Search

A new approach using scalar metasurfaces for the design of linearly polarized antennas is presented. The proposed method is based on the construction of the surface impedance Zs using a technique called "phase-matching," which employs the sum of two circular polarizations in phase opposition. This process allows for the achievement of good performance of the synthesized antenna, such as the reduction of side lobe levels and the attainment of an almost symmetric main lobe regardless of the pointing direction. Numerical and measurement results are also presented., Comment: in French language, JNM, Scienceconf, Jun 2022, Limoges (Universit{\'e}), France

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithm (GA), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems using a Deep Differentiable Reinforcement Learning (DDRL) algorithm in particle accelerators. We compare DDRL algorithm with Model Free Reinforcement Learning (MFRL), GA and Bayesian Optimization (BO) for simultaneous optimization of heat load and trip rates in the Continuous Electron Beam Accelerator Facility (CEBAF). The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraint for energy requirements of the beam. A physics-based surrogate model based on real data is developed. This surrogate model is differentiable and allows back-propagation of gradients. The results are evaluated in the form of a Pareto-front for two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.

ALMA surveys have suggested that protoplanetary disks are not massive enough to form the known exoplanet population, under the assumption that the millimeter continuum emission is optically thin. In this work, we investigate how the mass determination is influenced when the porosity of dust grains is considered in radiative transfer models. The results show that disks with porous dust opacities yield similar dust temperature, but systematically lower millimeter fluxes compared to disks incorporating compact dust grains. Moreover, we recalibrate the relation between dust temperature and stellar luminosity for a wide range of stellar parameters, and calculate the dust masses of a large sample of disks using the traditionally analytic approach. The median dust mass from our calculation is about 6 times higher than the literature result, and this is mostly driven by the different opacities of porous and compact grains. A comparison of the cumulative distribution function between disk dust masses and exoplanet masses show that the median exoplanet mass is about 2 times lower than the median dust mass, if grains are porous, and there are no exoplanetary systems with masses higher than the most massive disks. Our analysis suggests that adopting porous dust opacities may alleviate the mass budget problem for planet formation. As an example illustrating the combined effects of optical depth and porous dust opacities on the mass estimation, we conduct new IRAM/NIKA-2 observations toward the IRAS 04370+2559 disk and perform a detailed radiative transfer modeling of the spectral energy distribution. The best-fit dust mass is roughly 100 times higher than the value from the traditionally analytic calculation. Future spatially resolved observations at various wavelengths are required to better constrain the dust mass., Comment: 11 pages, 7 figures, Accepted for publication in A&A

Glioblastoma is a highly aggressive form of brain cancer characterized by rapid progression and poor prognosis. Despite advances in treatment, the underlying genetic mechanisms driving this aggressiveness remain poorly understood. In this study, we employed multimodal deep learning approaches to investigate glioblastoma heterogeneity using joint image/RNA-seq analysis. Our results reveal novel genes associated with glioblastoma. By leveraging a combination of whole-slide images and RNA-seq, as well as introducing novel methods to encode RNA-seq data, we identified specific genetic profiles that may explain different patterns of glioblastoma progression. These findings provide new insights into the genetic mechanisms underlying glioblastoma heterogeneity and highlight potential targets for therapeutic intervention.

Having a detailed knowledge of the intracluster medium (ICM) to infer the exact cluster physics such as the cluster dynamical state is crucial for cluster-based cosmological studies. This knowledge limits the accuracy and precision of mass estimation, a key parameter for such studies. In this paper, we conduct an in-depth analysis of cluster ACT-CL J0240.0+0116 using a multi-wavelength approach, with a primary focus on high angular resolution Sunyaev-Zeldovich (SZ) thermal component observations obtained under the NIKA2 Sunyaev-Zeldovich Large Programme (LPSZ). We create composite images using NIKA2, X-ray, and optical galaxy number density maps. The results reveal distinct signs of disturbance within the cluster with the distributions of gas and member galaxies that do not overlap. We also find suggestions of an inflow of matter onto the cluster from the southwestern direction. Ultimately, we classify the cluster as disturbed, using morphological indicators derived from its SZ, X-ray, and optical image. The cluster SZ signal is also contaminated by a strong central point source. We adopt different approaches to handling this contaminant and find the estimates of our pressure and hydrostatic mass profiles robust to the point source mitigation model. The cluster hydrostatic mass is estimated at $4.25^{+0.50}_{-0.45\, } \times 10^{14} \,\mathrm{M}_{\odot}$ for the case where the point source was masked. These values are consistent with the mass estimated using only X-ray data and with those from previous SZ studies of the Atacama cosmology telescope (ACT) survey, with improved precision on the mass estimate. Our findings strongly suggest that ACT-CL J0240.0+0116 is a disturbed cluster system, and the detailed observations and derived values serve as a compelling case study for the capabilities of the LPSZ in mapping the cluster ICM with high precision., Comment: 13 pages, 7 figures, submitted to A&A

The millimeter continuum emission from galaxies provides important information about cold dust, its distribution, heating, and role in their InterStellar Medium (ISM). This emission also carries an unknown portion of the free-free and synchrotron radiation. The IRAM 30m Guaranteed Time Large Project, Interpreting Millimeter Emission of Galaxies with IRAM and NIKA2 (IMEGIN) provides a unique opportunity to study the origin of the millimeter emission on angular resolutions of <18" in a sample of nearby galaxies. As a pilot study, we present millimeter observations of two IMEGIN galaxies, NGC 2146 (starburst) and NGC 2976 (peculiar dwarf) at 1.15 mm and 2 mm. Combined with the data taken with Spitzer, Herschel, Plank, WSRT, and the 100m Effelsberg telescopes, we model the infrared-to-radio Spectral Energy Distribution (SED) of these galaxies, both globally and at resolved scales, using a Bayesian approach to 1) dissect different components of the millimeter emission, 2) investigate the physical properties of dust, and 3) explore correlations between millimeter emission, gas, and Star Formation Rate (SFR). We find that cold dust is responsible for most of the 1.15 mm emission in both galaxies and at 2 mm in NGC 2976. The free-free emission emits more importantly in NGC 2146 at 2 mm. The cold dust emissivity index is flatter in the dwarf galaxy ($\beta = 1.3\pm 0.1$) compared to the starburst galaxy ($\beta = 1.7\pm 0.1$). Mapping the dust-to-gas ratio, we find that it changes between 0.004 and 0.01 with a mean of $0.006\pm0.001$ in the dwarf galaxy. In addition, no global balance holds between the formation and dissociation of H$_2$ in this galaxy. We find tight correlations between the millimeter emission and both the SFR and molecular gas mass in both galaxies., Comment: Accepted for publication in Astronomy & Astrophysics

We present resolved $3.6-250~\mu$m dust spectral energy distribution (SED) fitting for $\sim 800$ nearby galaxies. We measure the distribution of radiation field intensities heating the dust, the dust mass surface density ($\Sigma_{\rm d}$), and the fraction of dust in the form of polycyclic aromatic hydrocarbons (PAHs; $q_{\rm PAH}$). We find that the average interstellar radiation field ($\overline{U}$) is correlated both with stellar mass surface density ($\Sigma_{\star}$) and star formation rate surface density ($\Sigma_{\rm SFR}$), while more intense radiation fields are only correlated with $\Sigma_{\rm SFR}$. We show that $q_{\rm PAH}$ is a steeply decreasing function of $\Sigma_{\rm SFR}$, likely reflecting PAH destruction in H II regions. Galaxy integrated $q_{\rm PAH}$ is strongly, negatively correlated with specific star formation rate (sSFR) and offset from the star-forming ``main sequence'' ($\Delta$MS), suggesting that both metallicity and star formation intensity play a role in setting the global $q_{\rm PAH}$. We also find a nearly constant M$_{\rm d}$/M$_\star$ ratio for galaxies on the main sequence, with a lower ratio for more quiescent galaxies, likely due to their lower gas fractions. From these results, we construct prescriptions to estimate the radiation field distribution in both integrated and resolved galaxies. We test these prescriptions by comparing our predicted $\overline{U}$ to results of SED fitting for stacked "main sequence" galaxies at $0

Contextual Self-Modulation (CSM) is a potent regularization mechanism for the Neural Context Flow (NCF) framework which demonstrates powerful meta-learning of physical systems. However, CSM has limitations in its applicability across different modalities and in high-data regimes. In this work, we introduce two extensions: $i$CSM, which expands CSM to infinite-dimensional tasks, and StochasticNCF, which improves scalability. These extensions are demonstrated through comprehensive experimentation on a range of tasks, including dynamical systems with parameter variations, computer vision challenges, and curve fitting problems. $i$CSM embeds the contexts into an infinite-dimensional function space, as opposed to CSM which uses finite-dimensional context vectors. StochasticNCF enables the application of both CSM and $i$CSM to high-data scenarios by providing an unbiased approximation of meta-gradient updates through a sampled set of nearest environments. Additionally, we incorporate higher-order Taylor expansions via Taylor-Mode automatic differentiation, revealing that higher-order approximations do not necessarily enhance generalization. Finally, we demonstrate how CSM can be integrated into other meta-learning frameworks with FlashCAVIA, a computationally efficient extension of the CAVIA meta-learning framework (Zintgraf et al. 2019). FlashCAVIA outperforms its predecessor across various benchmarks and reinforces the utility of bi-level optimization techniques. Together, these contributions establish a robust framework for tackling an expanded spectrum of meta-learning tasks, offering practical insights for out-of-distribution generalization. Our open-sourced library, designed for flexible integration of self-modulation into contextual meta-learning workflows, is available at \url{github.com/ddrous/self-mod}., Comment: 23 pages, 11 figures, 5 tables

In Sunyaev-Zeldovich (SZ) cluster cosmology, two tools are needed to be able to exploit data from large scale surveys in the millimeter-wave domain. An accurate description of the IntraCluster Medium (ICM) pressure profile is needed along with the scaling relation connecting the SZ brightness to the mass. With its high angular resolution and large field of view, The NIKA2 camera, operating at 150 and 260 GHz, is perfectly suited for precise cluster SZ mapping. The SZ Large Program (LPSZ) of the NIKA2 collaboration is dedicated to the observation of a sample of 38 SZ-selected clusters at intermediate to high redshift and observed both in SZ and X-ray. The current status is that all LPSZ clusters have been observed and the analysis toward the final results is ongoing. We present in detail how NIKA2-LPSZ will obtain a robust estimation of the SZ-Mass scaling relation and how it will be used to obtain cosmological constraints., Comment: 4 pages, 3 figures, contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

The Earth Observation Satellite Planning (EOSP) is a difficult optimization problem with considerable practical interest. A set of requested observations must be scheduled on an agile Earth observation satellite while respecting constraints on their visibility window, as well as maneuver constraints that impose varying delays between successive observations. In addition, the problem is largely oversubscribed: there are much more candidate observations than what can possibly be achieved. Therefore, one must select the set of observations that will be performed while maximizing their weighted cumulative benefit, and propose a feasible schedule for these observations. As previous work mostly focused on heuristic and iterative search algorithms, this paper presents a new technique for selecting and scheduling observations based on Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL). GNNs are used to extract relevant information from the graphs representing instances of the EOSP, and DRL drives the search for optimal schedules. Our simulations show that it is able to learn on small problem instances and generalize to larger real-world instances, with very competitive performance compared to traditional approaches., Comment: Accepted at 17th European Workshop on Reinforcement Learning (EWRL 2024)

Discontinuous visibility changes remain a major bottleneck when optimizing surfaces within a physically-based inverse renderer. Many previous works have proposed sophisticated algorithms and data structures to sample visibility silhouettes more efficiently. Our work presents another solution: instead of differentiating a tentative surface locally, we differentiate a volumetric perturbation of a surface. We refer this as a many-worlds representation because it models a non-interacting superposition of conflicting explanations (worlds) of the input dataset. Each world is optically isolated from others, leading to a new transport law that distinguishes our method from prior work based on exponential random media. The resulting Monte Carlo algorithm is simpler and more efficient than prior methods. We demonstrate that our method promotes rapid convergence, both in terms of the total iteration count and the cost per iteration.

In this article, we study Poincare maps of harmonic fields in toroidal domains using a shape variational approach. Given a bounded domain of $\mathbb{R}^3$, we define its harmonic fields as the set of magnetic fields which are curl free and tangent to the boundary. For toroidal domains, this space is one dimensional, and one may thus single out a harmonic field by specifying a degree of freedom, such as the circulation along a toroidal loop. We are then interested in the Poincare maps of such fields restricted to the boundary, which produce diffeomorphisms of the circle. We begin by proving a general shape differentiability result of such Poincare maps in the smooth category, and obtain a general formula for the shape derivative. We then investigate two specific examples of interest; axisymmetric domains, and domains for which the harmonic field has a diophantine rotation number on the boundary. We prove that, in the first case, the shape derivative of the Poincare map is always identically zero, whereas in the second case, assuming an additional condition on the geometry of the domain, the shape derivative of the Poincare map may be any smooth function of the circle by choosing an appropriate perturbation of the domain.

We provide a mathematical analysis of the Dynamical Mean-Field Theory, a celebrated representative of a class of approximations in quantum mechanics known as embedding methods. We start by a pedagogical and self-contained mathematical formulation of the Dynamical Mean-Field Theory equations for the finite Hubbard model. After recalling the definition and properties of one-body time-ordered Green's functions and self-energies, and the mathematical structure of the Hubbard and Anderson impurity models, we describe a specific impurity solver, namely the Iterated Perturbation Theory solver, which can be conveniently formulated using Matsubara's Green's functions. Within this framework, we prove under certain assumptions that the Dynamical Mean-Field Theory equations admit a solution for any set of physical parameters. Moreover, we establish some properties of the solution(s)., Comment: 40 pages, 5 figures

Despite recent progress, the question of what regulates the star formation efficiency in galaxies remains one of the most debated problems in astrophysics. According to the dominant picture, star formation (SF) is regulated by turbulence and feedback, and the SFE is 1-2% per local free-fall time. In an alternate scenario, the SF rate in galactic disks is linearly proportional to the mass of dense gas above a critical density threshold. We aim to discriminate between these two pictures thanks to high-resolution observations tracing dense gas and young stellar objects (YSOs) for a comprehensive sample of 49 nearby massive SF complexes out to d < 3 kpc in the Galactic disk. We use data from CAFFEINE, a 350/450 $\mu$m survey with APEX/ArT\'eMiS of the densest portions of all southern molecular clouds, in combination with Herschel data to produce column density maps at 8" resolution. Our maps are free of saturation and resolve the structure of dense gas and the typical 0.1 pc width of molecular filaments at 3 kpc, which is impossible with Herschel data alone. Coupled with SFR estimates derived from Spitzer observations of the YSO content of the same clouds, this allows us to study the dependence of the SFE with density in the CAFFEINE clouds. We also combine our findings with existing SFE measurements in nearby clouds to extend our analysis down to lower column densities. Our results suggest that the SFE does not increase with density above the critical threshold and support a scenario in which the SFE in dense gas is approximately constant. However, the SFE measurements traced by Class I YSOs in nearby clouds are more inconclusive, since they are consistent with both the presence of a density threshold and a dependence on density above the threshold. Overall, we suggest that the SFE in dense gas is primarily governed by the physics of filament fragmentation into protostellar cores., Comment: In press; accepted: 18/05/2024

In this study, we investigate the potential of electronic interferometers for probing the quantum state of electromagnetic radiation on a chip at sub-nanosecond time scales. We propose to use single electron excitations propagating within an electronic Mach-Zehnder interferometer in the Aharonov-Bohm dominated regime. We discuss how information about the quantum state of the electromagnetic radiation is encoded into the interference contribution to the average outgoing electrical current. By investigating squeezed radiation and single edge magnetoplasmons probed by Leviton pulses in a realistic setup, we show that single electron interferometers have the potential to probe quantum radiation in the time domain with sub-nanosecond to pico-second time resolution. Our research could have significant implications for probing the fundamental properties of light in the microwave to tera-Hertz domains at extremely short time scales., Comment: 31 pages, 15 figures

Neural Ordinary Differential Equations (NODEs) often struggle to adapt to new dynamic behaviors caused by parameter changes in the underlying system, even when these dynamics are similar to previously observed behaviors. This problem becomes more challenging when the changing parameters are unobserved, meaning their value or influence cannot be directly measured when collecting data. To address this issue, we introduce Neural Context Flow (NCF), a robust and interpretable Meta-Learning framework that includes uncertainty estimation. NCF uses higher-order Taylor expansion to enable contextual self-modulation, allowing context vectors to influence dynamics from other domains while also modulating themselves. After establishing convergence guarantees, we empirically test NCF and compare it to related adaptation methods. Our results show that NCF achieves state-of-the-art Out-of-Distribution performance on 5 out of 6 linear and non-linear benchmark problems. Through extensive experiments, we explore the flexible model architecture of NCF and the encoded representations within the learned context vectors. Our findings highlight the potential implications of NCF for foundational models in the physical sciences, offering a promising approach to improving the adaptability and generalization of NODEs in various scientific applications. Our code is openly available at \url{https://github.com/ddrous/ncflow}., Comment: Accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science

Existence of quasi-static negative capacitance (QSNC) was proposed from an interpretation of the widely accepted Landau model of ferroelectrics. However, many works showed not to support the QSNC theory, making it controversial. In this letter we show the Landau model when used together with transition-state-theory, can connect various models including first-principles, Landau, Preisach and nucleation limited switching while it does not predict the existence of QSNC.

Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within 6-dimensional position-momentum spaces. However, the characterization of intricate features within 6-dimensional beam distributions using conventional diagnostic techniques necessitates hundreds of measurements, using many hours of valuable beam time. Novel phase space reconstruction techniques are needed to substantially reduce the number of measurements required to reconstruct detailed, high-dimensional beam features in order to resolve complex beam phenomena, and as feedback in precision beam shaping applications. In this study, we present a novel approach to reconstructing detailed 6-dimensional phase space distributions from experimental measurements using generative machine learning and differentiable beam dynamics simulations. We demonstrate that for a collection of synthetic beam distribution test cases that this approach can be used to resolve 6-dimensional phase space distributions using basic beam manipulations and as few as 20 2-dimensional measurements of the beam profile, without the need for prior data collection or model training. We also demonstrate an application of the reconstruction method in an experimental setting at the Argonne Wakefield Accelerator, where it is able to reconstruct the beam distribution and accurately predict previously unseen measurements 75x faster than previous methods.

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

The microbunching instability has been a long-standing issue for high-brightness free-electron lasers (FELs), and is a significant show-stopper to achieving full longitudinal coherence in the x-ray regime. This paper reports the first experimental demonstration of microbunching instability mitigation through transverse Landau damping, based on linear optics control in a dispersive region. Analytical predictions for the microbunching content are supported by numerical calculations of the instability gain and confirmed through the experimental characterization of the spectral brightness of the FERMI FEL under different transverse optics configurations of the transfer line between the linear accelerator and the FEL.

Job-Shop Scheduling Problem (JSSP) is a combinatorial optimization problem where tasks need to be scheduled on machines in order to minimize criteria such as makespan or delay. To address more realistic scenarios, we associate a probability distribution with the duration of each task. Our objective is to generate a robust schedule, i.e. that minimizes the average makespan. This paper introduces a new approach that leverages Deep Reinforcement Learning (DRL) techniques to search for robust solutions, emphasizing JSSPs with uncertain durations. Key contributions of this research include: (1) advancements in DRL applications to JSSPs, enhancing generalization and scalability, (2) a novel method for addressing JSSPs with uncertain durations. The Wheatley approach, which integrates Graph Neural Networks (GNNs) and DRL, is made publicly available for further research and applications., Comment: To be published at CPAIOR 2024

We develop a new framework to constrain the source redshift. The method jointly accounts for the detection/non-detection of spectral lines and the prior information from the photometric redshift and total infrared luminosity from spectral energy distribution analysis. The method uses the estimated total infrared luminosity to predict the line fluxes at given redshifts and generates model spectra. The redshift-dependent spectral models are then compared with the observed spectra to find the redshift. Results. We apply the aforementioned joint redshift analysis method to four high-z dusty star-forming galaxy candidates selected from the NIKA2 observations of the HLSJ091828.6+514223 (HLS) field, and further observed by NOEMA with blind spectral scans. These sources only have SPIRE/Herschel photometry as ancillary data. They were selected because of very faint or no SPIRE counterparts, as to bias the sample towards the highest redshift candidates. The method finds the spectroscopic redshift of 4 in the 5 NOEMA-counterpart detected sources, with z>3. Based on these measurements, we derive the CO/[CI] lines and millimeter continuum fluxes from the NOEMA data and study their ISM and star-formation properties. We find cold dust temperatures in some of the HLS sources compared to the general population of sub-millimeter galaxies, which might be related to the bias introduced by the SPIRE-dropout selection. Our sources, but one, have short gas depletion time of a few hundred Myrs, which is typical among high-z sub-millimeter galaxies. The only exception shows a longer gas depletion time, up to a few Gyrs, comparable to that of main-sequence galaxies at the same redshift. Furthermore, we identify a possible over-density of dusty star-forming galaxies at z=5.2, traced by two sources in our sample, as well as the lensed galaxy HLSJ091828.6+514223. (abridged), Comment: A&A in press

Tuning particle accelerators is a challenging and time-consuming task, but can be automated and carried out efficiently through the use of suitable optimization algorithms. With successful applications at various facilities, Bayesian optimization using Gaussian process modeling has proven to be a particularly powerful tool to address these challenges in practice. One of its major benefits is that it allows incorporating prior information, such as knowledge about the shape of the objective function or predictions based on archived data, simulations or surrogate models, into the model. In this work, we propose the use of a neural network model as an efficient way to include prior knowledge about the objective function into the Bayesian optimization process to speed up convergence. We report results obtained in simulations and experiments using neural network priors to perform optimization of electron and heavy-ion accelerator facilities, specifically the Linac Coherent Light Source and the Argonne Tandem Linear Accelerator System. Finally, we evaluate how the accuracy of the prior mean predictions affect optimization performance.

Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emittance ratios are critical for future linear colliders. Similarly, magnetized beams with significant cross-plane coupling are expected to enhance the performance of electron cooling in hadron beams. Preparing these beams requires precise control and characterization of the four-dimensional transverse phase space. In this study, we employ generative phase space reconstruction (GPSR) techniques to rapidly characterize magnetized and flat-beam phase-space distributions using a conventional quadrupole-scan method. The reconstruction technique is experimentally demonstrated on an electron beam produced at the Argonne Wakefield Accelerator and successfully benchmarked against conventional diagnostics techniques. Specifically, we show that predicted beam parameters from the reconstructed phase-space distributions (e.g. as magnetization and flat beam emittances) are in excellent agreement with those measured from the conventional diagnostic methods.

The rapid development of brain-inspired computing requires new artificial components and architectures for its hardware implementation. In this regard, memristive devices emerged as potential candidates for artificial synapses because of their ability to emulate the plasticity of the biological synapses. In this work, the synaptic behavior of the TiN/La2NiO4+{\delta}/Pt memristive devices based on thermally annealed La2NiO4+{\delta} films is thoroughly investigated. Using electron energy loss spectroscopy, we show that annealing using reducing (Ar) or oxidizing (O2) atmospheres affects the interstitial oxygen content ({\delta}) in the La2NiO4+{\delta} films. Electrical characterization shows that both devices exhibit long-term potentiation/depression and spike-timing-dependent plasticity, which makes them suitable for neuromorphic applications. At the same time, the Ar annealed TiN/La2NiO4+{\delta}/Pt device demonstrates non-volatile properties with low energy consumption during the learning process. On the other hand, in the O2 annealed TiN/La2NiO4+{\delta}/Pt device the resistive switching behavior is more volatile and requires more energy for synaptic learning. Finally, the simulation tools show that spiking neural network architectures with unsupervised learning rules based on the experimental data achieve high inference accuracy in the digit recognition task, which proves the potential of TiN/La2NiO4+{\delta}/Pt devices for artificial synapse applications.

In this paper, we introduce a new shape functional defined for toroidal domains that we call harmonic helicity, and study its shape optimization. Given a toroidal domain, we consider its associated harmonic field. The latter is the magnetic field obtained uniquely up to normalization when imposing zero normal trace and zero electrical current inside the domain. We then study the helicity of this field, which is a quantity of interest in magneto-hydrodynamics corresponding to the L2 product of the field with its image by the Biot--Savart operator. To do so, we begin by discussing the appropriate functional framework and an equivalent PDE characterization. We then focus on shape optimization, and we identify the shape gradient of the harmonic helicity. Finally, we study and implement an efficient numerical scheme to compute harmonic helicity and its shape gradient using finite elements exterior calculus.

This paper introduces Qrlew, an open source library that can parse SQL queries into Relations -- an intermediate representation -- that keeps track of rich data types, value ranges, and row ownership; so that they can easily be rewritten into differentially-private equivalent and turned back into SQL queries for execution in a variety of standard data stores. With Qrlew, a data practitioner can express their data queries in standard SQL; the data owner can run the rewritten query without any technical integration and with strong privacy guarantees on the output; and the query rewriting can be operated by a privacy-expert who must be trusted by the owner, but may belong to a separate organization.

Recent experiments have shown that the edge states of a quantum Hall sample can be coupled to a superconductor, so that incoming electrons in the edge states can be Andreev converted on the superconductor as coherent superpositions of an electron and a hole. In addition to this, single-electron emitters that operate in the gigahertz regime have been realized. Motivated by this remarkable experimental progress, we here analyze the Andreev reflections of incoming charge pulses on the interface with a superconductor. To this end, we employ a Wigner function representation that allows us to visualize the response function of the interface both in the time and in the frequency domain. We analyze the response of an interface between a normal-metal and a singlet or triplet superconductor with and without an insulating barrier in between them. As a special case, we analyze the Andreev reflections of clean single-particle excitations that are generated by the application of lorentzian-shaped voltage pulses to the contacts of the inputs. Our predictions may be tested in future experiments with edge states coupled to superconductors., Comment: 14 pages, 11 figures

Electron quantum optics explores the coherent propagation and interference of single-electron charge pulses in electronic nanoscale circuits that are similar to table-top setups with photons. So far, experiments with dynamic single-electron emitters have focused on normal-state conductors, however, the inclusion of superconductors would pave the way for a wide range of applications that exploit the electron-hole degree of freedom, for example, for quantum information processing or quantum sensing. Here, we propose and analyze a tunable mechanism for the on-demand conversion of single-electron pulses into holes through Andreev processes on a superconductor. To this end, we develop a Floquet-Nambu scattering formalism that allows us to describe the dynamic conversion of charge pulses on a superconductor, and we show that it is possible to generate arbitrary superpositions of electrons and holes with the degree of mixing controlled by the magnetic flux in an interferometric setup. We provide a detailed discussion of the optimal operating conditions in realistic situations and demonstrate that our proposal is feasible based on current technology., Comment: 7 pages, 3 figures

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.

A portable imaging system for the on-site detection of shoulder injury is necessary to identify its extent and avoid its development to severe condition. Here, firstly a microwave tomography system is introduced using state-of-the-art numerical modeling and parallel computing for imaging different tissues in the shoulder. The results show that the proposed method is capable of accurately detecting and localizing rotator cuff tears of different size. In the next step, an efficient design in terms of computing time and complexity is proposed to detect the variations in the injured model with respect to the healthy model. The method is based on finite element discretization and uses parallel preconditioners from the domain decomposition method to accelerate computations. It is implemented using the open source FreeFEM software.

To understand the evolution of dust properties in molecular clouds in the course of the star formation process, we constrain the changes in the dust emissivity index from star-forming filaments to prestellar and protostellar cores to T Tauri stars. Using the NIKA2 continuum camera on the IRAM 30~m telescope, we observed the Taurus B211/B213 filament at 1.2\,mm and 2\,mm with unprecedented sensitivity and used the resulting maps to derive the dust emissivity index $\beta$. Our sample of 105 objects detected in the $\beta$ map of the B211/B213 filament indicates that, overall, $\beta$ decreases from filament and prestellar cores ($\beta \sim 2\pm0.5$) to protostellar cores ($\beta \sim 1.2 \pm 0.2$) to T-Tauri protoplanetary disk ($\beta < 1$). The averaged dust emissivity index $\beta$ across the B211/B213 filament exhibits a flat ($\beta \sim 2\pm0.3$) profile. This may imply that dust grain sizes are rather homogeneous in the filament, start to grow significantly in size only after the onset of the gravitational contraction/collapse of prestellar cores to protostars, reaching big sizes in T Tauri protoplanetary disks. This evolution from the parent filament to T-Tauri disks happens on a timescale of about 1-2~Myr., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

High-resolution mapping of the hot gas in galaxy clusters is a key tool for cluster-based cosmological analyses. Taking advantage of the NIKA2 millimeter camera operated at the IRAM 30-m telescope, the NIKA2 SZ Large Program seeks to get a high-resolution follow-up of 38 galaxy clusters covering a wide mass range at intermediate to high redshift. The measured SZ fluxes will be essential to calibrate the SZ scaling relation and the galaxy clusters mean pressure profile, needed for the cosmological exploitation of SZ surveys. We present in this study a method to infer a mean pressure profile from cluster observations. We have designed a pipeline encompassing the map-making and the thermodynamical properties estimates from maps. We then combine all the individual fits, propagating the uncertainties on integrated quantities, such as $R_{500}$ or $P_{500}$, and the intrinsic scatter coming from the deviation to the standard self-similar model. We validate the proposed method on realistic LPSZ-like cluster simulations., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

We present the first preliminary results of the project \textit{ICED}, focusing on the face-on galaxy NGC4254. We use the millimetre maps observed with NIKA2 at IRAM-30m, as part of the IMEGIN Guaranteed Time Large Program, and of a wide collection of ancillary data (multi-wavelength photometry and gas phase spectral lines) that are publicly available. We derive the global and local properties of interstellar dust grains through infrared-to-radio spectral energy distribution fitting, using the hierarchical Bayesian code HerBIE, which includes the grain properties of the state-of-the-art dust model, THEMIS. Our method allows us to get the following dust parameters: dust mass, average interstellar radiation field, and fraction of small grains. Also, it is effective in retrieving the intrinsic correlations between dust parameters and interstellar medium properties. We find an evident anti-correlation between the interstellar radiation field and the fraction of small grains in the centre of NGC4254, meaning that, at strong radiation field intensities, very small amorphous carbon grains are efficiently destroyed by the ultra-violet photons coming from newly formed stars, through photo-desorption and sublimation. We observe a flattening of the anti-correlation at larger radial distances, which may be driven by the steep metallicity gradient measured in NGC4254., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

Three galaxy clusters selected from the XXL X-ray survey at high redshift and low mass ($z\sim1$ and $M_{500} \sim 1-2 \times 10^{14}$ M$_{\odot}$) were observed with NIKA2 to image their Sunyaev-Zel'dovich effect (SZ) signal. They all present an SZ morphology, together with the comparison with X-ray and optical data, that indicates dynamical activity related to merging events. Despite their disturbed intracluster medium, their high redshifts, and their low masses, the three clusters follow remarkably well the pressure profile and the SZ flux-mass relation expected from standard evolution. This suggests that the physics that drives cluster formation is already in place at $z \sim 1$ down to $M_{500} \sim 10^{14}$ M$_{\odot}$., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences