Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic value of troponin/creatinine ratio in patients with chest pain

Background The aim of this study was to investigate the prognostic value of the troponin/creatinine ratio in patients presenting with chest pain and to identify laboratory values affecting mortality. Methods Between October 1st, 2016 and April 30th, 2019, a total of 5,079 patients (2,844 males, 2,235 females; median age: 65 years; range, 49 to 83 years) who presented to the emergency department with chest pain and whose troponin and creatinine measurements were done were retrospectively analyzed. Laboratory data and 28-day mortality rates were evaluated. The patients were divided into two groups according to 28-day mortality rates after the initial emergency department admission as survivors (n=4,793) and non-survivors (n=286). Results There were statistically significant differences in the white blood cell count, C-reactive protein, creatinine, high-sensitivity troponin I, and troponin/creatinine ratio between the groups (p<0.05). The high-sensitivity troponin I ≥31.96 pg/dL, creatinine ≥1.11 mg/dL, C-reactive protein ≥43.94 mg/L, and troponin/creatinine ratio ≥25.12 were independent predictors of 28-day mortality (p<0.05). A C-reactive protein value of ≥43.94 mg/L was found to be more specific (81.14%) than the other markers for mortality. Conclusion Troponin/creatinine ratio is a predictor of mortality in patients presenting to the emergency department with chest pain and high-sensitivity troponin I, creatinine, and C-reactive protein seem to be independent risk factors for 28-day mortality.
A Unified Framework for Light Spanners

Seminal works on light spanners over the years provide spanners with optimal lightness in various graph classes, such as in general graphs, Euclidean spanners, and minor-free graphs. Three shortcomings of previous works on light spanners are: (i) The runtimes of these constructions are almost always sub-optimal, and usually far from optimal. (ii) These constructions are optimal in the standard and crude sense, but not in a refined sense that takes into account a wider range of involved parameters. (iii) The techniques are ad hoc per graph class, and thus can’t be applied broadly. This work aims at addressing these shortcomings by presenting a unified framework of light spanners in a variety of graph classes. Informally, the framework boils down to a transformation from sparse spanners to light spanners; since the state-of-the-art for sparse spanners is much more advanced than that for light spanners, such a transformation is powerful. First, we apply our framework to design fast constructions with optimal lightness for several graph classes. Second, we apply our framework to achieve more refined optimality bounds for several graph classes, i.e., the bounds remain optimal when taking into account a wider range of involved parameters, most notably є. Our new constructions are significantly better than the state-of-the-art for every examined graph class.
A turbulence model for 3-d flows with anisotropic structure of turbulence

A new turbulence model for flows in open channels with compound cross-sections is presented. The structure of turbulence in these channels can be anisotropic. This structure is described by the turbulent stress tensor that is presented here as the sum of two tensors, namely, normal and shear stress tensors. The normal and shear turbulent stresses are expressed by the turbulence intensities and the mixing length tensor (MLT), respectively. The turbulence intensities can be learned from measurements or another suitable approaches. One such approach that allows calculating the main component of the normal stresses is presented in the paper. The components of MLT are defined based on a new concept of generic mixing length (GML). The generic mixing length is assumed to depend on both distances; from the nearest wall and from the water surface. To demonstrate how the new model works the basic hydrodynamic equations (parabolic approximation of Reynolds equations) together with the turbulence model are solved. The well-known Patankar and Spalding (1972) algorithm was used when solving these equations. A series of numerical simulations were performed for different components of MLT and different channel geometries.
Soft Fluidic Closed-Loop Controller for Untethered Underwater Gliders

Soft underwater robots typically explore bio-inspired designs at the expense of power efficiency when compared to traditional underwater robots, which limits their practical use in real-world applications. We leverage a fluidic closed-loop controller to actuate a passive underwater glider. A soft hydrostatic pressure sensor is configured as a bang-bang controller actuating a swim bladder made from silicone balloons. Our underwater glider oscillates between the water surface and 4 m depth while traveling 15 m translationally. The fluidic underwater glider demonstrates a power efficiency of 28 mW/m. This work demonstrates a low-cost and power-efficient underwater glider and non-electronic controller. Due to its simple design, low cost, and ease of fabrication using FDM printing and soft lithography, it serves as a starting point for the exploration of non-electronic underwater soft robots.
MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences

Point cloud sequences are commonly used to accurately detect 3D objects in applications such as autonomous driving. Current top-performing multi-frame detectors mostly follow a Detect-and-Fuse framework, which extracts features from each frame of the sequence and fuses them to detect the objects in the current frame. However, this inevitably leads to redundant computation since adjacent frames are highly correlated. In this paper, we propose an efficient Motion-guided Sequential Fusion (MSF) method, which exploits the continuity of object motion to mine useful sequential contexts for object detection in the current frame. We first generate 3D proposals on the current frame and propagate them to preceding frames based on the estimated velocities. The points-of-interest are then pooled from the sequence and encoded as proposal features. A novel Bidi-rectional Feature Aggregation (BiFA) module is further proposed to facilitate the interactions of proposal features across frames. Besides, we optimize the point cloud pooling by a voxel-based sampling technique so that millions of points can be processed in several milliseconds. The proposed MSF method achieves not only better efficiency than other multi-frame detectors but also leading accuracy, with 83.12% and 78.30% mAP on the LEVEL1 and LEVEL2 test sets of Waymo Open Dataset, respectively. Codes can be found at https://github.com/skyhehe123/MSF.
Adapting U-Net for linear elastic stress estimation in polycrystal Zr microstructures

A variant of the U-Net convolutional neural network architecture is proposed to estimate linear elastic compatibility stresses in a-Zr (hcp) polycrystalline grain structures. Training data was generated using VGrain software with a regularity alpha of 0.73 and uniform random orientation for the grain structures and ABAQUS to evaluate the stress welds using the finite element method. The initial dataset contains 200 samples with 20 held from training for validation. The network gives speedups of around 200x to 6000x using a CPU or GPU, with signifcant memory savings, compared to finite element analysis with a modest reduction in accuracy of up to 10%. Network performance is not correlated with grain structure regularity or texture, showing generalisation of the network beyond the training set to arbitrary Zr crystal structures. Performance when trained with 200 and 400 samples was measured, finding an improvement in accuracy of approximately 10% when the size of the dataset was doubled.
Mean-variance constrained priors have finite maximum Bayes risk in the normal location model

Consider a normal location model $X \mid \theta \sim N(\theta, \sigma^2)$ with known $\sigma^2$. Suppose $\theta \sim G_0$, where the prior $G_0$ has zero mean and unit variance. Let $G_1$ be a possibly misspecified prior with zero mean and unit variance. We show that the squared error Bayes risk of the posterior mean under $G_1$ is bounded, uniformly over $G_0, G_1, \sigma^2>0$.
Self-Driven Photo-Polarized Water Molecule-Triggered Graphene-Based Photodetector

Flowing water can be used as an energy source for generators, providing a major part of the energy for daily life. However, water is rarely used for information or electronic devices. Herein, we present the feasibility of a polarized liquid-triggered photodetector in which polarized water is sandwiched between graphene and a semiconductor. Due to the polarization and depolarization processes of water molecules driven by photogenerated carriers, a photo-sensitive current can be repeatedly produced, resulting in a high-performance photodetector. The response wavelength of the photodetector can be fine-tuned as a result of the free choice of semiconductors as there is no requirement of lattice match between graphene and the semiconductors. Under zero voltage bias, the responsivity and specific detectivity of Gr/NaCl (0.5 M)W/N-GaN reach values of 130.7 mA/W and 2.3 × 109 Jones under 350 nm illumination, respectively. Meanwhile, using a polar liquid photodetector can successfully read the photoplethysmography signals to produce accurate oxygen blood saturation and heart rate. Compared with the commercial pulse oximetry sensor, the average errors of oxygen saturation and heart rate in the designed photoplethysmography sensor are ~1.9% and ~2.1%, respectively. This study reveals that water can be used as a high-performance photodetector in informative industries.
Cooperative coloring of matroids

Let $M_1,M_2,\ldots,M_k$ be a collection of matroids on the same ground set $E$. A coloring $c:E \rightarrow \{1,2,\ldots,k\}$ is called \emph{cooperative} if for every color $j$, the set of elements in color $j$ is independent in $M_j$. We prove that such coloring always exists provided that every matroid $M_j$ is itself $k$-colorable (the set $E$ can be split into at most $k$ independent sets of $M_j$). We derive this fact from a generalization of Seymour's list coloring theorem for matroids, which asserts that every $k$-colorable matroid is $k$-list colorable, too. We also point on some consequences for the game-theoretic variants of cooperative coloring of matroids.
Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions

Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract the background for measurements of jets in relativistic heavy ion collisions. We show that the deep neural network is approximately the same as a method using the particle multiplicity in a jet. This demonstrates that interpretable machine learning methods can provide insight into underlying physical processes.
Observation of Periodic Systems: Bridge Centralized Kalman Filtering and Consensus-Based Distributed Filtering

Compared with linear time invariant systems, linear periodic system can describe the periodic processes arising from nature and engineering more precisely. However, the time-varying system parameters increase the difficulty of the research on periodic system, such as stabilization and observation. This article aims to consider the observation problem of periodic systems by bridging two fundamental filtering algorithms for periodic systems with a sensor network: consensus-on-measurement-based distributed filtering (CMDF) and centralized Kalman filtering (CKF). First, one mild convergence condition based on uniformly collective observability is established for CMDF, under which the filtering performance of CMDF can be formulated as a symmetric periodic positive semidefinite solution to a discrete-time periodic Lyapunov equation. Then, the closed form of the performance gap between CMDF and CKF is presented in terms of the information fusion steps and the consensus weights of the network. Moreover, it is pointed out that the estimation error covariance of CMDF exponentially converges to the centralized one with the fusion steps tending to infinity. Altogether, these new results establish a concise and specific relationship between distributed and centralized filterings, and formulate the tradeoff between the communication cost and distributed filtering performance on periodic systems. Finally, the theoretical results are verified with numerical experiments.
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators. Here, we present our experiences deploying two AI-guided simulation workflows across such heterogeneous systems. A unique aspect of our approach is our use of cloud-hosted management services to manage challenging aspects of cross-resource authentication and authorization, function-as-a-service (FaaS) function invocation, and data transfer. We show that these methods can achieve performance parity with systems that rely on direct connection between resources. We achieve parity by integrating the FaaS system and data transfer capabilities with a system that passes data by reference among managers and workers, and a user-configurable steering algorithm to hide data transfer latencies. We anticipate that this ease of use can enable routine use of heterogeneous resources in computational science.
Enhancing Unsupervised Audio Representation Learning via Adversarial Sample Generation

Existing audio analysis methods generally first transform the audio stream to spectrogram, and then feed it into CNN for further analysis. A standard CNN recognizes specific visual patterns over feature map, then pools for high-level representation, which overlooks the positional information of recognized patterns. However, unlike natural image, the semantic of an audio spectrogram is sensitive to positional change, as its vertical and horizontal axes indicate the frequency and temporal information of the audio, instead of naive rectangular coordinates. Thus, the insensitivity of CNN to positional change plays a negative role on audio spectrogram encoding. To address this issue, this paper proposes a new self-supervised learning mechanism, which enhances the audio representation by first generating adversarial samples (\textit{i.e.}, negative samples), then driving CNN to distinguish the embeddings of negative pairs in the latent space. Extensive experiments show that the proposed approach achieves best or competitive results on 9 downstream datasets compared with previous methods, which verifies its effectiveness on audio representation learning.
Bi-Directional Distribution Alignment for Transductive Zero-Shot Learning

Zero-shot learning (ZSL) suffers intensely from the domain shift issue, i.e., the mismatch (or misalignment) between the true and learned data distributions for classes without training data (unseen classes). By learning additionally from unlabelled data collected for the unseen classes, transductive ZSL (TZSL) could reduce the shift but only to a certain extent. To improve TZSL, we propose a novel approach Bi-VAEGAN which strengthens the distribution alignment between the visual space and an auxiliary space. As a result, it can reduce largely the domain shift. The proposed key designs include (1) a bi-directional distribution alignment, (2) a simple but effective L2-norm based feature normalization approach, and (3) a more sophisticated unseen class prior estimation. Evaluated by four benchmark datasets, Bi-VAEGAN11Code is available at https://github.com/Zhicaiwww/Bi-VAEGAN achieves the new state of the art under both the standard and generalized TZSL settings.
EGFR mutation prediction using F18-FDG PET-CT based radiomics features in non-small cell lung cancer

Lung cancer is the leading cause of cancer death in the world. Accurate determination of the EGFR (epidermal growth factor receptor) mutation status is highly relevant for the proper treatment of this patients. Purpose: The aim of this study was to predict the mutational status of the EGFR in non-small cell lung cancer patients using radiomics features extracted from PET-CT images. Methods: Retrospective study that involve 34 patients with lung cancer confirmed by histology and EGFR status mutation assessment. A total of 2.205 radiomics features were extracted from manual segmentation of the PET-CT images using pyradiomics library. Both computed tomography and positron emission tomography images were used. All images were acquired with intravenous iodinated contrast and F18-FDG. Preprocessing includes resampling, normalization, and discretization of the pixel intensity. Three methods were used for the feature selection process: backward selection (set 1), forward selection (set 2), and feature importance analysis of random forest model (set 3). Nine machine learning methods were used for radiomics model building. Results: 35.2% of patients had EGFR mutation, without significant differences in age, gender, tumor size and SUVmax. After the feature selection process 6, 7 and 17 radiomics features were selected, respectively in each group. The best performances were obtained by Ridge Regression in set 1: AUC of 0.826 (95% CI, 0.811 - 0.839), Random Forest in set 2: AUC of 0.823 (95% CI, 0.808 - 0.838) and Neural Network in set 3: AUC of 0.821 (95% CI, 0.808 - 0.835). Conclusion: The radiomics features analysis has the potential of predicting clinically relevant mutations in lung cancer patients through a non-invasive methodology.
Mass classification in mammography with multi-agent based fusion of human and machine intelligence

Although the computer-aided diagnosis (CAD) system can be applied for classifying the breast masses, the effects of this method on improvement of the radiologist’ accuracy for distinguishing malignant from benign lesions still remain unclear. This study provided a novel method to classify breast masses by integrating the intelligence of human and machine. In this research, 224 breast masses were selected in mammography from database of DDSM with Breast Imaging Reporting and Data System (BI-RADS) categories. Three observers (a senior and a junior radiologist, as well as a radiology resident) were employed to independently read and classify these masses utilizing the Positive Predictive Values (PPV) for each BI-RADS category. Meanwhile, a CAD system was also implemented for classification of these breast masses between malignant and benign. To combine the decisions from the radiologists and CAD, the fusion method of the Multi-Agent was provided. Significant improvements are observed for the fusion system over solely radiologist or CAD. The area under the receiver operating characteristic curve (AUC) of the fusion system increased by 9.6%, 10.3% and 21% compared to that of radiologists with senior, junior and resident level, respectively. In addition, the AUC of this method based on the fusion of each radiologist and CAD are 3.5%, 3.6% and 3.3% higher than that of CAD alone. Finally, the fusion of the three radiologists with CAD achieved AUC value of 0.957, which was 5.6% larger compared to CAD. Our results indicated that the proposed fusion method has better performance than radiologist or CAD alone.
Parametric Surface Constrained Upsampler Network for Point Cloud

Designing a point cloud upsampler, which aims to generate a clean and dense point cloud given a sparse point representation, is a fundamental and challenging problem in computer vision. A line of attempts achieves this goal by establishing a point-to-point mapping function via deep neural networks. However, these approaches are prone to produce outlier points due to the lack of explicit surface-level constraints. To solve this problem, we introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions, where the new generated points are then constrained on the underlying surface. These designs are integrated into two different networks for two tasks that take advantages of upsampling layers -- point cloud upsampling and point cloud completion for evaluation. The state-of-the-art experimental results on both tasks demonstrate the effectiveness of the proposed method. The implementation code will be available at https://github.com/corecai163/PSCU.
A mimetic finite difference based quasi-static magnetohydrodynamic solver for force-free plasmas in tokamak disruptions

Force-free plasmas are a good approximation where the plasma pressure is tiny compared with the magnetic pressure, which is the case during the cold vertical displacement event (VDE) of a major disruption in a tokamak. On time scales long compared with the transit time of Alfven waves, the evolution of a force-free plasma is most efficiently described by the quasi-static magnetohydrodynamic (MHD) model, which ignores the plasma inertia. Here we consider a regularized quasi-static MHD model for force-free plasmas in tokamak disruptions and propose a mimetic finite difference (MFD) algorithm. The full geometry of an ITER-like tokamak reactor is treated, with a blanket module region, a vacuum vessel region, and the plasma region. Specifically, we develop a parallel, fully implicit, and scalable MFD solver based on PETSc and its DMStag data structure for the discretization of the five-field quasi-static perpendicular plasma dynamics model on a 3D structured mesh. The MFD spatial discretization is coupled with a fully implicit DIRK scheme. The algorithm exactly preserves the divergence-free condition of the magnetic field under the resistive Ohm's law. The preconditioner employed is a four-level fieldsplit preconditioner, which is created by combining separate preconditioners for individual fields, that calls multigrid or direct solvers for sub-blocks or exact factorization on the separate fields. The numerical results confirm the divergence-free constraint is strongly satisfied and demonstrate the performance of the fieldsplit preconditioner and overall algorithm. The simulation of ITER VDE cases over the actual plasma current diffusion time is also presented.
Hybrid Kinetic/Fluid numerical method for the Vlasov-Poisson-BGK equation in the diffusive scaling

This short note presents an extension of the hybrid, model-adaptation method introduced in [T. ~Laidin, \textit{arXiv 2202.03696}, 2022] for linear collisional kinetic equations in a diffusive scaling to the nonlinear mean-field Vlasov-Poisson-BGK model. The aim of the approach is to reduce the computational cost by taking advantage of the lower dimensionality of the asymptotic model while reducing the overall error. It relies on two criteria motivated by a perturbative approach to obtain a dynamic domain adaptation. The performance of the method and the conservation of mass are illustrated through numerical examples.