Search

Interdependent directed networks are essential in understanding the dynamics of real-world systems such as power grids and communication networks. A minor malfunction in such systems can propagate and lead to catastrophic consequences in a process known as cascading failures, which highlights the need for effective recovery strategies that help to mitigate or avoid eventual damages. In this work, we analyze the impact of a recovery strategy on cascading failures in two interdependent directed networks, where a fraction $q$ of nodes have single dependencies. After a random removal of a fraction $1 - p$ of nodes, we repair nodes in the contour of each giant strongly connected component, with probability $\gamma$. We find that the sustained application of the strategy leads to an abrupt transition between full collapse and complete recovery. Phase diagrams in the $(p, \gamma)$ plane reveal three regions: one where the system collapses despite intervention, another where the strategy ensures recovery, and a third where no intervention is necessary to avoid collapse. For obtaining these results, we develop an analytical framework using node percolation and generating functions that aligns well with simulation results., Comment: 21 pages, 6 figures

Digitalization appears as a lever to enhance agriculture sustainability. However, existing works on digital agriculture's own sustainability remain scarce, disregarding the environmental effects of deploying digital devices on a large-scale. We propose a bottom-up method to estimate the carbon footprint of digital agriculture scenarios considering deployment of devices over a diversity of farm sizes. It is applied to two use-cases and demonstrates that digital agriculture encompasses a diversity of devices with heterogeneous carbon footprints and that more complex devices yield higher footprints not always compensated by better performances or scaling gains. By emphasizing the necessity of considering the multiplicity of devices, and the territorial distribution of farm sizes when modelling digital agriculture deployments, this study highlights the need for further exploration of the first-order effects of digital technologies in agriculture., Comment: Journal of Industrial Ecology, In press, 10.1111/jiec.13568

In this paper, we tackle the problem of reconstructing earlier tumour configurations starting from a single spatial measurement at a later time. We describe the tumour evolution through a diffuse interface model coupling a Cahn-Hilliard-type equation for the tumour phase field to a reaction-diffusion equation for a key nutrient proportion, also accounting for chemotaxis effects. We stress that the ability to reconstruct earlier tumour states is crucial for calibrating the model used to predict the tumour dynamics and also to identify the areas where the tumour initially began to develop. However, backward-in-time inverse problems are well-known to be severely ill-posed, even for linear parabolic equations. Moreover, we also face additional challenges due to the complexity of a non-linear fourth-order parabolic system. Nonetheless, we can establish uniqueness by using logarithmic convexity methods under suitable a priori assumptions. To further address the ill-posedness of the inverse problem, we propose a Tikhonov regularisation approach that approximates the solution through a family of constrained minimisation problems. For such problems, we analytically derive the first-order necessary optimality conditions. Finally, we develop a computationally efficient numerical approximation of the optimisation problems by employing standard $C^0$-conforming first-order finite elements. We conduct numerical experiments on several pertinent test cases and observe that the proposed algorithm consistently meets expectations, delivering accurate reconstructions of the original ground truth., Comment: 54 pages

The development of mathematical models of cancer informed by time-resolved measurements has enabled personalised predictions of tumour growth and treatment response. However, frequent cancer monitoring is rare, and many tumours are treated soon after diagnosis with limited data. To improve the predictive capabilities of cancer models, we investigate the problem of recovering earlier tumour states from a single spatial measurement at a later time. Focusing on prostate cancer, we describe tumour dynamics using a phase-field model coupled with two reaction-diffusion equations for a nutrient and the local prostate-specific antigen. We generate synthetic data using a discretisation based on Isogeometric Analysis. Then, building on our previous analytical work (Beretta et al., SIAP (2024)), we propose an iterative reconstruction algorithm based on the Landweber scheme, showing local convergence with quantitative rates and exploring an adaptive step size that leads to faster reconstruction algorithms. Finally, we run simulations demonstrating high-quality reconstructions even with long time horizons and noisy data., Comment: 37 pages

The ringdown phase following a binary black hole coalescence is a powerful tool for measuring properties of the remnant black hole. Future gravitational wave detectors will increase the precision of these measurements and may be sensitive to the environment surrounding the black hole. This work examines how environments affect the ringdown from a binary coalescence. Our analysis shows that for astrophysical parameters and sensitivity of planned detectors, the ringdown signal is indistinguishable from its vacuum counterpart, suggesting that ringdown-only analyses can reliably extract the (redshifted) mass and spin of the remnant black hole. These conclusions include models with spectral instabilities, suggesting that these are not relevant from an observational viewpoint. Deviations from inspiral-only estimates could then enhance the characterisation of environmental effects present during the coalescence., Comment: 5 pages, 3 figures

Chirality pervades natural processes from the atomic to the cosmic scales, crucially impacting molecular chemistry and pharmaceutics. Traditional chirality sensing methods face challenges in sensitivity and efficiency, prompting the quest of novel chiral recognition solutions based on nanophotonics. In this work we theoretically investigate the possibility to carry out enantiomeric discrimination by measuring the spontaneous emission rate of chiral molecules on twisted two-dimensional materials. We first present a general theoretical framework based on dyadic Green's functions to calculate the chiral contribution to the decay rate in the presence of a generic chiral bilayer interface. We then combine this theory with density functional theory to obtain numerical estimates of the decay rate of helical bilayer nanographene molecules placed on top of twisted bilayer graphene., Comment: 9 pages, 2 figures, 6 appendices. Draft version. Comments are welcome!

The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of applications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.

As mental health care systems worldwide struggle to meet demand, there is increasing focus on using language models to infer neuropsychiatric conditions or psychopathological traits from language production. Yet, so far, this research has only delivered solutions with limited clinical applicability, due to insufficient consideration of ethical questions crucial to ensuring the synergy between possible applications and model design. To accelerate progress towards clinically applicable models, our paper charts the ethical landscape of research on language-based inference of psychopathology and provides a practical tool for researchers to navigate it. We identify seven core ethical principles that should guide model development and deployment in this domain, translate them into ELLIPS, an ethical toolkit operationalizing these principles into questions that can guide researchers' choices with respect to data selection, architectures, evaluation, and model deployment, and provide a case study exemplifying its use. With this, we aim to facilitate the emergence of model technology with concrete potential for real-world applicability.

This work is devoted to the design of a checkerboard air-gas heat exchanger suitable for industrial applications. The design of the heat exchanger is optimized in order to obtain the maximum increase of the outlet air temperature, considering different geometrical design parameters and including manufacturing constraints. The heat exchanger efficiency has been assessed by means of the $\epsilon$-NTU method. The perfomances are compared with traditional finned recuperators and appreciable enhancement of the exchanger efficiency has been observed adopting the new design., Comment: 17 pages, 11 figures, 6 tables

In this paper we investigate the existence of solutions and their weak-strong uniqueness property for a PDE system modelling damage in viscoelastic materials. In fact, we address two solution concepts, weak and strong solutions. For the former, we obtain a global-in-time existence result, but the highly nonlinear character of the system prevents us from proving their uniqueness. For the latter, we prove local-in-time existence. Then, we show that the strong solution, as long as it exists, is unique in the class of weak solutions. This weak-strong uniqueness statement is proved by means of a suitable relative energy inequality.

The Extreme Energy Events (EEE) Project, a joint project of the Centro Fermi (Museo Storico della Fisica e Centro Studi e Ricerche "E.Fermi") and INFN, has a dual purpose: a scientific research program on cosmic rays at ground level and an intense outreach and educational program. The project consists in a network of about 60 tracking detectors, called telescopes, mostly hosted in Italian High Schools. Each telescope is made by three Multigap Resistive Plate Chambers, operated so far with a gas mixture composed by 98% C$_2$H$_2$F$_4$ and 2% SF$_6$. Due to its high Global Warming Potential, a few years ago the EEE collaboration has started an extensive R&D on alternative mixtures environmentally sustainable and compatible with the current experimental setup and operational environment. Among other gas mixtures, the one with helium and hydrofluoroolefin R1234ze gave the best result during the preliminary tests performed with two of the network telescopes. The detector has proved to reach performance levels comparable to those obtained with previous mixtures, without any modification of the hardware. We will discuss the first results obtained with the new mixture, tested with different percentages of the two components., Comment: 14 pages, 6 figures, submitted to JINST

Underground gas storage is a versatile tool for managing energy resources and addressing pressing environmental concerns. While natural gas is stored in geological formations since the beginning of the 20th century, hydrogen has recently been considered as a potential candidate toward a more flexible and sustainable energy infrastructure. Furthermore, these formations can also be used to sequester environmentally harmful gases such as CO2. When such operations are implemented in faulted basins, however, safety concerns may arise due to the possible reactivation of pre-existing faults, which could result in (micro)-seismicity events. In the Netherlands, it has been recently noted that fault reactivation can occur "unexpectedly" during the life of an underground gas storage (UGS) site, even when stress conditions are not expected to cause a failure. The present two-part work aims to develop a modeling framework to investigate the physical mechanisms causing such occurrences and define a safe operational bandwidth for pore pressure variation for UGS operations in the faulted reservoirs of the Rotliegend formation, the Netherlands. In this follow-up paper, we investigate in detail the mechanisms and crucial factors that result in fault reactivation at various stages of a UGS. The mathematical and numerical model described in Part I is used, also accounting for the effect of geochemical dissolution on reservoir and caprock weakening. TThe study investigates the risks of fault activation caused by the storage of different fluids for various purposes, such as long-term CO2 sequestration, CH4 and N2 injection and extraction cycles, and N2 permanent storage. The results show how geomechanical properties and reservoir operating conditions may increase the risk of fault reactivation at various UGS stages. Finally, operational guidelines for improving secure storage operations are presented., Comment: 31 pages, 24 figures, 4 tables

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Over the past few years, ionic conductors have gained a lot of attention given the possibility to implement them in various applications such as supercapacitors, batteries or fuel cells as well as for resistive memories. Especially, layered two-dimensional (2D) crystals such as h-BN, graphene oxide and MoSe2 have shown to provide unique properties originating from the specific 2D confinement of moving ions. Two important parameters are the ion conductivity and the chemical stability over a wide range of operating conditions. In this vein, Rb2Ti2O5 has been recently found displaying remarkable properties such as superionic conduction and colossal equivalent dielectric constant. Here, a first approach to the study of the electrical properties of layered Rb2Ti2O5 at the 100-nanometer scale is presented. Characterizations by means of micro-Raman spectroscopy and atomic force microscope (AFM) measurements of mechanically exfoliated RTO nanocrystals via the so-called adhesive-tape technique are reported. Finally, the results of electrical measurements performed on an exfoliated RTO nanocrystals are presented, and are found to be consistent with the results obtained on macroscopic crystals. 4, Comment: 4 figures

Topic models are useful tools for discovering latent semantic structures in large textual corpora. Topic modeling historically relied on bag-of-words representations of language. This approach makes models sensitive to the presence of stop words and noise, and does not utilize potentially useful contextual information. Recent efforts have been oriented at incorporating contextual neural representations in topic modeling and have been shown to outperform classical topic models. These approaches are, however, typically slow, volatile and still require preprocessing for optimal results. We present Semantic Signal Separation ($S^3$), a theory-driven topic modeling approach in neural embedding spaces. $S^3$ conceptualizes topics as independent axes of semantic space, and uncovers these with blind-source separation. Our approach provides the most diverse, highly coherent topics, requires no preprocessing, and is demonstrated to be the fastest contextually sensitive topic model to date. We offer an implementation of $S^3$, among other approaches, in the Turftopic Python package., Comment: 26 pages, 9 figures (main manuscript has 9 pages and 4 figures)

The drift-barrier hypothesis states that random genetic drift constrains the refinement of a phenotype under natural selection. The influence of effective population size and the genome-wide deleterious mutation rate were studied theoretically, and an inverse relationship between mutation rate and genome size has been observed for many species. However, the effect of the recessive gene count, an important feature of the genomic architecture, is unknown. In a Wright-Fisher model, we studied the mutation burden for a growing number of N completely recessive and lethal disease genes. Diploid individuals are represented with a binary $2 \times N$ matrix denoting wild-type and mutated alleles. Analytic results for specific cases were complemented by simulations across a broad parameter regime for gene count, mutation and recombination rates. Simulations revealed transitions to higher mutation burden and prevalence within a few generations that were linked to the extinction of the wild-type haplotype (least-loaded class). This metastability, that is, phases of quasi-equilibrium with intermittent transitions, persists over $100\,000$ generations. The drift-barrier hypothesis is confirmed by a high mutation burden resulting in population collapse. Simulations showed the emergence of mutually exclusive haplotypes for a mutation rate above 0.02 lethal equivalents per generation for a genomic architecture and population size representing complex multicellular organisms such as humans. In such systems, recombination proves pivotal, preventing population collapse and maintaining a mutation burden below 10. This study advances our understanding of gene pool stability, and particularly the role of the number of recessive disorders. Insights into Muller`s ratchet dynamics are provided, and the essential role of recombination in curbing mutation burden and stabilizing the gene pool is demonstrated., Comment: 21 pages, 4 figures

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

Partial differential equation (PDE) models for infectious disease have received renewed interest in recent years. Most models of this type extend classical compartmental formulations with additional terms accounting for spatial dynamics, with Fickian diffusion being the most common such term. However, while diffusion may be appropriate for modeling vector-borne diseases, or diseases among plants or wildlife, the spatial propagation of airborne diseases in human populations is heavily dependent on human contact and mobility patterns, which are not necessarily well-described by diffusion. By including an additional chemotaxis-inspired term, in which the infection is propagated along the positive gradient of the susceptible population (from regions of low- to high-density of susceptibles), one may provide a more suitable description of these dynamics. This article introduces and analyzes a mathematical model of infectious disease incorporating a modified chemotaxis-type term. The model is analyzed mathematically and the well-posedness of the resulting PDE system is demonstrated. A series of numerical simulations are provided, demonstrating the ability of the model to naturally capture important phenomena not easily observed in standard diffusion models, including propagation over long spatial distances over short time scales and the emergence of localized infection hotspots

The availability of cancer measurements over time enables the personalised assessment of tumour growth and therapeutic response dynamics. However, many tumours are treated after diagnosis without collecting longitudinal data, and cancer monitoring protocols may include infrequent measurements. To facilitate the estimation of disease dynamics and better guide ensuing clinical decisions, we investigate an inverse problem enabling the reconstruction of earlier tumour states by using a single spatial tumour dataset and a biomathematical model describing disease dynamics. We focus on prostate cancer, since aggressive cases of this disease are usually treated after diagnosis. We describe tumour dynamics with a phase-field model driven by a generic nutrient ruled by reaction-diffusion dynamics. The model is completed with another reaction-diffusion equation for the local production of prostate-specific antigen, which is a key prostate cancer biomarker. We first improve previous well-posedness results by further showing that the solution operator is continuously Fr\'echet differentiable. We then analyse the backward inverse problem concerning the reconstruction of earlier tumour states starting from measurements of the model variables at the final time. Since this problem is severely ill-posed, only very weak conditional stability of logarithmic type can be recovered from the terminal data. However, by restricting the unknowns to a compact subset of a finite-dimensional subspace, we can derive an optimal Lipschitz stability estimate., Comment: 25 pages

The quantum phase estimation algorithm stands as the primary method for determining the ground state energy of a molecular electronic Hamiltonian on a quantum computer. In this context, the ability to initialize a classically tractable state that has a strong overlap with the desired ground state is critical as it directly affects the runtime of the algorithm. However, several numerical studies have shown that this overlap decays exponentially with system size. In this work, we demonstrate that this decay can be alleviated by optimizing the molecular orbital basis, for an initial state constructed from a single Slater determinant. We propose a practical method to achieve this optimization without knowledge of the true molecular ground state and test this method numerically. By comparing the resulting optimized orbitals to the natural orbitals, we find improved overlap. Specifically, for four iron-sulfur molecules, which are known to suffer from the mentioned decay, we show that our method yields one to two orders of magnitude improvement compared to localized molecular orbitals.

We demonstrate that phase-matched frequency upconversion of ultrafast laser light can be extended to shorter wavelengths by using longer driving laser wavelengths. Experimentally, we show that the phase-matching cutoff for harmonic generation in argon increases from 45 to 100 eV when the driving laser wavelength is increased from 0.8 to 1.3 micrometers. Phase matching is also obtained at higher pressures using a longer-wavelength driving laser, mitigating the unfavorable scaling of the single-atom response. Theoretical calculations suggest that phase-matched high harmonic frequency upconversion driven by mid-infrared pulses could be extended to extremely high photon energies., Comment: 10 pages, 3 figures

Efficient algorithms and solvers are required to provide optimal or near-optimal solutions quickly and enable organizations to react promptly to dynamic situations such as supply chain disruptions or changing customer demands. State-of-the-art mixed-integer programming (MIP) solvers are crafted to tackle a wide variety of problems, yet many real-world situations are characterized by problem instances that originate from a narrow distribution. This has inspired the creation of tailored approaches that exploit historical data to inform heuristic design. Deep learning (DL) methods are typically used in this context to extract patterns from data, but they require large datasets and comprehensive hyperparameter tuning for strong performance. This article describes a one-shot learning heuristic that leverages solutions discovered within the branch-and-bound tree to construct a model with minimal overhead. We evaluate our method on the locomotive assignment problem (LAP) and sets of MIPLIB instances that contain constraints based on special ordered sets of type 1. Experimental results include a comparison with multiple primal heuristics and state-of-the-art MIP solvers. We show that the method is most effective with CPLEX in terms of the average primal gap.

Analogue test structures were fabricated using the Tower Partners Semiconductor Co. CMOS 65 nm ISC process. The purpose was to characterise and qualify this process and to optimise the sensor for the next generation of Monolithic Active Pixels Sensors for high-energy physics. The technology was explored in several variants which differed by: doping levels, pixel geometries and pixel pitches (10-25 $\mu$m). These variants have been tested following exposure to varying levels of irradiation up to 3 MGy and $10^{16}$ 1 MeV n$_\text{eq}$ cm$^{-2}$. Here the results from prototypes that feature direct analogue output of a 4$\times$4 pixel matrix are reported, allowing the systematic and detailed study of charge collection properties. Measurements were taken both using $^{55}$Fe X-ray sources and in beam tests using minimum ionizing particles. The results not only demonstrate the feasibility of using this technology for particle detection but also serve as a reference for future applications and optimisations.

Application of Neural Networks to river hydraulics is fledgling, despite the field suffering from data scarcity, a challenge for machine learning techniques. Consequently, many purely data-driven Neural Networks proved to lack predictive capabilities. In this work, we propose to mitigate such problem by introducing physical information into the training phase. The idea is borrowed from Physics-Informed Neural Networks which have been recently proposed in other contexts. Physics-Informed Neural Networks embed physical information in the form of the residual of the Partial Differential Equations (PDEs) governing the phenomenon and, as such, are conceived as neural solvers, i.e. an alternative to traditional numerical solvers. Such approach is seldom suitable for environmental hydraulics, where epistemic uncertainties are large, and computing residuals of PDEs exhibits difficulties similar to those faced by classical numerical methods. Instead, we envisaged the employment of Neural Networks as neural operators, featuring physical constraints formulated without resorting to PDEs. The proposed novel methodology shares similarities with data augmentation and regularization. We show that incorporating such soft physical information can improve predictive capabilities.

The computational cost of quantum algorithms for physics and chemistry is closely linked to the spectrum of the Hamiltonian, a property that manifests in the necessary rescaling of its eigenvalues. The typical approach of using the 1-norm as an upper bound to the spectral norm to rescale the Hamiltonian suits the most general case of bounded Hermitian operators but neglects the influence of symmetries commonly found in chemical systems. In this work, we introduce a hierarchy of symmetry-aware spectral bounds that provide a unified understanding of the performance of quantum phase estimation algorithms using block-encoded electronic structure Hamiltonians. We present a variational and numerically tractable method for computing these bounds, based on orbital optimization, to demonstrate that the computed bounds are smaller than conventional spectral bounds for a variety of molecular benchmark systems. We also highlight the unique analytical and numerical scaling behavior of these bounds in the thermodynamic and complete basis set limits. Our work shows that there is room for improvement in reducing the 1-norm, not yet achieved through methods like double factorization and tensor hypercontraction, but highlights potential challenges in improving the performance of current quantum algorithms beyond small constant factors through 1-norm reduction techniques alone., Comment: 11 pages, 3 figures, 1 table

Quantum phase estimation based on qubitization is the state-of-the-art fault-tolerant quantum algorithm for computing ground-state energies in chemical applications. In this context, the 1-norm of the Hamiltonian plays a fundamental role in determining the total number of required iterations and also the overall computational cost. In this work, we introduce the symmetry-compressed double factorization (SCDF) approach, which combines a compressed double factorization of the Hamiltonian with the symmetry shift technique, significantly reducing the 1-norm value. The effectiveness of this approach is demonstrated numerically by considering various benchmark systems, including the FeMoco molecule, cytochrome P450, and hydrogen chains of different sizes. To compare the efficiency of SCDF to other methods in absolute terms, we estimate Toffoli gate requirements, which dominate the execution time on fault-tolerant quantum computers. For the systems considered here, SCDF leads to a sizeable reduction of the Toffoli gate count in comparison to other variants of double factorization or even tensor hypercontraction, which is usually regarded as the most efficient approach for qubitization.

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors.