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Astronomical observations and numerical simulations are providing increasing evidence that resistive effects in plasmas around black holes play an important role in determining the phenomenology observed from these objects. In this spirit, we present a general approach to the study of a Penrose process driven by plasmoids that are produced at reconnection sites along current sheets. Our formalism is meant to determine the physical conditions that make a plasmoid-driven Penrose process energetically viable and can be applied to scenarios that are matter- or magnetic-field-dominated, that is, in magnetohydrodynamical or force-free descriptions. Our approach is genuinely multidimensional and hence allows one to explore conditions that are beyond the ones explored so far and that have been restricted to the equatorial plane, thus providing a direct contact with numerical simulations exhibiting an intense reconnection activity outside the equatorial plane. Finally, our analysis does not resort to ad-hoc assumptions about the dynamics of the plasma or adopts oversimplified and possibly unrealistic models to describe the kinematics of the plasma. On the contrary, we study the dynamics of the plasma starting from a well-known configuration, that of an equilibrium torus with a purely toroidal magnetic field whose "ergobelt", i.e. the portion penetrating the ergosphere, naturally provides a site to compute, self-consistently, the occurrence of reconnection and estimate the energetics of a plasmoid-driven Penrose process., Comment: v1: 5 pages + 3 appendix, 4 figures

In this paper, we first show that current learning-based video codecs, specifically the SSF codec, are not suitable for real-world applications due to the mismatch between the encoder and decoder caused by floating-point round-off errors. To address this issue, we propose the static quantization of the hyper prior decoding path. The quantization parameters are determined through an exhaustive search of all possible combinations of observers and quantization schemes from PyTorch. For the SSF codec, when encoding and decoding on different machines, the proposed solution effectively mitigates the mismatch issue and enhances compression efficiency results by preventing severe image quality degradation. When encoding and decoding are performed on the same machine, it constrains the average BD-rate increase to 9.93% and 9.02% for UVG and HEVC-B sequences, respectively., Comment: 4 pages, submitted to ISCAS2025

This research presents an online path planner for Unmanned Aerial Vehicles (UAVs) that can handle dynamic obstacles and UAV motion constraints, including maximum curvature and desired orientations. Our proposed planner uses a NURBS path representation and a Differential Evolution algorithm, incorporating concepts from the Velocity Obstacle approach in a constraint function. Initial results show that our approach is feasible and provides a foundation for future extensions to three-dimensional (3D) environments., Comment: Accepted to the 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)

In this work we discuss how femtoscopic analysis can shed light on the nature of the two lightest axial charmed mesons, denominated as $D_1(2430)$ and $D_1(2420)$, whose masses are similar but widths are different. Their properties are reasonably described taking into account meson-meson coupled channel dynamics and a bare quark-model pole constituting the lowest-order amplitudes. Two different bare quark-model states are used in order to accommodate the different scattering lengths coming from the lattice QCD calculations for the $D\pi$ and $D^*\pi$ systems and the data from the ALICE Collaboration on the $D\pi$ system. The amplitudes are employed as inputs to determine the correlation functions for the $D^{*}\pi$ and $D\rho$ channels and identify the signatures associated with the lowest-lying axial charmed mesons., Comment: 6 pages; 2 figures; Proceedings of The 10th International Conference on Quarks and Nuclear Physics, (QNP 2024) Jul 8-12, 2024, Barcelona, Spain

A good deal of science and technology concepts and methods rely on comparing and relating entities in quantitative terms. Among the several possible approaches, similarity indices allow some interesting features, especially the ability to quantify how much two entities resemble one another. In this work, the Jaccard similarity for comparing non-zero real-valued vectors is modified so as to estimate similarity while focusing on the distinct parts of the signals. The resulting operator, which is called partially proportional similarity index, not only allows more strict comparisons, but also paves the way to develop an adaptive approach to similarity estimation in which the size and orientation of the comparisons adapt to those of a respective calibration field expressing how the observed features are related to original counterparts. Being a particularly relevant concept in data analysis and modeling, emphasis is placed on presenting and discussing the concept of calibration field and how they can be taken into account while performing similarity comparisons. Several results are described which illustrate the potential of the reported concepts and approaches for enhancing, and even simultaneously normalizing to some extent the representation of entities in terms of their features, as frequently required in scientific modeling and pattern recognition., Comment: 46 pages, 32 figures

The Jaccard similarity index has often been employed in science and technology as a means to quantify the similarity between two sets. When modified to operate on real-valued values, the Jaccard similarity index can be applied to compare vectors, an operation which plays a central role in visualization, classification, and modeling. The present work aims at developing an analytical approach for estimating the probability density of the Jaccard similarity values as implied by set of data elements characterized by specific statistical densities, with emphasis on the uniform and normal cases. Several theoretical and practical situations can benefit directly from such an approach, as it allows several of the properties of the similarity comparisons among a given dataset to be better understood and anticipated. Situations in which the described approach can be applied include the estimation and visualization of data interrelationships in terms of similarity networks, as well as diverse problems in data analysis, pattern recognition and scientific modeling. In addition to presenting the analytical developments and results, examples are also provided in order to illustrate the potential of the approach. The work also includes extension of the reported developments to modifications of the Jaccard index intended for regularization and control of the sharpness of the implemented comparisons.

Supermassive binary black holes in galactic centers are potential multimessenger sources in gravitational waves and electromagnetic radiation. To find such objects, isolating unique electromagnetic signatures of their accretion flow is key. With the aid of three-dimensional general-relativistic magnetohydrodynamic (GRMHD) simulations that utilize an approximate, semi-analytic, super-imposed spacetime metric, we identify two such signatures for merging binaries. Both involve magnetic reconnection and are analogous to plasma processes observed in the solar corona. The first, like colliding flux tubes that can cause solar flares, involves colliding jets that form an extended reconnection layer, dissipating magnetic energy and causing the two jets to merge. The second, akin to coronal mass ejection events, involves the accretion of magnetic field lines onto both black holes; these magnetic fields then twist, inflate, and form a trailing current sheet, ultimately reconnecting and driving a hot outflow. We provide estimates for the associated electromagnetic emission for both processes, showing that they likely accelerate electrons to high energies and are promising candidates for continuous, stochastic, and/or quasi-periodic higher energy electromagnetic emission. We also show that the accretion flows around each black hole can display features associated with the magnetically arrested state. However, simulations with black hole spins misaligned with the orbital plane and simulations with larger Bondi radii saturate at lower values of horizon-penetrating magnetic flux than standard magnetically arrested disks, leading to weaker, intermittent jets due to feedback from the weak jets or equatorial flux tubes ejected by reconnecting field lines near the horizon., Comment: 21 pages, 8 Figures, Submitted to ApJL. Animations available at https://www.youtube.com/playlist?list=PL3pLmTeUPcqThCLYhkSrkEBOUutrnSJWq

We explore the concept of negative pressure and its relevance in a variety of physical contexts: the expansion of the universe, mixture theory, cavitation, and the capillary effect in plants. Using thermodynamic arguments, we discuss the intricate connection between negative pressure and negative thermal expansion. We highlight the fact that metastable states and competing phases are often associated with the emergence of negative pressure. We also propose a new link between the effective Gr\"uneisen parameter and nucleation theory., Comment: 15 pages, 5 figs, comments are welcome

This thesis addresses the theory of topological spaces and the foundations of persistence theory. We will discuss chain complexes and the associated simplicial homology groups, as well as their relationship with singular homology theory. Moreover, we present the fundamental concepts of algebraic topology, including exact and short exact sequences and relative homology groups derived from quotienting with subspaces of a topological space. These tools are used to prove the Excision Theorem in algebraic topology. Subsequently, the theorem is applied to demonstrate the equivalence of simplicial and singular homology for triangulable topological spaces, i.e. those topological spaces which admit a simplicial structure. This enables a more general theory of homology to be adopted in the study of filtrations of point clouds. The chapter on homological persistence makes use of these tools throughout. We develop the theory of persistent homology, the homology of filtrations of topological spaces, and the corresponding dual concept of persistent cohomology. This work aims to provide mathematicians with a robust foundation for productive engagement with the aforementioned theories. The majority of the proofs have been rewritten to clarify the relationships between the techniques discussed. The novel aspect of this contribution is the canonical presentation of persistence theory and the associated ideas through a rigorous mathematical treatment for triangulable topological spaces and closing some gaps in the existing literature., Comment: Bachelor's thesis

We frame dynamic persuasion in a partial observation stochastic control game with an ergodic criterion. The Receiver controls the dynamics of a multidimensional unobserved state process. Information is provided to the Receiver through a device designed by the Sender that generates the observation process. The commitment of the Sender is enforced. We develop this approach in the case where all dynamics are linear and the preferences of the Receiver are linear-quadratic. We prove a verification theorem for the existence and uniqueness of the solution of the HJB equation satisfied by the Receiver's value function. An extension to the case of persuasion of a mean field of interacting Receivers is also provided. We illustrate this approach in two applications: the provision of information to electricity consumers with a smart meter designed by an electricity producer; the information provided by carbon footprint accounting rules to companies engaged in a best-in-class emissions reduction effort. In the first application, we link the benefits of information provision to the mispricing of electricity production. In the latter, we show that when firms declare a high level of best-in-class target, the information provided by stringent accounting rules offsets the Nash equilibrium effect that leads firms to increase pollution to make their target easier to achieve., Comment: 2 figures

A significant interest has emerged recently in assessing whether collimated and ultra-relativistic outflows can be produced by a long-lived remnant from a binary neutron-star (BNS) merger, with different approaches leading to different outcomes. To clarify some of the aspect of this process, we report the results of long-term (\ie $\sim~110\,{\rm ms}$) state-of-the-art general-relativistic magnetohydrodynamics simulations of the inspiral and merger of a BNS system of magnetized stars. We find that after $\sim~50\,{\rm ms}$ from the merger, an $\alpha$-$\Omega$~dynamo driven by the magnetorotational instability (MRI) sets-in in the densest regions of the disk and leads to the breakout of the magnetic-field lines from the accretion disk around the remnant. The breakout, which can be associated with the violation of the Parker-stability criterion, is responsible for the generation of a collimated, magnetically-driven outflow with only mildly relativistic velocities that is responsible for a violent eruption of electromagnetic energy. We provide evidence that this outflow is partly collimated via a Blandford-Payne mechanism driven by the open field lines anchored in the inner disk regions. Finally, by including or not the radiative transport via neutrinos, we determine the role they play in the launching of the collimated wind. In this way, we conclude that the mechanism of magnetic-field breakout we observe is robust and takes place even without neutrinos. Contrary to previous expectations, the inclusion of neutrinos absorption and emission leads to a smaller baryon pollution in polar regions, and hence accelerates the occurrence of the breakout, yielding a larger electromagnetic luminosity. Given the mildly relativistic nature of these disk-driven breakout outflows, it is difficult to consider them responsible for the jet phenomenology observed in short gamma-ray bursts.

The Industrial Internet of Things (IIoT) paradigm has emerged as a transformative force, revolutionizing industrial processes by integrating advanced wireless technologies into traditional procedures to enhance their efficiency. The importance of this paradigm shift has produced a massive, yet heterogeneous, proliferation of scientific contributions. However, these works lack a standardized and cohesive characterization of the IIoT framework coming from different entities, like the 3rd Generation Partnership Project (3GPP) or the 5G Alliance for Connected Industries and Automation (5G-ACIA), resulting in divergent perspectives and potentially hindering interoperability. To bridge this gap, this article offers a unified characterization of (i) the main IIoT application domains, (ii) their respective requirements, (iii) the principal technological gaps existing in the current literature, and, most importantly, (iv) we propose a systematic approach for assessing and addressing the identified research challenges. Therefore, this article serves as a roadmap for future research endeavors, promoting a unified vision of the IIoT paradigm and fostering collaborative efforts to advance the field.

Large Language Models (LLMs) are poised to revolutionize healthcare. Ophthalmology-specific LLMs remain scarce and underexplored. We introduced an open-source, specialized LLM for ophthalmology, termed Language Enhanced Model for Eye (LEME). LEME was initially pre-trained on the Llama2 70B framework and further fine-tuned with a corpus of ~127,000 non-copyrighted training instances curated from ophthalmology-specific case reports, abstracts, and open-source study materials. We benchmarked LEME against eight other LLMs, namely, GPT-3.5, GPT-4, three Llama2 models (7B, 13B, 70B), PMC-LLAMA 13B, Meditron 70B, and EYE-Llama (another ophthalmology-specific LLM). Evaluations included four internal validation tasks: abstract completion, fill-in-the-blank, multiple-choice questions (MCQ), and short-answer QA. External validation tasks encompassed long-form QA, MCQ, patient EHR summarization, and clinical QA. Evaluation metrics included Rouge-L scores, accuracy, and expert evaluation of correctness, completeness, and readability. In internal validations, LEME consistently outperformed its counterparts, achieving Rouge-L scores of 0.20 in abstract completion (all p<0.05), 0.82 in fill-in-the-blank (all p<0.0001), and 0.22 in short-answer QA (all p<0.0001, except versus GPT-4). In external validations, LEME excelled in long-form QA with a Rouge-L of 0.19 (all p<0.0001), ranked second in MCQ accuracy (0.68; all p<0.0001), and scored highest in EHR summarization and clinical QA (ranging from 4.24 to 4.83 out of 5 for correctness, completeness, and readability). LEME's emphasis on robust fine-tuning and the use of non-copyrighted data represents a breakthrough in open-source ophthalmology-specific LLMs, offering the potential to revolutionize execution of clinical tasks while democratizing research collaboration.

In a temporal forest each edge has an associated set of time labels that specify the time instants in which the edges are available. A temporal path from vertex $u$ to vertex $v$ in the forest is a selection of a label for each edge in the unique path from $u$ to $v$, assuming it exists, such that the labels selected for any two consecutive edges are non-decreasing. We design linear-size data structures that maintain a temporal forest of rooted trees under addition and deletion of both edge labels and singleton vertices, insertion of root-to-node edges, and removal of edges with no labels. Such data structures can answer temporal reachability, earliest arrival, and latest departure queries. All queries and updates are handled in polylogarithmic worst-case time. Our results can be adapted to deal with latencies. More precisely, all the worst-case time bounds are asymptotically unaffected when latencies are uniform. For arbitrary latencies, the update time becomes amortized in the incremental case where only label additions and edge/singleton insertions are allowed as well as in the decremental case in which only label deletions and edge/singleton removals are allowed. To the best of our knowledge, the only previously known data structure supporting temporal reachability queries is due to Brito, Albertini, Casteigts, and Traven\c{c}olo [Social Network Analysis and Mining, 2021], which can handle general temporal graphs, answers queries in logarithmic time in the worst case, but requires an amortized update time that is quadratic in the number of vertices, up to polylogarithmic factors., Comment: ISAAC 2024

We construct and discuss generic N=1 and N=2 Carroll dilaton supergravity in two dimensions. We apply our general results to the supersymmetric Carroll-Jackiw-Teitelboim model, including a discussion of specific boundary conditions. For N=2 Carroll dilaton supergravity, we find two versions, dubbed "democratic" and "despotic"., Comment: 33pp

Protein compartmentalization in the frame of a liquid-liquid phase separation is a key mechanism to optimize spatiotemporal control of biological systems. Such a compartmentalization process reduces the intrinsic noise in protein concentration due to stochasticity in gene expression. Employing Flory-Huggins solution theory, Avramov/Casalini's model, and the Gr\"uneisen parameter, we unprecedentedly propose a cellular Griffiths-like phase (CGLP), which can impact its functionality and self-organization. The here-proposed CGLP is key ranging from the understanding of primary organisms' evolution to the treatment of diseases. Our findings pave the way for an alternative Biophysics approach to investigate coacervation processes., Comment: 13 pages, 2 figures, suppl. material upon request

Logic Tensor Networks (LTN) is a Neuro-Symbolic framework that effectively incorporates deep learning and logical reasoning. In particular, LTN allows defining a logical knowledge base and using it as the objective of a neural model. This makes learning by logical reasoning possible as the parameters of the model are optimized by minimizing a loss function composed of a set of logical formulas expressing facts about the learning task. The framework learns via gradient-descent optimization. Fuzzy logic, a relaxation of classical logic permitting continuous truth values in the interval [0,1], makes this learning possible. Specifically, the training of an LTN consists of three steps. Firstly, (1) the training data is used to ground the formulas. Then, (2) the formulas are evaluated, and the loss function is computed. Lastly, (3) the gradients are back-propagated through the logical computational graph, and the weights of the neural model are changed so the knowledge base is maximally satisfied. LTNtorch is the fully documented and tested PyTorch implementation of Logic Tensor Networks. This paper presents the formalization of LTN and how LTNtorch implements it. Moreover, it provides a basic binary classification example., Comment: 5 pages, 2 figures

Simulation-based testing is widely used to assess the reliability of Autonomous Driving Systems (ADS), but its effectiveness is limited by the operational design domain (ODD) conditions available in such simulators. To address this limitation, in this work, we explore the integration of generative artificial intelligence techniques with physics-based simulators to enhance ADS system-level testing. Our study evaluates the effectiveness and computational overhead of three generative strategies based on diffusion models, namely instruction-editing, inpainting, and inpainting with refinement. Specifically, we assess these techniques' capabilities to produce augmented simulator-generated images of driving scenarios representing new ODDs. We employ a novel automated detector for invalid inputs based on semantic segmentation to ensure semantic preservation and realism of the neural generated images. We then perform system-level testing to evaluate the ADS's generalization ability to newly synthesized ODDs. Our findings show that diffusion models help increase the ODD coverage for system-level testing of ADS. Our automated semantic validator achieved a percentage of false positives as low as 3\%, retaining the correctness and quality of the generated images for testing. Our approach successfully identified new ADS system failures before real-world testing., Comment: 10 pages + 2 for references

Many real-world tasks are intuitive for a human to perform, but difficult to encode algorithmically when utilizing a robot to perform the tasks. In these scenarios, robotic systems can benefit from expert demonstrations to learn how to perform each task. In many settings, it may be difficult or unsafe to use a physical robot to provide these demonstrations, for example, considering cooking tasks such as slicing with a knife. Extended reality provides a natural setting for demonstrating robotic trajectories while bypassing safety concerns and providing a broader range of interaction modalities. We propose the Robot Action Demonstration in Extended Reality (RADER) system, a generic extended reality interface for learning from demonstration. We additionally present its application to an existing state-of-the-art learning from demonstration approach and show comparable results between demonstrations given on a physical robot and those given using our extended reality system., Comment: In submission

In search of substrates for adsorption of He atoms allowing for novel quantum phases in restricted geometry we study the case of borophane. We focus on two allotropes of borophane, alpha-4H and Rect-2H. With a suitable Density Functional Theory we characterize the adsorption potential of a He atom on such crystalline substrates finding its corrugation, the preferential adsorption sites and the energy barrier between sites. Rect-2H borophane is particularly interesting due to thepresence of ridges in the adsorption potential with modest energy barriers in one direction of the basal plane and much higher barrier in the orthogonal direction, thus forming channels for motion of the adsorbed atoms. We study the adsorption of He-4 on Rect-2H borophane using Path Integral Monte Carlo simulations. In the first adsorbed layer the He-4 atoms are rather delocalized along a channel with no exchanges between channels. This strong anisotropy is present also in the first few additional adsorption layers of He-4 with presence of ordered and of disordered regions. In the second and the fifth layers at low temperature we find superfluidity on the length scale of the simulated systems. In the second layer the superfluidity is one-dimensional along the grooves. In the fifth layer the state is a strongly anisotropic two-dimensional superfluid at low coverage, with a crossover to an isotropic one at layer completion. Starting from the sixth layer the adsorbed He-4 film evolves toward a three-dimensional superfluid. Our main prediction is that Rect-2H borophane as a substrate will allow to probe 1D superfluidity in the second absorption layer, as well as the evolution from a 2D anisotropic superfluid to an isotropic one in the fifth layer, and eventually the onset of 3D superfluidity for higher coverages., Comment: 13 pages, 17 figures

The subject of features normalization plays an important central role in data representation, characterization, visualization, analysis, comparison, classification, and modeling, as it can substantially influence and be influenced by all of these activities and respective aspects. The selection of an appropriate normalization method needs to take into account the type and characteristics of the involved features, the methods to be used subsequently for the just mentioned data processing, as well as the specific questions being considered. After briefly considering how normalization constitutes one of the many interrelated parts typically involved in data analysis and modeling, the present work addressed the important issue of feature normalization from the perspective of uniform and proportional (right skewed) features and comparison operations. More general right skewed features are also considered in an approximated manner. Several concepts, properties, and results are described and discussed, including the description of a duality relationship between uniform and proportional feature spaces and respective comparisons, specifying conditions for consistency between comparisons in each of the two domains. Two normalization possibilities based on non-centralized dispersion of features are also presented, and also described is a modified version of the Jaccard similarity index which incorporates intrinsically normalization. Preliminary experiments are presented in order to illustrate the developed concepts and methods., Comment: 31 pages, 10 figures

We present Benchpress, a benchmarking suite for evaluating the performance and range of functionality of multiple quantum computing software development kits. This suite consists of a collection of over $1000$ tests measuring key performance metrics for a wide variety of operations on quantum circuits comprised of up to $930$ qubits and $\mathcal{O}(10^{6})$ two-qubit gates, as well as an execution framework for running the tests over multiple quantum software packages in a unified manner. We give a detailed overview of the benchmark suite, its methodology, and generate representative results over seven different quantum software packages. The flexibility of the Benchpress framework allows for benchmarking that not only keeps pace with quantum hardware improvements but can preemptively gauge the quantum circuit processing costs of future device architectures. Being open-source, Benchpress ensures the transparency and verification of performance claims., Comment: 11 pages, 4 figures, 2 tables

Black hole (BH) - neutron star (NS) binary mergers are not only strong sources of gravitational waves (GWs), but they are also candidates for joint detections in the GW and electromagnetic (EM) spectra. However, the possible emergence of an EM signal from these binaries is determined by a complex combination of the equation of state (EOS), the BH spin, and the mass ratio. In this second paper in a series, we present a systematic exploration of the possible space of binary parameters in terms of the mass ratio and BH spin so as to construct a complete description of the dynamical processes accompanying a BHNS binary merger. This second work relies not only on the initial data presented in the companion paper I, but also on the predictions via quasi-equilibrium (QE) sequences on the outcome of the binary. In this way, and for the first time, we are able to relate the predictions of QE analyses with the results of accurate general-relativistic magnetohydrodynamic simulations. In addition to a careful investigation of the evolution of the BH mass and spin as a result of the merger, the total remnant rest-mass of the resulting accretion disk and its properties, and of the corresponding post-merger GW emission, special attention is paid to the conditions that lead to tidal disruption. Leveraging QE calculations, we are able to verify the reliability of stringent predictions about the occurrence or not of a plunge and to measure the `strength' of the tidal disruption when it takes place. Finally, using a novel contraction of the Riemann tensor in a tetrad comoving with the fluid introduced in paper I, we are able to point out the onset of the instability to tidal disruption. This new diagnostic can be employed not only to determine the occurrence of the disruption, but also to characterize it in terms of the binary parameters., Comment: 26 pages, 19 figures. Movies of BHNS evolutions can be accessed at https://youtu.be/s8n1qrPKdVY, at https://youtu.be/frxP4TnSLc4m, at https://youtu.be/MoD4sbvev_o and at https://youtu.be/7B0oOe-nFYs

Black hole - neutron star (BHNS) mergers are a promising target of current gravitational-wave (GW) and electromagnetic (EM) searches, being the putative origin of ultra-relativistic jets, gamma-ray emission, and r-process nucleosynthesis. However, the possibility of any EM emission accompanying a GW detection crucially depends on the amount of baryonic mass left after the coalescence, i.e. whether the neutron star (NS) undergoes a `tidal disruption' or `plunges' into the black hole (BH) while remaining essentially intact. As the first of a series of two papers, we here report the most systematic investigation to date of quasi-equilibrium sequences of initial data across a range of stellar compactnesses $\mathcal{C}$, mass ratios $q$, BH spins $\chi_{_{\rm BH}}$, and equations of state satisfying all present observational constraints. Using an improved version of the elliptic initial-data solver FUKA, we have computed more than $1000$ individual configurations and estimated the onset of mass-shedding or the crossing of the innermost stable circular orbit in terms of the corresponding characteristic orbital angular velocities $\Omega_{_{\rm MS}}$ and $\Omega_{_{\rm ISCO}}$ as a function of $\mathcal{C}, q$, and $\chi_{_{\rm BH}}$. To the best of our knowledge, this is the first time that the dependence of these frequencies on the BH spin is investigated. In turn, by setting $\Omega_{_{\rm MS}} = \Omega_{_{\rm ISCO}}$ it is possible to determine the separatrix between the `tidal disruption' or `plunge' scenarios as a function of the fundamental parameters of these systems, namely, $q, \mathcal{C}$, and $\chi_{_{\rm BH}}$. Finally, we present a novel analysis of quantities related to the tidal forces in the initial data and discuss their dependence on spin and separation., Comment: 26 pages and 26 figures

The X-ray Integral Field Unit (X-IFU) is an instrument of ESA's future NewAthena space observatory, with the goal to provide high-energy resolution ($<$ 4 eV at X-ray energies up to 7 keV) and high-spatial resolution (9") spectroscopic imaging over the X-ray energy range from 200 eV to 12 keV, by means of an array of about 1500 transition-edge sensors (TES) read out via SQUID time-division multiplexing (TDM). A TDM-based laboratory test-bed has been assembled at SRON, hosting an array of $75\times 75\ \upmu$m$^2$ TESs that are read out via 2-column $\times$ 32-row TDM. A system component that is critical to high-performance operation is the wiring harness that connects the room-temperature electronics to the cryogenic readout componentry. We report here on our characterization of such a test-bed, whose harness has a length close to what envisioned for X-IFU, which allowed to achieve a co-added energy resolution at a level of 2.7~eV FWHM at 6~keV via 32-row readout. In addition, we provide an outlook on the integration of TDM readout into the X-IFU Focal-Plane Assembly Development Model., Comment: Submitted for publication to Journal of Astronomical Telescopes, Instrumentation and Systems. arXiv admin note: text overlap with arXiv:2403.02978

The femtoscopic $\phi N$ correlation function is studied within a hadronic effective Lagrangian approach with coupled channels, based on hidden local symmetry. The results are compared with the data recently reported by the ALICE collaboration. We find that the correlation function has very different features for the $\phi N$ system in the spin 1/2 and 3/2 configurations, with the spin-averaged combination matching well with the experimental data. A strong attraction, leading to the formation of a $\phi N$ bound state or a very prominent cusp is found in the spin 3/2 case. The correlation function for spin 1/2, on the other hand, is strongly impacted by the negative parity $N^*(1895)$ nucleon resonance., Comment: Updated version with some typos corrected and new references added

Currently envisaged tests for probing the quantum nature of the gravitational interaction in the low-energy regime typically focus either on the quantized center-of-mass degrees of freedom of two spherically-symmetric test masses or on the rotational degrees of freedom of non-symmetric masses under a gravitational interaction governed by the Newtonian potential. In contrast, here we investigate the interaction between the angular momenta of spherically-symmetric test masses considering a tree-level relativistic correction related to frame-dragging that leads to an effective dipolar interaction between the angular momenta. In this approach, the mass of the probes is not directly relevant; instead, their angular momentum plays the central role. We demonstrate that, while the optimal entangling rate is achieved with a maximally delocalized initial state, significant quantum correlations can still arise between two rotating systems even when each is initialized in an eigenstate of rotation. Additionally, we examine the robustness of the generated entanglement against typical sources of noise and observe that our combination of angular momentum and spherically-symmetric test-masses mitigates the impact of many common noise sources.

Pattern recognition constitutes a particularly important task underlying a great deal of scientific and technologica activities. At the same time, pattern recognition involves several challenges, including the choice of features to represent the data elements, as well as possible respective transformations. In the present work, the classification potential of the Euclidean distance and a dissimilarity index based on the coincidence similarity index are compared by using the k-neighbors supervised classification method respectively to features resulting from several types of transformations of one- and two-dimensional symmetric densities. Given two groups characterized by respective densities without or with overlap, different types of respective transformations are obtained and employed to quantitatively evaluate the performance of k-neighbors methodologies based on the Euclidean distance an coincidence similarity index. More specifically, the accuracy of classifying the intersection point between the densities of two adjacent groups is taken into account for the comparison. Several interesting results are described and discussed, including the enhanced potential of the dissimilarity index for classifying datasets with right skewed feature densities, as well as the identification that the sharpness of the comparison between data elements can be independent of the respective supervised classification performance., Comment: 25 page and 16 figures

An infinite game on the set of real numbers appeared in Matthew Baker's work [Math. Mag. 80 (2007), no. 5, pp. 377--380] in which he asks whether it can help characterize countable subsets of the reals. This question is in a similar spirit to how the Banach-Mazur Game characterizes meager sets in an arbitrary topological space. In a recent paper, Will Brian and Steven Clontz prove that in Baker's game, Player II has a winning strategy if and only if the payoff set is countable. They also asked if it is possible, in general linear orders, for Player II to have a winning strategy on some uncountable set. To this we give a positive answer and moreover construct, for every infinite cardinal $\kappa$, a dense linear order of size $\kappa$ on which Player II has a winning strategy on all payoff sets. We finish with some future research questions, further underlining the difficulty in generalizing the characterization of Brian and Clontz to linear orders.

In this paper, we introduce Rorqual, a protocol designed to enhance the performance of the Narwhal Mempool by integrating Trusted Execution Environments (TEEs). Both Narwhal and Roqual are protocols based on a Directed Acyclic Graph (DAG). Compared to Narwhal, Rorqual achieves significant reductions in latency and increases throughput by streamlining the steps required to include a vertex in the DAG. The use of TEEs also reduces the communication complexity of the protocol while maintaining low computational costs. Through rigorous analysis, we demonstrate the protocol's robustness under both normal and adversarial conditions, highlighting its improvements in throughput, latency, and security., Comment: 11 pages, 1 table, 9 algorithms

During the last years, algorithms known as Convolutional Neural Networks (CNNs) had become increasingly popular, expanding its application range to several areas. In particular, the image processing field has experienced a remarkable advance thanks to this algorithms. In IoT, a wide research field aims to develop hardware capable of execute them at the lowest possible energy cost, but keeping acceptable image inference time. One can get around this apparently conflicting objectives by applying design and training techniques. The present work proposes a generic hardware architecture ready to be implemented on FPGA devices, supporting a wide range of configurations which allows the system to run different neural network architectures, dynamically exploiting the sparsity caused by pruning techniques in the mathematical operations present in this kind of algorithms. The inference speed of the design is evaluated over different resource constrained FPGA devices. Finally, the standard pruning algorithm is compared against a custom pruning technique specifically designed to exploit the scheduling properties of this hardware accelerator. We demonstrate that our hardware-aware pruning algorithm achieves a remarkable improvement of a 45 % in inference time compared to a network pruned using the standard algorithm., Comment: 8 pages, 7 figure, thesis for the title of Electronic Engineer attained in 2021 at the Universidad Tecnologica Nacional (UTN), Argentina

We investigate the gravitational origin of the Tsallis entropy, characterized by the nonadditive index $\delta$. Utilizing Wald's formalism within the framework of $f(R)$ modified theories of gravity, we evaluate the entropy on the black hole horizon for constant curvature solutions to the spherically symmetric vacuum field equations. In so doing, we demonstrate that the Tsallis entropy can be effectively derived from a generalization of the Lagrangian $\mathcal{L}\propto R^{1+\epsilon}$, where $\epsilon=\delta-1$ quantifies small deviations from general relativity. In conclusion, we examine the physical implications of our findings in light of cosmological observations and elaborate on the possibility of solving the thermodynamic instability of Schwarzschild black holes., Comment: 8 pages. Accepted for publication in Physics Letters B

Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. Ideally, between these two extreme models lies a continuous range of models that adopt different levels of sequentiality. This paper proposes a graph generative model, called Insert-Fill-Halt (IFH), that supports the specification of a sequentiality degree. IFH is based upon the theory of Denoising Diffusion Probabilistic Models (DDPM), designing a node removal process that gradually destroys a graph. An insertion process learns to reverse this removal process by inserting arcs and nodes according to the specified sequentiality degree. We evaluate the performance of IFH in terms of quality, run time, and memory, depending on different sequentiality degrees. We also show that using DiGress, a diffusion-based one-shot model, as a generative step in IFH leads to improvement to the model itself, and is competitive with the current state-of-the-art., Comment: Accepted at the 27th European Conference on Artificial Intelligence (ECAI 24)

Two graphs having the same spectrum are said to be cospectral. Two graphs such that the absolute values of their nonzero eigenvalues coincide are singularly cospectral graphs. Cospectrality implies singular cospectrality, but the converse may be false. In this paper, we present sufficient conditions for two circulant graphs, with an even number of vertices, to be noncospectral singularly cospectral graphs. In this analysis, we study when a pair of these graphs have the same or distinct inertia. In addition, we show that two singularly cospectral circulant graphs with an odd prime number of vertices are isomorphic.

A signed graph is one that features two types of edges: positive and negative. Balanced signed graphs are those in which all cycles contain an even number of positive edges. In the adjacency matrix of a signed graph, entries can be $0$, $-1$, or $1$, depending on whether $ij$ represents no edge, a negative edge, or a positive edge, respectively. The index of the adjacency matrix of a signed graph $\dot{G}$ is less or equal to the index of the adjacency matrix of its underlying graph $G$, i.e., $\lambda_1(\dot{G}) \le \lambda_1(G)$. Indeed, if $\dot{G}$ is balanced, then $\lambda_1(\dot{G})=\lambda_1(G)$. This inequality becomes strict when $\dot{G}$ is an unbalanced signed graph. Recently, Brunetti and Stani\'c found the whole list of unbalanced signed graphs on $n$ vertices with maximum (resp. minimum) spectral radius. To our knowledge, there has been little research on this problem when unbalanced signed graphs are confined to specific graph classes. In this article, we demonstrate that there is only one unbalanced signed bipartite graph on $n$ vertices with maximum spectral radius, up to an operation on the signed edges known as switching. Additionally, we investigate unbalanced signed complete bipartite graphs on $n$ vertices with a bounded number of edges and maximum spectral radius, where the negative edges induce a tree.

We study a mathematical model to describe the evolution of a city, which is determined by the interaction of two large populations of agents, workers and firms. The map of the city is described by a network with the edges representing at the same time residential areas and communication routes. The two populations compete for space while interacting through the labour market. The resulting model is described by a two population Mean-Field Game system coupled with an Optimal Transport problem.We prove existence and uniqueness of the solution and we provide several numerical simulations.

We investigate an infinite dimensional partial differential equation of Isaacs' type, which arises from a zero-sum differential game between two masses. The evolution of the two masses is described by a controlled transport/continuity equation, where the control is given by the vector velocity field. Our study is set in the framework of the viscosity solutions theory in Hilbert spaces, and we prove the uniqueness of the value functions as solutions of the Isaacs equation.

The original goal of this paper was a novel, fast atomic-snapshot protocol for asynchronous message-passing systems. In the process of defining what fast means exactly, we faced a number of interesting issues that arise when conventional time metrics are applied to asynchronous implementations. We discovered some gaps in latency claims made in earlier work on snapshot algorithms, which hampers their comparative time-complexity analysis. We then came up with a new unifying time-complexity analysis that captures the latency of an operation in an asynchronous, long-lived implementation, which allowed us to formally grasp latency improvements of our solution with respect to the state-of-the-art protocols: optimal latency in fault-free runs without contention, short constant latency in fault-free runs with contention, the worst-case latency proportional to the number of failures, and constant, close to optimal amortized latency.

We report on the discovery of polarized X-ray emission from an accreting millisecond pulsar. During a 10-day-long coverage of the February 2024 outburst of SRGA J144459.2-604207, the Imaging X-ray Polarimetry Explorer (IXPE) detected an average polarization degree of the 2-8 keV emission of 2.3% +/- 0.4% at an angle of 59{\deg} +/- 6{\deg} (East of North; uncertainties quoted at the 1${\sigma}$ confidence level). The polarized signal shows a significant energy dependence with a degree of 4.0% +/- 0.5% between 3 and 6 keV and < 2% (90% c.l.) in the 2-3 keV range. We used NICER, XMM-Newton, and NuSTAR observations to obtain an accurate pulse timing solution and perform a phase-resolved polarimetric analysis of IXPE data. We did not detect any significant variability of the Stokes parameters Q and U with the spin and the orbital phases. We used the relativistic rotating vector model to show that a moderately fan-beam emission from two point-like spots at a small magnetic obliquity ($\simeq$ 10{\deg}) is compatible with the observed pulse profile and polarization properties. IXPE also detected 52 type-I X-ray bursts, with a recurrence time $\Delta t_{rec}$ increasing from 2 to 8 h as a function of the observed count rate $C$ as as $\Delta t_{rec} \simeq C^{-0.8}$ We stacked the emission observed during all the bursts and obtained an upper limit on the polarization degree of 8.5% (90% c.l.)., Comment: 13 pages, 9 figures, submitted to ApJL

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

We consider the task of performing Jaccard similarity queries over a large collection of items that are dynamically updated according to a streaming input model. An item here is a subset of a large universe $U$ of elements. A well-studied approach to address this important problem in data mining is to design fast-similarity data sketches. In this paper, we focus on global solutions for this problem, i.e., a single data structure which is able to answer both Similarity Estimation and All-Candidate Pairs queries, while also dynamically managing an arbitrary, online sequence of element insertions and deletions received in input. We introduce and provide an in-depth analysis of a dynamic, buffered version of the well-known $k$-MinHash sketch. This buffered version better manages critical update operations thus significantly reducing the number of times the sketch needs to be rebuilt from scratch using expensive recovery queries. We prove that the buffered $k$-MinHash uses $O(k \log |U|)$ memory words per subset and that its amortized update time per insertion/deletion is $O(k \log |U|)$ with high probability. Moreover, our data structure can return the $k$-MinHash signature of any subset in $O(k)$ time, and this signature is exactly the same signature that would be computed from scratch (and thus the quality of the signature is the same as the one guaranteed by the static $k$-MinHash). Analytical and experimental comparisons with the other, state-of-the-art global solutions for this problem given in [Bury et al.,WSDM'18] show that the buffered $k$-MinHash turns out to be competitive in a wide and relevant range of the online input parameters.

In this work, we examine the optical behaviors and thermodynamic phase structures using shadow analysis for four $\mathbb{S}\mathbb{T}\mathbb{U}$ black holes. The study is conducted for four cases of charge configurations on the parameter space $\mathcal{M}\left(q_1,q_2,q_3,q_4\right)$. As a matter of fact, both the electric charge as a parameter and the parameter space $\mathcal{M}\left(q_1,q_2,q_3,q_4\right)$ affect the geometry of the black hole shadow, particularly the size of the shadow. We also introduce a constraint on the charge of the black hole from the observational results of the M87$^\star$ {\color{black}and Sgr A$^\star$} shadow. Furthermore, we show that the electric charge and the parameter space $\mathcal{M}\left(q_1,q_2,q_3,q_4\right)$ have a non-trivial impact on the variation of the energy emission rate. Interestingly enough, we find novel scenarios in which the evaporation is slower, which causes the lifetime of the black holes to be considerably elongated. On the other side, the phase structure of four $\mathbb{S}\mathbb{T}\mathbb{U}$ black holes is explored for two cases of electric charge configuration. The findings show a perfect correlation between the shadow and event horizon radii. This correlation is, in fact, helpful in discovering the phase transition in terms of the shadow radius. In addition, the microstructure is being analyzed in terms of shadow analysis, providing similar behavior to the ordinary situation of the Ruppeiner formalism., Comment: 32 pages, 14 labeled figures, accepted for publication in Classical and Quantum Gravity

MoS2 is one of the most investigated and promising transition-metal dichalcogenides. Its popularity stems from the interesting properties of the monolayer phase, which can serve as the fundamental block for numerous applications. In this paper, we propose an atomistic perspective on the modulation of thermal transport properties in monolayer MoS2 through strategic defect engineering, specifically the introduction of sulfur vacancies. Using a combination of molecular dynamics simulations and lattice dynamics calculations, we show how various distributions of sulfur vacancies -- ranging from random to periodically arranged configurations -- affect its thermal conductivity. Notably, we observe that certain periodic arrangements restore the thermal conductivity of the pristine system, due to a minimized interaction between acoustic and optical phonons facilitated by the imposed superperiodicity. This research deepens the understanding of phononic heat transport in two-dimensional materials and introduces a different point-of-view for phonon engineering in nanoscale devices, offering a pathway to enhance device performance and longevity through tailored thermal management strategies.

We investigate the classification performance of graph neural networks with graph-polynomial features, poly-GNNs, on the problem of semi-supervised node classification. We analyze poly-GNNs under a general contextual stochastic block model (CSBM) by providing a sharp characterization of the rate of separation between classes in their output node representations. A question of interest is whether this rate depends on the depth of the network $k$, i.e., whether deeper networks can achieve a faster separation? We provide a negative answer to this question: for a sufficiently large graph, a depth $k > 1$ poly-GNN exhibits the same rate of separation as a depth $k=1$ counterpart. Our analysis highlights and quantifies the impact of ``graph noise'' in deep GNNs and shows how noise in the graph structure can dominate other sources of signal in the graph, negating any benefit further aggregation provides. Our analysis also reveals subtle differences between even and odd-layered GNNs in how the feature noise propagates.

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, protocols and more, to empower innovation., Comment: 8 pages, 1 figure, 9 references

In the context of the CERN EP R&D on monolithic sensors and the ALICE ITS3 upgrade, the Tower Partners Semiconductor Co (TPSCo) 65 nm process has been qualified for use in high energy physics, and adopted for the ALICE ITS3 upgrade. An Analog Pixel Test Structure (APTS) featuring fast per pixel operational-amplifier-based buffering for a small matrix of four by four pixels, with a sensor with a small collection electrode and a very non-uniform electric field, was designed to allow detailed characterization of the pixel performance in this technology. Several variants of this chip with different pixel designs have been characterized with a (120 GeV/$c$) positive hadron beam. This result indicates that the APTS-OA prototype variants with the best performance achieve a time resolution of 63 ps with a detection efficiency exceeding 99% and a spatial resolution of 2 $\mu$m, highlighting the potential of TPSCo 65nm CMOS imaging technology for high-energy physics and other fields requiring precise time measurement, high detection efficiency, and excellent spatial resolution.

Estimating many-body effects that deviate from an independent particle approach, has long been a key research interest in condensed matter physics. Layered cuprates are prototypical systems, where electron-electron interactions are found to strongly affect the dynamics of single-particle excitations. It is however, still unclear how the electron correlations influence charge excitations, such as plasmons, which have been variously treated with either weak or strong correlation models. In this work, we demonstrate the hybridised nature of collective valence charge fluctuations leading to dispersing acoustic-like plasmons in hole-doped La$_{1.84}$Sr$_{0.16}$CuO$_{4}$ and electron-doped La$_{1.84}$Ce$_{0.16}$CuO$_{4}$ using the two-particle probe, resonant inelastic x-ray scattering. We then describe the plasmon dispersions in both systems, within both the weak mean-field Random Phase Approximation (RPA) and strong coupling $t$-$J$-$V$ models. The $t$-$J$-$V$ model, which includes the correlation effects implicitly, accurately describes the plasmon dispersions as resonant excitations outside the single-particle intra-band continuum. In comparison, a quantitative description of the plasmon dispersion in the RPA approach is obtained only upon explicit consideration of re-normalized electronic band parameters. Our comparative analysis shows that electron correlations significantly impact the low-energy plasmon excitations across the cuprate doping phase diagram, even at long wavelengths. Thus, complementary information on the evolution of electron correlations, influenced by the rich electronic phases in condensed matter systems, can be extracted through the study of two-particle charge response., Comment: 6 Figures

Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.

In a spherically symmetric and static spacetime of a compact object, such as that of a Schwarzschild black hole, the light-ring is a 2-sphere where photons experience the only possible circular orbits. As a "Gedankenexperiment", we imagine an advanced civilisation able to populate the light-ring of a nonrotating black hole of mass $M$ with photons having a fine-tuned impact parameter that allows their orbits to be exactly circular with radius $r=3M$. As the number of photons in the light-ring increases in time, its mass will no longer be negligible and hence it will impact on the background spacetime, that is, it will "self-gravitate". We here consider two different routes to assign a nonzero mass to the light-ring that are either based on a discrete concentration of photons on a specific radial location or on a suitable distribution of photons in a given region. In both cases, and using the Einstein equations, we find that the inclusion of the energy from the accumulated photons leads to the generation of new light-rings. Such new light-rings can either appear at well-defined but discrete locations, or be fused in a well-defined region. In either case, we show that such light-ring configurations are dynamically unstable and a small perturbation, either via the inclusion of an additional photon onto the light-ring or via the absorption of a photon by the black hole, leads to a catastrophic destruction of the light-ring structures., Comment: 6 pages, 1 figure

Despite the remarkable success of LLMs in English, there is a significant gap in performance in non-English languages. In order to address this, we introduce a novel recipe for creating a multilingual synthetic instruction tuning dataset, sPhinX, which is created by selectively translating instruction response pairs from English into 50 languages. We test the effectiveness of sPhinx by using it to fine-tune two state-of-the-art models, Mistral-7B and Phi-Small and then evaluating them across a comprehensive suite of multilingual benchmarks that test reasoning, question answering, reading comprehension and machine translation. Our results show that Mistral-7B and Phi-Small fine-tuned with sPhinX perform better on an average by 5%pt for both the models when compared to the base variants of these models. We also devise a strategy to incorporate N-shot examples in each fine-tuning sample which further boosts the performance of these models by 9%pt and 4%pt respectively respectively compared to vanilla fine-tuning. To show efficacy of our data curation approach, we also directly translate our original dataset to the target languages, and observe an increase of 7%pt and 4%pt on both the models respectively. sPhinX outperforms other multilingual instruction tuning datasets in both efficiency and diversity, reducing dataset creation costs. It also maintains strong performance on standard English LLM benchmarks, with minimal regression., Comment: 20 pages, 12 tables, 5 figures