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Multi-robot systems enhance efficiency and productivity across various applications, from manufacturing to surveillance. While single-robot motion planning has improved by using databases of prior solutions, extending this approach to multi-robot motion planning (MRMP) presents challenges due to the increased complexity and diversity of tasks and configurations. Recent discrete methods have attempted to address this by focusing on relevant lower-dimensional subproblems, but they are inadequate for complex scenarios like those involving manipulator robots. To overcome this, we propose a novel approach that %leverages experience-based planning by constructs and utilizes databases of solutions for smaller sub-problems. By focusing on interactions between fewer robots, our method reduces the need for exhaustive database growth, allowing for efficient handling of more complex MRMP scenarios. We validate our approach with experiments involving both mobile and manipulator robots, demonstrating significant improvements over existing methods in scalability and planning efficiency. Our contributions include a rapidly constructed database for low-dimensional MRMP problems, a framework for applying these solutions to larger problems, and experimental validation with up to 32 mobile and 16 manipulator robots.

Understanding complex interactions within microbiomes is essential for exploring their roles in health and disease. However, constructing reliable microbiome networks often poses a challenge due to variations in the output of different network inference algorithms. To address this issue, we present CMiNet, an R package designed to generate a consensus microbiome network by integrating results from multiple established network construction methods. CMiNet incorporates nine widely used algorithms, including Pearson, Spearman, Biweight Midcorrelation (Bicor), SparCC, SpiecEasi, SPRING, GCoDA, and CCLasso, along with a novel algorithm based on conditional mutual information (CMIMN). By combining the strengths of these algorithms, CMiNet generates a single, weighted consensus network that provides a more stable and comprehensive representation of microbial interactions. The package includes customizable functions for network construction, visualization, and analysis, allowing users to explore network structures at different threshold levels and assess connectivity and reliability. CMiNet is designed to handle both quantitative and compositional data, ensuring broad applicability for researchers aiming to understand the intricate relationships within microbiome communities. Availability: Source code is freely available at https://github.com/solislemuslab/CMiNet.

The problem of imputing multivariate time series spans a wide range of fields, from clinical healthcare to multi-sensor systems. Initially, Recurrent Neural Networks (RNNs) were employed for this task; however, their error accumulation issues led to the adoption of Transformers, leveraging attention mechanisms to mitigate these problems. Concurrently, the promising results of diffusion models in capturing original distributions have positioned them at the forefront of current research, often in conjunction with Transformers. In this paper, we propose replacing time-oriented Transformers with State-Space Models (SSM), which are better suited for temporal data modeling. Specifically, we utilize the latest SSM variant, S6, which incorporates attention-like mechanisms. By embedding S6 within Mamba blocks, we develop a model that integrates SSM, Graph Neural Networks, and node-oriented Transformers to achieve enhanced spatiotemporal representations. Implementing these architectural modifications, previously unexplored in this field, we present Time series Imputation with Bi-directional mamba blocks and diffusion models (TIMBA). TIMBA achieves superior performance in almost all benchmark scenarios and performs comparably in others across a diverse range of missing value situations and three real-world datasets. We also evaluate how the performance of our model varies with different amounts of missing values and analyse its performance on downstream tasks. In addition, we provide the original code to replicate the results., Comment: 14 pages, 7 tables and 2 figures

Working with Resonance Chiral Theory, within the two resonance multiplets saturation scheme, we satisfy leading (and some subleading) chiral and asymptotic QCD constraints and accurately fit simultaneously the $\pi^{0},\eta,\eta^{\prime}$ transition form factors, for single and double virtuality. In the latter case, we supplement the few available measurements with lattice data to ensure a faithful description. Mainly due to the new results for the doubly virtual case, we improve over existing descriptions for the $\eta$ and $\eta^\prime$. Our evaluation of the corresponding pole contributions to the hadronic light-by-light piece of the muon $g-2$ read: $a_\mu^{\pi^{0}\text{-}\rm{pole}}=\left(60.4\pm0.5^{+3.2}_{-1.8}\right)\times10^{-11}$, $a_\mu^{\eta\text{-}\mathrm{pole}}=\left(15.2\pm0.5^{+1.1}_{-0.7}\right)\times10^{-11}$ and $a_\mu^{\eta^\prime\text{-}\rm{pole}}=\left(14.4\pm0.8^{+1.4}_{-1.0}\right)\times10^{-11}$, for a total of $a_\mu^{\pi^0+\eta+\eta^{\prime}\text{-}\rm{pole}}=\left(90.0\pm1.1^{+3.7}_{-2.2}\right)\times10^{-11}$, where the first and second error are the statistical and systematic uncertainties, respectively., Comment: 46 pages, 7 figures

Transition metal dichalcogenides (TMDs) exhibit unique properties and potential applications when reduced to one-dimensional (1D) nanoribbons (NRs), owing to quantum confinement and high edge densities. However, effective growth methods for self-aligned TMD NRs are still lacking. We demonstrate a versatile approach for the lattice-guided growth of dense, aligned TMD NR arrays via chemical vapor deposition (CVD) on anisotropic sapphire substrates, without the need for tailored surface steps. This method enables the synthesis of NRs with widths below 10 nm, with their longitudinal axis parallel to the zigzag direction. The growth is influenced by both the substrate and CVD temperature, indicating the role of anisotropic precursor diffusion and substrate interaction. The 1D nature of the NRs was asserted by the observation of Coulomb blockade at low temperature. Pronounced catalytic activity was observed at the edges of the NRs, indicating their promise for efficient catalysis., Comment: 39 pages, 24 figures

In this work we performed numerical simulations for the Ising model on three dimensional lattices with coordination number equal 5. With Monte Carlo simulations in the static case we evaluated the critical temperature and the static critical exponents $\nu$, $\gamma$ and $\beta$. Once that we have the critical temperature value we investigated the dynamical critical behavior with Glauber dynamics, starting from disordered states. From our simulations we obtained the values for the dynamic exponent $z=2.037(8)$, the exponent for the autocorrelation $\lambda/z=1.364(5)$, the exponent of the critical initial increase $\theta'=0.109(8)$ and the asymptotic value of the fluctuation-dissipation ratio $X^\infty=0.433(1)$. All of these results are in good agreement to the previous values reported for the 3D Ising model universality class., Comment: 7 pages, 8 figures

The tumor microenvironment (TME) plays a critical role in cancer cell proliferation, invasion, and resistance to therapy. A principal component of the TME is the tumor immune microenvironment (TIME), which includes various immune cells such as macrophages. Depending on the signals received from environmental elements like IL-4 or IFN-$\gamma$, macrophages can exhibit pro-inflammatory (M1) or anti-inflammatory (M2) phenotypes. This study uses an enhanced agent-based model to simulate interactions within the TIME, focusing on the dynamic behavior of macrophages. We examine the response of cancer cell populations to alterations in macrophages, categorized into three different behaviors: M0 (initial-inactive), M1 (immune-upholding), and M2 (immune-repressing), as well as environmental differentiations. The results highlight the significant impact of macrophage modulation on tumor proliferation and suggest potential therapeutic strategies targeting these immune cells., Comment: 12 pages, 8 figures, 0 tables

Unlike Right Atrium (RA), Left Atrium (LA) presents distinctive challenges, including much thinner myocardial walls, complex and irregular morphology, as well as diversity in individual's structure, making off-the-shelf methods designed for the Left Ventricle (LV) may not work in the context of the left atrium. To overcome aforementioned challenges, we are the first to present comprehensive technical workflow designed for 4D registration modeling to automatically analyze LA motion using high-resolution 3D Cine MR images. We integrate segmentation network and 4D registration process to precisely delineate LA segmentation throughout the full cardiac cycle. Additionally, an image 4D registration network is employed to extract LA displacement vector fields (DVFs). Our findings show the potential of proposed end to end framework in providing clinicians with novel regional biomarkers for left atrium motion tracking and deformation, carrying significant clinical implications.

Osgood functions in the source term are used to produce results for non-existence of local solutions into the framework of non-Gaussian diffusion equations. The critical exponent for non-existence of local solutions is found to depend on the fractional derivative, the non-Gaussian diffusion and the non-linear term. The instantaneous blow-up phenomenon is studied by exploiting estimates of the fundamental solutions. Nevertheless, theory of super-solutions and fixed points are combined for showing existence of global solutions. In this case, the critical exponent for existence of global solutions depends only on the last two parameters above., Comment: 12 pages

Automatic lexical simplification is a task to substitute lexical items that may be unfamiliar and difficult to understand with easier and more common words. This paper presents MultiLS-SP/CA, a novel dataset for lexical simplification in Spanish and Catalan. This dataset represents the first of its kind in Catalan and a substantial addition to the sparse data on automatic lexical simplification which is available for Spanish. Specifically, MultiLS-SP is the first dataset for Spanish which includes scalar ratings of the understanding difficulty of lexical items. In addition, we describe experiments with this dataset, which can serve as a baseline for future work on the same data., Comment: Submitted to the 40th edition of the SEPLN Conference. Under Revision

In this paper, we research into the anomalous Chromomagnetic Dipole Moment (CMDM), denoted as $\hat{\mu}_{q}^{BLHM}$, of the light quarks $q=(u, c, d, s, b)$ within the framework of the Bestest Little Higgs Model (BLHM) as an extension of the Standard Model (SM). Our investigation encompasses novel interactions among the light quarks, the heavy quark $B$, and the heavy bosons $(W^{\prime\pm}, H^{\pm}, \phi^{\pm}, \eta^{\pm})$, incorporating the extended Cabibbo-Kobayashi-Maskawa (CKM) matrix characteristic of the BLHM. We thoroughly explore the permissible parameter space, yielding a spectrum of CMDM values ranging from $10^{-10}$ to $10^{-3}$., Comment: 12 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:2403.08021

The Jacobson Coordinatization Theorem describes the structure of unitary Jordan algebras containing the algebra $H_n(F)$ of symmetric nxn matrices over a field F with the same identity element, for $n\geq 3$. In this paper we extend the Jacobson Coordinatization Theorem for n=2. Specifically, we prove that if J is a unitary Jordan algebra containing the Jordan matrix algebra $H_2(F)$ with the same identity element, then J has a form $J=H_2(F)\otimes A_0+k\otimes A_1$, where $A=A_0+A_1$ is a $Z_2$-graded Jordan algebra with a partial odd Leibniz bracket {,} an $k=e_{12}-e_{21}\in M_2(F)$ with the multiplication given by $(a\otimes b)(c\otimes d)=ac\otimes bd + [a,c]\otimes \{b,d\},$ the commutator [a,c] is taken in $M_2(F)$., Comment: 24 pages

It has been long-understood that final state rescattering effects provide $\mathcal{O}(1)$ corrections to hadronic meson decays rates, such as $\eta\to\pi\pi\pi$ and $\eta^{\prime}\to\eta\pi\pi$. Hence, one would expect that such effects would be just as important in axio-hadronic $\eta$ and $\eta^{\prime}$ decays, such as $\eta^{(\prime)}\to\pi\pi a$, where $a$ is an axion or axion-like particle (ALP). And indeed they are, as we show in this paper by using the treatment of dispersion relations to include the effects of strong final state interactions in several axio-hadronic processes, namely, $\eta^{(\prime)}\to\pi^0\pi^0 a$, $\eta^{(\prime)}\to\pi^+\pi^- a$, and $\eta^{\prime}\to\eta\pi^0 a$. We also compute the perturbative, leading order decay rates for multiple ALP emission, such as in $\eta^{(\prime)}\to\pi^{0}aa$, $\eta^{\prime}\to\eta aa$ and $\eta^{(\prime)}\to aaa$, and briefly discuss the expected corrections from strong interactions and the processes that must be considered for an accurate rate estimation of these multi-ALP decay channels., Comment: 29 pages, 9 figures

We study the magnetic Laplacian on the Lieb lattice, and prove Cantor spectrum for arbitrary irrational magnetic flux. We also provide a complete spectral analysis for the reduced one-dimensional Hamiltonian, proving Cantor spectra for all irrational frequencies, and sharp arithmetic phase transitions. Part of our analysis reveals a novel coexistence phenomenon of point spectrum and absolutely/singular continuous spectrum.

Many scientists view science as value-free, despite the fact that both epistemic and non-epistemic values structure scientific inquiry. Current ethics training usually focuses on transmitting knowledge about high-level ethical concepts or rules and is widely regarded as ineffective. We argue that ethics training will be more effective at improving ethical decision making if it focuses on connecting values to science. We pull from philosophy and psychology to define ethical decision making using the Four Component Model. This model states that in order to make an ethical decision someone must consider four components: moral sensitivity, moral reasoning, moral motivation, and moral implementation. We formed a moderated fellowship of fourteen science faculty from different disciplines who met for ten sessions over the course of a year, where they discussed the values embedded in different scientific norms. We then conducted interviews before and after the year-long fellowship that involved guided reflection of scenarios where there was some kind of ethical misconduct where the scientific practice required value judgements (e.g using unpublished data in their own work). We looked at how the fellowship affected the scientists' ability to recognize ethical dimensions regarding the scenarios. We found that this fellowship improved moral sensitivity, but their moral reasoning does not improve. We outlined our approach on how to look at scientists' ethical decision making and made recommendations on how to improve our approach. This work can inform future ethical training to align better with what scientists value and introduce useful concepts from philosophy and psychology to education research in physics.

Unsupervised learning has become a staple in classical machine learning, successfully identifying clustering patterns in data across a broad range of domain applications. Surprisingly, despite its accuracy and elegant simplicity, unsupervised learning has not been sufficiently exploited in the realm of phylogenetic tree inference. The main reason for the delay in adoption of unsupervised learning in phylogenetics is the lack of a meaningful, yet simple, way of embedding phylogenetic trees into a vector space. Here, we propose the simple yet powerful split-weight embedding which allows us to fit standard clustering algorithms to the space of phylogenetic trees. We show that our split-weight embedded clustering is able to recover meaningful evolutionary relationships in simulated and real (Adansonia baobabs) data.

Text simplification, crucial in natural language processing, aims to make texts more comprehensible, particularly for specific groups like visually impaired Spanish speakers, a less-represented language in this field. In Spanish, there are few datasets that can be used to create text simplification systems. Our research has the primary objective to develop a Spanish financial text simplification dataset. We created a dataset with 5,314 complex and simplified sentence pairs using established simplification rules. We also compared our dataset with the simplifications generated from GPT-3, Tuner, and MT5, in order to evaluate the feasibility of data augmentation using these systems. In this manuscript we present the characteristics of our dataset and the findings of the comparisons with other systems. The dataset is available at Hugging face, saul1917/FEINA.

This work presents Adaptive Robot Coordination (ARC), a novel hybrid framework for multi-robot motion planning (MRMP) that employs local subproblems to resolve inter-robot conflicts. ARC creates subproblems centered around conflicts, and the solutions represent the robot motions required to resolve these conflicts. The use of subproblems enables an inexpensive hybrid exploration of the multi-robot planning space. ARC leverages the hybrid exploration by dynamically adjusting the coupling and decoupling of the multi-robot planning space. This allows ARC to adapt the levels of coordination efficiently by planning in decoupled spaces, where robots can operate independently, and in coupled spaces where coordination is essential. ARC is probabilistically complete, can be used for any robot, and produces efficient cost solutions in reduced planning times. Through extensive evaluation across representative scenarios with different robots requiring various levels of coordination, ARC demonstrates its ability to provide simultaneous scalability and precise coordination. ARC is the only method capable of solving all the scenarios and is competitive with coupled, decoupled, and hybrid baselines., Comment: This work has been submitted for review

It has been demonstrated that the amount of data is crucial in data-driven machine learning methods. Data is always valuable, but in some tasks, it is almost like gold. This occurs in engineering areas where data is scarce or very expensive to obtain, such as predictive maintenance, where faults are rare. In this context, a mechanism to generate synthetic data can be very useful. While in fields such as Computer Vision or Natural Language Processing synthetic data generation has been extensively explored with promising results, in other domains such as time series it has received less attention. This work specifically focuses on studying and analyzing the use of different techniques for data augmentation in time series for classification and regression problems. The proposed approach involves the use of diffusion probabilistic models, which have recently achieved successful results in the field of Image Processing, for data augmentation in time series. Additionally, the use of meta-attributes to condition the data augmentation process is investigated. The results highlight the high utility of this methodology in creating synthetic data to train classification and regression models. To assess the results, six different datasets from diverse domains were employed, showcasing versatility in terms of input size and output types. Finally, an extensive ablation study is conducted to further support the obtained outcomes., Comment: In peer reviewing process