Miracle-dz/UniFAR-FTUs huggingface dataset:
American Heart Association’s Life’s Simple 7: Lifestyle Recommendations, Polygenic Risk, and Lifetime Risk of Coronary Heart Disease

Background: Understanding the effect of lifestyle and genetic risk on the lifetime risk of coronary heart disease (CHD) is important to improving public health initiatives. Our objective was to quantify remaining lifetime risk and years free of CHD according to polygenic risk and the American Heart Association’s Life’s Simple 7 (LS7) guidelines in a population-based cohort study. Methods: Our analysis included data from participants of the ARIC (Atherosclerosis Risk in Communities) study: 8372 White and 2314 Black participants; 45 years of age and older; and free of CHD at baseline examination. A polygenic risk score (PRS) comprised more than 6 million genetic variants was categorized into low (<20th percentile), intermediate, and high (>80th percentile). An overall LS7 score was calculated at baseline and categorized into “poor,” “intermediate,” and “ideal” cardiovascular health. Lifetime risk and CHD-free years were computed according to polygenic risk and LS7 categories. Results: The overall remaining lifetime risk was 27%, ranging from 16.6% in individuals with an ideal LS7 score to 43.1% for individuals with a poor LS7 score. The association of PRS with lifetime risk differed according to ancestry. In White participants, remaining lifetime risk ranged from 19.8% to 39.3% according to increasing PRS categories. Individuals with a high PRS and poor LS7 had a remaining lifetime risk of 67.1% and 15.9 fewer CHD-free years than did those with intermediate polygenic risk and LS7 scores. In the high-PRS group, ideal LS7 was associated with 20.2 more CHD-free years compared with poor LS7. In Black participants, remaining lifetime risk ranged from 19.1% to 28.6% according to increasing PRS category. Similar lifetime risk estimates were observed for individuals of poor LS7 regardless of PRS category. In the high-PRS group, an ideal LS7 score was associated with only 4.5 more CHD-free years compared with a poor LS7 score. Conclusions: Ideal adherence to LS7 recommendations was associated with lower lifetime risk of CHD for all individuals, especially in those with high genetic susceptibility. In Black participants, adherence to LS7 guidelines contributed to lifetime risk of CHD more so than current PRSs. Improved PRSs are needed to properly evaluate genetic susceptibility for CHD in diverse populations.
Utility and Limitations of Transient Elastography to Monitor Hepatic Steatosis, Hepatic Fibrosis, and Methotrexate-Associated Hepatic Disease in Psoriasis: A Systematic Review.

Objective Psoriasis is associated with hepatic steatosis, fibrosis, and methotrexate-associated liver injury. There is a need for reliable methods to monitor liver disease in psoriasis. Transient elastography (TE) is a validated non-invasive method for assessing hepatic steatosis and fibrosis. Psoriasis-specific TE studies have been limited until recently. Here, we review the utility and limitations of TE to detect and monitor liver disease in the context of psoriasis. Methods A comprehensive search using OVID, PubMed, and gray literature was conducted (2005-November 2019) to identify studies of TE use in psoriasis for assessment of hepatic steatosis and fibrosis. Results Fifteen studies met inclusion criteria. A total of 1,536 patients with psoriasis or psoriatic arthritis were represented. TE-detected liver fibrosis is associated with age, diabetes, obesity, and severity of psoriasis. TE successfully evaluates hepatic steatosis and fibrosis. Elastography has a high negative predictive value and specificity in the context of methotrexate-associated liver fibrosis in psoriasis; however, reported associations between abnormal elastography results and cumulative methotrexate dose varied significantly despite methotrexate's association with hepatotoxicity and fibrosis. The presence of central adiposity is associated with increased TE failure rate. Limitation The TE studies included in this review date from 2007 to 2019, which could contribute to publication bias, as the technique of TE has improved over this time period. Conclusion TE is a useful and non-invasive modality to detect hepatic steatosis and fibrosis in psoriasis. Dermatologists might consider TE in psoriatic patients and concomitant risk factors for fibrosis with the understanding that failure rates may be higher in patients with central adiposity.
Brief assessment of diabetes‐specific psychological flexibility in racially and income diverse youth with type 1 diabetes

Diabetes‐specific psychological flexibility (the ability to engage in valued behaviors, be open to internal experiences, with present‐moment awareness, while living with diabetes) is associated with HbA1c and health‐related quality of life in youth with type 1 diabetes (T1D). Having brief diabetes‐specific psychological flexibility assessments that perform equivalently across diverse individuals is important for research and clinical work addressing health disparities. The present study aimed to create 9‐and 3‐item short forms (DAASito‐9 and ‐3) of the Diabetes Acceptance and Action Scale (DAAS‐22), and evaluate their validity, reliability, and measurement invariance (MI).
Staphylococcus aureus and Acinetobacter baumannii Inhibit Osseointegration of Orthopedic Implants

Bacterial infections routinely cause inflammation and thereby impair osseointegration of orthopedic implants. Acinetobacter spp. , which cause osteomyelitis following trauma, on or off the battlefield, were, however, reported to cause neither osteomyelitis nor osteolysis in rodents. We therefore compared the effects of Acinetobacter strain M2 to those of Staphylococcus aureus in a murine implant infection model. Sterile implants and implants with adherent bacteria were inserted in the femur of mice. ABSTRACT Bacterial infections routinely cause inflammation and thereby impair osseointegration of orthopedic implants. Acinetobacter spp. , which cause osteomyelitis following trauma, on or off the battlefield, were, however, reported to cause neither osteomyelitis nor osteolysis in rodents. We therefore compared the effects of Acinetobacter strain M2 to those of Staphylococcus aureus in a murine implant infection model. Sterile implants and implants with adherent bacteria were inserted in the femur of mice. Bacterial burden, levels of proinflammatory cytokines, and osseointegration were measured. All infections were localized to the implant site. Infection with either S. aureus or Acinetobacter strain M2 increased the levels of proinflammatory cytokines and the chemokine CCL2 in the surrounding femurs, inhibited bone formation around the implant, and caused loss of the surrounding cortical bone, leading to decreases in both histomorphometric and biomechanical measures of osseointegration. Genetic deletion of TLR2 and TLR4 from the mice partially reduced the effects of Acinetobacter strain M2 on osseointegration but did not alter the effects of S. aureus. This is the first report that Acinetobacter spp. impair osseointegration of orthopedic implants in mice, and the murine model developed for this study will be useful for future efforts to clarify the mechanism of implant failure due to Acinetobacter spp. and to assess novel diagnostic tools or therapeutic agents.
A unique case of driving while under the influence of isopropanol

Isopropanol intoxication, whether accidental or intentional, is not unusual. Alcoholics have long‐resorted to isopropanol because of its availability and cost. The incidence of isopropanol intoxication with driving, however, has been reported only a few times in the literature. A unique case is reported where an individual intentionally drank isopropanol and was involved in a minor traffic incident. The individual’s impairment was not consistent with a preliminary breath test of 0.062%. The officer proceeded to an evidential breath test, but received an “interferent‐detected” result, and then requested a drug recognition expert (DRE). A full DRE evaluation was performed, indicating central nervous system depressants, marijuana, and narcotic analgesics. Laboratory analysis included chromatographic testing for volatiles and an immunoassay drug screen. Results were positive for isopropanol and acetone; no other compounds were detected. This case report provides a unique comprehensive evaluation of a driver under the influence of isopropanol.
Field Investigation and Hydrological Modelling of a Subarctic Wetland - the Deer River Watershed

Recently, investigation and conservation of subarctic wetlands has been recognized as an attractive route. To gain insight of the interactions between hydrology and atmosphere of the second largest wetland in Canada - the Hudson Bay Lowlands (HBL), the semi-distributed land use-based runoff process (SLURP) hydrological model was applied to a typical subarctic wetland - the Deer River watershed over a 20-year period (1978-1997). Sensitivity analysis, calibration and validation of the model identified a number of distinguishable hydrological features of subarctic wetlands as well as model deficiencies. Snowmelt was the major source of water recharge in subarctic wetlands and constituted approximately half of the average annual runoff in the Deer River watershed. The peaks of the simulated spring runoff were 34% lower than the observed ones in average which could be attributed to the effects of shallow permafrost that impeded the infiltration of melt water. Runoff of rainfall water during the summer season occurred only during storms due to canopy interception, depression storage, soil porosity, impermeable permafrost, and intensive evapotranspiration. A lag of 2-8 days between the peaks of streamflow and rainfall was observed through both field investigation and modeling results. The numerous seasonally connected ponds/lakes stretching over the middle and lower reach of the watershed behaved as buffers and significantly prolonged the concentration time in summer and fall. The findings will help build a scientific basis for advancing the knowledge of the hydrologic cycle and impacts of climatic changes on sub-arctic wetlands.
ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise

In recent years, large pre-trained Transformer-based language models have led to dramatic improvements in many natural language understanding tasks. To train these models with increasing sizes, many neural network practitioners attempt to increase the batch sizes in order to leverage multiple GPUs to improve training speed. However, increasing the batch size often makes the optimization more difficult, leading to slow convergence or poor generalization that can require orders of magnitude more training time to achieve the same model quality. In this paper, we explore the steepness of the loss landscape of large-batch optimization for adapting pre-trained Transformer-based language models to domain-specific tasks and find that it tends to be highly complex and irregular, posing challenges to generalization on downstream tasks. To tackle this challenge, we propose ScaLA, a novel and efficient method to accelerate the adaptation speed of pre-trained transformer networks. Different from prior methods, we take a sequential game-theoretic approach by adding lightweight adversarial noise into large-batch optimization, which significantly improves adaptation speed while preserving model generalization. Experiment results show that ScaLA attains 2.7--9.8$\times$ adaptation speedups over the baseline for GLUE on BERT-base and RoBERTa-large, while achieving comparable and sometimes higher accuracy than the state-of-the-art large-batch optimization methods. Finally, we also address the theoretical aspect of large-batch optimization with adversarial noise and provide a theoretical convergence rate analysis for ScaLA using techniques for analyzing non-convex saddle-point problems.
Emergence of Superconductivity on the Border of Antiferromagnetic Order in RbMn6Bi5 under High Pressure: A New Family of Mn-Based Superconductors

We report the discovery of superconductivity on the border of antiferromagnetic order in a quasi-one-dimensional material RbMn6Bi5 via measurements of resistivity and magnetic susceptibility under high pressures. Its phase diagram of temperature versus pressure resembles those of many magnetism-mediated superconducting systems. With increasing pressure, its antiferromagnetic ordering transition with T N = 83 K at ambient pressure is first enhanced moderately and then suppressed completely at a critical pressure of P c ≈ 13 GPa, around which bulk superconductivity emerges and exhibits a dome-like T c(P) with a maximal Tconset≈9.5 K at about 15 GPa. In addition, the superconducting state around P c is characterized by a large upper critical field μ 0 H c2(0) exceeding the Pauli paramagnetic limit, implying a possible unconventional paring mechanism. The present study, together with our recent work on KMn6Bi5 (the maximum Tconset≈9.3 K), makes AMn6Bi5 (A = alkali metal) a new family of Mn-based superconductors with relatively high T c.
Hydrodynamics of Collisions and Close Encounters between Stellar Black Holes and Main-sequence Stars

Recent analyses have shown that close encounters between stars and stellar black holes occur frequently in dense star clusters. Depending upon the distance at closest approach, these interactions can lead to dissipating encounters such as tidal captures and disruptions, or direct physical collisions, all of which may be accompanied by bright electromagnetic transients. In this study, we perform a wide range of hydrodynamic simulations of close encounters between black holes and main-sequence stars that collectively cover the parameter space of interest, and we identify and classify the various possible outcomes. In the case of nearly head-on collisions, the star is completely disrupted with roughly half of the stellar material becoming bound to the black hole. For more distant encounters near the classical tidal-disruption radius, the star is only partially disrupted on the first pericenter passage. Depending upon the interaction details, the partially disrupted stellar remnant may be tidally captured by the black hole or become unbound (in some cases, receiving a sufficiently large impulsive kick from asymmetric mass loss to be ejected from its host cluster). In the former case, the star will undergo additional pericenter passages before ultimately being disrupted fully. Based on the properties of the material bound to the black hole at the end of our simulations (in particular, the total bound mass and angular momentum), we comment upon the expected accretion process and associated electromagnetic signatures that are likely to result.
Powering Stellar Magnetism: Energy Transfers in Cyclic Dynamos of Sun-like Stars

We use the anelastic spherical harmonic code to model the convective dynamo of solar-type stars. Based on a series of 15 3D MHD simulations spanning four bins in rotation and mass, we show what mechanisms are at work in these stellar dynamos with and without magnetic cycles and how global stellar parameters affect the outcome. We also derive scaling laws for the differential rotation and magnetic field based on these simulations. We find a weaker trend between differential rotation and stellar rotation rate, ( ΔΩ∝(∣Ω∣/Ω⊙)0.46 ) in the MHD solutions than in their HD counterpart ∣Ω∣/Ω⊙0.66 ), yielding a better agreement with the observational trends based on power laws. We find that for a fluid Rossby number between 0.15 ≲ Ro f ≲ 0.65, the solutions possess long magnetic cycle, if Ro f ≲ 0.42 a short cycle and if Ro f ≳ 1 (antisolar-like differential rotation), a statistically steady state. We show that short-cycle dynamos follow the classical Parker–Yoshimura rule whereas the long-cycle period ones do not. We also find efficient energy transfer between reservoirs, leading to the conversion of several percent of the star's luminosity into magnetic energy that could provide enough free energy to sustain intense eruptive behavior at the star’s surface. We further demonstrate that the Rossby number dependency of the large-scale surface magnetic field in the simulation ( BL,surf∼Rof−1.26 ) agrees better with observations ( BV∼Ros−1.4±0.1 ) and differs from dynamo scaling based on the global magnetic energy ( Bbulk∼Rof−0.5 ).
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings

Graph isomorphism testing is usually approached via the comparison of graph invariants. Two popular alternatives that offer a good trade-off between expressive power and computational efficiency are combinatorial (i.e., obtained via the Weisfeiler-Leman (WL) test) and spectral invariants. While the exact power of the latter is still an open question, the former is regularly criticized for its limited power, when a standard configuration of uniform pre-coloring is used. This drawback hinders the applicability of Message Passing Graph Neural Networks (MPGNNs), whose expressive power is upper bounded by the WL test. Relaxing the assumption of uniform pre-coloring, we show that one can increase the expressive power of the WL test ad infinitum. Following that, we propose an efficient pre-coloring based on spectral features that provably increase the expressive power of the vanilla WL test. The above claims are accompanied by extensive synthetic and real data experiments. The code to reproduce our experiments is available at https://github.com/TPFI22/Spectral-and-Combinatorial
FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks

Distributed methods for training models on graph datasets have recently grown in popularity, due to the size of graph datasets as well as the private nature of graphical data like social networks. However, the graphical structure of this data means that it cannot be disjointly partitioned between different learning clients, leading to either significant communication overhead between clients or a loss of information available to the training method. We introduce Federated Graph Convolutional Network (FedGCN), which uses federated learning to train GCN models with optimized convergence rate and communication cost. Compared to prior methods that require communication among clients at each iteration, FedGCN preserves the privacy of client data and only needs communication at the initial step, which greatly reduces communication cost and speeds up the convergence rate. We theoretically analyze the tradeoff between FedGCN’s convergence rate and communication cost under different data distributions, introducing a general framework can be generally used for the analysis of all edge-completion-based GCN training algorithms. Experimental results demonstrate the effectiveness of our algorithm and validate our theoretical analysis1.
A Vector-Signal-Based Analysis for Salient Synchronous Motors Oriented to Energy-Efficient Current Controls

This paper consists of two main parts. The first part newly proposes a basically and commonly important mathematical model in new closed form for synchronous motors of salient pole characteristics, which indicates with clear physical meaning motor dynamics, torque generation relation and energy transfer relation in general frame. The second part extends the former model-based analytical results to the analysis of energy-efficient but desired-torque-producing current controls such as minimum current control, unity power factor current control and maximum power factor current control. New analytical methods for generating current command in real time based on torque command are given, which are essential for realizing these attractive energy-efficient control schemes. Interesting phenomena of interior states of motors occurring during the controls are also newly clarified. Analysis with clear physical meaning is carried out in general frame and most of the results are new.
Investigating Why Contrastive Learning Benefits Robustness Against Label Noise

Self-supervised Contrastive Learning (CL) has been recently shown to be very effective in preventing deep networks from overfitting noisy labels. Despite its empirical success, the theoretical understanding of the effect of contrastive learning on boosting robustness is very limited. In this work, we rigorously prove that the representation matrix learned by contrastive learning boosts robustness, by having: (i) one prominent singular value corresponding to each sub-class in the data, and significantly smaller remaining singular values; and (ii) {a large alignment between the prominent singular vectors and the clean labels of each sub-class. The above properties enable a linear layer trained on such representations to effectively learn the clean labels without overfitting the noise. } We further show that the low-rank structure of the Jacobian of deep networks pre-trained with contrastive learning allows them to achieve a superior performance initially, when fine-tuned on noisy labels. Finally, we demonstrate that the initial robustness provided by contrastive learning enables robust training methods to achieve state-of-the-art performance under extreme noise levels, e.g., an average of 27.18\% and 15.58\% increase in accuracy on CIFAR-10 and CIFAR-100 with 80\% symmetric noisy labels, and 4.11\% increase in accuracy on WebVision.
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy

The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural architecture search (NAS). Although they have been widely adopted and used to tune real-world NAS algorithms, these benchmarks are limited to small search spaces and focus solely on image classification. Recently, several new NAS benchmarks have been introduced that cover significantly larger search spaces over a wide range of tasks, including object detection, speech recognition, and natural language processing. However, substantial differences among these NAS benchmarks have so far prevented their widespread adoption, limiting researchers to using just a few benchmarks. In this work, we present an in-depth analysis of popular NAS algorithms and performance prediction methods across 25 different combinations of search spaces and datasets, finding that many conclusions drawn from a few NAS benchmarks do not generalize to other benchmarks. To help remedy this problem, we introduce NAS-Bench-Suite, a comprehensive and extensible collection of NAS benchmarks, accessible through a unified interface, created with the aim to facilitate reproducible, generalizable, and rapid NAS research. Our code is available at https://github.com/automl/naslib.
Implicit Regularization Towards Rank Minimization in ReLU Networks

We study the conjectured relationship between the implicit regularization in neural networks, trained with gradient-based methods, and rank minimization of their weight matrices. Previously, it was proved that for linear networks (of depth 2 and vector-valued outputs), gradient flow (GF) w.r. t. the square loss acts as a rank minimization heuristic. However, understanding to what extent this generalizes to nonlinear networks is an open problem. In this paper, we focus on nonlinear ReLU networks, providing several new positive and negative results. On the negative side, we prove (and demonstrate empirically) that, unlike the linear case, GF on ReLU networks may no longer tend to minimize ranks, in a rather strong sense (even approximately, for"most"datasets of size 2). On the positive side, we reveal that ReLU networks of sufficient depth are provably biased towards low-rank solutions in several reasonable settings.