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The development of Kondo lattice coherence in UTe2 leads to the formation of a heavy Fermi liquid state from which superconductivity emerges at lower temperature. In Kondo lattice systems, the nuclear magnetic resonance (NMR) and muon Knight shift have proven to be particularly sensitive to the properties of the developing heavy-electron fluid. Here we report muon Knight shift measurements on high-quality UTe2 single crystals grown by a molten salt-flux method. Together with previous data from a single crystal grown by a chemical-vapor transport method, our results show the contribution of the heavy-electron liquid to the muon Knight shift increases below a crossover temperature T* ~ 30 K in accord with a universal scaling function of T/T* for heavy-fermion materials. An observed departure from this universal scaling below a temperature T ~ 12 K at certain muon stopping sites signifies a reversal of the Kondo hybridization and a relocalization of U 5f moments with an antiferromagnetic coupling. The preservation of universal scaling at a different muon site demonstrates a coexistence of itinerant and localized 5f electron states preceding the superconducting phase transition., Comment: 8 pages, 4 figures

Direct-ink writing leverages the rheological complexity of yield-stress fluids to construct complex geometries, particularly those with large gaps across internal structures. However, extensional rheological properties have rarely been considered in work that studies rheology-printability correlations. Here, we test our hypothesis that extensional properties correlate with drawability, a key indicator of printability that signifies speed robustness, printing resolution, and gap-spanning performance. We formulated cementitious suspensions using hydroxyapatite (HAp) particles, independently tuning them for yield stress and extensibility, two crucial rheological properties, and test-printed. To enhance extensibility, we incorporated hydroxypropyl methylcellulose as a polymeric modifier, but this enhancement may decrease as yield stress increases, presenting a challenge in materials design. We modulated particle interactions to achieve a wide range of yield stress and extensibility, allowing for rigorous testing of our hypothesis. This approach created inks with high extensibility and high yield stress, generally considered mutually exclusive properties. We evaluated correlations between drawability and key rheological properties, finding the strongest positive correlation with extensional failure strains (strain-to-break) rather than yield stress. We establish a bijective property-manufacturing relationship (one-on-one mapping of shear yield stress to buildability and extensional strain-to-break to drawability) by combining our findings on drawability with previous studies on buildability. This relationship provides a comprehensive framework for designing high-performance inks that can be self-supporting, capable of high-speed printing, and allow gap-spanning features.

Data-driven software is increasingly being used as a critical component of automated decision-support systems. Since this class of software learns its logic from historical data, it can encode or amplify discriminatory practices. Previous research on algorithmic fairness has focused on improving average-case fairness. On the other hand, fairness at the extreme ends of the spectrum, which often signifies lasting and impactful shifts in societal attitudes, has received significantly less emphasis. Leveraging the statistics of extreme value theory (EVT), we propose a novel fairness criterion called extreme counterfactual discrimination (ECD). This criterion estimates the worst-case amounts of disadvantage in outcomes for individuals solely based on their memberships in a protected group. Utilizing tools from search-based software engineering and generative AI, we present a randomized algorithm that samples a statistically significant set of points from the tail of ML outcome distributions even if the input dataset lacks a sufficient number of relevant samples. We conducted several experiments on four ML models (deep neural networks, logistic regression, and random forests) over 10 socially relevant tasks from the literature on algorithmic fairness. First, we evaluate the generative AI methods and find that they generate sufficient samples to infer valid EVT distribution in 95% of cases. Remarkably, we found that the prevalent bias mitigators reduce the average-case discrimination but increase the worst-case discrimination significantly in 5% of cases. We also observed that even the tail-aware mitigation algorithm -- MiniMax-Fairness -- increased the worst-case discrimination in 30% of cases. We propose a novel ECD-based mitigator that improves fairness in the tail in 90% of cases with no degradation of the average-case discrimination., Comment: In IEEE/ACM 47th International Conference on Software Engineering (ICSE'25)

Altermagnets are a novel platform to realize exotic electromagnetic properties distinct from those of conventional ferromagnets and antiferromagnets. We report results of micro-focused angle-resolved photoemission spectroscopy (ARPES) on RuO2, of which altermagnetic nature has been under fierce debate. We have elucidated the band structure of the (100), (110) and (101) surfaces of a bulk single crystal. We found that, irrespective of the surface orientation, the experimental band structures obtained by ARPES commonly show a semi-quantitative agreement with the bulk-band calculation for the nonmagnetic phase, but display a severe disagreement with that for the antiferromagnetic phase. Moreover, spin-resolved ARPES signifies a negligible spin polarization for the bulk bands. These results suggest the absence of antiferromagnetism and altermagnetic spin splitting. Furthermore, we identified a flat surface band and a dispersive one near the Fermi level at the (100)/(110) and (101) surfaces, respectively, both of which are attributed to the topological surface states associated with the bulk Dirac nodal lines. The present ARPES results suggest that the crystal-orientation-dependent topological surface/interface states need to be taken into account to properly capture the transport and catalytic characteristics of RuO2., Comment: 29 pages, 9 figures

Clustering large high-dimensional datasets with diverse variable is essential for extracting high-level latent information from these datasets. Here, we developed an unsupervised clustering algorithm, we call "Village-Net". Village-Net is specifically designed to effectively cluster high-dimension data without priori knowledge on the number of existing clusters. The algorithm operates in two phases: first, utilizing K-Means clustering, it divides the dataset into distinct subsets we refer to as "villages". Next, a weighted network is created, with each node representing a village, capturing their proximity relationships. To achieve optimal clustering, we process this network using a community detection algorithm called Walk-likelihood Community Finder (WLCF), a community detection algorithm developed by one of our team members. A salient feature of Village-Net Clustering is its ability to autonomously determine an optimal number of clusters for further analysis based on inherent characteristics of the data. We present extensive benchmarking on extant real-world datasets with known ground-truth labels to showcase its competitive performance, particularly in terms of the normalized mutual information (NMI) score, when compared to other state-of-the-art methods. The algorithm is computationally efficient, boasting a time complexity of O(N*k*d), where N signifies the number of instances, k represents the number of villages and d represents the dimension of the dataset, which makes it well suited for effectively handling large-scale datasets., Comment: Software available at https://villagenet.streamlit.app/

Safety is the main concern in the aviation industry, where even minor operational issues can lead to serious consequences. This study addresses the need for comprehensive aviation accident analysis by leveraging natural language processing (NLP) and advanced AI models to classify the phase of flight from unstructured aviation accident analysis narratives. The research aims to determine whether the phase of flight can be inferred from narratives of post-accident events using NLP techniques. The classification performance of various deep learning models was evaluated. For single RNN-based models, LSTM achieved an accuracy of 63%, precision 60%, and recall 61%. BiLSTM recorded an accuracy of 64%, precision 63%, and a recall of 64%. GRU exhibited balanced performance with an accuracy and recall of 60% and a precision of 63%. Joint RNN-based models further enhanced predictive capabilities. GRU-LSTM, LSTM-BiLSTM, and GRU-BiLSTM demonstrated accuracy rates of 62%, 67%, and 60%, respectively, showcasing the benefits of combining these architectures. To provide a comprehensive overview of model performance, single and combined models were compared in terms of the various metrics. These results underscore the models' capacity to classify the phase of flight from raw text narratives, equipping aviation industry stakeholders with valuable insights for proactive decision-making. Therefore, this research signifies a substantial advancement in the application of NLP and deep learning models to enhance aviation safety., Comment: Aviation Safety, Deep learning algorithms, Flight phase, NLP, ASN, and Classification

We report new magnetic heat capacity measurements of a high quality single crystal of the dipole-octupole pyrochlore Ce$_2$Hf$_2$O$_7$ down to a temperature of $T = 0.02$ K, a factor of three lower than those previously reported. These show a two-peaked structure, with a Schottky-like peak at $T_1 \sim 0.065$ K, similar to what is observed in its sister Ce-pyrochlores Ce$_2$Zr$_2$O$_7$ and Ce$_2$Sn$_2$O$_7$. However a second, sharper peak is observed at $T_2 \sim 0.025$ K, which signifies the entrance to its ground state, as even the most abrupt low-temperature extrapolation to $C_P=0$ at $T = 0$ K gives a full accounting of $R\ln(2)$ in entropy, associated with the well isolated pseudospin-1/2 doublet for Ce$^{3+}$ in this environment. The ground state could be conventionally ordered, although theory predicts a much larger anomaly in $C_P$, at much higher temperatures than the measured $T_2$, for expectations from an all-in all-out ground state of the nearest-neighbor XYZ Hamiltonian for Ce$_2$Hf$_2$O$_7$. The sharp low-temperature peak could also signify a cross-over from a classical to a quantum spin liquid regime. The diffuse magnetic neutron scattering observed from Ce$_2$Hf$_2$O$_7$ at low temperatures between $T_2$ and $T_1$ resembles that observed from Ce$_2$Zr$_2$O$_7$, which is well established as a $\pi$-flux quantum spin ice., Comment: Main Text (7 pages, 4 figures), Supplemental Material (15 pages, 17 figures)

Time-reversal symmetry breaking (TRSB) in magnetic topological insulators induces a Dirac gap in the topological surface state (TSS), leading to exotic phenomena such as the quantum anomalous Hall effect. Yet, the interplay between TRSB and topology in superconductors remains underexplored due to limited suitable materials. Here we employ zero-field muon spin relaxation ($\mu$SR) as a sensitive probe of TRSB to map out the electronic phase diagrams of iron-chalcogenide superconductors FeSe$_{1-x}$Te$_{x}$. For the Te composition $x=0.64$ with the highest superconducting transition temperature $T_{\rm c}=14.5$ K, which is known to host a TSS and Majorana zero modes within vortices, we detect spontaneous magnetic fields below $T_{\rm c}$ distinct from a magnetic order. This signifies a TRSB superconducting state in the bulk, revealing the convergence of unconventional TRSB superconductivity with topologically nontrivial electronic structures in FeSe$_{1-x}$Te$_{x}$. Given the relatively high $T_{\rm c}$ and the tunability of the Fermi level through chemical substitution, iron-chalcogenide superconductors offer an intriguing platform for investigating the synergy between topological superconductivity and TRSB., Comment: 20 pages, 4 figures