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A long standing problem in abstract harmonic analysis concerns the strong Arens irregularity (sAir, for short) of the Fourier algebra $A(G)$ of a locally compact group $G.$ The groups for which $A(G)$ is known to be sAir are all amenable. So far this class includes the abelian groups, the discrete amenable groups, the second countable amenable groups $G$ such that $\overline{[G, G]}$ is not open in $G,$ the groups of the form $\prod_{i=0}^\infty G_i$ where each $G_i$, $i\ge1$, is a non-trivial metrizable compact group and $G_0$ is an amenable second countable locally compact group, the groups of the form $G_0\times G$, where $G$ is a compact group whose local weight $w(G)$ has uncountable cofinality and $G_0$ is any locally compact amenable group with $w(G_0)\le w(G)$, and the compact group $SU(2).$ We were primarily concerned with the groups for which $A(G)$ is sAir. We introduce a new class of $\ell^1$-bases in Banach algebras. These new $\ell^1$-bases enable us, among other results, to unify most of the results related to Arens products proved in the past seventy years since Arens defined his products. This includes the strong Arens irregularity of algebras in harmonic analysis, and in particular almost all the cases mentioned above for the Fourier algebras. In addition, we also show that $A(G)$ is sAir for compact connected groups with an infinite dual rank. The $\ell^1$-bases for the Fourier algebra are constructed with coefficients of certain irreducible representations of the group. With this new approach using $\ell^1$-bases, the rich algebraic structure of the algebras and semigroups under study such as the second dual of Banach algebras with an Arens product or certain semigroup compactifications (the Stone-\v Cech compactification of an infinite discrete group, for instance) is also unveiled

This letter presents the measurement of the energy-dependent neutrino-nucleon cross section in tungsten and the differential flux of muon neutrinos and anti-neutrinos. The analysis is performed using proton-proton collision data at a center-of-mass energy of $13.6 \, {\rm TeV}$ and corresponding to an integrated luminosity of $(65.6 \pm 1.4) \, \mathrm{fb^{-1}}$. Using the active electronic components of the FASER detector, $338.1 \pm 21.0$ charged current muon neutrino interaction events are identified, with backgrounds from other processes subtracted. We unfold the neutrino events into a fiducial volume corresponding to the sensitive regions of the FASER detector and interpret the results in two ways: We use the expected neutrino flux to measure the cross section, and we use the predicted cross section to measure the neutrino flux. Both results are presented in six bins of neutrino energy, achieving the first differential measurement in the TeV range. The observed distributions align with Standard Model predictions. Using this differential data, we extract the contributions of neutrinos from pion and kaon decays.

In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data from solar active regions, recorded by solar imaging observatories, are transformed into multivariate time series to enable solar flare prediction using temporal window-based analysis. In the realm of multivariate time series-driven solar flare prediction, addressing severe class imbalance with effective strategies for multivariate time series representation learning is key to developing robust predictive models. Traditional methods often struggle with overfitting to the majority class in prediction tasks where major solar flares are infrequent. This work presents EXCON, a contrastive representation learning framework designed to enhance classification performance amidst such imbalances. EXCON operates through four stages: obtaining core features from multivariate time series data; selecting distinctive contrastive representations for each class to maximize inter-class separation; training a temporal feature embedding module with a custom extreme reconstruction loss to minimize intra-class variation; and applying a classifier to the learned embeddings for robust classification. The proposed method leverages contrastive learning principles to map similar instances closer in the feature space while distancing dissimilar ones, a strategy not extensively explored in solar flare prediction tasks. This approach not only addresses class imbalance but also offers a versatile solution applicable to univariate and multivariate time series across binary and multiclass classification problems. Experimental results, including evaluations on the benchmark solar flare dataset and multiple time series archive datasets with binary and multiclass labels, demonstrate EXCON's efficacy in enhancing classification performance., Comment: This work has been accepted at the 2024 IEEE International Conference on Big Data (IEEE BigData 2024) on October 27, 2024, as a main conference paper

Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of applications and tasks. The challenge of many state-of-the-art ML models is a lack of transparency and interpretability. In this work, we introduce M-CELS, a counterfactual explanation model designed to enhance interpretability in multidimensional time series classification tasks. Our experimental validation involves comparing M-CELS with leading state-of-the-art baselines, utilizing seven real-world time-series datasets from the UEA repository. The results demonstrate the superior performance of M-CELS in terms of validity, proximity, and sparsity, reinforcing its effectiveness in providing transparent insights into the decisions of machine learning models applied to multivariate time series data., Comment: Accepted at ICMLA 2024. arXiv admin note: text overlap with arXiv:2410.20539

Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability. To overcome these challenges, we introduce an advanced framework that integrates the advantages of an autoencoder-generated embedding space with the adversarial training dynamics of GANs. This method employs two discriminators: one to specifically guide the generator and another to refine both the autoencoder's and generator's output. Additionally, our framework incorporates a novel autoencoder-based loss function and supervision from a teacher-forcing supervisor network, which captures the stepwise conditional distributions of the data. The generator operates within the latent space, while the two discriminators work on latent and feature spaces separately, providing crucial feedback to both the generator and the autoencoder. By leveraging this dual-discriminator approach, we minimize information loss in the embedding space. Through joint training, our framework excels at generating high-fidelity time series data, consistently outperforming existing state-of-the-art benchmarks both qualitatively and quantitatively across a range of real and synthetic multivariate time series datasets., Comment: This work has been accepted at BigData 2024 on October 26, 2024, as a regular paper for oral presentation

As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. Recently, a novel counterfactual explanation model, CELS, has been introduced. CELS learns a saliency map for the interest of an instance and generates a counterfactual explanation guided by the learned saliency map. While CELS represents the first attempt to exploit learned saliency maps not only to provide intuitive explanations for the reason behind the decision made by the time series classifier but also to explore post hoc counterfactual explanations, it exhibits limitations in terms of high validity for the sake of ensuring high proximity and sparsity. In this paper, we present an enhanced approach that builds upon CELS. While the original model achieved promising results in terms of sparsity and proximity, it faced limitations in validity. Our proposed method addresses this limitation by removing mask normalization to provide more informative and valid counterfactual explanations. Through extensive experimentation on datasets from various domains, we demonstrate that our approach outperforms the CELS model, achieving higher validity and producing more informative explanations.

The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are characterised by high-energy deposits in the electromagnetic calorimeter and no signal in the veto scintillators. One event is observed, compared to a background expectation of $0.44 \pm 0.39$ events, which is entirely dominated by neutrino interactions. World-leading constraints on ALPs are obtained for masses up to $300~\text{MeV}$ and couplings to the Standard Model W gauge boson, $g_{aWW}$, around $10^{-4}$ GeV$^{-1}$, testing a previously unexplored region of parameter space. Other new particle models that lead to the same experimental signature, including ALPs coupled to gluons or photons, U(1)$_B$ gauge bosons, up-philic scalars, and a Type-I two-Higgs doublet model, are also considered for interpretation, and new constraints on previously viable parameter space are presented in this paper., Comment: 37 pages, 22 figures

RF-GAP has recently been introduced as an improved random forest proximity measure. In this paper, we present PF-GAP, an extension of RF-GAP proximities to proximity forests, an accurate and efficient time series classification model. We use the forest proximities in connection with Multi-Dimensional Scaling to obtain vector embeddings of univariate time series, comparing the embeddings to those obtained using various time series distance measures. We also use the forest proximities alongside Local Outlier Factors to investigate the connection between misclassified points and outliers, comparing with nearest neighbor classifiers which use time series distance measures. We show that the forest proximities may exhibit a stronger connection between misclassified points and outliers than nearest neighbor classifiers.

Major solar flares are abrupt surges in the Sun's magnetic flux, presenting significant risks to technological infrastructure. In view of this, effectively predicting major flares from solar active region magnetic field data through machine learning methods becomes highly important in space weather research. Magnetic field data can be represented in multivariate time series modality where the data displays an extreme class imbalance due to the rarity of major flare events. In time series classification-based flare prediction, the use of contrastive representation learning methods has been relatively limited. In this paper, we introduce CONTREX, a novel contrastive representation learning approach for multivariate time series data, addressing challenges of temporal dependencies and extreme class imbalance. Our method involves extracting dynamic features from the multivariate time series instances, deriving two extremes from positive and negative class feature vectors that provide maximum separation capability, and training a sequence representation embedding module with the original multivariate time series data guided by our novel contrastive reconstruction loss to generate embeddings aligned with the extreme points. These embeddings capture essential time series characteristics and enhance discriminative power. Our approach shows promising solar flare prediction results on the Space Weather Analytics for Solar Flares (SWAN-SF) multivariate time series benchmark dataset against baseline methods., Comment: This work has been accepted at ICMLA 2024 on September 7, 2024, as a short paper for poster presentation

Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by utilizing advanced data preprocessing and classification methods on a multivariate time series-based dataset of photospheric magnetic field parameters. First, our study employs a novel preprocessing pipeline that includes missing value imputation, normalization, balanced sampling, near decision boundary sample removal, and feature selection to significantly boost prediction accuracy. Second, we integrate contrastive learning with a GRU regression model to develop a novel classifier, termed ContReg, which employs dual learning methodologies, thereby further enhancing prediction performance. To validate the effectiveness of our preprocessing pipeline, we compare and demonstrate the performance gain of each step, and to demonstrate the efficacy of the ContReg classifier, we compare its performance to that of sequence-based deep learning architectures, machine learning models, and findings from previous studies. Our results illustrate exceptional True Skill Statistic (TSS) scores, surpassing previous methods and highlighting the critical role of precise data preprocessing and classifier development in time series-based solar flare prediction., Comment: This work has been accepted at ICMLA 2024 on September 7, 2024, as a regular paper for an oral presentation

Generating time series data using Generative Adversarial Networks (GANs) presents several prevalent challenges, such as slow convergence, information loss in embedding spaces, instability, and performance variability depending on the series length. To tackle these obstacles, we introduce a robust framework aimed at addressing and mitigating these issues effectively. This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs. This framework benefits from a time series-based loss function and oversight from a supervisory network, both of which capture the stepwise conditional distributions of the data effectively. The generator functions within the latent space, while the discriminator offers essential feedback based on the feature space. Moreover, we introduce an early generation algorithm and an improved neural network architecture to enhance stability and ensure effective generalization across both short and long time series. Through joint training, our framework consistently outperforms existing benchmarks, generating high-quality time series data across a range of real and synthetic datasets with diverse characteristics., Comment: This work has been accepted at ICMLA 2024 on September 7, 2024, as a regular paper for an oral presentation

Clarivate's Web of Science (WoS) and Elsevier's Scopus have been for decades the main sources of bibliometric information. Although highly curated, these closed, proprietary databases are largely biased towards English-language publications, underestimating the use of other languages in research dissemination. Launched in 2022, OpenAlex promised comprehensive, inclusive, and open-source research information. While already in use by scholars and research institutions, the quality of its metadata is currently being assessed. This paper contributes to this literature by assessing the completeness and accuracy of OpenAlex's metadata related to language, through a comparison with WoS, as well as an in-depth manual validation of a sample of 6,836 articles. Results show that OpenAlex exhibits a far more balanced linguistic coverage than WoS. However, language metadata is not always accurate, which leads OpenAlex to overestimate the place of English while underestimating that of other languages. If used critically, OpenAlex can provide comprehensive and representative analyses of languages used for scholarly publishing. However, more work is needed at infrastructural level to ensure the quality of metadata on language., Comment: Reference list updated and corrected, corresponding author's email contact added, minor typographical errors corrected

Recent neutrino-nucleus cross-section measurements of observables characterising kinematic imbalance from the T2K, MicroBooNE and MINERvA experiments are used to benchmark predictions from widely used neutrino interaction event generators. Given the different neutrino energy spectra and nuclear targets employed by the three experiments, comparisons of model predictions to their measurements breaks degeneracies that would be present in any single measurement. In particular, the comparison of T2K and MINERvA measurements offers a probe of energy dependence, whilst a comparison of T2K and MicroBooNE investigates scaling with nuclear target. In order to isolate the impact of individual nuclear effects, model comparisons are made following systematic alterations to: the nuclear ground state; final state interactions and multi-nucleon interaction strength. The measurements are further compared to the generators used as an input to DUNE/SBN and T2K/Hyper-K analyses. Whilst no model is able to quantitatively describe all the measurements, evidence is found for mis-modelling of A-scaling in multi-nucleon interactions and it is found that tight control over how energy is distributed among hadrons following final state interactions is likely to be crucial to achieving improved agreement. Overall, this work provides a novel characterisation of neutrino interactions whilst offering guidance for refining existing generator predictions., Comment: 23 pages, 22 figures. Minor updates to the text to improve clarity. Added references

Garth Dales asked whether a Banach algebra $\mA$ with an Arens regular closed ideal $\mJ$ and an Arens regular quotient $\mA/\mJ$ is necessarily Arens regular. We prove in this note that, for a class of Banach algebras including the standard algebras in harmonic analysis, Garth's conditions force the algebra to be even reflexive. We also give examples of Banach algebras with Garth's conditions, that are not Arens regular.

Let $\mathcal{A}$ be a weakly sequentially complete Banach algebra containing a bounded approximate identity that is an ideal in its second dual $\mathcal{A}^{\ast\ast}$, we call such an algebra a Wesebai algebra. In the present paper we examine the Arens regularity properties of closed ideals of algebras in the Wesebai class. We observe that, although Wesebai algebras are always strongly Arens irregular, a variety of Arens regularity properties can be observed within their closed ideals. After characterizing Arens regular ideals and strongly Arens irregular ideals, we proceed to particularize to the main examples of \wasabi algebras, the convolution group algebras $L^1(G)$, $G$ compact, and the Fourier algebras $A(\Gamma)$, $\Gamma$ discrete and amenable. We find examples of Arens regular ideals in $L^1(G)$ and $A(\Gamma)$, both reflexive and nonreflexive and examples of strongly Arens irregular ideals that are not in the \wasabi class. For this, we construct, in many noncommutative groups, a new class of Riesz sets which are not $\Lambda(p)$, for any $p>1$. Our approach also shows that every infinite Abelian group contains a Rosenthal set that is not $\Lambda(p)$, for any $p>0$. These latter results could be of independent interest., Comment: A few stylistic improvements were introduced. New title

This paper presents the first results of the study of high-energy electron and muon neutrino charged-current interactions in the FASER$\nu$ emulsion/tungsten detector of the FASER experiment at the LHC. A subset of the FASER$\nu$ volume, which corresponds to a target mass of 128.6~kg, was exposed to neutrinos from the LHC $pp$ collisions with a centre-of-mass energy of 13.6~TeV and an integrated luminosity of 9.5 fb$^{-1}$. Applying stringent selections requiring electrons with reconstructed energy above 200~GeV, four electron neutrino interaction candidate events are observed with an expected background of $0.025^{+0.015}_{-0.010}$, leading to a statistical significance of 5.2$\sigma$. This is the first direct observation of electron neutrino interactions at a particle collider. Eight muon neutrino interaction candidate events are also detected, with an expected background of $0.22^{+0.09}_{-0.07}$, leading to a statistical significance of 5.7$\sigma$. The signal events include neutrinos with energies in the TeV range, the highest-energy electron and muon neutrinos ever detected from an artificial source. The energy-independent part of the interaction cross section per nucleon is measured over an energy range of 560--1740 GeV (520--1760 GeV) for $\nu_e$ ($\nu_{\mu}$) to be $(1.2_{-0.7}^{+0.8}) \times 10^{-38}~\mathrm{cm}^{2}\,\mathrm{GeV}^{-1}$ ($(0.5\pm0.2) \times 10^{-38}~\mathrm{cm}^{2}\,\mathrm{GeV}^{-1}$), consistent with Standard Model predictions. These are the first measurements of neutrino interaction cross sections in those energy ranges.

The Forward Search Experiment (FASER) at CERN's Large Hadron Collider (LHC) has recently directly detected the first collider neutrinos. Neutrinos play an important role in all FASER analyses, either as signal or background, and it is therefore essential to understand the neutrino event rates. In this study, we update previous simulations and present prescriptions for theoretical predictions of neutrino fluxes and cross sections, together with their associated uncertainties. With these results, we discuss the potential for possible measurements that could be carried out in the coming years with the FASER neutrino data to be collected in LHC Run 3 and Run 4., Comment: 19 pages, 10 figures

This paper presents Haris, an advanced autonomous mobile robot system for tracking the location of vehicles in crowded car parks using license plate recognition. The system employs simultaneous localization and mapping (SLAM) for autonomous navigation and precise mapping of the parking area, eliminating the need for GPS dependency. In addition, the system utilizes a sophisticated framework using computer vision techniques for object detection and automatic license plate recognition (ALPR) for reading and associating license plate numbers with location data. This information is subsequently synchronized with a back-end service and made accessible to users via a user-friendly mobile app, offering effortless vehicle location and alleviating congestion within the parking facility. The proposed system has the potential to improve the management of short-term large outdoor parking areas in crowded places such as sports stadiums. The demo of the robot can be found on https://youtu.be/ZkTCM35fxa0?si=QjggJuN7M1o3oifx., Comment: Accepted in 2024 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2024

Colorectal cancer is a public health problem.Approximately 30 to 50 \% of colorectal tumors are caused by mutations in the KRAS gene.These mutations induce uncontrolled proliferation.To date,There is no approved effective treatment for the mutated KRAS oncogene.Farnesyltransferase (FTI) inhibitors are considered a therapeutic target against the mutated KRAS oncogene.Tipifarnib is a farnesyltransferase inhibitor that was analyzed in a Phase II trial.In the present study, the three-dimensional structure of farnesyltransferase complexed with tipifarnib [1SA4] was used as a basis to exploit the characteristics of tipifarnib.A pharmacophore model was generated based on the structure using the Asinex (Gold and Platinum Collections) database.A total of 299 molecules were obtained after screening.The 299 molecules were anchored to the tipifarnib binding site in the farnesyltransferase crystal structure for docking analysis.During the molecular docking process, the pharmacophore that was modeled, and was used as a constraint to eliminate the molecules that do not satisfy the pharmacophore.Finally, four Hits identified as farnesyltransferase inhibitors for biological tests. Keywords: colorectal cancer, structure-based pharmacophore, molecular docking, KRAS, farnesyltransferase inhibitors, Virtual Screening.

Recent works have shown that deep learning (DL) models can effectively learn city-wide crowd-flow patterns, which can be used for more effective urban planning and smart city management. However, DL models have been known to perform poorly on inconspicuous adversarial perturbations. Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep crowd-flow prediction models in particular have remained largely unexplored. In this paper, we perform a rigorous analysis of the adversarial vulnerabilities of DL-based crowd-flow prediction models under multiple threat settings, making three-fold contributions. (1) We propose CaV-detect by formally identifying two novel properties - Consistency and Validity - of the crowd-flow prediction inputs that enable the detection of standard adversarial inputs with 0% false acceptance rate (FAR). (2) We leverage universal adversarial perturbations and an adaptive adversarial loss to present adaptive adversarial attacks to evade CaV-detect defense. (3) We propose CVPR, a Consistent, Valid and Physically-Realizable adversarial attack, that explicitly inducts the consistency and validity priors in the perturbation generation mechanism. We find out that although the crowd-flow models are vulnerable to adversarial perturbations, it is extremely challenging to simulate these perturbations in physical settings, notably when CaV-detect is in place. We also show that CVPR attack considerably outperforms the adaptively modified standard attacks in FAR and adversarial loss metrics. We conclude with useful insights emerging from our work and highlight promising future research directions.

With the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. To improve the trust and transparency of AI-based systems, the EXplainable Artificial Intelligence (XAI) field has emerged. The XAI paradigm is bifurcated into two main categories: feature attribution and counterfactual explanation methods. While feature attribution methods are based on explaining the reason behind a model decision, counterfactual explanation methods discover the smallest input changes that will result in a different decision. In this paper, we aim at building trust and transparency in time series models by using motifs to generate counterfactual explanations. We propose Motif-Guided Counterfactual Explanation (MG-CF), a novel model that generates intuitive post-hoc counterfactual explanations that make full use of important motifs to provide interpretive information in decision-making processes. To the best of our knowledge, this is the first effort that leverages motifs to guide the counterfactual explanation generation. We validated our model using five real-world time-series datasets from the UCR repository. Our experimental results show the superiority of MG-CF in balancing all the desirable counterfactual explanations properties in comparison with other competing state-of-the-art baselines., Comment: 13 pages, accepted at 2-nd Workshop on Explainable and Ethical AI - ICPR 2022

We study gravitational wave production in an expanding Universe during the first stages following inflation, and investigate the consequences of the Gauss-Bonnet term on the inflationary parameters for a power-law inflation model with a GB coupling term. Moreover, we perform the analyses on the preheating parameters involving the number of e-folds $N_{pre}$, and the temperature of thermalization $T_{th},$ and show that it's sensitive to the parameters $n$, and $\gamma$, the parameter $\gamma$ is proposed to connect the density energy at the end of inflation to the preheating energy density. We set a correlation of gravitational wave energy density spectrum with the spectral index $n_{s}$ detected by the cosmic microwave background experiments$.$ The density spectrum $\Omega_{gw}$ shows good consistency with observation for $\gamma = 10^{3}$ and $10^{6}$. Our findings suggest that the generation of gravitational waves (GWs) during preheating can satisfy the constraints from Planck's data.

The paper presents Barrow holographic dark energy (infrared cut-off is the Hubble horizon) suggested by Barrow recently (Physics Letters B 808 (2020): 135643) in an anisotropic Bianchi type-I Universe within the framework of $% f\left( Q\right) $ symmetric teleparallel gravity, where the non-metricity scalar $Q$ is responsible for the gravitational interaction. We consider two cases: Interacting and non-interacting models of pressureless dark matter and Barrow holographic dark energy by solving $f\left( Q\right) $ symmetric teleparallel field equations. To find the exact solutions of the field equations, we assume that the time-redshift relation follows a Lambert function distribution as $t\left( z\right) =\frac{mt_{0}}{l}g\left( z\right) $, where $g\left( z\right) =LambertW\left[ \frac{l}{m}e^{\frac{l-\ln \left( 1+z\right) }{m}}\right] $, $m$ and $l$ are non-negative constants and $t_{0}$ represents the age of the Universe. Moreover, we discuss several cosmological parameters such as energy density, equation of state (EoS) and skewness parameters, squared sound speed, and $(\omega _{B}-\omega _{B}^{^{\prime }})$ plane. Finally, we found the values of the deceleration parameter (DP) for the Lambert function distribution as $q_{(z=0)}=-0.45$ and $q_{(z=-1)}=-1$ which are consistent with recent observational data, i.e. DP evolves with cosmic time from initial deceleration to late-time acceleration., Comment: IJGMMP accepted version

As machine learning and deep learning models have become highly prevalent in a multitude of domains, the main reservation in their adoption for decision-making processes is their black-box nature. The Explainable Artificial Intelligence (XAI) paradigm has gained a lot of momentum lately due to its ability to reduce models opacity. XAI methods have not only increased stakeholders' trust in the decision process but also helped developers ensure its fairness. Recent efforts have been invested in creating transparent models and post-hoc explanations. However, fewer methods have been developed for time series data, and even less when it comes to multivariate datasets. In this work, we take advantage of the inherent interpretability of shapelets to develop a model agnostic multivariate time series (MTS) counterfactual explanation algorithm. Counterfactuals can have a tremendous impact on making black-box models explainable by indicating what changes have to be performed on the input to change the final decision. We test our approach on a real-life solar flare prediction dataset and prove that our approach produces high-quality counterfactuals. Moreover, a comparison to the only MTS counterfactual generation algorithm shows that, in addition to being visually interpretable, our explanations are superior in terms of proximity, sparsity, and plausibility., Comment: Appeared in ACM SIGKDD Workshop on Mining and Learning from Time Series (KDD-MiLeTS 2022)

Radio access network (RAN) slicing is a key element in enabling current 5G networks and next-generation networks to meet the requirements of different services in various verticals. However, the heterogeneous nature of these services' requirements, along with the limited RAN resources, makes the RAN slicing very complex. Indeed, the challenge that mobile virtual network operators (MVNOs) face is to rapidly adapt their RAN slicing strategies to the frequent changes of the environment constraints and service requirements. Machine learning techniques, such as deep reinforcement learning (DRL), are increasingly considered a key enabler for automating the management and orchestration of RAN slicing operations. Nerveless, the ability to generalize DRL models to multiple RAN slicing environments may be limited, due to their strong dependence on the environment data on which they are trained. Federated learning enables MVNOs to leverage more diverse training inputs for DRL without the high cost of collecting this data from different RANs. In this paper, we propose a federated deep reinforcement learning approach for RAN slicing. In this approach, MVNOs collaborate to improve the performance of their DRL-based RAN slicing models. Each MVNO trains a DRL model and sends it for aggregation. The aggregated model is then sent back to each MVNO for immediate use and further training. The simulation results show the effectiveness of the proposed DRL approach.