The source count dipole from wide-area radio continuum surveys allows us to test the cosmological standard model. Many radio sources have multiple components, which can cause an overdispersion of the source counts distribution. We account for this effect via a new Bayesian estimator, based on the negative binomial distribution. Combining the two best understood wide-area surveys, NVSS and RACS-low, and the deepest wide-area survey, LoTSS-DR2, we find that the source count dipole exceeds its expected value as the kinematic dipole amplitude from standard cosmology by a factor of $3.67 \pm 0.49$ -- a $5.4\sigma$ discrepancy.

We introduce GalaxyGenius, a Python package designed to produce synthetic galaxy images tailored to different telescopes based on hydrodynamical simulations. Its implementation will support and advance research on galaxies in the era of large-scale sky surveys. The package comprises three main modules: data preprocessing, ideal data cube generation, and mock observation. Specifically, the preprocessing module extracts necessary properties of star and gas particles for a selected subhalo from hydrodynamical simulations and creates the execution file for the following radiative transfer procedure. Subsequently, building on the above information, the ideal data cube generation module executes a widely used radiative transfer project, specifically the SKIRT, to perform the SED assignment for each particle and the radiative transfer procedure to produce an IFU-like ideal data cube. Lastly, the mock observation module takes the ideal data cube and applies the throughputs of aiming telescopes, while also incorporating the relevant instrumental effects, point spread functions (PSFs), and background noise to generate the required mock observational images of galaxies. To showcase the outcomes of GalaxyGenius, we created a series of mock images of galaxies based on the IllustrisTNG and EAGLE simulations for both space and ground-based surveys, spanning ultraviolet (UV) to infrared (IR) wavelength coverage, including CSST, Euclid, HST, JWST, Roman, and HSC. GalaxyGenius offers a flexible framework to generate mock galaxy images with customizable recipes. These generated images can serve as valuable references for verifying and validating new approaches in astronomical research. They can also serve as training sets for relevant studies using deep learning in cases where real observational data are insufficient.

High-order harmonic generation (HHG) in solids has emerged as a versatile platform for exploring ultrafast and quantum-coherent phenomena in condensed matter. Recent advances reveal Berry-phase and topological effects in harmonic emission, strong-field control of excitons and lattice motion, the generation of nonclassical light states driven by quantum and squeezed fields, and the emergence of orbital-angular-momentum transfer in solid-state high-harmonic generation. Nanostructured and hybrid plasmonic-semiconductor platforms enable enhanced and spectrally tunable HHG, while interferometric and cryogenic setups allow attosecond-resolved phase measurements. On the theoretical side, multiband and topological models incorporating dephasing, propagation, and electron-hole coherence effects have deepened our understanding of the interplay between interband and intraband dynamics. These developments establish solid-state HHG as a bridge between ultrafast spectroscopy, quantum optics, and material science, paving the way toward quantum-engineered attosecond sources and coherent control of light-matter interactions in solids.

Intense light-matter interaction largely relies on the use of high-power light sources, creating fields comparable to, or even stronger than, the field keeping the electrons bound in atoms. Under such conditions, the interaction induces highly nonlinear processes such as high harmonic generation, in which the low-frequency photons of a driving laser field are upconverted into higher-frequency photons. These processes have enabled numerous groundbreaking advances in atomic, molecular, and optical physics, and they form the foundation of attosecond science. Until recently, however, such processes were typically described using semi-classical approximations, since the quantum properties of the light field were not required to explain the observables. This has changed in the recent past. Ongoing theoretical and experimental advances show that fully quantized descriptions of intense light-matter interactions, which explicitly incorporate the quantum nature of the light field, open new avenues for both fundamental research and technological applications at the fully quantized level. These advances emerge from the convergence of quantum optics with strong-field physics and ultrafast science. Together, they have given rise to the field of quantum optics and quantum electrodynamics of strong-field processes.

In its beginnings, the physics of intense laser-matter interactions was the physics of multiphoton processes. The theory was reduced then to high-order perturbation theory, while treating matter and light in a quantum manner. With the advent of chirped pulse amplification developed by D. Strickland and G. Mourou, which enabled generation of ultra-intense, ultra-short, coherent laser pulses, the need for a quantum electrodynamics description of electromagnetic (EM) fields practically ceased to exist and lost relevance. Contemporary attoscience (AS), and more generally ultrafast laser physics, awarded the Nobel Prize in 2023 to P. Agostini, F. Krausz, and A. L'Huillier, commonly uses the classical description of EM fields while keeping a fully quantum description of matter. The progress and successes of AS in the last 40 years have been spectacular, with an enormous amount of fascinating investigations in basic research and technology. Yet a central question remains: can ultrafast laser physics continue to advance without reintroducing quantum electrodynamics and quantum optics into its description of light-matter interactions? This article discusses future perspectives at the intersection of strong-field physics and quantum optics.

Bright squeezed vacuum (BSV) is an intense quantum state of light with zero mean electric field and huge photon number fluctuations, sufficiently intense to drive extreme nonlinear processes and imprint nonclassical statistics. However, the temporal structure of single BSV shots has not been fully characterized. Here, we retrieve the spectral and temporal pulse characteristics of a set of single-peak BSV shots. It is obtained by realizing a femtosecond BSV source at 1040 nm with a single spatial mode and perform single-shot spectral interferometry with a fully characterized coherent-state reference pulse. Our approach reveals that the group delay is consistent between the various shots, resulting in an average pulse duration of 27.2 fs, much shorter than the pump pulse, and a variation of 5.5 fs (standard deviation). We also observe a characteristic nodal structure in the spectral interferograms, demonstrating the BSV's random phase ambiguity of $\pi$ rad. Our approach demonstrates that BSV is a viable source of femtosecond light pulses for attosecond sub-cycle metrology of ultrafast electron dynamics.

Bone density prediction via CT scans to estimate T-scores is crucial, providing a more precise assessment of bone health compared to traditional methods like X-ray bone density tests, which lack spatial resolution and the ability to detect localized changes. However, CT-based prediction faces two major challenges: the high computational complexity of transformer-based architectures, which limits their deployment in portable and clinical settings, and the imbalanced, long-tailed distribution of real-world hospital data that skews predictions. To address these issues, we introduce MedConv, a convolutional model for bone density prediction that outperforms transformer models with lower computational demands. We also adapt Bal-CE loss and post-hoc logit adjustment to improve class balance. Extensive experiments on our AustinSpine dataset shows that our approach achieves up to 21% improvement in accuracy and 20% in ROC AUC over previous state-of-the-art methods.

Prostate cancer, a growing global health concern, necessitates precise diagnostic tools, with Magnetic Resonance Imaging (MRI) offering high-resolution soft tissue imaging that significantly enhances diagnostic accuracy. Recent advancements in explainable AI and representation learning have significantly improved prostate cancer diagnosis by enabling automated and precise lesion classification. However, existing explainable AI methods, particularly those based on frameworks like generative adversarial networks (GANs), are predominantly developed for natural image generation, and their application to medical imaging often leads to suboptimal performance due to the unique characteristics and complexity of medical image. To address these challenges, our paper introduces three key contributions. First, we propose ProjectedEx, a generative framework that provides interpretable, multi-attribute explanations, effectively linking medical image features to classifier decisions. Second, we enhance the encoder module by incorporating feature pyramids, which enables multiscale feedback to refine the latent space and improves the quality of generated explanations. Additionally, we conduct comprehensive experiments on both the generator and classifier, demonstrating the clinical relevance and effectiveness of ProjectedEx in enhancing interpretability and supporting the adoption of AI in medical settings. Code will be released at this https URL

The effectiveness of testing in uncovering software defects depends not only on the characteristics of the test inputs and how thoroughly they exercise the software, but also on the quality of the oracles used to determine whether the software behaves as expected. Therefore, assessing the quality of oracles is crucial to improve the overall effectiveness of the testing process. Existing metrics have been used for this purpose, but they either fail to provide a comprehensive basis for guiding oracle improvement, or they are tailored to specific types of oracles, thus limiting their generality. In this paper, we introduce state field coverage, a novel metric for assessing oracle quality. This metric measures the proportion of an object's state, as statically defined by its class fields, that an oracle may access during test execution. The main intuition of our metric is that oracles with a higher state field coverage are more likely to detect faults in the software under analysis, as they inspect a larger portion of the object states to determine whether tests pass or not. We implement a mechanism to statically compute the state field coverage metric. Being statically computed, the metric is efficient and provides direct guidance for improving test oracles by identifying state fields that remain unexamined. We evaluate state field coverage through experiments involving 273 representation invariants and 249,027 test assertions. The results show that state field coverage is a well-suited metric for assessing oracle quality, as it strongly correlates with the oracles' fault-detection ability, measured by mutation score.

We investigate the use of light beams carrying orbital angular momentum (OAM) in the context of high harmonic generation (HHG) within semiconductor crystals. Our contribution deals with the transfer and conservation of OAM in the strong-field regime, from the driving laser field to the generated harmonics. To this end, in this work, we combine the semiconductor Bloch equations with the thin slab model to simulate the generation of high-order harmonics in semiconductor media and to compute the features of the far-field harmonics. We demonstrate that this theoretical approach is capable of satisfactorily reproducing previously published experimental features of the generated harmonics in ZnO driven by a Laguerre-Gauss beam. Our research not only deepens the understanding of light-solid interactions but also heralds the dawn of bright, structured XUV coherent radiation sources with unparalleled potential across diverse technological areas, paving the way for enhanced functionalities in fields such as microscopy, spectroscopy, and optical communication.

We propose a concept of a superconducting photodiode - a device that transforms the energy and `spin' of an external electromagnetic field into the rectified steady-state supercurrent and develop a microscopic theory describing its properties. For this, we consider a two-dimensional thin film cooled down below the temperature of superconducting transition with the injected dc supercurrent and exposed to an external electromagnetic field with a frequency smaller than the superconducting gap. As a result, we predict the emergence of a photoexcited quasiparticle current, and, as a consequence, oppositely oriented stationary flow of Cooper pairs. The strength and direction of this photoinduced supercurrent depend on (i) such material properties as the effective impurity scattering time and the nonequilibrium quasiparticles' energy relaxation time and (ii) such electromagnetic field properties as its frequency and polarization.

We present a joint experimental and theoretical study of non-sequential double ionization (NSDI) in argon driven by a 3100-nm laser source. The correlated photoelectron momentum distribution (PMD) shows a strong dependence on the pulse duration, and the evolution of the PMD can be explained by an envelope-induced intensity effect. Determined by the time difference between tunneling and rescattering, the laser vector potential at the ionization time of the bound electron will be influenced by the pulse duration, leading to different drift momenta. Such a mechanism is extracted through a classical trajectory Monte Carlo-based model and it can be further confirmed by quantum mechanical simulations. This work sheds light on the importance of the pulse duration in NSDI and improves our understanding of the strong field tunnel-recollision dynamics under mid-IR laser fields.

A deep learning-based computational method has been proposed for soft matter dynamics -- the deep Onsager-Machlup method (DOMM), which combines the brute forces of deep neural networks (DNNs) with the fundamental physics principle -- Onsager-Machlup variational principle (OMVP). In the DOMM, the trial solution to the dynamics is constructed by DNNs that allow us to explore a rich and complex set of admissible functions. It outperforms the Ritz-type variational method where one has to impose carefully-chosen trial functions. This capability endows the DOMM with the potential to solve rather complex problems in soft matter dynamics that involve multiple physics with multiple slow variables, multiple scales, and multiple dissipative processes. Actually, the DOMM can be regarded as an extension of the deep Ritz method constructed using variational formulations of physics models to solve static problems in physics as discussed in our former work [Wang et al, Soft Matter, 2022, 18, 6015-6031]. In this work, as the first step, we focus on the validation of the DOMM as a useful computational method by using it to solve several typical soft matter dynamic problems: particle diffusion in dilute solutions, and two-phase dynamics with and without hydrodynamics. The predicted results agree very well with the analytical solution or numerical solution from traditional computational methods. These results show the accuracy and convergence of DOMM and justify it as an alternative computational method for solving soft matter dynamics.

Aluminum nitride is a traditional wide-bandgap semiconductor that has been widely used in high-power electronic and optoelectronic devices. Recently, scandium-doped aluminum nitride (AlScN) was shown to host ferroelectricity with high remnant polarization and excellent thermal stability. However, its practical use is currently limited by its high coercive field, $E_c$. Understanding the atomic-scale switching mechanism is essential to guide strategies for reducing $E_c$. Here, we combine density functional theory and machine-learning molecular dynamics to investigate polarization switching mechanisms in AlScN over various Sc concentrations and applied electric fields. We find that collective switching induces excessive lattice strain and is therefore unlikely to occur. Rather, pre-existing domain walls relieve strain and lead to a distinct switching dynamics, with the associated switching mechanism being field dependent. More precisely, at low electric fields, switching proceeds via gradual domain-wall propagation, well described by the Kolmogorov-Avram-Ishibashi model; meanwhile high fields trigger additional nucleation events, producing rapid and more homogeneous reversal, whose mixed switching process is better described by the simultaneous non-linear nucleation and growth model. These findings highlight the critical role of domain-wall dynamics in nitride ferroelectrics and suggest that domain engineering provides a viable route to control coercive fields and enhance device performance.

The advantage of attosecond measurements is the possibility of time-resolving ultrafast quantum phenomena of electron dynamics. Many such measurements are of interferometric nature, and therefore give access to the phase. Likewise, weak measurements are intrinsically interferometric and specifically take advantage of interfering probability amplitudes, therefore encoding the phase information of the process. In this work, we show that attosecond interferometry experiments can be seen as a weak measurement, which unveils how this notion is connected to strong field physics and attosecond science. In particular, we show how the electron trajectory picks up a new phase, which occurs due to the weak measurement of the process. This phase can show significant contributions in the presence of spectral features of the measured system. Furthermore, extending this approach to include non-classical driving fields shows that the generated harmonics exhibit non-trivial features in their quantum state and photon statistics. This opens the path towards investigations of attosecond quantum interferometry experiments.

Density Functional Theory (DFT) calculations, within the virtual crystal alloy approximation, are performed, along with the development of a Landau-type model employing a symmetry-allowed analytical expression of the internal energy and having parameters being determined from first principles, to investigate properties and energetics of Al1-xScxN ferroelectric nitrides in their hexagonal forms. These DFT computations and this model predict the existence of two different types of minima, namely the 4-fold-coordinated wurtzite (WZ) polar structure and a 5-times paraelectric hexagonal phase (to be denoted as H5), for any Sc composition up to 40%. The H5 minimum progressively becomes the lowest energy state within hexagonal symmetry as the Sc concentration increases from 0 to 40%. Furthermore, the model points out to several key findings. Examples include the crucial role of the coupling between polarization and strains to create the WZ minimum, in addition to polar and elastic energies, and that the origin of the H5 state overcoming the WZ phase as the global minimum within hexagonal symmetry when increasing the Sc composition mostly lies in the compositional dependency of only two parameters, one linked to the polarization and another one being purely elastic in nature. Other examples are that forcing Al1-xScxN systems to have no or a weak change in lattice parameters when heating them allows to reproduce well their finite-temperature polar properties, and that a value of the axial ratio close to that of the ideal WZ structure does imply a large polarization at low temperatures but not necessarily at high temperatures because of the ordered-disordered character of the temperature-induced formation of the WZ state. Such findings should allow for a better fundamental understanding of (Al,Sc)N ferroelectric nitrides, which may be used to design efficient devices operating at low voltages.

In this work we study the flux density dependence of the redshift distribution of low-frequency radio sources observed in the LOFAR Two-metre Sky Survey (LoTSS) deep fields and apply it to estimate the clustering length of the large-scale structure of the Universe, examining flux density limited samples (1 mJy, 2 mJy, 4 mJy and 8 mJy) of LoTSS wide field radio sources. We utilise and combine the posterior probability distributions of photometric redshift determinations for LoTSS deep field observations from three different fields (Boötes, Lockman hole and ELAIS-N1, together about $26$ square degrees of sky), which are available for between $91\%$ to $96\%$ of all sources above the studied flux density thresholds and observed in the area covered by multi-frequency data. We estimate uncertainties by a bootstrap method. We apply the inferred redshift distribution on the LoTSS wide area radio sources from the HETDEX field (LoTSS-DR1; about $424$ square degrees) and make use of the Limber approximation and a power-law model of three dimensional clustering to measure the clustering length, $r_0$, for various models of the evolution of clustering. We find that the redshift distributions from all three LoTSS deep fields agree within expected uncertainties. We show that the radio source population probed by LoTSS at flux densities above $1$ mJy has a median redshift of at least $0.9$. At $2$ mJy, we measure the clustering length of LoTSS radio sources to be $r_0 = (10.1\pm 2.6) \ h^{-1}$Mpc in the context of the comoving clustering model. Our findings are in agreement with measurements at higher flux density thresholds at the same frequency and with measurements at higher frequencies in the context of the comoving clustering model.

Quantum state engineering of light is of great interest for quantum technologies, particularly generating non-classical states of light, and is often studied through quantum conditioning approaches. Recently, we demonstrated that such approaches can be applied in intense laser-atom interactions to generate optical "cat" states by using intensity measurements and classical post-selection of the measurement data. Post-processing of the sampled data set allows to select specific events corresponding to measurement statistics as if there would be non-classical states of light leading to these measurement outcomes. However, to fully realize the potential of this method for quantum state engineering, it is crucial to thoroughly investigate the role of the involved measurements and the specifications of the post-selection scheme. We illustrate this by analyzing post-selection schemes recently developed for the process of high harmonic generation, which enables generating optical cat states bright enough to induce non-linear phenomena. These findings provide significant guidance for quantum light engineering and the generation of high-quality, intense optical cat states for applications in non-linear optics and quantum information science.

The classical Kramers-Henneberger transformation connects, via a series of unitary transformations, the dynamics of a quantum particle of mass $m$ located in a trap at position $\alpha(t)$, with the dynamics of a charge $e$ moving in an electric field $e{\cal{E}}(t)=-m\ddot{\alpha}(t)$ within the dipole approximation. In this paper, we extend the classical Kramers-Henneberger transformation to the quantum electrodynamic and quantum optical realm, by explicitly treating the trap location quantum mechanically, thus taking into account the quantum fluctuations of the time-dependent displacement force. Compared to the classical case, we show that quantum electrodynamic corrections appear, and we propose an optomechanical realization for the quantized position of the trap to show that such corrections can manifest in state-of-the-art experiments. These results open the path to novel quantum simulation of quantum electrodynamics and quantum optics of attoscience and ultrafast physics by using ultracold trapped atoms and ions.

Let $B\subset A$ be a left or right bounded extension of finite dimensional algebras. We use the Jacobi-Zariski long nearly exact sequence to show that $B$ satisfies Han's conjecture if and only if $A$ does, regardless if the extension splits or not. We provide conditions ensuring that an extension by arrows and relations is left or right bounded. Finally we give a structure result for extensions of an algebra given by a quiver and admissible relations, and examples of non split left or right bounded extensions.