Search

We present a lattice calculation of the Hadronic Vacuum Polarization (HVP) contribution of the strange and charm quarks to the anomalous magnetic moment of the muon in isospin symmetric QCD. We employ the gauge configurations generated by the Extended Twisted Mass Collaboration (ETMC) with $N_f = 2 + 1 + 1$ flavors of Wilson-clover twisted-mass quarks at five lattice spacings and at values of the quark mass parameters that are close and/or include the isospin symmetric QCD point of interest. After computing the small corrections necessary to precisely match this point, and carrying out an extrapolation to the continuum limit based on the data at lattice spacings $a \simeq 0.049, 0.057, 0.068, 0.080$~fm and spatial lattice sizes up to $L \simeq 7.6$~fm, we obtain $a_\mu^{\rm HVP}(s) = (53.57 \pm 0.63) \times 10^{-10}$ and $a_\mu^{\rm HVP}(c) = (14.56 \pm 0.13) \times 10^{-10}$, for the quark-connected strange and charm contributions, respectively. Our findings agree well with the corresponding results by other lattice groups., Comment: 33 pages, 18 figures, 3 tables

Convolutional Neural Networks (CNNs) have seen significant performance improvements in recent years. However, due to their size and complexity, they function as black-boxes, leading to transparency concerns. State-of-the-art saliency methods generate local explanations that highlight the area in the input image where a class is identified but cannot explain how a concept of interest contributes to the prediction, which is essential for bias mitigation. On the other hand, concept-based methods, such as TCAV (Testing with Concept Activation Vectors), provide insights into how sensitive is the network to a concept, but cannot compute its attribution in a specific prediction nor show its location within the input image. This paper introduces a novel post-hoc explainability framework, Visual-TCAV, which aims to bridge the gap between these methods by providing both local and global explanations for CNN-based image classification. Visual-TCAV uses Concept Activation Vectors (CAVs) to generate saliency maps that show where concepts are recognized by the network. Moreover, it can estimate the attribution of these concepts to the output of any class using a generalization of Integrated Gradients. This framework is evaluated on popular CNN architectures, with its validity further confirmed via experiments where ground truth for explanations is known, and a comparison with TCAV. Our code will be made available soon., Comment: Preprint currently under review

Single Particle Imaging techniques at X-ray lasers have made significant strides, yet the challenge of determining the orientation of freely rotating molecules during delivery remains. In this study, we propose a novel method to partially retrieve the relative orientation of proteins exposed to ultrafast X-ray pulses by analyzing the fragmentation patterns resulting from Coulomb explosions. We simulate these explosions for 45 proteins in the size range 100 -- 4000 atoms using a hybrid Monte Carlo/Molecular Dynamics approach and capture the resulting ion ejection patterns with virtual detectors. Our goal is to exploit information from the explosion to infer orientations of proteins at the time of X-ray exposure. Our results demonstrate that partial orientation information can be extracted, particularly for larger proteins. Our findings can be integrated into existing reconstruction algorithms such as Expand-Maximize-Compress, to improve their efficiency and reduce the need for high-quality diffraction patterns. This method offers a promising avenue for enhancing Single Particle Imaging by leveraging measurable data from the Coulomb explosion to provide valuable insights about orientation., Comment: 11 pages, 4 figures, one column double spacing

We simulated the Coulomb explosion dynamics due to the fast ionization induced by high-intensity X-rays in six proteins that share similar atomic content and shape. We followed and projected the trajectory of the fragments onto a virtual detector, providing a unique explosion footprint. After collecting 500 explosion footprints for each protein, we utilized principal component analysis and t-distributed stochastic neighbor embedding to classify these. The results show that the classification algorithms were able to separate proteins on the basis of explosion footprints from structurally similar proteins into distinct groups. The explosion footprints, therefore, provide a unique identifier for each of the proteins. We envision that method could be used concurrently with single particle coherent imaging experiments to provide additional information on shape, mass, or conformation., Comment: 6 pages. 4 figures

In the past decade, photo-induced superconducting-like behaviors have been reported in a number of materials driven by intense pump fields. Of particular interest is the high-$T_c$ cuprate $\rm Y Ba_2 Cu_2 O_{6+x}$, where such effect has been reported up to the so-called pseudogap temperature $T^* \sim 300-400$ K. In a recent tour-de-force experiment, a transient magnetic field which is proportional to and in the same direction of an applied field has been observed outside the sample, suggestive of flux exclusion due to the Meissner effect. In this paper, we point out that the transient magnetic field could be explained by a model of bilayers of copper-oxygen planes with a local superconducting phase variable persisting up to the pseudo-gap temperature at equilibrium. Under pumping, the time evolution is described by a driven sine-Gordon equation. In the presence of an external magnetic field, this model exhibits a novel instability which amplifies the current at the edges of the bilayer formed by defects or grain boundaries, producing a giant paramagnetic magnetization in the same direction as the applied field. We present how this scenario can fit most of the available data and propose additional experimental tests which can distinguish our proposal from the Meissner flux exclusion scenario. To the extent that this model can account for the data, we conclude that the experiments have the important consequence of revealing the presence of local pairing in the pseudogap phase. More broadly, this work provides a new mechanism for amplifying external magnetic fields at ultra-fast time scales., Comment: 11 pages, 5 figures + Supplementary

Ontologies often lack explicit disjointness declarations between classes, despite their usefulness for sophisticated reasoning and consistency checking in Knowledge Graphs. In this study, we explore the potential of Large Language Models (LLMs) to enrich ontologies by identifying and asserting class disjointness axioms. Our approach aims at leveraging the implicit knowledge embedded in LLMs, using prompt engineering to elicit this knowledge for classifying ontological disjointness. We validate our methodology on the DBpedia ontology, focusing on open-source LLMs. Our findings suggest that LLMs, when guided by effective prompt strategies, can reliably identify disjoint class relationships, thus streamlining the process of ontology completion without extensive manual input. For comprehensive disjointness enrichment, we propose a process that takes logical relationships between disjointness and subclass statements into account in order to maintain satisfiability and reduce the number of calls to the LLM. This work provides a foundation for future applications of LLMs in automated ontology enhancement and offers insights into optimizing LLM performance through strategic prompt design. Our code is publicly available on GitHub at https://github.com/n28div/llm-disjointness., Comment: Accepted at KBC-LM'24 workshop at ISWC 2024

Aerospace manufacturing companies, such as Thales Alenia Space, design, develop, integrate, verify, and validate products characterized by high complexity and low volume. They carefully document all phases for each product but analyses across products are challenging due to the heterogeneity and unstructured nature of the data in documents. In this paper, we propose a hybrid methodology that leverages Knowledge Graphs (KGs) in conjunction with Large Language Models (LLMs) to extract and validate data contained in these documents. We consider a case study focused on test data related to electronic boards for satellites. To do so, we extend the Semantic Sensor Network ontology. We store the metadata of the reports in a KG, while the actual test results are stored in parquet accessible via a Virtual Knowledge Graph. The validation process is managed using an LLM-based approach. We also conduct a benchmarking study to evaluate the performance of state-of-the-art LLMs in executing this task. Finally, we analyze the costs and benefits of automating preexisting processes of manual data extraction and validation for subsequent cross-report analyses., Comment: Paper Accepted at ISWC 2024 In-Use Track

The DA{\Phi}NE operations during the last year have been devoted to deliver a statistically significant data sample to perform the first-ever measurement of kaonic deuterium X-ray transitions to the fundamental level. Operations for the SIDDHARTA-2 detector using a deuterium gas target started officially on the second half of May 2023, and have been organized in several runs here described., Comment: 7 pages, 3 figures, prepared for the INFN Laboratori Nazionali di Frascati 2023 activity report

While neutrinos are often treated as a background for many dark matter experiments, these particles offer a new avenue for physics: the detection of core-collapse supernovae. Supernovae are extremely energetic, violent and complex events that mark the death of massive stars. During their collapse stars emit a large number of neutrinos in a short burst. These neutrinos carry 99\% of the emitted energy which makes their detection fundamental in understanding supernovae. This paper illustrates how COSINUS (Cryogenic Observatory for SIgnatures seen in Next-generation Underground Searches), a sodium iodide (NaI) based dark matter search, will be sensitive to the next galactic core-collapse supernova. The experiment is composed of two separate detectors which will be sensitive to far and nearby supernovae. The inner core of the experiment will consist of NaI crystals operating as scintillating calorimeters, mainly sensitive to the Coherent Elastic Scattering of Neutrinos (CE$\nu$NS) against the Na and I nuclei. The low mass of the cryogenic detectors gives the experiment a sensitivity to close supernovae below 1kpc without pileup. They will see up to hundreds of CE$\nu$NS events from a supernova happening at 200pc. The crystals reside at the center of a cylindrical 230T water tank, instrumented with 30 photomultipliers. This tank acts as a passive and active shield able to detect the Cherenkov radiation induced by impinging charged particles from ambient and cosmogenic radioactivity. A supernova near the Milky Way Center (10kpc) will be easily detected inducing $\sim$60 measurable events, and the water tank will have a 3$\sigma$ sensitivity to supernovae up to 22kpc, seeing $\sim$10 events. This paper shows how, even without dedicated optimization, modern dark matter experiments will also play their part in the multi-messenger effort to detect the next galactic core-collapse supernova.

The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionization. However, foreground contamination poses challenges for detecting this signal, and image recovery will heavily rely on effective mitigation methods. We introduce \texttt{SERENEt}, a deep-learning framework designed to recover the 21-cm signal from SKA-Low's foreground-contaminated observations, enabling the detection of ionized (HII) and neutral (HI) regions during reionization. \texttt{SERENEt} can recover the signal distribution with an average accuracy of 75 per cent at the early stages ($\overline{x}_\mathrm{HI}\simeq0.9$) and up to 90 per cent at the late stages of reionization ($\overline{x}_\mathrm{HI}\simeq0.1$). Conversely, HI region detection starts at 92 per cent accuracy, decreasing to 73 per cent as reionization progresses. Beyond improving image recovery, \texttt{SERENEt} provides cylindrical power spectra with an average accuracy exceeding 93 per cent throughout the reionization period. We tested \texttt{SERENEt} on a 10-degree field-of-view simulation, consistently achieving better and more stable results when prior maps were provided. Notably, including prior information about HII region locations improved 21-cm signal recovery by approximately 10 per cent. This capability was demonstrated by supplying \texttt{SERENEt} with ionizing source distribution measurements, showing that high-redshift galaxy surveys of similar observation fields can optimize foreground mitigation and enhance 21-cm image construction., Comment: 16 pages, 14 figures, 1 table

Polarization mode dispersion (PMD) in optical fibers poses a major challenge for maintaining the fidelity of quantum states for quantum communications. In this work, a comprehensive model linking the probability of quantum measurement errors (infidelity) to PMD is developed and validated by experimental measurements of differential group delay and quantum bit error rate (QBER). Our research proposes effective methods to mitigate PMD effects for broadband entangled photons and evaluates the impact of higher-order PMD effects. The model provides an experimentally verified framework for the optimization of commercial quantum key distribution systems in deployed fiber optic lines., Comment: 11 pages, 8 figures, research paper

Alphabetic codes and binary search trees are combinatorial structures that abstract search procedures in ordered sets endowed with probability distributions. In this paper, we design new linear-time algorithms to construct alphabetic codes, and we show that the obtained codes are not too far from being optimal. Moreover, we exploit our results on alphabetic codes to provide new bounds on the average cost of optimal binary search trees. Our results improve on the best-known bounds on the average cost of optimal binary search trees present in the literature., Comment: Accepted by IEEE Transaction on Information Theory

A set of $2^n$ candidates is presented to a commission. At every round, each member of this commission votes by pairwise comparison, and one-half of the candidates is deleted from the tournament, the remaining ones proceeding to the next round until the $n$-th round (the final one) in which the final winner is declared. The candidates are arranged on a board in a given order, which is maintained among the remaining candidates at all rounds. A study of the size of the commission is carried out in order to obtain the desired result of any candidate being a possible winner. For $2^n$ candidates with $n \geq 3$, we identify a voting profile with $4n -3$ voters such that any candidate could win just by choosing a proper initial order of the candidates. Moreover, in the setting of a random number of voters, we obtain the same results, with high probability, when the expected number of voters is large., Comment: 14 pages

Despite their success, Large-Language Models (LLMs) still face criticism as their lack of interpretability limits their controllability and reliability. Traditional post-hoc interpretation methods, based on attention and gradient-based analysis, offer limited insight into the model's decision-making processes. In the image field, Concept-based models have emerged as explainable-by-design architectures, employing human-interpretable features as intermediate representations. However, these methods have not been yet adapted to textual data, mainly because they require expensive concept annotations, which are impractical for real-world text data. This paper addresses this challenge by proposing a self-supervised Interpretable Concept Embedding Models (ICEMs). We leverage the generalization abilities of LLMs to predict the concepts labels in a self-supervised way, while we deliver the final predictions with an interpretable function. The results of our experiments show that ICEMs can be trained in a self-supervised way achieving similar performance to fully supervised concept-based models and end-to-end black-box ones. Additionally, we show that our models are (i) interpretable, offering meaningful logical explanations for their predictions; (ii) interactable, allowing humans to modify intermediate predictions through concept interventions; and (iii) controllable, guiding the LLMs' decoding process to follow a required decision-making path.

Quantum key distribution (QKD) via satellite links is the only currently viable solution to create quantum-backed secure communication at a global scale. To achieve intercontinental coverage with available technology one must adopt a "flying trusted node" paradigm, in which users fully trust the satellite platform. Here, inspired by the concept of distributed secret sharing and the imminent projected launch of several QKD-equipped satellites, we propose a parallel trusted node approach, in which key distribution is mediated by several satellites in parallel. This has the effect of distributing the trust, removing single points of failure and reducing the necessary assumptions. In addition, we discuss the versatility that an optical ground station should provide to execute such a protocol and, in general, to be fully integrated into a multi-party global quantum network., Comment: 6 pages, 2 figures

Several combinatorial optimization problems can be solved with NISQ devices once that a corresponding quadratic unconstrained binary optimization (QUBO) form is derived. The aim of this work is to drastically reduce the variables needed for these QUBO reformulations in order to unlock the possibility to efficiently obtain optimal solutions for a class of optimization problems with NISQ devices. This is achieved by introducing novel tools that allow an efficient use of slack variables, even for problems with non-linear constraints, without the need to approximate the starting problem. We divide our new techniques in two independent parts, called the iterative quadratic polynomial and the master-satellite methods. Hence, we show how to apply our techniques in case of an NP-hard optimization problem inspired by a real-world financial scenario called Max-Profit Balance Settlement. We follow by submitting several instances of this problem to two D-wave quantum annealers, comparing the performances of our novel approach with the standard methods used in these scenarios. Moreover, this study allows to appreciate several performance differences between the D-wave Advantage and Advantage2 quantum annealers., Comment: Minor changes

Observing and understanding the origin of the very-high-energy (VHE) spectral component in gamma-ray bursts (GRBs) has been challenging because of the lack of sensitivity in MeV-GeV observations, so far. The majestic GRB 221009A, known as the brightest of all times (BOAT), offers a unique opportunity to identify spectral components during the prompt and early afterglow phases and probe their origin. Analyzing simultaneous observations spanning from keV to TeV energies, we identified two distinct spectral components during the initial 20 minutes of the burst. The second spectral component peaks between $10-300$ GeV, and the bolometric fluence (10 MeV-10 TeV) is estimated to be greater than 2$\times10^{-3}$ erg/ cm$^{2}$. Performing broad-band spectral modeling, we provide constraints on the magnetic field and the energies of electrons accelerated in the external relativistic shock. We interpret the VHE component as an afterglow emission that is affected by luminous prompt MeV radiation at early times., Comment: Comments and suggestions are welcome

Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors. State-of-the-art explainability methods generate saliency maps to show where a specific class is identified, without providing a detailed explanation of the model's decision process. Striving to address such a need, we introduce a post-hoc method that explains the entire feature extraction process of a Convolutional Neural Network. These explanations include a layer-wise representation of the features the model extracts from the input. Such features are represented as saliency maps generated by clustering and merging similar feature maps, to which we associate a weight derived by generalizing Grad-CAM for the proposed methodology. To further enhance these explanations, we include a set of textual labels collected through a gamified crowdsourcing activity and processed using NLP techniques and Sentence-BERT. Finally, we show an approach to generate global explanations by aggregating labels across multiple images., Comment: International Joint Conference on Artificial Intelligence 2024 (to be published)

In the recent work "Non-reciprocal topological solitons in active metamaterials" (see arXiv:2312.03544v1), for an analytical understanding of the system under consideration, the authors derive an ordinary differential equation for the sine-Gordon (anti)soliton velocity, with the perturbation theory in the adiabatic approximation, via the inverse scattering transform formalism, see Eq. (3) in their work. Here we note that the latter equation for the (anti)soliton velocity also follows from an energy balance approach., Comment: 1 page; comment on arXiv:2312.03544

We present a lattice determination of the inclusive decay rate of the process $\tau\mapsto X_{us} \nu_\tau$ in which the $\tau$ lepton decays into a generic hadronic state $X_{us}$ with $\bar u s$ flavour quantum numbers. Our results have been obtained in $n_f=2+1+1$ iso-symmetric QCD with full non-perturbative accuracy, without any OPE approximation and, except for the presently missing long-distance isospin-breaking corrections, include a solid estimate of all sources of theoretical uncertainties. This has been possible by using the Hansen-Lupo-Tantalo method [1] that we have already successfully applied in [2] to compute the inclusive decay rate of the process $\tau\mapsto X_{ud} \nu_\tau$ in the $\bar u d$ flavour channel. By combining our first-principles theoretical results with the presently-available experimental data we extract the CKM matrix element $\vert V_{us}\vert$, the Cabibbo angle, with a $0.9$\% accuracy, dominated by the experimental error., Comment: 9 pages, 5 figures, 1 table. Version accepted by PRL, expanded technical discussion moved to an appendix, results unchanged

In many applied sciences a popular analysis strategy for high-dimensional data is to fit many multivariate generalized linear models in parallel. This paper presents a novel approach to address the resulting multiple testing problem by combining a recently developed sign-flip test with permutation-based multiple-testing procedures. Our method builds upon the univariate standardized flip-scores test which offers robustness against misspecified variances in generalized linear models, a crucial feature in high-dimensional settings where comprehensive model validation is particularly challenging. We extend this approach to the multivariate setting, enabling adaptation to unknown response correlation structures. This approach yields relevant power improvements over conventional multiple testing methods when correlation is present.

A central challenge in quantum networking is transferring quantum states between different physical modalities, such as between flying photonic qubits and stationary quantum memories. One implementation entails using spin-photon interfaces that combine solid-state spin qubits, such as color centers in diamond, with photonic nanostructures. However, while high-fidelity spin-photon interactions have been demonstrated on isolated devices, building practical quantum repeaters requires scaling to large numbers of interfaces yet to be realized. Here, we demonstrate integration of nanophotonic cavities containing tin-vacancy (SnV) centers in a photonic integrated circuit (PIC). Out of a six-channel quantum micro-chiplet (QMC), we find four coupled SnV-cavity devices with an average Purcell factor of ~7. Based on system analyses and numerical simulations, we find with near-term improvements this multiplexed architecture can enable high-fidelity quantum state transfer, paving the way towards building large-scale quantum repeaters., Comment: to be published in Optica Quantum

Quantum-chemical subsystem and embedding methods require complex workflows that may involve multiple quantum-chemical program packages. Moreover, such workflows require the exchange of voluminous data that goes beyond simple quantities such as molecular structures and energies. Here, we describe our approach for addressing this interoperability challenge by exchanging electron densities and embedding potentials as grid-based data. We describe the approach that we have implemented to this end in a dedicated code, PyEmbed, currently part of a Python scripting framework. We discuss how it has facilitated the development of quantum-chemical subsystem and embedding methods, and highlight several applications that have been enabled by PyEmbed, including WFT-in-DFT embedding schemes mixing non-relativistic and relativistic electronic structure methods, real-time time-dependent DFT-in-DFT approaches, the density-based many-body expansion, and workflows including real-space data analysis and visualization. Our approach demonstrates in particular the merits of exchanging (complex) grid-based data, and in general the potential of modular software development in quantum chemistry, which hinges upon libraries that facilitate interoperability.

In this work we introduce a novel subsystem-based electronic structure embedding method that combines the projection-based block-orthogonalized Manby-Miller embedding (BOMME) with the density-based Frozen Density Embedding (FDE) methods. Our approach is effective for systems in which the building blocks interact at varying strengths while still maintaining a lower computational cost compared to a quantum simulation of the entire system. To evaluate the performance of our method, we assess its ability to reproduce the X-ray absorption spectra (XAS) of chloride and fluoride anions in aqueous solutions (based on a 50-water droplet model) via real-time time-dependent density functional theory (rt-TDDFT) calculations. We employ an ensemble approach to compute XAS for the K- and L-edges, utilizing multiple snapshots of configuration space obtained from classical molecular dynamics simulations with a polarizable force field. Configurational averaging influences both the broadening of spectral features and their intensities, with contributions to the final intensities originating from different geometry configurations. We found that embedding models that are too approximate for halide-water specific interactions, as in the case of FDE, fail to reproduce the experimental spectrum for chloride. Meanwhile, BOMME tends to overestimate intensities, particularly for higher energy features because of finite-size effects. Combining FDE for the second solvation shell and retaining BOMME for the first solvation shell mitigates this effect, resulting in an overall improved agreement within the energy range of the experimental spectrum. Additionally, we compute the transition densities of the relevant transitions, confirming that these transitions occur within the halide systems. Thus, our real-time QM/QM/QM embedding method proves to be a promising approach for modeling XAS of solvated systems.

Efforts in the fight against Climate Change are increasingly oriented towards new energy efficiency strategies in Smart Grids (SGs). In 2018, with proper legislation, the European Union (EU) defined the Renewable Energy Community (REC) as a local electrical grid whose participants share their self-produced renewable energy, aiming at reducing bill costs by taking advantage of proper incentives. That action aspires to accelerate the spread of local renewable energy exploitation, whose costs could not be within everyone's reach. Since a REC is technically an SG, the strategies above can be applied, and specifically, practical Energy Management Systems (EMSs) are required. Therefore, in this work, an online Hierarchical EMS (HEMS) is synthesized for REC cost minimization to evaluate its superiority over a local self-consumption approach. EU technical indications (as inherited from Italy) are diligently followed, aiming for results that are as realistic as possible. Power flows between REC nodes, or Microgrids (MGs) are optimized by taking Energy Storage Systems (ESSs) and PV plant costs, energy purchase costs, and REC incentives. A hybrid Fuzzy Inference System - Genetic Algorithm (FIS-GA) model is implemented with the GA encoding the FIS parameters. Power generation and consumption, which are the overall system input, are predicted by a LSTM trained on historical data. The proposed hierarchical model achieves good precision in short computation times and outperforms the self-consumption approach, leading to about 20% savings compared to the latter. In addition, the Explainable AI (XAI), which characterizes the model through the FIS, makes results more reliable thanks to an excellent human interpretation level. To finish, the HEMS is parametrized so that it is straightforward to switch to another Country's technical legislation framework., Comment: 26 pages, 18 figures

We describe a method to compute photon-matter interaction and atomic dynamics with X-ray lasers using a hybrid code based on classical molecular dynamics and collisional-radiative calculations. The forces between the atoms are dynamically computed based on changes to their electronic occupations and the free electron cloud created due to the irradiation of photons in the X-ray spectrum. The rapid transition from neutral solid matter to dense plasma phase allows the use of screened potentials, which reduces the number of non-bonded interactions required to compute. In combination with parallelisation through domain decomposition, large-scale molecular dynamics and ionisation induced by X-ray lasers can be followed. This method is applicable for large enough samples (solids, liquids, proteins, viruses, atomic clusters and crystals) that when exposed to an X-ray laser pulse turn into a plasma in the first few femtoseconds of the interaction. We show several examples of the applicability of the method and we quantify the sizes that the method is suitable for. For large systems, we investigate non-thermal heating and scattering of bulk water, which we compare to previous experiments. We simulate molecular dynamics of a protein crystal induced by an X-ray pump, X-ray probe scheme, and find good agreement of the damage dynamics with experiments. For single particle imaging, we simulate ultrafast dynamics of a methane cluster exposed to a femtosecond X-ray laser. In the context of coherent diffractive imaging we study the fragmentation as given by an X-ray pump X-ray probe setup to understand the evolution of radiation damage., Comment: 12 pages, 11 figures, 50 references