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Multi-modal Large Language Models (MLLMs) have achieved remarkable success by integrating visual and textual modalities. However, they incur significant computational overhead due to the large number of vision tokens processed, limiting their practicality in resource-constrained environments. We introduce Language-Guided Vision Token Pruning (LVPruning) for MLLMs, an effective yet simple method that significantly reduces the computational burden while preserving model performance. LVPruning employs cross-attention modules to compute the importance of vision tokens based on their interaction with language tokens, determining which to prune. Importantly, LVPruning can be integrated without modifying the original MLLM parameters, which makes LVPruning simple to apply or remove. Our experiments show that LVPruning can effectively reduce up to 90% of vision tokens by the middle layer of LLaVA-1.5, resulting in a 62.1% decrease in inference Tera Floating-Point Operations Per Second (TFLOPs), with an average performance loss of just 0.45% across nine multi-modal benchmarks.

The rapid advancement of digital technologies in the first quarter of the 21st century has introduced significant transformations in various fields, such as communication, healthcare, and education. However, it has also led to an increase in the use and disposal of electronic devices, resulting in environmental challenges related to Waste Electrical and Electronic Equipment (WEEE), also known as e-waste. This phenomenon is observed in schools, where the integration and renewal of equipment have become essential for the development and implementation of new teaching strategies. Based on a technological research project, we present how students from a public school in S\~ao Paulo's countryside conducted e-waste reuse processes, applying the principles of metarecycling and physics knowledge to build a portable battery (power bank) and a smartphone charger powered by a dynamo attached to a bicycle. The appropriation of the relationships between science, technology, and social aspects was facilitated by validating the chargers through characterization tests of the charging time provided by the power bank and the bicycle-installed device under riding conditions. Educational actions within the community, involving concepts of sustainability, clean energy, and health benefits through physical exercise, were guided by the United Nations Sustainable Development Goals (SDGs)., Comment: 15 pages, in Portuguese language, 10 figures

Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of numerical solvers. These include powerful methods for simulating spin dynamics both in and out of equilibrium. Uniquely, it features a broad generalization of classical and semiclassical approaches to SU(N) coherent states, which is useful for studying systems exhibiting strong spin-orbit coupling or local entanglement effects. Sunny also offers a well-developed framework for calculating the dynamical spin structure factor, enabling direct comparison with scattering experiments. Ease of use is a priority, with tools for symmetry-guided modeling and interactive visualization.

Optimization challenges span a wide array of fields, from logistics and scheduling to finance, materials science, and drug discovery. Among these, Quadratic Unconstrained Binary Optimization (QUBO) problems are especially significant due to their computational complexity and their potential as a key application for quantum computing. In this work, we introduce an approach for solving QUBO problems using hybrid qubit-qumode bosonic quantum computers$\unicode{x2014}$devices that manipulate and measure the quantum states of light within microwave cavity resonators. We map problems with soft and hard constraints onto the Hamiltonian of a hybrid quantum system, consisting of a single qubit coupled to multiple qumodes. The optimal solution is encoded in the ground state of the system, which is revealed by photon-number measurements. Trial states are prepared through universal qubit-qumode circuits, employing echoed conditional displacement (ECD) gates in combination with qubit rotations. Our approach demonstrates the immense potential of hybrid quantum systems, showcasing their ability to efficiently tackle complex optimization problems in both academia and industry., Comment: 12 pages, 11 figures

We present preliminary results of 5 years' monitoring of the radial velocity of Alpha Sco, performed at the Astronomical Observatory of the Universidad de Los Andes in Bogot\'a, Colombia. The data include 580 spectra acquired on 153 nights between March 2015 and March 2020. The aim of this study is to probe the dynamics of the star's atmosphere on all possible time-scales through the variations in observed radial velocity. At present, our findings are consistent with previous results from other observers, and the combination of older and new data make it possible to assess the several periodicities. A detailed study of these results, including the convective motions in the photosphere, is still in progress., Comment: 5 pages, 4 figures, 2 tables, accepted in RevMexAA

We investigate the spin dynamics of the cuprate ladder Sr$_{2.5}$Ca$_{11.5}$Cu$_{24}$O$_{41}$ to elucidate the behavior of its intrinsically doped holes. Combining high-resolution neutron spectroscopy and density matrix renormalization group calculations enables a comprehensive analysis of the collective magnetic dynamics. We find a general absence of magnetic signatures from unpaired charges, indicating holes within the system form strongly bound localized Cooper pairs. A one-band Hubbard model fails to match the spectral features but a straightforward extension to a large attractive nearest-neighbor interaction quantitatively explains our results. Our finding shows the significance of additional interactions beyond the long-predicted quantum spin pairing in the ($d$-wave) charge pairing process. Considering the parallels between ladders and two-dimensional cuprates, these results are potentially relevant for square lattices as well., Comment: Main text plus supplementary material

Here we analyse ways to achieve deep subthreshold parametric squeezing of fluctuations beyond the $-6$~dB limit of single degree-of-freedom parametric resonators. One way of accomplishing this is via a lock-in amplifier feedback loop. Initially, we calculate the phase-dependent parametric amplification with feedback of an added ac signal. In one approach, we use the averaging method to obtain the amplification gain, while in the second approach, we obtain the ac response of the parametric amplifier with feedback using the harmonic balance method. In this latter approach, the feedback is proportional to an integral term that emulates the cosine quadrature output of a lock-in amplifier multiplied by a sine at the same tone of the lock-in. We find that the gain obtained via these two methods are the same whenever the integration time span of the integral is a multiple of the tone period. When this is not the case, we can obtain considerable deamplification. Finally, we analyse the response of the parametric resonator with feedback, described by this integro-differential model, to an added white noise in the frequency domain. Using this model we were able to calculate, in addition to squeezing, the noise spectral density in this resonator with feedback. Very strong squeezing or cooling can be obtained., Comment: 22 pages, 8 figures

The rapid integration of Internet of Things (IoT) devices into enterprise environments presents significant security challenges. Many IoT devices are released to the market with minimal security measures, often harbouring an average of 25 vulnerabilities per device. To enhance cybersecurity measures and aid system administrators in managing IoT patches more effectively, we propose an innovative framework that predicts the time it will take for a vulnerable IoT device to receive a fix or patch. We developed a survival analysis model based on the Accelerated Failure Time (AFT) approach, implemented using the XGBoost ensemble regression model, to predict when vulnerable IoT devices will receive fixes or patches. By constructing a comprehensive IoT vulnerabilities database that combines public and private sources, we provide insights into affected devices, vulnerability detection dates, published CVEs, patch release dates, and associated Twitter activity trends. We conducted thorough experiments evaluating different combinations of features, including fundamental device and vulnerability data, National Vulnerability Database (NVD) information such as CVE, CWE, and CVSS scores, transformed textual descriptions into sentence vectors, and the frequency of Twitter trends related to CVEs. Our experiments demonstrate that the proposed model accurately predicts the time to fix for IoT vulnerabilities, with data from VulDB and NVD proving particularly effective. Incorporating Twitter trend data offered minimal additional benefit. This framework provides a practical tool for organisations to anticipate vulnerability resolutions, improve IoT patch management, and strengthen their cybersecurity posture against potential threats., Comment: Accepted to Australasian Information Security Conference (AISC) 2025

The future mobile network has the complex mission of distributing available radio resources among various applications with different requirements. The radio access network slicing enables the creation of different logical networks by isolating and using dedicated resources for each group of applications. In this scenario, the radio resource scheduling (RRS) is responsible for distributing the radio resources available among the slices to fulfill their service-level agreement (SLA) requirements, prioritizing critical slices while minimizing the number of intent violations. Moreover, ensuring that the RRS can deal with a high diversity of network scenarios is essential. Several recent papers present advances in machine learning-based RRS. However, the scenarios and slice variety are restricted, which inhibits solid conclusions about the generalization capabilities of the models after deployment in real networks. This paper proposes an intent-based RRS using multi-agent reinforcement learning in a radio access network (RAN) slicing context. The proposed method protects high-priority slices when the available radio resources cannot fulfill all the slices. It uses transfer learning to reduce the number of training steps required. The proposed method and baselines are evaluated in different network scenarios that comprehend combinations of different slice types, channel trajectories, number of active slices and users' equipment (UEs), and UE characteristics. The proposed method outperformed the baselines in protecting slices with higher priority, obtaining an improvement of 40% and, when considering all the slices, obtaining an improvement of 20% in relation to the baselines. The results show that by using transfer learning, the required number of training steps could be reduced by a factor of eight without hurting performance.

We use a combination of ultra-high-resolution inelastic neutron scattering and Monte Carlo numerical simulations to study the thermodynamics and the structure of spin excitations in the spin-supersolid phase of the triangular lattice XXZ easy axis antiferromagnet K$_2$Co(SeO$_3$)$_2$ and its evolution in a magnetic field. BKT transitions heralding the onset of Ising and supersolid order are detected. Above the supersolid phase the value of Wannier entropy is experimentally recovered. At low temperatures, with an experimental resolution of about 23 $\mu$eV, no discrete coherent magnon modes are resolved within a broad continuum of scattering. In addition to gapless excitations, a pseudo-Goldstone mode with a small energy gap of 0.06 meV is found. A second excitation continuum is seen at higher energy, in place of single-spin-flip excitations of the Ising model. In applied fields the continuum gradually morphs into coherent spin waves, with the Goldstone and pseudo-Goldstone sectors showing distinct evolution. The agreement between experiment and numerical simulations is excellent on the quantitative level., Comment: 18 pages, 25 figures

Lorentz invariance violation is a feature of several quantum gravity models in which Lorentz symmetry is broken at high energies, leading to potential changes in particle behavior and interactions. In this study, we investigate vacuum Cherenkov radiation, a reaction in which an electron spontaneously emits a photon. This process, forbidden when considering unbroken Lorentz symmetry, is a phenomenological consequence of some quantum gravity models. We derive, for the first time, the spectra for the vacuum Cherenkov reaction, and confirm our results numerically. These results can be used to derive limits on Lorentz invariance violation., Comment: 12 pages, 6 figures

We report a comprehensive study on the origin of the enigmatic disordered ground state within the Shastry-Sutherland lattice, BaCe$_2$ZnS$_5$, at low temperatures. The magnetization and heat capacity data show a lack of magnetic ordering down to 73 mK. We deploy a localized spin dimer model which can accurately reproduce the dynamic structure factor of the neutron data, magnetization and heat capacity data. Remarkably, the intra-dimer exchange interaction shows strong XY-type anisotropy and the ground state of BaCe$_2$ZnS$_5$ is in an entangled state $(|\uparrow\uparrow> - |\downarrow\downarrow>)/\sqrt{2}$. This is in contrast to the singlet dimer state that is obtained for Heisenberg interactions. These results confirm that BaCe$_2$ZnS$_5$ is in a quantum paramagnet state consisting of entangled spin dimer states.

Significant progress has been made over the past decades towards unveiling the sources of the most energetic particles in nature, the ultra-high-energy cosmic rays (UHECRs). Despite these advancements, the exact astrophysical sites capable of accelerating these particles to such extreme energies remain largely unknown. Moreover, the mechanisms by which they achieve these extreme energies are poorly understood. Here, I provide a concise overview of the theory underlying the acceleration and propagation of UHECRs. I then critically discuss three recent results that could help unveil their origins: the reported excess around Centaurus A, the correlation with starburst galaxies, and the efforts to jointly model the energy spectrum, composition, and arrival directions. Finally, I discuss strategies for advancing this field, emphasising the need for refined theoretical models, the challenges in building them, and the potential for new observatories to shed light on the mysteries of UHECRs., Comment: 20 pages, 3 figures; invited review to appear in the proceedings of the 28th European Cosmic Ray Symposium (ECRS2024)

In this work, we introduce the notion of a partial action of a group on a strict monoidal category. We propose, in the context of Monoidal categories, new constructions analogous to those existing for partial group actions over an algebra such as the globalization, the subalgebra of partial invariants, and the partial smash product., Comment: 41 pages

Let $\Bbbk$ be a field, $H$ a Hopf algebra over $\Bbbk$, and $R = (_iM_j)_{1 \leq i,j \leq n}$ a generalized matrix algebra. In this work, we establish necessary and sufficient conditions for $H$ to act partially on $R$. To achieve this, we introduce the concept of an opposite covariant pair and demonstrate that it satisfies a universal property. In the special case where $H = \Bbbk G$ is the group algebra of a group $G$, we recover the conditions given in \cite{BP} for the existence of a unital partial action of $G$ on $R$.

Speeding significantly contributes to traffic accidents, posing ongoing risks despite advancements in automotive safety technologies. This study investigates how auditory alerts influence speeding behavior across different demographic groups, focusing on drivers' age and experience levels. Using a mobile application to collect real-time driving data, we conducted a field study in Copenhagen/Denmark that included various driving environments and controlled auditory warnings for speed limit violations. Our results revealed that auditory alerts were unexpectedly associated with an increased frequency and duration of speeding incidents. The impact of these alerts varied by experience level: intermediate drivers showed reduced speeding duration in response to alerts, whereas novice and highly experienced drivers tended to speed for more extended periods after receiving alerts. These findings underscore the potential benefits of adaptive, experience-sensitive alert systems tailored to driver demographics, suggesting that personalized alerts may enhance safety more effectively than standardized approaches., Comment: Length: 10 pages, 5 figures, 2 tables. Supplementary materials: Source code available on GitHub

In 1989 H.Karcher rewrote the theory of elliptic functions through an approach that is much more geometrical than analytical. Therewith he obtained an optimal control over the behaviour and image values of these functions, which allowed for their broad application in minimal surfaces. Our work is devoted to presenting the theory of elliptic functions according to Karcher's approach, as well as some of its applications in minimal surfaces. The original publication was a mini-course that can be downloaded at https://www.dm.ufscar.br/profs/lobos/IIEPG, Comment: in Portuguese language

The temperature-dependent phonons are a generalization of interatomic force constants varying in T, which as found widespread use in computing the thermal transport of materials. A formal justification for using this combination to access thermal conductivity in anharmonic crystals, beyond the harmonic approximation and perturbation theory, is still lacking. In this work, we derive a theory of heat transport of anharmonic crystals, using the mode-coupling theory of anharmonic lattice dynamics. Starting from the Green-Kubo formula, we develop the thermal conductivity tensor based on the system's dynamical susceptibility, or spectral function. Our results account for both the diagonal and off-diagonal contributions of the heat current, with and without collective effects. We implement our theory in the TDEP package, and have notably introduced a Monte Carlo scheme to compute phonon scattering due to third- and fourth-order interactions, achieving a substantial reduction in computational cost which enables full convergence of such calculations for the first time. We apply our methodology to systems with varying regimes of anharmonicity and thermal conductivity to demonstrate its universality. These applications highlight the importance of the phonon renormalizations and their interactions beyond the harmonic order. Overall, our work advances the understanding of thermal conductivity in anharmonic crystals and provides a theoretically robust framework for predicting heat transport in complex materials.

Quantum computing offers promising new avenues for tackling the long-standing challenge of simulating the quantum dynamics of complex chemical systems, particularly open quantum systems coupled to external baths. However, simulating such non-unitary dynamics on quantum computers is challenging since quantum circuits are specifically designed to carry out unitary transformations. Furthermore, chemical systems are often strongly coupled to the surrounding environment, rendering the dynamics non-Markovian and beyond the scope of Markovian quantum master equations like Lindblad or Redfield. In this work, we introduce a quantum algorithm designed to simulate non-Markovian dynamics of open quantum systems. Our approach enables the implementation of arbitrary quantum master equations on noisy intermediate-scale quantum (NISQ) computers. We illustrate the method as applied in conjunction with the numerically exact hierarchical equations of motion (HEOM) method. The effectiveness of the resulting quantum HEOM algorithm (qHEOM) is demonstrated as applied to simulations of the non-Lindbladian electronic energy and charge transfer dynamics in models of the carotenoid-porphyrin-C60 molecular triad dissolved in tetrahydrofuran and the Fenna-Matthews-Olson complex., Comment: 58 pages, 17 figures

Discontinuous Named Entity Recognition (DNER) presents a challenging problem where entities may be scattered across multiple non-adjacent tokens, making traditional sequence labelling approaches inadequate. Existing methods predominantly rely on custom tagging schemes to handle these discontinuous entities, resulting in models tightly coupled to specific tagging strategies and lacking generalisability across diverse datasets. To address these challenges, we propose TriG-NER, a novel Triplet-Grid Framework that introduces a generalisable approach to learning robust token-level representations for discontinuous entity extraction. Our framework applies triplet loss at the token level, where similarity is defined by word pairs existing within the same entity, effectively pulling together similar and pushing apart dissimilar ones. This approach enhances entity boundary detection and reduces the dependency on specific tagging schemes by focusing on word-pair relationships within a flexible grid structure. We evaluate TriG-NER on three benchmark DNER datasets and demonstrate significant improvements over existing grid-based architectures. These results underscore our framework's effectiveness in capturing complex entity structures and its adaptability to various tagging schemes, setting a new benchmark for discontinuous entity extraction., Comment: Accepted at WWW 2025. Code will be made available at https://github.com/adlnlp/trig_ner

Identifying experimental probes capable of diagnosing extreme quantum behavior is widely regarded as one of the foremost challenges in modern condensed matter physics. Here, we propose a novel approach for detecting chiral Kitaev spin liquid states through measurements of the local dynamical spin structure factor on the boundary using scanning tunneling microscopy (STM). We specifically focus on unpaired ("dangling") Majorana fermions, which naturally emerge along boundaries of Kitaev spin liquids, and can serve as indicators of chiral boundary modes under broad conditions, thereby offering a clear signature of these exotic quantum states.

In this paper, we consider 1D agent-based and kinetic models of aggregation with reversals. In particular, we fit a Gamma distribution to represent the run times in myxobacteria and analyze numerically the importance of non-exponential reversal times. We demonstrate that non-exponential reversal times aid aggregation and result in tighter aggregates. We compare and contrast the behavior of agent-based and kinetic models, and also consider kinetic models with aggregation driven by chemotaxis. Thus, incorporating non-exponential reversal times into models of aggregation can be particularly important for reproducing experimental data, such as aggregate persistence and dispersal.

A promising way to test the physics of the accelerated expansion of the Universe is by studying the growth rate of matter fluctuations, which can be parameterized by the matter energy density parameter to the power $\gamma$, the so-called growth index. It is well-known that the $\Lambda$CDM cosmology predicts $\gamma=0.55$. However, using observational data, Ref. \citep{Nguyen:2023fip} measured a much higher $\gamma=0.633^{+0.025}_{-0.024}$, excluding the $\Lambda$CDM value within $3.7\sigma$. In this work, we analyze whether Dark Energy (DE) with the Equation of State (EoS) parameter described by the CPL parametrization can significantly modify $\gamma$ with respect to the $\Lambda$CDM one. Besides the usual Smooth DE (SDE) scenario, where DE perturbations are neglected on small scales, we also consider the case of Clustering Dark Energy (CDE), which has more potential to impact the growth of matter perturbations. In order to minimally constrain the background evolution and assess the largest meaningful $\gamma$ distribution, we use data from $32$ Cosmic Chronometers, $H(z$), data points. In this context, we found that both SDE and CDE models described by the CPL parametrization can not provide a significant number of $\gamma$ samples compatible with the value determined in Nguyen et al. (2023). Therefore, explaining the measured value of $\gamma$ is a challenge for DE models. Moreover, we present new fitting functions for $\gamma$, which are more accurate and general than the one proposed in Linder (2005) for SDE, and, for the first time, fitting functions for CDE models., Comment: 10 pages, 7 figures and 4 tables

The Raman active G mode in graphene exhibits strong coupling to electrons, yet the comprehensive treatment of this interaction in the calculation of its temperature-dependent Raman spectrum remains incomplete. In this study, we calculate the temperature dependence of the G mode frequency and linewidth, and successfully explain the experimental trend, by accounting for the contributions arising from the first-order electron-phonon coupling, electron-mediated phonon-phonon coupling, and standard lattice anharmonicity. The generality of our approach enables its broad applicability to study phonon dynamics in materials where both electron-phonon coupling and anharmonicity are important., Comment: 14 pages, 7 figures, SI included in "anc" folder

Recent experimental work indicates that the Kitaev spin liquid may be realizable close to zero magnetic field in exfoliated $\alpha$-RuCl$_3$ flakes, thus providing a more versatile setting for studying non-Abelian anyons. Here, we propose a robust nanoscale signature of the Kitaev spin liquid that results from its edge states and manifests in the low-energy spin dynamics. In particular, we highlight a singular peak in the dynamical spin structure factor of a zigzag edge whose energy scales linearly with only one component of the magnetic field. This sharp feature in the local spin dynamics directly reflects the bond-directional Kitaev spin interactions and, more generally, the projective symmetries of the Kitaev spin liquid. We demonstrate that our proposed signature survives in the presence of edge disorder as well as non-Kitaev interactions, and provide detailed guidelines for experimentally observing it using inelastic electron tunneling spectroscopy and color-center relaxometry., Comment: 6+2 pages, 4 figures

Context: Requirements Engineering for AI-based systems (RE4AI) presents unique challenges due to the inherent volatility and complexity of AI technologies, necessitating the development of specialized methodologies. It is crucial to prepare upcoming software engineers with the abilities to specify high-quality requirements for AI-based systems. Goal: This research aims to evaluate the effectiveness and applicability of Goal-Oriented Requirements Engineering (GORE), specifically the KAOS method, in facilitating requirements elicitation for AI-based systems within an educational context. Method: We conducted an empirical study in an introductory software engineering class, combining presentations, practical exercises, and a survey to assess students' experience using GORE. Results: The analysis revealed that GORE is particularly effective in capturing high-level requirements, such as user expectations and system necessity. However, it is less effective for detailed planning, such as ensuring privacy and handling errors. The majority of students were able to apply the KAOS methodology correctly or with minor inadequacies, indicating its usability and effectiveness in educational settings. Students identified several benefits of GORE, including its goal-oriented nature and structured approach, which facilitated the management of complex requirements. However, challenges such as determining goal refinement stopping criteria and managing diagram complexity were also noted. Conclusion: GORE shows significant potential for enhancing requirements elicitation in AI-based systems. While generally effective, the approach could benefit from additional support and resources to address identified challenges. These findings suggest that GORE can be a valuable tool in both educational and practical contexts, provided that enhancements are made to facilitate its application., Comment: 10 pages, SBQS

Hybrid quantum-classical computing algorithms offer significant potential for accelerating the calculation of the electronic structure of strongly correlated molecules. In this work, we present the first quantum simulation of conical intersections (CIs) in a biomolecule, cytosine, using a superconducting quantum computer. We apply the Contracted Quantum Eigensolver (CQE) -- with comparisons to conventional Variational Quantum Deflation (VQD) -- to compute the near-degenerate ground and excited states associated with the conical intersection, a key feature governing the photostability of DNA and RNA. The CQE is based on an exact ansatz for many-electron molecules in the absence of noise -- a critically important property for resolving strongly correlated states at CIs. Both methods demonstrate promising accuracy when compared with exact diagonalization, even on noisy intermediate-scale quantum computers, highlighting their potential for advancing the understanding of photochemical and photobiological processes. The ability to simulate these intersections is critical for advancing our knowledge of biological processes like DNA repair and mutation, with potential implications for molecular biology and medical research.

In an era increasingly dominated by digital platforms, the spread of misinformation poses a significant challenge, highlighting the need for solutions capable of assessing information veracity. Our research contributes to the field of Explainable Artificial Antelligence (XAI) by developing transformer-based fact-checking models that contextualise and justify their decisions by generating human-accessible explanations. Importantly, we also develop models for automatic evaluation of explanations for fact-checking verdicts across different dimensions such as \texttt{(self)-contradiction}, \texttt{hallucination}, \texttt{convincingness} and \texttt{overall quality}. By introducing human-centred evaluation methods and developing specialised datasets, we emphasise the need for aligning Artificial Intelligence (AI)-generated explanations with human judgements. This approach not only advances theoretical knowledge in XAI but also holds practical implications by enhancing the transparency, reliability and users' trust in AI-driven fact-checking systems. Furthermore, the development of our metric learning models is a first step towards potentially increasing efficiency and reducing reliance on extensive manual assessment. Based on experimental results, our best performing generative model \textsc{ROUGE-1} score of 47.77, demonstrating superior performance in generating fact-checking explanations, particularly when provided with high-quality evidence. Additionally, the best performing metric learning model showed a moderately strong correlation with human judgements on objective dimensions such as \texttt{(self)-contradiction and \texttt{hallucination}, achieving a Matthews Correlation Coefficient (MCC) of around 0.7.}, Comment: Forthcoming in Engineering Applications of Artificial Intelligence

The excitonic insulator, a quantum mechanical state arising from exciton condensation, was proposed theoretically many years ago but has yet to be experimentally confirmed. The discovery of correlated transition metal oxides based on $4d$ and $5d$ elements, where the on-site Coulomb repulsion is comparable to the dominant hopping amplitude, presents a unique opportunity to study exciton condensation. By constructing an effective mean field Raman operator for the Hubbard model, we derive the low-energy electronic Raman scattering cross section, demonstrating Raman spectroscopy as a powerful tool for detecting exciton condensation. Here, we demonstrate that Raman scattering directly reveals exciton condensation in the bilayer iridate Sr$_3$Ir$_2$O$_7$ under pressure., Comment: The main text is 6 pages long, including 3 figures, and the supplemental material is 8 pages long, including 1 figure

Humanitarian organizations can enhance their effectiveness by analyzing data to discover trends, gather aggregated insights, manage their security risks, support decision-making, and inform advocacy and funding proposals. However, data about violent incidents with direct impact and relevance for humanitarian aid operations is not readily available. An automatic data collection and NLP-backed classification framework aligned with humanitarian perspectives can help bridge this gap. In this paper, we present HumVI - a dataset comprising news articles in three languages (English, French, Arabic) containing instances of different types of violent incidents categorized by the humanitarian sector they impact, e.g., aid security, education, food security, health, and protection. Reliable labels were obtained for the dataset by partnering with a data-backed humanitarian organization, Insecurity Insight. We provide multiple benchmarks for the dataset, employing various deep learning architectures and techniques, including data augmentation and mask loss, to address different task-related challenges, e.g., domain expansion. The dataset is publicly available at https://github.com/dataminr-ai/humvi-dataset.

Multi-Q magnetic structures on triangular lattices, with their two-dimensional topological spin texture, have attracted significant interest. However, unambiguously confirming their formation by excluding the presence of three equally-populated single-Q domains remains challenging. In the metallic triangular lattice antiferromagnet Co1/3TaS2, two magnetic ground states have been suggested at different temperature ranges, with the low-temperature phase being a triple-Q structure corresponding to the highest-density Skyrmion lattice. Using inelastic neutron scattering (INS) and advanced spin dynamics simulations, we demonstrate a clear distinction in the excitation spectra between the single-Q and triple-Q phases of Co1/3TaS2 and, more generally, a triangular lattice. First, we refined the spin Hamiltonian by fitting the excitation spectra measured in its paramagnetic phase, allowing us to develop an unbiased model independent of magnetic ordering. Second, we observed that the two magnetically ordered phases in Co1/3TaS2 exhibit markedly different behaviors in their long-wavelength Goldstone modes. Our spin model, derived from the paramagnetic phase, confirms that these behaviors originate from the single-Q and triple-Q nature of the respective ordered phases, providing unequivocal evidence of the single-Q to triple-Q phase transition in Co1/3TaS2. Importantly, we propose that the observed contrast in the long-wavelength spin dynamics between the single-Q and triple-Q orderings is universal, offering a potentially unique way to distinguish a generic triple-Q ordering on a triangular lattice from its multi-domain single-Q counterparts. We describe its applicability with examples of similar hexagonal systems forming potential triple-Q orderings. (For the full abstract, please refer to the manuscript), Comment: 14 pages, 6 figures excluding Appendices

The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery. We discuss the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. Applications to drug discovery are highlighted, including molecular property prediction and molecular generation. We provide a balanced perspective, emphasizing both the potential benefits and the challenges that must be addressed.

In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's performance when the obstacles form a narrow gap and the robot is a black-box (i.e. the dynamics are not known analytically, but interacting with the system is cheap). To address this challenge, this paper presents NeuralPARC. The method extends the authors' prior Piecewise Affine Reach-avoid Computation (PARC) method to systems modeled by rectified linear unit (ReLU) neural networks, which are trained to represent parameterized trajectory data demonstrated by the robot. NeuralPARC computes the reachable set of the network while accounting for modeling error, and returns a set of states and parameters with which the black-box system is guaranteed to reach the goal and avoid obstacles. Through numerical experiments, NeuralPARC is shown to outperform PARC in generating provably-safe extreme vehicle drift parking maneuvers, as well as enabling safety on an autonomous surface vehicle (ASV) subjected to large disturbances and controlled by a deep reinforcement learning (RL) policy., Comment: This work has been submitted for possible publication

Dissipative tunneling remains a cornerstone effect in quantum mechanics. In chemistry, it plays a crucial role in governing the rates of chemical reactions, often modeled as the motion along the reaction coordinate from one potential well to another. The relative positions of energy levels in these wells strongly influence the reaction dynamics. Chemical research will benefit from a fully adjustable, asymmetric double-well equipped with precise measurement capabilities of the tunneling rates. In this paper, we show a quantum simulator system that consists of a continuously driven Kerr parametric oscillator with a third order non-linearity that can be operated in the quantum regime to create a fully tunable asymmetric double-well. Our experiment leverages a low-noise, all-microwave control system with a high-efficiency readout, based on a tunnel Josephson junction circuit, of the which-well information. We explore the reaction rates across the landscape of tunneling resonances in parameter space. We uncover two new and counter-intuitive effects: (i) a weak asymmetry can significantly decrease the activation rates, even though the well in which the system is initialized is made shallower, and (ii) the width of the tunneling resonances alternates between narrow and broad lines as a function of the well depth and asymmetry. We predict by numerical simulations that both effects will also manifest themselves in ordinary chemical double-well systems in the quantum regime. Our work is a first step for the development of analog molecule simulators of proton transfer reactions based on quantum superconducting circuits., Comment: Edited some of the text and figures for clarity and corrected typos

Chemical reactions are commonly described by the reactive flux transferring population from reactants to products across a double-well free energy barrier. Dynamics often involves barrier recrossing and quantum effects like tunneling, zero-point energy motion and interference, which traditional rate theories, such as transition-state theory, do not consider. In this study, we investigate the feasibility of simulating reaction dynamics using a parametrically driven bosonic superconducting Kerr-cat device. This approach provides control over parameters defining the double-well free energy profile, as well as external factors like temperature and the coupling strength between the reaction coordinate and the thermal bath of non-reactive degrees of freedom. We demonstrate the effectiveness of this protocol by showing that the dynamics of proton transfer reactions in prototypical benchmark model systems, such as hydrogen bonded dimers of malonaldehyde and DNA base pairs, could be accurately simulated on currently accessible Kerr-cat devices., Comment: 53 pages including references and supporting material, 19 figures, revised

Plastic waste in aquatic environments poses severe risks to marine life and human health. Autonomous robots can be utilized to collect floating waste, but they require accurate object identification capability. While deep learning has been widely used as a powerful tool for this task, its performance is significantly limited by outdoor light conditions and water surface reflection. Light polarization, abundant in such environments yet invisible to the human eye, can be captured by modern sensors to significantly improve litter detection accuracy on water surfaces. With this goal in mind, we introduce PoTATO, a dataset containing 12,380 labeled plastic bottles and rich polarimetric information. We demonstrate under which conditions polarization can enhance object detection and, by providing raw image data, we offer an opportunity for the research community to explore novel approaches and push the boundaries of state-of-the-art object detection algorithms even further. Code and data are publicly available at https://github.com/luisfelipewb/ PoTATO/tree/eccv2024., Comment: ECCV24 TRICKY workshop, Sep 2024, Milano (Italy), Italy

Unmanned aerial vehicles (UAV) have been recognized as a versatile platform for a wide range of services. During the flight, these vehicles must avoid collisions to operate safely. In this way, they demand to keep spatial awareness, i.e., to know others in their coverage area. However, mobility and positioning aspects hamper building UAV network infrastructure to support reliable basic services. Thus, such vehicles call for a location service with up-to-date information resilient to false location injection threats. This work proposes FlySafe, a resilient UAVs location sharing service that employs opportunistic approaches to deliver UAVs' location. FlySafe takes into account the freshness of UAVs' location to maintain their spatial awareness. Further, it counts on the age of the UAV's location information to trigger device discovery. Simulation results showed that FlySafe achieved spatial awareness up to 94.15% of UAV operations, being resilient to~false locations injected in the network. Moreover, the accuracy in device discovery achieved 94.53% with a location error of less than 2 m., Comment: This work has been submitted to the Elsevier (Pervasive and Mobile Computing Journal) for possible publication

We classify the hypersurfaces of $\mathbb{Q}^3\times\mathbb{R}$ with three distinct constant principal curvatures, where $\varepsilon \in \{1,-1\}$ and $\mathbb{Q}^3$ denotes the unit sphere $\mathbb{S}^3$ if $\varepsilon = 1$, whereas it denotes the hyperbolic space $\mathbb{H}^3$ if $\varepsilon = -1$. We show that they are cylinders over isoparametric surfaces in $\mathbb{Q}^3$, filling an intriguing gap in the existing literature. We also prove that the hypersurfaces with constant principal curvatures of $\mathbb{Q}^3\times\mathbb{R}$ are isoparametric. Furthermore, we provide the complete classification of the extrinsically homogeneous hypersurfaces in $\mathbb{Q}^3\times\mathbb{R}$.

The initialization of quantum states or Quantum State Preparation (QSP) is a basic subroutine in quantum algorithms. In the worst case, general QSP algorithms are expensive due to the application of multi-controlled gates required to build them. Here, we propose an algorithm that detects whether a given quantum state can be factored into substates, increasing the efficiency of compiling the QSP circuit when we initialize states with some level of disentanglement. The simplification is done by eliminating controls of quantum multiplexers, significantly reducing circuit depth and the number of CNOT gates with a better execution and compilation time than the previous QSP algorithms. Considering efficiency in terms of depth and number of CNOT gates, our method is competitive with the methods in the literature. However, when it comes to run-time and compilation efficiency, our result is significantly better, and the experiments show that by increasing the number of qubits, the gap between the temporal efficiency of the methods increases.

One way to study the spread of disease is through mathematical models. The most successful models compartmentalize the host population according to their infectious stage, e.g., susceptible (S), infected (I), exposed (E), and recovered (R). The composition of these compartments leads to the SI, SIS, SIR, and SEIR models. In this Chapter, we present and compare three formulations of SI, SIS, SIR, and SEIR models in the framework of standard (integer operators), fractional (Caputo sense), and fractal derivatives (Hausdorff sense). As an application of the SI model, we study the evolution of AIDS cases in Bangladesh from 2001 to 2021. For this case, our simulations suggest that fractal formulation describes the data well. For the SIS model, we consider syphilis data from Brazil from 2006 to 2017. In this case, the three frameworks describe the data with good accuracy. We used data from Influenza A to adjust the SIR model in previous approaches and observed that the fractional formulation was better. The last application considers the COVID-19 data from India in the range 2020-04-10 to 2020-12-31 to adjust the parameters of the SEIR model. The standard formulation fits the data better than the other approaches. As a common result, all models exhibit steady solutions in the different formulations. The time to reach a steady solution is correlated to the considered approach. The standard and fractal formulations reach the steady state earlier when compared with the fractional formulation.

We develop a systematic large-$N$ expansion based on the Schwinger boson representation of SU(4) coherent states of dimers for the paradigmatic spin-$1/2$ bilayer square lattice Heisenberg antiferromagnet. This system exhibits a quantum phase transition between a quantum paramagnetic state and a N\'eel order state, driven by the coupling constant $g = J'/J$, which is defined as the ratio between the inter-dimer $J'$ and intra-dimer $J$ exchange interactions. We demonstrate that this approach accurately describes static and dynamic properties on both sides of the quantum phase transition. The critical coupling constant $g_c \approx 0.42$ and the dynamic spin structure factor reproduce quantum Monte Carlo results with high precision. Notably, the $1/N$ corrections reveal the longitudinal mode of the magnetically ordered phase along with the overdamping caused by its decay into the two-magnon continuum. The present large-$N$ $SU(N)$ Schwinger boson theory can be extended to more general cases of quantum paramagnets that undergo a quantum phase transition into magnetically ordered states.

In a recent study by Euclid collaboration, the halo mass function (HMF) has been fitted with accuracy better than $1\%$ for the $\Lambda$CDM model. Several parameters were introduced and fitted against N-body simulations, assuming the usual linearly extrapolated matter density contrast at the collapse time, $\delta_c$, as a basic threshold for halo formation. As a result, a new function that multiplies $\delta_c$ was introduced, producing an effective threshold that varies both with redshift and mass scale. We show that the redshift evolution of this effective threshold is similar to the one of the linear extrapolated matter density contrast at the virialization time, $\delta_{\rm v}$. Assuming the Euclid HMF as a fiducial model, we refit the Sheth-Tormen (ST) HMF using $\delta_{\rm v}$ as a threshold. This new fit improves the agreement between ST-HMF and the Euclid one with respect to Despali et al. (2016) fit, specially at high masses. Interestingly, the parameters $a$ and $p$ in this refit have values closer to the Press-Schechter limit of the ST-HMF, showing that the use of $\delta_{\rm v}$ can provide semi-analytical HMF less dependent on extra parameters. Moreover, we analyze the consistency of the ST-HMF fitted with $\delta_{\rm v}$ in smooth dark energy models with time-varying equation of state, finding an overall good agreement with the evolution of halo abundances expected from the linear evolution of perturbations and the Euclid HMF extrapolated to these scenarios. These findings suggest that the use $\delta_{\rm v}$ as a basic function to describe the threshold for halo formation can be a good guide when considering extrapolations for models beyond $\Lambda$CDM, which are typically harder to study in simulations., Comment: 17 pages, 8 figures and a publicly available code

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Neutrinos at ultrahigh energies can originate both from interactions of cosmic rays at their acceleration sites and through cosmic-ray interactions as they propagate through the universe. These neutrinos are expected to have a low flux which drives the need for instruments with large effective areas. Radio observations of the inclined air showers induced by tau neutrino interactions in rock can achieve this, because radio waves can propagate essentially unattenuated through the hundreds of kilometers of atmosphere. Proposed arrays for radio detection of tau neutrinos focus on either arrays of inexpensive receivers distributed over a large area, the GRAND concept, or compact phased arrays on elevated mountains, the BEACON concept, to build up a large detector area with a low trigger threshold. We present a concept that combines the advantages of these two approaches with a trigger driven by phased arrays at a moderate altitude (1 km) and sparse, high-gain outrigger receivers for reconstruction and background rejection. We show that this design has enhanced sensitivity at 100 PeV over the two prior designs with fewer required antennas and discuss the need for optimized antenna designs., Comment: ARENA2024 Conference Proceeding PoS(ARENA2024)058

In this work, effects of constant and time-dependent vaccination rates on the Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) seasonal model are studied. Computing the Lyapunov exponent, we show that typical complex structures, such as shrimps, emerge for given combinations of constant vaccination rate and another model parameter. In some specific cases, the constant vaccination does not act as a chaotic suppressor and chaotic bands can exist for high levels of vaccination (e.g., $> 0.95$). Moreover, we obtain linear and non-linear relationships between one control parameter and constant vaccination to establish a disease-free solution. We also verify that the total infected number does not change whether the dynamics is chaotic or periodic. The introduction of a time-dependent vaccine is made by the inclusion of a periodic function with a defined amplitude and frequency. For this case, we investigate the effects of different amplitudes and frequencies on chaotic attractors, yielding low, medium, and high seasonality degrees of contacts. Depending on the parameters of the time-dependent vaccination function, chaotic structures can be controlled and become periodic structures. For a given set of parameters, these structures are accessed mostly via crisis and in some cases via period-doubling. After that, we investigate how the time-dependent vaccine acts in bi-stable dynamics when chaotic and periodic attractors coexist. We identify that this kind of vaccination acts as a control by destroying almost all the periodic basins. We explain this by the fact that chaotic attractors exhibit more desirable characteristics for epidemics than periodic ones in a bi-stable state.

This study investigates vulnerabilities in dependencies of sampled open-source software (OSS) projects, the relationship between these and overall project security, and how developers' behaviors and practices influence their mitigation. Through analysis of OSS projects, we have identified common issues in outdated or unmaintained dependencies, including pointer dereferences and array bounds violations, that pose significant security risks. We have also examined developer responses to formal verifier reports, noting a tendency to dismiss potential issues as false positives, which can lead to overlooked vulnerabilities. Our results suggest that reducing the number of direct dependencies and prioritizing well-established libraries with strong security records are effective strategies for enhancing the software security landscape. Notably, four vulnerabilities were fixed as a result of this study, demonstrating the effectiveness of our mitigation strategies., Comment: 16 pages

As the adoption of Internet of Things (IoT) devices continues to rise in enterprise environments, the need for effective and efficient security measures becomes increasingly critical. This paper presents a cost-efficient platform to facilitate the pre-deployment security checks of IoT devices by predicting potential weaknesses and associated attack patterns. The platform employs a Bidirectional Long Short-Term Memory (Bi-LSTM) network to analyse device-related textual data and predict weaknesses. At the same time, a Gradient Boosting Machine (GBM) model predicts likely attack patterns that could exploit these weaknesses. When evaluated on a dataset curated from the National Vulnerability Database (NVD) and publicly accessible IoT data sources, the system demonstrates high accuracy and reliability. The dataset created for this solution is publicly accessible.

Active galactic nuclei (AGN) are among the main candidates for ultra-high-energy cosmic ray (UHECR) sources. However, while some theoretical and phenomenological works favor AGNs as the main sources, recent works have shown that using the very-high-energy (VHE) $\gamma$-ray flux as a proxy for the UHECR flux leads to a bad agreement with data. In this context, the energy spectrum and composition data are hardly fitted. At the same time, the arrival directions map is badly described and a spurious dipole direction is produced. In this work, we propose a possible solution to these contradictions. Using the observed $\gamma$-ray flux as a proxy may carry the implicit assumption of beamed UHECR emission and, consequently, its beam will remain collimated up to its detection on Earth. We show that assuming an isotropic UHECR emission and correcting the $\gamma$-ray emission proxy by Doppler boosting can overcome the problem. The combined fit of the spectrum and composition is improved, with a change of reduced $\chi^2$ from 4.6 to 3.1. In particular, the tension between the observed and modeled dipole directions can be reduced from $5.9 \ (2.1)\sigma$ away from the data to $3.5 \ (1.1)\sigma$ for $E > 8$ EeV ($E > 32$ EeV). We also show that this effect is particularly important when including AGNs of different classes in the same analysis, such as radio galaxies and blazars., Comment: Accepted for publication in The Astrophysical Journal (ApJ)