Higher-order dynamics refer to mechanisms where collective mutual or synchronous interactions differ fundamentally from their pairwise counterparts through the concept of many-body interactions. Phenomena absent in pairwise models, such as catastrophic activation, hysteresis, and hybrid transitions, emerge naturally in higher-order interacting systems. Thus, the simulation of contagion dynamics on higher-order structures is algorithmically and computationally challenging due to the complexity of propagation through hyperedges of arbitrary order. To address this issue, optimized Gillespie algorithms were constructed for higher-order structures by means of phantom processes: events that do not change the state of the system but still account for time progression. We investigate the algorithm's performance considering the susceptible-infected-susceptible (SIS) epidemic model with critical mass thresholds on hypergraphs. Optimizations were assessed on networks of different sizes and levels of heterogeneity in both connectivity and order interactions, in a high epidemic prevalence regime. Algorithms with phantom processes are shown to outperform standard approaches by several orders of magnitude in the limit of large sizes. Indeed, a high computational complexity scaling $\mathcal{O}(N^2)$ with system size $N$ of the standard algorithms is improved to low complexity scaling nearly as $\mathcal{O}(N)$. The optimized methods allow for the simulation of highly heterogeneous networks with millions of nodes within affordable computation costs, significantly surpassing the size range and order heterogeneity currently considered.

In capitalist societies, only a single right can be fully exerted without constraints of any kind: the limitless accumulation of wealth. Such imperative or prime axiom is the ultimate cause of the raising waves of inequalities observed today. In this work we extended the agent-based model proposed by Castro de Oliveira arXiv:1711.06164 to study the effects of non-uniform income redistribution policies and tax evasion on the final steady-state wealth distribution of economic agents. Our simulational results strongly support that well designed policies of income redistribution and rigid control of tax planning possibilities are unavoidable instruments to promote the raise of more economically egalitarian and sustainable societies.

We report a comprehensive experimental investigation of orbital-to-charge conversion in metallic and semiconductor materials, emphasizing the fundamental roles of the inverse orbital Hall effect (IOHE) and the inverse orbital Rashba effect. Using spin pumping driven by ferromagnetic resonance (SP-FMR) and the spin Seebeck effect (SSE), we demonstrate efficient orbital current generation and detection in YIG/Pt/NM structures, where NM is either a metal or a semiconductor. A central finding is the dominance of orbital contributions over spin-related effects, even in systems with weak spin-orbit coupling. In particular, a large enhancement of the SP-FMR and SSE signals is observed in the presence of naturally oxidized Cu in different heterostructures. Furthermore, we identify positive and negative IOHE signals in Ti and Ge, respectively, and extract orbital diffusion lengths in both systems using a diffusive model. Our results confirm the presence of orbital transport and offer valuable insights that may guide the further development of orbitronics.

We introduce two novel tree search algorithms that use a policy to guide search. The first algorithm is a best-first enumeration that uses a cost function that allows us to prove an upper bound on the number of nodes to be expanded before reaching a goal state. We show that this best-first algorithm is particularly well suited for `needle-in-a-haystack' problems. The second algorithm is based on sampling and we prove an upper bound on the expected number of nodes it expands before reaching a set of goal states. We show that this algorithm is better suited for problems where many paths lead to a goal. We validate these tree search algorithms on 1,000 computer-generated levels of Sokoban, where the policy used to guide the search comes from a neural network trained using A3C. Our results show that the policy tree search algorithms we introduce are competitive with a state-of-the-art domain-independent planner that uses heuristic search.

The Kitaev model belongs to an unconventional class of two-dimensional spin systems characterized by anisotropic, bond-dependent interactions that give rise to Quantum Spin Liquid (QSL) states. These exotic phases, marked by the absence of magnetic ordering even at zero temperature, support fractionalized excitations and emergent gauge fields. A particularly compelling feature of the Kitaev model is its exact solvability, which reveals low-energy excitations in the form of itinerant Majorana fermions-quasiparticles that obey non-Abelian statistics and are of central interest in topological quantum computation due to their inherent robustness against local perturbations and decoherence. Despite extensive theoretical advancements, the experimental identification of QSLs remains challenging, as conventional magnetic probes fail to detect their defining properties. In this work, we present a theoretical investigation of spin current injection from a superconducting metal into a Kitaev quantum spin liquid. By employing a spintronic framework, we derive the dynamics of the injected spin current and demonstrate how its signatures can be traced back to the underlying Majorana excitations in the spin liquid phase. Superconductivity plays a pivotal role in this context, not only as a source of coherent quasiparticles but also as a platform with potential for interfacing with topological quantum devices. Our analysis contrasts the Kitaev-superconductor interface with conventional ferromagnetic junctions, where spin transport is carried by magnons, and highlights distinctive features in the spin current response. These findings open new directions for the detection of QSLs and contribute to the broader effort of integrating topological quantum materials into scalable quantum technologies.

Infections diseases are marked by recovering time distributions which can be far from the exponential one associated with Markovian/Poisson processes, broadly applied in epidemic compartmental models. In the present work, we tackled this problem by investigating a susceptible-infected-recovered-susceptible model on networks with $\eta$ independent infectious compartments (SI$_{\eta}$RS), each one with a Markovian dynamics, that leads to a Gamma-distributed recovering times. We analytically develop a theory for the epidemic lifespan on star graphs with a center and $K$ leaves showing that the epidemic lifespan scales with a non-universal power-law $\tau_{K}\sim K^{\alpha/\mu\eta}$ plus logarithm corrections, where $\alpha^{-1}$ and $\mu^{-1}$ are the mean waning immunity and recovering times, respectively. Compared with standard SIRS dynamics with $\eta=1$ and the same mean recovering time, the epidemic lifespan on star graphs is severely reduced as the number of stages increases. In particular, the case $\eta\rightarrow\infty$ leads to a finite lifespan. Numerical simulations support the approximated analytical calculations. For the SIS dynamics, numerical simulations show that the lifespan increases exponentially with the number of leaves, with a nonuniversal rate that decays with the number of infectious compartments. We investigated the SI$_{\eta}$RS dynamics on power-law networks with degree distribution $P(K)\sim k^{-\gamma}$. When \gamma<5/2, the epidemic spreading is ruled by a maximum $k$-core activation, the alteration of the hub activity time does not alter either the epidemic threshold or the localization pattern. For \gamma>3, where hub mutual activation is at work, the localization is reduced but not sufficiently to alter the threshold scaling with the network size. Therefore, the activation mechanisms remain the same as in the case of Markovian healing.

This paper investigates the capabilities of text-to-audio music generation models in producing long-form music with prompts that change over time, focusing on soundtrack generation for Tabletop Role-Playing Games (TRPGs). We introduce Babel Bardo, a system that uses Large Language Models (LLMs) to transform speech transcriptions into music descriptions for controlling a text-to-music model. Four versions of Babel Bardo were compared in two TRPG campaigns: a baseline using direct speech transcriptions, and three LLM-based versions with varying approaches to music description generation. Evaluations considered audio quality, story alignment, and transition smoothness. Results indicate that detailed music descriptions improve audio quality while maintaining consistency across consecutive descriptions enhances story alignment and transition smoothness.

We consider a complete study of the influence of the cavity size on the spontaneous decay of an atom excited state, roughly approximated by a harmonic oscillator. We confine the oscillator-field system in a spherical cavity of radius $R$, perfectly reflective, and work in the formalism of dressed coordinates and states, which allows to perform non-perturbative calculations for the probability of the atom to decay spontaneously from the first excited state to the ground state. In free space, $R\to\infty$, we obtain known exact results an for sufficiently small $R$ we have developed a power expansion calculation in this parameter. Furthermore, for arbitrary cavity size radius, we developed numerical computations and showed complete agreement with the exact one for $R\to\infty$ and the power expansion results for small cavities, in this way showing the robustness of our results. We have found that in general the spontaneous decay of an excited state of the atom increases with the cavity size radius and vice versa. For sufficiently small cavities the atom practically does not suffers spontaneous decay, whereas for large cavities the spontaneous decay approaches the free-space $R\to\infty$ value. On the other hand, for some particular values of the cavity radius, in which the cavity is in resonance with the natural frequency of the atom, the spontaneous decay transition probability is increased compared to the free-space case. Finally, we showed how the probability spontaneous decay go from an oscillatory time behaviour, for finite cavity radius, to an almost exponential decay, for free space.

We investigate the role of clustering on the critical behavior of the contact process (CP) on small-world networks using the Watts-Strogatz (WS) network model with an edge rewiring probability p. The critical point is well predicted by a homogeneous cluster-approximation for the limit of vanishing clustering (p close to 1). The critical exponents and dimensionless moment ratios of the CP are in agreement with those predicted by the mean-field theory for any p > 0. This independence on the network clustering shows that the small-world property is a sufficient condition for the mean-field theory to correctly predict the universality of the model. Moreover, we compare the CP dynamics on WS networks with rewiring probability p = 1 and random regular networks and show that the weak heterogeneity of the WS network slightly changes the critical point but does not alter other critical quantities of the model.

Cross-docking is a logistics strategy that minimizes the storage and picking functions of conventional warehouses. The objective is to unload the cargo from inbound trucks and directly load it into outbound trucks, with little or no storage. The success of the strategy depends on an efficient transshipment operation. This work undertakes a study of truck scheduling in a cross-docking center. The problem is modeled as a two-machine flow shop scheduling problem with precedence constraints, with the objective of minimizing the makespan. The proposed method is based on a Lagrangean relaxation solved by a Volume algorithm over a time-indexed formulation. We use polynomial time heuristics for generating efficient upper and lower bounds in a computationally efficient time, outperforming current results in the literature for small and large size instances.

We discuss on existence, nonexistence and uniqueness of positive viscosity solutions for Lane-Emden systems involving the fractional Laplacian on bounded domains. As a byproduct, we obtain the critical hyperbole associated to the these systems.

Group algebras have been used in the context of Coding Theory since the beginning of the latter, but not in its full power. The work of Ferraz and Polcino Milies entitled Idempotents in group algebras and minimal abelian codes (Finite Fields and their Applications, 13, (2007) 382-393) gave origin to many thesis and papers linking these two subjects. In these works, the techniques of group algebras are mainly brought into play for the computing of the idempotents that generate the minimal codes and the minimum weight of such codes. In this paper I summarize the main results of the work done by doctorate students and research partners of Polcino Milies and Ferraz.

We present some experimental and simulation results that reproduces the Ostwald ripening (gas diffusion among bubbles) for air bubbles in a liquid fluid. Concerning the experiment, there it is measured the time evolution of bubbles mean radius, number of bubbles and radius size distribution. One of the main results shows that, while the number of bubbles decreases in time the bubbles mean radius increases, hence, it follows that the smaller bubbles disappear whereas the -- potentially dangerous for the diver -- larger bubbles grow up. Consequently, this effect suggests a possible contribution of the Ostwald ripening to the decompression sickness, and if so, it should be pursued its implementation to the Reduced Gradient Bubble Model (RGBM) so as to build up dive tables and computer programs for further diving tests.

The thermodynamic behavior of mixed systems containing the anionic surfactant sodium dodecyl sulfate (SDS) and the nonionic surfactant polyethylene glycol dodecyl ether (Brij L4) in aqueous solutions was investigated. Electrical conductivity and interfacial tension measurements were employed to investigate the concentration-dependent properties of these surfactant mixtures. Two main experiments were conducted: i) constant ratio experiment: the overall surfactant concentration was varied while maintaining a fixed ratio between SDS and Brij L4. It was shown that the critical micelle concentration (CMC) determined from electrical conductivity measurements does not indicate the formation of the first mixed micelles as observed using tensiometry, but the point from which the adsorption of counterions by the existing micelles became important. By using the Regular Solution Theory (RST), it was found that the interaction between SDS and Brij L4 is synergistic, driven by dipole-ion interactions of the hydrophilic regions of the two surfactants. ii) Fixed SDS concentration while increase the Brij L4 concentration: the resulting electrical conductivity exhibited non-trivial behavior which complexity arose from simultaneous phenomena of incorporation of free SDS molecules into the micelles as Brij L4 concentration increased, leading to a decrease in electrical conductivity; liberation of some adsorbed counterions from the mixed micelles to the solution, which increases the conductivity and increase of viscosity due to Brij L4 further addition that leads to a sequential decrease in electrical conductivity.

We analyze critical time series of the order parameter generated with active to inactive phase transitions of spreading dynamics running on the top of heterogeneous networks. Different activation mechanisms that govern the dynamics near the critical point were investigated. The time series were analyzed using the visibility graph (VG) method where a disassortative degree correlation of the VG is a signature of criticality. In contrast, assortative correlation is associated with offcritical dynamics. The signature of criticality given by the VG is confirmed for collective activation phenomena, as in the case of homogeneous networks. Similarly, for a localized activation driven by a densely connected set of hubs, identified by a maximum k-core decomposition, critical times series were also successfully identified by the VG method. However, in the case of activation driven by sparsely distributed hubs, the time series criticality is blurred, being observable only for huge systems. In the case of strong structural localization induced by the presence of rare regions, an assortative VG degree correlation, typical of off-critical series, is observed. We conclude that while macroscopic times series remain good proxies for the analysis of criticality for collective or maximum k-core activation, systems under spatial localization can postpone the signatures of or, in case of extreme localization, lead to false negatives for criticality of time series.

Neural models are one of the most popular approaches for music generation, yet there aren't standard large datasets tailored for learning music directly from game data. To address this research gap, we introduce a novel dataset named NES-VMDB, containing 98,940 gameplay videos from 389 NES games, each paired with its original soundtrack in symbolic format (MIDI). NES-VMDB is built upon the Nintendo Entertainment System Music Database (NES-MDB), encompassing 5,278 music pieces from 397 NES games. Our approach involves collecting long-play videos for 389 games of the original dataset, slicing them into 15-second-long clips, and extracting the audio from each clip. Subsequently, we apply an audio fingerprinting algorithm (similar to Shazam) to automatically identify the corresponding piece in the NES-MDB dataset. Additionally, we introduce a baseline method based on the Controllable Music Transformer to generate NES music conditioned on gameplay clips. We evaluated this approach with objective metrics, and the results showed that the conditional CMT improves musical structural quality when compared to its unconditional counterpart. Moreover, we used a neural classifier to predict the game genre of the generated pieces. Results showed that the CMT generator can learn correlations between gameplay videos and game genres, but further research has to be conducted to achieve human-level performance.

The Thief Orienteering Problem (ThOP) is a multi-component problem that combines features of two classic combinatorial optimization problems: Orienteering Problem and Knapsack Problem. The ThOP is challenging due to the given time constraint and the interaction between its components. We propose an Ant Colony Optimization algorithm together with a new packing heuristic to deal individually and interactively with problem components. Our approach outperforms existing work on more than 90% of the benchmarking instances, with an average improvement of over 300%.

With the increasing use of smartphones, instant messaging platforms turned into important communication tools. According to WhatsApp, more than 100 billion messages are sent each day on the app. Communication on these platforms has allowed individuals to express themselves in other types of media, rather than simple text, including audio, videos, images, and stickers. Particularly, stickers are a new multimedia format that emerged with messaging apps, promoting new forms of interactions among users, especially in the Brazilian context, transcending their role as a mere form of humor to become a key element in political strategy. In this regard, we investigate how stickers are being used, unveiling unique characteristics that these media bring to WhatsApp chats and the political use of this new media format. To achieve that, we collected a large sample of messages from WhatsApp public political discussion groups in Brazil and analyzed the sticker messages shared in this context

We study the Hamiltonian dynamics of a five-dimensional Chern-Simons theory for the gauge algebra $C_5$ of Izaurieta, Rodriguez and Salgado, the so-called S$_H$-expansion of the 5D (anti-)de Sitter algebra (a)ds, based on the cyclic group $\mathbb{Z}_4$. The theory consists of a 1-form field containing the (a)ds gravitation variables and 1-form field transforming in the adjoint representation of (a)ds. The gravitational part of the action necessarily contains a term quadratic in the curvature, beyond the Einstein-Hilbert and cosmological terms, for any choice of the two independent coupling constants. The total action is also invariant under a new local symmetry, called "crossed diffeomorphisms", beyond the usual space-time diffeomorphisms. The number of physical degrees of freedom is computed. The theory is shown to be "generic" in the sense of Ba\~nados, Garay and Henneaux, i.e., the constraint associated to the time diffeomorphisms is not independent from the other constraints.

We tackle two long-standing problems related to re-expansions in heuristic search algorithms. For graph search, A* can require $\Omega(2^{n})$ expansions, where $n$ is the number of states within the final $f$ bound. Existing algorithms that address this problem like B and B' improve this bound to $\Omega(n^2)$. For tree search, IDA* can also require $\Omega(n^2)$ expansions. We describe a new algorithmic framework that iteratively controls an expansion budget and solution cost limit, giving rise to new graph and tree search algorithms for which the number of expansions is $O(n \log C)$, where $C$ is the optimal solution cost. Our experiments show that the new algorithms are robust in scenarios where existing algorithms fail. In the case of tree search, our new algorithms have no overhead over IDA* in scenarios to which IDA* is well suited and can therefore be recommended as a general replacement for IDA*.