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The inference and training stages of Graph Neural Networks (GNNs) are often dominated by the time required to compute a long sequence of matrix multiplications between the sparse graph adjacency matrix and its embedding. To accelerate these stages, we first propose the Compressed Binary Matrix (CBM) storage format to succinctly represent the binary adjacency matrix of an unweighted graph. Then, we show how to generalize this representation to normalized adjacency matrices of unweighted graphs which arise in the context of GNNs. Finally, we develop efficient matrix multiplication kernels based on this compressed representation. The matrix multiplication kernels proposed in this work never require more scalar operations than classic sparse matrix multiplication algorithms. Experimental evaluation shows that the matrix multiplication strategies proposed outperform the current state-of-the-art implementations provided by Intel MKL, achieving speedups close to 5$\times$. Furthermore, our optimized matrix-multiplication strategies accelerated the inference time of a GNN by up to $3\times$.

HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder is a space-borne mission based on a constellation of six nano-satellites flying in a low-Earth orbit (LEO). The 3U CubeSats, to be launched in early 2025, host miniaturized instruments with a hybrid Silicon Drift Detector/GAGG:Ce scintillator photodetector system, sensitive to X-rays and gamma-rays in a large energy band. HERMES will operate in conjunction with Australian Space Industry Responsive Intelligent Thermal (SpIRIT) 6U CubeSat, launched in December 2023. HERMES will probe the temporal emission of bright high-energy transients such as Gamma-Ray Bursts (GRBs), ensuring a fast transient localization in a field of view of several steradians exploiting the triangulation technique. HERMES intrinsically modular transient monitoring experiment represents a keystone capability to complement the next generation of gravitational wave experiments. In this paper we outline the scientific case, development and programmatic status of the mission, Comment: 13 pages, 16 figures. Proceedings of SPIE "Astronomical Telescopes and Instrumentation" 2024. arXiv admin note: text overlap with arXiv:2210.13866

In this work, we present a novel framework for Best Arm Identification (BAI) under fairness constraints, a setting that we refer to as \textit{F-BAI} (fair BAI). Unlike traditional BAI, which solely focuses on identifying the optimal arm with minimal sample complexity, F-BAI also includes a set of fairness constraints. These constraints impose a lower limit on the selection rate of each arm and can be either model-agnostic or model-dependent. For this setting, we establish an instance-specific sample complexity lower bound and analyze the \textit{price of fairness}, quantifying how fairness impacts sample complexity. Based on the sample complexity lower bound, we propose F-TaS, an algorithm provably matching the sample complexity lower bound, while ensuring that the fairness constraints are satisfied. Numerical results, conducted using both a synthetic model and a practical wireless scheduling application, show the efficiency of F-TaS in minimizing the sample complexity while achieving low fairness violations.

Context: Software Quality Assurance (SQA) is a fundamental part of software engineering to ensure stakeholders that software products work as expected after release in operation. Machine Learning (ML) has proven to be able to boost SQA activities and contribute to the development of quality software systems. In this context, Causal Reasoning is gaining increasing interest as a methodology to solve some of the current ML limitations. It aims to go beyond a purely data-driven approach by exploiting the use of causality for more effective SQA strategies. Objective: Provide a broad and detailed overview of the use of causal reasoning for SQA activities, in order to support researchers to access this research field, identifying room for application, main challenges and research opportunities. Methods: A systematic literature review of causal reasoning in the SQA research area. Scientific papers have been searched, classified, and analyzed according to established guidelines for software engineering secondary studies. Results: Results highlight the primary areas within SQA where causal reasoning has been applied, the predominant methodologies used, and the level of maturity of the proposed solutions. Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain, but other tasks like testing are rapidly gaining popularity. Both causal inference and causal discovery are exploited, with the Pearl's graphical formulation of causality being preferred, likely due to its intuitiveness. Tools to favour their application are appearing at a fast pace - most of them after 2021. Conclusions: The findings show that causal reasoning is a valuable means for SQA tasks with respect to multiple quality attributes, especially during V&V, evolution and maintenance to ensure reliability, while it is not yet fully exploited for phases like ..., Comment: Preprint Journal Information and Software Technology

In recent years, the study of biological transportation networks has attracted significant interest, focusing on their self-regulating, demand-driven nature. This paper examines a mathematical model for these networks, featuring nonlinear elliptic equations for pressure and an auxiliary variable, and a reaction-diffusion parabolic equation for the conductivity tensor, introduced in \cite{portaro2022emergence}. The model, based on an energy functional with diffusive and metabolic terms, allows for various entropy generating functions, facilitating its application to different biological scenarios. We proved a local well-posedness result for the problem in H\"older spaces employing Schauder and semigroup theory. Then, after a suitable parameter reduction through scaling, we computed the numerical solution for the proposed system using a recently developed ghost nodal finite element method \cite{astuto2024nodal}. An interesting aspect emerges when the solution is very articulated and the branches occupy a wide region of the domain.

There is mounting evidence that a second quantum revolution based on the technological capabilities to detect and manipulate single quantum particles (e.g., electrons, photons, ions, etc), a feat not achieved during the first quantum revolution, is progressing fast. It is expected that in less than 10 years, this second quantum revolution shall have a significant impact over numerous industries, including finance, medicine, energy, transportation, etc. Quantum computers threaten the status quo of cybersecurity, due to known quantum algorithms that can break asymmetric encryption, which is what gives us the ability to communicate securely using a public channel. Considering the world's dependence on digital communication through data exchange and processing, retaining the ability to communicate securely even once quantum computers come into play, cannot be stressed enough. Two solutions are available: Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC); which, we emphasise, are not mutually exclusive. The EuroQCI initiative, of which EQUO is a part of, focuses on QKD and aims to build a network whereby EU countries can communicate securely through QKD. To this aim, the DEP (Digital Europe Programme) project aims to bring technological matureness to QKD by deploying a QKD test network and, through this exercise, understand what is lacking from an operator's point of view when the time to integrate QKD in their network comes., Comment: IEEE 2024 International Conference on Quantum Communications, Networking, and Computing (QCNC)

This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM-driven RL is the exploitation of a finite-state machine that decomposes the agent's task into different subtasks. PROB-IRM uses a state-of-the-art inductive logic programming framework robust to noisy examples to learn RMs from noisy traces using the Bayesian posterior degree of beliefs, thus ensuring robustness against inconsistencies. Pivotal for the results is the interleaving between RM learning and policy learning: a new RM is learned whenever the RL agent generates a trace that is believed not to be accepted by the current RM. To speed up the training of the RL agent, PROB-IRM employs a probabilistic formulation of reward shaping that uses the posterior Bayesian beliefs derived from the traces. Our experimental analysis shows that PROB-IRM can learn (potentially imperfect) RMs from noisy traces and exploit them to train an RL agent to solve its tasks successfully. Despite the complexity of learning the RM from noisy traces, agents trained with PROB-IRM perform comparably to agents provided with handcrafted RMs., Comment: Preprint accepted for publication to the 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024)

We study the behavior of shallow water waves propagating over bathymetry that varies periodically in one direction and is constant in the other. Plane waves traveling along the constant direction are known to evolve into solitary waves, due to an effective dispersion. We apply multiple-scale perturbation theory to derive an effective constant-coefficient system of Boussinesq-type equations that not only accurately describe these waves but also predict their full two-dimensional shape in some detail. Numerical experiments confirm the good agreement between the effective equations and the variable-bathymetry shallow water equations.

We present the results of a molecular survey of long period comets C/2021 A1 (Leonard) and C/2022 E3 (ZTF). Comet C/2021 A1 was observed with the IRAM 30-m radio telescope in November-December 2021 before perihelion when it was closest to the Earth. We observed C/2022 E3 in January-February 2023 with the Odin 1-m space telescope and IRAM 30-m, shortly after its perihelion, and when it was closest to the Earth. Snapshots were obtained during 12-16 November 2021 period for comet C/2021 A1. Spectral surveys were undertaken over the 8-13 December 2021 period for comet C/2021 A1 (8, 16, and 61 GHz bandwidth in the 3 mm, 2 mm, and 1 mm window) and over the 3-7 February 2023 period for comet C/2022 E3 (25 and 61 GHz at 2 and 1mm). We report detections of 14 molecular species (HCN, HNC, CH3CN, HNCO, NH2CHO, CH3OH, H2CO, HCOOH, CH3CHO, H2S, CS, OCS, C2H5OH and aGg-(CH2OH)2 ) in both comets plus HC3N and CH2OHCHO marginally detected in C/2021 A1 and CO and H2O (with Odin detected in C/2022 E3. The spatial distribution of several species is investigated. Significant upper limits on the abundances of other molecules and isotopic ratios are also presented. The activity of comet C/2021 A1 did not vary significantly between 13 November and 13 December 2021. Short-term variability in the outgassing of comet C/2022 E3 on the order of +-20% is present and possibly linked to its 8h rotation period. Both comets exhibit rather low abundances relative to water for volatiles species such as CO (< 2%) and H2S (0.15%). Methanol is also rather depleted in comet C/2021 A1 (0.9%). Following their revised photo-destruction rates, HNCO and HCOOH abundances in comets have been reevaluated. Both molecules are relatively enriched in these two comets (0.2% relative to water). We cannot exclude that these species could be produced by the dissociation of ammonium salts., Comment: To be published in Astronomy & Astrophysics

Morphing Attack Detection (MAD) is a relevant topic that aims to detect attempts by unauthorised individuals to access a "valid" identity. One of the main scenarios is printing morphed images and submitting the respective print in a passport application process. Today, small datasets are available to train the MAD algorithm because of privacy concerns and the limitations resulting from the effort associated with the printing and scanning of images at large numbers. In order to improve the detection capabilities and spot such morphing attacks, it will be necessary to have a larger and more realistic dataset representing the passport application scenario with the diversity of devices and the resulting printed scanned or compressed images. Creating training data representing the diversity of attacks is a very demanding task because the training material is developed manually. This paper proposes two different methods based on transfer-transfer for automatically creating digital print/scan face images and using such images in the training of a Morphing Attack Detection algorithm. Our proposed method can reach an Equal Error Rate (EER) of 3.84% and 1.92% on the FRGC/FERET database when including our synthetic and texture-transfer print/scan with 600 dpi to handcrafted images, respectively., Comment: Paper under revision process in Journal

We study the wave form emitted by a particle moving along an arbitrary (in general open) geodesic of the Schwarzschild geometry. The mathematical problem can be phrased in terms of quantities in ${\cal N}=2$ supersymmetric gauge theories that can be calculated by using localization and the AGT correspondence. In particular through this mapping, the post-Newtonian expansion of the wave form is expressed as a double instanton sum with rational coefficients that resums all tail contributions into Gamma functions and exponentials. The formulae we obtain are valid for generic values of the orbital quantum numbers $\ell$ and $m$. For $\ell=2,3$ we check explicitly that our results agree with the small mass ratio limit of the wave forms derived in the Multipole Post-Minkowskian and the amplitudes approaches. We show how the so-called tail and tail of tail contributions to the wave form arise in our approach. Finally, we derive a universal formula for the soft limit of the wave form that resums all logarithmic divergent terms of the form $\omega^{n-1} (\log \omega)^n$., Comment: 29 pages

In this paper, we present MADE-WIC, a large dataset of functions and their comments with multiple annotations for technical debt and code weaknesses leveraging different state-of-the-art approaches. It contains about 860K code functions and more than 2.7M related comments from 12 open-source projects. To the best of our knowledge, no such dataset is publicly available. MADE-WIC aims to provide researchers with a curated dataset on which to test and compare tools designed for the detection of code weaknesses and technical debt. As we have fused existing datasets, researchers have the possibility to evaluate the performance of their tools by also controlling the bias related to the annotation definition and dataset construction. The demonstration video can be retrieved at https://www.youtube.com/watch?v=GaQodPrcb6E., Comment: Accepted at ASE@Tool Demonstrations

In this work we address partial wave decompositions of thermal one-point functions in conformal field theories on $S^1 \times S^{d-1}$. With the help of Casimir differential equations we develop efficient algorithms to compute the relevant conformal blocks for an external field of arbitrary spin and with any spin exchange along the thermal circle, at least in three dimensions. This is achieved by identifying solutions to the Casimir equations with a special class of spherical functions in the harmonic analysis of the conformal group. The resulting blocks are then applied to study the decomposition of one-point functions of the scalar $\phi^2$ and the stress tensor $T$ for a three-dimensional free scalar field $\phi$. We are able to read off averaged OPE coefficients into exchanged fields of high weight and spin for a complete set of tensor structures. We also extract an asymptotic behaviour of conformal blocks and use it to analyse the density of heavy-heavy-light OPE coefficients for spinning operators, comparing it with semi-classical predictions, such as the dimensions of operators at large charge.

We use parsimonious diffusion maps (PDMs) to discover the latent dynamics of high-fidelity Navier-Stokes simulations with a focus on the 2D fluidic pinball problem. By varying the Reynolds number, different flow regimes emerge, ranging from steady symmetric flows to quasi-periodic asymmetric and turbulence. We show, that the proposed non-linear manifold learning scheme, identifies in a crisp manner the expected intrinsic dimension of the underlying emerging dynamics over the parameter space. In particular, PDMs, estimate that the emergent dynamics in the oscillatory regime can be captured by just two variables, while in the chaotic regime, the dominant modes are three as anticipated by the normal form theory. On the other hand, proper orthogonal decomposition (POD)/PCA, most commonly used for dimensionality reduction in fluid mechanics, does not provide such a crisp separation between the dominant modes. To validate the performance of PDMs, we also computed the reconstruction error, by constructing a decoder using Geometric Harmonics. We show that the proposed scheme outperforms the POD/PCA over the whole Reynolds number range. Thus, we believe that the proposed scheme will allow for the development of more accurate reduced order models for high-fidelity fluid dynamics simulators, thus relaxing the curse of dimensionality in numerical analysis tasks such as bifurcation analysis, optimization and control.

In pathology, accurate and efficient analysis of Hematoxylin and Eosin (H\&E) slides is crucial for timely and effective cancer diagnosis. Although many deep learning solutions for nuclei instance segmentation and classification exist in the literature, they often entail high computational costs and resource requirements, thus limiting their practical usage in medical applications. To address this issue, we introduce a novel convolutional neural network, NuLite, a U-Net-like architecture designed explicitly on Fast-ViT, a state-of-the-art (SOTA) lightweight CNN. We obtained three versions of our model, NuLite-S, NuLite-M, and NuLite-H, trained on the PanNuke dataset. The experimental results prove that our models equal CellViT (SOTA) in terms of panoptic quality and detection. However, our lightest model, NuLite-S, is 40 times smaller in terms of parameters and about 8 times smaller in terms of GFlops, while our heaviest model is 17 times smaller in terms of parameters and about 7 times smaller in terms of GFlops. Moreover, our model is up to about 8 times faster than CellViT. Lastly, to prove the effectiveness of our solution, we provide a robust comparison of external datasets, namely CoNseP, MoNuSeg, and GlySAC. Our model is publicly available at https://github.com/CosmoIknosLab/NuLite

We present results of our study devoted to the development of a time correction algorithm needed to precisely synchronize a free-running Rubidium atomic clock with the Coordinated Universal Time (UTC). This R&D is performed in view of the Hyper-Kamiokande (HK) experiment currently under construction in Japan, which requires a synchronization with UTC and between its different experimental sites with a precision better than $100$ ns. We use a Global Navigation Satellite System (GNSS) receiver to compare a PPS and a $10$ MHz signal, generated by a free-running Rubidium clock, to the Global Positioning System (GPS) Time signal. We use these comparisons to correct the time series (time stamps) provided by the Rubidium clock signal. We fit the difference between Rubidium and GPS Time with polynomial functions of time over a certain integration time window to extract a correction of the Rubidium time stamps in offline or online mode. In online mode, the latest fit results are used for the correction until a new comparison to GPS Time becomes available. We show that with an integration time window of around $10^4$ seconds, we can correct the time stamps drift, caused by the frequency random walk noise and the deterministic frequency drift of the free running Rubidium clock, so that the time difference with respect to GPS Time stays within a $\pm5$ ns range in both offline or online correction mode. Presented results could be of interest for other experiments in the field of neutrino physics and multi-messenger astrophysics., Comment: 32 pages, 15 figures, manuscript prepared for submission to NIM A

One of the frontiers of nanotechnology is advancing beyond the periodic self-assembly of materials. Icosahedral quasicrystals, aperiodic in all directions, represent one of the most challenging targets that have yet to be experimentally realized at the colloidal scale. Previous attempts have required meticulous human-designed building blocks and often resulted in interactions beyond current experimental capabilities. In this work, we introduce a framework for generating experimentally accessible designs that self-assemble into quasicrystalline arrangements. We present a design for icosahedral DNA-origami building blocks and demonstrate, through molecular simulations, their successful assembly into the target quasicrystalline structure. Our results highlight the feasibility of using automated design protocols to achieve complex quasicrystalline patterns, paving the way for new applications in material science and nanotechnology.

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.

Several experiments on molecular and metallic glasses have shown that the ability of vapor deposition to produce ultrastable glasses is correlated with their structural and thermodynamic properties. Here we investigate the vapor deposition of a class of tetrahedral materials (including silicon and water) via molecular dynamics simulations of the generalized Stillinger-Weber potential. By changing a single parameter that controls the local tetrahedrality, we show that the emergence of ultrastable behavior is correlated with an increase in the fragility of the model. At the same time, while the mobility of the surface compared to the bulk shows only slight changes at low temperature, with increasing the tetrahedrality, it displays a significant enhancement towards the glass transition temperature. Our results point towards a strong connection between bulk dynamics, surface dynamics and glass-ultrastability ability in this class of materials.

The main objective consists in generalizing a well-known It{\^o} formula of J. Jacod and A. Shiryaev: given a c{\`a}dl{\`a}g process S, there is an equivalence between the fact that S is a semimartingale with given characteristics (B^k , C, $\nu$) and a It{\^o} formula type expansion of F (S), where F is a bounded function of class C2. This result connects weak solutions of path-dependent SDEs and related martingale problems. We extend this to the case when S is a weak Dirichlet process. A second aspect of the paper consists in discussing some untreated features of stochastic calculus for finite quadratic variation processes.

The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of 'organizational alignment': what makes research 'doable' is the successful alignment of three levels of work organization (the social world, the laboratory, and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI research to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research for them. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.

In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred. Following the recent line of research on algorithms endowed with additional predictive features, we revisit this problem by allowing the decision maker to acquire additional information on the actions to be selected. In particular, we study the power of \emph{best-action queries}, which reveal beforehand the identity of the best action at a given time step. In practice, predictive features may be expensive, so we allow the decision maker to issue at most $k$ such queries. We establish tight bounds on the performance any algorithm can achieve when given access to $k$ best-action queries for different types of feedback models. In particular, we prove that in the full feedback model, $k$ queries are enough to achieve an optimal regret of $\Theta\left(\min\left\{\sqrt T, \frac Tk\right\}\right)$. This finding highlights the significant multiplicative advantage in the regret rate achievable with even a modest (sublinear) number $k \in \Omega(\sqrt{T})$ of queries. Additionally, we study the challenging setting in which the only available feedback is obtained during the time steps corresponding to the $k$ best-action queries. There, we provide a tight regret rate of $\Theta\left(\min\left\{\frac{T}{\sqrt k},\frac{T^2}{k^2}\right\}\right)$, which improves over the standard $\Theta\left(\frac{T}{\sqrt k}\right)$ regret rate for label efficient prediction for $k \in \Omega(T^{2/3})$.

The online joint replenishment problem (JRP) is a fundamental problem in the area of online problems with delay. Over the last decade, several works have studied generalizations of JRP with different cost functions for servicing requests. Most prior works on JRP and its generalizations have focused on the clairvoyant setting. Recently, Touitou [Tou23a] developed a non-clairvoyant framework that provided an $O(\sqrt{n \log n})$ upper bound for a wide class of generalized JRP, where $n$ is the number of request types. We advance the study of non-clairvoyant algorithms by providing a simpler, modular framework that matches the competitive ratio established by Touitou for the same class of generalized JRP. Our key insight is to leverage universal algorithms for Set Cover to approximate arbitrary monotone subadditive functions using a simple class of functions termed \textit{disjoint}. This allows us to reduce the problem to several independent instances of the TCP Acknowledgement problem, for which a simple 2-competitive non-clairvoyant algorithm is known. The modularity of our framework is a major advantage as it allows us to tailor the reduction to specific problems and obtain better competitive ratios. In particular, we obtain tight $O(\sqrt{n})$-competitive algorithms for two significant problems: Multi-Level Aggregation and Weighted Symmetric Subadditive Joint Replenishment. We also show that, in contrast, Touitou's algorithm is $\Omega(\sqrt{n \log n})$-competitive for both of these problems.

The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs) has swiftly garnered attention because of its straightforwardness, precision, and resilience, along with its aptitude for transfer learning, which has been demonstrated through significant achievements in global BCI competitions. This paper presents a comprehensive review of recent advancements in the integration of deep learning with Riemannian geometry to enhance EEG signal decoding in BCIs. Our review updates the findings since the last major review in 2017, comparing modern approaches that utilize deep learning to improve the handling of non-Euclidean data structures inherent in EEG signals. We discuss how these approaches not only tackle the traditional challenges of noise sensitivity, non-stationarity, and lengthy calibration times but also introduce novel classification frameworks and signal processing techniques to reduce these limitations significantly. Furthermore, we identify current shortcomings and propose future research directions in manifold learning and riemannian-based classification, focusing on practical implementations and theoretical expansions, such as feature tracking on manifolds, multitask learning, feature extraction, and transfer learning. This review aims to bridge the gap between theoretical research and practical, real-world applications, making sophisticated mathematical approaches accessible and actionable for BCI enhancements.

Semiconductor nanocrystals (quantum dots) are light emitters with high quantum yield that are relatively easy to manufacture. There is therefore much interest in their possible application for the development of high-performance scintillators for use in high-energy physics. However, few previous studies have focused on the response of these materials to high-energy particles. To evaluate the potential for the use of nanocomposite scintillators in calorimetry, we are performing side-by-side tests of fine-sampling shashlyk calorimeter prototypes with both conventional and nanocomposite scintillators using electron and minimum-ionizing particle beams, allowing direct comparison of the performance obtained., Comment: 3 pages, 2 figures, 1 table

We discuss the dynamics of a (neutral) test particle in Topological Star spacetime undergoing scattering processes by a superposed test radiation field, a situation that in a 4D black hole spacetime is known as relativistic Poynting-Robertson effect, paving the way for future studies involving radiation-reaction effects. Furthermore, we study self-force-driven evolution of a scalar field, perturbing the Top-Star spacetime with a scalar charge current. The latter for simplicity is taken to be circular, equatorial and geodetic. To perform this study, besides solving all the self-force related problem (regularization of all divergences due to the self-field, mode sum regularization, etc.), we had to adapt the 4D Mano-Suzuki-Takasugi formalism to the present 5D situation. Finally, we have compared this formalism with the (quantum) Seiberg-Witten formalism, both related to the solutions of a Heun Confluent Equation, but appearing in different contexts in the literature, black hole perturbation theory the first, quantum curves in super-Yang-Mills theories the second., Comment: 31 pages, 3 figures for 8 eps files, revtex macros used

A global approximation method of Nystr\"om type is explored for the numerical solution of a class of nonlinear integral equations of the second kind. The cases of smooth and weakly singular kernels are both considered. In the first occurrence, the method uses a Gauss-Legendre rule whereas in the second one resorts to a product rule based on Legendre nodes. Stability and convergence are proved in functional spaces equipped with the uniform norm and several numerical tests are given to show the good performance of the proposed method. An application to the interior Neumann problem for the Laplace equation with nonlinear boundary conditions is also considered., Comment: 31 pages, 1 figure

The $\beta$ decay of one-neutron halo nucleus $^{11}$Be was investigated using the Warsaw Optical Time Projection Chamber (OTPC) detector to measure $\beta$-delayed charged particles. The results of two experiments are reported. In the first one, carried out in LNS Catania, the absolute branching ratio for $\beta$-delayed $\alpha$ emission was measured by counting incoming $^{11}$Be ions stopped in the detector and the observed decays with the emission of $\alpha$ particle. The result of 3.27(46)\% is in good agreement with the literature value. In the second experiment, performed at the HIE-ISOLDE facility at CERN, bunches containing several hundreds of $^{11}$Be ions were implanted into the OTPC detector followed by the detection of decays with the emission of charged particles. The energy spectrum of $\beta$-delayed $\alpha$ particles was determined in the full energy range. It was analysed in the R-matrix framework and was found to be consistent with the literature. The best description of the spectrum was obtained assuming that the two $3/2^+$ and one $1/2^+$ states in $^{11}$B are involved in the transition. The search for $\beta$-delayed emission of protons was undertaken. Only the upper limit for the branching ratio for this process of $(2.2 \pm 0.6_{\rm stat} \pm 0.6_{\rm sys}) \times 10^{-6}$ could be determined. This value is in conflict with the result published in [Ayyad et al. Phys. Rev. Lett. 123, 082501 (2019)] but does agree with the limit reported in [Riisager et al., Eur. Phys. J. A (2020) 56:100], Comment: 16 pages, 18 figures, submitted to Phys. Rev. C

We report measurements of time-dependent $CP$ asymmetries in $B^0 \to K^0_S \pi^0 \gamma$ decays based on a data sample of $(388\pm6)\times10^6$ $B\bar{B}$ events collected at the $\Upsilon(4S)$ resonance with the Belle II detector. The Belle II experiment operates at the SuperKEKB asymmetric-energy $e^+e^-$ collider. We measure decay-time distributions to determine $CP$-violating parameters $S$ and $C$. We determine these parameters for two ranges of $K^0_S \pi^0$ invariant mass: $m(K^0_S \pi^0)\in (0.8, 1.0)$ $GeV/c^2$, which is dominated by $B^0 \to K^{*0} (\to K^0_S \pi^0) \gamma$ decays, and a complementary region $m(K^0_S \pi^0)\in (0.6, 0.8)\cup(1.0, 1.8)$ $GeV/c^2$. Our results have improved precision as compared to previous measurements and are consistent with theory predictions., Comment: 10 pages, 4 figures

We present measurements of $B^{+}\rightarrow\rho^{+}\gamma$ and $B^{0}\rightarrow\rho^{0}\gamma$ decays using a combined data sample of $772 \times 10^6$ $B\overline{B}$ pairs collected by the Belle experiment and $387\times 10^6$ $B\overline{B}$ pairs collected by the Belle II experiment in $e^{+}e^{-}$ collisions at the $\Upsilon (4S)$ resonance. After an optimized selection, a simultaneous fit to the Belle and Belle II data sets yields $114\pm 12$ $B^{+}\rightarrow\rho^{+}\gamma$ and $99\pm 12$ $B^{0}\rightarrow\rho^{0}\gamma$ decays. The measured branching fractions are $(13.1^{+2.0 +1.3}_{-1.9 -1.2})\times 10^{-7}$ and $(7.5\pm 1.3^{+1.0}_{-0.8})\times 10^{-7}$ for $B^{+}\rightarrow\rho^{+}\gamma$ and $B^{0}\rightarrow\rho^{0}\gamma$ decays, respectively, where the first uncertainty is statistical and the second is systematic. We also measure the isospin asymmetry $A_{\rm I}(B\rightarrow\rho\gamma)=(10.9^{+11.2 +7.8}_{-11.7 -7.3})\%$ and the direct CP asymmetry $A_{CP}(B^{+}\rightarrow\rho^{+}\gamma)=(-8.2\pm 15.2^{+1.6}_{-1.2})\%$., Comment: 12 pages, 4 figures

In this paper we study the exponential twist, i.e. a path-integral exponential change of measure, of a Markovian reference probability measure $\P$. This type of transformation naturally appears in variational representation formulae originating from the theory of large deviations and can be interpreted in some cases, as the solution of a specific stochastic control problem. Under a very general Markovian assumption on $\P$, we fully characterize the exponential twist probability measure as the solution of a martingale problem and prove that it inherits the Markov property of the reference measure. The ''generator'' of the martingale problem shows a drift depending on a ''generalized gradient'' of some suitable ''value function'' $v$.

This Letter proposes a new signature for confining dark sectors at the LHC. Under the assumption of a QCD-like hidden sector, hadronic jets containing stable dark bound states originating from hidden strong dynamics, known as semi-visible jets, could manifest in proton-proton collisions. In the proposed simplified model, a heavy $Z'$ mediator coupling to SM quarks allows the resonant production of dark quarks, subsequently hadronizing in stable and unstable dark bound states. The unstable dark bound states can then decay back to SM quarks via the same $Z'$ portal or photons via a lighter pseudo-scalar portal (such as an axion-like particle). This mechanism creates a new signature where semi-visible jets are enriched in non-isolated photons. We show that these exotic jets evade the phase space probed by current LHC searches exploiting jets or photons due to the expected high jet neutral electromagnetic fraction and photons candidates non-isolation, respectively. In the proposed analysis strategy to tackle such signature, we exploit jets as final state objects to represent the underlying QCD-like hidden sector. We show that, by removing any selection on the neutral electromagnetic fraction from the jet identification criteria, higher signal efficiency can be reached. To enhance the signal-to-background discrimination, we train a deep neural network as a jet tagger that exploits differences in the substructure of signal and background jets. We estimate that with the available triggers for Run 2 and this new strategy, a high mass search can claim a $5 \sigma$ discovery (exclusion) of the $Z'$ boson with a mass up to 5 TeV (5 TeV) with the full Run 2 data of the LHC when the fraction of unstable dark hadrons decaying to photons pairs is around 30 %, and with a coupling of the $Z'$ to SM quarks of 0.25., Comment: 11 pages, 7 figures, to be submitted as a Letter to EPJC. arXiv admin note: text overlap with arXiv:2212.11523

Vulnerabilities in software security can remain undiscovered even after being exploited. Linking attacks to vulnerabilities helps experts identify and respond promptly to the incident. This paper introduces VULDAT, a classification tool using a sentence transformer MPNET to identify system vulnerabilities from attack descriptions. Our model was applied to 100 attack techniques from the ATT&CK repository and 685 issues from the CVE repository. Then, we compare the performance of VULDAT against the other eight state-of-the-art classifiers based on sentence transformers. Our findings indicate that our model achieves the best performance with F1 score of 0.85, Precision of 0.86, and Recall of 0.83. Furthermore, we found 56% of CVE reports vulnerabilities associated with an attack were identified by VULDAT, and 61% of identified vulnerabilities were in the CVE repository.

Nowadays, threat reports from cybersecurity vendors incorporate detailed descriptions of attacks within unstructured text. Knowing vulnerabilities that are related to these reports helps cybersecurity researchers and practitioners understand and adjust to evolving attacks and develop mitigation plans. This paper aims to aid cybersecurity researchers and practitioners in choosing attack extraction methods to enhance the monitoring and sharing of threat intelligence. In this work, we examine five feature extraction methods (TF-IDF, LSI, BERT, MiniLM, RoBERTa) and find that Term Frequency-Inverse Document Frequency (TF-IDF) outperforms the other four methods with a precision of 75\% and an F1 score of 64\%. The findings offer valuable insights to the cybersecurity community, and our research can aid cybersecurity researchers in evaluating and comparing the effectiveness of upcoming extraction methods.

A rapidly growing number of applications rely on a small set of closed-source language models (LMs). This dependency might introduce novel security risks if LMs develop self-recognition capabilities. Inspired by human identity verification methods, we propose a novel approach for assessing self-recognition in LMs using model-generated "security questions". Our test can be externally administered to keep track of frontier models as it does not require access to internal model parameters or output probabilities. We use our test to examine self-recognition in ten of the most capable open- and closed-source LMs currently publicly available. Our extensive experiments found no empirical evidence of general or consistent self-recognition in any examined LM. Instead, our results suggest that given a set of alternatives, LMs seek to pick the "best" answer, regardless of its origin. Moreover, we find indications that preferences about which models produce the best answers are consistent across LMs. We additionally uncover novel insights on position bias considerations for LMs in multiple-choice settings., Comment: Code to reproduce experiments and replicate findings is made available at https://github.com/trdavidson/self-recognition

We establish a verification theorem, inspired by those existing in stochastic control, to demonstrate how a pair of progressively measurable controls can form a Nash equilibrium in a stochastic zero-sum differential game. Specifically, we suppose that a pathwise-type Isaacs condition is satisfied together with the existence of what is termed a quasi-strong solution to the Bellman-Isaacs (BI)equations. In that case we are able to show that the value of the game is achieved and corresponds exactly to the unique solution of the BI equations. Those have also been applied for improving a well-known verification theorem in stochastic control theory. In so doing, we have implemented new techniques of stochastic calculus via regularizations, developing specific chain rules.

In the present work, with the intent of exploring the out-of-equilibrium polymerization of active patchy particles in linear chains, we study a suspension of active bivalent Brownian particles (ABBPs). At all studied temperatures and densities, ABBPs self-assemble in aggregating chains, as opposed to the uniformly space-distributed chains observed in the corresponding passive systems. The main effect of activity, other than inducing chain aggregation, is to reduce the chain length and favor alignment of the propulsion vectors in the bonding process. At low activities, attraction dominates over activity in the bonding process, leading self-assembly to occur randomly regardless of the particle orientations.Interestingly, we find that at the lowest temperature, as density increases, chains aggregate forming a novel state: MISP, i.e., Motility-Induced Spirals, where spirals are characterised by a finite angular velocity. On the contrary, at the highest temperature, density and activity, chains aggregate forming a different novel state (a spinning crystalline cluster) characterized by a compact and hexagonal ordered structure, both translating and rotating. The rotation arises from an effective torque generated by the presence of competing domains where particles self-propel in the same direction.

We report the first evidence for the $h_{b}(\text{2P}) \to \Upsilon(\text{1S})\eta$ transition with a significance of $3.5$ standard deviations. The decay branching fraction is measured to be $\mathcal{B}[h_{b}(\text{2P}) \to \Upsilon(\text{1S})\eta]=(7.1 ~^{+3.7} _{-3.2}\pm 0.8)\times10^{-3}$, which is noticeably smaller than expected. We also set upper limits on $\pi^0$ transitions of $\mathcal{B}[h_{b}(\text{2P}) \to \Upsilon(\text{1S})\pi^0] < 1.8\times10^{-3}$, and $\mathcal{B}[h_{b}(\text{1P})\to \Upsilon(\text{1S})\pi^0] < 1.8\times10^{-3}$, at the $90\%$ confidence level. These results are obtained with a $131.4$~fb$^{-1}$ data sample collected near the $\Upsilon(\text{5S})$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider., Comment: to be submitted to PRL

This study presents a comprehensive analysis of the band structure in NiPS$_3$, a van der Waals layered antiferromagnet, utilizing high-resolution synchrotron-based angle-resolved photoemission spectroscopy (ARPES) and corroborative density functional theory (DFT) calculations. By tuning the parameters of the light source, we obtained a very clear and wide energy range band structure of NiPS$_3$. Comparison with DFT calculations allows for the identification of the orbital character of the observed bands. Our DFT calculations perfectly match the experimental results, and no adaptations were made to the calculations based on the experimental outcomes. The appearance of novel electronic structure upon alkali metal dosing (AMD) were also obtained in this ARPES study. Above valence band maximum, structure of conduction bands and bands from defect states were firstly observed in NiPS$_3$. We provide the direct determination of the band gap of NiPS$_3$ as 1.3 eV from the band structure by AMD. In addition, detailed temperature dependent ARPES spectra were obtained across a range that spans both below and above the N\'eel transition temperature of NiPS$_3$. We found that the paramagnetic and antiferromagnetic states have almost identical spectra, indicating the highly localized nature of Ni $d$ states., Comment: 4 figures

For any causal nonlinear electrodynamics theory that is "self-dual" (electromagnetic $U(1)$-duality invariant), the Legendre-dual pair of Lagrangian and Hamiltonian densities $\{\mathcal{L},\mathcal{H}\}$ are constructed from functions $\{\ell,h\}$ on ${\bf R}^+$ related to a particle-mechanics Lagrangian and Hamiltonian. We show how a `duality' relating $\ell$ to $h$ implies that $\mathcal{L}$ and $\mathcal{H}$ are related by a simple map between appropriate pairs of variables. We also discuss Born's "Legendre self-duality" and implications of a new "$\Phi$-parity" duality. Our results are illustrated with many examples., Comment: 43 pages

The layered van der Waals trichalcogenide NiPS3 has attracted widespread attention due to its unique optical, magnetic, and electronic properties. The complexity of NiPS3 itself, however, has also led to ongoing debates regarding its characteristics such as the existence of self-doped ligand holes. In this study, X-ray absorption spectroscopy and resonant inelastic X-ray scattering have been applied to investigate the electronic structure of NiPS3. With the aid of theoretical calculations using the charge-transfer multiplet model, we provide experimental evidence for NiPS3 being a Mott-Hubbard insulator rather than a charge-transfer insulator. Moreover, we explain why some previous XAS studies have concluded that NiPS3 is a charge-transfer insulator by comparing surface and bulk sensitive spectra., Comment: 6 figures

We report a study of the hadronic transitions $\chi_{bJ}(2P)\to\omega\Upsilon(1S)$, with $\omega\to\pi^{+}\pi^{-}\pi^{0}$, using $28.2\times10^6~\Upsilon(3S)$ mesons recorded by the Belle detector. We present the first evidence for the near--threshold transition $\chi_{b0}(2P)\to\omega\Upsilon(1S)$, the analog of the charm sector decay $\chi_{c1}(3872)\to\omega J/\psi$, with a branching fraction of $B\big(\chi_{b0}(2P)\to\omega\Upsilon(1S)\big) = \big(0.55\pm0.19\pm0.07\big)\%$. We also obtain branching fractions of $B\big(\chi_{b1}(2P)\to\omega\Upsilon(1S)\big) = \big(2.39{}^{+0.20}_{-0.19}\pm0.24\big)\%$ and $B\big(\chi_{b2}(2P)\to\omega\Upsilon(1S)\big) = \big(0.47{}^{+0.13}_{-0.12}\pm0.06\big)\%$, confirming the measurement of the $\omega$ transitions of the $J=1,2~P$--wave states. The ratio for the $J=2$ to $J=1$ transitions is also measured and found to differ by 3.3 standard deviations from the expected value in the QCD multipole expansion., Comment: 6 pages, 2 figures

We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. We adopt an information-theoretical viewpoint and start from the instance-specific lower bound of the number of samples that have to be collected to identify a nearly-optimal policy. Deriving this lower bound along with the optimal exploration strategy entails solving an intricate optimization problem and requires a model of the system. In turn, most existing sample optimal exploration algorithms rely on estimating the model. We derive an approximation of the instance-specific lower bound that only involves quantities that can be inferred using model-free approaches. Leveraging this approximation, we devise an ensemble-based model-free exploration strategy applicable to both tabular and continuous Markov decision processes. Numerical results demonstrate that our strategy is able to identify efficient policies faster than state-of-the-art exploration approaches

Machine Learning (ML) systems are capable of reproducing and often amplifying undesired biases. This puts emphasis on the importance of operating under practices that enable the study and understanding of the intrinsic characteristics of ML pipelines, prompting the emergence of documentation frameworks with the idea that ``any remedy for bias starts with awareness of its existence''. However, a resource that can formally describe these pipelines in terms of biases detected is still amiss. To fill this gap, we present the Doc-BiasO ontology, a resource that aims to create an integrated vocabulary of biases defined in the \textit{fair-ML} literature and their measures, as well as to incorporate relevant terminology and the relationships between them. Overseeing ontology engineering best practices, we re-use existing vocabulary on machine learning and AI, to foster knowledge sharing and interoperability between the actors concerned with its research, development, regulation, among others. Overall, our main objective is to contribute towards clarifying existing terminology on bias research as it rapidly expands to all areas of AI and to improve the interpretation of bias in data and downstream impact.