Miracle-dz/UniFAR-FTUs huggingface dataset:
Verification and Validation: the Path to Predictive Scale-Resolving Simulations of Turbulence

This work investigates the importance of verification and validation (V&V) to achieve predictive scale-resolving simulations (SRS) of turbulence, i.e., computations capable of resolving a fraction of the turbulent flow scales. Toward this end, we propose a novel but simple V&V strategy based on grid and physical resolution refinement studies that can be used even when the exact initial flow conditions are unknown, or reference data are unavailable. This is particularly relevant for transient and transitional flow problems, as well as for the improvement of turbulence models. We start by presenting a literature survey of results obtained with distinct SRS models for flows past circular cylinders. It confirms the importance of V&V by illustrating a large variability of results, which is independent of the selected mathematical model and Reynolds number. The proposed V&V strategy is then used on three representative problems of practical interest. The results illustrate that it is possible to conduct reliable verification and validation exercises with SRS models, and evidence the importance of V&V to predictive SRS of turbulence. Most notably, the data also confirm the advantages and potential of the proposed V&V strategy: separate assessment of numerical and modeling errors, enhanced flow physics analysis, identification of key flow phenomena, and ability to operate when the exact flow conditions are unknown or reference data are unavailable.
Nitrification and coupled nitrification-denitrification associated with a soil-manure interface

Nitrification and denitrification in soil often occur in close proximity so that a substantial part of the NO 3 - formed by nitrification afterwards diffuses to the anaerobic denitrification zone where it is reduced to N 2 . The stable N isotope based isotope pairing method was used to study nitrification and coupled nitrification-denitrification associated with a manure hot-spot in soil contained within a diffusion chamber. During the 3-wk incubation period, a very active nitrification zone developed within 0.7 to 1 mm from the aerobic-anaerobic soil-manure interface, and within this thin zone, all NH 4 + liberated from the manure was oxidized. The short distance between the nitrification zone and the anaerobic manure facilitated coupled nitrification-denitrification, which turned out to be the main sink for liberated inorganic N with maximum rates of 140 nmol N-N 2 cm -2 h -1 , thereby removing 90% of the inorganic N liberated during the incubation period. The applicability of the isotopic pairing method was verified by using variable concentrations of 15 NO 3 - and by selectively inhibiting nitrification with acetylene. The change in 14 NO 3 - profile following addition of acetylene was simulated by a diffusion reaction model to obtain estimates of the underestimation of denitrification by use of the commonly used acetylene blockage technique. Incubation periods with acetylene of 1, 3, 6, and 12 h reduced the coupled nitrification-denitrification rate to 46, 25, 16, and 10%, respectively, of the original rate, and use of the acetylene technique for determining denitrification in manure-amended soils thus cannot be recommended.
Decision Theoretic Bootstrapping

The design and testing of supervised machine learning models combine two fundamental distributions: (1) the training data distribution (2) the testing data distribution. Although these two distributions are identical and identifiable when the data set is infinite; they are imperfectly known (and possibly distinct) when the data is finite (and possibly corrupted) and this uncertainty must be taken into account for robust Uncertainty Quantification (UQ). We present a general decision-theoretic bootstrapping solution to this problem: (1) partition the available data into a training subset and a UQ subset (2) take $m$ subsampled subsets of the training set and train $m$ models (3) partition the UQ set into $n$ sorted subsets and take a random fraction of them to define $n$ corresponding empirical distributions $\mu_{j}$ (4) consider the adversarial game where Player I selects a model $i\in\left\{ 1,\ldots,m\right\} $, Player II selects the UQ distribution $\mu_{j}$ and Player I receives a loss defined by evaluating the model $i$ against data points sampled from $\mu_{j}$ (5) identify optimal mixed strategies (probability distributions over models and UQ distributions) for both players. These randomized optimal mixed strategies provide optimal model mixtures and UQ estimates given the adversarial uncertainty of the training and testing distributions represented by the game. The proposed approach provides (1) some degree of robustness to distributional shift in both the distribution of training data and that of the testing data (2) conditional probability distributions on the output space forming aleatory representations of the uncertainty on the output as a function of the input variable.
Robust-to-outliers square-root LASSO, simultaneous inference with a MOM approach

We consider the least-squares regression problem with unknown noise variance, where the observed data points are allowed to be corrupted by outliers. Building on the median-of-means (MOM) method introduced by Lecue and Lerasle Ann.Statist. 48(2):906-931(April 2020) in the case of known noise variance, we propose a general MOM approach for simultaneous inference of both the regression function and the noise variance, requiring only an upper bound on the noise level. Interestingly, this generalization requires care due to regularity issues that are intrinsic to the underlying convex-concave optimization problem. In the general case where the regression function belongs to a convex class, we show that our simultaneous estimator achieves with high probability the same convergence rates and a similar risk bound as if the noise level was unknown, as well as convergence rates for the estimated noise standard deviation. In the high-dimensional sparse linear setting, our estimator yields a robust analog of the square-root LASSO. Under weak moment conditions, it jointly achieves with high probability the minimax rates of estimation $s^{1/p} \sqrt{(1/n) \log(p/s)}$ for the $\ell_p$-norm of the coefficient vector, and the rate $\sqrt{(s/n) \log(p/s)}$ for the estimation of the noise standard deviation. Here $n$ denotes the sample size, $p$ the dimension and $s$ the sparsity level. We finally propose an extension to the case of unknown sparsity level $s$, providing a jointly adaptive estimator $(\widetilde \beta, \widetilde \sigma, \widetilde s)$. It simultaneously estimates the coefficient vector, the noise level and the sparsity level, with proven bounds on each of these three components that hold with high probability.
Enhanced AGCM3D: A Highly Scalable Dynamical Core of Atmospheric General Circulation Model Based on Leap-Format

—The ﬁnite-difference dynamical core based on the equal-interval latitude-longitude mesh has been widely used for numerical simulations of the Atmospheric General Circulation Model (AGCM). Previous work utilizes different ﬁltering schemes to alleviate the instability problem incurred by the unequal physical spacing at different latitudes, but they all incur high communication and computation overhead and become a scaling bottleneck. This paper proposes a highly scalable 3d dynamical core based on a new leap-format ﬁnite-difference computing scheme. It generalizes the usual ﬁnite-difference format with adaptive wider intervals and is able to maintain the computational stability in the grid updating. Therefore, the costly ﬁltering scheme is eliminated. The new scheme is parallelized with a shifting communication method and implemented with ﬁne communication optimizations based on a 3D decomposition. With the proposed leap-format computation scheme, the communication overhead of the AGCM is signiﬁcantly reduced and good load balance is exhibited. The simulation results verify the correctness of the new leap-format scheme. The new leap-format dynamical core scales up to 196,608 CPU cores and achieves the speed of 7.4 simulation-year-per-day (SYPD) and 2.0x speedup on average over the latest implementation at a high resolution of 25KM.
Resolving discs and mergers in z ∼ 2 heavily reddened quasars and their companion galaxies with ALMA

We present sub-arcsecond resolution ALMA imaging of the CO(3-2) emission in two z ∼ 2.5 heavily reddened quasars (HRQs) ULASJ1234+0907 and ULASJ2315+0143 – and their companion galaxies. Dynamical modeling of the resolved velocity fields enables us to constrain the molecular gas morphologies and host galaxy masses. Combining the new data with extensive multi-wavelength observations, we are able to study the relative kinematics of different molecular emission lines, the molecular gas fractions and the locations of the quasars on the MBH-Mgal relation. Despite having similar black-hole properties, the two HRQs display markedly different host galaxy properties and local environments. J1234 has a very massive host – Mdyn ∼ 5 × 1011M and two companion galaxies that are similarly massive located within 200 kpc of the quasar. The molecular gas fraction is low (∼6%). The significant ongoing star formation in the host galaxy is entirely obscured at rest-frame UV and optical wavelengths. J2315 is resolved into a close-separation major-merger (Δr=15 kpc; Δv=170 km/s) with a ∼1:2 mass ratio. The total dynamical mass is estimated to be . 1011M and the molecular gas fraction is high (>45%). A new HSC image of the galaxy shows unobscured UV-luminous star-forming regions co-incident with the extended reservoir of cold molecular gas in the merger. We use the outputs from the Illustris simulations to track the growth of such massive black holes from z ∼ 6 to the present day. While J1234 is consistent with the simulated z ∼ 2 relation, J2315 has a black hole that is over-massive relative to its host galaxy.
3D Solar Coronal Loop Reconstructions with Machine Learning

The magnetic field plays an essential role in the initiation and evolution of different solar phenomena in the corona. The structure and evolution of the 3D coronal magnetic field are still not very well known. A way to ascertain the 3D structure of the coronal magnetic field is by performing magnetic field extrapolations from the photosphere to the corona. In previous work, it was shown that by prescribing the 3D-reconstructed loops’ geometry, the magnetic field extrapolation produces a solution with a better agreement between the modeled field and the reconstructed loops. This also improves the quality of the field extrapolation. Stereoscopy, which uses at least two view directions, is the traditional method for performing 3D coronal loop reconstruction. When only one vantage point of the coronal loops is available, other 3D reconstruction methods must be applied. Within this work, we present a method for the 3D loop reconstruction based on machine learning. Our purpose for developing this method is to use as many observed coronal loops in space and time for the modeling of the coronal magnetic field. Our results show that we can build machine-learning models that can retrieve 3D loops based only on their projection information. Ultimately, the neural network model will be able to use only 2D information of the coronal loops, identified, traced, and extracted from the extreme-ultraviolet images, for the calculation of their 3D geometry.
Mapping the impact of papers on various status groups: A new excellence mapping tool based on citation and reader scores

In over five years, Bornmann, Stefaner, de Moya Anegon, and Mutz (2014) and Bornmann, Stefaner, de Moya Anegon, and Mutz (2014, 2015) have published several releases of the www.excellencemapping.net tool revealing (clusters of) excellent institutions worldwide based on citation data. With the new release, a completely revised tool has been published. It is not only based on citation data (bibliometrics), but also Mendeley data (altmetrics). Thus, the institutional impact measurement of the tool has been expanded by focusing on additional status groups besides researchers such as students and librarians. Furthermore, the visualization of the data has been completely updated by improving the operability for the user and including new features such as institutional profile pages. In this paper, we describe the datasets for the current excellencemapping.net tool and the indicators applied. Furthermore, the underlying statistics for the tool and the use of the web application are explained.
Refining Neural Networks with Compositional Explanations

Neural networks are prone to learning spurious correlations from biased datasets, and are thus vulnerable when making inferences in a new target domain. Prior work reveals spurious patterns via post-hoc model explanations which compute the importance of input features, and further eliminates the unintended model behaviors by regular-izing importance scores with human knowledge. However, such regularization technique lacks ﬂex-ibility and coverage, since only importance scores towards a pre-deﬁned list of features are adjusted, while more complex human knowledge such as feature interaction and pattern generalization can hardly be incorporated. In this work, we propose to reﬁne a learned model by collecting human-provided compositional explanations on the models’ failure cases. By describing generalizable rules about spurious patterns in the explanation, more training examples can be matched and regularized, tackling the challenge of regularization coverage. We additionally introduce a regularization term for feature interaction to support more complex human rationale in reﬁning the model. We demonstrate the effectiveness of the proposed approach on two text classiﬁcation tasks by showing improved performance in target domain after reﬁnement. †
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. We address the trade-off between reconstruction quality and number of parts with Neural Parts, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing. Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision. Evaluations on ShapeNet, D-FAUST and FreiHAND demonstrate that our primitives can capture complex geometries and thus simultaneously achieve geometrically accurate as well as interpretable reconstructions using an order of magnitude fewer primitives than state-of-the-art shape abstraction methods.
The impact of using biased performance metrics on software defect prediction research

Context: Software engineering researchers have undertaken many experiments investigating the potential of software defect prediction algorithms. Unfortunately, some widely used performance metrics are known to be problematic, most notably F1, but nevertheless F1 is widely used. Objective: To investigate the potential impact of using F1 on the validity of this large body of research. Method: We undertook a systematic review to locate relevant experiments and then extract all pairwise comparisons of defect prediction performance using F1 and the un-biased Matthews correlation coefficient (MCC). Results: We found a total of 38 primary studies. These contain 12,471 pairs of results. Of these, 21.95% changed direction when the MCC metric is used instead of the biased F1 metric. Unfortunately, we also found evidence suggesting that F1 remains widely used in software defect prediction research. Conclusions: We reiterate the concerns of statisticians that the F1 is a problematic metric outside of an information retrieval context, since we are concerned about both classes (defect-prone and not defect-prone units). This inappropriate usage has led to a substantial number (more than one fifth) of erroneous (in terms of direction) results. Therefore we urge researchers to (i) use an unbiased metric and (ii) publish detailed results including confusion matrices such that alternative analyses become possible.
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles

As neural networks become the tool of choice to solve an increasing variety of problems in our society, adversarial attacks become critical. The possibility of generating data instances deliberately designed to fool a network's analysis can have disastrous consequences. Recent work has shown that commonly used methods for model training often result in fragile abstract representations that are particularly vulnerable to such attacks. This paper presents a visual framework to investigate neural network models subjected to adversarial examples, revealing how models' perception of the adversarial data differs from regular data instances and their relationships with class perception. Through different use cases, we show how observing these elements can quickly pinpoint exploited areas in a model, allowing further study of vulnerable features in input data and serving as a guide to improving model training and architecture.
Robust optimal periodic control using guaranteed Euler's method

In this paper, we consider the application of optimal periodic control sequences to switched dynamical systems. The control sequence is obtained using a finite-horizon optimal method based on dynamic programming. We then consider Euler approximate solutions for the system extended with bounded perturbations. The main result gives a simple condition on the perturbed system for guaranteeing the existence of a stable limit cycle of the unperturbed system. An illustrative numerical example is provided which demonstrates the applicability of the method.
Higher Performance Visual Tracking with Dual-Modal Localization

Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy. While most existing works fail to operate simultaneously on both, we investigate in this work the problem of conflicting performance between accuracy and robustness. We first conduct a systematic comparison among existing methods and analyze their restrictions in terms of accuracy and robustness. Specifically, 4 formulations-offline classification (OFC), offline regression (OFR), online classification (ONC), and online regression (ONR)-are considered, categorized by the existence of online update and the types of supervision signal. To account for the problem, we resort to the idea of ensemble and propose a dual-modal framework for target localization, consisting of robust localization suppressing distractors via ONR and the accurate localization attending to the target center precisely via OFC. To yield a final representation (i.e, bounding box), we propose a simple but effective score voting strategy to involve adjacent predictions such that the final representation does not commit to a single location. Operating beyond the real-time demand, our proposed method is further validated on 8 datasets-VOT2018, VOT2019, OTB2015, NFS, UAV123, LaSOT, TrackingNet, and GOT-10k, achieving state-of-the-art performance.
Ideal triangles, hyperbolic surfaces and the Thurston metric on Teichm{\"u}ller space

These are notes on the hyperbolic geometry of surfaces, Teichm{\"u}ller spaces and Thurston's metric on these spaces. They are associated with lectures I gave at the Morningside Center of Mathematics of the Chinese Academy of Sciences in March 2019 and at the Chebyshev Laboratory of the Saint Petersburg State University in May 2019. In particular, I survey several results on the behavior of stretch lines, a distinguished class of geodesics for Thurston's metric and I point out several analogies between this metric and Teichm{\"u}ller's metric. Several open questions are addressed. The final version of these notes will appear in the book"Moduli Spaces and Locally Symmetric Spaces", edited by L. Ji and S. -T. Yau, International Press, 2021.
A Novel Sensitivity Metric For Mixed-Precision Quantization With Synthetic Data Generation

Post-training quantization is a representative technique for compressing neural networks, making them smaller and more efficient for deployment on edge devices. However, an inaccessible user dataset often makes it difficult to ensure the quality of the quantized neural network in practice. In addition, existing approaches may use a single uniform bit-width across the network, resulting in significant accuracy degradation at extremely low bit-widths. To utilize multiple bit-width, sensitivity metric plays a key role in balancing accuracy and compression. In this paper, we propose a novel sensitivity metric that considers the effect of quantization error on task loss and interaction with other layers. Moreover, we develop labeled data generation methods that are not dependent on a specific operation of the neural network. Our experiments show that the proposed metric better represents quantization sensitivity, and generated data are more feasible to apply to mixed-precision quantization.
Hopscotching jellyfish: combining different duty cycle kinematics can lead to enhanced swimming performance

Jellyfish (Medusozoa) have been deemed the most energy-efficient animals in the world. Their bell morphology and relatively simple nervous systems make them attractive to robotocists. Although, the science community has devoted much attention to understanding their swimming performance, there is still much to be learned about the jet propulsive locomotive gait displayed by prolate jellyfish. Traditionally, computational scientists have assumed uniform duty cycle kinematics when computationally modeling jellyfish locomotion. In this study we used fluid-structure interaction modeling to determine possible enhancements in performance from shuffling different duty cycles together across multiple Reynolds numbers and contraction frequencies. Increases in speed and reductions in cost of transport were observed as high as 80% and 50%, respectively. Generally, the net effects were greater for cases involving lower contraction frequencies. Overall, robust duty cycle combinations were determined that led to enhanced or impeded performance.
Comment on: Crossover of Charge Fluctuations across the Strange Metal Phase Diagram

In a recent paper by Husain et al. [PRX 9, 041062 (2019)], the two-particle electronic excitations in Bi2Sr2CaCu2O8+x have been studied by Electron Energy-Loss Spectroscopy in reflection (R-EELS) in the “strange metal” range between underdoped and overdoped materials. The authors conclude that there are no well defined plasmons. Rather they obtain a momentum-independent continuum which they discuss in terms of holographic theories. In this Comment it is pointed out that the experimental results are in stark contrast to previous EELS in transmission (T-EELS), Resonant Inelastic X-ray Scattering (RIXS), and optical studies. The differences can be probably explained by an inaccurate momentum scale in the R-EELS experiments. Furthermore, it is shown, that many material specific experimental results from T-EELS, R-EELS, RIXS, and optical spectroscopy can be explained by a more traditional extended Lindhard model. This model describes the energy, the width, and the dispersion of normal and accustic plasmons in cuprates, as well as the continuum. The latter is explained by electron-hole excitations inside a lifetime broadened conduction band. This continuum is directly related to the scattering rates of the charge carriers, which in turn, by a feed back process, lead to the continuum.