Weak and strong electronic correlations in Fe superconductors

In this chapter the strength of electronic correlations in the normal phase of Fe-superconductors is discussed. It will be shown that the agreement between a wealth of experiments and DFT+DMFT or similar approaches supports a scenario in which strongly-correlated and weakly-correlated electrons coexist in the conduction bands of these materials. I will then reverse-engineer the realistic calculations and justify this scenario in terms of simpler behaviors easily interpreted through model results. All pieces come together to show that Hund's coupling, besides being responsible for the electronic correlations even in absence of a strong Coulomb repulsion is also the origin of a subtle emergent behavior: orbital decoupling. Indeed Hund's exchange decouples the charge excitations in the different Iron orbitals involved in the conduction bands thus causing an independent tuning of the degree of electronic correlation in each one of them. The latter becomes sensitive almost only to the offset of the orbital population from half-filling, where a Mott insulating state is invariably realized at these interaction strengths. Depending on the difference in orbital population a different 'Mottness' affects each orbital, and thus reflects in the conduction bands and in the Fermi surfaces depending on the orbital content.Comment: Book Chapte

ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts

Eye movements in reading play a crucial role in psycholinguistic research studying the cognitive mechanisms underlying human language processing. More recently, the tight coupling between eye movements and cognition has also been leveraged for language-related machine learning tasks such as the interpretability, enhancement, and pre-training of language models, as well as the inference of reader- and text-specific properties. However, scarcity of eye movement data and its unavailability at application time poses a major challenge for this line of research. Initially, this problem was tackled by resorting to cognitive models for synthesizing eye movement data. However, for the sole purpose of generating human-like scanpaths, purely data-driven machine-learning-based methods have proven to be more suitable. Following recent advances in adapting diffusion processes to discrete data, we propose ScanDL, a novel discrete sequence-to-sequence diffusion model that generates synthetic scanpaths on texts. By leveraging pre-trained word representations and jointly embedding both the stimulus text and the fixation sequence, our model captures multi-modal interactions between the two inputs. We evaluate ScanDL within- and across-dataset and demonstrate that it significantly outperforms state-of-the-art scanpath generation methods. Finally, we provide an extensive psycholinguistic analysis that underlines the model's ability to exhibit human-like reading behavior. Our implementation is made available at https://github.com/DiLi-Lab/ScanDL

ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts

Eye movements in reading play a crucial role in psycholinguistic research studying the cognitive mechanisms underlying human language processing. More recently, the tight coupling between eye movements and cognition has also been leveraged for language-related machine learning tasks such as the interpretability, enhancement, and pre-training of language models, as well as the inference of reader- and text-specific properties. However, scarcity of eye movement data and its unavailability at application time poses a major challenge for this line of research. Initially, this problem was tackled by resorting to cognitive models for synthesizing eye movement data. However, for the sole purpose of generating human-like scanpaths, purely data-driven machine-learning-based methods have proven to be more suitable. Following recent advances in adapting diffusion processes to discrete data, we propose ScanDL, a novel discrete sequence-to-sequence diffusion model that generates synthetic scanpaths on texts. By leveraging pre-trained word representations and jointly embedding both the stimulus text and the fixation sequence, our model captures multi-modal interactions between the two inputs. We evaluate ScanDL within- and across-dataset and demonstrate that it significantly outperforms state-of-the-art scanpath generation methods. Finally, we provide an extensive psycholinguistic analysis that underlines the model's ability to exhibit human-like reading behavior. Our implementation is made available at https://github.com/DiLi-Lab/ScanDL.Comment: EMNLP 202

Efficient creation of dipolar coupled nitrogen-vacancy spin qubits in diamond

Coherently coupled pairs or multimers of nitrogen-vacancy defect electron spins in diamond have many promising applications especially in quantum information processing (QIP) but also in nanoscale sensing applications. Scalable registers of spin qubits are essential to the progress of QIP. Ion implantation is the only known technique able to produce defect pairs close enough to allow spin coupling via dipolar interaction. Although several competing methods have been proposed to increase the resulting resolution of ion implantation, the reliable creation of working registers is still to be demonstrated. The current limitation are residual radiation-induced defects, resulting in degraded qubit performance as trade-off for positioning accuracy. Here we present an optimized estimation of nanomask implantation parameters that are most likely to produce interacting qubits under standard conditions. We apply our findings to a well-established technique, namely masks written in electron-beam lithography, to create coupled defect pairs with a reasonable probability. Furthermore, we investigate the scaling behavior and necessary improvements to efficiently engineer interacting spin architectures

Variation of Absorption Angstrom Exponent in Aerosols From Different Emission Sources

The absorption Angstrom exponent (AAE) describes the spectral dependence of light absorption by aerosols. AAE is typically used to differentiate between different aerosol types for example., black carbon, brown carbon, and dust particles. In this study, the variation of AAE was investigated mainly in fresh aerosol emissions from different fuel and combustion types, including emissions from ships, buses, coal-fired power plants, and residential wood burning. The results were assembled to provide a compendium of AAE values from different emission sources. A dual-spot aethalometer (AE33) was used in all measurements to obtain the light absorption coefficients at seven wavelengths (370-950 nm). AAE(470/950) varied greatly between the different emission sources, ranging from -0.2 +/- 0.7 to 3.0 +/- 0.8. The correlation between the AAE(470/950) and AAE(370-950) results was good (R-2 = 0.95) and the mean bias error between these was 0.02. In the ship engine exhaust emissions, the highest AAE(470/950) values (up to 2.0 +/- 0.1) were observed when high sulfur content heavy fuel oil was used, whereas low sulfur content fuels had the lowest AAE(470/950) (0.9-1.1). In the diesel bus exhaust emissions, AAE(470/950) increased in the order of acceleration (0.8 +/- 0.1), deceleration (1.1 +/- 0.1), and steady driving (1.2 +/- 0.1). In the coal-fired power plant emissions, the variation of AAE(470/950) was substantial (from -0.1 +/- 2.1 to 0.9 +/- 1.6) due to the differences in the fuels and flue gas cleaning conditions. Fresh wood-burning derived aerosols had AAE(470/950) from 1.1 +/- 0.1 (modern masonry heater) to 1.4 +/- 0.1 (pellet boiler), lower than typically associated with wood burning, while the burn cycle phase affected AAE variation.Peer reviewe

Contribution of brown carbon to light absorption in emissions of European residential biomass combustion appliances

Residential biomass combustion significantly contributes to light-absorbing carbonaceous aerosols in the atmosphere, impacting the earth's radiative balance at regional and global levels. This study investigates the contribution of brown carbon (BrC) to the total particulate light absorption in the wavelength range of 370–950 nm (BrC370–950) and the particulate absorption Ångström exponents (AAE470/950) in 15 different European residential combustion appliances using a variety of wood-based fuels. BrC370–950 was estimated to be from 1 % to 21 % for wood log stoves and 10 % for a fully automatized residential pellet boiler. Correlations between the ratio of organic to elemental carbon (OC / EC) and BrC370–950 indicated that a one-unit increase in OC / EC corresponded to approximately a 14 % increase in BrC370–950. Additionally, BrC370–950 was clearly influenced by the fuel moisture content and the combustion efficiency, while the effect of the combustion appliance type was less prominent. AAE470/950 of wood log combustion aerosols ranged from 1.06 to 1.61. By examining the correlation between AAE470/950 and OC / EC, an AAE470/950 close to unity was found for pure black carbon (BC) particles originating from residential wood combustion. This supports the common assumption used to differentiate light absorption caused by BC and BrC. Moreover, diesel aerosols exhibited an AAE470/950 of 1.02, with BrC contributing only 0.66 % to the total absorption, aligning with the assumption employed in source apportionment. These findings provide important data to assess the BrC from residential wood combustion with different emission characteristics and confirm that BrC can be a major contributor to particulate UV and near-UV light absorption for northern European wood stove emissions with relatively high OC / EC ratios.</p

Simulating Kilobots within ARGoS: models and experimental validation

The Kilobot is a popular platform for swarm robotics research due to its low cost and ease of manufacturing. Despite this, the effort to bootstrap the design of new behaviours and the time necessary to develop and debug new behaviours is considerable. To make this process less burdensome, high-performing and flexible simulation tools are important. In this paper, we present a plugin for the ARGoS simulator designed to simplify and accelerate experimentation with Kilobots. First, the plugin supports cross-compiling against the real robot platform, removing the need to translate algorithms across different languages. Second, it is highly configurable to match the real robot behaviour. Third, it is fast and allows running simulations with several hundreds of Kilobots in a fraction of real time. We present the design choices that drove our work and report on experiments with physical robots performed to validate simulated behaviours

Solvent-surface interactions control the phase structure in laser-generated iron-gold core-shell nanoparticles

This work highlights a strategy for the one-step synthesis of FeAu nanoparticles by the pulsed laser ablation of alloy targets in the presence of different solvents. This method allows particle generation without the use of additional chemicals; hence, solvent-metal interactions could be studied without cross effects from organic surface ligands. A detailed analysis of generated particles via transmission electron microscopy in combination with EDX elemental mapping could conclusively verify that the nature of the used solvent governs the internal phase structure of the formed nanoparticles. In the presence of acetone or methyl methacrylate, a gold shell covering a non-oxidized iron core was formed, whereas in aqueous media, an Au core with an Fe3O4 shell was generated. This core-shell morphology was the predominant species found in >90% of the examined nanoparticles. These findings indicate that fundamental chemical interactions between the nanoparticle surface and the solvent significantly contribute to phase segregation and elemental distribution in FeAu nanoparticles. A consecutive analysis of resulting Fe@Au core-shell nanoparticles revealed outstanding oxidation resistance and fair magnetic and optical properties. In particular, the combination of these features with high stability magnetism and plasmonics may create new opportunities for this hybrid material in imaging applications

Higher-order assemblies of oligomeric cargo receptor complexes form the membrane scaffold of the Cvt vesicle

Selective autophagy is the mechanism by which large cargos are specifically sequestered for degradation. The structural details of cargo and receptor assembly giving rise to autophagic vesicles remain to be elucidated. We utilize the yeast cytoplasm-to-vacuole targeting (Cvt) pathway, a prototype of selective autophagy, together with a multi-scale analysis approach to study the molecular structure of Cvt vesicles. We report the oligomeric nature of the major Cvt cargo Ape1 with a combined 2.8 Å X-ray and negative stain EM structure, as well as the secondary cargo Ams1 with a 6.3 Å cryo-EM structure. We show that the major dodecameric cargo prApe1 exhibits a tendency to form higher-order chain structures that are broken upon interaction with the receptor Atg19 in vitro The stoichiometry of these cargo-receptor complexes is key to maintaining the size of the Cvt aggregate in vivo Using correlative light and electron microscopy, we further visualize key stages of Cvt vesicle biogenesis. Our findings suggest that Atg19 interaction limits Ape1 aggregate size while serving as a vehicle for vacuolar delivery of tetrameric Ams1