Search

The dynamics of swimming bacteria depend on the properties of their habitat media. Recently it was shown that the motion of swimming bacteria dispersed directly in a non-toxic water-based lyotropic chromonic liquid crystal can be controlled by the director field of the liquid crystal. Here we investigate whether the macroscopic polar order of a ferroelectric nematic liquid crystal (NF) can be recognized by bacteria B. Subtilis swimming in a water dispersion adjacent to a glassy NF film by surface interactions alone. We show that B. Subtilis tends to move in the direction antiparallel to the spontaneous electric polarization at the NF surface. Their speed was found to be the same with or without a polar NF layer. In contrast to observation on crystal ferroelectric films, the bacteria do not get immobilized. These observations may offer a pathway to creation of polar microinserts to direct bacterial motion in-vivo., Comment: 29 pages, 15 figures

The tetragonal heavy-fermion superconductor CeRh$_2$As$_2$ ($T_{\rm c}=0.3$ K) exhibits an exceptionally high critical field of 14 T for $\textbf{B} \parallel \textbf{c}$. It undergoes a field-driven first-order phase transition between superconducting (SC) states, potentially transitioning from spin-singlet to spin-triplet superconductivity. To elucidate the underlying pairing mechanism, we probe spin fluctuations in CeRh$_2$As$_2$ using neutron scattering. We find dynamic $(\pi,\pi)$ antiferromagnetic spin correlations with an anisotropic quasi-two-dimensional correlation volume. Our data place an upper limit of 0.31 $\mu_{\rm B}$ on the staggered magnetization of corresponding N\'{e}el orders at $T=0.08$ K. Density functional theory (DFT) calculations, treating Ce $4f$ electrons as core states, show that the AFM wave vector connects significant areas of the Fermi surface. Our findings show the dominant excitations in CeRh$_2$As$_2$ for $\hbar\omega< 1.2$~meV are magnetic and indicate superconductivity in CeRh$_2$As$_2$ is mediated by AFM spin fluctuations associated with a proximate quantum critical point., Comment: 7+6 pages, 4+4 figures

Kondo lattice systems are recognized for potentially hosting a variety of rich topological phases. Several pioneering studies have demonstrated significant anomalous Hall and anomalous Nernst effects in these systems, attributed to the Berry curvature of the hybridization bands. In this study, we investigate UBiTe, a ferromagnetic Kondo lattice system. Our findings reveal that the intrinsic contribution to the anomalous Hall conductivity is closely tied to the Kondo coherence temperature. Moreover, we demonstrate that slight shifts in the Fermi level across three different samples significantly influence this intrinsic contribution, in alignment with the Berry curvature localized within the narrow hybridization bands. This provides a stark contrast to the less pronounced sensitivity observed in weakly correlated Weyl semimetals, underscoring the distinctive electronic properties of Kondo lattice systems. The anomalous Hall conductivity of one samples ranks among the highest reported for topological magnetic materials.

Weakly-supervised diffusion models (DMs) in anomaly segmentation, leveraging image-level labels, have attracted significant attention for their superior performance compared to unsupervised methods. It eliminates the need for pixel-level labels in training, offering a more cost-effective alternative to supervised methods. However, existing methods are not fully weakly-supervised because they heavily rely on costly pixel-level labels for hyperparameter tuning in inference. To tackle this challenge, we introduce Anomaly Segmentation with Forward Process of Diffusion Models (AnoFPDM), a fully weakly-supervised framework that operates without the need of pixel-level labels. Leveraging the unguided forward process as a reference for the guided forward process, we select hyperparameters such as the noise scale, the threshold for segmentation and the guidance strength. We aggregate anomaly maps from guided forward process, enhancing the signal strength of anomalous regions. Remarkably, our proposed method outperforms recent state-of-the-art weakly-supervised approaches, even without utilizing pixel-level labels., Comment: v4: added appendices and fixed some typos

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES'22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model achieved superior ischemic lesion detection and segmentation accuracy on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm's segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model's generalizability. The algorithm's outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm (https://github.com/Tabrisrei/ISLES22_Ensemble) that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)radiologists. Second, we show the potential for biomedical challenge outputs to extend beyond the challenge's initial objectives, demonstrating their real-world clinical applicability.

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero., Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted to IEEE Transactions on Medical Imaging (2024). Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/10628648

Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions. Deep learning-based regression techniques to predict brain age from magnetic resonance imaging (MRI) scans have shown great accuracy recently. However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects. This weakens the reliability of predicted brain age as a valid biomarker for downstream clinical applications. Here, we reformulate the brain age prediction task from regression to classification to address the issue of systematic bias. Recognizing the importance of preserving ordinal information from ages to understand aging trajectory and monitor aging longitudinally, we propose a novel ORdinal Distance Encoded Regularization (ORDER) loss that incorporates the order of age labels, enhancing the model's ability to capture age-related patterns. Extensive experiments and ablation studies demonstrate that this framework reduces systematic bias, outperforms state-of-art methods by statistically significant margins, and can better capture subtle differences between clinical groups in an independent AD dataset. Our implementation is publicly available at https://github.com/jaygshah/Robust-Brain-Age-Prediction., Comment: Accepted in WACV 2024

Induced by quenched disorder, random-singlet states occur in a variety of low-dimensional spin-1/2 antiferromagnets, some of them candidates for quantum spin liquids. Here we report measurements of the specific heat, magnetization, and magnetic susceptibility of nanocrystalline Sr21Bi8Cu2(CO3)2O41, a quasi-one-dimensional spin-1/2 antiferromagnet with alternating bonds. The results uncover the predominant presence of random-singlet spin pairs in this material, with a logarithmic probability distribution, P(J), of the renormalized, emergent exchange interaction, J, in zero magnetic field and P(J) proportional to 1/J in magnetic fields. We postulate that these unexpected J dependences, in contrast to the usual P(J) \propto 1/$J^\gamma$ with 0 < $\gamma$ < 1, and possibly also the dichotomy, arise from the finite size of the nanocrystals. Scaling functions for the specific heat and magnetization reproduce our magnetocaloric-effect data, with no adjustable parameters.

We report a detailed study of the specific heat and magnetic susceptibility of single crystals of a spin liquid candidate: the hyper-honeycomb Cu oxalate framework compound [(C$_2$H$_5$)$_3$NH]$_2$Cu$_2$(C$_2$O$_4$)$_3$. The specific heat shows no anomaly associated with a magnetic transition at low temperatures down to $T\sim$ 180 mK in zero magnetic field. We observe a large linear-in-$T$ contribution to the specific heat $\gamma T$, $\gamma = 98(1)$ mK/mol K$^{2}$, at low temperatures, indicative of the presence of fermionic excitations despite the Mott insulating state. The low-$T$ specific heat is strongly suppressed by applied magnetic fields $H$, which induce an energy gap, $\Delta (H)$, in the spin-excitation spectrum. We use the four-component relativistic density-functional theory (DFT) to calculate the magnetic interactions, including the Dzyaloshinskii-Moriya antisymmetric exchange, which causes an effective staggered field acting on one copper sublattice. The magnitude and field dependence of the field-induced gap, $\Delta (H) \propto H^{2/3}$, are accurately predicted by the soliton mass calculated from the sine-Gordon model of weakly coupled antiferromagnetic Heisenberg chains with all parameters determined by our DFT calculations. Thus our experiment and calculations are entirely consistent with a model of [(C$_2$H$_5$)$_3$NH]$_2$Cu$_2$(C$_2$O$_4$)$_3$ in which anisotropic magnetic exchange interactions due to Jahn-Teller distortion cause one copper sublattice to dimerize, leaving a second sublattice of weakly coupled antiferromagnetic chains. We also show that this model quantitatively accounts for the measured temperature-dependent magnetic susceptibility. Thus [(C$_2$H$_5$)$_3$NH]$_2$Cu$_2$(C$_2$O$_4$)$_3$ is a canonical example of a one-dimensional spin-1/2 Heisenberg antiferromagnet and not a resonating-valence-bond quantum spin liquid, as previously proposed., Comment: 8 pages, 6 figures

Soft robots have the ability to adapt to their environment, which makes them suitable for use in disaster areas and agricultural fields, where their mobility is constrained by complex terrain. One of the main challenges in developing soft terrestrial robots is that the robot must be soft enough to adapt to its environment, but also rigid enough to exert the required force on the ground to locomote. In this paper, we report a pneumatically driven, soft modular appendage made of silicone for a terrestrial robot capable of generating specific mechanical movement to locomote and transport loads in the desired direction. This two-segmented soft appendage uses actuation in between the joint and the lower segment of the appendage to ensure adequate rigidity to exert the required force to locomote. A prototype of a soft-rigid-bodied tethered physical robot was developed and two sets of experiments were carried out in both air and underwater environments to assess its performance. The experimental results address the effectiveness of the soft appendage to generate adequate force to navigate through various environments and our design method offers a simple, low-cost, and efficient way to develop terradynamically capable soft appendages that can be used in a variety of locomotion applications.

The ability to classify images is dependent on having access to large labeled datasets and testing on data from the same domain that the model can train on. Classification becomes more challenging when dealing with new data from a different domain, where gathering and especially labeling a larger image dataset for retraining a classification model requires a labor-intensive human effort. Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain. The problem with these unsupervised models lies in their unsupervised nature. For lack of annotations, it is not possible to use the traditional supervised metrics to evaluate these translation models to pick the best-saved checkpoint model. This paper introduces a new method called Domain-knowledge Inspired Pseudo Supervision (DIPS) which utilizes domain-informed Gaussian Mixture Models to generate pseudo annotations to enable the use of traditional supervised metrics. This method was designed specifically to support cross-domain classification applications contrary to other typically used metrics such as the FID which were designed to evaluate the model in terms of the quality of the generated image from a human-eye perspective. DIPS proves its effectiveness by outperforming various GAN evaluation metrics, including FID, when selecting the optimal saved checkpoint model. It is also evaluated against truly supervised metrics. Furthermore, DIPS showcases its robustness and interpretability by demonstrating a strong correlation with truly supervised metrics, highlighting its superiority over existing state-of-the-art alternatives. The code and data to replicate the results can be found on the official Github repository: https://github.com/Hindawi91/DIPS, Comment: arXiv admin note: text overlap with arXiv:2212.09107

Spin triplet superconductor UTe$_{2}$ is widely believed to host a quasi-two-dimensional Fermi surface, revealed by first principal calculations, photoemission and quantum oscillation measurements. An outstanding question still remains as to the existence of a three-dimensional Fermi surface pocket, which is crucial for our understanding of the exotic superconducting and topological properties of UTe$_{2}$. This 3D Fermi surface pocket appears in various theoretical models with different physics origins but has not been detected experimentally. Here for the first time, we provide concrete evidence for a relatively isotropic, small Fermi surface pocket of UTe$_{2}$ via quantum oscillation measurements. In addition, we observed high frequency quantum oscillations corresponding to electron-hole tunneling between adjacent electron and hole pockets. The coexistence of 2D and 3D Fermi surface pockets, as well as the breakdown orbits, provides a test bed for theoretical models and aid the realization of a unified understanding of superconducting state of UTe$_{2}$ from the first-principles approach.

Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation. Unsupervised disease detection methods, such as anomaly detection, can significantly reduce human effort in these scenarios. While anomaly detection typically focuses on learning from images of healthy subjects only, real-world situations often present unannotated datasets with a mixture of healthy and diseased subjects. Recent studies have demonstrated that utilizing such unannotated images can improve unsupervised disease and anomaly detection. However, these methods do not utilize knowledge specific to registered neuroimages, resulting in a subpar performance in neurologic disease detection. To address this limitation, we propose Brainomaly, a GAN-based image-to-image translation method specifically designed for neurologic disease detection. Brainomaly not only offers tailored image-to-image translation suitable for neuroimages but also leverages unannotated mixed images to achieve superior neurologic disease detection. Additionally, we address the issue of model selection for inference without annotated samples by proposing a pseudo-AUC metric, further enhancing Brainomaly's detection performance. Extensive experiments and ablation studies demonstrate that Brainomaly outperforms existing state-of-the-art unsupervised disease and anomaly detection methods by significant margins in Alzheimer's disease detection using a publicly available dataset and headache detection using an institutional dataset. The code is available from https://github.com/mahfuzmohammad/Brainomaly., Comment: Accepted in WACV 2024

Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing. The INSTANCE released a training set of 100 cases with ground-truth and a validation set with 30 cases without ground-truth labels that were available to the participants. A held-out testing set with 70 cases is utilized for the final evaluation and ranking. The methods from different participants are ranked based on four metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), Relative Volume Difference (RVD) and Normalized Surface Dice (NSD). A total of 13 teams submitted distinct solutions to resolve the challenges, making several baseline models, pre-processing strategies and anisotropic data processing techniques available to future researchers. The winner method achieved an average DSC of 0.6925, demonstrating a significant growth over our proposed baseline method. To the best of our knowledge, the proposed INSTANCE challenge releases the first intracranial hemorrhage segmentation benchmark, and is also the first challenge that intended to resolve the anisotropic problem in 3D medical image segmentation, which provides new alternatives in these research fields., Comment: Summarized paper for the MICCAI INSTANCE 2022 Challenge

The interaction between strong correlation and Berry curvature is an open territory of in the field of quantum materials. Here we report large anomalous Hall conductivity in a Kondo lattice ferromagnet USbTe which is dominated by intrinsic Berry curvature at low temperatures. However, the Berry curvature induced anomalous Hall effect does not follow the scaling relation derived from Fermi liquid theory. The onset of the Berry curvature contribution coincides with the Kondo coherent temperature. Combined with ARPES measurement and DMFT calculations, this strongly indicates that Berry curvature is hosted by the flat bands induced by Kondo hybridization at the Fermi level. Our results demonstrate that the Kondo coherence of the flat bands has a dramatic influence on the low temperature physical properties associated with the Berry curvature, calling for new theories of scaling relations of anomalous Hall effect to account for the interaction between strong correlation and Berry curvature., Comment: Accepted by Nature Communications

The detection of critical heat flux (CHF) is crucial in heat boiling applications as failure to do so can cause rapid temperature ramp leading to device failures. Many machine learning models exist to detect CHF, but their performance reduces significantly when tested on data from different domains. To deal with datasets from new domains a model needs to be trained from scratch. Moreover, the dataset needs to be annotated by a domain expert. To address this issue, we propose a new framework to support the generalizability and adaptability of trained CHF detection models in an unsupervised manner. This approach uses an unsupervised Image-to-Image (UI2I) translation model to transform images in the target dataset to look like they were obtained from the same domain the model previously trained on. Unlike other frameworks dealing with domain shift, our framework does not require retraining or fine-tuning of the trained classification model nor does it require synthesized datasets in the training process of either the classification model or the UI2I model. The framework was tested on three boiling datasets from different domains, and we show that the CHF detection model trained on one dataset was able to generalize to the other two previously unseen datasets with high accuracy. Overall, the framework enables CHF detection models to adapt to data generated from different domains without requiring additional annotation effort or retraining of the model., Comment: This work has been submitted to the Expert Systems With Applications Journal on Sep 25, 2022

In the recently discovered heavy fermion superconductor CeRh2As2, a magnetic field induced phase transition has been observed inside the superconducting state, which is proposed to be a transition from an even to an odd-parity superconducting state. The odd-parity superconducting state and its large upper critical field has been explained by local inversion symmetry breaking and consequent Rashba spin-orbit coupling. Here we report the experimental tuning of the parity of superconductivity in CeRh2As2 via applied pressure. Superconductivity is enhanced by pressure after its initial suppression, forming a second dome. However, the odd-parity state only exists in the first dome and the second dome is dominated by the even-parity state. This parity switch of superconductivity under pressure is consistent with the competition between Rashba spin-orbit coupling and interlayer hopping in determining the parity of superconductivity in systems with local inversion symmetry breaking. Our results provide insightful guidance on how to look for new odd-parity superconductors.

Spin-triplet topological superconductors should exhibit many unprecedented electronic properties including fractionalized electronic states relevant to quantum information processing. Although UTe$_2$ may embody such bulk topological superconductivity, its superconductive order-parameter $\Delta(\mathbf{k})$ remains unknown. Many diverse forms for $\Delta(\mathbf{k})$ are physically possible in such heavy fermion materials. Moreover, intertwined density waves of spin (SDW), charge (CDW) and pairs (PDW) may interpose, with the latter state exhibiting spatially modulating superconductive order-parameter $\Delta(\mathbf{r})$, electron pair density and pairing energy-gap. Hence, the newly discovered CDW state in UTe$_2$ motivates the prospect that a PDW state may exist in this material. To search for it, we visualize the pairing energy-gap with $\mu$$eV$-scale energy-resolution using superconductive STM tips. We detect three PDWs, each with peak-peak gap modulations circa 10 $\mu$$eV$ and at incommensurate wavevectors $\mathbf{P}_{i=1,2,3}$ that are indistinguishable from the wavevectors $\mathbf{Q}_{i=1,2,3}$ of the prevenient CDW. Concurrent visualization of the UTe$_2$ superconductive PDWs and the non-superconductive CDWs reveals that every $\mathbf{P}_i$ : $\mathbf{Q}_i$ pair exhibits a relative phase $\delta\phi \approx \pi$. From these observations, and given UTe$_2$ as a spin-triplet superconductor, this PDW state should be a spin-triplet pair density wave. While such states do exist in superfluid $^{3}$He, for superconductors they are unprecedented., Comment: 39 pages, 14 figures

Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET images. In this work, we describe our solution to HECKTOR 2022 segmentation task. We re-sample all images to a common resolution, crop around head and neck region, and train SegResNet semantic segmentation network from MONAI. We use 5-fold cross validation to select best model checkpoints. The final submission is an ensemble of 15 models from 3 runs. Our solution (team name NVAUTO) achieves the 1st place on the HECKTOR22 challenge leaderboard with an aggregated dice score of 0.78802., Comment: HECKTOR22 segmentation challenge. MICCAI 2022. arXiv admin note: text overlap with arXiv:2209.09546

Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs. In this work, we describe our solution to INSTANCE 2022. We use a 2D segmentation network, SegResNet from MONAI, operating slice-wise without resampling. The final submission is an ensemble of 18 models. Our solution (team name NVAUTO) achieves the top place in terms of Dice metric (0.721), and overall rank 2. It is implemented with Auto3DSeg., Comment: INSTANCE22 challenge report, MICCAI2022. arXiv admin note: substantial text overlap with arXiv:2209.09546

Automated anomaly detection from medical images, such as MRIs and X-rays, can significantly reduce human effort in disease diagnosis. Owing to the complexity of modeling anomalies and the high cost of manual annotation by domain experts (e.g., radiologists), a typical technique in the current medical imaging literature has focused on deriving diagnostic models from healthy subjects only, assuming the model will detect the images from patients as outliers. However, in many real-world scenarios, unannotated datasets with a mix of both healthy and diseased individuals are abundant. Therefore, this paper poses the research question of how to improve unsupervised anomaly detection by utilizing (1) an unannotated set of mixed images, in addition to (2) the set of healthy images as being used in the literature. To answer the question, we propose HealthyGAN, a novel one-directional image-to-image translation method, which learns to translate the images from the mixed dataset to only healthy images. Being one-directional, HealthyGAN relaxes the requirement of cycle consistency of existing unpaired image-to-image translation methods, which is unattainable with mixed unannotated data. Once the translation is learned, we generate a difference map for any given image by subtracting its translated output. Regions of significant responses in the difference map correspond to potential anomalies (if any). Our HealthyGAN outperforms the conventional state-of-the-art methods by significant margins on two publicly available datasets: COVID-19 and NIH ChestX-ray14, and one institutional dataset collected from Mayo Clinic. The implementation is publicly available at https://github.com/mahfuzmohammad/HealthyGAN., Comment: International Workshop on Simulation and Synthesis in Medical Imaging, MICCAI, 2022