Miracle-dz/UniFAR-FTUs huggingface dataset:
Deciphering Faint Gyrosynchrotron Emission from a Coronal Mass Ejection Using Spectropolarimetric Radio Imaging

Measurements of the plasma parameters of coronal mass ejections (CMEs), particularly the magnetic field and nonthermal electron population entrained in the CME plasma, are crucial to understand their propagation, evolution, and geo-effectiveness. Spectral modeling of gyrosynchrotron (GS) emission from CME plasma has been regarded as one of the most promising remote-sensing techniques for estimating spatially resolved CME plasma parameters. Imaging the very low flux density CME GS emission in close proximity to the Sun with orders of magnitude higher flux density has, however, proven to be rather challenging. This challenge has only recently been met using the high dynamic range imaging capability of the Murchison Widefield Array (MWA). Although routine detection of GS is now within reach, the challenge has shifted to constraining the large number of free parameters in GS models, a few of which are degenerate, using the limited number of spectral points at which the observations are typically available. These degeneracies can be broken using polarimetric imaging. For the first time, we demonstrate this using our recently developed capability of high-fidelity polarimetric imaging on the data from the MWA. We show that spectropolarimetric imaging, even when only sensitive upper limits on circularly polarization flux density are available, is not only able to break the degeneracies but also yields tighter constraints on the plasma parameters of key interest than possible with total intensity spectroscopic imaging alone.
Production of hydrogen isotopes and charged pions in p (3.5 GeV) + Nb reactions

The double differential production cross sections, $d^2\sigma/d\Omega dE$, for hydrogen isotopes and charged pions in the reaction of p + Nb at 3.5 GeV proton beam energy have been measured by the High Acceptance DiElectron Spectrometer (HADES). Thanks to the high acceptance of HADES at forward emission angles and usage of its magnetic field, the measured energy range of hydrogen isotopes could be significantly extended in comparison to the relatively scarce experimental data available in the literature. The data provide information about the development of the intranuclear cascade in the proton-nucleus collisions. They can as well be utilized to study the rate of energy/momentum dissipation in the nuclear systems and the mechanism of elementary and composite particle production in excited nuclear matter at normal density. Data of this type are important also for technological and medical applications. Our results are compared to models developed to describe the processes relevant to nuclear spallation (INCL++) or oriented to probe either the elementary hadronic processes in nuclear matter or the behavior of compressed nuclear matter (GiBUU).
Graviton noise on tidal forces and geodesic congruences

In this work we continue with our recent study, using the Feynman-Vernon worldline influence action and the Schwinger-Keldysh closed-time-path formalism, to consider the effects of quantum noise of gravitons on the motion of point masses. This effect can be regarded as due to a stochastic tensorial force whose correlator is given by the graviton noise kernel associated with the Hadamard function of the quantized gravitational field. Solving the Langevin equation governing the motion of the separation of two masses, the fluctuations of the separation due to the graviton noise can be obtained for various states of the quantum field. Since this force has the stretching and compressing effects like the tidal force, we can view it as one. We therefore derive the expressions for, and estimate the magnitude of, this tidal force for the cases of the Minkowski and the squeezed vacua. The influence of this force on the evolution of the geodesic congruence through the Raychaudhuri equation is then studied and the effects of quantum graviton noise on the shear and rotation tensors presented.
Automated Software Testing Starting from Static Analysis: Current State of the Art

— The mass production of complex software has made it impossible to manually test it for security vulnerabilities. Automated security testing tools come in a variety of flavors, function at various stages of software development, and target different categories of software vulnerabilities. It is great that we have a plethora of automated tools to choose from, but it is a problem that their adoption and recognition are not prominent. The purpose of this study is to explore the possibilities of existing techniques while also broadening the horizon by exploring the future of security testing tools and related techniques.
Model Reproducibility Study on Left Atrial Fibres

This work presents an open-source software pipeline to create patient-speciﬁc left atrial models with ﬁbre orientations and a ﬁbrosis map, suitable for electrophysiology simulations. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) ﬁbre orientations mapped from a diﬀusion tensor MRI (DTMRI) human atlas, (3) ﬁbrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, ﬁbrosis distribution in the left atrial body, and ﬁbre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workﬂow allows a single model to be created in 16.72 ˘ 12.25 minutes. Similarity was measured with shape, percentage of ﬁbres oriented in the same direction, and intra-class correlation coeﬃcient (ICC) for the ﬁbrosis calculation. Shape diﬀered noticeably only with users’ selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; ﬁbrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); ﬁbre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median ˘ IQR of the absolute diﬀerence of the total activation times was 2.02 ˘ 2.45 ms for inter, and 1.37 ˘ 2.45 ms for intra. Also, the average ˘ sd of the mean CV diﬀerence was -0.00404 ˘ 0.0155 m { s for inter, and 0.0021 ˘ 0.0115 m { s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean ˘ sd SSIM for inter and intra were 0.648 ˘ 0.21 and 0.608 ˘ 0.15, respectively. Although we found notable diﬀerences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated ﬁbres, and image resolution accuracy of segmentation tools.
Quasi-Static Motion Simulation and Slip Prediction of Articulated Planetary Rovers Using a Kinematic Approach

Wheel slips are unavoidable when moving on a 3D rough surface. They are mainly due to geometrical features of contact surfaces. In this paper, we propose a model for predicting rover motion and contact slips by using a kineto-static model coupled with a linear contact model derived from semiempirical tire/terramechanics approaches. The paper also introduces a coherent approach for motion simulation of uneven articulated rovers which is computationally efficient and can then be used for autonomous on-line path planning. Model results are compared to another numerical model based on a multibody dynamic model including frictional contacts. The well-known rocker-bogie chassis, a highly articulated structure, is chosen to illustrate results and their comparison. Results demonstrate that for a slow motion, the proposed model approximates with a good accuracy the general behavior of the robot with a minimal time computation.
Cross-Population Amplitude Coupling in High-Dimensional Oscillatory Neural Time Series

An important outstanding problem in analysis of neural data is to characterize interactions across brain regions from high-dimensional multiple-electrode recordings during a behavioral experiment. A leading theory, based on a considerable body of research, is that oscillations represent coordinated activity across populations of neurons. We sought to quantify time-varying covariation of oscillatory amplitudes across two brain regions, during a memory task, based on neural potentials recorded from 96 electrodes in each region. We extended probabilistic Canonical Correlation Analysis (CCA) to the time series setting, which provides a new interpretation of multiset CCA based on cross-correlation of latent time series. Because the latent time series covariance matrix is high-dimensional, we assumed sparsity of partial correlations within a range of possible interesting time series lead-lag effects to derive procedures for estimation and inference. We found the resulting methodology to perform well in realistic settings, and we applied it to data recorded from prefrontal cortex and visual area V4 to produce results that are highly plausible based on existing literature.
Consciousness is learning: predictive processing systems that learn by binding may perceive themselves as conscious

Machine learning algorithms have achieved superhuman performance in specific complex domains. Yet learning online from few examples and efficiently generalizing across domains remains elusive. In humans such learning proceeds via declarative memory formation and is closely associated with consciousness. Predictive processing has been advanced as a principled Bayesian inference framework for understanding the cortex as implementing deep generative perceptual models for both sensory data and action control. However, predictive processing offers little direct insight into fast compositional learning or the mystery of consciousness. Here we propose that through implementing online learning by hierarchical binding of unpredicted inferences, a predictive processing system may flexibly generalize in novel situations by forming working memories for perceptions and actions from single examples, which can become short- and long-term declarative memories retrievable by associative recall. We argue that the contents of such working memories are unified yet differentiated, can be maintained by selective attention and are consistent with observations of masking, postdictive perceptual integration, and other paradigm cases of consciousness research. We describe how the brain could have evolved to use perceptual value prediction for reinforcement learning of complex action policies simultaneously implementing multiple survival and reproduction strategies. 'Conscious experience' is how such a learning system perceptually represents its own functioning, suggesting an answer to the meta problem of consciousness. Our proposal naturally unifies feature binding, recurrent processing, and predictive processing with global workspace, and, to a lesser extent, the higher order theories of consciousness.
Least-Squares Neural Network (LSNN) Method for Linear Advection-Reaction Equation: General Discontinuous Interface

We studied the least-squares ReLU neural network method (LSNN) for solving linear advection-reaction equation with discontinuous solution in [Cai, Zhiqiang, Jingshuang Chen, and Min Liu. “Least-squares ReLU neural network (LSNN) method for linear advection-reaction equation. ” Journal of Computational Physics 443 (2021), 110514]. The method is based on a least-squares formulation and uses a new class of approximating functions: ReLU neural network (NN) functions. A critical and additional component of the LSNN method, diﬀering from other NN-based methods, is the introduction of a proper designed discrete diﬀerential operator. In this paper, we study the LSNN method for problems with arbitrary discontinuous interfaces. First, we show that ReLU NN functions with depth (cid:100) log 2 ( d + 1) (cid:101) + 1 can approximate any d -dimensional step function on arbitrary discontinuous interfaces with any prescribed accuracy. By decomposing the solution into continuous and discontinuous parts, we prove theoretically that discretization error of the LSNN method using ReLU NN functions with depth (cid:100) log 2 ( d +1) (cid:101) +1 is mainly determined by the continuous part of the solution provided that the solution jump is constant. Numerical results for both two and three dimensional problems with various discontinuous interfaces show that the LSNN method with enough layers is accurate and does not exhibit the common Gibbs phenomena along the discontinuous interface.
Waveform uncertainty quantification and interpretation for gravitational-wave astronomy

We demonstrate how to quantify the frequency-domain amplitude and phase accuracy of waveform models, δA and δφ, in a form that could be marginalized over in gravitational-wave inference using techniques currently applied for quantifying calibration uncertainty. For concreteness, waveform uncertainties affecting neutron-star inspiral measurements are considered, and post-hoc error estimates from a variety of waveform models are made by comparing time-domain and frequency-domain analytic models with multiple-resolution numerical simulations. These waveform uncertainty estimates can be compared to GW170817 calibration envelopes or to Advanced LIGO and Virgo calibration goals. Signal-specific calibration and waveform uncertainties are compared to statistical fluctuations in gravitational-wave observatories, giving frequency-dependent modeling requirements for detectors such as Advanced LIGO Plus, Cosmic Explorer, or Einstein Telescope. Finally, the distribution of waveform error for the GW170817 posterior is computed from tidal models and compared to the constraints on δφ or δA from GWTC-1 by Edelman et al. In general, δφ and δA can also be interpreted in terms of unmodeled astrophysical energy transfer within or from the source system.
Optimal Algorithms for Latent Bandits with Cluster Structure

We consider the problem of latent bandits with cluster structure where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \emph{latent} clusters such that the mean reward vectors of users within the same cluster are identical. At each round, a user, selected uniformly at random, pulls an arm and observes a corresponding noisy reward. The goal of the users is to maximize their cumulative rewards. This problem is central to practical recommendation systems and has received wide attention of late \cite{gentile2014online, maillard2014latent}. Now, if each user acts independently, then they would have to explore each arm independently and a regret of $\Omega(\sqrt{\mathsf{MNT}})$ is unavoidable, where $\mathsf{M}, \mathsf{N}$ are the number of arms and users, respectively. Instead, we propose LATTICE (Latent bAndiTs via maTrIx ComplEtion) which allows exploitation of the latent cluster structure to provide the minimax optimal regret of $\widetilde{O}(\sqrt{(\mathsf{M}+\mathsf{N})\mathsf{T}})$, when the number of clusters is $\widetilde{O}(1)$. This is the first algorithm to guarantee such strong regret bound. LATTICE is based on a careful exploitation of arm information within a cluster while simultaneously clustering users. Furthermore, it is computationally efficient and requires only $O(\log{\mathsf{T}})$ calls to an offline matrix completion oracle across all $\mathsf{T}$ rounds.
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network

Temperature field prediction is of great importance in the thermal design of systems engineering, and building the surrogate model is an effective way for the task. Generally, large amounts of labeled data are required to guarantee a good prediction performance of the surrogate model, especially the deep learning model, which have more parameters and better representational ability. However, labeled data, especially high-fidelity labeled data, are usually expensive to obtain and sometimes even impossible. To solve this problem, this paper proposes a pithy deep multi-fidelity model (DMFM) for temperature field prediction, which takes advantage of low-fidelity data to boost the performance with less high-fidelity data. First, a pre-train and fine-tune paradigm are developed in DMFM to train the low-fidelity and high-fidelity data, which significantly reduces the complexity of the deep surrogate model. Then, a self-supervised learning method for training the physics-driven deep multi-fidelity model (PD-DMFM) is proposed, which fully utilizes the physics characteristics of the engineering systems and reduces the dependence on large amounts of labeled low-fidelity data in the training process. Two diverse temperature field prediction problems are constructed to validate the effectiveness of DMFM and PD-DMFM, and the result shows that the proposed method can greatly reduce the dependence of the model on high-fidelity data.
The emergence of spatial patterns for compartmental reaction kinetics coupled by two bulk diffusing species with comparable diffusivities

Originating from the pioneering study of Alan Turing, the bifurcation analysis predicting spatial pattern formation from a spatially uniform state for diffusing morphogens or chemical species that interact through nonlinear reactions is a central problem in many chemical and biological systems. From a mathematical viewpoint, one key challenge with this theory for two component systems is that stable spatial patterns can typically only occur from a spatially uniform state when a slowly diffusing ‘activator’ species reacts with a much faster diffusing ‘inhibitor’ species. However, from a modelling perspective, this large diffusivity ratio requirement for pattern formation is often unrealistic in biological settings since different molecules tend to diffuse with similar rates in extracellular spaces. As a result, one key long-standing question is how to robustly obtain pattern formation in the biologically realistic case where the time scales for diffusion of the interacting species are comparable. For a coupled one-dimensional bulk-compartment theoretical model, we investigate the emergence of spatial patterns for the scenario where two bulk diffusing species with comparable diffusivities are coupled to nonlinear reactions that occur only in localized ‘compartments’, such as on the boundaries of a one-dimensional domain. The exchange between the bulk medium and the spatially localized compartments is modelled by a Robin boundary condition with certain binding rates. As regulated by these binding rates, we show for various specific nonlinearities that our one-dimensional coupled PDE-ODE model admits symmetry-breaking bifurcations, leading to linearly stable asymmetric steady-state patterns, even when the bulk diffusing species have equal diffusivities. Depending on the form of the nonlinear kinetics, oscillatory instabilities can also be triggered. Moreover, the analysis is extended to treat a periodic chain of compartments. This article is part of the theme issue ‘New trends in pattern formation and nonlinear dynamics of extended systems’.
Comparison of the number of history in Monte Carlo Simulation Programs

The use of the Monte Carlo technique in a reliable and inexpensive way without the need for a standard radioactive source in determining the detector efficiency is becoming widespread every passing day. It is important to model the detector with the real dimensions for an accurate and precise results for the method. Another parameter as important as detector modeling is the number of histories in the simulation code examined in this study. In this study, the effect of the number of histories on the efficiency was examined in detail using different simulation codes. The results obtained in this work, at least 10 7 particle numbers should be used in all three programs where the uncertainty is below 1%. If the existing facilities are sufficient, it can be increased to 10 8 s in case of having a more equipped and fast computer. However, going higher than this value does not make any sense as seen from the study.
Sea ice noise‐generating processes

An ambient noise model is presented for sound generated in compact ice by ridging, microcracking, and mixed layer shearing. The model describes sound intensity as the sum of these different processes and the sum of intensities propagated from all source locations. The source intensity changes as a function of time for each frequency and can depend on material parameters that affect behavior, such as ice thickness. The processes are identified by their driving forces. The large‐scale stress state is related to the local stress in each ice floe, which, when small, can cause local cracking but does not contribute to large‐scale deformations. If the stress level is large enough to cause ridging, rafting, and leading, then large‐scale deformations occur. Independently of these large‐scale deformations, the ice can move as a rigid body, which shears the mixed layer. Each of these processes is assumed to have its own spectral signature, and each is driven by a different environmental variable. Local cracking is .
A Comprehensive Survey of Graph-level Learning

—Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as input, has been applied to many tasks including comparison, regression, classiﬁcation, and more. Traditional approaches to learning a set of graphs tend to rely on hand-crafted features, such as substructures. But while these methods beneﬁt from good interpretability, they often suffer from computational bottlenecks as they cannot skirt the graph isomorphism problem. Conversely, deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and decoding graphs into low-dimensional representations. As a result, these deep graph learning methods have been responsible for many successes. Yet, there is no comprehensive survey that reviews graph-level learning starting with traditional learning and moving through to the deep learning approaches. This article ﬁlls this gap and frames the representative algorithms into a systematic taxonomy covering traditional learning, graph-level deep neural networks, graph-level graph neural networks, and graph pooling. To ensure a thoroughly comprehensive survey, the evolutions, interactions, and communications between methods from four different branches of development are also examined. This is followed by a brief review of the benchmark data sets, evaluation metrics, and common downstream applications. The survey concludes with 13 future directions of necessary research that will help to overcome the challenges facing this booming ﬁeld.