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Here we report on the first measurements of the $\gamma$-ray strength functions and nuclear level densities of ${}^{112,114}$Sn performed at the 9~MV Tandem accelerator facilities at IFIN-HH using the Oslo method. We extract thermodynamic properties as well as both gross and fine properties of the pygmy dipole resonance for systematic comparison in the chain of Sn isotopes. The results are compared with microscopic models implemented in the TALYS reaction code, and the fully microscopic quasiparticle-phonon model for the underlying nuclear structure of the dipole strength in ${}^{112,114}$Sn. The experimental data and theoretical results are further included into the cross-section and reaction rate calculations for the $(\mathrm{n},\gamma)$ production reaction of the $p$-process nuclei ${}^{112,114}$Sn showing a significant increase in reaction rates at high temperatures compared to existing nuclear databases., Comment: 21 pages, 17 figures

The paper presents a wireless system integrated with a machine learning (ML) model for structural health monitoring (SHM) of carbon fiber reinforced polymer (CFRP) structures, primarily targeting aerospace applications. The system collects data via carbon nanotube (CNT) piezoresistive sensors embedded within CFRP coupons, wirelessly transmitting these data to a central server for processing. A deep neural network (DNN) model predicts mechanical properties and can be extended to forecast structural failures, facilitating proactive maintenance and enhancing safety. The modular design supports scalability and can be embedded within digital twin frameworks, offering significant benefits to aircraft operators and manufacturers. The system utilizes an ML model with a mean absolute error (MAE) of 0.14 on test data for forecasting mechanical properties. Data transmission latency throughout the entire system is less than one second in a LAN setup, highlighting its potential for real-time monitoring applications in aerospace and other industries. However, while the system shows promise, challenges such as sensor reliability under extreme environmental conditions and the need for advanced ML models to handle diverse data streams have been identified as areas for future research., Comment: 16 pages, 14 figures

Axion-like particles (ALPs) have been searched for with a quasi-parallel stimulated resonant photon-photon collider sensitive to the sub-eV mass range by focusing two-color near-infrared pulse lasers into a vacuum. In this work, we have developed a specialized coronagraphy to mitigate the dominant background photons from optical elements by introducing an eclipse filter. The observed number of signal-like photons was found to be consistent with residual background photons from optical elements through an additional test by degrading the focal point overlapping factor between the two lasers. We then extended the exclusion region in the relation between ALP-photon coupling, $g/M$, and the ALP mass $m$, reaching the most sensitive point $g/M = 4.2\times10^{-7}\,\mathrm{GeV^{-1}}$ at $m = 0.15\,\mathrm{eV}$ for pseudoscalar ALPs., Comment: 23 pages, 9 figures. arXiv admin note: text overlap with arXiv:2208.09880

This work examines the hypothesis that intravoxel incoherent motion MRI (IVIM) can quantify local cerebral blood flow (qCBF), infarct volume, and define the ischemic penumbra for determination of the perfusion-diffusion mismatch (PWI/DWI) volume in a setting of acute ischemic stroke. Eight experiments were conducted in a pre-clinical middle cerebral artery occlusion (MCAO) model. IVIM and dynamic susceptibility contrast (DSC) imaging were acquired 2.5hr post-MCAO. IVIM was post-processed using software written in-house to produce parametric images of local qCBF, Water Transport Time (WTT), diffusion, and subsequently, PWI/DWI mismatch. These IVIM image parameters were compared with delay-and-dispersion-corrected local-AIF DSC perfusion image parameters including Tmax, qCBF, mean transit time (MTT), and mean diffusivity for DSC PWI/DWI mismatch. Final infarct volume was measured 4hrs post-occlusion. Early (2.5hr post-occlusion) DSC qCBF and IVIM qCBF in the diffusion negative MCA territory correlated strongly (slope=1.00, p=0.01,R2=0.69,Lins CCC=0.71), and both DSC and IVIM qCBF values negatively correlated with final infarct volume (R2=0.78,R2=0.61 respectively). The volume of hypoperfusion measured at 2.5 hours from DSC qCBF and from IVIM qCBF both predicted final infarct volume with good sensitivity and correlation (slope=2.08, R2=0.67, slope=2.50,R2=0.68 respectively). IVIM PWI/DWI ratio was correlated with infarct growth (R2=0.70) and WTT correlated with MTT (slope=0.82,R2=0.60). IVIM qCBF correlated strongly with local-AIF DSC qCBF and IVIM PWI/DWI correlated strongly with infarct growth. Both DSC and IVIM quantitative perfusion image acquired early after occlusion were able to predict final infarct volume, and IVIM simultaneous PWI/DWI ratio predicted infarct growth., Comment: 16 pages, 5 figures, Appendix

The gradient mapping norm is a strong and easily verifiable stopping criterion for first-order methods on composite problems. When the objective exhibits the quadratic growth property, the gradient mapping norm minimization problem can be solved by online parameter-free and adaptive first-order schemes with near-optimal worst-case rates. In this work we address problems where quadratic growth is absent, a class for which no methods with all the aforementioned properties are known to exist. We formulate a template whose instantiation recovers the existing Performance Estimation derived approaches. Our framework provides a simple human-readable interpretation along with runtime convergence rates for these algorithms. Moreover, our template can be used to construct a quasi-online parameter-free method applicable to the entire class of composite problems while retaining the optimal worst-case rates with the best known proportionality constant. The analysis also allows for adaptivity. Preliminary simulation results suggest that our scheme is highly competitive in practice with the existing approaches, either obtained via Performance Estimation or employing Accumulative Regularization., Comment: 42 pages, 3 figures

Introduced by Polyak in 1966, the class of strongly quasiconvex functions includes some interesting nonconvex members, like the square root of the Euclidean norm or ratios with a nonnegative strongly convex numerator and a concave and positive denominator. This survey collects the vast majority of the results involving strongly quasiconvex functions available in the literature at the moment, presenting, in particular, algorithms for minimizing such functions, and suggests some directions where additional investigations would be welcome.

The SIDDHARTA-2 collaboration aims to measure for the first time the shift and width induced on the $1s$ level of kaonic deuterium by the strong interaction. In the preliminary phase to the experiment, a test run using a Helium-4 target was performed to optimize the performance of the full experimental apparatus. This preliminary study highlighted the possibility to measure transition lines coming from intermediate mass kaonic atoms, such as kaonic carbon and kaonic aluminum. In order to measure transitions where strong interaction is manifesting at higher energies, out of the energy range of the SIDDHARTA-2 apparatus, the collaboration is testing a new detector system which exploits a novel compound semiconductor, the Cadmium Zinc Telluride. Tests are now running at DA$\Phi$NE to study the performance of this detector, exploring the possibility to build a dedicated setup., Comment: 10 pages, 3 figures, eQCD conference

It is known that a properly rescaled version of Sinai's random walk converges in distribution to Brox's diffusion. In this article we quantify this convergence by considering a specific coupling between Sinai's walk and Brox's diffusion. Our method relies on convergence results for martingale problems considered in the rough path setting.

The rise of electron spin qubit architectures for quantum computing processors has led to a strong interest in designing and integrating ferromagnets to induce stray magnetic fields for electron dipole spin resonance (EDSR). The integration of nanomagnets imposes however strict layout and processing constraints, challenging the arrangement of different gating layers and the control of neighboring qubit frequencies. This work reports a successful integration of nano-sized cobalt control gates into a multi-gate FD-SOI nanowire with nanometer-scale dot-to-magnet pitch, simultaneously exploiting electrical and ferromagnetic properties of the gate stack at nanoscale. The electrical characterization of the multi-gate nanowire exhibits full field effect functionality of all ferromagnetic gates from room temperature to 10 mK, proving quantum dot formation when ferromagnets are operated as barrier gates. The front-end-of-line (FEOL) compatible gate-first integration of cobalt is examined by energy dispersive X-ray spectroscopy and high/low frequency capacitance characterization, confirming the quality of interfaces and control over material diffusion. Insights into the magnetic properties of thin films and patterned control-gates are provided by vibrating sample magnetometry and electron holography measurements. Micromagnetic simulations anticipate that this structure fulfills the requirements for EDSR driving for magnetic fields higher than 1 T, where a homogeneous magnetization along the hard magnetic axis of the Co gates is expected. The FDSOI architecture showcased in this study provides a scalable alternative to micromagnets deposited in the back-end-of-line (BEOL) and middle-of-line (MOL) processes, while bringing technological insights for the FEOL-compatible integration of Co nanostructures in spin qubit devices., Comment: 15 pages, 7 figures

This work reports a pulsed laser-assisted synthesis, detailed structural characterization, and study of plasmonic properties of three sets of TiN/TiNO thin films with high electron density. The first two sets of TiN films were grown at 600C and 700C under a high vacuum condition. The third set of TiN film was grown in the presence of 5 mTorr of molecular oxygen at 700C. The results have shown that TiN films deposited in a high vacuum are metallic, have large optical reflectance, and have high optical and electrical conductivity. The TiN films, grown in 5 mTorr O2, were partially oxidized and semiconducting with room temperature resistivity nearly three times larger than those of the TiN films grown under high vacuum conditions. The optical conductivity of these films was analyzed using a Kramers-Kronig transformation of reflectance and a Lorentz-Drude model; the optical conductivity determined by these two different methods agreed very well. To corroborate our experimental spectral observations, we have calculated the phonon dispersions and Raman active modes of TiNO using the virtual crystal approximation. A comparative analysis of the phonon dispersions between rutile TiO2 and rocksalt TiNO has shown that the incorporation of nitrogen atoms does not significantly alter the phonon dispersions of rutile TiO2. However, it results in the emergence of new phonon modes at approximately 7.128 THz at the Gamma point, which corresponds to the experimentally observed Multi-Photon Phase-MPP. From the collateral study of experimental results and theoretical corroboration, a suitable multi-layer optical model was proposed for the TiN/TINO epitaxial thin films to extract the individual complex dielectric function from which many other optical parameters can be calculated.

We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our approach uses a high-quality curated dataset of time and location annotations in a corpus of epidemiology papers to train an encoder-decoder architecture. We also explored the use of data augmentation techniques during training. Our findings suggest that a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurate predict the relevant scenario information of a particular entity or event., Comment: 9 pages, 7 figures

It is generally assumed that the entropy of activation associated with dislocation glide, a process controlling body-centered cubic metals strength, is either constant or related to the enthalpy and stress through the empirical Meyer-Neldel (MN) law. We present an atomistic investigation revealing that while both cases can be encountered depending on the refinement of simulation methodology, the MN law for this process is an artifact of simple radial models of interatomic interaction which predict a rugged energy landscape, and is not necessarily connected to the temperature-evolution of the bulk modulus as historically thought. Comparing a classical and a dedicated machine-learning (ML) interatomic potential, we show surprisingly large differences between the classical harmonic theory and anharmonic calculations with the classical model, impacting dislocation velocities by orders of magnitude, while both predictions are in excellent agreement using the ML, which predicts a constant activation entropy. Simulations based on classical potentials, a cornerstone of materials modelling, could be broadly affected by these over-estimated entropic effects.

The paper studies the possible interplay between matter and geometry in scalar tensor theories of gravitation where the energy--momentum tensor is directly coupled with the Einstein tensor. After obtaining the scalar tensor representation of the $f(R, G_{\mu\nu}T^{\mu\nu})$ gravity, the analysis continue with an approach based on the thermodynamics of irreversible processes in open systems. To this regard, various thermodynamic properties are directly obtained in this manner, like the matter creation (annihilation) rate and the corresponding creation (annihilation) pressure. In the case of the Roberson--Walker metric several analytic and numerical solutions are found in the asymptotic regime. In the last part of the manuscript a specific parametrization for the Hubble rate is constrained using the Markov Chain Monte Carlo algorithms in the case of cosmic chronometers (CC) and BAO observations, obtaining an approximate numerical solution which can describe the cosmological model. For this model, we have obtained by fine--tuning some numerical solutions which exhibit creation mechanisms in different specific regimes.

We propose SLAMFuse, an open-source SLAM benchmarking framework that provides consistent crossplatform environments for evaluating multi-modal SLAM algorithms, along with tools for data fuzzing, failure detection, and diagnosis across different datasets. Our framework introduces a fuzzing mechanism to test the resilience of SLAM algorithms against dataset perturbations. This enables the assessment of pose estimation accuracy under varying conditions and identifies critical perturbation thresholds. SLAMFuse improves diagnostics with failure detection and analysis tools, examining algorithm behaviour against dataset characteristics. SLAMFuse uses Docker to ensure reproducible testing conditions across diverse datasets and systems by streamlining dependency management. Emphasizing the importance of reproducibility and introducing advanced tools for algorithm evaluation and performance diagnosis, our work sets a new precedent for reliable benchmarking of SLAM systems. We provide ready-to-use docker compatible versions of the algorithms and datasets used in the experiments, together with guidelines for integrating and benchmarking new algorithms. Code is available at https://github.com/nikolaradulov/slamfuse, Comment: 8 pages, 8 figures, Equal contribution of first two authors, Accepted at the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

Natural Language Inference (NLI) evaluation is crucial for assessing language understanding models; however, popular datasets suffer from systematic spurious correlations that artificially inflate actual model performance. To address this, we propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. This categorization significantly reduces spurious correlation measures, with examples labeled as having the highest difficulty showing markedly decreased performance and encompassing more realistic and diverse linguistic phenomena. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset, surpassing other dataset characterization techniques. Our research addresses limitations in NLI dataset construction, providing a more authentic evaluation of model performance with implications for diverse NLU applications., Comment: Accepted at EMNLP 2024 Main Conference

Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the strong links between weather and climate modelling, this raises the question whether machine learning models could also revolutionize climate science, for example by informing mitigation and adaptation to climate change or to generate larger ensembles for more robust uncertainty estimates. Here, we show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states corresponding to pre-industrial, present-day, and future 2.9K warmer climates. This indicates that the dynamics shaping the weather on short timescales may not differ fundamentally in a changing climate. It also demonstrates out-of-distribution generalization capabilities of the machine learning models that are a critical prerequisite for climate applications. Nonetheless, two of the models show a global-mean cold bias in the forecasts for the future warmer climate state, i.e. they drift towards the colder present-day climate they have been trained for. A similar result is obtained for the pre-industrial case where two out of three models show a warming. We discuss possible remedies for these biases and analyze their spatial distribution, revealing complex warming and cooling patterns that are partly related to missing ocean-sea ice and land surface information in the training data. Despite these current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science and transform established approaches by complementing conventional physics-based models., Comment: 14 pages, 4 figures

Creating visually appealing composites requires optimizing both text and background for compatibility. Previous methods have focused on simple design strategies, such as changing text color or adding background shapes for contrast. These approaches are often destructive, altering text color or partially obstructing the background image. Another method involves placing design elements in non-salient and contrasting regions, but this isn't always effective, especially with patterned backgrounds. To address these challenges, we propose a generative approach using a diffusion model. This method ensures the altered regions beneath design assets exhibit low saliency while enhancing contrast, thereby improving the visibility of the design asset., Comment: 14 pages, 5 figures, Paper sent and accepted as a poster at PRICAI 2024

We investigate the steady state phase diagram of fermionic atoms subjected to an optical lattice and coupled to a high finesse optical cavity with photon losses. The coupling between the atoms and the cavity field is induced by a transverse pump beam. Taking fluctuations around the mean-field solutions into account, we find that a transition to a self-organized phase takes place at a critical value of the pump strength. In the self-organized phase the cavity field takes a finite expectation value and the atoms show a modulation in the density. Surprisingly, at even larger pump strengths two self-organized stable solutions of the cavity field and the atoms occur, signaling the presence of a bistability. We show that the bistable behavior is induced by the atoms-cavity fluctuations and is not captured by the the mean-field approach.

We explore the behavior of the Hall response of a Bose-Hubbard triangular ladder in a magnetic field as a function of the repulsive on-site atomic interactions. We consider a wide range of interaction strengths, from the weakly interacting limit to the hardcore regime. This is realized by computing the Hall polarization following the quench of a weak linear potential which induces the flow of a current through the system, using time-dependent matrix product state numerical simulations. We complement our understanding in the regime of small magnetic fields by analytical calculations of the equilibrium value of the Hall polarization for non-interacting bosonic atoms, or under a mean-field assumption. The Bose-Hubbard triangular flux ladder exhibits a rich phase diagram, containing Meissner, vortex and biased-chiral superfluid phases. We show that the Hall response can be employed to fingerprint the various chiral state, the frustration effects occurring in the limit of strong interactions, and the phase boundaries of the equilibrium phase diagram.

Manual parallelization of code remains a significant challenge due to the complexities of modern software systems and the widespread adoption of multi-core architectures. This paper introduces OMPar, an AI-driven tool designed to automate the parallelization of C/C++ code using OpenMP pragmas. OMPar integrates Large Language Models (LLMs) through two key components: OMPify, which assesses loop parallelization potential, and MonoCoder-OMP, a new fine-tuned model which generates precise OpenMP pragmas. The evaluation of OMPar follows the same rigorous process applied to traditional tools like source-to-source AutoPar and ICPC compilers: (1) ensuring the generated code compiles and runs correctly in serial form, (2) assessing performance with the gradual addition of threads and corresponding physical cores, and (3) verifying and validating the correctness of the code's output. Benchmarks from HeCBench and ParEval are used to evaluate accuracy and performance. Experimental results demonstrate that OMPar significantly outperforms traditional methods, achieving higher accuracy in identifying parallelizable loops and generating efficient pragmas. Beyond accuracy, OMPar offers advantages such as the ability to work on partial or incomplete codebases and the capacity to continuously learn from new code patterns, enhancing its parallelization capabilities over time. These results underscore the potential of LLMs in revolutionizing automatic parallelization techniques, paving the way for more efficient and scalable parallel computing systems.

This is a survey article, presenting few of the mathematical achievements of J.-P. Demailly. We discuss a few aspects of his approach for Fujita conjecture, and results around the K\"ahler cone of a compact K\"ahler manifold. The article will appear in a special issue of "Gazette des Math\'ematiciens"., Comment: En fran\c{c}ais dans le texte (written in French)

Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, they are prone to invalidating the very assumptions used in their own construction. This is due to the linearity of the underlying system, inherently assumed and formulated in most data-driven control approaches, which may falsely generalize the behavior of the system beyond the behavior experienced in the data. This paper seeks to mitigate these problems by enforcing consistency of the newly designed closed-loop systems with data and slow down any distributional shifts in the joint state-input space. This is achieved through incorporating affine regularization terms and linear matrix inequality constraints to data-driven approaches, resulting in convex semi-definite programs that can be efficiently solved by standard software packages. We discuss the optimality conditions of these programs and then conclude the paper with a numerical example that further highlights the problem of premature generalization beyond data and shows the effectiveness of our proposed approaches in enhancing the safety of data-driven control methods., Comment: 13 pages, 3 figures, 2 tables

Nowadays, deep neural networks are used for solving complex tasks in several critical applications and protecting both their integrity and intellectual property rights (IPR) has become of utmost importance. To this end, we advance WaterMAS, a substitutive, white-box neural network watermarking method that improves the trade-off among robustness, imperceptibility, and computational complexity, while making provisions for increased data payload and security. WasterMAS insertion keeps unchanged the watermarked weights while sharpening their underlying gradient space. The robustness is thus ensured by limiting the attack's strength: even small alterations of the watermarked weights would impact the model's performance. The imperceptibility is ensured by inserting the watermark during the training process. The relationship among the WaterMAS data payload, imperceptibility, and robustness properties is discussed. The secret key is represented by the positions of the weights conveying the watermark, randomly chosen through multiple layers of the model. The security is evaluated by investigating the case in which an attacker would intercept the key. The experimental validations consider 5 models and 2 tasks (VGG16, ResNet18, MobileNetV3, SwinT for CIFAR10 image classification, and DeepLabV3 for Cityscapes image segmentation) as well as 4 types of attacks (Gaussian noise addition, pruning, fine-tuning, and quantization). The code will be released open-source upon acceptance of the article.

A remarkable feature of the elephant trunk is the pronounced wrinkling that enables its great flexibility. Here, we devise a general mathematical model that accounts for characteristic skin wrinkles formed during morphogenesis in elephant trunk. Using physically realistic parameters and operating within the theoretical framework of nonlinear morphoelasticity, we elucidate analytically and numerically the effect of skin thickness, relative stiffness and differential growth on the physiological pattern of transverse wrinkles distributed along the trunk. We conclude that, since the skin and muscle components have similar material properties, geometric parameters, such as curvature, play important roles. In particular, our model predicts that, in the proximal region close to the skull, where curvature is lower, fewer wrinkles form and sooner than in the distal narrower region where more wrinkles develop. Similarly, less wrinkling is found on the ventral side, which is flatter, compared to the dorsal side. In summary, the mechanical compatibility between the skin and the muscle enables them to grow seamlessly, while the wrinkled skin acts as a protective barrier that is both thicker and more flexible than the unwrinkled skin.

A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations. The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution. The model is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated using surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. The DDM outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. The model also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.

The underlying framework for controlling autonomous robots and complex automation applications are Operating Systems (OS) capable of scheduling perception-and-control tasks, as well as providing real-time data communication to other robotic peers and remote cloud computers. In this paper, we introduce CyberCortex AI, a robotics OS designed to enable heterogeneous AI-based robotics and complex automation applications. CyberCortex AI is a decentralized distributed OS which enables robots to talk to each other, as well as to High Performance Computers (HPC) in the cloud. Sensory and control data from the robots is streamed towards HPC systems with the purpose of training AI algorithms, which are afterwards deployed on the robots. Each functionality of a robot (e.g. sensory data acquisition, path planning, motion control, etc.) is executed within a so-called DataBlock of Filters shared through the internet, where each filter is computed either locally on the robot itself, or remotely on a different robotic system. The data is stored and accessed via a so-called Temporal Addressable Memory (TAM), which acts as a gateway between each filter's input and output. CyberCortex AI has two main components: i) the CyberCortex AI inference system, which is a real-time implementation of the DataBlock running on the robots' embedded hardware, and ii) the CyberCortex AI dojo, which runs on an HPC computer in the cloud, and it is used to design, train and deploy AI algorithms. We present a quantitative and qualitative performance analysis of the proposed approach using two collaborative robotics applications: i) a forest fires prevention system based on an Unitree A1 legged robot and an Anafi Parrot 4K drone, as well as ii) an autonomous driving system which uses CyberCortex AI for collaborative perception and motion control.

The main motivation behind this paper stems from a notable gap in the existing literature: the absence of a discrete counterpart to the Laplace-Beltrami operator on Riemannian manifolds, which can be effectively used to solve PDEs. We consider that the natural approach to pioneer this field is to first explore one of the simplest non-trivial (i.e., non-Euclidean) scenario, specifically focusing on the $2$-dimensional hyperbolic space $\mathbb{H}^2$. To this end, we present two variants of discrete finite-difference operator tailored to this constant negatively curved space, both serving as approximations to the (continuous) Laplace-Beltrami operator within the $\mathrm{L}^2$ framework. Moreover, we prove that the discrete heat equation associated to both aforesaid operators exhibits stability and converges towards the continuous heat-Beltrami Cauchy problem on $\mathbb{H}^2$. Eventually, we illustrate that a discrete Laplacian specifically designed for the geometry of the hyperbolic space yields a more precise approximation and offers advantages from both theoretical and computational perspectives.

Polonium isotopes having two protons above the shell closure at $Z=82$ show a wide variety of low-lying high-spin isomeric states across the whole chain. The structure of neutron-deficient isotopes up to $^{210}$Po ($N=126$) is well established as they are easily produced through various methods. However, there is not much information available for the neutron-rich counterparts for which only selective techniques can be used for their production. We report on the first fast-timing measurements of yrast states up to the 8$^+$ level in $^{214,216,218}$Po isotopes produced in the $\beta^-$ decay of $^{214,216,218}$Bi at ISOLDE, CERN. In particular, our new half-life value of 607(14) ps for the 8$_1^+$ state in $^{214}$Po is nearly 20 times shorter than the one available in literature and comparable with the newly measured half-lives of 409(16) and 628(25) ps for the corresponding 8$_1^+$ states in $^{216,218}$Po, respectively. The measured $B(E2;8_1^+ \to 6_1^+)$ transition probability values follow an increasing trend relative to isotope mass, reaching a maximum for $^{216}$Po. The increase contradicts the previous claims of isomerism for the $8^+$ yrast states in neutron-rich $^{214}$Po and beyond. Together with the other measured yrast transitions, the $B(E2)$ values provide a crucial test of the different theoretical approaches describing the underlying configurations of the yrast band. The new experimental results are compared to shell-model calculations using the KHPE and H208 effective interactions and their pairing modified versions, showing an increase in configuration mixing when moving towards the heavier isotopes., Comment: 6 pages, 5 figures (main manuscript), 2 pages (supplemental material)

In recent years, Large Language Models (LLMs) have achieved almost human-like performance on various tasks. While some LLMs have been trained on multilingual data, most of the training data is in English; hence, their performance in English greatly exceeds other languages. To our knowledge, we are the first to collect and translate a large collection of texts, instructions, and benchmarks and train, evaluate, and release open-source LLMs tailored for Romanian. We evaluate our methods on four different categories, including academic benchmarks, MT-Bench (manually translated), and a professionally built historical, cultural, and social benchmark adapted to Romanian. We argue for the usefulness and high performance of RoLLMs by obtaining state-of-the-art results across the board. We publicly release all resources (i.e., data, training and evaluation code, models) to support and encourage research on Romanian LLMs while concurrently creating a generalizable recipe, adequate for other low or less-resourced languages., Comment: Accepted at The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024 Findings). arXiv admin note: text overlap with arXiv:2405.07703

Answering questions about the spatial properties of the environment poses challenges for existing language and vision foundation models due to a lack of understanding of the 3D world notably in terms of relationships between objects. To push the field forward, multiple 3D Q&A datasets were proposed which, overall, provide a variety of questions, but they individually focus on particular aspects of 3D reasoning or are limited in terms of data modalities. To address this, we present Space3D-Bench - a collection of 1000 general spatial questions and answers related to scenes of the Replica dataset which offers a variety of data modalities: point clouds, posed RGB-D images, navigation meshes and 3D object detections. To ensure that the questions cover a wide range of 3D objectives, we propose an indoor spatial questions taxonomy inspired by geographic information systems and use it to balance the dataset accordingly. Moreover, we provide an assessment system that grades natural language responses based on predefined ground-truth answers by leveraging a Vision Language Model's comprehension of both text and images to compare the responses with ground-truth textual information or relevant visual data. Finally, we introduce a baseline called RAG3D-Chat integrating the world understanding of foundation models with rich context retrieval, achieving an accuracy of 67% on the proposed dataset.

We analyzed the Hartree-Fock approximation for an electron system. The interaction between particles is modeled by a non-Coulombian potential. We analyzed both the three-dimensional and two-dimensional systems. We obtained accurate analytical results for the particle energy, the particle velocity, the ground state energy of the system as well as the momentum dependent density of states. The previous classical results for the Coulombian case were reobtained as particular cases., Comment: 23 pages, 3 figures

In-context learning (ICL) has proven to be an effective strategy for improving the performance of large language models (LLMs) with no additional training. However, the exact mechanism behind this performance improvement remains unclear. This study is the first to show how ICL surfaces memorized training data and to explore the correlation between this memorization and performance on downstream tasks across various ICL regimes: zero-shot, few-shot, and many-shot. Our most notable findings include: (1) ICL significantly surfaces memorization compared to zero-shot learning in most cases; (2) demonstrations, without their labels, are the most effective element in surfacing memorization; (3) ICL improves performance when the surfaced memorization in few-shot regimes reaches a high level (about 40%); and (4) there is a very strong correlation between performance and memorization in ICL when it outperforms zero-shot learning. Overall, our study uncovers memorization as a new factor impacting ICL, raising an important question: to what extent do LLMs truly generalize from demonstrations in ICL, and how much of their success is due to memorization?, Comment: v2

We introduce a novel framework to financial time series forecasting that leverages causality-inspired models to balance the trade-off between invariance to distributional changes and minimization of prediction errors. To the best of our knowledge, this is the first study to conduct a comprehensive comparative analysis among state-of-the-art causal discovery algorithms, benchmarked against non-causal feature selection techniques, in the application of forecasting asset returns. Empirical evaluations demonstrate the efficacy of our approach in yielding stable and accurate predictions, outperforming baseline models, particularly in tumultuous market conditions.

We propose a solution for handling abort commands given to robots. The solution is exemplified with a running scenario with household kitchen robots. The robot uses planning to find sequences of actions that must be performed in order to gracefully cancel a previously received command. The Planning Domain Definition Language (PDDL) is used to write a domain to model kitchen activities and behaviours, and this domain is enriched with knowledge from online ontologies and knowledge graphs, like DBPedia. We discuss the results obtained in different scenarios.

The main purpose of this paper is to study the special fractional-order Chen-Lee system, using the Caputo fractional derivatives. For this fractional model we investigate the existence and uniqueness of solution of initial value problem, asymptotic stability of its equilibrium states, stabilization problem using appropriate control and numerical integration., Comment: 13 pages, 2 figures

We propose a novel data-driven network framework for forecasting problems related to E-mini S\&P 500 and CBOE Volatility Index futures, in which products with different expirations act as distinct nodes. We provide visual demonstrations of the correlation structures of these products in terms of their returns, realized volatility, and trading volume. The resulting networks offer insights into the contemporaneous movements across the different products, illustrating how inherently connected the movements of the future products belonging to these two classes are. These networks are further utilized by a multi-channel Graph Convolutional Network to enhance the predictive power of a Long Short-Term Memory network, allowing for the propagation of forecasts of highly correlated quantities, combining the temporal with the spatial aspect of the term structure.

We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL outline for a code function comprises multiple statements written in concise prose, which partition the code and summarize its main ideas in the style of literate programming. Crucially, we find that modern LLMs can generate accurate and high-quality NL outlines in practice. Moreover, NL outlines enable a bidirectional sync between code and NL, allowing changes in one to be automatically reflected in the other. We discuss many use cases for NL outlines: they can accelerate understanding and navigation of code and diffs, simplify code maintenance, augment code search, steer code generation, and more. We then propose and compare multiple LLM prompting techniques for generating outlines and ask professional developers to judge outline quality. Finally, we present two case studies applying NL outlines toward code review and the difficult task of malware detection.

Many temporal safety properties of higher-order programs go beyond simple event sequencing and require an automaton register (or "accumulator") to express, such as input-dependency, event summation, resource usage, ensuring equal event magnitude, computation cost, etc. Some steps have been made towards verifying more basic temporal event sequences via reductions to fair termination [Murase et al. 2016] or some input-dependent properties through deductive proof systems [Nanjo et al. 2018]. However, there are currently no automated techniques to verify the more general class of register-automaton safety properties of higher-order programs. We introduce an abstract interpretation-based analysis to compute dependent, register-automata effects of recursive, higher-order programs. We capture properties of a program's effects in terms of automata that summarizes the history of observed effects using an accumulator register. The key novelty is a new abstract domain for context-dependent effects, capable of abstracting relations between the program environment, the automaton control state, and the accumulator value. The upshot is a dataflow type and effect system that computes context-sensitive effect summaries. We demonstrate our work via a prototype implementation that computes dependent effect summaries (and validates assertions) for OCaml-like recursive higher order programs. As a basis of comparison, we describe reductions to assertion checking for effect-free programs, and demonstrate that our approach outperforms prior tools Drift and RCaml/PCSat. Overall, across a set of 21 new benchmarks, RCaml/PCSat could not verify any, Drift verified 9 benchmarks, and evDrift verified 19; evDrift also had a 30.5x over Drift on those benchmarks that both tools could solve.

The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community., Comment: https://huggingface.co/spaces/CONDA-Workshop/Data-Contamination-Database

Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather such as the ECMWF ERA5. These datasets have been made freely available to the wider research community, including the commercial sector, which has been a major factor in the rapid rise of ML forecast systems and the levels of accuracy they have achieved. However, historical reanalyses used for training and real-time analyses used for initial conditions are produced by data assimilation, an optimal blending of observations with a physics-based forecast model. As such, many ML forecast systems have an implicit and unquantified dependence on the physics-based models they seek to challenge. Here we propose a new approach, training a neural network to predict future weather purely from historical observations with no dependence on reanalyses. We use raw observations to initialise a model of the atmosphere (in observation space) learned directly from the observations themselves. Forecasts of crucial weather parameters (such as surface temperature and wind) are obtained by predicting weather parameter observations (e.g. SYNOP surface data) at future times and arbitrary locations. We present preliminary results on forecasting observations 12-hours into the future. These already demonstrate successful learning of time evolutions of the physical processes captured in real observations. We argue that this new approach, by staying purely in observation space, avoids many of the challenges of traditional data assimilation, can exploit a wider range of observations and is readily expanded to simultaneous forecasting of the full Earth system (atmosphere, land, ocean and composition).

When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in the transmission network. We extract these patterns from utility outage data, determine some key statistics of these patterns, and then show how to generate new patterns consistent with these statistics. The generated patterns provide a new and easily feasible way to model the overall effect of the protection system at the scale of a large transmission system. This new generative modeling of protection is expected to contribute to simulations of disturbances in large grids so that they can better quantify the risk of blackouts. Analysis of the pattern sizes suggests an index that describes how much outages spread in the transmission network at the fast timescale.

We survey old and new results on the existence of moduli spaces of semistable coherent sheaves both in algebraic and in complex geometry.

High-dimensional panels of time series arise in many scientific disciplines such as neuroscience, finance, and macroeconomics. Often, co-movements within groups of the panel components occur. Extracting these groupings from the data provides a course-grained description of the complex system in question and can inform subsequent prediction tasks. We develop a novel methodology to model such a panel as a restricted vector autoregressive process, where the coefficient matrix is the weighted adjacency matrix of a stochastic block model. This network time series model, which we call the Network Informed Restricted Vector Autoregression (NIRVAR) model, yields a coefficient matrix that has a sparse block-diagonal structure. We propose an estimation procedure that embeds each panel component in a low-dimensional latent space and clusters the embedded points to recover the blocks of the coefficient matrix. Crucially, the method allows for network-based time series modelling when the underlying network is unobserved. We derive the bias, consistency and asymptotic normality of the NIRVAR estimator. Simulation studies suggest that the NIRVAR estimated embedded points are Gaussian distributed around the ground truth latent positions. On three applications to finance, macroeconomics, and transportation systems, NIRVAR outperforms competing models in terms of prediction and provides interpretable results regarding group recovery., Comment: 27 pages

In this paper we study an equivalence relation defined on the set of cycle-free $d$-partitions of the complete graph $K_{2d}$. We discuss a conjecture which states that this equivalence relation has only one equivalence class, and show that the conjecture is equivalent with the so called twin-star hypothesis. We check the conjecture in the case $d=4$ and disuses how this relates to the determinant-like map $det^{S^2}$., Comment: Comments are welcome

Rendering realistic images from 3D reconstruction is an essential task of many Computer Vision and Robotics pipelines, notably for mixed-reality applications as well as training autonomous agents in simulated environments. However, the quality of novel views heavily depends of the source reconstruction which is often imperfect due to noisy or missing geometry and appearance. Inspired by the recent success of reference-based super-resolution networks, we propose MaRINeR, a refinement method that leverages information of a nearby mapping image to improve the rendering of a target viewpoint. We first establish matches between the raw rendered image of the scene geometry from the target viewpoint and the nearby reference based on deep features, followed by hierarchical detail transfer. We show improved renderings in quantitative metrics and qualitative examples from both explicit and implicit scene representations. We further employ our method on the downstream tasks of pseudo-ground-truth validation, synthetic data enhancement and detail recovery for renderings of reduced 3D reconstructions., Comment: Accepted to ECCV 2024; Project Page: see https://boelukas.github.io/mariner/

Research in the field of language models is rapidly evolving, with many open models being released to the public. Openly available pretraining corpora usually focus on only a handful of languages, with many others either missing completely or extremely underrepresented. In this report, we introduce FuLG, a hundred-fifty-billion-token Romanian corpus extracted from CommonCrawl. We present our methodology for filtering FuLG and compare it via ablation studies against existing Romanian corpora.

We present closed forms for several functions that are fundamental in number theory and we explain the method used to obtain them. Concretely, we find formulas for the p-adic valuation, the number-of-divisors function, the sum-of-divisors function, Euler's totient function, the modular inverse, the integer part of the root, the integer part of the logarithm, the multiplicative order and the discrete logarithm. Although these are very complicated, they only involve elementary operations, and to our knowledge no other closed form of this kind is known for the aforementioned functions.

The European Committee for Future Accelerators (ECFA) Early-Career Researcher (ECR) panel, which represents the interests of the ECR community to ECFA, presents in this document its initiatives and activities in the year 2023. This report summarises the process of the first big turnover in the panel composition at the start of 2023 and reports on the activities of the active working groups - either pursued from before or newly established. The overarching goal of the ECFA-ECR panel is to better understand and support the diverse interests of early-career researchers in the ECFA community and beyond., Comment: Editors: Jan-Hendrik Arling, Cecilia Borca, Armin Ilg, Arnau Morancho Tarda, Holly Pacey, Marko Pesut, Elisabetta Spadaro Norella and Marta Urbaniak. arXiv admin note: substantial text overlap with arXiv:2212.11238