IA Scholar Query: The Nature and Meaning of Perturbations in Geometric Computing.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgTue, 01 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Ray theory for elastic wave propagation in graded metamaterials
https://scholar.archive.org/work/xjnyszsnqja7hm7qzcz37do3he
We present a ray theory for modeling elastic wave propagation in spatially graded mechanical metamaterials. Wave propagation in periodic metamaterials has been well studied, motivated by their beneficial wave steering and bandgap properties. By contrast, comparably little work has explored wave propagation in spatially graded metamaterials despite the increased design opportunities, largely due to the lack of efficient modeling techniques. We develop a ray theory to model waves in graded metamaterials based on high-frequency asymptotics and the assumption of local periodicity. This work builds upon the well-developed ray theories that are fundamental in a wide range of fields, from optics to seismology. Our derivations produce a practical framework for computing approximate wave fields in graded metamaterials. Ray trajectories are computed by independently solving a system of ordinary differential equations for each ray, requiring only knowledge of local dispersion relations throughout the metamaterial, which vary smoothly in space due to grading. Equations for the wave amplitude along rays are also derived in the two-dimensional setting. We show that the form of the ray tracing equations are nearly identical to those for smooth solids in seismic ray theory, with the primary difference being the dispersion relations. A numerical framework for computing ray solutions is demonstrated on a mass-spring network with analytical dispersion relations as well as a truss metamaterial that requires the numerical evaluation of dispersion relations. Through these examples, we demonstrate that ray theory provides an efficient means of studying the fascinating behavior of waves in graded metamaterials such as wave guiding along curved trajectories.Charles Jacob Dorn, Dennis M. Kochmannwork_xjnyszsnqja7hm7qzcz37do3heTue, 01 Nov 2022 00:00:00 GMTDynamic Processes Modeling in a Peristaltic Pump with a Hydraulic Drive for the Bingham Fluid
https://scholar.archive.org/work/otsvzf3m7je6dknsybu2qpi4pm
At present peristaltic pumps are widely used in many branches of industry and national economy. Simplicity of construction, processability and possibility of pumping liquids with big quantity of solid particles are the main advantages while using peristaltic pumps. Therefore development of methods of rational choice of parameters at designing of peristaltic pumps is the actual problem. To develop universal mathematical models of dynamic processes in peristaltic pumps for definition of rational technical parameters. In dynamic processes we propose to use differential equations of motion in the Lagrange form, where the angle of rotation of the pump rotor is taken as a universal coordinate. Mathematical model of dynamic processes in peristaltic pump with hydraulic drive has been created on the base of differential equation. The function of resistance forces caused by gravity forces of mixture particles in the hose reel has been determined. On the basis of the non-linear model of the resistance forces to the flow of the fluid Bingham method of constructing the dependence of the pressure drop on the angular velocity of the rotor to determine the resistance forces to the flow of the fluid has been proposed. The result of dynamic processes simulation is the determination of interrelationship of technological parameters of the device functioning: the velocity of the medium and pump performance is increasing at reducing the length of the diverting hose and reducing the height of its rise; a significant influence on the average speed has plastic viscosity of the environment; a significant change in the yield strength has an insignificant impact on the speed.Olha Dvirna, Vladimir Shatokhin, Yaroslav Ivanchuk, Natalia Veselovskaya, Wojciech Jurczakwork_otsvzf3m7je6dknsybu2qpi4pmSat, 01 Oct 2022 00:00:00 GMTIsadore M. Singer (1924–2021) In Memoriam Part 1: Scientific Works
https://scholar.archive.org/work/aejx3oq2lvch5gdpwoqpzzlbqe
Robert Bryant, Jean-Michel Bismut, Jeff Cheeger, Phillip Griffiths, Simon Donaldson, Nigel Hitchin, H Blaine Lawson, Michail Gromov, Adam Marcus, Daniel Spielman, Nikhil Srivastava, Edward Wittenwork_aejx3oq2lvch5gdpwoqpzzlbqeSat, 01 Oct 2022 00:00:00 GMTHuman-controllable and structured deep generative models
https://scholar.archive.org/work/2feku6i5y5dbbcdwe7p66fkkai
Deep generative models are a class of probabilistic models that attempts to learn the underlying data distribution. These models are usually trained in an unsupervised way and thus, do not require any labels. Generative models such as Variational Autoencoders and Generative Adversarial Networks have made astounding progress over the last years. These models have several benefits: eased sampling and evaluation, efficient learning of low-dimensional representations for downstream tasks, and better understanding through interpretable representations. However, even though the quality of these models has improved immensely, the ability to control their style and structure is limited. Structured and human-controllable representations of generative models are essential for human-machine interaction and other applications, including fairness, creativity, and entertainment. This thesis investigates learning human-controllable and structured representations with deep generative models. In particular, we focus on generative modelling of 2D images. For the first part, we focus on learning clustered representations. We propose semi-parametric hierarchical variational autoencoders to estimate the intensity of facial action units. The semi-parametric model forms a hybrid generative-discriminative model and leverages both parametric Variational Autoencoder and non-parametric Gaussian Process autoencoder. We show superior performance in comparison with existing facial action unit estimation approaches. Based on the results and analysis of the learned representation, we focus on learning Mixture-of-Gaussians representations in an autoencoding framework. We deviate from the conventional autoencoding framework and consider a regularized objective with the Cauchy-Schwarz divergence. The Cauchy-Schwarz divergence allows a closed-form solution for Mixture-of-Gaussian distributions and, thus, efficiently optimizing the autoencoding objective. We show that our model outperforms existing Variational Autoencoders in density estimation, clu [...]Dieu Linh Tran, Maja Panticwork_2feku6i5y5dbbcdwe7p66fkkaiTue, 27 Sep 2022 00:00:00 GMTNetwork Toxicology Guided Mechanism Study on the Association between Thyroid Function and Exposures to Polychlorinated Biphenyls Mixture
https://scholar.archive.org/work/leptvzdrezhqncmwzfukuiwkde
Polychlorinated biphenyls (PCBs) are persistent and highly toxic pollutants, which can accumulate in organisms and produce toxic effects, especially damaging the function of thyroid hormones. So far, the molecular mechanism of PCBs mixture and their metabolites interfering with thyroid hormones has not been studied thoroughly except for individual compounds. In this study, PubMed, Web of Science, and STITCH databases were used to search PCBs and their corresponding target proteins. The intersection of PCBs and thyroid hormone dysfunction target proteins was obtained from GeneCards. The "compounds-targets-pathways" network was constructed by Cytoscape software. And KEGG and Go analyses were performed for key targets. Finally, molecular docking was used to verify the binding effect. Four major active components, five key targets, and 10 kernel pathways were successfully screened by constructing the network. Functional enrichment analysis showed that the interference was mediated by cancer, proteoglycans, PI3K-Akt, thyroid hormone, and FoxO signaling pathways. The molecular docking results showed that the binding energies were less than -5 kcal·mol-1. PCBs and their metabolites may act on the key targets of MAPK3, MAPK1, RXRA, PIK3R1, and TP53. The toxic effect of sulfated and methyl sulfone PCBs is greater. The method of screening targets based on the simultaneous action of multiple PCBs can provide a reference for other research. The targets were not found in previous metabolite toxicity studies. It also provides a bridge for the toxic effects and experimental research of PCBs and their metabolites in the future.Chunxia Li, Hong Xing, Qiaoyu He, Jing Liu, Hong Liu, Yue Li, Xiaopeng Chen, Aleksandra Buhawork_leptvzdrezhqncmwzfukuiwkdeTue, 27 Sep 2022 00:00:00 GMTAdversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation
https://scholar.archive.org/work/3rz4ylgkarh2vn4cac2uxs6xb4
A common challenge posed to robust semantic segmentation is the expensive data annotation cost. Existing semi-supervised solutions show great potential for solving this problem. Their key idea is constructing consistency regularization with unsupervised data augmentation from unlabeled data for model training. The perturbations for unlabeled data enable the consistency training loss, which benefits semi-supervised semantic segmentation. However, these perturbations destroy image context and introduce unnatural boundaries, which is harmful for semantic segmentation. Besides, the widely adopted semi-supervised learning framework, i.e. mean-teacher, suffers performance limitation since the student model finally converges to the teacher model. In this paper, first of all, we propose a context friendly differentiable geometric warping to conduct unsupervised data augmentation; secondly, a novel adversarial dual-student framework is proposed to improve the Mean-Teacher from the following two aspects: (1) dual student models are learned independently except for a stabilization constraint to encourage exploiting model diversities; (2) adversarial training scheme is applied to both students and the discriminators are resorted to distinguish reliable pseudo-label of unlabeled data for self-training. Effectiveness is validated via extensive experiments on PASCAL VOC2012 and Cityscapes. Our solution significantly improves the performance and state-of-the-art results are achieved on both datasets. Remarkably, compared with fully supervision, our solution achieves comparable mIoU of 73.4% using only 12.5% annotated data on PASCAL VOC2012. Our codes and models are available at https://github.com/cao-cong/ADS-SemiSeg.Cong Cao, Tianwei Lin, Dongliang He, Fu Li, Huanjing Yue, Jingyu Yang, Errui Dingwork_3rz4ylgkarh2vn4cac2uxs6xb4Tue, 27 Sep 2022 00:00:00 GMTSIT 45: An interacting, compact, and star-forming isolated galaxy triplet
https://scholar.archive.org/work/mgzxn4hnjjbbjcsaxnx3py2lju
The merging system SIT 45 (UGC 12589) is an unusual isolated galaxy triplet, consisting of three merging late-type galaxies, out of 315 systems in the SIT (SDSS-based catalogue of Isolated Triplets). The main aims of this work are to study its dynamical evolution and star formation history (SFH), as well as its dependence on its local and large-scale environment. To study its dynamics, parameters such as the velocity dispersion (σ_v), the harmonic radius (R_H), the crossing time (H_0t_c), and the virial mass (M_vir), along with the compactness of the triplet (S) were considered. To constrain the SFH, we used CIGALE to fit its observed spectral energy distribution using multi-wavelength data from the ultraviolet to the infrared. According to its SFH, SIT 45 presents star-formation, where the galaxies also present recent (∼200 Myr) star-formation increase, indicating that this activity may have been triggered by the interaction. Its dynamical configuration suggests that the system is highly evolved in comparison to the SIT. However this is not expected for systems composed of star-forming late-type galaxies, based on observations in compact groups. We conclude that SIT 45 is a system of three interacting galaxies that are evolving within the same dark matter halo, where its compact configuration is a consequence of the on-going interaction, rather than due to a long-term evolution (as suggested from its H_0t_c value). We consider two scenarios for the present configuration of the triplet, one where one of the members is a tidal galaxy, and another where this galaxy arrives to the system after the interaction. Both scenarios need further exploration. The isolated triplet SIT 45 is therefore an ideal system to study short timescale mechanisms (∼ 10^8 years), such as starbursts triggered by interactions which are more frequent at higher redshift.D. Grajales-Medina, M. Argudo-Fernández, P. Vásquez-Bustos, S. Verley, M. Boquien, S. Salim, S. Duarte Puertas, U. Lisenfeld, D. Espada, H. Salas-Olavework_mgzxn4hnjjbbjcsaxnx3py2ljuMon, 26 Sep 2022 00:00:00 GMTRigid comparison geometry for Riemannian bands and open incomplete manifolds
https://scholar.archive.org/work/37crgel3nbfinjzemh6o2sxgdi
Comparison theorems are foundational to our understanding of the geometric features implied by various curvature constraints. This paper considers manifolds with a positive lower bound on either scalar, 2-Ricci, or Ricci curvature, and contains a variety of theorems which provide sharp relationships between this bound and notions of width. Some inequalities leverage geometric quantities such as boundary mean curvature, while others involve topological conditions in the form of linking requirements or homological constraints. In several of these results open and incomplete manifolds are studied, one of which partially addresses a conjecture of Gromov in this setting. The majority of results are accompanied by rigidity statements which isolate various model geometries – both complete and incomplete – including a new characterization of round lens spaces, and other models that have not appeared elsewhere. As a byproduct, we additionally give new and quantitative proofs of several classical comparison statements such as Bonnet-Myers' and Frankel's Theorem, as well as a version of Llarull's Theorem and a notable fact concerning asymptotically flat manifolds. The results that we present vary significantly in character, however a common theme is present in that the lead role in each proof is played by spacetime harmonic functions, which are solutions to a certain elliptic equation originally designed to study mass in mathematical general relativity.Sven Hirsch, Demetre Kazaras, Marcus Khuri, Yiyue Zhangwork_37crgel3nbfinjzemh6o2sxgdiMon, 26 Sep 2022 00:00:00 GMTVariational Inference as Iterative Projection in a Bayesian Hilbert Space with Application to Robotic State Estimation
https://scholar.archive.org/work/zc2p2a2kvnhavhqolmt76mjaw4
Variational Bayesian inference is an important machine-learning tool that finds application from statistics to robotics. The goal is to find an approximate probability density function (PDF) from a chosen family that is in some sense 'closest' to the full Bayesian posterior. Closeness is typically defined through the selection of an appropriate loss functional such as the Kullback-Leibler (KL) divergence. In this paper, we explore a new formulation of variational inference by exploiting the fact that (most) PDFs are members of a Bayesian Hilbert space under careful definitions of vector addition, scalar multiplication and an inner product. We show that, under the right conditions, variational inference based on KL divergence can amount to iterative projection, in the Euclidean sense, of the Bayesian posterior onto a subspace corresponding to the selected approximation family. We work through the details of this general framework for the specific case of the Gaussian approximation family and show the equivalence to another Gaussian variational inference approach. We furthermore discuss the implications for systems that exhibit sparsity, which is handled naturally in Bayesian space, and give an example of a high-dimensional robotic state estimation problem that can be handled as a result. We provide some preliminary examples of how the approach could be applied to non-Gaussian inference and discuss the limitations of the approach in detail to encourage follow-on work along these lines.Timothy D. Barfoot, Gabriele M. T. D'Eleuteriowork_zc2p2a2kvnhavhqolmt76mjaw4Mon, 26 Sep 2022 00:00:00 GMTNewton–Okounkov bodies of curve classes
https://scholar.archive.org/work/6k2cnuhayfg2haltn7l3exinm4
The purpose of the paper is to initiate the development of the theory of Newton Okounkov bodies of curve classes. Our denition is based on making a fundamental property of NewtonOkounkov bodies hold also in the curve case: the volume of the NewtonOkounkov body of a curve is a volume-type function of the original curve. This construction allows us to conjecture a new relation between NewtonOkounkov bodies, we prove it in certain cases.Lucie Deveywork_6k2cnuhayfg2haltn7l3exinm4Mon, 26 Sep 2022 00:00:00 GMTObservation of confinement-induced resonances in a 3D lattice
https://scholar.archive.org/work/akup3ndj6jfb5dgzvmmuncowca
We report on the observation of confinement-induced resonances for strong three-dimensional (3D) confinement in a lattice potential. Starting from a Mott-insulator state with predominantly single-site occupancy, we detect loss and heating features at specific values for the confinement length and the 3D scattering length. Two independent models, based on the coupling between the center-of-mass and the relative motion of the particles as mediated by the lattice, predict the resonance positions to a good approximation, suggesting a universal behavior. Our results extend confinement-induced resonances to any dimensionality and open up an alternative method for interaction tuning and controlled molecule formation under strong 3D confinement.Deborah Capecchi, Camilo Cantillano, Manfred J. Mark, Florian Meinert, Andreas Schindewolf, Manuele Landini, Alejandro Saenz, Fabio Revuelta, Hanns-Christoph Nägerlwork_akup3ndj6jfb5dgzvmmuncowcaMon, 26 Sep 2022 00:00:00 GMTAblation Path Saliency
https://scholar.archive.org/work/2my6va7pifb7zlvpv4iervqlmi
Various types of saliency methods have been proposed for explaining black-box classification. In image applications, this means highlighting the part of the image that is most relevant for the current decision. We observe that several of these methods can be seen as edge cases of a single, more general procedure based on finding a particular ablation path through the classifier's domain. This gives additional geometric insight to the existing methods. We also demonstrate that this ablation path method can be used as a technique in its own right, the higher computational cost being traded against additional information given by the path.Justus Sagemüller, Olivier Verdierwork_2my6va7pifb7zlvpv4iervqlmiMon, 26 Sep 2022 00:00:00 GMTThe Dynamics and Geometry of Semi-Hyperbolic Rational Semigroups
https://scholar.archive.org/work/w3blhjbmcnhojfdmpmyk4whwqq
We study skew-product dynamics for a large class of finitely-generated semi–hyperbolic semigroups of rational maps acting on the Riemann sphere, which generalizes both the theory of iteration of a single rational map of a single complex variable complex/holomorphic dynamics) and the theory of countable alphabet conformal iterated function systems (CIFSs). We construct the thermodynamic formalism for such dynamical systems and geometric potentials by developing the notion of nice families that extend to the case of our highly disconnected skew product phase space the powerful notion of nice sets due to Rivera–Letelier and Przytycki, and the allied earlier notion of K(V) sets due to Denker and the last named author. We leverage out techniques to prove the existence and uniqueness of equilibrium states for a wide class of Hölder potentials, and concomitant statistical laws: central limit theorem, law of iterated logarithm, and exponential decay of correlations. We devote lots of space and effort to control (non-recurrent) critical points which is a notoriously challenging task even for a single rational function; more generators add qualitatively new challenges. Beyond dynamics, but still with dynamical methods, we advance the study of finer fractal geometrical properties of the intricate Julia sets associated to such systems and, in particular, via equilibrium states, we perform a multifractal analysis of Lyapunov exponents. We use the Nice Open Set Condition (NOSC) introduced by the last two authors, and apply our new techniques to settle a long-standing problem in the theory of rational semigroups by proving that for our class of semigroups the Hausdorff dimension of each fiber Julia set is strictly smaller than the Hausdorff dimension of the global Julia set of the semigroup.Jason Atnip, Hiroki Sumi, Mariusz Urbańskiwork_w3blhjbmcnhojfdmpmyk4whwqqMon, 26 Sep 2022 00:00:00 GMTJump Law of Co-State in Optimal Control for State-Dependent Switched Systems and Applications
https://scholar.archive.org/work/e7zuu6e2afec3kqlvzqhlpexwi
This paper presents the jump law of co-states in optimal control for state-dependent switched systems. The number of switches and the switching modes are assumed to be known a priori. A proposed jump law is rigorously derived by theoretical analysis and illustrated by simulation results. An algorithm is then proposed to solve optimal control for state-dependent hybrid systems. Through numerical simulations, we further show that the proposed approach is more efficient than existing methods in solving optimal control for state-dependent switched systems.Mi Zhou, Erik I. Verriest, Yue Guan, Chaouki Abdallahwork_e7zuu6e2afec3kqlvzqhlpexwiMon, 26 Sep 2022 00:00:00 GMTSupergravity: Application in Particle Physics
https://scholar.archive.org/work/xy7myhzenvcovfxz53juh3ktqe
We provide a pedagogical introduction to N=1 supergravity/supersymmetry in relation to particle physics. The various steps in the construction of a generic N=1 supergravity model are briefly described, and we focus on its low energy supersymmetric limit. The conditions for supersymmetry and supergravity breaking are investigated, and realistic mechanisms suitable for particle physics identified. We then study the model-building aspects of 'softly-broken' supersymmetric extensions of the Standard Model and discuss several of their phenomenological features.Florian Domingo, Michel Rausch de Traubenbergwork_xy7myhzenvcovfxz53juh3ktqeMon, 26 Sep 2022 00:00:00 GMTTurbulence via intermolecular potential: A weakly compressible model of gas flow at low Mach number
https://scholar.archive.org/work/nkhetdj5tzexhhaqpur6pnyebu
In our recent works we proposed a theory of turbulence in inertial gas flow via the mean field effect of an intermolecular potential. We found that, in inertial flow, turbulence indeed spontaneously develops from a laminar initial condition, just as observed in nature and experiments. However, we also found that density and temperature in our inertial flow model behave unrealistically. The goal of the current work is to demonstrate technical possibility of modeling compressible, turbulent flow at low Mach number where both density and temperature behave in a more realistic fashion. Here we focus on a new treatment of the pressure variable, which constitutes a compromise between compressible, incompressible and inertial flow. Similarly to incompressible flow, the proposed equation for the pressure variable is artificial, rather than derived directly from kinetic formulation. However, unlike that for incompressible flow, our pressure equation only damps the divergence of velocity, instead of setting it directly to zero. We find that turbulence develops in our weakly compressible model much like it does in the inertial flow model, but density and temperature behave more realistically.Rafail V. Abramovwork_nkhetdj5tzexhhaqpur6pnyebuMon, 26 Sep 2022 00:00:00 GMTInflationary phenomenology of quadratic gravity in the Palatini formulation
https://scholar.archive.org/work/quaon6ge45evvaaglpa5fvs3zm
Angelos Lykkas, University Of Ioanninawork_quaon6ge45evvaaglpa5fvs3zmMon, 26 Sep 2022 00:00:00 GMTMultiplicity of positive solutions for a class of nonhomogeneous elliptic equations in the hyperbolic space
https://scholar.archive.org/work/72kb77c77nfyfl2um4bi5ncfgy
The paper is concerned with positive solutions to problems of the type -Δ_𝔹^N u - λ u = a(x) |u|^p-1 u + f in 𝔹^N, u ∈ H^1(𝔹^N), where 𝔹^N denotes the hyperbolic space, 1 0. Subsequently, we establish the existence of two positive solutions for a(x) ≡ 1 and prove asymptotic estimates for positive solutions using barrier-type arguments. The proofs for existence combine variational arguments, key energy estimates involving hyperbolic bubbles.Debdip Ganguly, Diksha Gupta, K. Sreenadhwork_72kb77c77nfyfl2um4bi5ncfgyMon, 26 Sep 2022 00:00:00 GMTParton distributions need representative sampling
https://scholar.archive.org/work/sahuqttytjasnpwkjt6nmjgnn4
In global QCD fits of parton distribution functions (PDFs), a large part of the estimated uncertainty on the PDFs originates from the choices of parametric functional forms and fitting methodology. We argue that these types of uncertainties can be underestimated with common PDF ensembles in high-stake measurements at the Large Hadron Collider and Tevatron. A fruitful approach to quantify these uncertainties is to view them as arising from sampling of allowed PDF solutions in a multidimensional parametric space. This approach applies powerful insights gained in recent statistical studies of large-scale population surveys and quasi-Monte Carlo integration methods. In particular, PDF fits may be affected by the big data paradox, which stipulates that more experimental data do not automatically raise the accuracy of PDFs – close attention to the data quality and sampling of possible PDF solutions is as essential. To test if the sampling of the PDF uncertainty of an experimental observable is truly representative of all acceptable solutions, we introduce a technique ("a hopscotch scan") based on a combination of parameter scans and stochastic sampling. With this technique, we show that the PDF uncertainty on key LHC cross sections at 13 TeV obtained with the public NNPDF4.0 fitting code is larger than the nominal uncertainty obtained with the published NNPDF4.0 Monte-Carlo replica sets. For example, the uncertainties on the charm distribution at a large momentum fraction x and gluon PDF at small x are enlarged. In PDF ensembles obtained in the analytic minimization (Hessian) formalism, the tolerance on the PDF uncertainty must be based on sufficiently complete sampling of PDF functional forms and choices of the experiments.Aurore Courtoy, Joey Huston, Pavel Nadolsky, Keping Xie, Mengshi Yan, C.-P. Yuanwork_sahuqttytjasnpwkjt6nmjgnn4Mon, 26 Sep 2022 00:00:00 GMTRoadmap on Electronic Structure Codes in the Exascale Era
https://scholar.archive.org/work/72kg6tpwnncrxlfs77h32a3zde
Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.Vikram Gavini, Stefano Baroni, Volker Blum, David R. Bowler, Alexander Buccheri, James R. Chelikowsky, Sambit Das, William Dawson, Pietro Delugas, Mehmet Dogan, Claudia Draxl, Giulia Galli, Luigi Genovese, Paolo Giannozzi, Matteo Giantomassi, Xavier Gonze, Marco Govoni, Andris Gulans, François Gygi, John M. Herbert, Sebastian Kokott, Thomas D. Kühne, Kai-Hsin Liou, Tsuyoshi Miyazaki, Phani Motamarri, Ayako Nakata, John E. Pask, Christian Plessl, Laura E. Ratcliff, Ryan M. Richard, Mariana Rossi, Robert Schade, Matthias Scheffler, Ole Schütt, Phanish Suryanarayana, Marc Torrent, Lionel Truflandier, Theresa L. Windus, Qimen Xu, Victor W.-Z. Yu, Danny Perezwork_72kg6tpwnncrxlfs77h32a3zdeMon, 26 Sep 2022 00:00:00 GMT