Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardioembolic vs. Noncardioembolic Strokes in Atrial Fibrillation: Frequency and Effect of Antithrombotic Agents in the Stroke Prevention in Atrial Fibrillation Studies

Background: While atrial fibrillation (AF) increases the risk of cardioembolic stroke, some ischemic strokes in AF patients are noncardioembolic. Objectives: To assess ischemic stroke mechanisms in AF and to compare their responses to antithrombotic therapies. Methods: On-therapy analyses of ischemic strokes occurring in 3,950 participants in the Stroke Prevention in Atrial Fibrillation I–III clinical trials. Strokes were classified by presumed mechanism according to specified neurologic features by neurologists unaware of antithrombotic therapy. Results: Of 217 ischemic strokes, 52% were classified as probably cardioembolic, 24% as noncardioembolic, and 24% as of uncertain cause (i.e., 68% of classifiable infarcts were deemed cardioembolic). Compared to those receiving placebo or no antithrombotic therapy, the proportion of cardioembolic stroke was lower in patients taking adjusted-dose warfarin (p = 0.02), while the proportion of noncardioembolic stroke was lower in those taking aspirin (p = 0.06). Most (56%) ischemic strokes occurring in AF patients taking adjusted-dose warfarin were noncardioembolic vs. 16% of strokes in those taking aspirin. Adjusted-dose warfarin reduced cardioembolic strokes by 83% (p < 0.001) relative to aspirin. Cardioembolic strokes were particularly disabling (p = 0.05). Conclusions: Most ischemic strokes in AF patients are probably cardioembolic, and these are sharply reduced by adjusted-dose warfarin. Aspirin in AF patients appears to primarily reduce noncardioembolic strokes. AF patients at highest risk for stroke have the highest rates of cardioembolic stroke and have the greatest reduction in stroke by warfarin.
Scylla: A Mesos Framework for Container Based MPI Jobs

Open source cloud technologies provide a wide range of support for creating customized compute node clusters to schedule tasks and managing resources. In cloud infrastructures such as Jetstream and Chameleon, which are used for scientific research, users receive complete control of the Virtual Machines (VM) that are allocated to them. Importantly, users get root access to the VMs. This provides an opportunity for HPC users to experiment with new resource management technologies such as Apache Mesos that have proven scalability, flexibility, and fault tolerance. To ease the development and deployment of HPC tools on the cloud, the containerization technology has matured and is gaining interest in the scientific community. In particular, several well known scientific code bases now have publicly available Docker containers. While Mesos provides support for Docker containers to execute individually, it does not provide support for container inter-communication or orchestration of the containers for a parallel or distributed application. In this paper, we present the design, implementation, and performance analysis of a Mesos framework, Scylla, which integrates Mesos with Docker Swarm to enable orchestration of MPI jobs on a cluster of VMs acquired from the Chameleon cloud [1]. Scylla uses Docker Swarm for communication between containerized tasks (MPI processes) and Apache Mesos for resource pooling and allocation. Scylla allows a policy-driven approach to determine how the containers should be distributed across the nodes depending on the CPU, memory, and network throughput requirement for each application.
He abundances in disc galaxies

We investigate how the stellar and gas-phase He abundances evolve as a function of time within simulated star-forming disc galaxies with different star formation histories. We make use of a cosmological chemodynamical simulation for galaxy formation and evolution, which includes star formation as well as energy and chemical enrichment feedback from asymptotic giant branch stars, core-collapse supernovae, and Type Ia supernovae. The predicted relations between the He mass fraction, Y, and the metallicity, Z, in the interstellar medium of our simulated disc galaxies depend on the galaxy star formation history. In particular, dY/dZ is not constant and evolves as a function of time, depending on the specific chemical element that we choose to trace Z; in particular, dY/dXO and dY/dXC increase as a function of time, whereas dY/dXN decreases. In the gas-phase, we find negative radial gradients of Y, due to the inside-out growth of our simulated galaxy discs as a function of time; this gives rise to longer chemical enrichment timescales in the outer galaxy regions, where we find lower average values for Y and Z. Finally, by means of chemical-evolution models, in the galactic bulge and inner disc, we predict steeper Y vs. age relations at high Z than in the outer galaxy regions. We conclude that for calibrating the assumed Y − Z relation in stellar models, C, N, and C+N are better proxies for the metallicity than O because they show steeper and less scattered relations.
Formation of disc galaxies around z ∼ 2

We present combined evolution of morphological and stellar properties of galaxies on the two sides of z=2 (2.0 2 to ~70% for z 2 to z<2, pure spheroids maintain roughly the same values. Additionally, while bulge hosting discs witness an expansion in scale length (~1.3 times), their bulge sizes as well as bulge to total light ratio see no evolution, suggesting that z~2 is pre-dominantly a disc formation period.
Triage of astrometric binaries – how to find triple systems and dormant black hole secondaries in the Gaia orbits

Preparing for the expected wealth of Gaia detections, we consider here a simple algorithm for classifying unresolved astrometric binaries with main-sequence (MS) primary into three classes: binaries with a probable MS secondary, with two possible values for the mass ratio; probable hierarchical triple MS systems with an astrometric secondary as a close binary, with a limited range of mass-ratio values; and binaries with a compact-object secondary, with a minimal value of the mass ratio. This is done by defining a unitless observational parameter ‘Astrometric Mass-Ratio Function’ (AMRF), $\mathcal {A}$, of a binary, based on primary-mass estimation, in addition to the astrometric parameters – the angular semimajor axis, the period, and the parallax. We derive the $\mathcal {A}$ value that differentiates the three classes by forward modelling representative binaries of each class, assuming some mass–luminosity relation. To demonstrate the potential of the algorithm, we consider the orbits of 98 Hipparcos astrometric binaries with MS primaries, using the Hipparcos parallaxes and the primary-mass estimates. For systems with known spectroscopic orbital solution, our results are consistent with the spectroscopic elements, validating the suggested approach. The algorithm will be able to identify hierarchical triple systems and dormant neutron star and black hole companions in the Gaia astrometric binaries.
Scaling and conformal symmetries for plane gravitational waves

The isometries of an exact plane gravitational wave are symmetries for both massive and massless particles. Their conformal extensions are in fact chrono-projective transformations introduced earlier by Duval et al are symmetries for massless particles. Homotheties are universal chrono-projective symmetries for any profile. Chrono-projective transformations also generate new conserved quantities for the underlying non-relativistic systems in the Bargmann framework. Homotheties play a similar role for the lightlike "vertical" coordinate as isometries play for the transverse coordinates.
After‐effects of repetitive stimulation at low frequency on fast‐contracting motor units of cat muscle.

Twitch and tetanic contractions of single motor units of the cat peroneus tertius muscle were examined after application of a test allowing their identification as either fast fatigable (f.f.) or fast fatigue‐resistant (f.r.) or fast intermediate (f.i.) or slow units as established by Burke, Levine, Tsairis & Zajac (1973). The test was found to leave two kinds of after‐effects in f.f. , f.r. and f.i. units whereas it did not affect slow units. The first after‐effect was an early and brief potentiation of twitch tension occurring in all f.r. and f.i. units and in most f.f. units. The second after‐effect, termed ‘delayed fatigue', was a prolonged depression of tension output, that developed slowly following the early potentiation in all f.f. and f.i. units and more than half of the f.r. units. One hour after the test, unfused tetanic contractions elicited by 20‐40/sec stimulation were deeply depressed in motor units that had been left without stimulation since the end of the test. Recovery took place in 3‐5 hr. Motor units affected by delayed fatigue could nevertheless be made to develop nearly normal tension by gradual build‐up upon prolonged stimulation at 30‐40/sec. Maximal tetanic contractions elicited by 200/sec stimulation were much less depressed during delayed fatigue than unfused tetanic contractions. These observations suggest that contractile mechanism were not impaired by delayed fatigue. Since absence of change in muscle action potential indicated that excitation of muscle fibres was not affected either, delayed fatigue might be due to a temporary failure of excitation‐contraction coupling.
Intentional Attention Mask Transformation for Robust CNN Classification

Convolutional Neural Networks have achieved impressive results in various tasks, but interpreting the internal mechanism is a challenging problem. To tackle this problem, we exploit a multi-channel attention mechanism in feature space. Our network architecture allows us to obtain an attention mask for each feature while existing CNN visualization methods provide only a common attention mask for all features. We apply the proposed multi-channel attention mechanism to multi-attribute recognition task. We can obtain different attention mask for each feature and for each attribute. Those analyses give us deeper insight into the feature space of CNNs. Furthermore, our proposed attention mechanism naturally derives a method for improving the robustness of CNNs. From the observation of feature space based on the proposed attention mask, we demonstrate that we can obtain robust CNNs by intentionally emphasizing features that are important for attributes. The experimental results for the benchmark dataset show that the proposed method gives high human interpretability while accurately grasping the attributes of the data, and improves network robustness.
Factorised Neural Relational Inference for Multi-Interaction Systems

Many complex natural and cultural phenomena are well modelled by systems of simple interactions between particles. A number of architectures have been developed to articulate this kind of structure, both implicitly and explicitly. We consider an unsupervised explicit model, the NRI model, and make a series of representational adaptations and physically motivated changes. Most notably we factorise the inferred latent interaction graph into a multiplex graph, allowing each layer to encode for a different interaction-type. This fNRI model is smaller in size and significantly outperforms the original in both edge and trajectory prediction, establishing a new state-of-the-art. We also present a simplified variant of our model, which demonstrates the NRI's formulation as a variational auto-encoder is not necessary for good performance, and make an adaptation to the NRI's training routine, significantly improving its ability to model complex physical dynamical systems.
Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.
Weakly Modular Maximization: Applications, Hardness, Tractability, and Efficient Algorithms

Maximization of non-submodular set functions has been attracting a lot of attention since many set functions that appear in practice lack submodularity. In this paper, we study maximization of weakly modular functions, which has both weak submodularity and weak supermodularity. Actually, weakly modular maximization (WMM) can model various important (possibly non-submodular) maximization problems including feature selection and production planning. We reveal two theoretical properties of WMM: We prove a hardness result related to improving an existing approximation guarantee achieved by the greedy algorithm, and we show that WMM is fixed-parameter tractable under certain conditions. The latter result provides a new time–accuracy trade-off for `0-constrained minimization as a corollary. We then prove that efficient multi-stage algorithms can perform well for WMM; the obtained theoretical guarantees generalize and improve some existing results. Experiments confirm the practical utility of the multi-stage algorithms.
Coding theory for noiseless channels realized by anonymous oblivious mobile robots

We propose an information transmission scheme by a swarm of anonymous oblivious mobile robots on a graph. The swarm of robots travel from a sender vertex to a receiver vertex to transmit a symbol generated at the sender. The codeword for a symbol is a pair of an initial configuration at the sender and a set of terminal configurations at the receiver. The set of such codewords forms a code. We analyze the performance of the proposed scheme in terms of its code size and transmission delay. We first demonstrate that a lower bound of the transmission delay depends on the size of the swarm, and the code size is upper bounded by an exponent of the size of the swarm. We then give two algorithms for a swarm of a fixed size. The first algorithm realizes a near optimal code size with a large transmission delay. The second algorithm realizes an optimal transmission delay with a smaller code size. We then consider information transmission by swarms of different sizes and present upper bounds of the expected swarm size by the two algorithms. We also present lower bounds by Shannon's lemma and noiseless coding theorem.
Endoscopic Transesophageal and Endoscopic Transbronchial Real-Time Ultrasound-Guided Biopsy

The goal of preoperative staging of non-small-cell lung cancer is to identify patients who will benefit from surgical resection. Various imaging and less invasive modalities are now available to improve therapy decision-making, and with the introduction of multimodality treatment of lung cancer, proper staging of this disease is becoming more and more important. This staging process is therefore not only a question of determining the prognosis, but it is also necessary information for institution of the right treatment. Proper staging and restaging of lung cancer should also be a must in the evaluation of the different treatments of lung cancer in controlled clinical trials. In lung cancer, endoscopic ultrasound scanning (EUS) is emerging as an accurate, nonsurgical alternative to staging the mediastinum through EUS-fine-needle aspiration (EUS-FNA). The author presents publications on evaluating EUS in diagnosing lymph node involvement in lung cancer and tumor ingrowths in the mediastinum. With EUS it is possible to guide FNA with direct visualization of the needle path into the lymph nodes in real time. Although this method is only able to visualize the posterior path and the inferior parts of the mediastinum, it makes it possible to visualize the aortopulmonary window. The limitation of EUS is a sensitivity of about 90%; nonetheless, this method is more precise than other staging procedures except for mediastinoscopy, which is limited to only the anterior parts of the mediastinum.
Time-varying Autoregression with Low Rank Tensors

We present a windowed technique to learn parsimonious time-varying autoregressive models from multivariate timeseries. This unsupervised method uncovers spatiotemporal structure in data via non-smooth and non-convex optimization. In each time window, we assume the data follow a linear model parameterized by a potentially different system matrix, and we model this stack of system matrices as a low rank tensor. Because of its structure, the model is scalable to high-dimensional data and can easily incorporate priors such as smoothness over time. We find the components of the tensor using alternating minimization and prove that any stationary point of this algorithm is a local minimum. In a test case, our method identifies the true rank of a switching linear system in the presence of noise. We illustrate our model's utility and superior scalability over extant methods when applied to several synthetic and real examples, including a nonlinear dynamical system, worm behavior, sea surface temperature, and monkey brain recordings.
Fixed dosage of low‐molecular‐weight heparins causes large individual variation in coagulability, only partly correlated to body weight

Summary. Backgrounds: Low‐molecular‐weight heparins (LMWHs) are routinely given without the control of their effect on coagulation. The endogenous thrombin potential (ETP) is a sensitive detector of the heparin effect. Question: What is the interindividual variation in TG after a fixed dose of LMWH in normal volunteers, is it explained by variation in weight? Methods: Subcutaneous (s.c.) injection, in 12 healthy volunteers, of 9000 aXa‐units of unfractionated heparin (UFH) and of three heparins with narrow MW distribution around 10.5, 6.0 and 4.5 kD. Measurement of anti‐thrombin (aIIa) and antifactor Xa (aXa)‐activities and ETP at 11 time points over 24 h. Results: The coefficient of variation (CV) of the AUCs of aXa‐ and aIIa‐activities is 50% for UFH and 22–37% for LMWHs. Because of the hyperbolic form of the dose–response curve, the CV of the inhibition of the ETP is lower: 32% for UFH and 13–21% for the LMWHs. Fixed dosage of LMWH caused under‐dosage in 10–13% of the samples and over‐dosage in 5–11%. High or low response is an individual property independent of the type of heparin injected and only partially explained by variation in body weight. Conclusion: Optimized individual dosage of LMWH is possible through recognition of high and low responders, which requires one measurement of the heparin concentration or, preferably, the heparin effect on the ETP, 2–5 h after a first injection.
Isolated Heisenberg magnet as a quantum time crystal

Local observables of closed many-body quantum systems are generally believed to quickly equilibrate after a quench, according to the eigenstate thermalization hypothesis. Here, the authors show that the paradigmatic Heisenberg quantum magnet violates this fundamental assumption. They analytically find measurable local correlation functions that show perpetual oscillations, while at the same time all single-particle observables equilibrate. They link their existence to the presence of dynamical symmetries and obtain a fractal dependence of their oscillation frequency on the interaction strength.
Pediatric and Neonatal Staphylococcus aureus Bacteremia Epidemiology, Risk Factors, and Outcome

Objective. To evaluate the impact of methicillin-resistant Staphylococcus aureus on the prevalence of S. aureus bloodstream infection among children. Methods. Retrospective analysis of demographic data, risk factors for infection, and clinical outcomes for children (age, less than 18 years) with S. aureus bacteremia hospitalized at a children's hospital during 2001–2006. Results. We identified 164 episodes of S. aureus bacteremia among 151 children. The prevalence of bacteremia due to methicillin-susceptible S. aureus during 2001–2003 was approximately the same as that during 2004–2006 (29 and 30 cases, respectively, per 10,000 hospitalized children [hereafter, “per 10,000 hospitalizations”]), but the prevalence of bacteremia due to methicillin-resistant S. aureus increased from 4 to 11 cases, respectively, per 10,000 hospitalizations (P = .015). A total of 48% of infections involved children who had S. aureus-positive blood cultures less than 3 days after hospital admission. Seventy-four percent of these children had a preexisting comorbidity. When the prevalence of S. aureus bacteremia was stratified by race, sex, or age, neonates hospitalized at birth and Hispanic children had significantly reduced risks of infection. Children younger than 1 year of age (excluding neonates hospitalized at birth) had an increased prevalence of hospital-onset S. aureus bacteremia. There was a disproportionate increase in the risk of S. aureus bacteremia for each additional week of hospitalization among children with hospital-onset S. aureus bacteremia. Children with methicillin-resistant S. aureus bacteremia had a longer hospital stay, were transferred to another facility at a greater rate than they were discharged home, and had a greater mortality rate, compared with children with methicillin-susceptible S. aureus bacteremia. Conclusion. This study documents the prevalence of S. aureus bacteremia among children with a high risk for acquiring this infection, and it describes populations of children who are at higher risk for bacteremia due to either methicillin-susceptible or methicillin-resistant S. aureus. Methods to improve prevention of S. aureus bacteremia are needed for children with healthcare-associated risk factors for S. aureus bacteremia.