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In this paper we present the Advancing Racial Equity in Community College Model which maps out the organizational conditions shaping institutional transformation. Focused on two dimensions, the level of "organizational support" and "shared responsibility" to enact equity, we describe four quadrants with distinct organizational conditions that shape how equity advocates design, build, and sustain equity efforts. With well-documented racial inequities and renewed calls for racial justice across higher education, it demands new ways of exploring and understanding how institutional actors leading equity efforts are nested within differing organizational contexts that can enable as well as restrict the enactment and success of racial equity efforts. Our model helps equity advocates gain an "awareness" of known barriers to implementation in higher education, assess the readiness of their campus for racialized change, and take action to build the necessary institutional support and capacity to move the work forward.

In this paper we establish hierarchic control for the wave equation in a non cylindrical domain $\widehat{Q}$ of $\mathbb{R}^{n + 1}$. We assume that we can act in the dynamic of the system by a hierarchy of controls. According to the formulation given by H. Von Stackelberg \cite{S}, there are local controls, called followers and global controls, called leaders. In fact, one considers situations where there are two cost (objective) functions. One possible way is to cut the control into two parts, one being thought of as "the leader" and the other one as "the follower". This situation is studied in the paper, with one of the cost functions being of the controllability type. Existence and uniqueness is proven. The optimality system is given in the paper., Comment: 9 pages. arXiv admin note: text overlap with arXiv:2501.06672

Using density functional theory, we investigate the electronic structure of the alpha phase of an antimony monolayer in its isolated form and in contact to the (111) surface of gold. We demonstrate that the isolated single-layer actually displays a slightly modulated puckering that stabilizes the monolayer, not a uniform one as often assumed. Moreover, it has dramatic consequences on the electronic band structure: the material is a semiconductor with low-dispersing bands near the Brillouin zone center. By further application of about 12% strain on the armchair direction, a double-cone features develops wherein an electronic bandgap of about 21~meV is found. When in contact with a Au(111) surface, a strong interaction with gold arises, as it appears clearly from (i) substantial atomic displacements compared to the isolated form, and (ii) hybridization of Sb and Au orbitals. The latter profoundly modifies the electronic band structure by strengthening the spin-orbit splitting of hybridized bands and spoiling the double-cone feature whose manipulation through substrate-induced strain appears therefore questionable, at least in the simulated epitaxial implementation., Comment: 7 pages, 5 figures

This paper is devoted to the study of the approximate controllability for a one-dimensional wave equation in domains with moving boundary. This equation models the motion of a string where an endpoint is fixed and the other one is moving. When the speed of the moving endpoint is less than the characteristic speed, the controllability of this equation is established. We present the following results: the existence and uniqueness of Nash equilibrium, the approximate controllability with respect to the leader control, and the optimality system for the leader control., Comment: 13 pages

We study a Stackelberg strategy subject to the evolutionary linearized micropolar fluids equations in domains with moving boundaries, considering a Nash multi-objective equilibrium (non necessarily cooperative) for the "follower players" (as is called in the economy field) and an optimal problem for the leader player with approximate controllability objective. We will obtain the following main results : the existence and uniqueness of Nash equilibrium and its characterization, the approximate controllability of the linearized micropolar system with respect to the leader control and the existence and uniqueness of the Stackelberg-Nash problem, where the optimality system for the leader is given., Comment: 28 pages

This manuscript deals with a hierarchical control problem for Oldroyd equation under the Stackelberg-Nash strategy. The Oldroyd equation model is defined by non-regular coefficients, that is, they are bounded measurable functions. We assume that we can act in the dynamic of the system by a hierarchy of controls, where one main control (the leader) and several additional secondary control (the followers) act in order to accomplish their given tasks: controllability for the leader and optimization for followers. We obtain the existence and uniqueness of Nash equilibrium and its characterization, the approximate controllability with respect to the leader control, and the optimality system for leader control., Comment: 28 pages

Maximum-entropy methods, rooted in the inverse Ising/Potts problem from statistical mechanics, have become indispensable tools for modeling pairwise interactions in disciplines such as bioinformatics, ecology, and neuroscience. Despite their remarkable success, these methods often overlook high-order interactions that may be crucial in complex systems. Conversely, while modern machine learning approaches can capture such interactions, existing interpretable frameworks are computationally expensive, making it impractical to assess the relevance of high-order interactions in real-world scenarios. Restricted Boltzmann Machines (RBMs) offer a computationally efficient alternative by encoding statistical correlations via hidden nodes in a bipartite neural network. Here, we present a method that maps RBMs exactly onto generalized Potts models with interactions of arbitrary high order. This approach leverages large-$N$ approximations, facilitated by the simple architecture of the RBM, to enable the efficient extraction of effective many-body couplings with minimal computational cost. This mapping also enables the development of a general formal framework for the extraction of effective higher-order interactions in arbitrarily complex probabilistic models. Additionally, we introduce a robust formalism for gauge fixing within the generalized Potts model. We validate our method by accurately recovering two- and three-body interactions from synthetic datasets. Additionally, applying our framework to protein sequence data demonstrates its effectiveness in reconstructing protein contact maps, achieving performance comparable to state-of-the-art inverse Potts models. These results position RBMs as a powerful and efficient tool for investigating high-order interactions in complex systems., Comment: 13 Pages and 3 Figures

We show that the 32 $H(z)$ data from cosmic chronometers have overestimated uncertainties and make use of a Bayesian method to correct and reduce it. We then use the corrected data to constrain flat $\Lambda$CDM and O$\Lambda$CDM parameters. For the flat $\Lambda$CDM model, we got as result $H_{0} = 67.1\pm 4.0$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega _{m} = 0.333 ^{+0.041}_{-0.057}$. While for the O$\Lambda$CDM model, we found $H_{0} = 67.2\pm 4.8$ km s$^{-1}$ Mpc$^{-1}$, $\Omega _{m} = 0.36\pm 0.16$ and $\Omega _{\Lambda} =0.71 ^{+0.36}_{-0.28}$. These results goes from $22\%$ up to $28\%$ uncertainty reduction when compared to the constraints of the both uncorrected models.

Sedimentation in hydroelectric reservoirs is strongly impacted by anthropogenic activities within their upstream drainage basins. These activities, encompassing soil erosion and various other human-induced actions, have significant consequences for sedimentation patterns. This issue has been a subject of prolonged study, as sedimentation directly undermines the water storage capacity of reservoirs, consequently diminishing the overall efficiency of hydroelectric operations. Several scientists have dedicated their efforts to addressing the matter of reservoir sedimentation. This pursuit has led to the formulation of an indicator known as Sediment Trap Efficiency (STE), serving as a metric that quantifies the proportion of sedimentation within reservoirs relative to the influx of sediment from their upstream sources. This study seeks to present findings pertaining to carbon trapping efficiency observed across seven hydroelectric reservoirs in Brazil. The objective is to demonstrate the substantial relevance of carbon accumulation within these aquatic environments within the context of the carbon balance frameworks previously established., Comment: 20 pages, 8 figures, 6 tables

A calorimetric detector for minimally disruptive measurements of atomic hydrogen beams is described. The calorimeter measures heat released by the recombination of hydrogen atoms into molecules on a thin wire. As a demonstration, the angular distribution of a beam with a peak intensity of $\approx 10^{16} \,{\rm{atoms}}/{(\rm{cm}^2 \rm{s})}$ is measured by translating the wire across the beam. The data agree well with an analytic model of the beam from the thermal hydrogen atom source. Using the beam shape model, the relative intensity of the beam can be determined to 5% precision or better at any angle.

Searching for information on the internet and digital platforms to satisfy an information need requires effective retrieval solutions. However, such solutions are not yet available for Tetun, making it challenging to find relevant documents for text-based search queries in this language. To address these challenges, this study investigates Tetun text retrieval with a focus on the ad-hoc retrieval task. It begins by developing essential language resources -- including a list of stopwords, a stemmer, and a test collection -- which serve as foundational components for solutions tailored to Tetun text retrieval. Various strategies are then explored using both document titles and content to evaluate retrieval effectiveness. The results show that retrieving document titles, after removing hyphens and apostrophes without applying stemming, significantly improves retrieval performance compared to the baseline. Efficiency increases by 31.37%, while effectiveness achieves an average gain of 9.40% in MAP@10 and 30.35% in nDCG@10 with DFR BM25. Beyond the top-10 cutoff point, Hiemstra LM demonstrates strong performance across various retrieval strategies and evaluation metrics. Contributions of this work include the development of Labadain-Stopwords (a list of 160 Tetun stopwords), Labadain-Stemmer (a Tetun stemmer with three variants), and Labadain-Avaliad\'or (a Tetun test collection containing 59 topics, 33,550 documents, and 5,900 qrels).

The Variational Quantum Eigensolver (VQE) is a promising hybrid algorithm, utilizing both quantum and classical computers to obtain the ground state energy of molecules. In this context, this study applies VQE to investigate the ground state of protocatechuic acid, analyzing its performance with various Ans\"atze and active spaces. Subsequently, all VQE results were compared to those obtained with the Hartree-Fock (HF) method. The results demonstrate that Ans\"atze, like unitary coupled-cluster singles and doubles (UCCSD) and variations of Hardware-Efficient, generally achieve accuracy close to that of HF. Furthermore, the increase in active space has led to the models becoming more difficult to converge to values with a greater distance from the correct energy. In summary, the findings of this study reinforce the use of VQE as a powerful tool for analyzing molecular ground state energies. Finally, the results underscore the critical importance of Ansatz selection and active space size in VQE performance, providing valuable insights into its potential and limitations., Comment: 10 pages, 6 figures, 1 table

This paper presents a new construction of non-Anosov Partially Hyperbolic Geodesic flows. Our construction is closely related to the construction made by Carneiro and Pujals, the novelty is the use of conformal deformations to produce the examples. Some of the necessary conditions appear more naturally and are easier to check. Besides that, we could enumerate the conditions required to produce partially hyperbolic geodesic flow examples. We show how to produce examples with metrics that are non-positively curved and with a finner analysis we can prove ergodicity for the Liouville measure and uniqueness of the measure of maximal entropy. These examples lie on the boundary of Anosov metrics, allowing us to also produce metrics with partially hyperbolic geodesic flows and conjugate points., Comment: 34 pages

Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics., Comment: 22 pages, 13 figures, 10 tables

This proposal outlines the future plans of the Brazilian High-Energy Physics (HEP) community for upcoming collider experiments. With the construction of new particle colliders on the horizon and the ongoing operation of the High-Luminosity LHC, several research groups in Brazil have put forward technical proposals, covering both hardware and software contributions, as part of the Brazilian contribution to the global effort. The primary goal remains to foster a unified effort within the Brazilian HEP community, optimizing resources and expertise to deliver a high-impact contribution to the international HEP community., Comment: 24 pages, submitted to the Latin American Strategy Forum for Research Infrastructure

The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute with some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with $\Lambda$CDM and the predicted value of the Hubble parameter at redshift zero is $H_{0}=68.798\pm 6.340(1\sigma) \text{ km}\text{ s}^{-1}\text{ Mpc}^{-1}$., Comment: 21 pages, 19 figures, Universe 10 (2024) 12

The tension between direct measurements of the Hubble constant and those stemming from Cosmic Microwave Background probes has triggered a multitude of studies. The connection between cosmology and particle physics has shown to be a valuable approach to addressing the Hubble tension. In particular, increasing the number of relativistic degrees of freedom in the early universe helps alleviate the problem. In this work, we write down effective field theory describing relativistic dark matter production in association with neutrinos leading to a larger $H_0$. We derive limits on the effective energy scale that governs this relativistic production of dark matter as a function of the dark matter mass for fermion, vector, and scalar dark matter fields. In particular, scalar dark matter particles are more effective in increasing the effective number of relativistic species. Also, if they have weak scale masses, then the relativistic production of dark matter should be governed by Planck scale effective operators in order to alleviate the Hubble tension., Comment: 9 pages, 8 figures

In this article we show that the empirical measure of certain continuous time random walks satisfies a strong large deviation principle with respect to a topology introduced in~\cite{MV2016} by Mukherjee and Varadhan. This topology is natural in models which exhibit an invariance with respect to spatial translations. Our result applies in particular to the case of simple random walk and complements the results obtained in~\cite{MV2016} in which the large deviation principle has been established for the empirical measure of Brownian motion., Comment: 20 pages

This paper introduces novel deep reinforcement learning (Deep-RL) techniques using parallel distributional actor-critic networks for navigating terrestrial mobile robots. Our approaches use laser range findings, relative distance, and angle to the target to guide the robot. We trained agents in the Gazebo simulator and deployed them in real scenarios. Results show that parallel distributional Deep-RL algorithms enhance decision-making and outperform non-distributional and behavior-based approaches in navigation and spatial generalization., Comment: Paper accepted at the 24th International Conference on Control, Automation and Systems (ICCAS)

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV., Comment: Minor typo in table 4 corrected

In this study, we investigated Rydberg atom-based microwave electrometry using polarization spectroscopy in a room-temperature vapor cell. By measuring Autler-Townes splitting in the electromagnetically induced transparency (EIT) spectrum, we determined that the minimum measurable microwave electric field is approximately five times lower than conventional EIT techniques. The results are well reproduced by a full optical Bloch equation model, which takes into account all the hyperfine levels involved. Subsequently, the EIT setup was used to characterize a custom microwave cylindrical lens, which increases the field at the focus by a factor of three, decreasing the minimum measurable microwave electric field by the same amount. Our results indicate that the combination of polarization spectroscopy and a microwave lens may enhance microwave electrometry.

The Koopman operator enables the analysis of nonlinear dynamical systems through a linear perspective by describing time evolution in the infinite-dimensional space of observables. Here this formalism is applied to shear flows, specifically the minimal flow unit plane Couette flow and a nine-dimensional model of turbulent shear flow between infinite parallel free-slip walls under a sinusoidal body force (Moehlis et al., New J. Phys. 6, 56 (2004)). We identify a finite set of observables and approximate the Koopman operator using neural networks, following the method developed by Constante-Amores et al., Chaos, 34(4), 043119 (2024). Then the time evolution is determined with a method here denoted as "Projected Koopman Dynamics". Using a high-dimensional approximate Koopman operator directly for the plane Couette system is computationally infeasible due to the high state space dimension of direct numerical simulations (DNS). However, the long-term dynamics of the dissipative Navier-Stokes equations are expected to live on a lower-dimensional invariant manifold. Motivated by this fact, we perform dimension reduction on the DNS data with a variant of the IRMAE-WD (implicit rank-minimizing autoencoder-weight decay) autoencoder (Zeng et al., MLST 5(2), 025053 (2024)) before applying the Koopman approach. We compare the performance of data-driven state space (neural ordinary differential equation) and Projected Koopman Dynamics approaches in manifold coordinates for plane Couette flow. Projected Koopman Dynamics effectively captures the chaotic behavior of Couette flow and the nine-dimensional model, excelling in both short-term tracking and long-term statistical analysis, outperforming other frameworks applied to these shear flows.

Ultra-high-energy cosmic rays are known to be mainly of extragalactic origin, and their propagation is limited by energy losses, so their arrival directions are expected to correlate with the large-scale structure of the local Universe. In this work, we investigate the possible presence of intermediate-scale excesses in the flux of the most energetic cosmic rays from the direction of the supergalactic plane region using events with energies above 20 EeV recorded with the surface detector array of the Pierre Auger Observatory up to 31 December 2022, with a total exposure of 135,000 km^2 sr yr. The strongest indication for an excess that we find, with a post-trial significance of 3.1{\sigma}, is in the Centaurus region, as in our previous reports, and it extends down to lower energies than previously studied. We do not find any strong hints of excesses from any other region of the supergalactic plane at the same angular scale. In particular, our results do not confirm the reports by the Telescope Array collaboration of excesses from two regions in the Northern Hemisphere at the edge of the field of view of the Pierre Auger Observatory. With a comparable exposure, our results in those regions are in good agreement with the expectations from an isotropic distribution., Comment: submitted to ApJ

X-ray image-based disease diagnosis lies in ensuring the precision of identifying afflictions within the sample, a task fraught with challenges stemming from the occurrence of false positives and false negatives. False positives introduce the risk of erroneously identifying non-existent conditions, leading to misdiagnosis and a decline in patient care quality. Conversely, false negatives pose the threat of overlooking genuine abnormalities, potentially causing delays in treatment and interventions, thereby resulting in adverse patient outcomes. The urgency to overcome these challenges compels ongoing efforts to elevate the precision and reliability of X-ray image analysis algorithms within the computational framework. This study introduces modified pre-trained ResNet models tailored for multi-class disease diagnosis of X-ray images, incorporating advanced optimization strategies to reduce the execution runtime of training and inference tasks. The primary objective is to achieve tangible performance improvements through accelerated implementations of PyTorch, CUDA, Mixed- Precision Training, and Learning Rate Scheduler. While outcomes demonstrate substantial improvements in execution runtimes between normal training and CUDA-accelerated training, negligible differences emerge between various training optimization modalities. This research marks a significant advancement in optimizing computational approaches to reduce training execution time for larger models. Additionally, we explore the potential of effective parallel data processing using MPI4Py for the distribution of gradient descent optimization across multiple nodes and leverage multiprocessing to expedite data preprocessing for larger datasets., Comment: High Performance Computing course final term paper

The Pierre Auger Observatory is the most sensitive instrument to detect photons with energies above $10^{17}$ eV. It measures extensive air showers generated by ultra high energy cosmic rays using a hybrid technique that exploits the combination of a fluorescence detector with a ground array of particle detectors. The signatures of a photon-induced air shower are a larger atmospheric depth of the shower maximum ($X_{max}$) and a steeper lateral distribution function, along with a lower number of muons with respect to the bulk of hadron-induced cascades. In this work, a new analysis technique in the energy interval between 1 and 30 EeV (1 EeV = $10^{18}$ eV) has been developed by combining the fluorescence detector-based measurement of $X_{max}$ with the specific features of the surface detector signal through a parameter related to the air shower muon content, derived from the universality of the air shower development. No evidence of a statistically significant signal due to photon primaries was found using data collected in about 12 years of operation. Thus, upper bounds to the integral photon flux have been set using a detailed calculation of the detector exposure, in combination with a data-driven background estimation. The derived 95% confidence level upper limits are 0.0403, 0.01113, 0.0035, 0.0023, and 0.0021 km$^{-2}$ sr$^{-1}$ yr$^{-1}$ above 1, 2, 3, 5, and 10 EeV, respectively, leading to the most stringent upper limits on the photon flux in the EeV range. Compared with past results, the upper limits were improved by about 40% for the lowest energy threshold and by a factor 3 above 3 EeV, where no candidates were found and the expected background is negligible. The presented limits can be used to probe the assumptions on chemical composition of ultra-high energy cosmic rays and allow for the constraint of the mass and lifetime phase space of super-heavy dark matter particles., Comment: 19 pages, 22 figures

Tetun is one of Timor-Leste's official languages alongside Portuguese. It is a low-resource language with over 932,400 speakers that started developing when Timor-Leste restored its independence in 2002. The media mainly uses Tetun, and more than ten national online newspapers actively broadcast news in Tetun every day. However, since information retrieval-based solutions for Tetun do not exist, finding Tetun information on the internet is challenging. This work aims to investigate and develop solutions that can enable the application of information retrieval techniques to develop search solutions for Tetun. We present a preliminary result of an experiment conducted on the task of ad-hoc retrieval in Tetun.

The Cranfield paradigm has served as a foundational approach for developing test collections, with relevance judgments typically conducted by human assessors. However, the emergence of large language models (LLMs) has introduced new possibilities for automating these tasks. This paper explores the feasibility of using LLMs to automate relevance assessments, particularly within the context of low-resource languages. In our study, LLMs are employed to automate relevance judgment tasks, by providing a series of query-document pairs in Tetun as the input text. The models are tasked with assigning relevance scores to each pair, where these scores are then compared to those from human annotators to evaluate the inter-annotator agreement levels. Our investigation reveals results that align closely with those reported in studies of high-resource languages.

We report an investigation of the mass composition of cosmic rays with energies from 3 to 100 EeV (1 EeV=$10^{18}$ eV) using the distributions of the depth of shower maximum $X_\mathrm{max}$. The analysis relies on ${\sim}50,000$ events recorded by the Surface Detector of the Pierre Auger Observatory and a deep-learning-based reconstruction algorithm. Above energies of 5 EeV, the data set offers a 10-fold increase in statistics with respect to fluorescence measurements at the Observatory. After cross-calibration using the Fluorescence Detector, this enables the first measurement of the evolution of the mean and the standard deviation of the $X_\mathrm{max}$ distributions up to 100 EeV. Our findings are threefold: (1.) The evolution of the mean logarithmic mass towards a heavier composition with increasing energy can be confirmed and is extended to 100 EeV. (2.) The evolution of the fluctuations of $X_\mathrm{max}$ towards a heavier and purer composition with increasing energy can be confirmed with high statistics. We report a rather heavy composition and small fluctuations in $X_\mathrm{max}$ at the highest energies. (3.) We find indications for a characteristic structure beyond a constant change in the mean logarithmic mass, featuring three breaks that are observed in proximity to the ankle, instep, and suppression features in the energy spectrum., Comment: Version accepted for publication in Phys. Rev. D, 29 pages, 19 figures, 5 tables

We present measurements of the atmospheric depth of the shower maximum $X_\mathrm{max}$, inferred for the first time on an event-by-event level using the Surface Detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the $X_\mathrm{max}$ distributions up to energies of 100 EeV ($10^{20}$ eV), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the Fluorescence Detector data, we find evidence that the rate of change of the average $X_\mathrm{max}$ with the logarithm of energy features three breaks at $6.5\pm0.6~(\mathrm{stat})\pm1~(\mathrm{sys})$ EeV, $11\pm 2~(\mathrm{stat})\pm1~(\mathrm{sys})$ EeV, and $31\pm5~(\mathrm{stat})\pm3~(\mathrm{sys})$ EeV, in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured $X_\mathrm{max}$ distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 EeV and 100 EeV., Comment: Version accepted for publication in Phys. Rev. Lett., 9 pages, 3 figures, 1 table

Deep Reinforcement Learning (DRL) has emerged as a promising approach to enhancing motion control and decision-making through a wide range of robotic applications. While prior research has demonstrated the efficacy of DRL algorithms in facilitating autonomous mapless navigation for aerial and terrestrial mobile robots, these methods often grapple with poor generalization when faced with unknown tasks and environments. This paper explores the impact of the Delayed Policy Updates (DPU) technique on fostering generalization to new situations, and bolstering the overall performance of agents. Our analysis of DPU in aerial and terrestrial mobile robots reveals that this technique significantly curtails the lack of generalization and accelerates the learning process for agents, enhancing their efficiency across diverse tasks and unknown scenarios., Comment: IEEE 20th International Conference on Automation Science and Engineering (CASE)