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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.

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 < 1.5% (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: 10 pages, 10 figures, accepted for publication by A&A

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: Some refinements to improve clarity and simplify the analysis; Matches the published version; Conclusions unchanged

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

Pattern comparison represents a fundamental and crucial aspect of scientific modeling, artificial intelligence, and pattern recognition. Three main approaches have typically been applied for pattern comparison: (i) distances; (ii) statistical joint variation; (iii) projections; and (iv) similarity indices, each with their specific characteristics. In addition to arguing for intrinsic interesting properties of multiset-based similarity approaches, the present work describes a respectively based hierarchical agglomerative clustering approach which inherits the several interesting characteristics of the coincidence similarity index -- including strict comparisons allowing distinguishing between closely similar patterns, inherent normalization, as well as substantial robustness to the presence of noise and outliers in datasets. Two other hierarchical clustering approaches are considered, namely a multiset-based method as well as the traditional Ward's approach. After characterizing uniform and proportional features spaces and presenting the main basic concepts and methods, a comparison of relative performance between the three considered hierarchical methods is reported and discussed, with several interesting and important results. In particular, though intrinsically suitable for implementing proportional comparisons, the coincidence similarity methodology also works effectively in several types of data in uniform feature spaces, Comment: 32 pages, 22 figures

Implementing the concept of Dark Fluid with a Chaplygin-like equation of state within General Relativity, we construct a new higher-dimensional, static, and spherically symmetric anti-de Sitter (AdS) black hole solution. Energy conditions are explored alongside curvature singularity tools. The inspection at the level of the phase structure and $P-v$ critical behavior is carried out in the context of the extended phase space, where the cosmological constant appears as pressure. Our findings disclose non-trivial similarities between the small/large phase transition of AdS black holes surrounded by Chaplygin dark fluid and van der Waals systems' liquid/gas phase transition. This analysis offers insights into the physical interpretation of the $P-v$ diagram and identifies critical exponents that reveal the scaling behavior of thermodynamic quantities close to criticality in a universal manner. We finally deepen our understanding of the thermodynamic properties and microstructure of AdS black holes by leveraging the geometrothermodynamic formalism. Specifically, we employ tools, including Weinhold, Ruppeiner, Hendi-Panahiyan-Eslam-Momennia (HPEM) and Quevedo classes I and II. We show that each class of metrics predicts either the physical limitation point and/or the phase-transition critical points, with HPEM and Quevedo formulations providing richer information about the phase transitions. Altogether, this study contributes to advancing our knowledge of the role of Chaplygin gas in General Relativity and thoroughly examining the thermodynamic phase structure of high-dimensional AdS black holes under extreme conditions., Comment: 26 pages, 11 labeled figure, accepted for publication in Phys. Dark. Univ

Model-Based Engineering (MBE) has streamlined software development by focusing on abstraction and automation. The adoption of MBE in Maintenance and Evolution (MBM&E), however, is still limited due to poor tool support and a lack of perceived benefits. We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of MBM&E. In this sense, we argue that GenAI, driven by Foundation Models, offers promising potential for enhancing MBM&E tasks. With this possibility in mind, we introduce a research vision that contains a classification scheme for GenAI approaches in MBM&E considering two main aspects: (i) the level of augmentation provided by GenAI and (ii) the experience of the engineers involved. We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems. Furthermore, we outline challenges in this field as a research agenda to drive scientific and practical future solutions. With this proposed vision, we aim to bridge the gap between GenAI and MBM&E, presenting a structured and sophisticated way for advancing MBM&E practices.

Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems found in previous literature use one of three mental model. The Switch, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics. The Ladder, i.e., a risk-based approach that classifies systems according to the ethical risks they pose. And the Matrix, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, data input, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the conceptual tools needed to operationalise AI governance in practice.

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management.

Ethics based auditing (EBA) is a structured process whereby an entitys past or present behaviour is assessed for consistency with moral principles or norms. Recently, EBA has attracted much attention as a governance mechanism that may bridge the gap between principles and practice in AI ethics. However, important aspects of EBA (such as the feasibility and effectiveness of different auditing procedures) have yet to be substantiated by empirical research. In this article, we address this knowledge gap by providing insights from a longitudinal industry case study. Over 12 months, we observed and analysed the internal activities of AstraZeneca, a biopharmaceutical company, as it prepared for and underwent an ethics-based AI audit. While previous literature concerning EBA has focused on proposing evaluation metrics or visualisation techniques, our findings suggest that the main difficulties large multinational organisations face when conducting EBA mirror classical governance challenges. These include ensuring harmonised standards across decentralised organisations, demarcating the scope of the audit, driving internal communication and change management, and measuring actual outcomes. The case study presented in this article contributes to the existing literature by providing a detailed description of the organisational context in which EBA procedures must be integrated to be feasible and effective.

On the whole, the U.S. Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can both inform and learn from the European Artificial Intelligence Act (EU AIA)., Comment: Minds & Machines (2022)

Software modernization is an inherent activity of software engineering, as technology advances and systems inevitably become outdated. The term "software modernization" emerged as a research topic in the early 2000s, with a differentiation from traditional software evolution. Studies on this topic became popular due to new programming paradigms, technologies, and architectural styles. Given the pervasive nature of software today, modernizing legacy systems is paramount to provide users with competitive and innovative products and services. Despite the large amount of work available in the literature, there are significant limitations: (i) proposed approaches are strictly specific to one scenario or technology, lacking flexibility; (ii) most of the proposed approaches are not aligned with the current modern software development scenario; and (iii) due to a myriad of proposed modernization approaches, practitioners may be misguided on how to modernize legacies. In this work, our goal is to call attention to the need for advances in research and practices toward a well-defined software modernization domain. The focus is on enabling organizations to preserve the knowledge represented in legacy systems while taking advantages of disruptive and emerging technologies. Based on this goal, we put the different perspectives of software modernization in the context of contemporary software development. We also present a research agenda with 10 challenges to motivate new studies.

Synthetic data is becoming increasingly important for accelerating the development of language models, both large and small. Despite several successful use cases, researchers also raised concerns around model collapse and drawbacks of imitating other models. This discrepancy can be attributed to the fact that synthetic data varies in quality and diversity. Effective use of synthetic data usually requires significant human effort in curating the data. We focus on using synthetic data for post-training, specifically creating data by powerful models to teach a new skill or behavior to another model, we refer to this setting as Generative Teaching. We introduce AgentInstruct, an extensible agentic framework for automatically creating large amounts of diverse and high-quality synthetic data. AgentInstruct can create both the prompts and responses, using only raw data sources like text documents and code files as seeds. We demonstrate the utility of AgentInstruct by creating a post training dataset of 25M pairs to teach language models different skills, such as text editing, creative writing, tool usage, coding, reading comprehension, etc. The dataset can be used for instruction tuning of any base model. We post-train Mistral-7b with the data. When comparing the resulting model Orca-3 to Mistral-7b-Instruct (which uses the same base model), we observe significant improvements across many benchmarks. For example, 40% improvement on AGIEval, 19% improvement on MMLU, 54% improvement on GSM8K, 38% improvement on BBH and 45% improvement on AlpacaEval. Additionally, it consistently outperforms other models such as LLAMA-8B-instruct and GPT-3.5-turbo.

Software engineers have adopted the Dynamic Software Product Lines (DSPL) engineering practices to develop Dynamically Adaptable Software (DAS). DAS is seen as a DSPL application and must cope with a large number of configurations of features, Non-functional Requirements (NFRs), and contexts. However, the accurate representation of the impact of features over NFRs and contexts for the identification of optimal configurations is not a trivial task. Software engineers need to have domain knowledge and design DAS before deploying to satisfy those requirements. Aiming to handle them, we proposed an approach of Trade-off Analysis for DSPL at design-time, named ToffA-DSPL. It deals with the configuration selection process considering interactions between NFRs and contexts. We performed an exploratory study based on simulations to identify the usefulness of the ToffA-DSPL approach. In general, the configurations suggested by ToffA-DSPL provide high satisfaction levels of NFRs. Based on simulations, we evidenced that our approach aims to explore reuse and is useful for generating valid and optimal configurations. In addition, ToffA-DSPL enables software engineers to conduct trade-off analysis, evaluate changes in the context feature, and define an adaptation model from optimal configurations found in the analysis.

A spanner is a sparse subgraph of a given graph $G$ which preserves distances, measured w.r.t.\ some distance metric, up to a multiplicative stretch factor. This paper addresses the problem of constructing graph spanners w.r.t.\ the group Steiner metric, which generalizes the recently introduced beer distance metric. In such a metric we are given a collection of groups of required vertices, and we measure the distance between two vertices as the length of the shortest path between them that traverses at least one required vertex from each group. We discuss the relation between group Steiner spanners and classic spanners and we show that they exhibit strong ties with sourcewise spanners w.r.t.\ the shortest path metric. Nevertheless, group Steiner spanners capture several interesting scenarios that are not encompassed by existing spanners. This happens, e.g., for the singleton case, in which each group consists of a single required vertex, thus modeling the setting in which routes need to traverse certain points of interests (in any order). We provide several constructions of group Steiner spanners for both the all-pairs and single-source case, which exhibit various size-stretch trade-offs. Notably, we provide spanners with almost-optimal trade-offs for the singleton case. Moreover, some of our spanners also yield novel trade-offs for classical sourcewise spanners. Finally, we also investigate the query times that can be achieved when our spanners are turned into group Steiner distance oracles with the same size, stretch, and building time.

Turnover consists of moving into and out of professional employees in the company in a given period. Such a phenomenon significantly impacts the software industry since it generates knowledge loss, delays in the schedule, and increased costs in the final project. Despite the efforts made by researchers and professionals to minimize the turnover, more studies are needed to understand the motivation that drives Software Engineers to leave their jobs and the main strategies CEOs adopt to retain these professionals in software development companies. In this paper, we contribute a mixed methods study involving semi-structured interviews with Software Engineers and CEOs to obtain a wider opinion of these professionals about turnover and a subsequent validation survey with additional software engineers to check and review the insights from interviews. In studying such aspects, we identified 19 different reasons for software engineers' turnover and 18 more efficient strategies used in the software development industry to reduce it. Our findings provide several implications for industry and academia, which can drive future research.