Miracle-dz/UniFAR-FTUs huggingface dataset:
JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software

The aim of this work is the comparison of the performance of the three popular software platforms JAGS, NIMBLE and Stan. These probabilistic programming languages are able to automatically generate samples from the posterior distribution of interest using MCMC algorithms, starting from the specification of a Bayesian model, i.e. the likelihood and the prior. The final goal is to present a detailed analysis of their strengths and weaknesses to statisticians or applied scientists. In this way, we wish to contribute to make them fully aware of the pros and cons of this software. We carry out a systematic comparison of the three platforms on a wide class of models, prior distributions, and data generating mechanisms. Our extensive simulation studies evaluate the quality of the MCMC chains produced, the efficiency of the software and the goodness of fit of the output. We also consider the efficiency of the parallelization made by the three platforms.
Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM

Building object-level maps can facilitate robot-environment interactions (e.g. planning and manipulation), but objects could often have multiple probable poses when viewed from a single vantage point, due to symmetry, occlusion or perceptual failures. A robust object-level simultaneous localization and mapping (object SLAM) algorithm needs to be aware of this pose ambiguity. We propose to maintain and subsequently disambiguate the multiple pose interpretations to gradually recover a globally consistent world representation. The max-mixtures model is applied to implicitly and efficiently track all pose hypotheses, but the resulting formulation is non-convex, and therefore subject to local optima. To mitigate this problem, temporally consistent hypotheses are extracted, guiding the optimization into the global optimum. This consensus-informed inference method is applied online via landmark variable re-initialization within an incremental SLAM framework, iSAM2, for robust real-time performance. We demonstrate that this approach improves SLAM performance on both simulated and real object SLAM problems with pose ambiguity.
Positively Weighted Kernel Quadrature via Subsampling

We study kernel quadrature rules with convex weights. Our approach combines the spectral properties of the kernel with recombination results about point measures. This results in effective algorithms that construct convex quadrature rules using only access to i.i. d. samples from the underlying measure and evaluation of the kernel and that result in a small worst-case error. In addition to our theoretical results and the benefits resulting from convex weights, our experiments indicate that this construction can compete with the optimal bounds in well-known examples.
RankSRGAN: Super Resolution Generative Adversarial Networks With Learning to Rank

Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). To further improve the visual quality of super-resolved results, PIRM2018-SR Challenge employed perceptual metrics to assess the perceptual quality, such as PI, NIQE, and Ma. However, existing methods cannot directly optimize these indifferentiable perceptual metrics, which are shown to be highly correlated with human ratings. To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of different perceptual metrics. Specifically, we first train a Ranker which can learn the behaviour of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality. The most appealing part is that the proposed method can combine the strengths of different SR methods to generate better results. Furthermore, we extend our method to multiple Rankers to provide multi-dimension constraints for the generator. Extensive experiments show that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics and quality. Project page: https://wenlongzhang0517.github. io/Projects/RankSRGAN.
TESS survey of rotational and pulsational variability of mercury-manganese stars

Mercury–manganese (HgMn) stars are late-B upper main sequence chemically peculiar stars distinguished by large overabundances of heavy elements, slow rotation, and frequent membership in close binary systems. These stars lack strong magnetic fields typical of magnetic Bp stars but occasionally exhibit non-uniform surface distributions of chemical elements. The physical origin and the extent of this spot formation phenomenon remain unknown. Here, we use 2-min cadence light curves of 64 HgMn stars observed by the Transiting Exoplanet Survey Satellite (TESS) during the first 2 yr of its operation to investigate the incidence of rotational modulation and pulsations among HgMn stars. We found rotational variability with amplitudes of 0.1–3 mmag in 84 per cent of the targets, indicating ubiquitous presence of star-spots on HgMn-star surfaces. Rotational period measurements reveal six fast-rotating stars with periods below 1.2 d, including one ultra-fast rotator (HD 14228) with a 0.34-d period. We also identify several HgMn stars showing multiperiodic g-mode pulsations, tidally induced variation and eclipses in binary systems.
NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations

Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical models. In this manuscript we detail the various methodologies of PINNs and showcase the various types of problems a PINN software can solve. We then detail the inner workings of NeuralPDE.jl and show how a formulation structured around numerical quadrature gives rise to new loss functions which allow for adaptivity towards bounded error tolerances. We describe the various ways one can use the tool, detailing mathematical techniques like using extended loss functions for parameter estimation and operator discovery, to help potential users adopt these PINN-based techniques into their workflow. We showcase how NeuralPDE uses a purely symbolic formulation so that all of the underlying training code is generated from an abstract formulation, and show how to make use of GPUs and solve systems of PDEs. Afterwards we give a detailed performance analysis which showcases the trade-off between training techniques on a large set of PDEs. From these comprehensive performance benchmarks we provide guidelines for users of PINN software, such as mixing ADAM optimizers with techniques like BFGS and mixing robust quadrature techniques with faster quasi-random samplers. We end by focusing on a complex multiphysics example, the ∗Massachusetts Institute of Technology †Julia Computing ‡University of Turin §University of Ottawa ¶Carnegie Mellon University ‖Indian Institute of Technology, Roorkee ∗∗Ford Motor Company
A Novel Approach to Vehicle Pose Estimation using Automotive Radar

This paper presents a set of novel scan-matching techniques for vehicle pose estimation using automotive radar measurements. The proposed approach modifies the Normal Distributions Transform (NDT) -- a state-of-the-art scan-matching SLAM technique, widely used in lidar-based localization -- to account for particular aspects of radar environment perception. First, the polar NDT (PNDT) is introduced by solving the NDT problem in the polar coordinate system, natural for radar measurements. A better agreement between the measurement uncertainties and their representation in the scan-matching algorithm is achieved. Second, the extension of PNDT to take into account the Doppler measurements -- Doppler polar NDT (DPNDT) -- is proposed. Third, the SNR of detected targets is added to the optimization procedure to minimize the impact of RCS fluctuation. The improvement over the conventional NDT is demonstrated in numerical simulations and real data processing, showing the ability to decrease the localization error by a factor of 3 to 5, depending on the scenario, with a negligible increase in computational complexity. Finally, a DPNDT extension with the capability to compensate angular bias in array beam-forming is presented. Simulation results and real data processing show the possibility to correct it with the accuracy of $0.1^\circ$ almost in real-time.
Hybrid neural network reduced order modelling for turbulent flows with geometric parameters

Geometrically parametrized partial differential equations are currently widely used in many different fields, such as shape optimization processes or patient-specific surgery studies. The focus of this work is some advances on this topic, capable of increasing the accuracy with respect to previous approaches while relying on a high cost–benefit ratio performance. The main scope of this paper is the introduction of a new technique combining a classical Galerkin-projection approach together with a data-driven method to obtain a versatile and accurate algorithm for the resolution of geometrically parametrized incompressible turbulent Navier–Stokes problems. The effectiveness of this procedure is demonstrated on two different test cases: a classical academic back step problem and a shape deformation Ahmed body application. The results provide insight into details about the properties of the architecture we developed while exposing possible future perspectives for this work.
Diversity in sociotechnical machine learning systems

There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, different concepts of diversity are based on distinct rationales that should inform how we measure diversity in a given context. Similarly, the lack of specificity about the precise mechanisms underpinning diversity’s potential benefits can result in uninformative generalities, invalid experimental designs, and illicit interpretations of findings. In this work, we draw on research in philosophy, psychology, and social and organizational sciences to make three contributions: First, we introduce a taxonomy of different diversity concepts from philosophy of science, and explicate the distinct epistemic and political rationales underlying these concepts. Second, we provide an overview of mechanisms by which diversity can benefit group performance. Third, we situate these taxonomies of concepts and mechanisms in the lifecycle of sociotechnical machine learning systems and make a case for their usefulness in fair and accountable machine learning. We do so by illustrating how they clarify the discourse around diversity in the context of machine learning systems, promote the formulation of more precise research questions about diversity’s impact, and provide conceptual tools to further advance research and practice.
An Information Theory Approach to Physical Domain Discovery

The project of physics discovery is often equivalent to finding the most concise description of a physical system. The description with optimum predictive capability for a dataset generated by a physical system is one that minimizes both predictive error on the dataset and the complexity of the description. The discovery of the governing physics of a system can therefore be viewed as a mathematical optimization problem. We outline here a method to optimize the description of arbitrarily complex physical systems by minimizing the entropy of the description of the system. The Recursive Domain Partitioning (RDP) procedure finds the optimum partitioning of each physical domain into subdomains, and the optimum predictive function within each subdomain. Penalty functions are introduced to limit the complexity of the predictive function within each domain. Examples are shown in 1D and 2D. In 1D, the technique effectively discovers the elastic and plastic regions within a stress-strain curve generated by simulations of amorphous carbon material, while in 2D the technique discovers the free-flow region and the inertially-obstructed flow region in the simulation of fluid flow across a plate.
Dark sector interaction and the supernova absolute magnitude tension

It has been intensively discussed if modifications in the dynamics of the Universe at late times is able or not to solve the $H_0$ tension. On the other hand, it has also been argued that the $H_0$ tension is actually a tension on the supernova absolute magnitude $M_B$. In this work, we robustly constraint $M_B$ using Pantheon Supernovae Ia (SN) sample, Baryon Acoustic Oscillations (BAO), and Big Bang Nucleosynthesis (BBN) data, and assess the $M_B$ tension by comparing three theoretical models, namely the standard $\Lambda$CDM, the $w$CDM and a non-gravitational interaction (IDE) between dark energy (DE) and dark matter (DM). We find that the IDE model can solve the $M_B$ tension with a coupling different from zero at 95\% CL, confirming the results obtained using a $H_0$ prior.
RansomClave: Ransomware Key Management using SGX

Modern ransomware often generate and manage cryptographic keys on the victim’s machine, giving defenders an opportunity to capture exposed keys and recover encrypted data without paying the ransom. However, recent work has raised the possibility of future enclave-enhanced malware that could avoid such mitigations using emerging support for hardware-enforced secure enclaves in commodity CPUs. Nonetheless, the practicality of such enclave-enhanced malware and its potential impact on all phases of the ransomware lifecyle remain unclear. Given the demonstrated capacity of ransomware authors to innovate in order to better extort their victims (e.g. through the adoption of untraceable virtual currencies and anonymity networks), it is important to better understand the risks involved and identify potential mitigations. As a basis for comprehensive security and performance analysis of enclave-enhanced ransomware, we present RansomClave, a family of ransomware that securely manage their cryptographic keys using an enclave. We use RansomClave to explore the implications of enclave-enhanced ransomware for the key generation, encryption and key release phases of the ransomware lifecycle, and to identify potential limitations and mitigations. We propose two plausible victim models and analyse, from an attacker’s perspective, how RansomClave can protect cryptographic keys from each type of victim. We find that some existing mitigations are likely to be effective during the key generation and encryption phases, but that RansomClave enables new trustless key release schemes that could potentially improve attacker’s profitability and, by extension, make enclaves an attractive target for future attackers.
Subdifferentiation of Nonconvex Sparsity-Promoting Functionals on Lebesgue Spaces

Sparsity-promoting terms are incorporated into the objective functions of optimal control problems in order to ensure that optimal controls vanish on large parts of the underlying domain. Typical candidates for those terms are integral functions on Lebesgue spaces based on the lp-metric for p ∈ [0, 1) which are nonconvex as well as non-Lipschitz and, thus, variationally challenging. In this paper, we derive exact formulas for the Fréchet, limiting, and singular subdifferential of these functionals. These generalized derivatives can be used for the derivation of necessary optimality conditions for optimal control problems comprising such sparsity-promoting terms.
Routes to High Energy Cathodes of Sodium‐Ion Batteries

Sodium‐ion batteries (SIBs) are now being actively developed as low cost and sustainable alternatives to lithium‐ion batteries (LIBs) for large‐scale electric energy storage applications. In recent years, various inorganic and organic Na compounds, mostly mimicked from their Li counterparts, have been synthesized and tested for SIBs, and some of them indeed demonstrate comparable specific capacity to the presently developed LIB electrodes. However, the lack of suitable cathode materials is still a major obstacle to the commercial development of SIBs. Here, we present a brief review on the recent developments of SIB cathodes, with a focus on low cost and high energy density materials (> 450 Wh kg−1 vs Na) together with discussion of their Na‐storage mechanisms. The considerable differences in the structural requirements for Li‐ and Na‐storage reactions mean that it is not sufficient to design SIB cathode materials by simply mimicking LIB materials, and therefore great efforts are needed to discover new materials and reaction mechanisms to further develop variable cathodes for advanced SIB technology. Some directions for future research and possible strategies for building advanced cathode materials are also proposed here.
On Birkhoff Angles in Normed Spaces

Associated to Birkhoff orthogonality, we study Birkhoff angles in a normed space and present some of their basic properties. We also discuss how to decide whether an angle is more acute or more obtuse than another. In addition, given two vectors $x$ and $y$ in a normed space, we study the formula for Birkhoff `cosine' of the angle from $x$ to $y$ from which we can, in principal, compute the angle. Some examples will be presented.
Synchrotron light sources and X-ray free-electron-lasers

Synchrotron light sources and X-ray free-electron laser (FEL) facilities are unique tools providing extremely brilliant X-rays that allow the observation of matter with atomic spatial resolution. On the one hand, synchrotron light sources consist of electron circular accelerators and produce synchrotron radiation in bending magnets and undulators. On the other hand, X-ray FEL facilities are based on electron linear accelerators and generate more coherent and shorter pulses suitable for time-resolved experiments. In this contribution we will qualitatively describe synchrotron and X-ray FEL facilities. We will start explaining some fundamental concepts related to synchrotron and FEL radiation. We will then describe the two kinds of machines, including the history and current facilities, the typical layout, and some basic concepts about the electron beam dynamics and properties.
CONSTRUCTION AND VALIDATION OF SIMULATED SCENARIO FOR NURSING CARE TO COLOSTOMY PATIENTS

ABSTRACT Objective: to construct and validate a high fidelity clinical simulation scenario on nursing care to colostomy patients. Method: descriptive study of construction and validation of the appearance and content of a high fidelity clinical simulation scenario referring to nursing care for colostomy patients. To guide the elaboration of the scenario, a study was carried out in the literature on nursing care for the colostomy patient in the hospital environment. The scenario was constructed according to the items proposed by Fabri, based on the Bloom’s Taxonomy. For the selection of the nine experts, the criteria proposed by Fehring were used. The scenario, after being structured, was tested by a group of students from the 3rd and 4th years of graduation in nursing in a clinical simulation laboratory of a public university. It was considered 80% level of agreement. Results: in the validation of the scenario, all the experts agreed with the proposed items, suggesting the inclusion of some references, book chapters, skills training, preparation of reading material and checklist follow-up of participants. This scenario test allowed the identification of relevant contributions for adjustments of the simulated activity and allowed to test the debriefing with the support of the checklist. It was also evidenced the need to include information in the patient medical record and to increase the time of development of the scenario to solve the proposed objectives. Conclusion: the results show that for practical simulated well defined and successful are required to careful preparation, validation and prior testing of the planned activities.
Sensitivity analysis of modal quantities for underground cables

An accurate mathematical model, which takes into account both earth and conductor-sheath skin effects, is utilised for the computation of the high-frequency modal-attenuation constants and phase velocities of underground cables. The sensitivities of these quantities to cable dimensions, material properties, earth resistivity, spacing between cables, cable depth and frequency are evaluated for a typical underground cable system. The sensitivity analysis makes it possible to establish the effect of each parameter on modal propagation. The method of calculating the sensitivities can easily be extended to a crossbonded cable system provided the equivalent homogeneous parameters of the major section are determined
LATTICE BOLTZMANN SIMULATION OF HEAT TRANSFER ENHANCEMENT DURING MELTING BY USING NANOPARTICLES

Abstract– This research paper provides a mathematical modeling of heat transfer enhancement during melting process in a square cavity through dispersion of nanoparticles. The enthalpy-based lattice Boltzmann method (LBM) with a combination of D2Q9 and D2Q5 lattice models is used to solve density, velocity and temperature fields. The nano-enhanced phase change material (NEPCM) is composed of a dilute suspension of copper particles in water (ice) and is melted from the left. Also, in this study the sub-cooling case is neglected. Conduction heat transfer has been taken into account in the solid phase as well as natural convection in the liquid phase. Numerical simulations are performed for various volume fractions of nanoparticles and Rayleigh numbers ranging from 10 to 10. The validation of results is carried out by comparing the present results of natural convection and convection-dominated melting in a square cavity with those of existing earlier numerical studies. Predicated results illustrate that by suspending the nanoparticles in the fluid the thermal conductivity of NEPCM is increased in comparison with PCM. Also, by enhancing thermal conductivity and decreasing latent heat of fusion higher rates of heat release can be obtained.
Corrosion and biofilm for a composite coated iron observed by FTIR-ATR and Raman spectroscopy

Corrosion of metallic materials occurs in various ways. Recently, many researchers have pointed out that biofilm formed by a biofouling process may be a main cause for the initial stage of corrosion. From this viewpoint, the control of biofilm would help lead to corrosion prevention. In this paper, the authors studied the composite film of silane compounds with dispersed organometallic compounds that showed anti-fouling effect on pure iron substrates. The specimens were immersed in a laboratory biofilm reactor for 7 days to form biofilms on them. The surfaces of the specimens were observed by optical and scanning electron microscopy, Raman spectroscopy and FTIR-ATR spectroscopy. Finally, the effect of the composite coating on biofilm formation and corrosion characteristics was studied and shown to inhibit both.