Miracle-dz/UniFAR-FTUs huggingface dataset:
Mortality in Patients Hospitalized With Acute Myocardial Infarction Without Standard Modifiable Risk Factors: The ARIC Study Community Surveillance

Background Prevention strategies targeting standard modifiable cardiovascular risk factors (SMuRFs; diabetes, hypertension, smoking, hypercholesterolemia) are critical to improving cardiovascular disease outcomes. However, acute myocardial infarction (AMI) among individuals who lack 1 or more SMuRFs is not uncommon. Moreover, the clinical characteristics and prognosis of SMuRFless individuals are not well characterized. Methods and Results We analyzed AMI hospitalizations from 2000 to 2014 captured by the ARIC (Atherosclerosis Risk in Community) study community surveillance. AMI was classified by physician review using a validated algorithm. Clinical data, medications, and procedures were abstracted from the medical record. Main study outcomes included short‐ and long‐term mortality within 28 days and 1 year of AMI hospitalization. Between 2000 and 2014, a total of 742 (3.6%) of 20 569 patients with AMI were identified with no documented SMuRFs. Patients without SMuRFs were less likely to receive aspirin, nonaspirin antiplatelet therapy, or beta blockers and less often underwent angiography and revascularization. Compared with those with one or more SMuRFs, patients without SMuRFs had significantly higher 28‐day (odds ratio, 3.23 [95% CI, 1.78–5.88]) and 1‐year (hazard ratio, 2.09 [95% CI, 1.29–3.37]) adjusted mortality. When examined across 5‐year intervals from 2000 to 2014, the incidence of 28‐day mortality significantly increased for patients without SMuRFs (7% to 15% to 27%), whereas it declined for those with 1 or more SMuRFs (7% to 5% to 5%). Conclusions Individuals without SMuRFs presenting with AMI have an increased risk of all‐cause mortality with an overall lower prescription rate for guideline‐directed medical therapy. These findings highlight the need for evidence‐based pharmacotherapy during hospitalization and the need to discover new markers and mechanisms for early risk identification in this population.
Quasi-objective Nonlinear Principal Component Analysis and applications to the atmosphere

NonLinear Principal Component Analysis (NLPCA) using three-hidden-layer feed-forward neural networks can produce solutions that over-fit the data and are non-unique. These problems have been dealt with by subjective methods during the network training. This study shows that these problems are intrinsic due to the three-hidden-layer architecture. A simplified two-hidden-layer feed-forward neural network that has no encoding layer and no bottleneck and output biases is proposed. This new, compact NLPCA model alleviates these problems without employing the subjective methods and is called quasi-objective. The compact NLPCA is applied to the zonal winds observed at seven pressure levels between 10 and 70 hPa in the equatorial stratosphere to represent the Quasi-Biennial Oscillation (QBO) and investigate its variability and structure. The two nonlinear principal components of the dataset offer a clear picture of the QBO. In particular, their structure shows that the QBO phase consists of a predominant 28.4month cycle that is modulated by an 11-year cycle and a longer-period cycle. The significant difference in variability of the winds between cold and warm seasons and the tendency for a seasonal synchronization of the QBO phases are well captured. The one-dimensional NLPCA approximation of the dataset provides a better representation of the QBO than the classical principal component analysis and a better description of the asymmetry of the QBO between westerly and easterly shear zones and between their transitions. The compact NLPCA is then applied to the Arctic Oscillation (AO) index and aforementioned zonal winds to investigate the relationship of the AO with the QBO. The NLPCA of the AO index and zonalwinds dataset shows clearly that, of covariation of the two oscillations, the phase defined by the two nonlinear principal components progresses with a predominant 28.4-month periodicity, plus the 11-year and longer-period modulations. Large positive values of the AO index occur when westerlies prevail near the middle and upper levels of the equatorial stratosphere. Large negative values of the AO index arise when easterlies occupy over half the layer of the equatorial stratosphere.
Prethermalization and transient dynamics of the Multi-Channel Kondo systems under generic quantum quenches: Insights form Large-$N$ Schwinger-Keldysh approach

Understanding out-of-equilibrium many-body quantum systems is crucial in contemporary physics. However, capturing the universal dynamics of such systems remains challenging despite advanced numerical methods. We investigate the multi-channel Kondo impurity (MCKI) model, hosting an over-screened Kondo state with non-Fermi liquid characteristics. Using the large-N Schwinger-Keldysh approach, we study transient dynamics and long-time quasi-equilibrium properties following a sudden change in the Kondo coupling. We consider two initial states: over-screened Kondo and high-temperature Fermi liquid. In the over-screened Kondo state, we observe oscillations in spin-spin correlations and the Kondo order parameter, representing quantum revival of the entangled state. In the high-temperature Fermi liquid state, the absence of oscillations is attributed to the de-phasing mechanism. The system reaches quasi-equilibrium, manifested as incoherent thermalization between the impurity and conduction electrons. We observe a non-vanishing effective temperature difference between the impurity spin (Abrikosov fermion) and the composite boson formed with conduction electrons at the impurity site. This quasi-equilibrium is prethermalization, while complete thermalization occurs on an O(N) timescale with 1/N correction. We discuss the quantum cooling effect and quantum Boltzmann equations. Our study establishes a foundation for investigating large-N quantum field theory treatment of quantum many-body systems, revealing universal properties and a fresh perspective on prethermalization.
Assessing the Performance of 1D-Convolution Neural Networks to Predict Concentration of Mixture Components from Raman Spectra

An emerging application of Raman spectroscopy is monitoring the state of chemical reactors during biologic drug production. Raman shift intensities scale linearly with the concentrations of chemical species and thus can be used to analytically determine real-time concentrations using non-destructive light irradiation in a label-free manner. Chemometric algorithms are used to interpret Raman spectra produced from complex mixtures of bioreactor contents as a reaction evolves. Finding the optimal algorithm for a specific bioreactor environment is challenging due to the lack of freely available Raman mixture datasets. The RaMix Python package addresses this challenge by enabling the generation of synthetic Raman mixture datasets with controllable noise levels to assess the utility of different chemometric algorithm types for real-time monitoring applications. To demonstrate the capabilities of this package and compare the performance of different chemometric algorithms, 48 datasets of simulated spectra were generated using the RaMix Python package. The four tested algorithms include partial least squares regression (PLS), a simple neural network, a simple convolutional neural network (simple CNN), and a 1D convolutional neural network with a ResNet architecture (ResNet). The performance of the PLS and simple CNN model was found to be comparable, with the PLS algorithm slightly outperforming the other models on 83\% of the data sets. The simple CNN model outperforms the other models on large, high noise datasets, demonstrating the superior capability of convolutional neural networks compared to PLS in analyzing noisy spectra. These results demonstrate the promise of CNNs to automatically extract concentration information from unprocessed, noisy spectra, allowing for better process control of industrial drug production. Code for this project is available at github.com/DexterAntonio/RaMix.
Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar

Incoherent processing for synthetic aperture radar (SAR) is a promising approach that enables low implementation costs, simplified hardware designs and operations in high frequency spectrum compared to the conventional imaging methods using coherent processing. Existing non-convex phaseless imaging algorithms offer recovery guarantees over limited range of forward models. In recent years, several deep learning (DL) based techniques have been introduced with the goal of extending applicability of phaseless imaging techniques to wave-based imaging modalities by addressing fundamental challenges, such as, lack of redundancy, non-uniqueness issues encountered commonly with inverse scattering models. In this paper, we introduce a DL-based phaseless SAR imaging approach that is designed under the premise that the spectral estimation technique, widely used for initializing non-convex phase retrieval algorithms, has significance far beyond generating good initial points. We extend the iterative power method for spectral estimation by using deep denoisers at each iteration, and subsequently design a deep imaging network within the plug-and-play framework. Finally, we verify the feasibility of our approach using synthetic SAR measurements.
A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods to monitor the behaviors of their assets and protect them from critical attacks. In this paper, we systematically survey related research articles and industrial systems to highlight the current status of this arms race in enterprise network security. First, we discuss the taxonomy of distributed network attacks on enterprise assets, including distributed denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing methods in monitoring and classifying network behavior of enterprise hosts to verify their benign activities and isolate potential anomalies. Third, state-of-the-art detection methods for distributed network attacks sourced from external attackers are elaborated, highlighting their merits and bottlenecks. Fourth, as programmable networks and machine learning (ML) techniques are increasingly becoming adopted by the community, their current applications in network security are discussed. Finally, we highlight several research gaps on enterprise network security to inspire future research.
Boltzmann machines and quantum many-body problems

Analyzing quantum many-body problems and elucidating the entangled structure of quantum states is a significant challenge common to a wide range of fields. Recently, a novel approach using machine learning was introduced to address this challenge. The idea is to ‘embed’ nontrivial quantum correlations (quantum entanglement) into artificial neural networks. Through intensive developments, artificial neural network methods are becoming new powerful tools for analyzing quantum many-body problems. Among various artificial neural networks, this topical review focuses on Boltzmann machines and provides an overview of recent developments and applications.
Evaluating the Task Generalization of Temporal Convolutional Networks for Surgical Gesture and Motion Recognition Using Kinematic Data

Fine-grained activity recognition enables explainable analysis of procedures for skill assessment, autonomy, and error detection in robot-assisted surgery. However, existing recognition models suffer from the limited availability of annotated datasets with both kinematic and video data and an inability to generalize to unseen subjects and tasks. Kinematic data from the surgical robot is particularly critical for safety monitoring and autonomy, as it is unaffected by common camera issues such as occlusions and lens contamination. We leverage an aggregated dataset of six dry-lab surgical tasks from a total of 28 subjects to train activity recognition models at the gesture and motion primitive (MP) levels and for separate robotic arms using only kinematic data. The models are evaluated using the LOUO (Leave-One-User-Out) and our proposed LOTO (Leave-One-Task-Out) cross validation methods to assess their ability to generalize to unseen users and tasks respectively. Gesture recognition models achieve higher accuracies and edit scores than MP recognition models. But, using MPs enables the training of models that can generalize better to unseen tasks. Also, higher MP recognition accuracy can be achieved by training separate models for the left and right robot arms. For task-generalization, MP recognition models perform best if trained on similar tasks and/or tasks from the same dataset.
Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks

Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. This study uses social media and pre-trained language models to explore how user-generated data can predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature. The results show that new models outperform the previous approach with an accuracy rate of up to 97%. Analyzing the results while complementing past findings, we find that even tiny amounts of data (Like users’ bio descriptions) have the potential to predict mental disorders. We conclude that social media data is an excellent source of mental health screening, and pre-trained models can effectively automate this critical task.
Detection of Bosenovae with Quantum Sensors on Earth and in Space

In a broad class of theories, the accumulation of ultralight dark matter (ULDM) with particles of mass $10^{-22}~\textrm{eV}<m_{\phi}<1~\textrm{eV}$ leads the to formation of long-lived bound states known as boson stars. When the ULDM exhibits self-interactions, prodigious bursts of energy carried by relativistic bosons are released from collapsing boson stars in bosenova explosions. We extensively explore the potential reach of terrestrial and space-based experiments for detecting transient signatures of emitted relativistic bursts of scalar particles, including ULDM coupled to photons, electrons, and gluons, capturing a wide range of motivated theories. For the scenario of relaxion ULDM, we demonstrate that upcoming experiments and technology such as nuclear clocks as well as space-based interferometers will be able to sensitively probe orders of magnitude in the ULDM coupling-mass parameter space, challenging to study otherwise, by detecting signatures of transient bosenova events. Our analysis can be readily extended to different scenarios of relativistic scalar particle emission.
ManimML: Communicating Machine Learning Architectures with Animation

There has been an explosion in interest in machine learning (ML) in recent years due to its applications to science and engineering. However, as ML techniques have advanced, tools for explaining and visualizing novel ML algorithms have lagged behind. Animation has been shown to be a powerful tool for making engaging visualizations of systems that dynamically change over time, which makes it well suited to the task of communicating ML algorithms. However, the current approach to animating ML algorithms is to handcraft applications that highlight specific algorithms or use complex generalized animation software. We developed ManimML, an open-source Python library for easily generating animations of ML algorithms directly from code. We sought to leverage ML practitioners' preexisting knowledge of programming rather than requiring them to learn complex animation software. ManimML has a familiar syntax for specifying neural networks that mimics popular deep learning frameworks like Pytorch. A user can take a preexisting neural network architecture and easily write a specification for an animation in ManimML, which will then automatically compose animations for different components of the system into a final animation of the entire neural network. ManimML is open source and available at https://github.com/helblazer811/ManimML.
Flow Dynamics of a Dodecane Jet in Oxygen Crossflow at Supercritical Pressures

In advanced aeropropulsion engines, liquid fuel is often injected into the combustor at supercritical pressures, where flow dynamics are distinct from the subcritical counterpart. Large-eddy simulation, combined with real-fluid thermodynamics and transport processes of a liquid N-dodecane jet in oxygen crossflow, is presented at different supercritical pressures and jet-to-crossflow momentum flux ratios ([Formula: see text]). Various vortical structures are discussed in detail. The results show that, with the same velocity ratio of 0.75, the upstream shear layer (USL) is absolutely unstable at high supercritical pressure ([Formula: see text]) and convectively unstable at low supercritical pressure ([Formula: see text]), consistent with the empirical criterion at subcritical pressures ([Formula: see text]). While decreasing [Formula: see text] to 7.1 at low supercritical pressure, however, the USL remains convectively unstable, manifested by the variable dominant Strouhal number of the USL along the upstream jet trajectory. Such abnormal behavior can be attributed to the real-fluid effect induced by strong density stratification at low supercritical pressure, under which an inflection point in the upstream mixing layer renders a large density gradient and tends to stabilize the USL. Linear stability analysis further verifies these findings. The analysis of spatial mixing deficiencies reveals that the mixing efficiency is enhanced at a higher [Formula: see text].
Semi-supervised learning of order parameter in 2D Ising and XY models using Conditional Variational Autoencoders

We investigate the application of deep learning techniques employing the conditional variational autoencoders for semi-supervised learning of latent parameters to describe phase transition in the two-dimensional (2D) ferromagnetic Ising model and the two-dimensional XY model. For both models, we utilize spin configurations generated using the Wolff algorithms below and above the critical temperatures. For the 2D Ising model we find the latent parameter of conditional variational autoencoders is correlated to the known order parameter of magnetization more efficiently than their correspondence in variational autoencoders used previously. It can also clearly identify the restoration of the $\mathbb{Z}_2$ symmetry beyond the critical point. The critical temperature extracted from the latent parameter at larger lattices are found to be approaching its correct value. Similarly, for the 2D XY model, we find our chosen network with the latent representation of conditional variational autoencoders is equally capable of separating the two phases between the high and low temperatures, again at the correct critical temperature with reasonable accuracy. Together these results show that the latent representation of conditional variational autoencoders can be employed efficiently to identify the phases of condensed matter systems, without their prior knowledge.
Sparse RF Lens Antenna Array Design for AoA Estimation in Wideband Systems: Placement Optimization and Performance Analysis

In this paper, we propose a novel architecture for a lens antenna array (LAA) designed to work with a small number of antennas and enable angle-of-arrival (AoA) estimation for advanced 5G vehicle-to-everything (V2X) use cases that demand wider bandwidths and higher data rates. We derive a received signal in terms of optical analysis to consider the variability of the focal region for different carrier frequencies in a wideband multi-carrier system. By taking full advantage of the beam squint effect for multiple pilot signals with different frequencies, we propose a novel reconfiguration of antenna array (RAA) for the sparse LAA and a max-energy antenna selection (MS) algorithm for the AoA estimation. In addition, this paper presents an analysis of the received power at the single antenna with the maximum energy and compares it to simulation results. In contrast to previous studies on LAA that assumed a large number of antennas, which can require high complexity and hardware costs, the proposed RAA with MS estimation algorithm is shown meets the requirements of 5G V2X in a vehicular environment while utilizing limited RF hardware and has low complexity.
Toward a Scalable Census of Dashboard Designs in the Wild: A Case Study with Tableau Public

Dashboards remain ubiquitous artifacts for presenting or reasoning with data across different domains. Yet, there has been little work that provides a quantifiable, systematic, and descriptive overview of dashboard designs at scale. We propose a schematic representation of dashboard designs as node-link graphs to better understand their spatial and interactive structures. We apply our approach to a dataset of 25,620 dashboards curated from Tableau Public to provide a descriptive overview of the core building blocks of dashboards in the wild and derive common dashboard design patterns. To guide future research, we make our dashboard corpus publicly available and discuss its application toward the development of dashboard design tools.
Neutrino flares of radio blazars observed from TeV to PeV

Radio blazars have been linked both to individual high-energy neutrino events and to excesses in likelihood sky maps constructed from lower-energy neutrino data. However, the exact mechanism by which neutrinos are produced in these sources is still unknown. Here, we demonstrate that IceCube neutrinos with energies over 200 TeV, which were previously associated with bright radio blazars, are significantly more likely to be accompanied by flares of lower-energy events, compared to those lacking blazar counterparts. The parsec-scale core radio flux density of blazars, positioned within the error regions of energetic events, is strongly correlated with the likelihood of a day-scale lower-energy neutrino flare in directional and temporal coincidence with the high-energy event, reported by IceCube. The probability of a chance correlation is $3.6*10^{-4}$. This confirms the neutrino-blazar connection in a new and independent way, and provides valuable clues to understanding the origin of astrophysical neutrinos.