Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Skin Friction Measurements Including Recent High-Reynolds Number Results from NASA Langley NTF

Ralph D Watson"Flow Physics and Control BranchRobert M. Hall*Configuration Aerodynamics BranchJohn B. Anders"Flow Physics and Control BranchNASA, Langley Research CenterHampton, Virginia 23681ABSTRACTThis paper reviews flat plate skin friction datafrom early correlations of drag on plates in waterto measurements in the cryogenic environment ofThe NASA Langley National Transonic Facility(NTF) in late 1996. The flat plate (zero pressuregradient with negligible surface curvature)incompressible skin friction at high Reynoldsnumbers is emphasized in this paper, due to itsimportance in assessing the accuracy ofmeasurements, and as being important to theaerodynamics of large scale vehicles. Acorrelation of zero pressure gradient skin frictiondata minimizing extraneous effects between testsis often used as the first step in the calculation ofskin friction in complex flows. Early data compiledby Schoenherr for a range of momentum thicknessReynolds numbers, Re, from 860 to 370,000contained large scatter, but has provedsurprisingly accurate in its correlated form.Subsequent measurements in wind tunnels undermore carefully controlled conditions have providedinputs to this database, usually to a maximum Reof about 40,000. Data on a large axisymmetricmodel in the NASA Langley National TransonicFacility extends the upper limit in incompressibleRe to 619,800 using the van Driest transformation.Previous data, test techniques, and error sourcesare discussed, and the NTF data will be discussedin detail. The NTF Preston tube and Clauserinferred data accuracy is estimated to be within- 2 percent of a power-law curve fit, and fallsabove the Spalding theory by 1 percent at Re ofabout 600,000.1. INTRODUCTIONThe design of transport aircraft requires thataccurate estimates of skin friction be made atlength Reynolds numbers around 109 and Machnumbers of approximately 0.8, corresponding tocruise conditions 1. This estimate is often made byfirst calculating the flat plate incompressible skinfriction and then correcting for various effects suchas pressure gradient, three-dimensionality of theflow, compressibility, etc. Several baselinetheories/correlations are available, most of whichdiffer in the skin friction level predicted at highReynolds numbers where there has been a dearthof data.As will be shown, the most commonly usedcorrelations of skin friction do not agree to the* Senior Member AIAA"Associate Fellow AIAACopyright' 2000 by the AmericanInstitute of Aeronauticsand Astronautics,Inc. No copyright is assertedin the United States underTitle 17, U.S.Code. The U.S. Government has a royalty-freelicense to exercise all rights under the copyright claimed herein forgovernment purposes. All other rights are reserved by the copyright owner.1American Institute of Aeronautics and Astronautics, Inc.
Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings

Background In locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed. Methods We prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using near-patient tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a temporal external validation cohort. Findings 426 participants were recruited, of whom 89 (21{middle dot}0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0{middle dot}72 to 0{middle dot}74) and calibration (calibration slopes: 1{middle dot}01 to 1{middle dot}05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone. Interpretation We present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources. Funding M&eacutedecins Sans Fronti&egraveres, India.
Importance of input data normalization for the application of neural networks to complex industrial problems

Recent advances in artificial intelligence have allowed the application of such technologies in real industrial problems. We have studied the application of backpropagation neural networks to several problems of estimation and identification in nuclear power plants. These problems often have been reported to be very time-consuming in the training phase. Among the different approaches suggested to ease the backpropagation training process, input data pretreatment has been pointed out, although no specific procedure has been proposed. We have found that input data normalization with certain criteria, prior to a training process, is crucial to obtain good results as well as to fasten significantly the calculations. This paper shows how data normalization affects the performance error of parameter estimators trained to predict the value of several variables of a PWR nuclear power plant. The criteria needed to accomplish such data normalization are also described.
Understanding Square Loss in Training Overparametrized Neural Network Classifiers

Deep learning has achieved many breakthroughs in modern classification tasks. Numerous architectures have been proposed for different data structures but when it comes to the loss function, the cross-entropy loss is the predominant choice. Recently, several alternative losses have seen revived interests for deep classifiers. In particular, empirical evidence seems to promote square loss but a theoretical justification is still lacking. In this work, we contribute to the theoretical understanding of square loss in classification by systematically investigating how it performs for overparametrized neural networks in the neural tangent kernel (NTK) regime. Interesting properties regarding the generalization error, robustness, and calibration error are revealed. We consider two cases, according to whether classes are separable or not. In the general non-separable case, fast convergence rate is established for both misclassification rate and calibration error. When classes are separable, the misclassification rate improves to be exponentially fast. Further, the resulting margin is proven to be lower bounded away from zero, providing theoretical guarantees for robustness. We expect our findings to hold beyond the NTK regime and translate to practical settings. To this end, we conduct extensive empirical studies on practical neural networks, demonstrating the effectiveness of square loss in both synthetic low-dimensional data and real image data. Comparing to cross-entropy, square loss has comparable generalization error but noticeable advantages in robustness and model calibration.
UCD-CS at TREC 2021 Incident Streams Track

In recent years, the task of mining important information from social media posts during crises has become a focus of research for the purposes of assisting emergency response (ES). The TREC Incident Streams (IS) track is a research challenge organised for this purpose. The track asks participating systems to both classify a stream of crisis-related tweets into humanitarian aid related information types and estimate their importance regarding criticality. The former refers to a multi-label information type classification task and the latter refers to a priority estimation task. In this paper, we report on the participation of the University College Dublin School of Computer Science (UCD-CS) in TREC-IS 2021. We explored a variety of approaches, including simple machine learning algorithms, multi-task learning techniques, text augmentation, and ensemble approaches. The official evaluation results indicate that our runs achieve the highest scores in many metrics. To aid reproducibility, our code is publicly available at https://github.com/wangcongcong123/crisis-mtl.
Deep Efficient Continuous Manifold Learning for Time Series Modeling

Modeling non-euclidean data is drawing extensive attention along with the unprecedented successes of deep neural networks in diverse fields. Particularly, a symmetric positive definite matrix is being actively studied in computer vision, signal processing, and medical image analysis, due to its ability to learn beneficial statistical representations. However, owing to its rigid constraints, it remains challenging to optimization problems and inefficient computational costs, especially, when incorporating it with a deep learning framework. In this paper, we propose a framework to exploit a diffeomorphism mapping between Riemannian manifolds and a Cholesky space, by which it becomes feasible not only to efficiently solve optimization problems but also to greatly reduce computation costs. Further, for dynamic modeling of time-series data, we devise a continuous manifold learning method by systematically integrating a manifold ordinary differential equation and a gated recurrent neural network. It is worth noting that due to the nice parameterization of matrices in a Cholesky space, training our proposed network equipped with Riemannian geometric metrics is straightforward. We demonstrate through experiments over regular and irregular time-series datasets that our proposed model can be efficiently and reliably trained and outperforms existing manifold methods and state-of-the-art methods in various time-series tasks.
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models

We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques. The deep learning framework incorporates physical constraints on the flow, such as preserving incompressibility and global statistical invariants of velocity gradient tensor. The accuracy of the model is assessed using statistical and physics-based metrics. The data set comes from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent flow in a cubic box. Since the size of the dataset is memory intensive, we first generate a low-dimensional representation of the velocity data, and then pass it to a sequence prediction network that learns the spatial and temporal correlations of the underlying data. The dimensionality reduction is performed via extraction using Vector-Quantized Autoencoder (VQ-AE), which learns the discrete latent variables. For the sequence forecasting, the idea of Transformer architecture from natural language processing is used, and its performance compared against more standard Recurrent Networks (such as Convolutional LSTM). These architectures are designed and trained to perform a sequence to sequence multi-class classification task in which they take an input sequence with a fixed length (k) and predict a sequence with a fixed length (p), representing the future time instants of the flow. Our results for the short-term predictions show that the accuracy of results for both models deteriorates across predicted snapshots due to autoregressive nature of the predictions. Based on our diagnostics tests, the trained Conv-Transformer model outperforms the Conv-LSTM one and can accurately, both quantitatively and qualitatively, retain the large scales and capture well the inertial scales of flow but fails at recovering the small and intermittent fluid motions.
Milli-Hertz Gravitational-wave Background Produced by Quasiperiodic Eruptions

Extreme-mass-ratio inspirals (EMRIs) are important targets for future space-borne gravitational-wave (GW) detectors, such as the Laser Interferometer Space Antenna (LISA). Recent works suggest that EMRIs may reside in a population of newly discovered X-ray transients called “quasiperiodic eruptions” (QPEs). Here, we follow this scenario and investigate whether LISA could in the future detect the QPEs. We consider two specific models, in which the QPEs are made of either stellar-mass objects moving on circular orbits around massive black holes (MBHs) or white dwarfs (WDs) on eccentric orbits around MBHs. We find that in either case the five QPEs detected so far are too weak to be resolvable by LISA. However, if QPEs are made of eccentric WD–MBH binaries, they radiate GWs over a wide range of frequencies. The broad spectra overlap to form a background that peaks in the milli-Hertz band and has a signal-to-noise ratio of 9–17 even in the most pessimistic scenario. The presence of this GW background in the LISA band could impact future searches for seed black holes at high redshift as well as stellar-mass binary black holes in the local universe.
Algorithms based on operator-averaged operators

A class of algorithms comprised by certain semismooth Newton and active-set methods is able to solve convex minimization problems involving sparsity-inducing regularizers very rapidly; the speed advantage of methods from this class is a consequence of their ability to benefit from the sparsity of the corresponding solutions by solving smaller inner problems than conventional methods. The convergence properties of such conventional methods (e.g., the forward-backward and the proximal-Newton ones) can be studied very elegantly under the framework of iterations of scalar-averaged operators - this is not the case for the aforementioned class. However, we show in this work that by instead considering operator-averaged operators, one can indeed study methods of that class, and also to derive algorithms outside of it that may be more convenient to implement than existing ones. Additionally, we present experiments whose results suggest that methods based on operator-averaged operators achieve substantially faster convergence than conventional ones.
Singularities in Inflationary Cosmological Models

Due to the accelerated expansion of the universe, the possibilities for the formation of singularities has changed from the classical Big Bang and Big Crunch singularities to include a number of new scenarios. In recent papers it has been shown that such singularities may appear in inflationary cosmological models with a fractional power scalar field potential. In this paper we enlarge the analysis of singularities in scalar field cosmological models by the use of generalised power expansions of their Hubble scalars and their scalar fields in order to describe all possible models leading to a singularity, finding other possible cases. Unless a negative scalar field potential is considered, all singularities are weak and of type IV.
Pole position of the $a_1(1260)$ resonance in a three-body unitary framework

Daniel Sadasivan, ∗ Andrei Alexandru, 3, † Hakan Akdag, ‡ Felipe Amorim, § Ruaiŕı Brett, ¶ Chris Culver, ‖ Michael Döring, 6, ∗∗ Frank X. Lee, †† and Maxim Mai ‡‡ Ave Maria University, Ave Maria, FL 34142, USA The George Washington University, Washington, DC 20052, USA Department of Physics, University of Maryland, College Park, MD 20742, USA Helmholtz-Institut für Strahlenund Kernphysik (Theorie) and Bethe Center for Theoretical Physics, Universität Bonn, 53115 Bonn, Germany Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, United Kingdom Thomas Jefferson National Accelerator Facility, Newport News, VA 23606, USA
Experimental analysis of different simulation models for motorway traffic flow

The paper presents the first results of a comparison between two different motorway traffic models. The purpose is to operate a comparative evaluation of potentialities and limits in the two different approaches, by applying the models on a large scale motorway network with a complete traffic data base. The two different modelling approaches are: a model based on microscopic traffic theory and a macroscopic stochastic experimental model. Model performances have been verified comparing some simulation results to the real travel times and traffic flows measured on the network, in "noncongested traffic conditions". Travel times of all vehicles from origin to destination have been measured as well as all origin and destination flows by using data from the toll collection system (entrance and exit time for each vehicle). Models potentialities and limits in large-scale applications are also discussed.
KODIAQ-Z: Metals and Baryons in the Cool Intergalactic and Circumgalactic Gas at 2.2 ≲ z ≲ 3.6

We present the KODIAQ-Z survey aimed to characterize the cool, photoionized gas at 2.2 ≲ z ≲ 3.6 in 202 H i-selected absorbers with 14.6 ≤ logNHI < 20 that probe the interface between galaxies and the intergalactic medium (IGM). We find that gas with 14.6≤logNHI<20 at 2.2 ≲ z ≲ 3.6 can be metal-rich (−1.6 ≲ [X/H] ≲ − 0.2) as seen in damped Lyα absorbers (DLAs); it can also be very metal-poor ([X/H] < − 2.4) or even pristine ([X/H] < − 3.8), which is not observed in DLAs but is common in the IGM. For 16<logNHI<20 absorbers, the frequency of pristine absorbers is about 1%–10%, while for 14.6≤logNHI≤16 absorbers it is 10%–20%, similar to the diffuse IGM. Supersolar gas is extremely rare (<1%) at these redshifts. The factor of several thousand spread from the lowest to highest metallicities and large metallicity variations (a factor of a few to >100) between absorbers separated by less than Δv < 500 km s−1 imply that the metals are poorly mixed in 14.6≤logNHI<20 gas. We show that these photoionized absorbers contribute to about 14% of the cosmic baryons and 45% of the cosmic metals at 2.2 ≲ z ≲ 3.6. We find that the mean metallicity increases with N H i , consistent with what is found in z < 1 gas. The metallicity of gas in this column density regime has increased by a factor ∼8 from 2.2 ≲ z ≲ 3.6 to z < 1, but the contribution of the 14.6≤logNHI<19 absorbers to the total metal budget of the universe at z < 1 is a quarter of that at 2.2 ≲ z ≲ 3.6. We show that FOGGIE cosmological zoom-in simulations have a similar evolution of [X/H] with N H i , which is not observed in lower-resolution simulations. In these simulations, very metal-poor absorbers with [X/H] < − 2.4 at z ∼ 2–3 are tracers of inflows, while higher-metallicity absorbers are a mixture of inflows and outflows.
Multi-GeV Electron Bunches from an All-Optical Laser Wakefield Accelerator

We present the first demonstration of multi-GeV laser wakefield acceleration in a fully optically formed plasma waveguide, with an acceleration gradient as high as 25 GeV = m. The guide was formed via self-waveguiding of < 15 J, 45 fs ( < ∼ 300 TW) pulses over 20 cm in a low-density hydrogen gas jet, with accelerated electron bunches driven up to 5 GeV in quasimonoenergetic peaks of relative energy width as narrow as ∼ 15 %, with divergence down to ∼ 1 mrad and charge up to tens of picocoulombs. Energy gain is inversely correlated with on-axis waveguide density in the range N e 0 ¼ ð 1 . 3 – 3 . 2 Þ × 10 17 cm − 3 . We find that shot-to-shot stability of bunch spectra and charge are strongly dependent on the pointing of the injected laser pulse and gas jet uniformity. We also observe evidence of pump depletion-induced dephasing, a consequence of the long optical guiding distance.
Parsing with Pretrained Language Models, Multiple Datasets, and Dataset Embeddings

With an increase of dataset availability, the potential for learning from a variety of data sources has increased. One particular method to improve learning from multiple data sources is to embed the data source during training. This allows the model to learn generalizable features as well as distinguishing features between datasets. However, these dataset embeddings have mostly been used before contextualized transformer-based embeddings were introduced in the field of Natural Language Processing. In this work, we compare two methods to embed datasets in a transformer-based multilingual dependency parser, and perform an extensive evaluation. We show that: 1) embedding the dataset is still beneficial with these models 2) performance increases are highest when embedding the dataset at the encoder level 3) unsurprisingly, we confirm that performance increases are highest for small datasets and datasets with a low baseline score. 4) we show that training on the combination of all datasets performs similarly to designing smaller clusters based on language-relatedness.
q-TASEP with position-dependent slowing

We introduce a new interacting particle system on Z , slowed t -TASEP . It may be viewed as a q -TASEP with additional position-dependent slowing of jump rates, depending on a parameter t , which leads to discrete asymptotic ﬂuctuations at large time. If on the other hand t → 1 as time → ∞ , we prove 2. A central limit theorem, with convergence to the ﬁxed-time Gaussian marginal of a stationary solution to SDEs derived from the particle jump rates, and 3. A bulk limit to a certain explicit stationary Gaussian process on R , with scaling exponents characteristic of the Edwards-Wilkinson universality class in (1 + 1) dimensions. The proofs relate slowed t -TASEP to a certain Hall-Littlewood process, and use contour integral formulas for observables of this process. Unlike most previously studied Macdonald processes, this one involves only local interactions, resulting in asymptotics characteristic of (1 + 1) -dimensional rather than (2 + 1) -dimensional systems.
CMA-CLIP: Cross-Modality Attention CLIP for Image-Text Classification

Modern Web systems such as social media and e-commerce contain rich contents expressed in images and text. Leveraging information from multi-modalities can improve the performance of machine learning tasks such as classification and recommendation. In this paper, we propose the Cross-Modality Attention Contrastive Language-Image Pre-training (CMA-CLIP), a new framework which unifies two types of cross-modality attentions, sequence-wise attention and modality-wise attention, to effectively fuse information from image and text pairs. The sequence-wise attention enables the framework to capture the fine-grained relationship between image patches and text tokens, while the modality-wise attention weighs each modality by its relevance to the downstream tasks. In addition, by adding task specific modality-wise attentions and multilayer perceptrons, our proposed framework is capable of performing multi-task classification with multi-modalities. We conduct experiments on a Major Retail Website Product Attribute (MRWPA) dataset and two public datasets, Food101 and Fashion-Gen. The results show that CMA-CLIP outperforms the pre-trained and fine-tuned CLIP by an average of 11.9% in recall at the same level of precision on the MRWPA dataset for multi-task classification. It also surpasses the state-of-the-art method on Fashion-Gen Dataset by 5.5% in accuracy and achieves competitive performance on Food101 Dataset. Through detailed ablation studies, we further demonstrate the effectiveness of both cross-modality attention modules and our method's robustness against noise in image and text inputs, which is a common challenge in practice.