Miracle-dz/UniFAR-FTUs huggingface dataset:
Diamond Integrated Quantum Nanophotonics: Spins, Photons and Phonons

Integrated photonic devices in diamond have tremendous potential for many quantum applications, including long-distance quantum communication, quantum information processing, and quantum sensing. These devices benefit from diamond's combination of exceptional thermal, optical, and mechanical properties. Its wide electronic bandgap makes diamond an ideal host for a variety of optical active spin qubits that are key building blocks for quantum technologies. In landmark experiments, diamond spin qubits have enabled demonstrations of remote entanglement, memory-enhanced quantum communication, and multi-qubit spin registers with fault-tolerant quantum error correction, leading to the realization of multinode quantum networks. These advances put diamond at the forefront of solid-state material platforms for quantum information processing. Recent developments in diamond nanofabrication techniques provide a promising route to further scaling of these landmark experiments towards real-life quantum technologies. In this paper, we focus on the recent progress in creating integrated diamond quantum photonic devices, with particular emphasis on spin-photon interfaces, cavity optomechanical devices, and spin-phonon transduction. Finally, we discuss prospects and remaining challenges for the use of diamond in scalable quantum technologies.
Positron clouds within thunderstorms

We report the observation of two isolated clouds of positrons inside an active thunderstorm. These observations were made by the Airborne Detector for Energetic Lightning Emissions (ADELE), an array of six gamma-ray detectors, which flew on a Gulfstream V jet aircraft through the top of an active thunderstorm in August 2009. ADELE recorded two 511 keV gamma-ray count rate enhancements, 35 s apart, each lasting approximately 0.2 s. The enhancements, which were approximately a factor of 12 above background, were both accompanied by electrical activity as measured by a flat-plate antenna on the underside of the aircraft. The energy spectra were consistent with a source mostly composed of positron annihilation gamma rays, with a prominent 511 keV line clearly visible in the data. Model fits to the data suggest that the aircraft was briefly immersed in clouds of positrons, more than a kilometre across. It is not clear how the positron clouds were created within the thunderstorm, but it is possible they were caused by the presence of the aircraft in the electrified environment.
A High-Efficiency Deep-Learning-Based Antivibration Hammer Defect Detection Model for Energy-Efficient Transmission Line Inspection Systems

Automated inspection using unmanned aerial vehicles (UAVs) is an essential means to ensure safe operations of the power grid. Defect detection for antivibration hammers on transmission lines in inspection imagery is one of the critical tasks for automated UAV inspection. It needs a machine interpretation system to automatically detect numerous inspection images. In this paper, a high-efficiency model based on Cascade RCNN (region-convolutional neural network) is proposed to detect antivibration hammer defects with reduced costs and speedier response, which applies in energy-efficient transmission line inspection systems. Firstly, to reduce computational costs, this study modifies the Cascade RCNN with a probabilistic interpretation to achieve the best trade-off between the inference time and average precision. Secondly, an antivibration hammer defect detector (AVHDD) model is proposed that uses a deep layer aggregation-based feature extraction network and a highly effective weighted bidirectional feature fusion network to replace the original ResNet and FPN on the modified Cascade RCNN to further enhance the model performance. Finally, a fine classification (FC) scheme for the types of antivibration hammer defects is proposed based on defect features to rationalize the model. The AVHDD reached an experimental mAP of 97.24% when IoU = 0.75, which is 2.93% higher than the original Cascade RCNN, and the defect recall was 98.9% while also significantly improving the inference speed. Moreover, the experimental results indicate that the overall performance of the proposed model is superior to typical models, confirming its suitability for energy-efficient transmission line inspection systems.
Multipliers spaces and pseudo-differential operators

Let $\sigma(x,\xi) $ be a sufficiently regular function defined on $R^d \times R^d. $ The pseudo-differential operator with symbol $\sigma$ is defined on the Schwartz class by the formula: \[f\to\sigma f(x)=\int_{R^d} \sigma(x,\xi) \hat{f}(\xi)e^{2\pi ix\xi}d\xi, \] where $\hat{f}(\xi)=\int_{R^d} f(x)e^{-2\pi ix\xi}dx$ is the Fourier transform of $f. $ In this paper, we shall consider the regularity of the following type : \begin{description} \item[(a)] $| \partial_{\xi}^{\alpha}\sigma(x,\xi) | \leq A_{\alpha}(1+| \xi|) ^{-| \alpha|},$ \item[(b)] $| \partial_{\xi}^{\alpha}\sigma(x+y,\xi) -\partial_{\xi}^{\alpha}\sigma(x,\xi) | \leq A_{\alpha}\omega(| y|) (1+| \xi|) ^{-| \alpha|},$ \end{description} %
FORMING DISK GALAXIES IN WET MAJOR MERGERS. I. THREE FIDUCIAL EXAMPLES

Using three fiducial N-body+SPH simulations, we follow the merging of two disk galaxies that each have a hot gaseous halo component, and examine whether the merger remnant can be a spiral galaxy. The stellar progenitor disks are destroyed by violent relaxation during the merging and most of their stars form a classical bulge, while the remaining stars, as well as stars born during the merging times, form a thick disk and its bar. A new stellar disk forms subsequently and gradually in the remnant from the gas accreted mainly from the halo. It is vertically thin and well extended in its equatorial plane. A bar starts forming before the disk is fully in place, which is contrary to what is assumed in idealized simulations of isolated bar-forming galaxies, and has morphological features such as ansae and boxy/peanut bulges. Stars of different ages populate different parts of the box/peanut. A disky pseudobulge also forms, so that by the end of the simulation all three types of bulges coexist. The oldest stars are found in the classical bulge, followed by those of the thick disk, then by those in the thin disk. The youngest stars are in the spiral arms and the disky pseudobulge. The disk surface density profiles are of type II (exponential with downbending); the circular velocity curves are flat and show that the disks are submaximum in these examples: two clearly so and one near-borderline between maximum and submaximum. On average, only roughly between 10% and 20% of the stellar mass is in the classical bulge of the final models, i.e., much less than in previous simulations.
The Wide‐Field X and Gamma‐Ray Telescope ECLAIRs aboard the Gamma‐Ray Burst Multi‐Wavelength Space Mission SVOM

The X and Gamma-ray telescope ECLAIRs is foreseen to be launched on a low Earth orbit (h=630 km, i=30 degrees) aboard the SVOM satellite (Space-based multi-band astronomical Variable Objects Monitor), a French-Chinese mission with Italian contribution. Observations are expected to start in 2013. It has been designed to detect and localize Gamma-Ray Bursts (GRBs) or persistent sources of the sky, thanks to its wide field of view (about 2 sr) and its remarkable sensitivity in the 4-250 keV energy range, with enhanced imaging sensitivity in the 4-70 keV energy band. These characteristics are well suited to detect highly redshifted GRBs, and consequently to provide fast and accurate triggers to other onboard or ground-based instruments able to follow-up the detected events in a very short time from the optical wavelength bands up to the few MeV Gamma-Ray domain.
THERMAL STABILITY OF A THIN DISK WITH MAGNETICALLY DRIVEN WINDS

The absence of thermal instability in the high/soft state of black hole X-ray binaries, in disagreement with the standard thin disk theory, has been a long-standing riddle for theoretical astronomers. We have tried to resolve this question by studying the thermal stability of a thin disk with magnetically driven winds in the plane. It is found that disk winds can greatly decrease the disk temperature and thus help the disk become more stable at a given accretion rate. The critical accretion rate, , corresponding to the thermal instability threshold, is significantly increased in the presence of disk winds. For α = 0.01 and Bϕ = 10Bp, the disk is quite stable even for a very weak initial poloidal magnetic field []. However, when Bϕ = Bp or Bϕ = 0.1Bp, a somewhat stronger (but still weak) field (βp, 0 ∼ 200 or βp, 0 ∼ 20) is required to make the disk stable. Nevertheless, despite the great increase of , the luminosity threshold, corresponding to instability, remains almost constant or decreases slowly with increasing due to decreased gas temperature. The advection and diffusion timescales of the large-scale magnetic field threading the disk are also investigated in this work. We find that the advection timescale can be smaller than the diffusion timescale in a disk with winds, because the disk winds take away most of the gravitational energy released in the disk, resulting in the decrease of the magnetic diffusivity η and the increase of the diffusion timescale.
The Combined Impact of Canopy Stability and Soil NOx Exchange on Ozone Removal in a Temperate Deciduous Forest

Dry deposition is an important ozone sink that impacts ecosystem carbon and water cycling. Ozone dry deposition in forests is regulated by vertical transport, stomatal uptake, and non‐stomatal processes including chemical removal. However, accurate descriptions of these processes in deposition parameterizations are hindered by sparse observational constraints on individual sink terms. Here we quantify the contribution of canopy‐atmosphere turbulent exchange and chemical ozone removal by soil‐emitted nitric oxide (NO) to ozone deposition in a North‐Italian broadleaf deciduous forest. We apply a multi‐layer canopy exchange model to interpret campaign observations of nitrogen oxides (NOx = NO + NO2) and ozone exchange above and inside the forest canopy. Two state‐of‐science parameterizations of in‐canopy vertical diffusivity, based on above‐canopy wind speed or stability, do not reproduce the observed exchange suppressed by canopy‐top radiative heating, resulting in overestimated dry deposition velocities of 10%–19% during daytime. Applying observation‐derived vertical diffusivities in our simulations largely resolves this overestimation. Soil emissions are an important NOx source despite the observed high background NOx levels. Soil NOx emissions decrease the gradient between canopy and surface layer NOx mixing ratios, which suppresses simulated NOx deposition by 80% compared to a sensitivity simulation without soil emissions. However, a sensitivity analysis shows that the enhanced chemical ozone sink by reaction with soil‐emitted NO is offset by increased vertical ozone transport from aloft and suppressed dry deposition. Our results highlight the need for targeted observations of non‐stomatal ozone removal and turbulence‐resolving deposition simulations to improve quantification and model representation of forest ozone deposition.
Electron Wave Function in Armchair Graphene Nanoribbons

By using analytical solution of a tight-binding model for armchair nanoribbons, it is confirmed that the solution represents the standing wave formed by intervalley scattering and that pseudospin is invariant under the scattering. The phase space of armchair nanoribbon which includes a single Dirac point is specified. By examining the effects of boundary perturbations on the wave function, we suggest that the existance of a strong boundary potential is inconsistent with the observation in a recent scanning tunneling microscopy. Some of the possible electron-density superstructure patterns near a step armchair edge located on top of graphite are presented. It is demonstrated that a selection rule for the G band in Raman spectroscopy at graphene edge can be most easily reproduced with the analytical solution.
Lattice Modules over Rings of Bounded Random Variables

In the context of representation theorems for conditional and multi-period risk measures, we will extend the work, started in [9], on locally convex modules over the ring  $\lambda= L^\infty(G)$, by adding an order or a lattice structure. The dual spaces of lattice -modules turn out to be topologically and Dedekind-complete modules. For Banach lattice modules a version of the Namioka-Klee theorem will be proved, as well as the subdi erentiability of -convex functions on the interior of their domains. The completeness of $L^p_\lambda$ and $L^{(p2;p1)}_\lambda$-modules will be shown and their dual modules characterized. Similarly, we will establish the duality between Morse and their corresponding Orlicz $\lambda$ modules.
An operational link between MUBs and SICs

The existence of symmetric informationally complete positive operator valued measures (SIC POVMs, or simply SICs) in arbitrary dimensions and the existence of complete systems of d + 1 mutually unbiased bases (MUBs) in complex Hilbert spaces of dimensions d other than prime power have remained unsolved despite much effort expended on them over the last ten years or so. Connections with finite geometries have been exhibited for both SICs and MUBs, including the intriguing fact that the relevant geometries for either of them are equivalent (where they exist) [1, 2]. The construction schemes proposed so far for SICs and MUBs are essentially mathematically motivated and based on combinatorial, group theoretic, algebraic or finite geometric structures [3–14]. Here we elucidate a physically motivated connection between SICs and MUBs, which emerges quite naturally in some respects. Our main tools will be the generalisation of a SIC POVM into a SIC system and of MUBs into mutually unbiased POVMs. The basic idea is easily established by considering the qubit case: there a SIC is found to be a natural joint observable of three POVMs that are smearings of three mutually unbiased binary observables. It turns out that these POVMs have a similar relation as mutually unbiased bases and they will therefore be referred to as
Healthcare Students and Medical Residents as Second Victims: A Cross-Sectional Study

Background: The term second victim (SV) describes healthcare professionals who remain traumatized after being involved in a patient safety incident (PSI). They can experience various emotional, psychological, and physical symptoms. The phenomenon is quite common; it has been estimated that half of hospital workers will be an SV at least once in their career. Because recent literature has reported high prevalence (>30%) among nursing students, we studied the phenomenon among the whole population of healthcare students. Methods: We conducted a cross-sectional study with an online questionnaire among nursing students, medical students, and resident physicians at the teaching hospital of the University of the Piemonte Orientale located in Novara, Italy. The study included 387 individuals: 128 nursing students, 174 medical students, and 85 residents. Results: We observed an overall PSI prevalence rate of 25.58% (lowest in medical students, 14.37%; highest in residents, 43.53%). Of these, 62.63% experienced symptoms typical of an SV. The most common temporary symptom was the feeling of working badly (51.52%), whereas the most common lasting symptom was hypervigilance (51.52%). Notably, none of the resident physicians involved in a PSI spoke to the patient or the patient’s relatives. Conclusion: Our findings highlighted the risk incurred by healthcare students of becoming an SV, with a possible significant impact on their future professional and personal lives. Therefore, we suggest that academic institutions should play a more proactive role in providing support to those involved in a PSI.
Mesh generation with provable quality bounds

We consider the problem of generating a triangulation of provable quality for two and three dimensional polyhedral regions. That is, we seek a triangulation, allowing additional vertices called Steiner points, such that the triangular or tetrahedral elements have a bound on their shape. In three dimensions we also seek an upper bound on the number of tetrahedra in the triangulation. These triangulation algorithms find application in mesh generation for finite element methods. The polyhedral region must be bounded and well defined, but may have holes of arbitrary complexity. In three dimensions, we assume there are no restrictions on where we may place Steiner points. Our triangulation is optimal in the following two senses. First, our triangulation achieves the best possible aspect ratio up to a constant factor, which is our bound on element shape. Second, for any other triangulation of the same region into m triangles with bounded aspect ratio, our triangulation has size n = O(m). Such a triangulation is desired as an initial mesh for a finite element mesh refinement algorithm. Previous three dimensional triangulation schemes either worked only on a restricted class of input, or did not guarantee well-shaped tetrahedra, or were not able to bound the output size. We build on some of the ideas presented in previous work by Bern, Eppstein, and Gilbert, who have shown how to triangulate a two dimensional polyhedral region with holes, with similar quality and optimality bounds. In two dimensions, we assume the restriction that we may introduce Steiner points on the polygon's interior, but not on its boundary. Of all triangulations satisfying this restriction, our triangulation has the maximum minimum angle, up to a constant factor. This is the first known algorithm for this problem with provably optimal element shape. The algorithm solves several subproblems of mesh generation.
A Novel Strategy for Constructing an Integrated Linkage Map in an F1 Hybrid Population of Populus deltoides and Populus simonii

The genetic linkage maps of the traditional F2 population in inbred lines were estimated from the frequency of recombination events in both parents, providing full genetic information for genetic and genomic studies. However, in outbred forest trees, it is almost impossible to generate the F2 population because of their high heterozygosity and long generation times. We proposed a novel strategy to construct an integrated genetic linkage map that contained both parental recombination information, with restriction-site-associated DNA sequencing (RADSeq) data in an F1 hybrid population of Populus deltoides and Populus simonii. We selected a large number of specific RAD tags to construct the linkage map, each of which contained two SNPs, one heterozygous only in the female parent and the other heterozygous only in the male. Consequently, the integrated map contained a total of 1154 RAD tags and 19 linkage groups, with a total length of 5255.49 cM and an average genetic distance of 4.63 cM. Meanwhile, the two parent-specific linkage maps were also constructed with SNPs that were heterozygous in one parent and homozygous in the other. We found that the integrated linkage map was more consensus with the genomic sequences of P. simonii and P. deltoides. Additionally, the likelihood of the marker order in each linkage group of the integrated map was greater than that in both parental maps. The integrated linkage map was more accurate than the parent-specific linkage maps constructed in the same F1 hybrid population, providing a powerful genetic resource for identifying the quantitative trait loci (QTLs) with dominant effects, assembling genomic sequences, and performing comparative genomics in related Populus species. More importantly, this novel strategy can be used in other outbred species to build an integrated linkage map.
Supercoiling bacterial filaments

Cells of mutant strains of the bacterium B. subtilis fail to separate upon dividing, and form long fibers. A given fiber is initially straight, but once it grows to around 100 microns, it buckles and writhes. Soon after this initial instability the fiber rapidly braids up into a plectoneme. Modeling a fiber as a twisted elastic filament in a viscous fluid, we exploit the “natural” frame of space curves to formulate the dynamics of the filament’s shape and twist density. The resulting coupled nonlinear equations are used to display a remarkable nonlinear phenomenon: geometric untwisting of open filaments, in which twist strain relaxes through a transient writhing instability without axial rotation. Experimentally observed motions of the fibers of B. subtilis may be examples of this twisting process.
Modeling Aspects of Two‐Bead Microrheology

We revisit the Mason and Weitz (Phys. Rev. Lett., 74, 1995) and Levine and Lubensky (Phys. Rev. Lett., 85, 2000) analysis for one‐ and two‐bead microrheology. Our first motivation is the possibility of drawing inferences from experimental data about local diffusive properties of individual beads and nonlocal dynamic moduli of the medium separating the two beads. Our second motivation is the ability to perform direct numerical simulations of hydrodynamically coupled Brownian beads in soft matter. For both goals, we first must have a model for the coupling between these two transport properties. We reformulate the coupled generalized Langevin equations (GLE) following the scalar GLE analysis of Fricks et al. (J. Appl. Math., 2008), assuming an exponential series parametrization of both local and nonlocal memory kernels. We then show the two‐bead GLE model can be represented as a vector Ornstein‐Uhlenbeck process, which allows for a fast and statistically accurate numerical simulation of coupled bead paths (.
Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials

Metal–organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs using two deep learning algorithms, multilayer perceptron (MLP) and long short-term memory (LSTM) networks, with a hypothetical set of about 130,000 structures of MOFs with methane and carbon dioxide adsorption data at different pressures. The models were evaluated using 10 iterations of 10-fold cross validations and 100 holdout validations. The MLP and LSTM models performed similarly with high prediction accuracy. The models for predicting gas adsorption at a higher pressure outperformed the models for predicting gas adsorption at a lower pressure. The deep learning models are more accurate than the random forest models reported in the literature, especially for predicting gas adsorption capacities at low pressures. Our results demonstrated that deep learning algorithms have a great potential to generate models that can accurately predict the gas adsorption capacities of MOFs.
Electrical Conduction, Heat Conduction, Shear Viscosity and Entropy

We present here how to study steady linear transport phenomena by using entropy. We study the system and the environment together and identify their entropies. Concerning their interaction, quantum mechanics is considered in the study. A time parameter τ is therefore introduced to characterize the discrete nature of the quantum interactions. By combining τ and the entropy, an approach is constructed successfully to study electrical conduction, heat conduction and shear viscosity.