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We report the magnetic properties of the quantum triangular lattice antiferromagnet (TLAF) PrMgAl11O19 through magnetization and specific heat measurements. Strong magnetic anisotropy indicates the realization of an Ising-like magnetism in PrMgAl11O19 single crystal while no long-range magnetic ordering is realized down to 0.4 K. The splitting of the low-lying quasi-doublet into two singlets suggested by experimental data, is consistent with an effective pseudospin-1/2 scenario. The observed gapless excitations in zero field are attributed to induced quantum magnetism, they would be induced by magnetic interactions of an energy scale comparable with the splitting between the two singlets. Based on these results, we modeled the magnetic ground state of PrMgAl11O19 by a quantum Ising magnet with an intrinsic transverse field rather than a quantum spin liquid (QSL). In addition, our data show a non-monotonous response of the low-temperature specific heat to the external fields revealing a complex interplay between intrinsic and external magnetic fields.

The existence of high-$\alpha$ stars with inferred ages < 6 Gyr has been confirmed recently with large spectroscopic and photometric surveys. However, stellar mergers or binary interactions can induce properties associated with young ages, such as high mass, rapid rotation, or high activity, even in old populations. Literature studies have confirmed that at least some of these apparently young stars are old merger products. However, none have ruled out the possibility of genuinely young high-$\alpha$ stars. Because cool GKM dwarfs spin down, rapid rotation can be used to indicate youth. In this paper, we provide strong evidence that truly young high-$\alpha$ stars exist by studying high-$\alpha$ rotators in the Kepler and K2 field with abundance measurements from GALAH and APOGEE. After excluding close binaries using radial velocity (RV) measurements from Gaia DR3 and multi-epoch RVs from APOGEE, we find a total of 70 high-$\alpha$ rapid rotators with periods ~10-30 days, 29 of which have lithium measurements from GALAH, indicating that they have not gone through past mass transfer or stellar merger events. We identify 10 young high-$\alpha$ candidates with no signs of merger-induced mixing or close companions. One clear example is a G dwarf with a measurable rotation and an age of 1.98$^{+0.12}_{-0.28}$ Gyr that is likely a single star with multiple RV measurements from APOGEE, has significant lithium detection from GALAH (A(Li) = 1.79), and has no signs of planet engulfment., Comment: 21 pages, 13 figures, submitted to AJ

Vision transformers (ViT) have made substantial progress for classification tasks in computer vision. Recently, Gong et. al. '21, introduced attention-based modeling for several audio tasks. However, relatively unexplored is the use of a ViT for audio spoof detection task. We bridge this gap and introduce ViTs for this task. A vanilla baseline built on fine-tuning the SSAST (Gong et. al. '22) audio ViT model achieves sub-optimal equal error rates (EERs). To improve performance, we propose a novel attention-based contrastive learning framework (SSAST-CL) that uses cross-attention to aid the representation learning. Experiments show that our framework successfully disentangles the bonafide and spoof classes and helps learn better classifiers for the task. With appropriate data augmentations policy, a model trained on our framework achieves competitive performance on the ASVSpoof 2021 challenge. We provide comparisons and ablation studies to justify our claim., Comment: Proc. INTERSPEECH 2023

With the rapid growth in deepfake video content, we require improved and generalizable methods to detect them. Most existing detection methods either use uni-modal cues or rely on supervised training to capture the dissonance between the audio and visual modalities. While the former disregards the audio-visual correspondences entirely, the latter predominantly focuses on discerning audio-visual cues within the training corpus, thereby potentially overlooking correspondences that can help detect unseen deepfakes. We present Audio-Visual Feature Fusion (AVFF), a two-stage cross-modal learning method that explicitly captures the correspondence between the audio and visual modalities for improved deepfake detection. The first stage pursues representation learning via self-supervision on real videos to capture the intrinsic audio-visual correspondences. To extract rich cross-modal representations, we use contrastive learning and autoencoding objectives, and introduce a novel audio-visual complementary masking and feature fusion strategy. The learned representations are tuned in the second stage, where deepfake classification is pursued via supervised learning on both real and fake videos. Extensive experiments and analysis suggest that our novel representation learning paradigm is highly discriminative in nature. We report 98.6% accuracy and 99.1% AUC on the FakeAVCeleb dataset, outperforming the current audio-visual state-of-the-art by 14.9% and 9.9%, respectively., Comment: Accepted to CVPR 2024

The study of magnetic frustration in classical spin systems was motivated by the prediction and discovery of classical spin liquid states. These uncommon magnetic phases are characterized by a massive degeneracy of their ground state implying a finite magnetic entropy at zero temperature. While the classical spin liquid state was originally predicted in the Ising triangular lattice antiferromagnet in 1950, this state has never been experimentally observed in any triangular magnets. We report here the discovery of an electric analogue of classical spin liquids on a triangular lattice of uniaxial electric dipoles in EuAl12O19 . This new type of frustrated antipolar phase is characterized by a highly degenerate state at low temperature implying an absence of long-range antiferroelectric order, despite short-range antipolar correlations. Its dynamics are governed by a thermally activated process, slowing down upon cooling towards a complete freezing at zero temperature.

We report ferromagnetic ordering at $T_\mathrm {C}=1.3\,$K in the quasi-two dimensional magnet EuAl$_{12}$O$_{19}$ with large spins $S=7/2$. This ferromagnetic state was characterized by magnetization and specific heat measurements and the experimental results were compared with classical Monte Carlo simulations. They reveal a strong single ion anisotropy leading to an uniaxial spontaneous magnetization along the $c$ axis. Furthermore the application of a magnetic field in the hard magnetization plane $ab$ reduces the Curie temperature and it reaches zero temperature at a magnetic field of $\mu_\mathrm{0} H_c \approx 0.5\,$T.

Predicting the star formation rate (SFR) in galaxies is crucial to understand their evolution and morphology. To do so requires a fine understanding of how dense structures of gas are created and collapse. In that, turbulence and gravity play a major role. Within the gravo-turbulent framework, we assume that turbulence shapes the ISM, creating density fluctuations that, if gravitationally unstable, will collapse and form stars. The goal of this work is to quantify how different regimes of turbulence, characterized by the strength and compressibility of the driving, shape the density field. We are interested in the outcome in terms of SFR and how it compares with existing analytical models for the SFR. We run a series of hydrodynamical simulations of turbulent gas. The simulations are first conducted without gravity, so that the density and velocity are shaped by the turbulence driving. Gravity is then switched on, and the SFR is measured and compared with analytical models. The physics included in these simulations is very close to the one assumed in the classical gravo-turbulent SFR analytical models, which makes the comparison straightforward. We found that the existing analytical models convincingly agree with simulations at low Mach number, but we measure a much lower SFR in the simulation with a high Mach number. We develop, in a companion paper, an updated physically-motivated SFR model that reproduces well the inefficient high Mach regime of the simulations. Our work demonstrates that accurate estimations of the turbulent-driven replenishment time of dense structures and the dense gas spatial distribution are necessary to correctly predict the SFR in the high Mach regime. The inefficient high-Mach regime is a possible explanation for the low SFR found in dense and turbulent environments such as the centers of our Milky Way and other galaxies., Comment: 13 pages, 14 figures, accepted for publication in A&A

The star formation rate (SFR), the number of stars formed per unit of time, is a fundamental quantity in the evolution of the Universe. While turbulence is believed to play a crucial role in setting the SFR, the exact mechanism remains unclear. Turbulence promotes star formation by compressing the gas, but also slows it down by stabilizing the gas against gravity. Most widely-used analytical models rely on questionable assumptions, including: $i)$ integrating over the density PDF, a one-point statistical description that ignores spatial correlation, $ii)$ selecting self-gravitating gas based on a density threshold that often ignores turbulent dispersion, $iii)$ assuming the freefall time as the timescale for estimating SFR without considering the need to rejuvenate the density PDF, $iv)$ assuming the density PDF to be lognormal. Improving upon the only existing model that incorporates the spatial correlation of the density field, we present a new analytical model. We calculate the time needed to rejuvenate density fluctuations of a given density and spatial scale, revealing that it is generally much longer than the freefall time, rendering the latter inappropriate for use. We make specific predictions regarding the role of the Mach number, $ M $, and the driving scale of turbulence divided by the mean Jeans length. At low to moderate Mach numbers, turbulence does not reduce and may even slightly promote star formation by broadening the PDF. However, at higher Mach numbers, most density fluctuations are stabilized by turbulent dispersion, leading to a steep drop in the SFR as the Mach number increases., Comment: 16 pages, 7 figures, Accepted for publication in A&A

Molecular clouds (MC) are structures of dense gas in the interstellar medium (ISM), that extend from ten to a few hundred parsecs and form the main gas reservoir available for star formation. Hydrodynamical simulations of varying complexity are a promising way to investigate MC evolution and their properties. However, each simulation typically has a limited range in resolution and different cloud extraction algorithms are used, which complicates the comparison between simulations. In this work, we aim to extract clouds from different simulations covering a wide range of spatial scales. We compare their properties, such as size, shape, mass, internal velocity dispersion and virial state. We apply the Hop cloud detection algorithm on (M)HD numerical simulations of stratified ISM boxes and isolated galactic disk simulations that were produced using Flash Ramses and Arepo We find that the extracted clouds are complex in shape ranging from round objects to complex filamentary networks in all setups. Despite the wide range of scales, resolution, and sub-grid physics, we observe surprisingly robust trends in the investigated metrics. The mass spectrum matches in the overlap between simulations without rescaling and with a high-mass slope of $\mathrm{d} N/\mathrm{d}\ln M\propto-1$ in accordance with theoretical predictions. The internal velocity dispersion scales with the size of the cloud as $\sigma\propto R^{0.75}$ for large clouds ($R\gtrsim3\,\mathrm{pc}$). For small clouds we find larger sigma compared to the power-law scaling, as seen in observations, which is due to supernova-driven turbulence. Almost all clouds are gravitationally unbound with the virial parameter scaling as $\alpha_\mathrm{vir}\propto M^{-0.4}$, which is slightly flatter compared to observed scaling, but in agreement given the large scatter., Comment: 22 pages, 16 figures, proposed for acceptance in A&A

A comprehensive study of the structural properties of the heavily investigated rare-earth A2Ir2O7 series under extreme conditions is presented. The series is covered by studying iridates with A = Pr, Sm, Dy-Lu. Temperature- and pressure-dependent synchrotron X-ray powder diffraction experiments reveal robustness of the pyrochlore structure throughout the rare-earth series, down to 4 K and up to 20 GPa. The thermal expansivity and pressure compressibility are determined, including Debye temperature, bulk modulus and Gr\"uneisen parameter. Temperature and pressure evolution of the fractional coordinate of oxygen at 48f Wyckoff position, the sole free atomic position parameter, is investigated and discussed regarding the antiferromagnetic ordering of the Ir magnetic moments. In addition, the pressure evolution of the crystal structure of an IrO2 minority phase is followed. The tetragonal rutile-type structure is orthorhombically distorted at 15 GPa, and the orthorhombic structure is not fully stabilised up to 20 GPa., Comment: 24 pages, 14 figues

For ongoing studies of the role of rotation in stellar evolution, we require large catalogs of rotation periods for testing and refining gyrochronology. While there is a wealth of data from the Kepler and K2 missions, TESS presents both an opportunity and a challenge: despite its all-sky coverage, rotation periods remain hard to detect. We analyzed individual TESS sectors to detect short-period stellar rotation, using only parameters measured from light curves for a robust and unbiased method of evaluating detections. We used random forest classifiers for vetting, trained on a large corpus of period measurements in KELT data from the Oelkers et al. (2018) catalog and using TESS full-frame image light curves generated by eleanor (Feinstein et al. 2019). Finally, using data from the first 26 sectors of TESS, we analyzed 432,704 2-minute cadence single-sector light curves for FGKM dwarfs. We detected 16,800 periods in individual sector light curves, covering 10,909 distinct targets, and we present a catalog of the median period for each target as measured by a Lomb-Scargle periodogram., Comment: 23 pages, 15 figures, accepted for publication in The Astronomical Journal

Large Eddy Simulation (LES) was used to investigate the evolution of the soot size distribution in a series of turbulent nonpremixed bluff body flames, with different bluff body diameters. The new Bivariate Multi-Moment Sectional Method (BMMSM) is employed to characterize the size distribution. BMMSM combines elements of sectional methods and methods of moments and is capable of reproducing fractal aggregate morphology, thanks to its joint volume-surface formulation, all at relatively low computational costs with fewer transported soot scalars compared to traditional sectional methods. LES results show soot volume fraction profiles agreeing correctly with the experimental measurements, exhibiting significant improvement compared to previous work using the HMOM. The evolution of the particle size distribution function (PSDF) was examined across the flame series and show that the size distribution is less sensitive to the bluff body diameter than the overall soot volume fraction, which increases with increasing bluff body diameter. The PSDF across the flame exhibit different features compared to turbulent nonpremixed jet flames. The long residence times in the recirculation zone leads to a nearly bimodal size distribution, which eventually becomes bimodal in the downstream jet-like region. Further analysis indicates that a size distribution model is needed to correctly predict the soot evolution. Remarkably, due to improved descriptions of oxidation with BMMSM compared to HMOM, significant nucleation and condensation rates in both the recirculation zone and jet-like region were found using BMMSM, on the same order of magnitude as surface growth and oxidation, leading to the improved prediction of mean soot volume fraction compared to HMOM. This work reveals that the need of size distribution is crucial to both predict global soot quantities accurately and reproduce fundamental mechanisms.

A joint volume-surface formalism of the Multi-Moment Sectional Method (MMSM) is developed to describe the evolution of soot size distribution in turbulent reacting flows. The bivariate MMSM (or BMMSM) considers three statistical moments per section, including the total soot number density, total soot volume, and total soot surface area per section. A linear profile along the volume coordinate is considered to reconstruct the size distribution within each section, which weights a delta function along the surface coordinate. The closure for the surface considers that the primary particle diameter is constant so the surface/volume ratio constant within each section. The inclusion of the new variable in BMMSM allows for the description of soot's fractal aggregate morphology compared to the strictly spherical assumption of its univariate predecessor. BMMSM is shown to reproduce bimodal soot size distributions in simulations of one-dimensional laminar sooting flames as in experimental measurements. To demonstrate its performance in turbulent reacting flows, BMMSM is coupled to a Large Eddy Simulation framework to simulate a laboratory-scale turbulent nonpremixed jet flame. Computational results are validated against available experimental measurements of soot size distribution, showing the ability of BMMSM to reproduce the evolution of the size distribution from unimodal to bimodal moving downstream in the flame. In general, varying the number of sections has limited influence on results, and accurate results are obtained with as few as eight sections so 24 total degrees of freedom. The impact of using a different statistical model for soot (HMOM) is also investigated. The total computational cost of using BMMSM as low as approximately 44% more than the cost of HMOM. The new formulation results in a computationally efficient approach for the soot size distribution in turbulent reacting flows.

This study proposes the use of Machine Learning models to predict the early onset of sepsis using deidentified clinical data from Montefiore Medical Center in Bronx, NY, USA. A supervised learning approach was adopted, wherein an XGBoost model was trained utilizing 80\% of the train dataset, encompassing 107 features (including the original and derived features). Subsequently, the model was evaluated on the remaining 20\% of the test data. The model was validated on prospective data that was entirely unseen during the training phase. To assess the model's performance at the individual patient level and timeliness of the prediction, a normalized utility score was employed, a widely recognized scoring methodology for sepsis detection, as outlined in the PhysioNet Sepsis Challenge paper. Metrics such as F1 Score, Sensitivity, Specificity, and Flag Rate were also devised. The model achieved a normalized utility score of 0.494 on test data and 0.378 on prospective data at threshold 0.3. The F1 scores were 80.8\% and 67.1\% respectively for the test data and the prospective data for the same threshold, highlighting its potential to be integrated into clinical decision-making processes effectively. These results bear testament to the model's robust predictive capabilities and its potential to substantially impact clinical decision-making processes., Comment: 16 pages, 6 figures and 7 tables

State-of-the-art deepfake detection approaches rely on image-based features extracted via neural networks. While these approaches trained in a supervised manner extract likely fake features, they may fall short in representing unnatural `non-physical' semantic facial attributes -- blurry hairlines, double eyebrows, rigid eye pupils, or unnatural skin shading. However, such facial attributes are easily perceived by humans and used to discern the authenticity of an image based on human common sense. Furthermore, image-based feature extraction methods that provide visual explanations via saliency maps can be hard to interpret for humans. To address these challenges, we frame deepfake detection as a Deepfake Detection VQA (DD-VQA) task and model human intuition by providing textual explanations that describe common sense reasons for labeling an image as real or fake. We introduce a new annotated dataset and propose a Vision and Language Transformer-based framework for the DD-VQA task. We also incorporate text and image-aware feature alignment formulation to enhance multi-modal representation learning. As a result, we improve upon existing deepfake detection models by integrating our learned vision representations, which reason over common sense knowledge from the DD-VQA task. We provide extensive empirical results demonstrating that our method enhances detection performance, generalization ability, and language-based interpretability in the deepfake detection task.

Machine learning model bias can arise from dataset composition: sensitive features correlated to the learning target disturb the model decision rule and lead to performance differences along the features. Existing de-biasing work captures prominent and delicate image features which are traceable in model latent space, like colors of digits or background of animals. However, using the latent space is not sufficient to understand all dataset feature correlations. In this work, we propose a framework to extract feature clusters in a dataset based on image descriptions, allowing us to capture both subtle and coarse features of the images. The feature co-occurrence pattern is formulated and correlation is measured, utilizing a human-in-the-loop for examination. The analyzed features and correlations are human-interpretable, so we name the method Common-Sense Bias Discovery (CSBD). Having exposed sensitive correlations in a dataset, we demonstrate that downstream model bias can be mitigated by adjusting image sampling weights, without requiring a sensitive group label supervision. Experiments show that our method discovers novel biases on multiple classification tasks for two benchmark image datasets, and the intervention outperforms state-of-the-art unsupervised bias mitigation methods.

Cells continuously sense their surroundings to detect modifications and generate responses. Very often changes in extracellular concentrations initiate signaling cascades that eventually result in changes in gene expression. Increasing stimulus strengths can be encoded in increasing concentration amplitudes or increasing activation frequencies of intermediaries of the pathway. In this paper we show that the different way in which amplitude and frequency encoding map environmental changes endow cells with qualitatively different information transmission capabilities. While amplitude encoding is optimal for a limited range of stimuli strengths, frequency encoding can transmit information with equal reliability over much broader ranges. The qualitative difference between the two strategies stems from the scale invariant discriminating power of the first transducing step in frequency codification. The apparently redundant combination of both strategies in some cell types may then serve the purpose of expanding the span over which stimulus strengths can be reliably discriminated. In this paper we discuss a possible example of this mechanism in yeast., Comment: 12 pages, 5 figures

The conservation of spectral asymmetry is a fundamental feature of the ideal four-wave mixing process as it exists in a medium combining quadratic chromatic dispersion and third-order nonlinearity. We test in this paper the robustness of this invariant in an experimental configuration where the excitation conditions of an optical fiber are sequentially updated, mimicking infinite propagation. This theoretical and experimental study reveals the high sensitivity of the asymmetry to very slight deviations from the ideal case, and we show that our idealized system behaves as an intermediate case between the ideal case of non-cascaded fourwave mixing and propagation in a system governed by the nonlinear Schr{\"o}dinger equation.

Protostellar disks are the product of angular momentum conservation during the protostellar collapse. Understanding their formation is crucial because they are the birthplace of planets and because their formation is tightly related to star formation. Unfortunately, the initial properties of Class 0 disks and their evolution are still poorly constrained observationally and theoretically. We aim to better understand the mechanisms that set the statistics of disk properties as well as to study their formation in massive protostellar clumps. We also want to provide the community with synthetic disk populations to better interpret young disk observations. We use the ramses code to model star and disk formation in massive protostellar clumps with MHD including the effect of ambipolar diffusion and RT including the stellar radiative feedback. Those simulations, resolved up to the astronomical unit scale, allow to investigate the formation of disk populations. Magnetic fields play a crucial role in disk formation. A weaker initial field leads to larger and massive disks and weakens the stellar radiative feedback by increasing fragmentation. We find that ambipolar diffusion impacts disk and star formation and leads to very different disk magnetic properties. The stellar radiative feedback also have a strong influence, increasing the temperature and reducing fragmentation. Comparing our disk populations with observations reveals that our models with a mass-to-flux ratio of 10 seems to better reproduce observed disk sizes. This also sheds light on a tension between models and observations for the disk masses. The clump properties and physical modeling impact disk populations significantly. The tension between observations and models for disk mass estimates is critical to solve with synthetic observations in future years, in particular for our comprehension of planet formation., Comment: 20 pages, 15 figures, accepted for publication in Astronomy & Astrophysics

Prognostic Health Management (PHM) is designed to assess and monitor the health status of systems, anticipate the onset of potential failure, and prevent unplanned downtime. In recent decades, collecting massive amounts of real-time sensor data enabled condition monitoring (CM) and consequently, detection of abnormalities to support maintenance decision-making. Additionally, the utilization of PHM techniques can support energy sustainability efforts by optimizing energy usage and identifying opportunities for energy-saving measures. Escalators are efficient machines for transporting people and goods, and measuring energy consumption in time can facilitate PHM of escalators. Fixed loss energy, or no-load energy, of escalators denotes the energy consumption by an unloaded escalator. Fixed loss energy varies over time indicating varying operating conditions. In this paper, we propose to use escalators' fixed loss energy for PHM. We propose an approach to compute daily fixed loss energy based on energy consumption sensor data. The proposed approach is validated using a set of experimental data. The advantages and disadvantages of each approach are also presented, and recommendations are given. Finally, to illustrate PHM, we set up an EWMA chart for monitoring the fixed loss over time and demonstrate the potential in reducing energy costs associated with escalator operation.

The protection of Industrial Control Systems (ICS) that are employed in public critical infrastructures is of utmost importance due to catastrophic physical damages cyberattacks may cause. The research community requires testbeds for validation and comparing various intrusion detection algorithms to protect ICS. However, there exist high barriers to entry for research and education in the ICS cybersecurity domain due to expensive hardware, software, and inherent dangers of manipulating real-world systems. To close the gap, built upon recently developed 3D high-fidelity simulators, we further showcase our integrated framework to automatically launch cyberattacks, collect data, train machine learning models, and evaluate for practical chemical and manufacturing processes. On our testbed, we validate our proposed intrusion detection model called Minimal Threshold and Window SVM (MinTWin SVM) that utilizes unsupervised machine learning via a one-class SVM in combination with a sliding window and classification threshold. Results show that MinTWin SVM minimizes false positives and is responsive to physical process anomalies. Furthermore, we incorporate our framework with ICS cybersecurity education by using our dataset in an undergraduate machine learning course where students gain hands-on experience in practicing machine learning theory with a practical ICS dataset. All of our implementations have been open-sourced., Comment: accepted to the 20th Annual IEEE International Conference on Intelligence and Security Informatics (ISI)

We present a comparison of the Milky Way's star formation rate (SFR) surface density ($\Sigma_{\rm SFR}$) obtained with two independent state-of-the-art observational methods. The first method infers $\Sigma_{\rm SFR}$ from observations of the dust thermal emission from interstellar dust grains in far-infrared wavelengths registered in the Herschel infrared Galactic Plane Survey (Hi-GAL). The second method determines $\Sigma_{\rm SFR}$ by modeling the current population of O-, B-, and A-type stars in a 6 kpc $\times$ 6 kpc area around the Sun. We find an agreement between the two methods within a factor of two for the mean SFRs and the SFR surface density profiles. Given the broad differences between the observational techniques and the independent assumptions in the methods for computing the SFRs, this agreement constitutes a significant advance in our understanding of the star formation of our Galaxy and implies that the local SFR has been roughly constant over the past 10\,Myr., Comment: Accepted for publication at Astronomy & Astrophysics (11SEP2023). 8 pages, 7 figures

The refurbishment of an escalator is usually linked with its design life as recommended by the manufacturer. However, the actual useful life of an escalator should be determined by its operating condition which is affected by the runtime, workload, maintenance quality, vibration, etc., rather than age only. The objective of this project is to develop a comprehensive health condition analytic system for escalators to support refurbishment decisions. The analytic system consists of four parts: 1) online data gathering and processing; 2) a dashboard for condition monitoring; 3) a health index model; and 4) remaining useful life prediction. The results can be used for a) predicting the remaining useful life of the escalators, in order to support asset replacement planning and b) monitoring the real-time condition of escalators; including alerts when vibration exceeds the threshold and signal diagnosis, giving an indication of possible root cause (components) of the alert signal., Comment: 14 pages, 12 figures, 7 tables