with a temperature dependence of the torque amplitude following closely the behaviour of the powder magnetic susceptibility. The torque for field rotated in the honeycomb plane has a clear six-fold periodicity with a saw-tooth shape, reflecting the three-fold symmetry of the crystal structure and stabilization of different zigzag domains depending on the field orientation, with a torque amplitude that follows an order parameter form inside the zigzag phase. By comparing experimental data with theoretical calculations we identify the relevant anisotropic interactions and the role of the competition between different zigzag domains in this candidate Kitaev material.

The significant progress in the development of Large Language Models has contributed to blurring the distinction between human and AI-generated text. The increasing pervasiveness of AI-generated text and the difficulty in detecting it poses new challenges for our society. In this paper, we tackle the problem of detecting and attributing AI-generated text by proposing WhosAI, a triplet-network contrastive learning framework designed to predict whether a given input text has been generated by humans or AI and to unveil the authorship of the text. Unlike most existing approaches, our proposed framework is conceived to learn semantic similarity representations from multiple generators at once, thus equally handling both detection and attribution tasks. Furthermore, WhosAI is model-agnostic and scalable to the release of new AI text-generation models by incorporating their generated instances into the embedding space learned by our framework. Experimental results on the TuringBench benchmark of 200K news articles show that our proposed framework achieves outstanding results in both the Turing Test and Authorship Attribution tasks, outperforming all the methods listed in the TuringBench benchmark leaderboards., Comment: Accepted for publication at the 27th European Conference on Artificial Intelligence (ECAI-2024)

Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion model by strategic masking of the denoised structure with a diffused constrained structure prior to each diffusion step to steer the generation toward constrained outputs. Furthermore, we mathematically prove that SCIGEN effectively performs conditional sampling from the original distribution, which is crucial for generating stable constrained materials. We generate eight million compounds using Archimedean lattices as prototype constraints, with over 10% surviving a multi-staged stability pre-screening. High-throughput density functional theory (DFT) on 26,000 survived compounds shows that over 50% passed structural optimization at the DFT level. Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates., Comment: 512 pages total, 4 main figures + 218 supplementary figures

The global proliferation of social media platforms has transformed political communication, making the study of online interactions between politicians and voters crucial for understanding contemporary political discourse. In this work, we examine the dynamics of political messaging and voter behavior on Twitter during the 2022 Italian general elections. Specifically, we focus on voters who changed their political preferences over time (swing voters), identifying significant patterns of migration and susceptibility to propaganda messages. Our analysis reveals that during election periods, the popularity of politicians increases, and there is a notable variation in the use of persuasive language techniques, including doubt, loaded language, appeals to values, and slogans. Swing voters are more vulnerable to these propaganda techniques compared to non-swing voters, with differences in vulnerability patterns across various types of political shifts. These findings highlight the nuanced impact of social media on political opinion in Italy.

Several recent high-impact studies leverage large hospital-owned electrocardiographic (ECG) databases to model and predict patient mortality. MIMIC-IV, released September 2023, is the first comparable public dataset and includes 800,000 ECGs from a U.S. hospital system. Previously, the largest public ECG dataset was Code-15, containing 345,000 ECGs collected during routine care in Brazil. These datasets now provide an excellent resource for a broader audience to explore ECG survival modeling. Here, we benchmark survival model performance on Code-15 and MIMIC-IV with two neural network architectures, compare four deep survival modeling approaches to Cox regressions trained on classifier outputs, and evaluate performance at one to ten years. Our results yield AUROC and concordance scores comparable to past work (circa 0.8) and reasonable AUPRC scores (MIMIC-IV: 0.4-0.5, Code-15: 0.05-0.13) considering the fraction of ECG samples linked to a mortality (MIMIC-IV: 27\%, Code-15: 4\%). When evaluating models on the opposite dataset, AUROC and concordance values drop by 0.1-0.15, which may be due to cohort differences. All code and results are made public., Comment: 9 pages plus appendix, 2 figures

Verbs form the backbone of language, providing the structure and meaning to sentences. Yet, their intricate semantic nuances pose a longstanding challenge. Understanding verb relations through the concept of lexical entailment is crucial for comprehending sentence meanings and grasping verb dynamics. This work investigates the capabilities of eight Large Language Models in recognizing lexical entailment relations among verbs through differently devised prompting strategies and zero-/few-shot settings over verb pairs from two lexical databases, namely WordNet and HyperLex. Our findings unveil that the models can tackle the lexical entailment recognition task with moderately good performance, although at varying degree of effectiveness and under different conditions. Also, utilizing few-shot prompting can enhance the models' performance. However, perfectly solving the task arises as an unmet challenge for all examined LLMs, which raises an emergence for further research developments on this topic., Comment: Accepted for publication at The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP-2024) - Findings

Long-distance quantum communication using quantum repeaters is an enabling technology for secure communication, distributed quantum computing and quantum-enhanced sensing and metrology. As a building block of quantum repeaters, spin-photon entanglement has been demonstrated with both atomic and solid-state qubits. However, previously demonstrated qubits with long spin coherence do not directly emit photons into the low-loss telecom band that is needed for long-distance communication. Here, we demonstrate spin-photon entanglement using a single Er$^{3+}$ ion in a solid-state crystal, integrated into a silicon nanophotonic circuit. Direct emission into the telecom band enables an entanglement rate of 1.48 Hz over 15.6 km of optical fiber, with a fidelity of 73(3)$\%$. This opens the door to large-scale quantum networks based on scalable nanophotonic devices and many spectrally multiplexed Er$^{3+}$ ions., Comment: Added references

The triangular-lattice system $\alpha$-RuI$_3$ is isostructural to the widely-studied $\alpha$-RuCl$_3$ compound which was identified as a potential Kitaev system but exhibits, instead of spin liquid behaviour, a magnetically ordered zig-zag ground state which sets in below 14~K. Here we show experimentally that, in contrast, the spins in $\alpha$-RuI$_3$ remain dynamic down to at least 50~mK. We study the spin dynamics using muon-spin relaxation methods and determine the presence of low-frequency fluctuations which are characteristic of a two-dimensional system., Comment: 5 pages, 5 figures

The detection of Landau-level-like energy structures near the chemical potential of an insulator is essential to the search for a class of correlated electronic matter hosting charge-neutral fermions and Fermi surfaces, a long-proposed concept that remains elusive experimentally. Here we introduce and demonstrate that the magneto-thermoelectric response of a quantum insulator can reveal critical information not available via other approaches. We report large quantum oscillations (QOs) in the Seebeck response of the hole-doped insulating state of monolayer tungsten ditelluride (WTe2) in magnetic fields. The QOs remarkably undergo sign-changes as the field is swept, mimicking those in metals with Landau quantization. The sign-change in the thermoelectric response directly implies the presence of a field-induced Landau-level-like structure at the chemical potential of the insulator. Our results reinforce WTe2 as a platform for investigating insulating Landau levels and mobile neutral fermions in two-dimensional insulators., Comment: 19 pages, 9 figures

Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data. In this study, we introduce Cross-Care, the first benchmark framework dedicated to assessing biases and real world knowledge in LLMs, specifically focusing on the representation of disease prevalence across diverse demographic groups. We systematically evaluate how demographic biases embedded in pre-training corpora like $ThePile$ influence the outputs of LLMs. We expose and quantify discrepancies by juxtaposing these biases against actual disease prevalences in various U.S. demographic groups. Our results highlight substantial misalignment between LLM representation of disease prevalence and real disease prevalence rates across demographic subgroups, indicating a pronounced risk of bias propagation and a lack of real-world grounding for medical applications of LLMs. Furthermore, we observe that various alignment methods minimally resolve inconsistencies in the models' representation of disease prevalence across different languages. For further exploration and analysis, we make all data and a data visualization tool available at: www.crosscare.net., Comment: Submitted for review, data visualization tool available at: www.crosscare.net

Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on average error over the entire dataset as a criterion to select the parents, which can lead to an information loss due to aggregation of all test cases. Under epsilon-lexicase selection, the population goes to a selection pool that is iteratively reduced by using each test individually, discarding individuals with an error higher than the elite error plus the median absolute deviation (MAD) of errors for that particular test case. In an attempt to better capture differences in performance of individuals on cases, we propose a new criteria that splits errors into two partitions that minimize the total variance within partitions. Our method was embedded into the FEAT symbolic regression algorithm, and evaluated with the SRBench framework, containing 122 black-box synthetic and real-world regression problems. The empirical results show a better performance of our approach compared to traditional epsilon-lexicase selection in the real-world datasets while showing equivalent performance on the synthetic dataset., Comment: 9 pages, 13 figures, accepted in Genetic and Evolutionary Computation Conference (GECCO '24)

Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point out that the models are often overly complex due to redundant operations, introns, and bloat that arise during the iterative process, and can hinder the search with repeated exploration of bloated segments. Applying a fast heuristic algebraic simplification may not fully simplify the expression and exact methods can be infeasible depending on size or complexity of the expressions. We propose a novel agnostic simplification and bloat control for SR employing an efficient memoization with locality-sensitive hashing (LHS). The idea is that expressions and their sub-expressions traversed during the iterative simplification process are stored in a dictionary using LHS, enabling efficient retrieval of similar structures. We iterate through the expression, replacing subtrees with others of same hash if they result in a smaller expression. Empirical results shows that applying this simplification during evolution performs equal or better than without simplification in minimization of error, significantly reducing the number of nonlinear functions. This technique can learn simplification rules that work in general or for a specific problem, and improves convergence while reducing model complexity., Comment: 9 pages, 10 figures, accepted to GECCO-24

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussed., Comment: The 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024)

Co-based materials have recently been explored due to potential to realise complex bond-dependent anisotropic magnetism. Prominent examples include Na$_2$Co$_2$TeO$_6$, BaCo$_2$(AsO$_4$)$_2$, Na$_2$BaCo(PO$_4$)$_2$, and CoX$_2$ (X = Cl, Br, I). In order to provide insight into the magnetic interactions in these compounds, we make a comparative analysis of their local crystal electric field excitations spectra via Raman scattering measurements. Combining these measurements with theoretical analysis confirms the validity of $j_{\rm eff} = 1/2$ single-ion ground states for all compounds, and provides accurate experimental estimates of the local crystal distortions, which play a prominent role in the magnetic couplings between spin-orbital coupled Co moments., Comment: 11 pages, 7 figures

A supersolid is a quantum-entangled state of matter that exhibits the dual characteristics of superfluidity and solidity. \red{While theoretical studies have predicted that hard-core bosons with repulsive interactions on a triangular lattice can host a supersolid phase, experimental validation has remained elusive. Leveraging an exact mapping between bosons and spins, we investigate the supersolid phase in a spin-$\frac{1}{2}$ triangular-lattice antiferromagnet K$_2$Co(SeO$_3$)$_2$.} Here, we present the magnetic phase diagram and neutron scattering results for K$_2$Co(SeO$_3$)$_2$, which features nearest-neighbor Ising-like interactions with $J_z = 2.96(2)$ meV and $J_{\perp} = 0.21(3)$ meV. In zero field, neutron spectroscopy reveals the gradual development of a quasi-two-dimensional $\sqrt{3}\times\sqrt{3}$ magnetic order with Z$_3$ translational symmetry breaking (solidity) below 15 K. \red{At temperatures below 0.3 K, the fully developed supersolid phase is evidenced by the coexistence of a gapless Goldstone mode arising from broken U$(1)$ spin rotational symmetry (superfluidity), and a gapped pseudo-Goldstone mode associated with lifted accidental XY degeneracy (solidity).} In $\bf c$-axis-oriented magnetic fields 1.1 T $<$ $B$ $<$ 21 T, a prominent 1/3 magnetization plateau phase emerges, accompanied by a \red{plausible} high-field supersolid phase (17 T $<$ $B$ $<$ 21 T). Our results establish K$_2$Co(SeO$_3$)$_2$as an exceptional realization of a spin-$\frac{1}{2}$ triangular-lattice quantum Ising magnet, document its magnetic phase diagram featuring two supersolid phases, and uncover spectroscopic \red{signatures} of zero-field supersolidity in a triangular lattice antiferromagnet., Comment: 13 pages, 5 figures

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.

Misleading newsletters can shape individuals' perceptions, and pose a threat to societies; as we witnessed by lowering the severity of follow-up stay-at-home orders and burdening a significant challenge to the fight against COVID-19. In this research, we study (mis)information spreading, reanalyzing behavioral data on online sharing, and analyzing decision-making mechanisms using the Drift Diffusion Model (DDM). We find that subjects display an increased instinctive inclination towards sharing misleading news, but rational thinking significantly curbs this reaction, especially for more cautious and older individuals. On top of network structures with similar characteristics as X, Mastodon, and Facebook, we use an agent-based model to expand this individual knowledge to a large scale where individuals are exposed to (mis)information through friends and share (or not) content with probabilities driven by DDM. We found that the natural shape of these social online networks provides a fertile ground for any news to rapidly become viral. Yet we have found that, for the case of X, limiting the number of followers of the most connected users proves to be an appropriate and feasible containment strategy., Comment: 45 pages and 22 figures (taking into account the Supplementary Information)

The emergence of unveiling human-like behaviors in Large Language Models (LLMs) has led to a closer connection between NLP and human psychology. Scholars have been studying the inherent personalities exhibited by LLMs and attempting to incorporate human traits and behaviors into them. However, these efforts have primarily focused on commercially-licensed LLMs, neglecting the widespread use and notable advancements seen in Open LLMs. This work aims to address this gap by employing a set of 12 LLM Agents based on the most representative Open models and subject them to a series of assessments concerning the Myers-Briggs Type Indicator (MBTI) test and the Big Five Inventory (BFI) test. Our approach involves evaluating the intrinsic personality traits of Open LLM agents and determining the extent to which these agents can mimic human personalities when conditioned by specific personalities and roles. Our findings unveil that $(i)$ each Open LLM agent showcases distinct human personalities; $(ii)$ personality-conditioned prompting produces varying effects on the agents, with only few successfully mirroring the imposed personality, while most of them being ``closed-minded'' (i.e., they retain their intrinsic traits); and $(iii)$ combining role and personality conditioning can enhance the agents' ability to mimic human personalities. Our work represents a step up in understanding the dense relationship between NLP and human psychology through the lens of Open LLMs., Comment: Enhanced methodology and evaluation based on BFI in addition to MBTI, with expanded set of LLM agents. Author list changed w.r.t. the previous version (v1), see Acknowledgements

Er3+ plus V5+, and Er3+ plus Nb5+ co-doped CaWO4, formulas Ca1-xErxW1-xMxO4, were synthesized in air by a conventional solid-state method. A color change from white to pink was observed in the final products. An equal fraction of dopants was employed to obtain charge neutrality, and the limits of the solubility for our conditions are lower than x=0.15. The magnetic susceptibility data shows that that the magnetic coupling becomes increasingly antiferromagnetic with increasing Er3+content. The Curie-Weiss fit and isothermal magnetization imply that different degrees of spin-orbit coupling appear to be present in the two doping systems. No transitions were observed in the heat capacity data above 0.4 K.

In continuation of research into RuCl$_3$ and RuBr$_3$ as potential quantum spin liquids, a phase with unique magnetic order characterised by long-range quantum entanglement and fractionalised excitations, the compound RuI$_3$ has been recently synthesised. Here, we show RuI$_3$ is a moderately correlated metal with two bands crossing the Fermi level, implying the absence of any quantum spin liquids phase. We find that the Fermi surface as measured or calculated for a 2D ($k_\text{x},k_\text{y}$) slice at any $k_\text{z}$ lacks mirror symmetry, i.e. is pseudochiral. We link this phenomenon to the ABC stacking in the R$\bar{3}$ space group of $\alpha$-RuI$_3$, which is achiral but lacks any mirror or glide symmetries. We further provide a formal framework for understanding when such a pseudochiral electronic structure may be observed.

We report the high-pressure synthesis (6 GPa, 1200 $^{\circ}$C) and ambient pressure characterization of hexagonal HP-BaCoO$_3$. The material (with the 2H crystal structure) has a short intrachain Co-Co distance of about 2.07 $\text{\r{A}}$. Our magnetization investigation revealed robust diamagnetic behavior below approximately 130 K when exposed to weak applied magnetic fields (10 Oe) and a distinct half-levitation phenomenon below that temperature, such as is often observed for superconductors. Its field-dependent magnetization profile, however, unveils the characteristics of ferromagnetism, marked by a substantial magnetic retentivity of 0.22(1) ${\mu}_B$/Co at a temperature of 2 K. Electrical resistivity measurements indicate that HP-BaCoO$_3$ is a ferromagnetic insulator, not a superconductor.

Centralized social media platforms are currently experiencing a shift in user engagement, drawing attention to alternative paradigms like Decentralized Online Social Networks (DOSNs). The rising popularity of DOSNs finds its root in the accessibility of open-source software, enabling anyone to create a new instance (i.e., server) and participate in a decentralized network known as Fediverse. Despite this growing momentum, there has been a lack of studies addressing the effect of positive and negative interactions among instances within DOSNs. This work aims to fill this gap by presenting a preliminary examination of instances' polarization in DOSNs, focusing on Mastodon -- the most widely recognized decentralized social media platform, boasting over 10M users and nearly 20K instances to date. Our results suggest that polarization in the Fediverse emerges in unique ways, influenced by the desire to foster a federated environment between instances, also facilitating the isolation of instances that may pose potential risks to the Fediverse.

Magnetic Er3+ is doped into the well-studied frustrated normal spinel ZnCr2O4. Various spectroscopies are employed to prove that Er3+ successfully enters the spinel to form ZnCr2-xErxO4 for x less than 0.005. The low levels of Er3+ doping possible nonetheless have a significant effect on the frustrated magnetism and the ordering that is seen near 12 K in the undoped material.