Large language models (LLMs) are increasingly deployed as task-oriented agents, where success depends on their ability to generate accurate function calls under realistic, multilingual conditions. However, existing agent evaluations largely overlook cultural and linguistic diversity, often relying on monolingual or naively translated benchmarks. We introduce Ticket-Bench, a benchmark for multilingual agent evaluation in task-oriented scenarios. Ticket-Bench simulates the domain of soccer ticket purchases across six major languages: Portuguese, English, Spanish, German, Italian, and French. Using localized teams, cities, and user profiles to provide a higher level of realism. We evaluate a wide range of commercial and open-source LLMs, measuring function-calling accuracy and consistency across languages. Results show that reasoning-oriented models (e.g., GPT-5, Qwen3-235B) dominate performance but still exhibit notable cross-lingual disparities. These findings underscore the need for culturally aware, multilingual benchmarks to guide the development of robust LLM agents.

Autonomous artificial agents must be able to learn behaviors in complex environments without humans to design tasks and rewards. Designing these functions for each environment is not feasible, thus, motivating the development of intrinsic reward functions. In this paper, we propose using several cognitive elements that have been neglected for a long time to build an internal world model for an intrinsically motivated agent. Our agent performs satisfactory iterations with the environment, learning complex behaviors without needing previously designed reward functions. We used 18 Atari games to evaluate what cognitive skills emerge in games that require reactive and deliberative behaviors. Our results show superior performance compared to the state-of-the-art in many test cases with dense and sparse rewards.

Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consists of an incomplete set of distances, and the output is a set of points in Euclidean space that realizes the given distances. We survey some of the theory of Euclidean distance geometry and some of the most important applications: molecular conformation, localization of sensor networks and statics.

Organic Quantum Chains (OQCs) represent a newly synthesized class of carbon-based nanostructures whose quasi-one-dimensional nature gives rise to unconventional electronic and transport phenomena. Here we investigate the electronic and transport properties of recently synthesized OQCs [Nature Communications, 12, 5895 (2021)]. Structural stability was first assessed through molecular dynamics relaxation combined with density functional theory (DFT). The optimized coordinates are then used in a tight-binding model with exponentially decaying hopping parameterization, which reproduces the DFT results with high accuracy. Our calculations reveal a robust and nearly constant energy gap across several OQC configurations, in agreement with experimental data. We also identify emergent hierarchical states, characterized by distinct localization behaviors within sets of localized bands. Finally, we analyze different transport responses in scenarios involving the one-dimensional OQC coupled to carbon corrals, as observed in the experimental data, highlighting their potential as promising systems for application in carbon nanodevices.

The field of Automatic Music Generation has seen significant progress thanks to the advent of Deep Learning. However, most of these results have been produced by unconditional models, which lack the ability to interact with their users, not allowing them to guide the generative process in meaningful and practical ways. Moreover, synthesizing music that remains coherent across longer timescales while still capturing the local aspects that make it sound ``realistic'' or ``human-like'' is still challenging. This is due to the large computational requirements needed to work with long sequences of data, and also to limitations imposed by the training schemes that are often employed. In this paper, we propose a generative model of symbolic music conditioned by data retrieved from human sentiment. The model is a Transformer-GAN trained with labels that correspond to different configurations of the valence and arousal dimensions that quantitatively represent human affective states. We try to tackle both of the problems above by employing an efficient linear version of Attention and using a Discriminator both as a tool to improve the overall quality of the generated music and its ability to follow the conditioning signals.

The sum of random variables (RVs) appears extensively in wireless communications, at large, both conventional and advanced, and has been subject of longstanding research. The statistical characterization of the referred sum is crucial to determine the performance of such communications systems. Although efforts have been undertaken to unveil these sum statistics, e.g., probability density function (PDF) and cumulative distribution function (CDF), no general efficient nor manageable solutions capable of evaluating the exact sum PDF and CDF are available to date. The only formulations are given in terms of either the multi-fold Brennan's integral or the multivariate Fox H-function. Unfortunately, these methods are only feasible up to a certain number of RVs, meaning that when the number of RVs in the sum increases, the computation of the sum PDF and CDF is subject to stability problems, convergence issues, or inaccurate results. In this paper, we derive new, simple, exact formulations for the PDF and CDF of the sum of L independent and identically distributed {\alpha}-{\mu} RVs. Unlike the available solutions, the computational complexity of our analytical expressions is independent of the number of summands. Capitalizing on our unprecedented findings, we analyze, in exact and asymptotic manners, the performance of L-branch pre-detection equal-gain combining and maximal-ratio combining receivers over {\alpha}-{\mu} fading environments. The coding and diversity gains of the system for both receivers are analyzed and quantified. Moreover, numerical simulations show that the computation time reduces drastically when using our expressions, which are arguably the most efficient and manageable formulations derived so far.

The low battery autonomy of Unnamed Aerial Vehicles (UAVs or drones) can make smart farming (precision agriculture), disaster recovery, and the fighting against dengue vector applications difficult. This article considers two approaches, first enumerating the characteristics observed in these three IoT application types and then modeling an UAV's battery recharge coordination using the Agent-Based Simulation (ABS) approach. In this way, we propose that each drone inside the swarm does not communicate concerning this recharge coordination decision, reducing energy usage and permitting remote usage. A total of 6000 simulations were run to evaluate how two proposed policies, the BaseLine (BL) and ChargerThershold (CT) coordination recharging policy, behave in 30 situations regarding how each simulation sets conclude the simulation runs and how much time they work until recharging results. CT policy shows more reliable results in extreme system usage. This work conclusion presents the potential of these three IoT applications to achieve their perpetual service without communication between drones and ground stations. This work can be a baseline for future policies and simulation parameter enhancements.

The Lipkin-Meshkov-Glick is a simple, but not trivial, model of a quantum many-body system which allows us to solve the many-body Schr\"odinger equation without making any approximation. The model, which in its unperturbed case is composed only by two energy levels, includes two interacting terms. A first one, the $V$ interaction, which promotes or degrade pairs of particles, and a second one, the $W$ interaction, which scatters one particle in the upper and another in the lower energy level. In comparing this model with other approximation methods, the $W$ term interaction is often set to zero. In this paper, we show how the presence of this interaction changes the global structure of the system, generates degeneracies between the various eigenstates and modifies the energy eigenvalues structure. We present analytical solutions for systems of two and three particles and, for some specific cases, also for four, six and eight particles. The solutions for systems with more than eight particles are only numerical but their behaviour can be well understood by considering the extrapolations of the analytical results. Of particular interest it is the study of how the $W$ interaction affects the energy gap between the ground state and the first-excited state.

We investigate the computation of Hessian matrices via Automatic Differentiation, using a graph model and an algebraic model. The graph model reveals the inherent symmetries involved in calculating the Hessian. The algebraic model, based on Griewank and Walther's state transformations, synthesizes the calculation of the Hessian as a formula. These dual points of view, graphical and algebraic, lead to a new framework for Hessian computation. This is illustrated by developing edge_pushing, a new truly reverse Hessian computation algorithm that fully exploits the Hessian's symmetry. Computational experiments compare the performance of edge_pushing on sixteen functions from the CUTE collection against two algorithms available as drivers of the software ADOL-C, and the results are very promising.

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted considerable attention due to their tunable structural, electronic, and spin-related properties, particularly in the presence of point defects and molecular adsorbates. Motivated by these aspects, we have investigated using first-principles methods the magnetic properties induced by azanide (NH$_2$) and ammonia (NH$_3$) adsorption on defective monolayers of Molybdenum Disulfide (MoS$_2$) and Diselenide(MoSe$_2$). Spin-polarized density functional theory (DFT) was employed to investigate the impact of mono- and di-vacancies on the local spin environment and the role of molecular adsorption in modifying magnetic behavior. The results show that pristine chalcogen vacancies do not generate magnetism, whereas the adsorption of NH$_2$ and NH$_3$ creates localized magnetic moments in Mo-based dichalcogenides. A notable case occurs for MoSe$_2$, where NH$_3$ dissociation into NH$_2$ and H fragments on the same side of the surface produces a net magnetic moment of 2.0 $\mu_B$. Tests performed on W-based dichalcogenides under equivalent conditions showed no magnetic response, and are reported here only for comparison. These findings demonstrate that molecular adsorption combined with defect engineering can be a practical approach to tune magnetism in 2D materials, with potential relevance for spintronic and sensing applications.

Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accuracy, particularly by leveraging wearable sensor data. This survey comprehensively reviews current ML methodologies used in classifying Parkinsonian tremors, evaluating various tremor data acquisition methodologies, signal preprocessing techniques, and feature selection methods across time and frequency domains, highlighting practical approaches for tremor classification. The survey explores ML models utilized in existing studies, ranging from traditional methods such as Support Vector Machines (SVM) and Random Forests to advanced deep learning architectures like Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). We assess the efficacy of these models in classifying tremor patterns associated with PD, considering their strengths and limitations. Furthermore, we discuss challenges and discrepancies in current research and broader challenges in applying ML to PD diagnosis using wearable sensor data. We also outline future research directions to advance ML applications in PD diagnostics, providing insights for researchers and practitioners.

Carbon-based materials have attracted great attention due to their exceptional structural diversity and wide-ranging applications. Recently, a new two-dimensional carbon allotrope, named pentagraphene (PG), was proposed. In this study, we proposed three novel three-dimensional (3D) PG allotropes, named 3D-PG-$\alpha$, -$\beta$, and -$\gamma$, engineered through biaxial strain and controlled compression of 2D PG layers. Comprehensive stability analyses, including phonon dispersion and ab initio molecular dynamics simulations (AIMD), confirm their thermodynamic stability under room and high-temperature conditions. 3D-PG-$\alpha$ is the most stable, exhibiting a cohesive energy 0.5 eV/atom lower than the least stable structure, 3D-PG-$\gamma$. Electronic property characterization reveals semiconducting behavior for all structures, with indirect electronic band gaps ranging from 0.91 to 2.67 eV. The analyses of the mechanical properties showed significant anisotropy, with higher stiffness along the in-plane ($xy$-plane) direction. Optical properties highlight strong absorption along a wide range and a pronounced anisotropic response. Additionally, the absorption spectra exhibit activity in the visible region, and the refractive index and reflectivity indicate potential use in ultraviolet-blocking devices.

We show that the torsion-free rank of $H_i(M, \mathbb{Z}_p)$ has finite upper bound for $i \leq m$, where $M$ runs through the pro-$p$ subgroups of finite index in a pro-$p$ group $G$ that is (nilpotent of class $c$)-by-abelian such that $G/N'$ is of type $FP_{2cm}$.

We expand FLew with a unary connective whose algebraic counterpart is the operation that gives the greatest complemented element below a given argument. We prove that the expanded logic is conservative and has the Finite Model Property. We also prove that the corresponding expansion of the class of residuated lattices is an equational class.

This paper proposes the meeting of fuzzy logic with paraconsistency in a very precise and foundational way. Specifically, in this paper we introduce expansions of the fuzzy logic MTL by means of primitive operators for consistency and inconsistency in the style of the so-called Logics of Formal Inconsistency (LFIs). The main novelty of the present approach is the definition of postulates for this type of operators over MTL-algebras, leading to the definition and axiomatization of a family of logics, expansions of MTL, whose degree-preserving counterpart are paraconsistent and moreover LFIs.

Organic Quantum Chains (OQCs) represent a newly synthesized class of carbon-based nanostructures whose quasi-one-dimensional nature gives rise to unconventional electronic and transport phenomena. Here we investigate the electronic and transport properties of recently synthesized OQCs [Nature Communications, 12, 5895 (2021)]. Structural stability was first assessed through molecular dynamics relaxation combined with density functional theory (DFT). The optimized coordinates are then used in a tight-binding model with exponentially decaying hopping parameterization, which reproduces the DFT results with high accuracy. Our calculations reveal a robust and nearly constant energy gap across several OQC configurations, in agreement with experimental data. We also identify emergent hierarchical states, characterized by distinct localization behaviors within sets of localized bands. Finally, we analyze different transport responses in scenarios involving the one-dimensional OQC coupled to carbon corrals, as observed in the experimental data, highlighting their potential as promising systems for application in carbon nanodevices.

Two-dimensional (2D) silicates have emerged as a promising class of ultrathin materials, expanding the landscape of 2D systems beyond conventional van der Waals crystals. Their unique crystal chemistries and structural anisotropies make them attractive for applications ranging from sensors and flexoelectric devices to drug delivery and catalysis. To unlock their full potential, it is critical to understand their thickness-dependent mechanical properties within the family of 2D silicates. In this study, we investigate the nanomechanical and frictional behaviors of two structurally distinct natural silicates: layered Biotite and chain-structured Rhodonite. Using atomic force microscopy (AFM), we found that Rhodonite exhibits nearly ten times higher adhesion force and modulus response compared to Biotite. Despite this, Biotite demonstrates superior frictional performance, with ultrathin (5 nm) flakes showing a remarkably low coefficient of friction ($\sim 0.6 \times 10^{-3}$) versus Rhodonite ($\sim 3.6 \times 10^{-3}$). To further elucidate interlayer adhesion, density functional theory (DFT) calculations with Hubbard correction were employed. These findings offer valuable insights into the design and selection of 2D silicates for advanced mechanical and tribological applications.

The Zero-touch Network & Service Management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this paper, taking advantage of recent advances in generative Artificial Intelligence, we introduce the Network ConFiguration Generator (LLM-NetCFG) that employs Large Language Model and architects ZSM configuration agents by Large Language Models. LLM-NetCFG can automatically generate configurations, verify them, and configure network devices based on intents expressed in natural language. We also show the automation and verification of network configurations with minimum human intervention. Moreover, we explore the opportunities and challenges of integrating LLM in functional areas of network management to fully achieve ZSM.

Simulation of vehicle motion in planetary environments is challenging. This is due to the modeling of complex terrain, optical conditions, and terrain-aware vehicle dynamics. One of the critical issues of typical simulators is that they assume terrain is a rigid body, which limits their ability to render wheel traces and compute the wheel-terrain interactions. This prevents, for example, the use of wheel traces as landmarks for localization, as well as the accurate simulation of motion. In the context of lunar regolith, the surface is not rigid but granular. As such, there are differences in the rover's motion, such as sinkage and slippage, and a clear wheel trace left behind the rover, compared to that on a rigid terrain. This study presents a novel approach to integrating a terramechanics-aware terrain deformation engine to simulate a realistic wheel trace in a digital lunar environment. By leveraging Discrete Element Method simulation results alongside experimental single-wheel test data, we construct a regression model to derive deformation height as a function of contact normal force. The region of interest in a height map is retrieved from the wheel poses. The elevation values of corresponding pixels are subsequently modified using contact normal forces and the regression model. Finally, we apply the determined elevation change to each mesh vertex to render wheel traces during runtime. The deformation engine is integrated into our ongoing development of a lunar simulator based on NVIDIA's Omniverse IsaacSim. We hypothesize that our work will be crucial to testing perception and downstream navigation systems under conditions similar to outdoor or terrestrial fields. A demonstration video is available here: this https URL