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This work investigates the use of machine learning applied to the beam tracking problem in 5G networks and beyond. The goal is to decrease the overhead associated to MIMO millimeter wave beamforming. In comparison to beam selection (also called initial beam acquisition), ML-based beam tracking is less investigated in the literature due to factors such as the lack of comprehensive datasets. One of the contributions of this work is a new public multimodal dataset, which includes images, LIDAR information and GNSS positioning, enabling the evaluation of new data fusion algorithms applied to wireless communications. The work also contributes with an evaluation of the performance of beam tracking algorithms, and associated methodology. When considering as inputs the LIDAR data, the coordinates and the information from previously selected beams, the proposed deep neural network based on ResNet and using LSTM layers, significantly outperformed the other beam tracking models., Comment: 5 pages, conference: 2024 IEEE Latin-American Conference on Communications (LATINCOM)

Scanning Electron Microscopy (SEM) experiments provide detailed insights into material microstructures, enabling high-resolution imaging as well as crystallographic analysis through advanced techniques like Electron Backscatter Diffraction (EBSD). However, the interaction of the high-energy electron beam with the material can lead to localized heating, which may significantly impact specimen integrity, especially in applications requiring prolonged beam exposure, for instance when mapping the crystal structure using EBSD. This study examines electron-beam-induced heating effects on a model metal sample (iron), directly measuring the locally deposited electron beam energy with a MEMS-based heating device and validating these measurements through simulations, including Monte Carlo and Finite Element methods. The analysis focuses on the effects of various experimental parameters such as acceleration voltage (from 5 to 30 kV), beam current (from 0.17 nA to 22 nA), dwell time (from 1$\mu$s to 1ms) and sample tilt (0{\deg} to 70{\deg}). The findings reveal that local sample temperatures can increase by up to 70 {\deg}C during EBSD experiments, primarily affected by the choice in beam current and acceleration voltage, with beam current having the most significant impact.

A group element is called generalized torsion if a finite product of its conjugates is equal to the identity. We show that in a finitely generated abelian-by-finite group, an element is generalized torsion if and only if its image in the abelianization is torsion. We also prove a quantitative version with sharp bounds to the generalized exponent of these groups. In particular, we provide many examples of finitely presentable torsion-free groups in which all elements are generalized torsion. We also discuss positive generalized identities in abelian-by-finite groups and related classes.

Misclassified variables used in regression models, either as a covariate or as the response, may lead to biased estimators and incorrect inference. Even though Bayesian models to adjust for misclassification error exist, it has not been shown how these models can be implemented using integrated nested Laplace approximation (INLA), a popular framework for fitting Bayesian models due to its computational efficiency. Since INLA requires the latent field to be Gaussian, and the Bayesian models adjusting for covariate misclassification error necessarily introduce a latent categorical variable, it is not obvious how to fit these models in INLA. Here, we show how INLA can be combined with importance sampling to overcome this limitation. We also discuss how to account for a misclassified response variable using INLA directly without any additional sampling procedure. The proposed methods are illustrated through a number of simulations and applications to real-world data, and all examples are presented with detailed code in the supporting information.

In face of climate change and increasing urbanization, the predictive mosquito-borne diseases (MBD) transmission models require constant updates. Thus, is urgent to comprehend the driving forces of this non stationary behavior, observed through spatial and incidence expansion. We observed that temperature is a critical driver in predictive models for MBD transmission, also being consistently used in multiple reviewed papers with considerable incidence predictive capacity. Rainfall, however, have more subtle importance as moderate precipitation creates breeding sites for mosquitoes, but excessive rainfall can reduce larvae populations. We highlight the frequent use of mechanistic models, particularly those that integrate temperature-dependent biological parameters of disease transmission in incidence proxies as the Vectorial Capacity (VC) and temperature-based basic reproduction number $R_0(t)$, for example. These models show the importance of climate variables, but the socio-demographic factors are often not considered. This gap is a significant opportunity for future research to incorporate socio-demographic data into long-term predictive models for more comprehensive and reliable forecasts. With this survey, we outline the most promising paths to be followed by long-term MBD transmission research and highlighting the potential facing challenges. Thus, we offer a valuable foundation for enhancing disease forecasting models and supporting more effective public health interventions, specially in the long term.

We study the electronic band structure and optical response of a hybrid model, a $\alpha-\mathcal{T}_3$ model featuring a $\sqrt{3}\times\sqrt{3}$ Kekul\'e pattern modulation. Such a hybrid system may result from the depositing of adatoms in a hexagonal lattice, where the two sublattices are displaced in the perpendicular direction, like in germanene and silicene. We derive analytical expressions for the energy dispersion and the eigenfunctions using a tight-binding approximation of nearest-neighbor hopping electrons. The energy spectrum consists of a double-cone structure with Dirac points at zero momentum caused by Brillouin zone folding and a doubly degenerate flat band resulting from destructive quantum interference effects. Furthermore, we study the spectrum of intraband and interband transitions through the joint density of states, the optical conductivity, and the Drude spectral weight. We find new conductivity terms resulting from the opening of intervalley channels that are absent in the $\alpha-\mathcal{T}_3$ model and manifest themselves as van Hove singularities in the optical response. In particular, we identify an absorption window related to intervalley transport, which serves as a viable signature for detecting Kekul\'e periodicity in two-dimensional materials., Comment: 10 pages, 7 figures

Dengue is a climate-sensitive mosquito-borne disease with a complex transmission dynamic. Data related to climate, environmental and sociodemographic characteristics of the target population are important for project scenarios. Different datasets and methodologies have been applied to build complex models for dengue forecast, stressing the need to evaluate these models and their relative accuracy grounded on a reproducible methodology. The goal of this work is to describe and present Mosqlimate, a web-based platform composed by a dashboard, a data store, model and rediction registries and support for a community of practice in arbovirus forecasting. Multiple API endpoints give access to data for development, open registration of predictive models from different approaches and sharing of predictive models for arboviruses incidence, facilitating interaction between modellers and allowing for proper comparison of the performance of different registered models, by means of probabilistic scores. Epidemiological, entomological, climatic and sociodemographic datasets related to arboviruses in Brazil, are freely available for download, alongside full documentation., Comment: 10 pages, 2 figures, 6 tables

In this article, we introduce an original hybrid quantum-classical algorithm based on a variational quantum algorithm for solving systems of differential equations. The algorithm relies on a spectral method, which involves encoding the solution functions in the amplitudes of the quantum states generated by different parametrized circuits and transforms the task of solving the differential equations into an optimization problem. We first describe the principle of the algorithm from a theoretical point of view. We provide a detailed pseudo-code of the algorithm, on which we conduct a complexity analysis to highlight its scaling properties. We apply it to a set of examples, showcasing its applicability across diverse sets of differential equations. We discuss the advantages of our method and potential avenues for further exploration and refinement., Comment: The algorithm described in this paper is subject to a patent submission (EP24306601). Use of this method requires a license, which can be obtained from ColibriTD

Algebraic lattices are those obtained from modules in the ring of integers of algebraic number fields through the canonical or twisted embeddings. In turn, well-rounded lattices are those with maximal cardinality of linearly independent vectors in its set of minimal vectors. Both classes of lattices have been applied for signal transmission in some channels, such as wiretap channels. Recently, some advances have been made in the search for well-rounded lattices that can be realized as algebraic lattices. Moreover, some works have been published studying algebraic lattices obtained from modules in cyclic number fields of odd prime degree $p$. In this work, we generalize some results of a recent work of Tran et al. and we provide new constructions of well-rounded algebraic lattices from a certain family of modules in the ring of integers of each of these fields when $p$ is ramified in its extension over the field of rational numbers., Comment: 11 pages

Accurately describing the distribution of CO$_2$ in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems to aid implementation of international climate agreements. Large deep neural networks are poised to revolutionize weather prediction, which requires 3D modeling of the atmosphere. While similar in this regard, atmospheric transport modeling is subject to new challenges. Both, stable predictions for longer time horizons and mass conservation throughout need to be achieved, while IO plays a larger role compared to computational costs. In this study we explore four different deep neural networks (UNet, GraphCast, Spherical Fourier Neural Operator and SwinTransformer) which have proven as state-of-the-art in weather prediction to assess their usefulness for atmospheric tracer transport modeling. For this, we assemble the CarbonBench dataset, a systematic benchmark tailored for machine learning emulators of Eulerian atmospheric transport. Through architectural adjustments, we decouple the performance of our emulators from the distribution shift caused by a steady rise in atmospheric CO$_2$. More specifically, we center CO$_2$ input fields to zero mean and then use an explicit flux scheme and a mass fixer to assure mass balance. This design enables stable and mass conserving transport for over 6 months with all four neural network architectures. In our study, the SwinTransformer displays particularly strong emulation skill (90-day $R^2 > 0.99$), with physically plausible emulation even for forward runs of multiple years. This work paves the way forward towards high resolution forward and inverse modeling of inert trace gases with neural networks., Comment: Code: https://github.com/vitusbenson/carbonbench

We investigate the gravitational effects on the relativistic Dirac theory of a system such as a Hydrogen atom in the braneworld scenario. A gravitational dipole moment like contribution, arises in the nonrelativistic Hamiltonian of the system through an exact Foldy-Wouthuysen transformation. This term violates the equivalence principle for the weak interaction that is restored in the average over the spins. Furthermore, it feels the effects of the extra dimensions, so that in a Universe with two additional spatial dimensions its energy contribution is amplified by an order of $\sim 10^{16}\ \text{eV}$ concerning to the energy of this term for ordinary space. The compactification radius for a Universe with two extra dimensions is within the experimental limits, where deviations from the inverse square law are being tested. This suggests that the energy value for the gravitational dipole term in this scenario may lead us to search for traces of extra dimensions in atomic spectroscopy, as well as experimental constraints for these dimensions., Comment: 11 pages, one table

The influence of climate on mosquito-borne diseases like dengue and chikungunya is well-established, but comprehensively tracking long-term spatial and temporal trends across large areas has been hindered by fragmented data and limited analysis tools. This study presents an unprecedented analysis, in terms of breadth, estimating the SIR transmission parameters from incidence data in all 5,570 municipalities in Brazil over 14 years (2010-2023) for both dengue and chikungunya. We describe the Episcanner computational pipeline, developed to estimate these parameters, producing a reusable dataset describing all dengue and chikungunya epidemics that have taken place in this period, in Brazil. The analysis reveals new insights into the climate-epidemic nexus: We identify distinct geographical and temporal patterns of arbovirus disease incidence across Brazil, highlighting how climatic factors like temperature and precipitation influence the timing and intensity of dengue and chikungunya epidemics. The innovative Episcanner tool empowers researchers and public health officials to explore these patterns in detail, facilitating targeted interventions and risk assessments. This research offers a new perspective on the long-term dynamics of climate-driven mosquito-borne diseases and their geographical specificities linked to the effects of global temperature fluctuations such as those captured by the ENSO index., Comment: Submitted for peer review

In 2023, the CO2 growth rate was 3.37 +/- 0.11 ppm at Mauna Loa, 86% above the previous year, and hitting a record high since observations began in 1958, while global fossil fuel CO2 emissions only increased by 0.6 +/- 0.5%. This implies an unprecedented weakening of land and ocean sinks, and raises the question of where and why this reduction happened. Here we show a global net land CO2 sink of 0.44 +/- 0.21 GtC yr-1, the weakest since 2003. We used dynamic global vegetation models, satellites fire emissions, an atmospheric inversion based on OCO-2 measurements, and emulators of ocean biogeochemical and data driven models to deliver a fast-track carbon budget in 2023. Those models ensured consistency with previous carbon budgets. Regional flux anomalies from 2015-2022 are consistent between top-down and bottom-up approaches, with the largest abnormal carbon loss in the Amazon during the drought in the second half of 2023 (0.31 +/- 0.19 GtC yr-1), extreme fire emissions of 0.58 +/- 0.10 GtC yr-1 in Canada and a loss in South-East Asia (0.13 +/- 0.12 GtC yr-1). Since 2015, land CO2 uptake north of 20 degree N declined by half to 1.13 +/- 0.24 GtC yr-1 in 2023. Meanwhile, the tropics recovered from the 2015-16 El Nino carbon loss, gained carbon during the La Nina years (2020-2023), then switched to a carbon loss during the 2023 El Nino (0.56 +/- 0.23 GtC yr-1). The ocean sink was stronger than normal in the equatorial eastern Pacific due to reduced upwelling from La Nina's retreat in early 2023 and the development of El Nino later. Land regions exposed to extreme heat in 2023 contributed a gross carbon loss of 1.73 GtC yr-1, indicating that record warming in 2023 had a strong negative impact on the capacity of terrestrial ecosystems to mitigate climate change.

When extending the SM, e.g. with vector-like quarks (VLQ), one inevitably encounters more than one CP violating complex phases. In this work, we develop a method useful in reducing the number of complex phases in scenarios with one and two VLQs. We show that these VLQ-models can be described with just one or two physical complex phases respectively and present an explicit alternative parametrization for the quark mixing matrix. Making use of this Reduction of Complex Phases in the case of the Bento-Branco-Parada (BBP) model, i.e. the simplest implementation of the Nelson-Barr solution to the strong CP problem, one also recovers the single phase formulation of BBP model. We comment on how this specific scenario can be distinguished from the general VLQ case with weak-basis invariants, Comment: 18 pages

A locally recoverable code of locality $r$ over $\mathbb{F}_{q}$ is a code where every coordinate of a codeword can be recovered using the values of at most $r$ other coordinates of that codeword. Locally recoverable codes are efficient at restoring corrupted messages and data which make them highly applicable to distributed storage systems. Quasi-cyclic codes of length $n=m\ell$ and index $\ell$ are linear codes that are invariant under cyclic shifts by $\ell$ places. %Quasi-cyclic codes are generalizations of cyclic codes and are isomorphic to $\mathbb{F}_{q} [x]/ \langle x^m-1 \rangle$-submodules of $\mathbb{F}_{q^\ell} [x] / \langle x^m-1 \rangle$. In this paper, we decompose quasi-cyclic locally recoverable codes into a sum of constituent codes where each constituent code is a linear code over a field extension of $\mathbb{F}_q$. Using these constituent codes with set parameters, we propose conditions which ensure the existence of almost optimal and optimal quasi-cyclic locally recoverable codes with increased dimension and code length., Comment: 12 pages

Context: Due to advances in synthesizing lower dimensional materials there is the challenge of finding the wave equation that effectively describes quantum particles moving on 1D and 2D domains. Jensen and Koppe and Da Costa independently introduced a confining potential formalism showing that the effective constrained dynamics is subjected to a scalar geometry-induced potential; for the confinement to a curve, the potential depends on the curve's curvature function. Method: To characterize the $\pi$ electrons in polyenes, we follow two approaches. First, we utilize a weakened Coulomb potential associated with a spiral curve. The solution to the Schr\"{o}dinger equation with Dirichlet boundary conditions yields Bessel functions, and the spectrum is obtained analytically. We employ the particle-in-a-box model in the second approach, incorporating effective mass corrections. The $\pi$-$\pi^*$ transitions of polyenes were calculated in good experimental agreement with both approaches, although with different wave functions.

Advances in underwater imaging enable the collection of extensive seafloor image datasets that are necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering expedient mobilization of this crucial environmental information. Recent machine learning approaches provide opportunities to increase the efficiency with which seafloor image datasets are analyzed, yet large and consistent datasets necessary to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 2.6 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for use by the scientific community at https://doi.org/10.20383/103.0614.

This paper describes how to analyze the influence of Earth system variables on the errors when providing temperature forecasts. The initial framework to get the data has been based on previous research work, which resulted in a very interesting discovery. However, the aforementioned study only worked on individual correlations of the variables with respect to the error. This research work is going to re-use the main ideas but introduce three main novelties: (1) applying a data science approach by a few representative locations; (2) taking advantage of the rankings created by Spearman correlation but enriching them with other metrics looking for a more robust ranking of the variables; (3) evaluation of the methodology by learning random forest models for regression with the distinct experimental variations. The main contribution is the framework that shows how to convert correlations into rankings and combine them into an aggregate ranking. We have carried out experiments on five chosen locations to analyze the behavior of this ranking-based methodology. The results show that the specific performance is dependent on the location and season, which is expected, and that this selection technique works properly with Random Forest models but can also improve simpler regression models such as Bayesian Ridge. This work also contributes with an extensive analysis of the results. We can conclude that this selection based on the top-k ranked variables seems promising for this real problem, and it could also be applied in other domains., Comment: 17 pages, 19 figures, 5 tables, results SIMD UCLM - BGC Jena collaboration, research stay

We present the Evolving Graph Fourier Transform (EFT), the first invertible spectral transform that captures evolving representations on temporal graphs. We motivate our work by the inadequacy of existing methods for capturing the evolving graph spectra, which are also computationally expensive due to the temporal aspect along with the graph vertex domain. We view the problem as an optimization over the Laplacian of the continuous time dynamic graph. Additionally, we propose pseudo-spectrum relaxations that decompose the transformation process, making it highly computationally efficient. The EFT method adeptly captures the evolving graph's structural and positional properties, making it effective for downstream tasks on evolving graphs. Hence, as a reference implementation, we develop a simple neural model induced with EFT for capturing evolving graph spectra. We empirically validate our theoretical findings on a number of large-scale and standard temporal graph benchmarks and demonstrate that our model achieves state-of-the-art performance., Comment: Accepted as a full conference paper in the International Conference on Learning Representations 2024