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This study underscores the pivotal role of syntax feedback in augmenting the syntactic proficiency of students. Recognizing the challenges faced by learners in mastering syntactic nuances, we introduce a specialized dataset named Essay-Syntax-Instruct designed to enhance the understanding and application of English syntax among these students. Leveraging the capabilities of Large Language Models (LLMs) such as GPT3.5-Turbo, Llama-2-7b-chat-hf, Llama-2-13b-chat-hf, and Mistral-7B-Instruct-v0.2, this work embarks on a comprehensive fine-tuning process tailored to the syntax improvement task. Through meticulous evaluation, we demonstrate that the fine-tuned LLMs exhibit a marked improvement in addressing syntax-related challenges, thereby serving as a potent tool for students to identify and rectify their syntactic errors. The findings not only highlight the effectiveness of the proposed dataset in elevating the performance of LLMs for syntax enhancement but also illuminate a promising path for utilizing advanced language models to support language acquisition efforts. This research contributes to the broader field of language learning technology by showcasing the potential of LLMs in facilitating the linguistic development of Students., Comment: This paper has been accepted for presentation at AIEER 2024

Quantum correlations, like entanglement, represent the characteristic trait of quantum mechanics, and pose essential issues and challenges to the interpretation of this pillar of modern physics. Although quantum correlations are largely acknowledged as a major resource to achieve quantum advantage in many tasks of quantum technologies, their full quantitative description and the axiomatic basis underlying them are still under investigation. Previous works suggested that the origin of nonlocal correlations is grounded in principles capturing (from outside the quantum formalism) the essence of quantum uncertainty. In particular, the recently-introduced principle of Relativistic Independence gave rise to a new bound intertwining local and nonlocal correlations. Here we test such a bound by realizing together sequential and joint weak measurements on entangled photon pairs, allowing to simultaneously quantify both local and nonlocal correlations by measuring incompatible observables on the same quantum system without collapsing its state, a task typically forbidden in the traditional (projective) quantum measurement framework. Our results demonstrate the existence of a fundamental limit on the extent of quantum correlations, shedding light on the profound role of uncertainty in both enabling and balancing them., Comment: 23 pages, 3 figures

The confinement of electromagnetic radiation within extremely small volumes offers an effective means to significantly enhance light-matter interactions, to the extent that zero-point quantum vacuum fluctuations can influence and control the properties of materials. Here, we develop a theoretical framework for the quantum electrodynamics of graphene Landau levels embedded in a deep subwavelength hyperbolic cavity, where light is confined into ultrasmall mode volumes. By studying the spectrum, we discuss the emergence of polaritons, and disentangle the contributions of resonant quantum vacuum effects from those of purely electrostatic interactions. Finally, we study the hybridization between magnetoplasmons and the cavity's electromagnetic modes., Comment: 22 pages

In this paper, we study involutive non-degenerate set-theoretic solutions of the Yang-Baxter equation with regular displacement group. In particular, we completely describe the blocks of imprimitivity and the congruences of the irretractable ones, that we show belonging to the class of the latin solutions. Among these solutions, we characterise the simple ones having nilpotent permutation group. A more precise description involving the First Weyl Algebra will be provided when the displacement group is abelian and normal in the total permutation group, and we enumerate and classify the simple ones having minimal size $p^p$, for an arbitrary prime number $p$. Finally, we illustrate our results by some examples., Comment: 22 pages, Theorems 44 and 48 from the first version have been removed and replaced by other results. Some typos have been corrected. The main results of the paper remain unchanged

Text Classification (TC) stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through the lens of computer science and engineering. The past decade has seen deep learning revolutionize TC, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature is rich with datasets, models, and evaluation criteria, with English being the predominant language of focus, despite studies involving Arabic, Chinese, Hindi, and others. The efficacy of TC models relies heavily on their ability to capture intricate textual relationships and nonlinear correlations, necessitating a comprehensive examination of the entire TC pipeline. This monograph provides an in-depth exploration of the TC pipeline, with a particular emphasis on evaluating the impact of each component on the overall performance of TC models. The pipeline includes state-of-the-art datasets, text preprocessing techniques, text representation methods, classification models, evaluation metrics, current results and future trends. Each chapter meticulously examines these stages, presenting technical innovations and significant recent findings. The work critically assesses various classification strategies, offering comparative analyses, examples, case studies, and experimental evaluations. These contributions extend beyond a typical survey, providing a detailed and insightful exploration of TC.

We study prospects to detect axion-like particles (ALPs) with the upcoming near-infrared telescope SPHEREx. The signal under investigation is the ALP decay into two photons. Assuming dark matter (DM) to be in the form of ALPs, we analyze the signal from the DM halos of dwarf spheroidal galaxies, the Large Magellanic Cloud and the Milky Way. We find that SPHEREx can significantly improve current limits on the axion-photon coupling in the 0.5-3 eV ALP mass range., Comment: 8 pages, 2 figures

Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more resource-intensive than calculating their ground state counterparts. We present a quantum machine learning model that predicts excited-state properties from the molecular ground state for different geometric configurations. The model comprises a symmetry-invariant quantum neural network and a conventional neural network and is able to provide accurate predictions with only a few training data points. The proposed procedure is fully NISQ compatible. This is achieved by using a quantum circuit that requires a number of parameters linearly proportional to the number of molecular orbitals, along with a parameterized measurement observable, thereby reducing the number of necessary measurements. We benchmark the algorithm on three different molecules by evaluating its performance in predicting excited state transition energies and transition dipole moments. We show that, in many instances, the procedure is able to outperform various classical models that rely solely on classical features., Comment: 10 + 4 pages, 7 + 3 figures

Cryogenic phonon detectors are adopted in experiments searching for dark matter interactions or coherent elastic neutrino-nucleus scattering, thanks to the low energy threshold they can achieve. The phonon-mediated sensing of particle interactions in passive silicon absorbers has been demonstrated with Kinetic Inductance Detectors (KIDs). Targets with neutron number larger than silicon, however, feature higher cross section to neutrinos while multi-target absorbers in dark matter experiments would provide a stronger evidence of a possible signal. In this work we present the design, fabrication and operation of KIDs coupled to a germanium absorber, achieving phonon-sensing performance comparable to silicon absorbers. The device introduced in this work is a proof of concept for a scalable neutrino detector and for a multi-target dark matter experiment., Comment: 3 Figures, 2 Tables, 5 pages

Microresonators are powerful tools to enhance the efficiency of second-order nonlinear optical processes, such as second-harmonic generation, which can coherently bridge octave-spaced spectral bands. However, dispersion constraints such as phase-matching and doubly resonant conditions have so far limited demonstrations to narrowband operation. In this work, we overcome these limitations showing ultrabroadband resonant frequency doubling in a novel integrated device, wherein the resonant enhancement of pump and second harmonic are individually addressed in two distinct and linearly uncoupled microring resonators, each adjusted to target the respective spectral band. The two microresonators are designed and tuned independently, yet share a common interaction region that grants nonlinear coupling over a quasi-phase-matching bandwidth exceeding 200 nm, enabled by the inscription of a photoinduced $\chi^{(2)}$ grating. The system allows to not only conveniently disentangle the design parameters of the two microresonators but also to reconfigure the doubly resonant condition electrically, and the phase-matching condition optically. We demonstrate milliwatt-level addressable second-harmonic generation over the entire telecom band and then configure the device to generate and upconvert a Kerr frequency comb with bandwidth exceeding 100 nm and upconverted power up to 10 mW., Comment: Main text: 10 pages, 4 figures; SI: 4 pages, 5 figures

We demonstrate a robust signal extraction method for X-ray speckle-based tensor tomography. We validate the effectiveness of the method for several carbon fiber composites, highlighting its potential for industrial applications. The proposed method can be adapted to various acquisition schemes and wavefront-marking optical elements, making it a versatile and robust tool for X-ray scattering tensor tomography., Comment: under review in Applied Physics Letters

In this work, we unveil a novel tool for generating Italian crossword puzzles from text, utilizing advanced language models such as GPT-4o, Mistral-7B-Instruct-v0.3, and Llama3-8b-Instruct. Crafted specifically for educational applications, this cutting-edge generator makes use of the comprehensive Italian-Clue-Instruct dataset, which comprises over 30,000 entries including diverse text, solutions, and types of clues. This carefully assembled dataset is designed to facilitate the creation of contextually relevant clues in various styles associated with specific texts and keywords. The study delves into four distinctive styles of crossword clues: those without format constraints, those formed as definite determiner phrases, copular sentences, and bare noun phrases. Each style introduces unique linguistic structures to diversify clue presentation. Given the lack of sophisticated educational tools tailored to the Italian language, this project seeks to enhance learning experiences and cognitive development through an engaging, interactive platform. By meshing state-of-the-art AI with contemporary educational strategies, our tool can dynamically generate crossword puzzles from Italian educational materials, thereby providing an enjoyable and interactive learning environment. This technological advancement not only redefines educational paradigms but also sets a new benchmark for interactive and cognitive language learning solutions., Comment: This paper has been accepted for presentation at CLiC.it 2024

Agent-Based Modelling (ABM) has emerged as an essential tool for simulating social networks, encompassing diverse phenomena such as information dissemination, influence dynamics, and community formation. However, manually configuring varied agent interactions and information flow dynamics poses challenges, often resulting in oversimplified models that lack real-world generalizability. Integrating modern Large Language Models (LLMs) with ABM presents a promising avenue to address these challenges and enhance simulation fidelity, leveraging LLMs' human-like capabilities in sensing, reasoning, and behavior. In this paper, we propose a novel framework utilizing LLM-empowered agents to simulate social network users based on their interests and personality traits. The framework allows for customizable agent interactions resembling various social network platforms, including mechanisms for content resharing and personalized recommendations. We validate our framework using a comprehensive Twitter dataset from the 2020 US election, demonstrating that LLM-agents accurately replicate real users' behaviors, including linguistic patterns and political inclinations. These agents form homogeneous ideological clusters and retain the main themes of their community. Notably, preference-based recommendations significantly influence agent behavior, promoting increased engagement, network homophily and the formation of echo chambers. Overall, our findings underscore the potential of LLM-agents in advancing social media simulations and unraveling intricate online dynamics., Comment: Accepted at ASONAM2024

The marked power spectrum - a two-point correlation function of a transformed density field - has emerged as a promising tool for extracting cosmological information from the large-scale structure of the Universe. In this work, we present the first comprehensive analytical study of the marked power spectrum's sensitivity to primordial non-Gaussianity (PNG) of the non-local type. We extend previous effective field theory frameworks to incorporate PNG, developing a complete theoretical model that we validate against the Quijote simulation suite. Through a systematic Fisher analysis, we compare the constraining power of the marked power spectrum against traditional approaches combining the power spectrum and bispectrum (P+B). We explore different choices of mark parameters to evaluate their impact on parameter constraints, particularly focusing on equilateral and orthogonal PNG as well as neutrino masses. Our analysis shows that while marking up underdense regions yields optimal constraints in the low shot-noise regime, the marked power spectrum's performance for discrete tracers with BOSS-like number densities does not surpass that of P+B analysis at mildly non-linear scales ($k \lesssim 0.25 \,h/\text{Mpc}$). However, the marked approach offers several practical advantages, including simpler estimation procedures and potentially more manageable systematic effects. Our theoretical framework reveals how the marked power spectrum incorporates higher-order correlation information through terms resembling tree-level bispectra and power spectrum convolutions. This work establishes a robust foundation for applying marked statistics to future large-volume surveys., Comment: 18 pages, 7 figures

Minority languages are vital to preserving cultural heritage, yet they face growing risks of extinction due to limited digital resources and the dominance of artificial intelligence models trained on high-resource languages. This white paper proposes a framework to generate linguistic tools for low-resource languages, focusing on data creation to support the development of language models that can aid in preservation efforts. Sardinian, an endangered language, serves as the case study to demonstrate the framework's effectiveness. By addressing the data scarcity that hinders intelligent applications for such languages, we contribute to promoting linguistic diversity and support ongoing efforts in language standardization and revitalization through modern technologies.

Diagnostic Neutral Beam Injectors (DNBI), through the combined use of Charge Exchange Recombination Spectroscopy (CHERS) and Motional Stark effect diagnostics (MSE), are a well-known tool to access important information about magnetically confined plasmas, such as radial profiles of ion temperature, ion flow, impurity content and intensity and direction of the magnetic field. For this purpose, a DNBI was installed and operated in the RFX-mod experiment, which was designed to confine plasma mainly through the Reversed Field Pinch configuration. The DNBI, designed and built by the Budker Institute of Plasma Physics, was based on a source of positive hydrogen ions, accelerated to 50 keV and for an equivalent neutral beam current of about 5 A at the source. The beam could be modulated and the maximum overall duration was 50 ms. With the upgrade of RFX-mod to the present RFX-mod2 machine, the DNBI is being renovated to solve several plant faults and improve the overall reliability of the system. The 50 kV power supply is being improved, as well as the power supplies in the high voltage deck and its insulation transformer. The control system, originally based on CAMAC technology, was redesigned to be fully replaced. This contribution reviews the technical criticalities emerged in the DNBI check-up and the new solutions adopted to make the DNBI operative and more reliable., Comment: 6 pages (excl. highlights), 8 figures. Contribution to the 33rd Symposium on Fusion Technology (SOFT), 22-27 September 2024. This is a preprint for the "Fusion Engineering and Design" journal

The performance of the information reconciliation phase is crucial for quantum key distribution (QKD). Reverse reconciliation (RR) is typically preferred over direct reconciliation (DR) because it yields higher secure key rates. However, a significant challenge in continuous-variable (CV) QKD with discrete modulations (such as QAM) is that Alice lacks soft information about the symbol decisions made by Bob. This limitation restricts error correction to hard-decoding methods, with low reconciliation efficiency. This work introduces a reverse reconciliation softening (RRS) procedure designed for CV-QKD scenarios employing discrete modulations. This procedure generates a soft metric that Bob can share with Alice over a public channel, enabling her to perform soft-decoding error correction without disclosing any information to a potential eavesdropper. After detailing the RRS procedure, we investigate how the mutual information between Alice's and Bob's variables changes when the additional metric is shared. We show numerically that RRS improves the mutual information with respect to RR with hard decoding, practically achieving the same mutual information as DR with soft decoding. Finally, we test the proposed RRS for PAM-4 signalling with a rate 1/2 binary LDPC code and bit-wise decoding through numerical simulations, obtaining more than 1dB SNR improvement compared to hard-decoding RR., Comment: This manuscript was submitted to the 14th International ITG Conference on Systems, Communications and Coding (SCC 2025), March 10-13, 2025, Karlsruhe Institute of Technology, Karlsruhe

The NEO Coordination Centre (NEOCC) of the European Space Agency is an operational centre that, among other activities, computes the orbits of near-Earth objects and their probabilities of impact with the Earth. The NEOCC started providing information about near-Earth objects in 2012 on a dedicated web portal, accessible at https://neo.ssa.esa.int/. Since the beginning of the operational phase, many developments and improvements have been implemented regarding the software, the data provided, and the portal. One of the most important upgrades is that the NEOCC is now independently providing data through a newly developed Orbit Determination and Impact Monitoring system, named Aegis. All the data computed by Aegis is publicly available on the NEOCC web portal, and Aegis is also used to maintain all the major services offered. The most important services comprise an orbital catalogue of all known asteroids, a list of possible future impacts with the Earth (also called Risk List), a list of forthcoming close approaches, a set of graphical toolkits, and an on-demand ephemerides service. Many of the services are also available through dedicated APIs, which can be used to automatically retrieve data. Here we give an overview of the algorithms implemented in the Aegis software, and provide a summary of the services offered by the NEOCC that are supported by Aegis., Comment: Accepted for publication on CMDA

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

We study concavity properties of positive solutions to the Logarithmic Schr\"odinger equation $-\Delta u=u\, \log u^2$ in a general convex domain with Dirichlet conditions. To this aim, we analyse the auxiliary Lane-Emden problems $-\Delta u = \sigma\, (u^q-u)$ and build, for any $\sigma>0$ and $q>1$, solutions $u_q$ such that $u_q^{(1-q)/2}$ is convex. By choosing $\sigma_q=2/(1-q)$ and letting $q \to 1^+$ we eventually construct a solution $u$ of the Logarithmic Schr\"odinger equation such that $\log u$ is concave. This seems one of the few attempts in studying concavity properties for superlinear, sign changing sources. To get the result, we both make inspections on the constant rank theorem and develop Liouville theorems on convex epigraphs, which might be useful in other frameworks.

Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accurately capture complex, partially observable, and stochastic dynamics. The proposed method employs a dual-autoregressive mechanism and self-supervised training to achieve reliable long-horizon predictions without relying on domain-specific inductive biases, ensuring adaptability across diverse robotic tasks. We further propose a policy optimization framework that leverages world models for efficient training in imagined environments and seamless deployment in real-world systems. Through extensive experiments, our approach consistently outperforms state-of-the-art methods, demonstrating superior autoregressive prediction accuracy, robustness to noise, and generalization across manipulation and locomotion tasks. Notably, policies trained with our method are successfully deployed on ANYmal D hardware in a zero-shot transfer, achieving robust performance with minimal sim-to-real performance loss. This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.

The fast fashion industry's insatiable demand for new styles and rapid production cycles has led to a significant environmental burden. Overproduction, excessive waste, and harmful chemicals have contributed to the negative environmental impact of the industry. To mitigate these issues, a paradigm shift that prioritizes sustainability and efficiency is urgently needed. Integrating learning-based predictive analytics into the fashion industry represents a significant opportunity to address environmental challenges and drive sustainable practices. By forecasting fashion trends and optimizing production, brands can reduce their ecological footprint while remaining competitive in a rapidly changing market. However, one of the key challenges in forecasting fashion sales is the dynamic nature of consumer preferences. Fashion is acyclical, with trends constantly evolving and resurfacing. In addition, cultural changes and unexpected events can disrupt established patterns. This problem is also known as New Fashion Products Performance Forecasting (NFPPF), and it has recently gained more and more interest in the global research landscape. Given its multidisciplinary nature, the field of NFPPF has been approached from many different angles. This comprehensive survey wishes to provide an up-to-date overview that focuses on learning-based NFPPF strategies. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature review. In particular, we propose the first taxonomy that covers the learning panorama for NFPPF, examining in detail the different methodologies used to increase the amount of multimodal information, as well as the state-of-the-art available datasets. Finally, we discuss the challenges and future directions., Comment: Accepted at the Springer Nature Computer Science journal

By combining ab initio downfolding with cluster dynamical mean-field theory, we study the degree of correlations in the low-temperature structures of the breathing-mode kagome van der Waals materials Nb$_3$(F,Cl,Br,I)$_8$. We show that the Coulomb correlation strength steadily increases from I to Br, Cl, and F, allowing us to identify Nb$_3$I$_8$ as a weakly correlated (obstructed atomic) insulator whose gap is only mildly affected by the local Coulomb interaction. Nb$_3$Br$_8$ and Nb$_3$Cl$_8$ are strongly correlated insulators, whose gaps are significantly influenced by Coulomb-induced vertex corrections. Nb$_3$F$_8$ is a prototypical bulk Mott-insulator whose gap is initially opened by strong correlation effects. Angle-resolved photoemission spectroscopy measurements comparing Nb$_3$Br$_8$ and Nb$_3$I$_8$ allow us to experimentally confirm these findings by revealing spectroscopic footprints of the degree of correlation. Our calculations further predict that the entire material family can be tuned into correlated charge-transfer or Mott-insulating phases upon electron or hole doping. Our magnetic property analysis additionally shows that inter-layer magnetic frustrations in the high-temperature phase drive the lattice phase transition to the low-temperature structures. The accompanying bilayer hybridization through inter-layer dimerization yields magnetic singlet ground states in the Cl, Br, and I compounds. Our findings establish Nb$_3$X$_8$ as a robust, versatile, and tunable class for van der Waals-based Coulomb and Mott engineering and allow us to speculate on the symmetry-breaking effects necessary for the recently observed Josephson diode effect in NbSe$_2$/Nb$_3$Br$_8$/NbSe$_2$ heterostructures., Comment: 23 pages, 18 figures

We present a theoretical framework allowing to make an explicit connection between the phenomenology of QCD, namely the properties of the gluon correlator and Wilson loops, and a particular relativistic model for the description of nuclear matter and neutron stars, the chiral confining model. Starting with the Field Correlator Method, which incorporates explicitly and simultaneously confinement and chiral symmetry breaking, we describe how to obtain the response of the composite nucleon to the nuclear scalar field, the relative role of confinement and chiral symmetry breaking in the in-medium nucleon mass evolution, generating the three-body forces needed for the saturation mechanism., Comment: 55 pages, 5 figures

This paper investigates the challenges of developing large language models (LLMs) proficient in both multilingual understanding and medical knowledge. We demonstrate that simply translating medical data does not guarantee strong performance on clinical tasks in the target language. Our experiments reveal that the optimal language mix in training data varies significantly across different medical tasks. We find that larger models with carefully calibrated language ratios achieve superior performance on native-language clinical tasks. Furthermore, our results suggest that relying solely on fine-tuning may not be the most effective approach for incorporating new language knowledge into LLMs. Instead, data and computationally intensive pretraining methods may still be necessary to achieve optimal performance in multilingual medical settings. These findings provide valuable guidance for building effective and inclusive medical AI systems for diverse linguistic communities.

Nonlinear resonances of plasma waves in field-effect transitors enable a well-known photodetection mechanism, first introduced by Dyakonov and Shur in the Nineties, especially suited to the Terahertz (THz) frequency range. Theoretical analyses of the mechanism always assume that the gate of the transistor, which is coupled to the antenna receiving the THz signal, is as long as the channel itself, at odds with typical experimental devices, where short gates are usually employed, e.g. due to fabrication constraints. In this work we overcome this limitation and provide a complete theory of Dyakonov--Shur photodetection in the presence of short gates. We develop our theory in such a general fashion that allows us to also treat the case in which multiple gates are present. We find that a sub-optimal positioning of the gates can substantially decrease the detection efficiency of the device and provide a compact analytical formula to quickly estimate the optimal gate position.

Reconfigurable photonic integrated circuits (PICs) can implement arbitrary operations and signal processing functionalities directly in the optical domain. Run-time configuration of these circuits requires an electronic control layer to adjust the working point of their building elements and make them compensate for thermal drifts or degradations of the input signal. As the advancement of photonic foundries enables the fabrication of chips of increasing complexity, developing scalable electronic controllers becomes crucial for the operation of complex PICs. In this paper, we present an electronic application-specific integrated circuit (ASIC) designed for reconfiguration of PICs featuring numerous tuneable elements. Each channel of the ASIC controller independently addresses one optical component of the PIC, as multiple parallel local feedback loops are operated to achieve full control. The proposed design is validated through real-time reconfiguration of a 16-channel silicon photonics adaptive beam coupler. Results demonstrate automatic coupling of an arbitrary input beam to a single-mode waveguide, dynamic compensation of beam wavefront distortions and successful transmission of a 50 Gbit/s signal through an optical free-space link. The low power consumption and compactness of the electronic chip provide a scalable paradigm that can be seamlessly extended to larger photonic architectures., Comment: Manuscript contains both main text and supplementary information

AI workloads, often hosted in multi-tenant cloud environments, require vast computational resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants lack infrastructure insights, while providers lack workload details to optimize tasks like partitioning, scheduling, and fault tolerance. We propose the HarmonAIze project to redefine cloud abstractions, enabling cooperative optimization for improved performance, efficiency, resiliency, and sustainability. This paper outlines key opportunities, challenges, and a research agenda to realize this vision.

Among the known isotope effects in chemistry, electron spin conversion by nuclear spin is a potent mechanism governing the reactions of radical pairs. For the electron transfer between nonradical(s), this spin conversion does not work, and other isotope effects have been presumed to have negligible contributions. However, we have observed a nuclear-state-dependence anomaly in ion charge state distributions in the thermalization of energetic atomic ions in helium gas, the process between nonradical(s). It could be understood to arise from the change in the stability of the intermediate quasi-molecule state of the electron transfer caused by the difference in nuclear states. This should prompt a reconsideration of the influence of atomic nuclei on interatomic and intermolecular interactions., Comment: 12 pages of the main part and 4 pages of supplementary, 5 figures and 3 tables

The fundamental question how to describe Ohmic resistance at the nanoscale has been answered by Landauer in his seminal picture of the so-called Landauer resistivity dipole. This picture has been theoretically well understood, however experimentally there are only few studies due to the need for a non-invasive local probe. Here we use the nanometer lateral resolution of near-field photocurrent imaging to thoroughly characterize a buried monolayer - bilayer graphene interface as an ideal one dimensional defect for the Landauer resistivity dipole. Via systematic tuning of the overall charge carrier density and the current flow we are able to detect the formation of Landauer resistivity dipoles due to charge carrier accumulation around the one dimensional defects. We found that, for Fermi energy values near the charge neutrality point (i.e. at low hole or electron doping), the photocurrent exhibits the same polarity as the applied source-drain voltage, which is consistent with changes in carrier concentration induced by the Landauer resistivity dipoles. This signature is no longer evident at higher charge carrier density in agreement with the performed numerical calculations. Photocurrent nanoscopy can thus serve as non-invasive technique to study local dissipation at hidden interfaces.

Enhancing underwater images with non-uniform illumination (NUI) is crucial for improving visibility and visual quality in marine environments, where image degradation is caused by significant absorption and scattering effects. However, traditional model-based methods are often ineffective at capturing the complex illumination variations present in such images, resulting in limited visual improvements. On the other hand, learning-based approaches have shown promising results but face challenges due to the lack of specific datasets designed to effectively address the non-uniform illumination problem. To overcome these challenges, the Underwater Non-uniform Illumination Restoration Network (UNIR-Net) is introduced, a novel method that integrates illumination enhancement and attention blocks, along with visual refinement and contrast correction modules. This approach is specifically designed to mitigate the scattering and absorption effects that cause light attenuation in underwater environments. Additionally, the Paired Underwater Non-uniform Illumination (PUNI) dataset is presented, a paired resource that facilitates the restoration of underwater images under non-uniform illumination conditions. Extensive experiments conducted on the PUNI dataset and the large-scale real-world Non-Uniform Illumination Dataset (NUID), which contains underwater images with non-uniform illumination, demonstrate the robust generalization ability of UNIR-Net. This method outperforms existing approaches in both quantitative metrics and qualitative evaluations. Furthermore, UNIR-Net not only significantly enhances the visual quality of images but also improves performance in advanced computer vision tasks, such as semantic segmentation in underwater environments, highlighting its broad applicability and potential impact. The code of this method is available at https://github.com/xingyumex/UNIR-Net, Comment: Currently being prepared for journal submission

Generative models are nowadays widely used to generate graphical content used for multiple purposes, e.g. web, art, advertisement. However, it has been shown that the images generated by these models could reinforce societal biases already existing in specific contexts. In this paper, we focus on understanding if this is the case when one generates images related to various software engineering tasks. In fact, the Software Engineering (SE) community is not immune from gender and ethnicity disparities, which could be amplified by the use of these models. Hence, if used without consciousness, artificially generated images could reinforce these biases in the SE domain. Specifically, we perform an extensive empirical evaluation of the gender and ethnicity bias exposed by three versions of the Stable Diffusion (SD) model (a very popular open-source text-to-image model) - SD 2, SD XL, and SD 3 - towards SE tasks. We obtain 6,720 images by feeding each model with two sets of prompts describing different software-related tasks: one set includes the Software Engineer keyword, and one set does not include any specification of the person performing the task. Next, we evaluate the gender and ethnicity disparities in the generated images. Results show how all models are significantly biased towards male figures when representing software engineers. On the contrary, while SD 2 and SD XL are strongly biased towards White figures, SD 3 is slightly more biased towards Asian figures. Nevertheless, all models significantly under-represent Black and Arab figures, regardless of the prompt style used. The results of our analysis highlight severe concerns about adopting those models to generate content for SE tasks and open the field for future research on bias mitigation in this context.

In ab initio nuclear structure theory, accurately predicting electromagnetic observables, such as moments and transition rates, is essential for a comprehensive understanding of nuclear properties. However, computational limitations and conceptual difficulties often hinder the precise calculation of these observables. In this work, we extend machine learning methods for model-space extrapolations to electric quadrupole moments. We further present a new machine learning approach that leverages the correlations between energies, radii, and electromagnetic observables. By learning these correlations from no-core shell model calculations in accessible model spaces, this new model enables the prediction of converged electromagnetic observables from predictions of converged energies and radii, which can be obtained with established machine learning extrapolation tools. An essential property of our approach is the capability for uncertainty quantification, allowing for reliable predictions with combined statistical error estimates for many-body and interaction uncertainties. Being solely built upon the physical correlations of different observables, it can be generalized across different ab initio methods. We demonstrate the power of this new extrapolation scheme through a precision study of electric quadrupole moments across a wide range of p-shell nuclei., Comment: 11 pages, 12 figures

The precision advantages offered by harnessing the quantum states of sensors can be readily compromised by noise. However, when the noise has a different spatial function than the signal of interest, recent theoretical work shows how the advantage can be maintained and even significantly improved. In this work we experimentally demonstrate the associated sensing protocol, using trapped-ion sensors. An entangled state of multi-dimensional sensors is created that isolates and optimally detects a signal, whilst being insensitive to otherwise overwhelming noise fields with different spatial profiles over the sensor locations. The quantum protocol is found to outperform a perfect implementation of the best comparable strategy without sensor entanglement. While our demonstration is carried out for magnetic and electromagnetic fields over a few microns, the technique is readily applicable over arbitrary distances and for arbitrary fields, thus present a promising application for emerging quantum sensor networks.

PAX (antiProtonic Atom X-ray spectroscopy) is a new experiment with the aim to test strong-field quantum electrodynamics (QED) effects by performing high-precision x-ray spectroscopy of antiprotonic atoms. By utilizing advanced microcalorimeter detection techniques and a low-energy antiproton beam provided by the ELENA ring at CERN, gaseous targets will be used for the creation of antiprotonic atoms, and the measurement of transitions between circular Rydberg states will be conducted with up to two orders of magnitude improved accuracy over previous studies using high-purity germanium detectors. Our approach eliminates the longstanding issue of nuclear uncertainties that have hindered prior studies using highly charged ions, thus enabling direct and purely QED-focused measurements. By precisely probing atomic systems with electric fields up to two orders of magnitude above the Schwinger limit, PAX will test vacuum polarization and second-order QED corrections, opening new frontiers in fundamental physics and uncovering potential pathways to physics beyond the Standard Model.

The real-world data of power networks is often inaccessible due to privacy and security concerns, highlighting the need for tools to generate realistic synthetic network data. Existing methods leverage geographic tools like OpenStreetMap with heuristic rules to model system topology and typically focus on single-phase, balanced systems, limiting their applicability to real-world distribution systems, which are usually unbalanced. This work proposes a Bayesian Hierarchical Model (BHM) to generate unbalanced three-phase distribution systems learning from existing networks. The scheme takes as input the base topology and aggregated demand per node and outputs a three-phase unbalanced system. The proposed scheme achieves a Mean Absolute Percentage Error (MAPE) of less than $8\%$ across all phases, with computation times of 20.4 seconds for model training and 3.1 seconds per sample generation. The tool is applied to learn from publicly available SMART-DS dataset and applied to generate European 906 and IEEE-123 systems. We demonstrate the transfer learning capability of the proposed tool by leveraging a model trained on an observed system to generate a synthetic network for an unobserved system. Specifically, the tool is trained using the publicly available SMART-DS dataset and subsequently applied to generate synthetic networks for the European 906-bus system and the IEEE 123-bus system. This tool allows researchers to simulate realistic unbalanced three-phase power data with high accuracy and speed, enhancing planning and operational analysis for modern power grids.

I give a brief overview of our current understanding of the internal partonic 3D structure of nucleons in momentum space. I discuss some recent extractions of transverse-momentum-dependent distributions for quarks, whose analyses in the unpolarized case are reaching a theoretical precision comparable to collinear parton distribution functions. On the contrary, gluon transverse-momentum-dependent distributions are poorly known from a phenomenological point of view. I briefly review their general properties and sketch a recent model calculation covering all (un)polarized combinations at leading twist., Comment: 11 pages, 5 multiple figures. Extended version of the proceedings of the conference "Diffraction and Low-x", 8-14 Sept. 2024, Hotel Tonnara Trabia, Palermo (Italy), to appear in Acta Physica Polonica B - Proc. Suppl

GN-Vision is a compact, dual-modality imaging device designed to simultaneously localize the spatial origin of $\gamma$-ray and slow neutron sources, with potential applications in nuclear safety, security, and hadron therapy. The system utilizes two position-sensitive detection planes, combining Compton imaging techniques for $\gamma$-ray visualization with passive collimation for imaging slow and thermal neutrons (energies below 100 eV). This paper presents the first experimental outcomes from the initial GN-Vision prototype, focused on the development of its neutron imaging capabilities. Following this experimental assessment, we explore the device$'$s performance potential and discuss several Monte Carlo simulation-based optimizations aimed at refining the neutron collimation system. These optimizations seek to improve real-time imaging efficiency and cost-effectiveness, enhancing GN-Vision$'$s applicability for future practical deployments., Comment: 13 pages, 12 figures, submitted to App. Rad. Isot

Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to improve productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development., Comment: Accepted at 2nd ACM International Conference on AI Foundation Models and Software Engineering (FORGE 2025)

We prove that, to every abstract group $G$, we can associate a sequence of graphs $\Gamma_n$ such that the automorphism group of $\Gamma_n$ is isomorphic to $G$ and the genus of $\Gamma_n$ is an unbounded function of $n$., Comment: 3 pages, comments are welcome!

Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we have developed GRAPPA - a hybrid graph neural network for predicting vapor pressures of pure components. GRAPPA enables the prediction of the vapor pressure curve of basically any organic molecule, requiring only the molecular structure as input. The new model consists of three parts: A graph attention network for the message passing step, a pooling function that captures long-range interactions, and a prediction head that yields the component-specific parameters of the Antoine equation, from which the vapor pressure can readily and consistently be calculated for any temperature. We have trained and evaluated GRAPPA on experimental vapor pressure data of almost 25,000 pure components. We found excellent prediction accuracy for unseen components, outperforming state-of-the-art group contribution methods and other machine learning approaches in applicability and accuracy. The trained model and its code are fully disclosed, and GRAPPA is directly applicable via the interactive website ml-prop.mv.rptu.de., Comment: 38 pages, 12 figures

Experiments are conducted over smooth and rough walls to explore the influence of pressure gradient histories on skin friction and mean flow of turbulent boundary layers. Different pressure gradient histories are imposed on the boundary layer through an aerofoil mounted in the freestream. Hot-wire measurements are taken at different freestream velocities downstream of the aerofoil where the flow has locally recovered to zero pressure gradient but retains the history effects. Direct skin friction measurements are also made using oil film interferometry for smooth walls and a floating element drag balance for rough walls. The friction Reynolds number, $Re_\tau$, varies between $3000$ and $27000$, depending both on the surface conditions and the freestream velocity ensuring sufficient scale separation. Results align with previous findings, showing that adverse pressure gradients just upstream of the measurement location increase wake strength and reduce the local skin friction while favourable pressure gradients suppress the wake and increase skin friction. The roughness length scale, $y_0$, remains constant across different pressure gradient histories for rough wall boundary layers. Inspired by previous works, a new correlation is proposed to infer skin friction based on the mean flow. The difference in skin friction between an arbitrary pressure gradient history and zero pressure gradient condition can be predicted using only the local wake strength parameter ($\Pi$), and the variations in wake strength for different histories are related to a weighted integral of the pressure gradient history normalised by local quantities. This allows us to develop a general correlation that can be used to infer skin friction for turbulent boundary layers experiencing arbitrary pressure-gradient histories.

Ultra-soft narrow line Seyfert 1 (US-NLSy) are a poorly observed class of active galactic nuclei characterized by significant flux changes and an extreme soft X-ray excess. This peculiar spectral shape represents a golden opportunity to test whether the standard framework commonly adopted for modelling local AGN is still valid. We thus present the results on the joint XMM-Newton and HST monitoring campaign of the highly accreting US-NLSy RBS 1332. The optical-to-UV spectrum of RBS 1332 exhibits evidence of both a stratified narrow-line region and an ionized outflow, that produces absorption troughs over a wide range of velocities (from ~1500 km s-1 to ~1700 km s-1) in several high-ionization transitions (Lyalpha, N V, C IV). From a spectroscopic point of view, the optical/UV/FUV/X-rays emission of this source is due to the superposition of three distinct components which are best modelled in the context of the two-coronae framework in which the radiation of RBS 1332 can be ascribed to a standard outer disk, a warm Comptonization region and a soft coronal continuum. The present dataset is not compatible with a pure relativistic reflection scenario. Finally, the adoption of the novel model reXcor allowed us to determine that the soft X-ray excess in RBS 1332 is dominated by the emission of the optically thick and warm Comptonizing medium, and only marginal contribution is expected from relativistic reflection from a lamppost-like corona., Comment: 13 pages, 9 figures, accepted in A&A

The present study investigates the gravity-driven settling dynamics of non-Brownian suspensions of spherical and cubic particles within a triply periodic domain. The effect of solid volume fraction on the evolving microstructure of a suspension is numerically examined by employing the Rigid MultiBlob method under Stokes flow conditions. Our simulations accurately reproduce macroscopic trends observed in experiments and show strong agreement with established semi-empirical correlations over a wide range of volume fractions. It is observed that the hydrodynamic interactions between particles and the surrounding fluid dominate the settling mechanism at low to moderate solid volume fractions, whereas, frequent collisions between particles in a highly packed space tend to suppress velocity fluctuations at denser regime. The transport properties for dilute suspensions are found to be primarily shaped by an anisotropic microstructure, though this anisotropy decreases as many-body interactions significantly grow at higher volume fractions. Notably, cubic particles exhibit lower anisotropy in velocity fluctuations compared to spheres, due to more effective momentum and energy transfer from the gravity-driven direction to transverse directions. Moreover, cubic particles also display higher velocity fluctuations and particle rebounding after collisions, which may create a transient tendency for cubes to move against gravity more readily than spheres. The distinctive behaviors of cubic and spherical particles can notably influence the efficiency of size segregation in bidisperse mixtures. Interestingly, local velocity fluctuation intensity increases for both particle types in mixed suspensions, affecting the local sedimentation velocity field. Overall, spherical particles are found to settle faster than cubes at low to moderate volume fractions., Comment: 29 pages, 11 figures, submitted to the Journal of Fluid Mechanics

In the context of SHARK-NIR (System for coronagraphy with High Order adaptive optics in Z and H band), we present the development of SHINS, the SHARK-NIR INstrument control Software, in particular focusing on the changes introduced during the Assembly, Integration, and Test (AIT) phase. SHARK-NIR observing sessions will be carried out with "ESO-style" Observation Blocks (OBs) based on so-called Templates scripts that will be prepared by observers. We decided to develop Templates also for the large number of AIT tests (flexures, coronagraphic mask alignment, scientific camera performances...). Here we present the adopted HTTP API for the OBs generation and a web-based frontend that implements it. Taking advantage of this approach, we decided to expose APIs also for individual device movement and monitoring, as well as for general status. These APIs are then used in the web-based instrument control and synoptic panels. During the recent AIT phase, a potential collision issue between two motorized components emerged. While we are exploring the possibility of a hardware interlock, we present a software solution developed at the Observation Software level, that is also available while using other software such as engineering panels. The system is based on three protection layers and it has been successfully tested., Comment: 7 pages, 2 figures

We extend the concept of natural orbitals as an optimized single-particle basis for ab initio nuclear many-body calculations to hypernuclei and show that their superior properties, in particular accelerated convergence and independence of the underlying harmonic-oscillator frequency, can be directly transferred to the hypernuclear regime as demonstrated in no-core shell model calculations for selected p-shell hypernuclei. Moreover, the radial single-particle wavefunctions associated with the natural-orbital basis yield important structural information with respect to the different particle species allowing us to identify a hyperon halo in {\Lambda}He5. We further explore nucleonic and hyperonic halo structures in A=6 and A=7 singly-strange hypernuclei based on one-body densities and point-particle radii obtained from no-core shell model calculations with realistic interactions from chiral effective field theory., Comment: 7 pages, 7 figures

Hybrid multiterminal Josephson junctions (JJs) are expected to harbor a novel class of Andreev bound states (ABSs), including topologically nontrivial states in four-terminal devices. In these systems, topological phases emerge when ABSs depend on at least three superconducting phase differences, resulting in a three-dimensional (3D) energy spectrum characterized by Weyl nodes at zero energy. Here, we realize a four-terminal JJ in a hybrid Al/InAs heterostructure, where ABSs form a synthetic 3D band structure. We probe the energy spectrum using tunneling spectroscopy and identify spectral features associated with the formation of a tri-Andreev molecule, a bound state whose energy depends on three superconducting phases and, therefore, is able to host topological ABSs. The experimental observations are well described by a numerical model. The calculations predict the appearance of four Weyl nodes at zero energy within a gap smaller than the experimental resolution. These topological states are theoretically predicted to remain stable within an extended region of the parameter space, well accessible by our device. These findings establish an experimental foundation to study high-dimensional synthetic band structures in multiterminal JJs, and to realize topological Andreev bands.

On a symplectic manifold $(M, \omega)$, a spacefilling brane structure is a closed 2-form $F$ which determines a complex structure, with respect to which $F +i\omega$ is holomorphic symplectic. For holomorphic symplectic compact K\"ahler 4-manifolds, we show that the moduli space of spacefilling branes is smooth, and determine its dimension. The proof relies on the local Torelli theorem for K3 surfaces and tori., Comment: 24 pages

Current evaluations of defenses against prompt attacks in large language model (LLM) applications often overlook two critical factors: the dynamic nature of adversarial behavior and the usability penalties imposed on legitimate users by restrictive defenses. We propose D-SEC (Dynamic Security Utility Threat Model), which explicitly separates attackers from legitimate users, models multi-step interactions, and rigorously expresses the security-utility in an optimizable form. We further address the shortcomings in existing evaluations by introducing Gandalf, a crowd-sourced, gamified red-teaming platform designed to generate realistic, adaptive attack datasets. Using Gandalf, we collect and release a dataset of 279k prompt attacks. Complemented by benign user data, our analysis reveals the interplay between security and utility, showing that defenses integrated in the LLM (e.g., system prompts) can degrade usability even without blocking requests. We demonstrate that restricted application domains, defense-in-depth, and adaptive defenses are effective strategies for building secure and useful LLM applications. Code is available at \href{https://github.com/lakeraai/dsec-gandalf}{\texttt{https://github.com/lakeraai/dsec-gandalf}}., Comment: Niklas Pfister, V\'aclav Volhejn and Manuel Knott contributed equally

Planetary nebulae are formed by the matter ejected by low-to-intermediate mass stars (~0.8-8 times the mass of the Sun) towards the end of their lives. As hydrogen and then helium fuel sources run out, stars expand. During these giant phases stars also lose sizable amounts of mass. During the second giant phase, after the exhaustion of core helium, the mass loss is so great that stars lose a large fraction of their mass (50 - 90%), leaving behind a small, hot core, known as a white dwarf, surrounded by a nebula. Planetary nebulae are the result of many processes that shape and alter their ionization structure and chemical composition. The resulting nebula, illuminated by the ultraviolet-rich spectrum of the remnant very hot stellar core, is a spectacle of beauty and science. In this chapter, we show that these objects are invaluable laboratories for astrophysics, astrochemistry, and astromineralogy studies, with impact in many areas of Astronomy., Comment: 37 pages, 25 figures; this is a pre-print of a chapter for the Encyclopedia of Astrophysics (edited by I. Mandel, section editor F.R.N. Schneider) to be published by Elsevier as a Reference Module

Human pose estimation, a vital task in computer vision, involves detecting and localising human joints in images and videos. While single-frame pose estimation has seen significant progress, it often fails to capture the temporal dynamics for understanding complex, continuous movements. We propose Poseidon, a novel multi-frame pose estimation architecture that extends the ViTPose model by integrating temporal information for enhanced accuracy and robustness to address these limitations. Poseidon introduces key innovations: (1) an Adaptive Frame Weighting (AFW) mechanism that dynamically prioritises frames based on their relevance, ensuring that the model focuses on the most informative data; (2) a Multi-Scale Feature Fusion (MSFF) module that aggregates features from different backbone layers to capture both fine-grained details and high-level semantics; and (3) a Cross-Attention module for effective information exchange between central and contextual frames, enhancing the model's temporal coherence. The proposed architecture improves performance in complex video scenarios and offers scalability and computational efficiency suitable for real-world applications. Our approach achieves state-of-the-art performance on the PoseTrack21 and PoseTrack18 datasets, achieving mAP scores of 88.3 and 87.8, respectively, outperforming existing methods.