Miracle-dz/UniFAR-FTUs huggingface dataset:
Geodetic Rotation of the Solar System Bodies

Geodetic Rotation of the Solar System Bodies The problem of the geodetic (relativistic) rotation of the major planets, the Moon, and the Sun is studied by using DE404/LE404 ephemeris. For each body the files of the ecliptical components of the vectors of the angular velocity of the geodetic rotation are determined over the time span from AD1000 to AD3000 with one day spacing. The most essential terms of the geodetic rotation are found by means of the least squares method and spectral analysis methods.
Diaphragm Neurostimulation Mitigates Ventilation-Associated Brain Injury in a Preclinical Acute Respiratory Distress Syndrome Model

CONTEXT: In a porcine healthy lung model, temporary transvenous diaphragm neurostimulation (TTDN) for 50 hours mitigated hippocampal apoptosis and inflammation associated with mechanical ventilation (MV). HYPOTHESIS: Explore whether TTDN in combination with MV for 12 hours mitigates hippocampal apoptosis and inflammation in an acute respiratory distress syndrome (ARDS) preclinical model. METHODS AND MODELS: Compare hippocampal apoptosis, inflammatory markers, and serum markers of neurologic injury between never ventilated subjects and three groups of mechanically ventilated subjects with injured lungs: MV only (LI-MV), MV plus TTDN every other breath, and MV plus TTDN every breath. MV settings in volume control were tidal volume 8 mL/kg and positive end-expiratory pressure 5 cm H2O. Lung injury, equivalent to moderate ARDS, was achieved by infusing oleic acid into the pulmonary artery. RESULTS: Hippocampal apoptosis, microglia, and reactive-astrocyte percentages were similar between the TTDN-every-breath and never ventilated groups. The LI-MV group had a higher percentage of these measures than all other groups (p < 0.05). Transpulmonary driving pressure at study end was lower in the TTDN-every-breath group than in the LI-MV group; systemic inflammation and lung injury scores were not significantly different. The TTDN-every-breath group had considerably lower serum concentration of homovanillic acid (cerebral dopamine production surrogate) at study end than the LI-MV group (p < 0.05). Heart rate variability declined in the LI-MV group and increased in both TTDN groups (p < 0.05). INTERPRETATIONS AND CONCLUSIONS: In a moderate-ARDS porcine model, MV is associated with hippocampal apoptosis and inflammation, and TTDN mitigates that hippocampal apoptosis and inflammation.
M ay 2 01 0 On a theorem due to

The manifold M being closed and connected, we prove that every submanifold of T ∗M that is Hamiltonianly isotopic to the zero-section and that is invariant by a Tonelli flow is a graph. ANR Project BLANC07-3 187245, Hamilton-Jacobi and Weak KAM Theory ANR DynNonHyp Université d’Avignon et des Pays de Vaucluse, Laboratoire d’Analyse non linéaire et Géométrie (EA 2151), F-84 018Avignon, France. e-mail: Marie-Claude.Arnaud@univ-avignon.fr
The general observation period 2007 within the priority program on quantitative precipitation forecasting: concept and first results

In the year 2007 a General Observation Period (GOP) has been performed within the German Priority Program on Quantitative Precipitation Forecasting (PQP). By optimizing the use of existing instrumentation a large data set of in-situ and remote sensing instruments with special focus on water cycle variables was gathered over the full year cycle. The area of interest covered central Europe with increasing focus towards the Black Forest where the Convective and Orographically-induced Precipitation Study (COPS) took place from June to August 2007. Thus the GOP includes a variety of precipitation systems in order to relate the COPS results to a larger spatial scale. For a timely use of the data, forecasts of the numerical weather prediction models COSMO-EU and COSMO-DE of the German Meteorological Service were tailored to match the observations and perform model evaluation in a near real-time environment. The ultimate goal is to identify and distinguish between different kinds of model deficits and to improve process understanding.
Minimally invasive radical antegrade modular pancreatosplenectomy: routine vs. modified

Radical antegrade modular pancreatosplenectomy (RAMPS) was introduced in 2003 by Strasberg to improve survival outcomes in left-sided pancreatic ductal adenocarcinoma. Many investigators have shown the feasibility and safety of minimally invasive RAMPS (MI-RAMPS). However, the survival benefit of RAMPS is inconclusive, and possible risks following the procedure, such as exocrine and endocrine insufficiencies, cannot be ignored. Therefore, several modifications of RAMPS were designed. Modified RAMPS is not a specific technique but rather a reduced form of RAMPS that is undertaken without compromising oncologic principles. In this literature review, the surgical technique and strategies of MI-RAMPS were examined.
Symmetry breaking and gap opening in two-dimensional hexagonal lattices

The inhibition in wave propagation at band gap energies plays a central role in many areas of technology such as electronics (electron gaps), nanophotonics (light gaps) and phononics (acoustic gaps), among others. Here we demonstrate that metal surfaces featuring free-electron-like bands may become semiconducting by periodic nanostructuration. We combine scanning tunneling spectroscopy and angle-resolved photoemisssion to accurately determine the energy-dependent local density of states and band structure of the Ag/Cu(111) noble metal interface patterned with an array of triangular dislocations, demonstrating the existence of a 25 meV band gap that extends over the entire surface Brillouin zone. We prove that this gap is a general consequence of symmetry reduction in close-packed metallic overlayers; in particular, we show that the gap opening is due to the symmetry lowering of the wave vector group at the K point from C3v to C3.
GUAP: Graph Universal Attack Through Adversarial Patching

Graph neural networks (GNNs) are a class of effective deep learning models for node classification tasks; yet their predictive capability may be severely compromised under adversarially designed unnoticeable perturbations to the graph structure and/or node data. Most of the current work on graph adversarial attacks aims at lowering the overall prediction accuracy, but we argue that the resulting abnormal model performance may catch attention easily and invite quick counterattack. Moreover, attacks through modification of existing graph data may be hard to conduct if good security protocols are implemented. In this work, we consider an easier attack harder to be noticed, through adversarially patching the graph with new nodes and edges. The attack is universal: it targets a single node each time and flips its connection to the same set of patch nodes. The attack is unnoticeable: it does not modify the predictions of nodes other than the target. We develop an algorithm, named GUAP, that achieves high attack success rate but meanwhile preserves the prediction accuracy. GUAP is fast to train by employing a sampling strategy. We demonstrate that a 5% sampling in each epoch yields 20x speedup in training, with only a slight degradation in attack performance. Additionally, we show that the adversarial patch trained with the graph convolutional network transfers well to other GNNs, such as the graph attention network.
Online Learning of Smooth Functions

In this paper, we study the online learning of real-valued functions where the hidden function is known to have certain smoothness properties. Specifically, for $q \ge 1$, let $\mathcal F_q$ be the class of absolutely continuous functions $f: [0,1] \to \mathbb R$ such that $\|f'\|_q \le 1$. For $q \ge 1$ and $d \in \mathbb Z^+$, let $\mathcal F_{q,d}$ be the class of functions $f: [0,1]^d \to \mathbb R$ such that any function $g: [0,1] \to \mathbb R$ formed by fixing all but one parameter of $f$ is in $\mathcal F_q$. For any class of real-valued functions $\mathcal F$ and $p>0$, let $\text{opt}_p(\mathcal F)$ be the best upper bound on the sum of $p^{\text{th}}$ powers of absolute prediction errors that a learner can guarantee in the worst case. In the single-variable setup, we find new bounds for $\text{opt}_p(\mathcal F_q)$ that are sharp up to a constant factor. We show for all $\varepsilon \in (0, 1)$ that $\text{opt}_{1+\varepsilon}(\mathcal{F}_{\infty}) = \Theta(\varepsilon^{-\frac{1}{2}})$ and $\text{opt}_{1+\varepsilon}(\mathcal{F}_q) = \Theta(\varepsilon^{-\frac{1}{2}})$ for all $q \ge 2$. We also show for $\varepsilon \in (0,1)$ that $\text{opt}_2(\mathcal F_{1+\varepsilon})=\Theta(\varepsilon^{-1})$. In addition, we obtain new exact results by proving that $\text{opt}_p(\mathcal F_q)=1$ for $q \in (1,2)$ and $p \ge 2+\frac{1}{q-1}$. In the multi-variable setup, we establish inequalities relating $\text{opt}_p(\mathcal F_{q,d})$ to $\text{opt}_p(\mathcal F_q)$ and show that $\text{opt}_p(\mathcal F_{\infty,d})$ is infinite when $pd$. We also obtain sharp bounds on learning $\mathcal F_{\infty,d}$ for $p
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis

There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which, in turn, also motivates an urgent need for psychiatrists to fully understand the decision behavior of the used GNNs. However, most of the existing GNN explainers are either post-hoc in which another interpretive model needs to be created to explain a well-trained GNN, or do not consider the causal relationship between the extracted explanation and the decision, such that the explanation itself contains spurious correlations and suffers from weak faithfulness. In this work, we propose a granger causality-inspired graph neural network (CI-GNN), a built-in interpretable model that is able to identify the most influential subgraph (i.e., functional connectivity within brain regions) that is causally related to the decision (e.g., major depressive disorder patients or healthy controls), without the training of an auxillary interpretive network. CI-GNN learns disentangled subgraph-level representations α and β that encode, respectively, the causal and non-causal aspects of original graph under a graph variational autoencoder framework, regularized by a conditional mutual information (CMI) constraint. We theoretically justify the validity of the CMI regulation in capturing the causal relationship. We also empirically evaluate the performance of CI-GNN against three baseline GNNs and four state-of-the-art GNN explainers on synthetic data and three large-scale brain disease datasets. We observe that CI-GNN achieves the best performance in a wide range of metrics and provides more reliable and concise explanations which have clinical evidence. The source code and implementation details of CI-GNN are freely available at GitHub repository (https://github.com/ZKZ-Brain/CI-GNN/).
J an 2 02 3 GLOBAL WELL-POSEDNESS OF WEAK SOLUTIONS TO THE INCOMPRESSIBLE EULER EQUATIONS WITH HELICAL SYMMETRY IN R 3

We use a Lagrangian method to prove the global well-posedness of weak solutions to the three-dimensional Euler equations with helical sym- metry and without helical swirl in the whole space for initial vorticity in L 11 T L ∞ 1 ( R 3 ). The vortex transport formula is also obtained in our article.
On the Convergence of Stochastic Gradient Descent in Low-precision Number Formats

Deep learning models are dominating almost all artificial intelligence tasks such as vision, text, and speech processing. Stochastic Gradient Descent (SGD) is the main tool for training such models, where the computations are usually performed in single-precision floating-point number format. The convergence of single-precision SGD is normally aligned with the theoretical results of real numbers since they exhibit negligible error. However, the numerical error increases when the computations are performed in low-precision number formats. This provides compelling reasons to study the SGD convergence adapted for low-precision computations. We present both deterministic and stochastic analysis of the SGD algorithm, obtaining bounds that show the effect of number format. Such bounds can provide guidelines as to how SGD convergence is affected when constraints render the possibility of performing high-precision computations remote.
The K2 and TESS Synergy. II. Revisiting 26 Systems in the TESS Primary Mission

The legacy of NASA’s K2 mission has provided hundreds of transiting exoplanets that can be revisited by new and future facilities for further characterization, with a particular focus on studying the atmospheres of these systems. However, the majority of K2-discovered exoplanets have typical uncertainties on future times of transit within the next decade of greater than 4 hr, making observations less practical for many upcoming facilities. Fortunately, NASA’s Transiting Exoplanet Survey Satellite (TESS) mission is reobserving most of the sky, providing the opportunity to update the ephemerides for ∼300 K2 systems. In the second paper of this series, we reanalyze 26 single-planet, K2-discovered systems that were observed in the TESS primary mission by globally fitting their K2 and TESS light curves (including extended mission data where available), along with any archival radial velocity measurements. As a result of the faintness of the K2 sample, 13 systems studied here do not have transits detectable by TESS. In those cases, we refit the K2 light curve and provide updated system parameters. For the 23 systems with M * ≳ 0.6 M ⊙, we determine the host star parameters using a combination of Gaia parallaxes, spectral energy distribution fits, and MESA Isochrones and Stellar Tracks stellar evolution models. Given the expectation of future TESS extended missions, efforts like the K2 and TESS Synergy project will ensure the accessibility of transiting planets for future characterization while leading to a self-consistent catalog of stellar and planetary parameters for future population efforts.
Binary Black Hole Spins: Model Selection with GWTC-3

The origin of the spins of stellar-mass black holes is still controversial, and angular momentum transport inside massive stars is one of the main sources of uncertainty. Here, we apply hierarchical Bayesian inference to derive constraints on spin models from the 59 most confident binary black hole merger events in the third gravitational-wave transient catalogue (GWTC-3). We consider up to five parameters: chirp mass, mass ratio, redshift, effective spin, and precessing spin. For the model selection, we use a set of binary population synthesis simulations spanning drastically different assumptions for black hole spins and natal kicks. In particular, our spin models range from the maximal to minimal efficiency of angular momentum transport in stars. We find that if we include the precessing spin parameter into our analysis, models predicting only vanishingly small spins are in tension with GWTC-3 data. On the other hand, models in which most spins are vanishingly small but that also include a subpopulation of tidally spun-up black holes are a good match to the data. Our results show that the precessing spin parameter has a crucial impact on model selection.
Symmetric and Asymmetric Underplatform Dampers for Turbine Blades

Friction damping devices like underplatform dampers are widely used in turbomachinery applications to reduce vibration amplitudes and to increase lifetime and reliability of the bladed disk. Nowadays, in practical applications, a variety of different underplatform damper geometries is applied. Nevertheless, a detailed study of the in.uence of the geometric and dynamic properties of the damper is still not available. Within this paper the most frequently applied damper types like cylindrical and wedge as well as asymmetrical dampers are investigated and compared to each other with respect to their effectiveness. Especially the in.uence of the damper geometry on the resonance frequency and vibration amplitude is pointed out. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Ego-Only: Egocentric Action Detection without Exocentric Pretraining

We present Ego-Only, the ﬁrst training pipeline that enables state-of-the-art action detection on egocentric (ﬁrst-person) videos without any form of exocentric (third-person) pretraining. Previous approaches found that ego-centric models cannot be trained effectively from scratch and that exocentric representations transfer well to ﬁrst-person videos. In this paper we revisit these two observations. Motivated by the large content and appearance gap separating the two domains, we propose a strategy that enables effective training of egocentric models without ex-ocentric pretraining. Our Ego-Only pipeline is simple. It trains the video representation with a masked autoencoder ﬁnetuned for temporal segmentation. The learned features are then fed to an off-the-shelf temporal action localization method to detect actions. We evaluate our approach on two established egocentric video datasets: Ego4D and EPIC-Kitchens-100. On Ego4D, our Ego-Only is on-par with exo-centric pretraining methods that use an order of magnitude more labels. On EPIC-Kitchens-100, our Ego-Only even outperforms exocentric pretraining (by 2.1% on verbs and by 1.8% on nouns), setting a new state-of-the-art.
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection

Manipulated videos often contain subtle inconsistencies between their visual and audio signals. We propose a video forensics method, based on anomaly detection, that can identify these inconsistencies, and that can be trained solely using real, unlabeled data. We train an autoregressive model to generate sequences of audio-visual features, using feature sets that capture the temporal synchronization between video frames and sound. At test time, we then flag videos that the model assigns low probability. Despite being trained entirely on real videos, our model obtains strong performance on the task of detecting manipulated speech videos. Project site: https://cfeng16.github. io/audio-visual-forensics.
Liquid Film Atomization on Wall Edges—Separation Criterion and Droplets Formation Model

In order to predict the fuel mixture preparation inside the cylinder of port fuel injection engines, a model for the aerodynamic stripping of the fuel film deposited on the manifold walls is discussed, and a model for the fuel film separation and atomization near the sharp edges is developed. A separation criterion is set up using an analogy with Rayleigh-Taylor instabilities driven by the inertial forces of the liquid film. To determine the physical parameters of the resulting droplets, a liquid sheet atomization scheme is used. The critical value for the separation criterion is adjusted using experimental data obtained in 2D wind tunnel equipped with different steps shaped as a valve seat, and reproducing the main characteristics of the intake of spark ignition engine. CFD simulations are performed using the KMB code, a modified version of KIVA-2 already including a stochastic Lagrangian description of the spray, and an Eulerian liquid film model. Computations results for different operating conditions are in good agreement with the images of film separation and measured droplet size distributions.
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow

Gaussian mixture models form a flexible and expressive parametric family of distributions that has found applications in a wide variety of applications. Unfortunately, fitting these models to data is a notoriously hard problem from a computational perspective. Currently, only moment-based methods enjoy theoretical guarantees while likelihood-based methods are dominated by heuristics such as ExpectationMaximization that are known to fail in simple examples. In this work, we propose a new algorithm to compute the nonparametric maximum likelihood estimator (NPMLE) in a Gaussian mixture model. Our method is based on gradient descent over the space of probability measures equipped with the Wasserstein-Fisher-Rao geometry for which we establish convergence guarantees. In practice, it can be approximated using an interacting particle system where the weight and location of particles are updated alternately. We conduct extensive numerical experiments to confirm the effectiveness of the proposed algorithm compared not only to classical benchmarks but also to similar gradient descent algorithms with respect to simpler geometries. In particular, these simulations illustrate the benefit of updating both weight and location of the interacting particles.
Automated Misconfiguration Repair of Configurable Cyber-Physical Systems with Search: an Industrial Case Study on Elevator Dispatching Algorithms

Real-world Cyber-Physical Systems (CPSs) are usually configurable. Through parameters, it is possible to configure, select or unselect different system functionalities. While this provides high flexibility, it also becomes a source for failures due to misconfigurations. The large number of parameters these systems have and the long test execution time in this context due to the use of simulation-based testing make the manual repair process a cumbersome activity. Subsequently, in this context, automated repairing methods are paramount. In this paper, we propose an approach to automatically repair CPSs’ misconfigurations. Our approach is evaluated with an industrial CPS case study from the elevation domain. Experiments with a real building and data obtained from operation suggests that our approach outperforms a baseline algorithm as well as the state of the practice (i.e., manual repair carried out by domain experts).
A P ] 2 8 M ay 2 01 2 Convergence properties for a generalization of the Caginalp phase field system ∗ Giacomo Canevari and Pierluigi Colli

We are concerned with a phase field system consisting of two partial differential equations in terms of the variables thermal displacement, that is basically the time integration of temperature, and phase parameter. The system is a generalization of the well-known Caginalp model for phase transitions, when including a diffusive term for the thermal displacement in the balance equation and when dealing with an arbitrary maximal monotone graph, along with a smooth anti-monotone function, in the phase equation. A Cauchy-Neumann problem has been studied for such a system in [7], by proving well-posedness and regularity results, as well as convergence of the problem as the coefficient of the diffusive term for the thermal displacement tends to zero. The aim of this contribution is rather to investigate the asymptotic behaviour of the problem as the coefficient in front of the Laplacian of the temperature goes to 0: this analysis is motivated by the types III and II cases in the thermomechanical theory of Green and Naghdi. Under minimal assumptions on the data of the problems, we show a convergence result. Then, with the help of uniform regularity estimates, we discuss the rate of convergence for the difference of the solutions in suitable norms.