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We propose a number of powerful dynamic-epistemic logics for multi-agent information sharing and acts of publicly or privately accessing other agents' information databases. The static base of our logics is obtained by adding to standard epistemic logic comparative epistemic assertions, that can express epistemic superiority between groups or individuals, as well as a common distributed knowledge operator (that combines features of both common knowledge and distributed knowledge). On the dynamic side, we introduce actions by which epistemic superiority can be acquired: "sharing all one knows" (by e.g. giving access to one's information database to all or some of the other agents), as well as more complex informational events, such as hacking. We completely axiomatize several such logics and prove their decidability., Comment: 33 pages

We present a logical calculus for reasoning about information flow in quantum programs. In particular we introduce a dynamic logic that is capable of dealing with quantum measurements, unitary evolutions and entanglements in compound quantum systems. We give a syntax and a relational semantics in which we abstract away from phases and probabilities. We present a sound proof system for this logic, and we show how to characterize by logical means various forms of entanglement (e.g. the Bell states) and various linear operators. As an example we sketch an analysis of the teleportation protocol., Comment: 18 pages, date 2004, presented at the 2nd International Workshop on Quantum Programming Languages, affiliated to LICS 2004

We introduce a functional framework taylored to investigate the minimality and stability properties of the Ginzburg-Landau vortex of degree one on the whole plane. We prove that a renormalized Ginzburg-Landau energy is well-defined in that framework and that the vortex is its unique global minimizer up to the invariances by translation and phase shift. Our main result is a nonlinear coercivity estimate for the renormalized energy around the vortex, from which we can deduce its orbital stability as a solution to the Gross-Pitaevskii equation, the natural Hamiltonian evolution equation associated to the Ginzburg-Landau energy.

Due to their high cost of acquisition and operation, there are still a limited number of high-yield, high-flux neutron source facilities worldwide. In this context, laser-driven neutron sources offer a promising, cheaper alternative to those based on large-scale accelerators, with, in addition, the potential of generating compact neutron beams of high brightness and ultra-short duration. In particular, the predicted capability of next-generation petawatt (PW)-class lasers to accelerate protons beyond the 100 MeV range should unlock efficient neutron generation through spallation reactions. In this paper, this scenario is investigated numerically through particle-in-cell and Monte Carlo simulations, modeling, respectively, the laser acceleration of protons from thin-foil targets and their subsequent conversion into neutrons in secondary heavy-ion targets. Laser parameters relevant to the 1 PW LMJ-PETAL and 1-10 PW Apollon systems are considered. Under such conditions, neutron fluxes exceeding $10^{23}\,\rm n\,cm^{-2}\,s^{-1}$ are predicted, opening up attractive fundamental and applicative prospects., Comment: 16 pages, 13 figures

Particle-In-Cell codes are widely used for plasma physics simulations. It is often the case that particles within a computational cell need to be split to improve the statistics or, in the case of non-uniform meshes, to avoid the development of fictitious self-forces. Existing particle splitting methods are largely empirical and their accuracy in preserving the distribution function has not been evaluated in a quantitative way. Here we present a new method specifically designed for codes using adaptive mesh refinement. Although we point out that an exact, distribution function preserving method does exist, it requires a large number of split particles and its practical use is limited. We derive instead a method that minimizes the cost function representing the distance between the assignment function of the original particle and that of the sum of split particles. Depending on the interpolation degree and the dimension of the problem, we provide tabulated results for the weight and position of the split particles. This strategy represents no overhead in computing time and for a large enough number of split-particles it asymptotically tends to the exact solution.

This paper proposes a formal framework for modeling the interaction of causal and (qualitative) epistemic reasoning. To this purpose, we extend the notion of a causal model with a representation of the epistemic state of an agent. On the side of the object language, we add operators to express knowledge and the act of observing new information. We provide a sound and complete axiomatization of the logic, and discuss the relation of this framework to causal team semantics., Comment: This is the long version of a paper that is to be published in the post-proceedings of the 3rd Dali Workshop on Dynamic Logic: New Trends and Applications. The post-proceedings will be published by Springer as a Lecture Notes in Computer Science volume

The cosmic rays non-resonant streaming instability is believed to be the source of substantial magnetic field amplification. In this work we investigate the effects of the ambient plasma temperature on the instability and derive analytical expressions of its growth rate in the hot, demagnetized regime of interaction. To study its non-linear evolution we perform hybrid-PIC simulations for a wide range of temperatures. We find that in the cold limit about two-thirds of the cosmic rays drift kinetic energy is converted into magnetic energy. Increasing the temperature of the ambient plasma can substantially reduce the growth rate and the magnitude of the saturated magnetic field., Comment: Under review in MNRAS

We provide a variational construction of special solutions to the generalized surface quasi-geostrophic equations. These solutions take the form of N vortex patches with N-fold symmetry , which are steady in a uniformly rotating frame. Moreover, we investigate their asymptotic properties when the size of the corresponding patches vanishes. In this limit, we prove these solutions to be a desingularization of N Dirac masses with the same intensity, located on the N vertices of a regular polygon rotating at a constant angular velocity.

In the nineties, Klein, Majda and Damodaran have formally derived a simplified asymptotic motion law for the evolution of nearly parallel vortex filaments in the context of the three dimensional Euler equation for incompressible fluids. In the present work, we rigorously derive the corresponding asymptotic motion law in the context of the Gross-Pitaevskii equation., Comment: 33 pages

We investigate the time evolution of the six-component electron pressure tensor in a hybrid code studying consequences for the two-dimensional reconnection process in an initially perturbed Harris sheet. We put forward that two tensor components (a diagonal and a non-diagonal one) grow in an unstable way unless an isotropization operator is considered. This isotropization term is physically associated with an electron heat flux. As a consequence, we put forward that an enhanced value of a diagonal component is observed in the very middle of field reversal at sub-ion scale. Because of the increase of the kinetic pressure, the magnetic field is decreased in this electron layer, hence increasing the associated out-of-plane current at its edges and leading to its bifurcation. The bifurcation mechanism is based on the presence of electron pressure anisotropy, related to the gradient of inflow electron bulk velocity. The gradient in the inflow direction of the enhanced diagonal electron pressure tensor component results in the deceleration of the ions entering the X-point region. We suggest that bifurcated current sheets resulting from the anisotropies/agyrotropies of the six-component electron pressure tensor correspond to smaller reconnection rates comparing to non-bifurcated ones., Comment: 15 pages, 14 figures. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing

We present a PDE-based framework that generalizes Group equivariant Convolutional Neural Networks (G-CNNs). In this framework, a network layer is seen as a set of PDE-solvers where geometrically meaningful PDE-coefficients become the layer's trainable weights. Formulating our PDEs on homogeneous spaces allows these networks to be designed with built-in symmetries such as rotation in addition to the standard translation equivariance of CNNs. Having all the desired symmetries included in the design obviates the need to include them by means of costly techniques such as data augmentation. We will discuss our PDE-based G-CNNs (PDE-G-CNNs) in a general homogeneous space setting while also going into the specifics of our primary case of interest: roto-translation equivariance. We solve the PDE of interest by a combination of linear group convolutions and non-linear morphological group convolutions with analytic kernel approximations that we underpin with formal theorems. Our kernel approximations allow for fast GPU-implementation of the PDE-solvers, we release our implementation with this article in the form of the LieTorch extension to PyTorch, available at https://gitlab.com/bsmetsjr/lietorch . Just like for linear convolution a morphological convolution is specified by a kernel that we train in our PDE-G-CNNs. In PDE-G-CNNs we do not use non-linearities such as max/min-pooling and ReLUs as they are already subsumed by morphological convolutions. We present a set of experiments to demonstrate the strength of the proposed PDE-G-CNNs in increasing the performance of deep learning based imaging applications with far fewer parameters than traditional CNNs., Comment: 27 pages, 18 figures. v2 changes: - mentioned KerCNNs - added section Generalization of G-CNNs - clarification that the experiments utilized automatic differentiation and SGD. v3 changes: - streamlined theoretical framework - formulation and proof Thm.1 & 2 - expanded experiments. v4 changes: typos in Prop.5 and (20) v5/6 changes: minor revision

Magnetic reconnection occurs when two plasmas having co-planar but anti-parallel magnetic fields meet. At the contact point, the field is locally annihilated and the magnetic energy can be released into the surrounding plasma. Theory and numerical modelling still face many challenges in handling this complex process, the predictability of which remains elusive. Here we test, through a laboratory experiment conducted in a controlled geometry, the effect of changing the field topology from two-dimensional to three-dimensional. This is done by imposing an out-of-plane (guide) magnetic field of adjustable strength. A strong slowing down or even halting of symmetric reconnection is observed, even for a weak guide-field. Concomitantly, we observe a delayed heating of the plasma in the reconnection region and modified particle acceleration, with super-Alfvenic outflows ejected along the reconnection layer. These observations highlight the importance of taking into account three-dimensional effects in the many reconnection events taking place in natural and laboratory environments., Comment: 5 figures

We propose a new model for forming beliefs and learning about unknown probabilities (such as the probability of picking a red marble from a bag with an unknown distribution of coloured marbles). The most widespread model for such situations of 'radical uncertainty' is in terms of imprecise probabilities, i.e. representing the agent's knowledge as a set of probability measures. We add to this model a plausibility map, associating to each measure a plausibility number, as a way to go beyond what is known with certainty and represent the agent's beliefs about probability. There are a number of standard examples: Shannon Entropy, Centre of Mass etc. We then consider learning of two types of information: (1) learning by repeated sampling from the unknown distribution (e.g. picking marbles from the bag); and (2) learning higher-order information about the distribution (in the shape of linear inequalities, e.g. we are told there are more red marbles than green marbles). The first changes only the plausibility map (via a 'plausibilistic' version of Bayes' Rule), but leaves the given set of measures unchanged; the second shrinks the set of measures, without changing their plausibility. Beliefs are defined as in Belief Revision Theory, in terms of truth in the most plausible worlds. But our belief change does not comply with standard AGM axioms, since the revision induced by (1) is of a non-AGM type. This is essential, as it allows our agents to learn the true probability: we prove that the beliefs obtained by repeated sampling converge almost surely to the correct belief (in the true probability). We end by sketching the contours of a dynamic doxastic logic for statistical learning., Comment: In Proceedings TARK 2019, arXiv:1907.08335

This article is devoted to the study of the Hele-Shaw equation. We introduce an approach inspired by the water-wave theory. Starting from a reduction to the boundary, introducing the Dirichlet to Neumann operator and exploiting various cancellations, we exhibit parabolic evolution equations for the horizontal and vertical traces of the velocity on the free surface. This allows to quasi-linearize the equations in a very simple way. By combining these exact identities with convexity inequalities, we prove the existence of hidden Lyapunov functions of different natures. We also deduce from these identities and previous works on the water wave problem a simple proof of the well-posedness of the Cauchy problem. The analysis contains two side results of independent interest. Firstly, we give a principle to derive estimates for the modulus of continuity of a PDE under general assumptions on the flow. Secondly we prove a convexity inequality for the Dirichlet to Neumann operator., Comment: We have added some references

Total variation regularization and total variation flows (TVF) have been widely applied for image enhancement and denoising. To include a generic preservation of crossing curvilinear structures in TVF we lift images to the homogeneous space $M = \mathbb{R}^d \rtimes S^{d-1}$ of positions and orientations as a Lie group quotient in SE(d). For d = 2 this is called 'total roto-translation variation' by Chambolle & Pock. We extend this to d = 3, by a PDE-approach with a limiting procedure for which we prove convergence. We also include a Mean Curvature Flow (MCF) in our PDE model on M. This was first proposed for d = 2 by Citti et al. and we extend this to d = 3. Furthermore, for d = 2 we take advantage of locally optimal differential frames in invertible orientation scores (OS). We apply our TVF and MCF in the denoising/enhancement of crossing fiber bundles in DW-MRI. In comparison to data-driven diffusions, we see a better preservation of bundle boundaries and angular sharpness in fiber orientation densities at crossings. We support this by error comparisons on a noisy DW-MRI phantom. We also apply our TVF and MCF in enhancement of crossing elongated structures in 2D images via OS, and compare the results to nonlinear diffusions (CED-OS) via OS., Comment: Submission to the Seventh International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019). (v2) Typo correction in lemma 1. (v3) Typo correction last paragraph page 9

We investigate the linear stability of inviscid columnar vortices with respect to finite energy perturbations. For a large class of vortex profiles, we show that the linearized evolution group has a sub-exponential growth in time, which means that the associated growth bound is equal to zero. This implies in particular that the spectrum of the linearized operator is entirely contained in the imaginary axis. This contribution complements the results of a previous work, where spectral stability was established for the linearized operator in the enstrophy space., Comment: Major revision, including a complete rewriting of Section 3

Nitrous oxide (N2O) is a potent greenhouse gas emitted during biological wastewater treatment. A pseudo-mechanistic model describing three biological pathways for nitric oxide (NO) and N2O production was calibrated for mixed culture biomass from an activated sludge process using laboratory-scale experiments. The model (NDHA) comprehensively describes N2O producing pathways by both autotrophic ammonium oxidizing bacteria and heterotrophic bacteria. Extant respirometric assays and anaerobic batch experiments were designed to calibrate endogenous and exogenous processes (heterotrophic denitrification and autotrophic ammonium/nitrite oxidation) together with the associated net N2O production. Ten parameters describing heterotrophic processes and seven for autotrophic processes were accurately estimated (variance/mean < 25%). The model predicted NO and N2O dynamics at varying dissolved oxygen, ammonium and nitrite levels and was validated against an independent set of experiments with the same biomass. Aerobic ammonium oxidation experiments at two oxygen levels used for model evaluation (2 and 0.5 mg/L) indicated that both the nitrifier denitrification (42, 64%) and heterotrophic denitrification (7, 17%) pathways increased and dominated N2O production at high nitrite and low oxygen concentrations; while the nitrifier nitrification pathway showed the largest contribution at high dissolved oxygen levels (51, 19%). The uncertainty of the biological parameter estimates was propagated to N2O model outputs via Monte Carlo simulations as 95% confidence intervals. The accuracy of the estimated parameters resulted in a low uncertainty of the N2O emission factors (4.6 +- 0.6% and 1.2 +- 0.1%)., Comment: Text and Supporting Information

In a recent work Foulis and Pulmannov\' a \cite{Foulis2012} studied the logical connectives in lattice effect algebras. In this paper we extend their study and investigate further the logical calculus for which the lattice effect algebras can serve as semantic models. We shall first focus on some properties of lattice effect algebras and will then give a complete axiomatisation of this logic., Comment: 15 pagws, 2 figures

Columnar vortices are stationary solutions of the three-dimensional Euler equations with axial symmetry, where the velocity field only depends on the distance to the axis and has no component in the axial direction. Stability of such flows was first investigated by Lord Kelvin in 1880, but despite a long history the only analytical results available so far provide necessary conditions for instability under either planar or axisymmetric perturbations. The purpose of this paper is to show that columnar vortices are spectrally stable with respect to three-dimensional perturbations with no particular symmetry. Our result applies to a large family of velocity profiles, including the most common models in atmospheric flows and engineering applications. The proof is based on a homotopy argument, which allows us to concentrate in the spectral analysis of the linearized operator to a small neighborhood of the imaginary axis, where unstable eigenvalues can be excluded using integral identities and a careful study of the so-called critical layers., Comment: Final version including minor corrections. 52 pages, 1 figure