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This study addresses the critical issue of ensuring data security and efficiency in interconnected devices, especially in IoT environments. The objective is to design and implement a model using cryptographic algorithms to enhance data security in 5G networks. Challenges arise from the limited computational capabilities of IoT devices, which require the analysis and selection of cryptographic algorithms to achieve efficient data transmission. This study proposes a model that includes four levels of security, each employing different levels of encryption to provide better data security. Finally, cloud computing optimizes processing efficiency and resource utilization to improve data transmission., Comment: 8 pages

In this work we propose a novel method to ensure important entropy inequalities are satisfied semi-discretely when constructing reduced order models (ROMs) on nonlinear reduced manifolds. We are in particular interested in ROMs of systems of nonlinear hyperbolic conservation laws. The so-called entropy stability property endows the semi-discrete ROMs with physically admissible behaviour. The method generalizes earlier results on entropy-stable ROMs constructed on linear spaces. The ROM works by evaluating the projected system on a well-chosen approximation of the state that ensures entropy stability. To ensure accuracy of the ROM after this approximation we locally enrich the tangent space of the reduced manifold with important quantities. Using numerical experiments on some well-known equations (the inviscid Burgers equation, shallow water equations and compressible Euler equations) we show the improved structure-preserving properties of our ROM compared to standard approaches and that our approximations have minimal impact on the accuracy of the ROM. We additionally generalize the recently proposed polynomial reduced manifolds to rational polynomial manifolds and show that this leads to an increase in accuracy for our experiments.

The impact of intrinsic compressibility effects -- changes in fluid volume due to pressure variations -- on high-speed wall-bounded turbulence has often been overlooked or incorrectly attributed to mean property variations. To unambiguously quantify these intrinsic compressibility effects, we perform direct numerical simulations of compressible turbulent channel flows with nearly uniform mean properties. Our simulations reveal that intrinsic compressibility effects yield a significant upward shift in the logarithmic mean velocity profile that can be attributed to the reduction in the turbulent shear stress. This reduction stems from the weakening of the near-wall quasi-streamwise vortices. We in turn attribute this weakening to the spontaneous opposition of sweeps and ejections from the near-wall expansions and contractions of the fluid, and provide a theoretical explanation for this mechanism. Our results also demonstrate that intrinsic compressibility effects are responsible for the increase in the inner-scaled streamwise turbulence intensity in compressible flows compared to incompressible flows, previously regarded to be an effect of mean property variations., Comment: 29 pages, 17 figures

In particle-laden turbulent wall flows, lift forces can influence the near-wall turbulence. This has been recently observed in particle-resolved simulations, which, however, are too expensive to be used in upscaled models. Instead, point-particle simulations have been the method of choice to simulate the dynamics of these flows during the last decades. While this approach is simpler, cheaper, and physically sound for small inertial particles in turbulence, some issues remain. In the present work, we address challenges associated with lift force modelling in turbulent wall flows and the impact of lift forces in the near-wall flow. We performed direct numerical simulations (DNS) of small inertial point particles in turbulent channel flow for fixed Stokes number and mass loading while varying the particle size. Our results show that the particle dynamics in the buffer region, causing the apparent particle-to-fluid slip velocity to vanish, raises major challenges for accurately modelling lift forces. While our results confirm that lift forces have little influence on particle dynamics for sufficiently small particle sizes, for inner-scaled diameters of order one and beyond, lift forces become quite important near the wall. The different particle dynamics under lift forces results in the modulation of streamwise momentum transport in the near-wall region. We analyze this lift-induced turbulence modulation for different lift force models, and the results indicate that realistic models are critical for particle-modelled simulations to correctly predict turbulence modulation by particles in the near-wall region.

Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and storage constraints. While traditional optimization falls short in scalability; and heuristic approaches lack in achieving optimal outcomes, Reinforcement Learning (RL) offers a promising avenue by enabling the learning of optimal offloading strategies through iterative interactions. However, the efficacy of RL hinges on access to rich datasets and custom-tailored, realistic training environments. To address this, we introduce PeersimGym, an open-source, customizable simulation environment tailored for developing and optimizing task offloading strategies within computational networks. PeersimGym supports a wide range of network topologies and computational constraints and integrates a \textit{PettingZoo}-based interface for RL agent deployment in both solo and multi-agent setups. Furthermore, we demonstrate the utility of the environment through experiments with Deep Reinforcement Learning agents, showcasing the potential of RL-based approaches to significantly enhance offloading strategies in distributed computing settings. PeersimGym thus bridges the gap between theoretical RL models and their practical applications, paving the way for advancements in efficient task offloading methodologies.

The solution of large systems of nonlinear differential equations is needed for many applications in science and engineering. In this study, we present three main improvements to existing quantum algorithms based on the Carleman linearisation technique. First, by using a high-precision technique for the solution of the linearised differential equations, we achieve logarithmic dependence of the complexity on the error and near-linear dependence on time. Second, we demonstrate that a rescaling technique can considerably reduce the cost, which would otherwise be exponential in the Carleman order for a system of ODEs, preventing a quantum speedup for PDEs. Third, we provide improved, tighter bounds on the error of Carleman linearisation. We apply our results to a class of discretised reaction-diffusion equations using higher-order finite differences for spatial resolution. We show that providing a stability criterion independent of the discretisation can conflict with the use of the rescaling due to the difference between the max-norm and 2-norm. An efficient solution may still be provided if the number of discretisation points is limited, as is possible when using higher-order discretisations., Comment: 37 pages, 2 figures

The solution of linear systems of equations is the basis of many other quantum algorithms, and recent results provided an algorithm with optimal scaling in both the condition number $\kappa$ and the allowable error $\epsilon$ [PRX Quantum \textbf{3}, 0403003 (2022)]. That work was based on the discrete adiabatic theorem, and worked out an explicit constant factor for an upper bound on the complexity. Here we show via numerical testing on random matrices that the constant factor is in practice about 1,500 times smaller than the upper bound found numerically in the previous results. That means that this approach is far more efficient than might naively be expected from the upper bound. In particular, it is over an order of magnitude more efficient than using a randomised approach from [arXiv:2305.11352] that claimed to be more efficient., Comment: 12 pages, 33 figures

We propose the 3-momentum sphere intersection regularization applied to the one and two fermion line integrals at finite temperature and chemical potential. The quark-antiquark polarization function in this new regularization approach is equivalent to the usual 3-momentum regularization, when the absolute value of the external 3-momentum of the polarization is zero. Additionally, it respects the particle-antiparticle symmetry of meson states in the Nambu$-$Jona-Lasinio (NJL) model for all values of temperature and chemical potential. Without this symmetry, in-medium cross sections calculated in the 3-momentum regularized NJL model are not consistent. In order to demonstrate the difference between the usual 3-momentum regularization with the one proposed in this work, we study the quark-quark and quark-antiquark cross sections in both regularization schemes. To this end we use the standard $SU(3)$ NJL model, with four and six quark interactions. We observe major quantitative and qualitative differences when comparing quark-quark cross sections in both schemes. The quark-antiquark cross sections, on the other hand, are very similar in both regularizations, owning to the equivalence between the regularizations when the absolute value of the external 3-momentum is zero., Comment: 65 pages, 25 figures

The precise and efficient identification of the nature of the primary cosmic rays on an event-by-event basis stands as a fundamental aspiration for any cosmic ray observatory. In particular, the detection and characterization of gamma ray events are challenged by their occurrence within an overwhelmingly greater flux of charged cosmic rays spanning several orders of magnitude. The intricacies of distinguishing between cosmic ray compositions and the inherent uncertainties associated with hadronic interactions present formidable challenges, which, if not properly addressed, can introduce significant sources of systematic errors. This work introduces a novel composition discriminant variable, $P_{tail}^{\alpha}$, which quantifies the number of Water Cherenkov Detectors with a signal well above the mean signal observed in WCDs located at an equivalent distance from the shower core, in events with approximately the same energy at the ground. This new event variable is then shown to be, in the reconstructed energy range $10\,{\rm TeV}$ to $1.6\,{\rm PeV}$, well correlated with the total number of muons that hit, in the same event, all the observatory stations located at a distance greater than $200\,{\rm m}$ from the shower core. The two variables should thus have similar efficiencies in the selection of high-purity gamma event samples and in the determination of the nature of charged cosmic ray events., Comment: 6 pages, 7 figures

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features.

In this work we introduce the concept of Cartan-Polyakov loops, a special subset of Polyakov loops in the fundamental representation of the $\mathrm{SU}(N_c)$ group, with charges $k=1,\ldots,(N_c-1)/2$. It constitutes a sufficient set of independent degrees of freedom and it is used to parametrize the thermal Wilson line. Polyakov loops not contained in this set are classified as non-Cartan-Polyakov loops. Using properties of the characteristic polynomial of the thermal Wilson line, we write a non-Cartan-Polyakov loop charge decomposition formula. This formalism allows one to readily build effective models of quarks and gluons with an arbitrary number of colors. We apply it to the Polyakov$-$Nambu$-$Jona-Lasinio model and to an effective glue model, in the mean field approximation, showing how to directly extend these models to higher values of $N_c$., Comment: 28 pages

Phase estimation is used in many quantum algorithms, particularly in order to estimate energy eigenvalues for quantum systems. When using a single qubit as the probe (used to control the unitary we wish to estimate the eigenvalue of), it is not possible to measure the phase with a minimum mean-square error. In standard methods, there would be a logarithmic (in error) number of control qubits needed in order to achieve this minimum error. Here show how to perform this measurement using only two control qubits, thereby reducing the qubit requirements of the quantum algorithm. Our method corresponds to preparing the optimal control state one qubit at a time, while it is simultaneously consumed by the measurement procedure., Comment: 11 pages, 7 figures. Paper sent for Jonathan P. Dowling Memorial Special Issue

Is there an initialization for deep networks that requires no learning? ExplainFix adopts two design principles: the "fixed filters" principle that all spatial filter weights of convolutional neural networks can be fixed at initialization and never learned, and the "nimbleness" principle that only few network parameters suffice. We contribute (a) visual model-based explanations, (b) speed and accuracy gains, and (c) novel tools for deep convolutional neural networks. ExplainFix gives key insights that spatially fixed networks should have a steered initialization, that spatial convolution layers tend to prioritize low frequencies, and that most network parameters are not necessary in spatially fixed models. ExplainFix models have up to 100x fewer spatial filter kernels than fully learned models and matching or improved accuracy. Our extensive empirical analysis confirms that ExplainFix guarantees nimbler models (train up to 17\% faster with channel pruning), matching or improved predictive performance (spanning 13 distinct baseline models, four architectures and two medical image datasets), improved robustness to larger learning rate, and robustness to varying model size. We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned., Comment: Recently Published in Wiley WIREs Journal of Data Mining and Knowledge Discovery. This version has minor formatting differences and includes the supplementary appendix with the main document. Source code: https://github.com/adgaudio/ExplainFix/

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

Web3 aims at creating a decentralized platform that is competitive with modern cloud infrastructures that support today's Internet. However, Web3 is still limited, supporting only applications in the domains of content creation and sharing, decentralized financing, and decentralized communication. This is mainly due to the technologies supporting Web3: blockchain, IPFS, and libp2p, that although provide a good collection of tools to develop Web3 applications, are still limited in terms of design and performance. This motivates the need to better understand these technologies as to enable novel optimizations that can push Web3 to its full potential. Unfortunately, understanding the current behavior of a fully decentralized large-scale distributed system is a difficult task, as there is no centralized authority that has full knowledge of the system operation. To this end, in this paper we characterize the workload of IPFS, a key enabler of Web3. To achieve this, we have collected traces from accesses performed by users to one of the most popular IPFS gateways located in North America for a period of two weeks. Through the fine analysis of these traces, we gathered the amount of requests to the system, and found the providers of the requested content. With this data, we characterize both the popularity of requested and provided content, as well as their geo-location (by matching IP address with the MaxMind database). Our results show that most of the requests in IPFS are only to a few different content, that is provided by large portion of peers in the system. Furthermore, our analysis also shows that most requests are provided by the two largest portions of providers in the system, located in North America and Europe. With these insights, we conclude that the current IPFS architecture is sub-optimal and propose a research agenda for the future.

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses., Comment: 10 pages, 2 figures, 2 tables, presented at NeurIPS22@PAI4MH

Time-dependent linear differential equations are a common type of problem that needs to be solved in classical physics. Here we provide a quantum algorithm for solving time-dependent linear differential equations with logarithmic dependence of the complexity on the error and derivative. As usual, there is an exponential improvement over classical approaches in the scaling of the complexity with the dimension, with the caveat that the solution is encoded in the amplitudes of a quantum state. Our method is to encode the Dyson series in a system of linear equations, then solve via the optimal quantum linear equation solver. Our method also provides a simplified approach in the case of time-independent differential equations., Comment: 19 pages

Quantum algorithms for simulation of Hamiltonian evolution are often based on product formulae. The fractal method of Suzuki gives a systematic way to find arbitrarily high-order product formulae, but results in a large number of exponentials. On the other hand, product formulae with fewer exponentials can be found by numerical solution of simultaneous nonlinear equations. It is also possible to reduce the cost of long-time simulations by processing, where a kernel is repeated and a processor need only be applied at the beginning and end of the simulation. In this work, we found thousands of new product formulae of both 8th and 10th order, and numerically tested these formulae, together with many formulae from prior literature. We provide methods to fairly compare product formulae of different lengths and different orders. We have found a new 8th order processed product formula with exceptional performance, that outperforms all other tested product formulae for about eight orders of magnitude in system parameters $T$ (time) and $\epsilon$ (allowable error). That includes most reasonable combinations of parameters to be used in quantum algorithms., Comment: Greatly expanded with new solutions and new comparisons to prior work. 28 pages

Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (N=31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on covariables, such as age, sex, and brain structure volumes. We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively. Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community., Comment: 10 pages, 3 figures, Accepted in the Deep Generative Models workshop @ MICCAI 2022

This work is concerned with the effects or pedagogical influences of the so-called Sociolinguistics Approaches on Dominican College education, specifically at three groups of advanced EFL Classes at the State College, that is to say the Universidad Autonoma of Santo Domingo-UASD, during the last virtual and first semester of this year (2021-1), as a part of the Core Curriculum of the said institution. The emphasis was placed in determining how certain currents of the like had have a considerable influence in both the teacher's population as well as that of the students, resulting with not a new discovery, that Constructivism and Cognitivism were among others, like Vygotsky and Piaget the ones that still enjoy a great prestige in our academic world, not only for the EFL or ESL professors of the school of foreign languages, but also for the rest of the school and faculties such as Spanish letters, Philosophy, and Sociology. The results of this investigation are concretely offered through the discussion of the Research Questions in the final chapter of this research work, as a manner of conclusion.

Since the democratic opening of the country (the Dominican Republic), in past decades and the subsequent phenomena of Worldalization, Globalization and the Influences of the Knowledge Society and Computerization, our society and with it the national educational system at all levels has been undergoing a series of changes and influences not ignored in the order for the teachings of foreign languages in our institutions of higher education and specifically at the Universidad Autonoma de Santo Domingo-UASD-. In addition to the social, economic and political phenomena mentioned above, there are also the migrations of millions of Dominicans to mostly to the United States and Europe, which, in addition to the usual contribution in remittances, brings a new dimension of the Dominicanity as a result of the influence of the inevitable cultural and linguistic exchange that this phenomenon entails. Another phenomenon that occupies our attention, in addition to migration, is the recent development of the economic model of the Service industries, that is, free zones and tourism, which, like sugar mills and mining in certain areas of our national geography brought with them the first bilingual schools in the country in the recent past. This has caused a proliferation of language schools and institutes and courses like the English Immersion Programs sponsored by the Ministry of Higher Education, that have reaffirmed the imprint of two institutions: The Instituto Cultural Dominico-Americano (now a university) and the School of Languages of the UASD University, as the two of the oldest and leading institutions in the teaching of the English language in our country. In such a way, that a phenomenon that we must comprehend and understand in our students of advanced subjects in the language career (Degree Program), it is that of 'translanguaging' or translanguage, in order to allow the usage of Spanish-original words or expressions , and that occurs in a context where it is assumed that the only language to be expressed is English, as suggested by the most modern methodologies of language teaching that emphasize the unique use of the language to learning (Target language) and in a communicative way within the classroom, that is, the communicative approach is prioritized more than the learning of grammatical norms or rules as suggested by the so-called "Communicative Approach," (Larsen Freeman, 2011), it is in this exclusive context where it emerges in our students the practice of translanguaging or translanguage, (Not to be confused with Code-switching), which was a term coined by the Welsh pedagogue Cen Williams (1980) as a pedagogical practice in which students are asked to alternate several languages, for a more effective or receptive use, as for example, when they are in the presence of Latinisms or 'cognates' (words that have the same root [Latin] in both English and Spanish) in the English language. But the thing to be explored here it is, that sometimes students are forced in not using those words or expressions in the classroom due to a pedagogical practice in our Dominican context, (EFL Classes),whereas the prohibition or abrupt limitations of such practices in the American or British context are due to different reasons that implies extra-pedagogical motivations up to a point to be considered as rights and constitutional violations, (Bilingual Classrooms),and are consequently being examined here in two comparative cases, since both the Dominican and the Anglo-Saxon case are completely different national settings or scenarios. This research was developed through the examination of three different national cases, as a mean of comparing them in their respective particularities, similitudes, if any, and differences.

We assess the capabilities of a ground-based gamma-ray observatory to detect astrophysical neutrinos with energies in the $100\,{\rm TeV}$ to $100\,{\rm PeV}$ range. The identification of these events would be done through the measurement of very inclined extensive air showers induced by downward-going and upward-going neutrinos. The discrimination of neutrino-induced showers in the overwhelming cosmic-ray background is achieved by analysing the balance of the total electromagnetic and muonic signals of the shower at the ground. We demonstrate that a ${\rm km^2}$-scale wide field-of-view ground-based gamma-ray observatory could detect a couple of Very-High to Ultra-High energy (VHE-UHE) neutrino events per year with a reasonable pointing accuracy, making it an interesting facility for multi-messenger studies with both photons and neutrinos., Comment: 12 pages, 14 figures, accepted in Phys.Rev.D

Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generative models recently shown to produce excellent samples in computer vision (surpassing Generative Adversarial Networks), and to achieve log-likelihoods that are competitive with transformers while having fast inference times. Diffusion models can be applied to the latent representations learnt by autoencoders, making them easily scalable and great candidates for application to high dimensional data, such as medical images. Here, we propose a method based on diffusion models to detect and segment anomalies in brain imaging. By training the models on healthy data and then exploring its diffusion and reverse steps across its Markov chain, we can identify anomalous areas in the latent space and hence identify anomalies in the pixel space. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data involving synthetic and real pathological lesions with much reduced inference times, making their usage clinically viable.