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The existence of a set of $10$ Intrinsic Conformal Symmetries, which acts on three-dimensional hypersurfaces (spacelike or timelike), leads to the existence of two distinct families of 5D geometries. These models represent the general solutions of the bulk field equations where their energy-momentum tensor, includes only two components: a negative cosmological constant and a parallel pressure $p_{\parallel}$ aligned with the extra spatial dimension. Significantly, these models offer a novel perspective for investigating the impacts of spatial inhomogeneity and anisotropy on the cosmological evolution of the Universe, particularly within the context of the braneworld scenarios. It is shown that one of these families reduces to a \emph{fully inhomogeneous, anisotropic and conformally flat brane model} with a \emph{perfect fluid equation of state} and corresponds to the Stephani Universe (i.e. a Stephani brane) which implies that our model can be matched smoothly with the standard FRW model. We provide the generalized Friedmann and Raychaudhuri equations and we present how the additional quantities could affect the cosmological evolution. In particular we show that the new constituents are the terms $p_{\parallel }$, $\sigma ^{2}$ and the four acceleration of the brane observers that could affect the observational measurement of the Hubble parameter depending on which term dominates therefore provide us a potential answer to the Hubble tension and cosmic acceleration problems due to local inhomogeneities and anisotropies., Comment: 20 pages, no figures, Latex2e source file, one Appendix; v2 refined abstract, more extensive Introduction and Discussion, accepted for publication in General Relativity and Gravitation

We propose two methods for evaluating athermal recombination corrected (arc) displacement damage parameters in ion irradiations employing the computer code SRIM (Stopping and Range of Ions in Matter). The first method consists of post-processing the detailed SRIM output for all simulated damage events and re-calculating according to the arc damage model. In the second method, an approximate empirical formula is devised which gives the average displacements in the arc damage model as a function of the corresponding quantity according to the standard Norgett-Robinson-Torrens model, which is readily obtained from SRIM., Comment: 7 pages, 4 figures

The fundamental principles, potential applications, and ethical concerns of ChatGPT are analyzed and discussed in this study. Since ChatGPT emerged, it has gained a rapidly growing popularity, with more than 600 million users today. The development of ChatGPT was a significant mile-stone, as it demonstrated the potential of large-scale language models to generate natural language responses that are almost indistinguishable from those of a human. ChatGPT's operational principles, prospective applications, and ability to advance a range of human endeavours are discussed in the paper. However, much of the work discusses and poses moral and other problems that rely on the subject. To document the latter, we submitted 14 queries and captured the ChatGPT responses. ChatGPT appeared to be honest, self-knowledgeable, and careful with its answers. The authors come to the realization that since AI is already a part of society, the pervasiveness of the ChatGPT tool to the general public has once again brought to light concerns regarding AI in general. Still, they have moved from the domain of scientific community collective reflection at a conceptual level to everyday practice this time., Comment: 20 pages, 2 figures

We discuss the use of the Discrete Element Method (DEM) to simulate the dynamics of granular systems made up of elements with nontrivial geometries. The DEM simulator is GPU accelerated and can handle elements whose shape is defined as the union with overlap of diverse sets of spheres with user-specified radii. The simulator can also handle complex materials since each sphere in an element can have its own Young's modulus $E$, Poisson ratio $\nu$, friction coefficient $\mu$, and coefficient of restitution CoR. To demonstrate the simulator, we produce a "digital simulant" (DS), a replica of the GRC-1 lunar simulant. The DS follows an element size distribution similar but not identical to that of GRC-1. We validate the predictive attributes of the simulator via several numerical experiments: repose angle, cone penetration, drawbar pull, and rover incline-climbing tests. Subsequently, we carry out a sensitivity analysis to gauge how the slope vs. slip curves change when the element shape, element size, and friction coefficient change. The paper concludes with a VIPER rover simulation that confirms a recently proposed granular scaling law. The simulation involves more than 11 million elements composed of more than 34 million spheres of different radii. The simulator works in the Chrono framework and utilizes two GPUs concurrently. The GPU code for the simulator and all numerical experiments discussed are open-source and available on GitHub for reproducibility studies and unfettered use and distribution., Comment: Main text 28 pages, including 27 figures. Submitted to Engineering with Computers

In a recent paper, a new conformally flat metric was introduced, describing an expanding scalar field in a spherically symmetric geometry. The spacetime can be interpreted as a Schwarzschild-like model with an apparent horizon surrounding the curvature singularity. For the above metric, we present the complete conformal Lie algebra consisting of a six-dimensional subalgebra of isometries (Killing Vector Fields or KVFs) and nine proper conformal vector fields (CVFs). An interesting aspect of our findings is that there exists a gradient (proper) conformal symmetry (i.e., its bivector vanishes) which verifies the importance of gradient symmetries in constructing viable cosmological models. In addition, the 9-dimensional conformal algebra implies the existence of constants of motion along null geodesics that allow us to determine the complete solution of null geodesic equations., Comment: 4 pages, Presented at the 2nd Electronic Conference on Universe, 16 February 2 March 2023, published version

ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. Upon sufficiently broad adoption, such platforms reach economies of scale that bring greater component reuse while improving efficiency of system development and maintenance. For an end-to-end ML platform with broad adoption, scaling relies on pervasive ML automation and system integration to reach the quality we term self-serve that we define with ten requirements and six optional capabilities. With this in mind, we identify long-term goals for platform development, discuss related tradeoffs and future work. Our reasoning is illustrated on two commercially-deployed end-to-end ML platforms that host hundreds of real-time use cases -- one general-purpose and one specialized., Comment: 10 pages, 1 figure, 2 tables

In this work we would like to address the problem of the effect of bulk matter on the brane cosmological evolution in a general way. We assume that the spatial part of the brane metric is not maximally symmetric, therefore spatially inhomogeneous. However we retain the conformal flatness property of the standard cosmological model (FRW) i.e. the Weyl tensor of the induced 4D geometry is zero. We refer to it as Spatially Inhomogeneous Irrotational (SII) brane. It is shown that the model can be regarded as the 5D generalization of the SII spacetimes found recently [1]., Comment: 5 pages, accepted version prepared for the "2nd Electronic Conference on Universe" (16 Feb-2 Mar 2023)

We revisit the classical mechanism to produce $d-$dimensional spacetimes via Kaluza-Klein compactification. We made the assumption that the $\left(d+1\right) -$dimensional bulk geometry $\mathcal{M}$ admits a Homothetic Vector Field (HVF) $\mathbf{H}$ relaxing the existence of a zero i.e. $\mathbf{H}\neq 0$ holds on $\mathcal{M}$ and the homothetic bivector $F_{ab}(\mathbf{H})=0$. Under these circumstances we identify the origin of a positive cosmological constant in the $d-$dimensional spacetime as the homothetic factor representing a geometrical artifact of late time state of the $\left( d+1\right) -$dimensional bulk spacetime., Comment: 6 pages, no figures, uses iop class style; (v2) some minor corrections, version to appear in Modern Physics Letter A

Using purely geometrical methods we present a mechanism to solve the scalar field equations of motion (non-minimally coupled with gravity) in a spherically symmetric background. We found that the \emph{full }set of spacetimes, which are of Petrov type O (conformally flat) and admit a \emph{gradient} Conformal Vector Field, can be determined completely. It is shown that the full group of scalar field equations reduced to a \emph{single} equation that depends only on the distance $w=r^{2}-t^{2}$ leaving the metric function (equivalently the functional form of the scalar field or the potential) freely chosen. Depending on the structure of the metric or the potential $V$ (as a function of $\phi $) a solution can be found either analytically or via numerical integration. We provide physically sound examples and prove that (Anti)-de Sitter fits this scheme. We also reconstruct a recently found solution \cite{Strumia:2022kez} representing an expanding scalar bubble with metric that has a singularity and corresponds to what is termed as Anti-de Sitter crunch., Comment: 8 pages, no figures, uses iop class style; (v2) minor typos corrected; (v3) small extension in the abstract to indicate the inclusion of the non-minimally coupled scalar field within the article and some new references were added. Matches version to appear in Classical and Quantum Gravity as a Letter

The existence of self-similar solutions is discussed in symmetric teleparallel $f(Q)$-theory for a Friedmann-Lema\^itre-Robertson-Walker background geometry with zero and non-zero spatial curvature. For the four distinct families of connections which describe the specific cosmology in symmetric teleparallel gravity, the functional form of $f(Q)$ is reconstructed. Finally, to see if the analogy with General Relativity holds, we discuss the relation of the self-similar solutions with the asymptotic behaviour of more general $f(Q)$ functions., Comment: 25 pages, no figures, Latex2e source file, PRD accepted version

This paper investigates adaptive streaming codes over a three-node relayed network. In this setting, a source node transmits a sequence of message packets to a destination through a relay. The source-to-relay and relay-to-destination links are unreliable and introduce at most $N_1$ and $N_2$ packet erasures, respectively. The destination node must recover each message packet within a strict delay constraint $T$. The paper presents achievable streaming codes for all feasible parameters $\{N_1, N_2, T\}$ that exploit the fact that the relay naturally observes the erasure pattern occurring in the link from source to relay, thus it can adapt its relaying strategy based on these observations. In a recent work, Fong et al. provide streaming codes featuring channel-state-independent relaying strategies. The codes proposed in this paper achieve rates higher than the ones proposed by Fong et al. whenever $N_2 > N_1$, and achieve the same rate when $N_2 = N_1$. The paper also presents an upper bound on the achievable rate that takes into account erasures in both links in order to bound the rate in the second link. The upper bound is shown to be tighter than a trivial bound that considers only the erasures in the second link., Comment: Paper has been submitted to transactions on information theory

Coherent laser arrays compatible with silicon photonics are demonstrated in a waveguide geometry in epitaxially grown semiconductor membrane quantum well lasers transferred on substrates of silicon carbide and oxidised silicon; we record lasing thresholds as low as 60 mW of pump power. We study the emission of single lasers and arrays of lasers in the sub-mm range. We are able to create waveguide laser arrays with modal widths of approximately 5-10 {\mu}m separated by 10-20 {\mu}m, using real and reciprocal space imaging we study their emission characteristics and find that they maintain their mutual coherence while operating on either single or multiple longitudinal modes per lasing cavity., Comment: 12 pages, 7 figures

This paper investigates the performance of streaming codes in low-latency applications over a multi-link three-node relayed network. The source wishes to transmit a sequence of messages to the destination through a relay. Each message must be reconstructed after a fixed decoding delay. The special case with one link connecting each node has been studied by Fong et. al [1], and a multi-hop multi-link setting has been studied by Domanovitz et. al [2]. The topology with three nodes and multiple links is studied in this paper. Each link is subject to a different number of erasures due to different channel conditions. An information-theoretic upper bound is derived, and an achievable scheme is presented. The proposed scheme judiciously allocates rates for each link based on the concept of delay spectrum. The achievable scheme is compared to two baseline schemes and the scheme proposed in [2]. Experimental results show that this scheme achieves higher rates than the other schemes, and can achieve the upper bound even in non-trivial scenarios. The scheme is further extended to handle different propagation delays in each link, something not previously considered in the literature. Simulations over statistical channels show that the proposed scheme can outperform the simpler baseline under practical models.

Purpose: In this study, the recently emerged advances in Fuzzy Cognitive Maps (FCM) are investigated and employed, for achieving the automatic and non-invasive diagnosis of Coronary Artery Disease (CAD). Methods: A Computer-Aided Diagnostic model for the acceptable and non-invasive prediction of CAD using the State Space Advanced FCM (AFCM) approach is proposed. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the system and the interpretability of the decision mechanism. The proposed method is tested utilizing a CAD dataset from the Laboratory of Nuclear Medicine of the University of Patras. More specifically, two architectures of AFCMs are designed, and different parameter testing is performed. Furthermore, the proposed AFCMs, which are based on the new equations proposed recently, are compared with the traditional FCM approach. Results: The experiments highlight the effectiveness of the AFCM approach and the new equations over the traditional approach, which obtained an accuracy of 78.21%, achieving an increase of seven percent (+7%) on the classification task, and obtaining 85.47% accuracy. Conclusions: It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease. Conclusions and future research related to recent pandemic of coronavirus are provided., Comment: 14 pages

Babinet complementary patterns of a spectrally tunable metamaterial incorporating a nematic liquid crystal are normally assumed to exhibit the same tuning range. Here we show that for a hybrid, terahertz liquid crystal-metamaterial, the sensitivity of its resonances to the variations of the refractive index differs substantially for the two complementary patterns. This is due to a mismatch between the alignment of the liquid crystal and the direction of the local electric field induced in the patterns. Furthermore, and more intriguingly, our experimental data indicate that it is possible to shift the resonance of the positive metamaterial pattern beyond the limit imposed by the alignment mismatch. Our analysis suggests that the observed anomalous frequency shift result from the orientational optical nonlinearity of a nematic liquid crystal., Comment: 11 pages, 6 figures

Many malware campaigns use Microsoft (MS) Office documents as droppers to download and execute their malicious payload. Such campaigns often use these documents because MS Office is installed in billions of devices and that these files allow the execution of arbitrary VBA code. Recent versions of MS Office prevent the automatic execution of VBA macros, so malware authors try to convince users into enabling the content via images that, e.g. forge system or technical errors. In this work, we leverage these visual elements to construct lightweight malware signatures that can be applied with minimal effort. We test and validate our approach using an extensive database of malware samples and identify correlations between different campaigns that illustrate that some campaigns are either using the same tools or that there is some collaboration between them.

Large-scale Web-based services present opportunities for improving UI policies based on observed user interactions. We address challenges of learning such policies through model-free offline Reinforcement Learning (RL) with off-policy training. Deployed in a production system for user authentication in a major social network, it significantly improves long-term objectives. We articulate practical challenges, compare several ML techniques, provide insights on training and evaluation of RL models, and discuss generalizations., Comment: 9 pages, 8 figures, 3 tables

Many emerging multimedia streaming applications involve multiple users communicating under strict latency constraints. In this paper we study streaming codes for a network involving two source nodes, one relay node and a destination node. In our setting, each source node transmits a stream of messages, through the relay, to a destination, who is required to decode the messages under a strict delay constraint. For the case of a single source node, a class of streaming codes has been proposed by Fong et al. [2], using the concept of {\em delay-spectrum}. In the present work we present an in-depth analysis of the properties of delay-spectrum and apply them to develop streaming codes for our proposed setting through a novel framework. Our first scheme involves greedily selecting the rate on the link from relay to destination and using properties of the delay-spectrum to find feasible streaming codes that satisfy the required delay constraints. We provide a closed form expression for the achievable rate region and identify conditions when the proposed scheme is optimal by establishing a natural outer bound. Our second scheme builds upon this approach, but uses a numerical optimization-based approach to improve the achievable rate region over the first scheme. We demonstrate that our proposed schemes achieve significant improvements over baseline schemes based on single-user codes., Comment: 47 pages, 8 figures

This paper studies low-latency streaming codes for the multi-hop network. The source is transmitting a sequence of messages (streaming messages) to a destination through a chain of relays where each hop is subject to packet erasures. Every source message has to be recovered perfectly at the destination within a delay constraint of $T$ time slots. In any sliding window of $T+1$ time slots, we assume no more than $N_j$ erasures introduced by the $j$'th hop channel. The capacity in case of a single relay (a three-node network) was derived by Fong [1], et al. While the converse derived for the three-node case can be extended to any number of nodes using a similar technique (analyzing the case where erasures on other links are consecutive), we demonstrate next that the achievable scheme, which suggested a clever symbol-wise decode and forward strategy, can not be straightforwardly extended without a loss in performance. The coding scheme for the three-node network, which was shown to achieve the upper bound, was ``state-independent'' (i.e., it does not depend on specific erasure pattern). While this is a very desirable property, in this paper, we suggest a ``state-dependent'' (i.e., a scheme which depends on specific erasure pattern) and show that it achieves the upper bound up to the size of an additional header. Since, as we show, the size of the header does not depend on the field size, the gap between the achievable rate and the upper bound decreases as the field size increases.

Cardiovascular Diseases (CVD) and strokes produce immense health and economic burdens globally. Coronary Artery Disease (CAD) is the most common type of cardiovascular disease. Coronary Angiography, which is an invasive treatment, is also the standard procedure for diagnosing CAD. In this work, we illustrate a Medical Decision Support System for the prediction of Coronary Artery Disease (CAD) utilizing Fuzzy Cognitive Maps (FCMs). FCMs are a promising modeling methodology, based on human knowledge, capable of dealing with ambiguity and uncertainty, and learning how to adapt to the unknown or changing environment. The newly proposed MDSS is developed using the basic notions of Fuzzy Logic and Fuzzy Cognitive Maps, with some adjustments to improve the results. The proposed model, tested on a labelled CAD dataset of 303 patients, obtains an accuracy of 78.2% outmatching several state-of-the-art classification algorithms., Comment: 13 pages, Manuscript Submitted for Peer-Review, Pre-Print

In this study, the problem of automatically classifying pulmonary diseases, including the recently emerged COVID-19, from X-Ray images, is considered. While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. Deep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-Ray images, corresponding to 6 diseases is utilized for training MobileNet v2, which has been proven to achieve remarkable results in related tasks. The results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while an overall classification accuracy of the seven classes reaches 87.66%. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19., Comment: 14 pages, 1 figure, Submitted Manuscript

In this work, we employ the Synthetic Minority Oversampling Technique (SMOTE) to generate instances of the minority class of an imbalanced Coronary Artery Disease dataset. We firstly analyze the public dataset Z -- Alizadeh Sani, a dataset used for non-invasive prediction of CAD. We perform feature selection to exclude attributes unrelated to Coronary Artery Disease risk. The generation of new samples is performed using SMOTE, a technique commonly employed in machine learning tasks. We design Artificial Neural Networks, Decision Trees, and Support Vector Machines to classify both the original dataset and the augmented. The results demonstrate that data augmentation may be beneficial in specific cases, but it is not a panacea, and its application in a specific dataset should be carefully examined.

In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. The aim of the study is to evaluate the performance of state-of-the-art Convolutional Neural Network architectures proposed over recent years for medical image classification. Specifically, the procedure called transfer learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The dataset utilized in this experiment is a collection of 1427 X-Ray images. 224 images with confirmed Covid-19, 700 images with confirmed common pneumonia, and 504 images of normal conditions are included. The data was collected from the available X-Ray images on public medical repositories. With transfer learning, an overall accuracy of 97.82% in the detection of Covid-19 is achieved.

Lung Cancer is the most common cause of cancer-related death worldwide. Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures. Deep Learning has been proven as a popular and influential method in many medical imaging diagnosis areas. In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images derived from a PET/CT scanner. More specifically, we intend to develop experimental Convolutional Neural Network (CNN) architectures and conduct experiments, by tuning their parameters, to investigate their behavior, and to define the optimal setup for the accurate classification. For the experiments, we utilize PET/CT images obtained from the Laboratory of Nuclear Medicine of the University of Patras, and the publically available database called Lung Image Database Consortium Image Collection (LIDC-IDRI). Furthermore, we apply simple data augmentation to generate new instances and to inspect the performance of the developed networks. Classification accuracy of 91% and 93% on the PET/CT dataset and on a selection of nodule images form the LIDC-IDRI dataset, is achieved accordingly. The results demonstrate that CNNs are a trustworth method for nodule classification. Also, the experiment confirms that data augmentation enhances the robustness of the CNNs., Comment: 22 pages. arXiv admin note: text overlap with arXiv:1409.4842, arXiv:1512.07108 by other authors

We introduce a novel network-adaptive algorithm that is suitable for alleviating network packet losses for low-latency interactive communications between a source and a destination. Our network-adaptive algorithm estimates in real-time the best parameters of a recently proposed streaming code that uses forward error correction (FEC) to correct both arbitrary and burst losses, which cause a crackling noise and undesirable jitters, respectively in audio. In particular, the destination estimates appropriate coding parameters based on its observed packet loss pattern and sends them back to the source for updating the underlying code. Besides, a new explicit construction of practical low-latency streaming codes that achieve the optimal tradeoff between the capability of correcting arbitrary losses and the capability of correcting burst losses is provided. Simulation evaluations based on statistical losses and real-world packet loss traces reveal the following: (i) Our proposed network-adaptive algorithm combined with our optimal streaming codes can achieve significantly higher performance compared to uncoded and non-adaptive FEC schemes over UDP (User Datagram Protocol); (ii) Our explicit streaming codes can significantly outperform traditional MDS (maximum-distance separable) streaming schemes when they are used along with our network-adaptive algorithm., Comment: Presented in part at 27th ACM International Conference on Multimedia, October 2019; 13 pages, 24 figures, submitted to IEEE Transactions on Multimedia

We introduce a novel CNN-based feature point detector - GLAMpoints - learned in a semi-supervised manner. Our detector extracts repeatable, stable interest points with a dense coverage, specifically designed to maximize the correct matching in a specific domain, which is in contrast to conventional techniques that optimize indirect metrics. In this paper, we apply our method on challenging retinal slitlamp images, for which classical detectors yield unsatisfactory results due to low image quality and insufficient amount of low-level features. We show that GLAMpoints significantly outperforms classical detectors as well as state-of-the-art CNN-based methods in matching and registration quality for retinal images. Our method can also be extended to other domains, such as natural images. Training code and model weights are available at https://github.com/PruneTruong/GLAMpoints_pytorch.