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Traditional models of coronal oscillations rely on modelling the coronal structures that support them as compact cylindrical waveguides. Recently, an alternative model of the structure of the corona has been proposed, where the thin strand-like coronal loops observable in the EUV emission are a result of line-of-sight integration of warps in more complex coronal structures, referred to as the coronal veil model. We extend the implications of the coronal veil model of the solar corona to models of coronal oscillations. Using the convection-zone-to-corona simulations with the radiation-magnetohydrodynamics code Bifrost, we analysed the structure of the self-consistently formed simulated corona. We focus on the spatial variability of the volumetric emissivity of the Fe IX 171.073 {\AA} EUV line, and on the variability of the Alfv\'en speed, which captures the density and magnetic structuring of the simulated corona. We traced features associated with large magnitudes of the Alfv\'en speed gradient, which are the most likely to trap MHD waves and act as coronal waveguides, and looked for the correspondence with emitting regions which appear as strand-like loops in line-of-sight-integrated EUV emission. The waveguide filling factors corresponding to the fraction of the waveguides filled with plasma emitting in the given EUV wavelength range from 0.09 to 0.44. This suggests that we can observe only a small fraction of the waveguide. Similarly, the projected waveguide widths in the plane of the sky are several times larger than the widths of the apparent loops observable in EUV. We conclude that the 'coronal veil' structure is model-independent. As a result, we find a lack of straightforward correspondence between a peak in the integrated emission profile which constitutes an apparent coronal loop and regions of plasma bound by a large Alfv\'en speed gradient acting as waveguides., Comment: 8 pages, 7 figures, accepted for publication in A&A

The new definition of the ampere calls for a quantum current standard able to deliver a flow of elementary charges per second controlled with a relative uncertainty of one part in $10^8$. Despite many efforts, nanodevices handling electrons one by one have never been able to demonstrate such an accuracy. The alternative route based on applying Ohm's law to the Josephson voltage and quantum Hall standards recently reached the target uncertainty but this was at the expense of the application of error corrections, hampering simplicity and further improvement. As a result, national metrology institutes still lack an operational quantum current standard. Here, we present a new quantum current generator, combining both quantum standards and a superconducting cryogenic amplifier, free of error correction, which provides quantized currents driven by the Josephson microwave signal. We show that it can realize the ampere definition with the target uncertainty over a range extended from mA down to $\mu$A and improve end-user current measurements, which are up to now a hundred times less accurate. Other prospects include measuring large resistances using the new current standard in conjunction with a quantum voltmeter and, by exploiting its low-noise performances, bridging the gap with the lower currents of other quantum current sources., Comment: 6 pages, 5 figures, Methods, 6 Extended Data figures, 3 Extended Data Tables

The negatively charged boron vacancy ($\mathrm{V}_{\mathrm{B}}^-$) in hexagonal boron nitride (hBN) has garnered significant attention among defects in two-dimensional materials. This owes, in part, to its deterministic generation, well-characterized atomic structure, and optical polarizability at room temperature. We investigate the latter through extensive measurements probing both the ground and excited state polarization dynamics. We develop a semiclassical model based on these measurements that predicts a near-unity degree of spin polarization, surpassing other solid-state spin defects under ambient conditions. Building upon our model, we include the presence of nuclear spin degrees of freedom adjacent to the $\mathrm{V}_{\mathrm{B}}^-$ and perform a comprehensive set of Lindbladian numerics to investigate the hyperfine-induced polarization of the nuclear spins. Our simulations predict a number of important features that emerge as a function of magnetic field which are borne out by experiment.

Defects in crystals can have a transformative effect on the properties and functionalities of solid-state systems. Dopants in semiconductors are core components in electronic and optoelectronic devices. The control of single color centers is at the basis of advanced applications for quantum technologies. Unintentional defects can also be detrimental to the crystalline structure and hinder the development of novel materials. Whatever the research perspective, the identification of defects is a key but complicated, and often long-standing issue. Here, we present a general methodology to identify point defects by combining isotope substitution and polytype control, with a systematic comparison between experiments and first-principles calculations. We apply this methodology to hexagonal boron nitride (hBN) and its ubiquitous color center emitting in the ultraviolet spectral range. From isotopic purification of the host hBN matrix, a local vibrational mode of the defect is uncovered, and isotope-selective carbon doping proves that this mode belongs to a carbon-based center. Then, by varying the stacking sequence of the host hBN matrix, we unveil different optical responses to hydrostatic pressure for the non-equivalent configurations of this ultraviolet color center. We conclude that this defect is a carbon dimer in the honeycomb lattice of hBN. Our results show that tuning the stacking sequence in different polytypes of a given crystal provides unique fingerprints contributing to the identification of defects in 2D materials.

In this paper we study a class of weighted estimands, which we define as parameters that can be expressed as weighted averages of the underlying heterogeneous treatment effects. The popular ordinary least squares (OLS), two-stage least squares (2SLS), and two-way fixed effects (TWFE) estimands are all special cases within our framework. Our focus is on answering two questions concerning weighted estimands. First, under what conditions can they be interpreted as the average treatment effect for some (possibly latent) subpopulation? Second, when these conditions are satisfied, what is the upper bound on the size of that subpopulation, either in absolute terms or relative to a target population of interest? We argue that this upper bound provides a valuable diagnostic for empirical research. When a given weighted estimand corresponds to the average treatment effect for a small subset of the population of interest, we say its internal validity is low. Our paper develops practical tools to quantify the internal validity of weighted estimands.

We study the phase turbulence of the one-dimensional complex Ginzburg-Landau equation, in which the defect-free chaotic dynamics of the order parameter maps to a phase equation well approximated by the Kuramoto-Sivashinsky model. In this regime, the behaviour of the large wavelength modes is captured by the Kardar-Parisi-Zhang equation, determining universal scaling and statistical properties. We present numerical evidence of the existence of an additional scale-invariant regime, with dynamical scaling exponent $z=1$, emerging at scales which are intermediate between the microscopic, intrinsic to the modulational instability, and the macroscopic ones. We argue that this new regime is a signature of the universality class corresponding to the inviscid limit of the Kardar-Parisi-Zhang equation.

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

We introduce the notion of a contractible subshift. This is a strengthening of the notion of strong irreducibility, where we require that the gluings are given by a block map. We show that a subshift is a retract of a full shift if and only if it is a contractible SFT with a fixed point. For virtually polycyclic groups, contractibility implies dense periodic points. We introduce a ``homotopy theory'' framework for working with this notion, and ``contractibility'' is in fact simply an analog of the usual contractibility in algebraic topology. We also explore the symbolic dynamical analogs of homotopy equivalence and equiconnectedness of subshifts. Contractibility is implied by the map extension property of Meyerovitch, and among SFTs, it implies the finite extension property of Brice\~no, McGoff and Pavlov. We include thorough comparisons with these classes. We also encounter some new group-geometric notions, in particular a periodic variant of Gromov's asymptotic dimension of a group., Comment: 51 pages, 1 figure

In the context of autonomous navigation of terrestrial robots, the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and in commercial applications, where they are used for model based control and/or for localization and mapping. The more recent Embodied AI literature, on the other hand, focuses on modular or end-to-end agents trained in simulators like Habitat or AI-Thor, where the emphasis is put on photo-realistic rendering and scene diversity, but high-fidelity robot motion is assigned a less privileged role. The resulting sim2real gap significantly impacts transfer of the trained models to real robotic platforms. In this work we explore end-to-end training of agents in simulation in settings which minimize the sim2real gap both, in sensing and in actuation. Our agent directly predicts (discretized) velocity commands, which are maintained through closed-loop control in the real robot. The behavior of the real robot (including the underlying low-level controller) is identified and simulated in a modified Habitat simulator. Noise models for odometry and localization further contribute in lowering the sim2real gap. We evaluate on real navigation scenarios, explore different localization and point goal calculation methods and report significant gains in performance and robustness compared to prior work.

Superlattices are materials created by the alternating growth of two chemically different materials. The direct consequence of creating a superlattice is the folding of the Brillouin zone which gives rise to additional electronic bands and phonon modes. This has been successfully exploited to achieve new transport and optical properties in semiconductor superlattices, for example. Here, we show that multiferroic BiFeO$_3$/LaFeO$_3$ superlattices are more than just periodic chemical stacking. Using transmission electron microscopy, X-ray diffraction and first-principles calculations, we demonstrate the existence of a new order of FeO$_6$ octahedra, with a period along the growth direction about twice that of the chemical supercell, i.e. a superorder. The effect of this new structural order on the lattice dynamics is studied with ultrafast optical pump-probe experiments. While a mode at 1.2 THz is attributed solely to the chemical modulation of the superlattice, the existence of another 0.7 THz mode seems to be explained only by a double Brillouin zone folding in agreement with the structural description. Our work shows that multiferroic BiFeO$_3$/LaFeO$_3$ superlattices can be used to tune the spectrum of coherent THz phonons, and potentially that of magnons or electromagnons.

Sophorolipids are well-known scaled-up microbial glycolipid biosurfactants with a strong potential for commercialization due to their biological origin and mildness in contact with the skin and the environment compared to classical surfactants. However, their association properties in water are still poorly understood, they cannot be predicted and their behavior in solution challenges half a century of knowledge generated in the field of surfactant science. By studying forty different types of sophorolipids and sophorosides in water using small angle X-ray scattering, and optical and cryogenic transmission electron microscopy, this work provides better understanding of their structure-property relationship and identifies which chemical groups in their molecular structure have a critical influence on their self-assembly properties. Structural features like the number of sugar headgroups, acetylation, end-chain functional group, (un)saturation, lactonization and length of chain are adjusted to both rationalize their impact and understand their effect on self-assembly. The number of sugar groups, pH, (un)saturation and lactonization were found to have a critical impact on sophorolipid self-assembly. The chemical nature of the end-chain functional group and chain length were also found to have a possibly critical impact, depending on the specific type of chemical function (COOH and long chains are critical). Mono- and diacetylation, as well as the position of sophorose in the fatty acid ($\omega$, $\omega$-1), are not critical, i.e., they did not significantly influence sophorolipid self-assembly.

We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant ($H_0$) in the context of time delay cosmography. Assuming a Singular Isothermal Ellipsoid (SIE) mass profile for the deflector, we simulate time delay measurements, image position measurements, and modeled lensing parameters. We train the NRE to output the posterior distribution of $H_0$ given the time delay measurements, the relative Fermat potentials (calculated from the modeled parameters and the measured image positions), the deflector redshift, and the source redshift. We compare the accuracy and precision of the NRE with traditional explicit likelihood methods in the limit where the latter is tractable and reliable, using Gaussian noise to emulate measurement uncertainties in the input parameters. The NRE posteriors track the ones from the conventional method and, while they show a slight tendency to overestimate uncertainties, they can be combined in a population inference without bias., Comment: 5+3 pages, 2+2 figures, Accepted (spotlight talk) for the Machine Learning for Astrophysics Workshop at the 40th International Conference on Machine Learning (ICML 2023)

Multi-objective optimization is a ubiquitous problem that arises naturally in many scientific and industrial areas. Network routing optimization with multi-objective performance demands falls into this problem class, and finding good quality solutions at large scales is generally challenging. In this work, we develop a scheme with which near-term quantum computers can be applied to solve multi-objective combinatorial optimization problems. We study the application of this scheme to the network routing problem in detail, by first mapping it to the multi-objective shortest path problem. Focusing on an implementation based on the quantum approximate optimization algorithm (QAOA) -- the go-to approach for tackling optimization problems on near-term quantum computers -- we examine the Pareto plot that results from the scheme, and qualitatively analyze its ability to produce Pareto-optimal solutions. We further provide theoretical and numerical scaling analyses of the resource requirements and performance of QAOA, and identify key challenges associated with this approach. Finally, through Amazon Braket we execute small-scale implementations of our scheme on the IonQ Harmony 11-qubit quantum computer.

We analyse the signature and origin of transient structures embedded in the slow solar wind, and observed by the Wide-Field Imager for Parker Solar Probe (WISPR) during its first ten passages close to the Sun. WISPR provides a new in-depth vision on these structures, which have long been speculated to be a remnant of the pinch-off magnetic reconnection occurring at the tip of helmet streamers. We pursued the previous modelling works of Reville (2020b, 2022) that simulate the dynamic release of quasi-periodic density structures into the slow wind through a tearing-induced magnetic reconnection at the tip of helmet streamers. Synthetic WISPR white-light (WL) images are produced using a newly developed advanced forward modelling algorithm that includes an adaptive grid refinement to resolve the smallest transient structures in the simulations. We analysed the aspect and properties of the simulated WL signatures in several case studies that are typical of solar minimum and near-maximum configurations. Quasi-periodic density structures associated with small-scale magnetic flux ropes are formed by tearing-induced magnetic reconnection at the heliospheric current sheet and within 3-7Rs. Their appearance in WL images is greatly affected by the shape of the streamer belt and the presence of pseudo-streamers. The simulations show periodicities on ~90-180min, ~7-10hr and ~25-50hr timescales, which are compatible with WISPR and past observations. This work shows strong evidence for a tearing-induced magnetic reconnection contributing to the long-observed high variability of the slow solar wind., Comment: 23 pages, 14 figures, to appear in Astronomy & Astrophysics, associated movies available at https://doi.org/10.5281/zenodo.8135595

The structure-properties relationship of rhamnolipids, RLs, well known microbial bioamphiphiles (biosurfactants), is exlored in detail by coupling cryogenic transmission electron microscopy (cryo-TEM) and both ex situ and in situ small angle X-ray scattering (SAXS). The self-assembly of three RLs with reasoned variation of their molecular structure (RhaC10, RhaC10C10 and RhaRhaC10C10) and a rhamnose-free C10C10 fatty acid is studied in water as a function of pH. It is found that RhaC10 and RhaRhaC10C10 form micelles in a broad pH range and RhaC10C10 undergoes a micelle-to-vesicle transition from basic to acid pH occurring at pH 6.5. Modelling coupled to fitting SAXS data allows a good estimation of the hydrophobic core radius (or length), the hydrophilic shell thickness, the aggregation number and the surface area per RL. The essentially micellar morphology found for RhaC10 and

New MiZo line lists are presented for the D$_2$H$^+$ and D$_3^+$ isotopologues of H$_3^+$. These line lists plus the existing H$_3^+$ MiZATeP and the Sochi H$_2$D$^+$ line lists are updated using empirical energy levels generated using the MARVEL procedure for H$_3^+$, H$_2$D$^+$ and D$_2$H$^+$, and effective Hamiltonian energies for D$_3^+$ for which there is significantly less laboratory data available. These updates allow accurate frequencies for far infrared lines for these species to be predicted. Assignments of the energy levels of H$_3^+$ and D$_3^+$ are extended using a combination of high accuracy variational calculations and analysis of transition intensities. All line lists are made available via www.exomol.com.

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40\% pass@1 on HumanEval, and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.