Search

The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) in Barcelona (Spain). Our study encompasses relevant aspects within the federated experience, including model aggregation techniques, outlier management, the impact of individual clients, personalized learning, and the integration of exogenous sources of data. The performed evaluation is based on both prediction accuracy and sustainability, thus showcasing the environmental impact of employed FL algorithms in various settings. The findings from our study highlight FL as a promising and robust solution for mobile traffic prediction, emphasizing its twin merits as a privacy-conscious and environmentally sustainable approach, while also demonstrating its capability to overcome data heterogeneity and ensure high-quality predictions, marking a significant stride towards its integration in mobile traffic management systems.

The ROC curve is a statistical tool that analyses the accuracy of a diagnostic test in which a variable is used to decide whether an individual is healthy or not. Along with that diagnostic variable it is usual to have information of some other covariates. In some situations it is advisable to incorporate that information into the study, as the performance of the ROC curves can be affected by them. Using the covariate-adjusted, the covariate-specific or the pooled ROC curves we discuss how to decide if we can exclude the covariates from our study or not, and the implications this may have in further analyses of the ROC curve. A new test for comparing the covariate-adjusted and the pooled ROC curve is proposed, and the problem is illustrated by analysing a real database., Comment: 17 pages, 5 figures, 5 tables

Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without sharing their private data. While this privacy-preserving method shows potential, it struggles when data across clients is not independent and identically distributed (non-IID) data. The latter remains an unsolved challenge that can result in poorer model performance and slower training times. Despite the significance of non-IID data in FL, there is a lack of consensus among researchers about its classification and quantification. This systematic review aims to fill that gap by providing a detailed taxonomy for non-IID data, partition protocols, and metrics to quantify data heterogeneity. Additionally, we describe popular solutions to address non-IID data and standardized frameworks employed in FL with heterogeneous data. Based on our state-of-the-art review, we present key lessons learned and suggest promising future research directions.

In this paper we propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. We address the challenges of data privacy and heterogeneity in autonomous vehicle environments by proposing a personalized Federated Learning scenario that allows selective sharing of model parameters. Our method implements a coarse-to-fine approach, where clients share only the coarse feature extractors while keeping fine-grained features specific to local environments. We evaluate our approach against traditional centralized and single-client training schemes using the KITTI dataset combined with satellite imagery. Results demonstrate that our federated CVGL method achieves performance close to centralized training while maintaining data privacy. The proposed partial model sharing strategy shows comparable or slightly better performance than classical FL, offering significant reduced communication overhead without sacrificing accuracy. Our work contributes to more robust and privacy-preserving localization systems for autonomous vehicles operating in diverse environments, Comment: 6 pages, 2 figures, Preprint submitted to the IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)

This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard approach of KalmanNet, a recurrent neural network aiming to estimate the underlying system uncertainty of traditional Extended Kalman Filtering, and reformulate it by the adapt-then-combine concept to FedKalmanNet. The latter is trained in a distributed manner by a group of vehicles (or clients), with local training datasets consisting of vehicular location and velocity measurements, through a global server aggregation operation. The FedKalmanNet is then used by each vehicle to localize itself, by estimating the associated system uncertainty matrices (i.e, Kalman gain). Our aim is to actually demonstrate the benefits of collaborative training for state estimation in autonomous driving, over collaborative decision-making which requires rich V2X communication resources for measurement exchange and sensor fusion under real-time constraints. An extensive experimental and evaluation study conducted in CARLA autonomous driving simulator highlights the superior performance of FedKalmanNet over state-of-the-art collaborative decision-making approaches, in localizing vehicles without the need of real-time V2X communication.

Anomaly and missing data constitute a thorny problem in industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy is achieved with the utilization of large neural networks, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the privacy-preserving distributed paradigm, known as federated learning (FL). This framework allows edge devices to train and exchange models increasing also the communication cost. Thus, to deal with the increased communication, processing and storage challenges of the FL based deep anomaly detection NN pruning is expected to have significant benefits towards reducing the processing, storage and communication complexity. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that fusses the received local models broadcast and performs pruning generating a more compressed model. Experiments in the context of anomaly detection and missing value imputation demonstrate that the proposed FL scenario along with the proposed compressed-based method are able to achieve high compression rates (more than $99.7\%$) with negligible performance losses (less than $1.18\%$ ) as compared to the centralized solutions.

This study presents a machine learning framework for assessing similarity between audio content and predicting sentiment score. We construct a dataset containing audio samples from music covers on YouTube along with the audio of the original song, and sentiment scores derived from user comments, serving as proxy labels for content quality. Our approach involves extensive pre-processing, segmenting audio signals into 30-second windows, and extracting high-dimensional feature representations through Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Temporal characteristics. Leveraging these features, we train regression models to predict sentiment scores on a 0-100 scale, achieving root mean square error (RMSE) values of 3.420, 5.482, 2.783, and 4.212, respectively. Improvements over a baseline model based on absolute difference metrics are observed. These results demonstrate the potential of machine learning to capture sentiment and similarity in audio, offering an adaptable framework for AI applications in media analysis., Comment: 6 pages, 6 figures

Smart wireless environments enabled by multiple distributed Reconfigurable Intelligent Surfaces (RISs) have recently attracted significant research interest as a wireless connectivity paradigm for sixth Generation (6G) networks. In this paper, using random matrix theory methods, we calculate the mean of the sum Mutual Information (MI) for the correlated Multiple-Input Multiple-Output (MIMO) Multiple Access Channel (MAC) in the presence of multiple RISs, in the large-antenna number limit. We thus obtain the capacity region boundaries, after optimizing over the tunable RISs' phase configurations. Furthermore, we obtain a closed-form expression for the variance of the sum-MI metric, which together with the mean provides a tight Gaussian approximation for the outage probability. The derived results become relevant in the presence of fast-fading, when channel estimation is extremely challenging. Our numerical investigations showcased that, when the angle-spread in the neighborhood of each RIS is small, which is expected for higher carrier frequencies, the communication link strongly improves from optimizing the ergodic MI of the multiple RISs.We also found that, increasing the number of transmitting users in such MIMO-MAC-RIS systems results to rapidly diminishing sum-MI gains, hence, providing limits on the number of users that can be efficiently served by a given RIS., Comment: 14 pages, 5 figures, to appear in an IEEE Transactions

Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose monitoring along with smartwatches with embedded inertial sensors to collect glucose measurements and acceleration signals respectively, for the early detection of prediabetes. We propose a methodology based on signal processing and machine learning techniques. Two feature sets are extracted from the collected signals, based both on a dynamic modeling of the human glucose-homeostasis system and on the Glucose curve, inspired by three major glucose related blood tests. Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. We collected data from 22 participants for evaluation. The results are highly encouraging, demonstrating high sensitivity and precision. This work is a proof of concept, highlighting the potential of wearable devices in prediabetes assessment. Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used., Comment: 13 pages, 3 figures, 2 tables

Low Earth Orbit Satellite Networks (LSNs) are integral to supporting a broad range of modern applications, which are typically modeled as Service Function Chains (SFCs). Each SFC is composed of Virtual Network Functions (VNFs), where each VNF performs a specific task. In this work, we tackle two key challenges in deploying SFCs across an LSN. Firstly, we aim to optimize the long-term system performance by minimizing the average end-to-end SFC execution delay, given that each satellite comes with a pre-installed/cached subset of VNFs. To achieve optimal SFC placement, we formulate an offline Dynamic Programming (DP) equation. To overcome the challenges associated with DP, such as its complexity, the need for probability knowledge, and centralized decision-making, we put forth an online Multi-Agent Q-Learning (MAQL) solution. Our MAQL approach addresses convergence issues in the non-stationary LSN environment by enabling satellites to share learning parameters and update their Q-tables based on distinct rules for their selected actions. Secondly, to determine the optimal VNF subsets for satellite caching, we develop a Bayesian Optimization (BO)-based learning mechanism that operates both offline and continuously in the background during runtime. Extensive experiments demonstrate that our MAQL approach achieves near-optimal performance comparable to the DP model and significantly outperforms existing baselines. Moreover, the BO-based approach effectively enhances the request serving rate over time., Comment: 40 pages, 11 figure, 3 tables

3D pose estimation from a 2D cross-sectional view enables healthcare professionals to navigate through the 3D space, and such techniques initiate automatic guidance in many image-guided radiology applications. In this work, we investigate how estimating 3D fetal pose from freehand 2D ultrasound scanning can guide a sonographer to locate a head standard plane. Fetal head pose is estimated by the proposed Pose-GuideNet, a novel 2D/3D registration approach to align freehand 2D ultrasound to a 3D anatomical atlas without the acquisition of 3D ultrasound. To facilitate the 2D to 3D cross-dimensional projection, we exploit the prior knowledge in the atlas to align the standard plane frame in a freehand scan. A semantic-aware contrastive-based approach is further proposed to align the frames that are off standard planes based on their anatomical similarity. In the experiment, we enhance the existing assessment of freehand image localization by comparing the transformation of its estimated pose towards standard plane with the corresponding probe motion, which reflects the actual view change in 3D anatomy. Extensive results on two clinical head biometry tasks show that Pose-GuideNet not only accurately predicts pose but also successfully predicts the direction of the fetal head. Evaluations with probe motions further demonstrate the feasibility of adopting Pose-GuideNet for freehand ultrasound-assisted navigation in a sensor-free environment., Comment: Accepted by MICCAI2024

Network Function Virtualization (NFV) has shifted communication networks towards more adaptable software solutions, but this transition raises new security concerns, particularly in public cloud deployments. While Intel's Software Guard Extensions (SGX) offers a potential remedy, it requires complex application adaptations. This paper investigates AMD's Secure Encrypted Virtualization (SEV) as an alternative approach for securing NFV. SEV encrypts virtual machine (VM) memory, protecting it from threats, including those at the hypervisor level, without requiring application modifications. We explore the practicality and performance implications of executing native network function (NF) implementations in AMD SEV-SNP, the latest iteration of SEV. Our study focuses on running an unmodified Snort NF within SEV. Results show an average performance penalty of approximately 20% across various traffic and packet configurations, demonstrating a trade-off between security and performance that may be acceptable for many NFV deployments.

We construct a weakly compact convex subset of $\ell^2$ with nonempty interior that has an isolated maximal element, with respect to the lattice order $\ell _+^2$. Moreover, the maximal point cannot be supported by any strictly positive functional, showing that the Arrow-Barankin-Blackwell theorem fails. This example discloses the pertinence of the assumption that the cone has a bounded base for the validity of the result in infinite dimensions. Under this latter assumption, the equivalence of the notions of strict maximality and maximality is established

This paper presents a novel method to quantify seafarers' good seamanship during navigation scenarios with multi-vessel encounters -- in open and confined waters --, and to compute COLREG's-compliant trajectories for avoiding collision and grounding. The quantification of good seamanship requires knowledge about the state of the vessels (position, heading, and speed) and the surrounding sailing environment. Such information is accessible through the AIS system and the electronic nautical chart. The proposed method evaluates mutual collision risk by examining domain violations of each vessel, and comparing them to the seaman's actions. This results in a comprehensive metric of good seamanship. As risk free actions are not always possible in the resolution of a potential collision and grounding, the method adopts a branch-and-bound scheme to identify achievable maneuvers that minimize the risk. Further, the dynamic nature of vessel speed in congested scenarios is considered, recognizing potential changes in both own and target vessels' forward speeds. The proposed method is experimentally evaluated using historical AIS data and sea charts of Danish waters. This research contributes to the field by providing a more realistic perspective on seamanship in complex maritime environments., Comment: The 15th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS 2024)

The impressive physics and applications of intra- and interlayer excitons in a transition metal dichalcogenide twisted-bilayer make these systems compelling platforms for exploring the manipulation of their optoelectronic properties through electrical fields. This work studies the electrical control of excitonic complexes in twisted MoSe$_2$ homobilayer devices at room temperature. Gate-dependent micro-photoluminescence spectroscopy reveals an energy tunability of several meVs originating from the emission of excitonic complexes. Furthermore, our study investigates the twist-angle dependence of valley properties by fabricating devices with stacking angles of $\theta\sim1\degree$, $\theta\sim4\degree$ and $\theta\sim18\degree$. Strengthened by density functional theory calculations, the results suggest that, depending on the twist angle, the conduction band minima and hybridized states at the \textbf{Q}-point promote the formation of intervalley hybrid trions involving the \textbf{Q}-and \textbf{K}-points in the conduction band and the \textbf{K}-point in the valence band. By revealing the gate control of exciton species in twisted homobilayers, our findings open new avenues for engineering multifunctional optoelectronic devices based on ultrathin semiconducting systems.

Graph neural networks (GNNs) excel in learning from network-like data but often lack interpretability, making their application challenging in domains requiring transparent decision-making. We propose the Graph Kolmogorov-Arnold Network (GKAN), a novel GNN model leveraging spline-based activation functions on edges to enhance both accuracy and interpretability. Our experiments on five benchmark datasets demonstrate that GKAN outperforms state-of-the-art GNN models in node classification, link prediction, and graph classification tasks. In addition to the improved accuracy, GKAN's design inherently provides clear insights into the model's decision-making process, eliminating the need for post-hoc explainability techniques. This paper discusses the methodology, performance, and interpretability of GKAN, highlighting its potential for applications in domains where interpretability is crucial., Comment: 7 pages, 4 figures, under review

Optical photothermal microscopy is a powerful, emerging method that overcomes the diffraction limit in infrared hyperspectral imaging by utilizing a visible probe laser beam to detect local temperature-induced modulation at the visible diffraction limit. However, the spectral range of this technique has been limited by the tuning range of infrared sources, which is generally restricted to the fingerprint window with commercially available quantum cascade lasers. In this work, ultra-broadband synchrotron infrared radiation was used for infrared photothermal imaging and spectroscopy, spanning the entire mid-infrared range. Both optical- and fluorescence-detected photothermal modalities were performed, demonstrating improved spectral range when compared to optical photothermal microscopy using commercial sources and improved spatial resolution when compared to synchrotron micro-spectroscopy measurements. Following these initial validation studies, synchrotron Fourier-transform optical photothermal infrared spectroscopy (FT-OPTIR) in combination with synchrotron micro-spectroscopy measurements were used to differentiate cells in mouse brain tissue sections.

We study the problem of allocating a set of indivisible goods to a set of agents with additive valuation functions, aiming to achieve approximate envy-freeness up to any good ($\alpha$-EFX). The state-of-the-art results on the problem include that (exact) EFX allocations exist when (a) there are at most three agents, or (b) the agents' valuation functions can take at most two values, or (c) the agents' valuation functions can be represented via a graph. For $\alpha$-EFX, it is known that a $0.618$-EFX allocation exists for any number of agents with additive valuation functions. In this paper, we show that $2/3$-EFX allocations exist when (a) there are at most \emph{seven agents}, (b) the agents' valuation functions can take at most \emph{three values}, or (c) the agents' valuation functions can be represented via a \emph{multigraph}. Our results can be interpreted in two ways. First, by relaxing the notion of EFX to $2/3$-EFX, we obtain existence results for strict generalizations of the settings for which exact EFX allocations are known to exist. Secondly, by imposing restrictions on the setting, we manage to beat the barrier of $0.618$ and achieve an approximation guarantee of $2/3$. Therefore, our results push the \emph{frontier} of existence and computation of approximate EFX allocations, and provide insights into the challenges of settling the existence of exact EFX allocations., Comment: The conference version of this work has been accepted to the Twenty-Fifth ACM Conference on Economics and Computation (EC 2024)

We construct a differentiable locally Lipschitz function $f$ in $\mathbb{R}^{N}$ with the property that for every convex body $K\subset \mathbb{R}^N$ there exists $\bar x \in \mathbb{R}^N$ such that $K$ coincides with the set $\partial_L f(\bar x)$ of limits of derivatives $\{Df(x_n)\}_{n\geq 1}$ of sequences $\{x_n\}_{n\geq 1}$ converging to~$\bar x$. The technique can be further refined to recover all compact connected subsets with nonempty interior, disclosing an important difference between differentiable and continuously differentiable functions. It stems out from our approach that the class of these pathological functions contains an infinite dimensional vector space and is dense in the space of all locally Lipschitz functions for the uniform convergence., Comment: Revised version

In this work, we present and experimentally validate a passive photonic-integrated neuromorphic accelerator that uses a hardware-friendly optical spectrum slicing technique through a reconfigurable silicon photonic mesh. The proposed scheme acts as an analogue convolutional engine, enabling information preprocessing in the optical domain, dimensionality reduction and extraction of spatio-temporal features. Numerical results demonstrate that utilizing only 7 passive photonic nodes, critical modules of a digital convolutional neural network can be replaced. As a result, a 98.6% accuracy on the MNIST dataset was achieved, with a power consumption reduction of at least 26% compared to digital CNNs. Experimental results confirm these findings, achieving 97.7% accuracy with only 3 passive nodes., Comment: 18 pages, 10 figures, submitted to Optica Open

Can we assess a priori how well a knowledge graph embedding will perform on a specific downstream task and in a specific part of the knowledge graph? Knowledge graph embeddings (KGEs) represent entities (e.g., "da Vinci," "Mona Lisa") and relationships (e.g., "painted") of a knowledge graph (KG) as vectors. KGEs are generated by optimizing an embedding score, which assesses whether a triple (e.g., "da Vinci," "painted," "Mona Lisa") exists in the graph. KGEs have been proven effective in a variety of web-related downstream tasks, including, for instance, predicting relationships among entities. However, the problem of anticipating the performance of a given KGE in a certain downstream task and locally to a specific individual triple, has not been tackled so far. In this paper, we fill this gap with ReliK, a Reliability measure for KGEs. ReliK relies solely on KGE embedding scores, is task- and KGE-agnostic, and requires no further KGE training. As such, it is particularly appealing for semantic web applications which call for testing multiple KGE methods on various parts of the KG and on each individual downstream task. Through extensive experiments, we attest that ReliK correlates well with both common downstream tasks, such as tail or relation prediction and triple classification, as well as advanced downstream tasks, such as rule mining and question answering, while preserving locality.

In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. This combination offers high classification accuracy and a massive reduction in the number of trainable parameters of the digital machine-learning back-end. The photonic neuromorphic accelerator is based on a hardware-friendly passive optical spectrum slicing technique that is able to extract meaningful features from the generated spike-trains. The experimental scenario comprises the discrimination of artificial polymethyl methacrylate calibrated beads, having different diameters, flowing at a mean speed of 0.01m/sec. Classification accuracy, using only lightweight, digital machine-learning schemes has topped at 98.2%. On the other hand, by experimentally pre-processing the raw spike data through the proposed photonic neuromorphic spectrum slicer we achieved an accuracy of 98.6%. This performance was accompanied by a reduction in the number of trainable parameters at the classification back-end by a factor ranging from 8 to 22, depending on the configuration of the digital neural network., Comment: 21 pages, 11 figures, submitted to Scientific Reports - Springer Nature

The S-shaped swing of the linear polarization position angle (PPA) observed in many pulsars can be interpreted by the rotating vector model (RVM). However, efforts to fit the RVM for a large sample of pulsars observed with the MeerKAT telescope as a part of the Thousand-Pulsar-Array (TPA) programme, only succeeded for about half the cases. High time-resolution studies suggest that the failed cases arise due to the presence of orthogonal polarization modes, or highly disordered distribution of PPA points. One such example is PSR~J1645-0317. Recently it has been shown that the RVM can be recovered in this pulsar by using only time samples which are greater than 80% linearly polarized. In this work we test this novel approach on the brightest 249 pulsars from the TPA sample, of which 177 yield sufficient highly polarized samples to be amenable to our method. Remarkably, only 9 of these pulsars (5%) now fail to fit the RVM as opposed to 59% from the original analysis. This result favours the paradigm that the underlying mechanism is coherent curvature radiation., Comment: Delayed interminably by MNRAS

Mekler's construction is a powerful technique for building purely algebraic structures from combinatorial ones. Its power lies in the fact that it allows various model-theoretic tameness properties of the combinatorial structure to transfer to the algebraic one. In this paper, we push this ideology much further, describing a broad class of properties that transfer through Mekler's construction. This technique subsumes many well-known results and opens avenues for many more. As a straightforward application of our methods, we (1) obtain transfer principles for stably embedded pairs of Mekler groups and (2) construct the first examples of strictly $\mathsf{NFOP}_k$ pure groups for all $k\in\mathbb{N}_{>2}$. We also answer a question of Chernikov and Hempel on the transfer of burden., Comment: 45 pages. Preliminary version, comments welcome!

In a strong gravitational lensing system, the distorted light from a source is analysed to infer the properties of the lens. However, light emitted by the lens itself can contaminate the image of the source, introducing systematic errors in the analysis. We present a simple and efficient lens light model based on the well-tested multi-Gaussian expansion (MGE) method for representing galaxy surface brightness profiles, which we combine with a semi-linear inversion scheme for pixelized source modelling. Testing it against realistic mock lensing images, we show that our scheme can fit the lensed images to the noise level, with relative differences between the true input and best-fit lens light model remaining below 5%. We apply the MGE lens light model to 38 lenses from the HST SLACS sample. We find that the new scheme provides a good fit for the majority of the sample with only 3 exceptions -- these show clear asymmetric residuals in the lens light. We examine the radial dependence of the ellipticity and position angles and confirm that it is common for a typical lens galaxy to exhibit twisting, non-elliptical isophotes and boxy / disky isophotes. Our MGE lens light model will be a valuable tool for understanding the hidden complexity of the lens mass distribution., Comment: 22 pages, 15 figures. Submitted to MNRAS. Comments Welcome

We study possible tangles that can occur in singularities of solutions to plane Curve Shortening Flow. We exhibit solutions in which more complicated tangles with more than one self-intersection disappear into a singular point. It seems that there are many examples of this kind and that a complete classification presents a problem similar to the problem of classifying all knots in $\mathbb R^3$. As a particular example, we introduce the so-called $n$-loop curves, which generalize Matt Grayson's Figure-Eight curve, and we conjecture a generalization of the Coiculescu-Schwarz asymptotic bow-tie result, namely, a vanishing $n$-loop, when rescaled anisotropically to fit a square bounding box, converges to a "squeezed bow-tie," i.e. the curve $\{(x, y) : |x|\leq 1, y=\pm x^{n-1}\}\cup\{(\pm 1, y) : |y|\leq 1\}$. As evidence in support of the conjecture, we provide a formal asymptotic analysis on one hand, and a numerical simulation for the cases $n=3$ and $n=4$ on the other., Comment: 21 pages, 14 figures

We report on multi-resonance chirped distributed Bragg reflector (DBR) microcavities. These systems are employed to investigate the light-mater interaction with both intra- and inter-layer excitons of transition metal dichalcogenide (TMDC) bilayer heterostructures. The chirped DBRs consisting of SiO2 and Si3N4 layers with gradually changing thickness exhibit a broad stopband with a width exceeding 600 nm. Importantly, and in contrast to conventional single-resonance microcavities, our structures provide multiple resonances across a broad spectral range, which can be matched to spectrally distinct resonances of the embedded TMDC heterostructures. We study cavity-coupled emission of both intra- and inter-layer excitons from an integrated WSe2/MoSe2 heterostructure in a chirped microcavity system. We observe an enhanced interlayer exciton emission with a Purcell factor of 6.67 +- 1.02 at 4 K. Additionally, we take advantage of cavity-enhanced emission of the interlayer exciton to investigate its temperature-dependent luminescence lifetime, which yields a value of 60 ps at room temperature. Our approach provides an intriguing platform for future studies of energetically distant and confined excitons in different semiconducting materials, which paves the way for various applications such as microlasers and single-photon sources by enabling precise control and manipulation of excitonic interactions utilizing multimode resonance light-matter interaction.

Recent advancements in drone technology have shown that commercial off-the-shelf Micro Aerial Drones are more effective than large-sized drones for performing flight missions in narrow environments, such as swarming, indoor navigation, and inspection of hazardous locations. Due to their deployments in many civilian and military applications, safe and reliable communication of these drones throughout the mission is critical. The Crazyflie ecosystem is one of the most popular Micro Aerial Drones and has the potential to be deployed worldwide. In this paper, we empirically investigate two interference attacks against the Crazy Real Time Protocol (CRTP) implemented within the Crazyflie drones. In particular, we explore the feasibility of experimenting two attack vectors that can disrupt an ongoing flight mission: the jamming attack, and the hijacking attack. Our experimental results demonstrate the effectiveness of such attacks in both autonomous and non-autonomous flight modes on a Crazyflie 2.1 drone. Finally, we suggest potential shielding strategies that guarantee a safe and secure flight mission. To the best of our knowledge, this is the first work investigating jamming and hijacking attacks against Micro Aerial Drones, both in autonomous and non-autonomous modes., Comment: Accepted at IEEE International Conference on Communications (ICC) 2024

We extend dilation properties of Wiener amalgam spaces when the local and global componenets are Lebesgue spaces to a more general setting of Orlicz spaces. We recover the result of Cordero and Nicola when restricted to Lebesgue spaces. In addition, we prove continuity of the Zak transform on Wiener amalgam spaces with Orlicz spaces as their local components., Comment: 17 pages

In this work we show that several problems naturally modeled as Nonlinear Absolute Value Equations (NAVE), can be restated as Nonlinear Complementarity Problems (NCP) and solved efficiently using smoothing regularizing techniques under mild assumptions. Applications include ridge optimization and resolution of nonlinear ordinary differential equations.

We study the computational complexity of computing Bayes-Nash equilibria in first-price auctions with discrete value distributions and discrete bidding space, under general subjective beliefs. It is known that such auctions do not always have pure equilibria. In this paper, we prove that the problem of deciding their existence is NP-complete, even for approximate equilibria. On the other hand, it can be shown that mixed equilibria are guaranteed to exist; however, their computational complexity has not been studied before. We establish the PPAD-completeness of computing a mixed equilibrium and we complement this by an efficient algorithm for finding symmetric approximate equilibria in the special case of iid priors. En route to these results, we develop a computational equivalence framework between continuous and discrete first-price auctions, which can be of independent interest, and which allows us to transfer existing positive and negative results from one setting to the other. Finally, we show that correlated equilibria of the auction can be computed in polynomial time., Comment: 54 pages, 2 figures, Published in the 25th ACM Conference on Economics and Computation (EC'24)

Particle pair-correlations are broadly used to describe particle distributions in chemistry, physics, and material science. Many theoretical methods require the pair-correlation to predict material properties such as fluid flow, thermal properties, or wave propagation. In all these applications it is either important to calculate a pair-correlation from specific particle configurations, or vice-versa: determine the likely particle configurations from a pair-correlation which is needed to fabricate a particulate material. Most available methods to calculate the pair-correlation from a particle configuration require that the configuration be very large to avoid effects from the boundary. Here we show how to avoid boundary effects even for small particle configurations. Having small particle configurations leads to far more efficient numerical methods. We also demonstrate how to use techniques from smooth nonlinear optimisation to quickly recover a particle configuration from a pair-correlation.

We introduce a general technique for proving membership of search problems with exact rational solutions in PPAD, one of the most well-known classes containing total search problems with polynomial-time verifiable solutions. In particular, we construct a "pseudogate", coined the linear-OPT-gate, which can be used as a "plug-and-play" component in a piecewise-linear (PL) arithmetic circuit, as an integral component of the "Linear-FIXP" equivalent definition of the class. The linear-OPT-gate can solve several convex optimization programs, including quadratic programs, which often appear organically in the simplest existence proofs for these problems. This effectively transforms existence proofs to PPAD-membership proofs, and consequently establishes the existence of solutions described by rational numbers. Using the linear-OPT-gate, we are able to significantly simplify and generalize almost all known PPAD-membership proofs for finding exact solutions in the application domains of game theory, competitive markets, auto-bidding auctions, and fair division, as well as to obtain new PPAD-membership results for problems in these domains.

A classical result of variational analysis, known as Attouch theorem, establishes the equivalence between epigraphical convergence of a sequence of proper convex lower semicontinuous functions and graphical convergence of the corresponding subdifferential maps up to a normalization condition which fixes the integration constant. In this work, we show that in finite dimensions and under a mild boundedness assumption, we can replace subdifferentials (sets of vectors) by slopes (scalars, corresponding to the distance of the subdifferentials to zero) and still obtain the same characterization: namely, the epigraphical convergence of functions is equivalent to the epigraphical convergence of their slopes. This surprising result goes in line with recent developments on slope determination (Boulmezaoud, Cieutat, Daniilidis, 2018), (P\'erez-Aros, Salas, Vilches, 2021) and slope sensitivity (Daniilidis, Drusvyatskiy, 2023) for convex functions.

Generalised indiscernibles highlight a strong link between model theory and structural Ramsey theory. In this paper, we use generalised indiscernibles as tools to prove results in both these areas. More precisely, we first show that a reduct of an ultrahomogenous $\aleph_0$-categorical structure which has higher arity than the original structure cannot be Ramsey. In particular, the only nontrivial Ramsey reduct of the generically ordered random $k$-hypergraph is the linear order. We then turn our attention to model-theoretic dividing lines that are characterised by collapsing generalised indiscernibles, and prove, for these dividing lines, several transfer principles in (full and lexicographic) products of structures. As an application, we construct new algorithmically tame classes of graphs., Comment: 45 pages. Changes: fix of the proof of Theorem 3.3, and minor changes

Out-of-distribution (OOD) detection is essential to improve the reliability of machine learning models by detecting samples that do not belong to the training distribution. Detecting OOD samples effectively in certain tasks can pose a challenge because of the substantial heterogeneity within the in-distribution (ID), and the high structural similarity between ID and OOD classes. For instance, when detecting heart views in fetal ultrasound videos there is a high structural similarity between the heart and other anatomies such as the abdomen, and large in-distribution variance as a heart has 5 distinct views and structural variations within each view. To detect OOD samples in this context, the resulting model should generalise to the intra-anatomy variations while rejecting similar OOD samples. In this paper, we introduce dual-conditioned diffusion models (DCDM) where we condition the model on in-distribution class information and latent features of the input image for reconstruction-based OOD detection. This constrains the generative manifold of the model to generate images structurally and semantically similar to those within the in-distribution. The proposed model outperforms reference methods with a 12% improvement in accuracy, 22% higher precision, and an 8% better F1 score., Comment: Published in MICCAI 2023

Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, the audio-visual modelling in the latter case can be more challenging, due to the different sources of audio/video signals and the noise (both signal-level and semantic-level) in auditory signals -- usually speech. In this paper, we consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations that benefit various clinical tasks, without human expert annotation. A simple yet effective multi-modal self-supervised learning framework is proposed for this purpose. The proposed approach is able to localise anatomical regions of interest during ultrasound imaging, with only speech audio as a reference. Experimental evaluations on a large-scale clinical multi-modal ultrasound video dataset show that the proposed self-supervised method learns good transferable anatomical representations that boost the performance of automated downstream clinical tasks, even outperforming fully-supervised solutions.

Among all the available observational techniques for studying magnetic fields in the dense cold phase of the interstellar medium, linear polarization of spectral lines, referred to in the literature as the Goldreich-Kylafis effect (Goldreich & Kylafis 1981; hereafter "GK effect"), remains one of the most underutilized methods. In this study, we implement the GK effect into the multilevel, non-local thermodynamic equilibrium radiative transfer code PyRaTE. Different modes of polarized radiation are treated individually with separate optical depths computed for each polarization direction. We benchmark our implementation against analytical results and provide tests for various limiting cases. In agreement with previous theoretical results, we find that in the multilevel case the amount of fractional polarization decreases when compared to the two-level approximation, but this result is subject to the relative importance between radiative and collisional processes. Finally, we post-process an axially symmetric, non-ideal magnetohydrodynamic chemo-dynamical simulation of a collapsing prestellar core and provide theoretical predictions regarding the shape (as a function of velocity) of the polarization fraction of CO during the early stages in the evolution of molecular clouds. The code is freely available to download., Comment: 13 pages and 11 figures. Submitted to A&A. Comments welcome

We establish existence of steepest descent curves emanating from almost every point of a regular locally Lipschitz quasiconvex functions, where regularity means that the sweeping process flow induced by the sublevel sets is reversible. We then use max-convolution to regularize general quasiconvex functions and obtain a result of the same nature in a more general setting.

We report an easy-to-fabricate microcavity design to produce optically pumped high-$\beta$ quantum dot microlasers. Our cavity concept is based on a buried photonic-defect for tight lateral mode confinement in a quasi-planar microcavity system, which includes an upper dielectric distributed Bragg reflector (DBR) as a promising alternative to conventional III-V semiconductor DBRs. Through the integration of a photonic-defect, we achieve low mode volumes as low as 0.28 $\mu$m$^3$, leading to enhanced light-matter interaction, without the additional need for complex lateral nanoprocessing of micropillars. We fabricate semiconductor-dielectric hybrid microcavities, consisting of Al$_{0.9}$Ga$_{0.1}$As/GaAs bottom DBR with 33.5 mirror pairs, dielectric SiO$_{2}$/SiN$_x$ top DBR with 5, 10, 15, and 19 mirror pairs, and photonic-defects with varying lateral size in the range of 1.5 $\mu$m to 2.5 $\mu$m incorporated into a one-$\lambda/n$ GaAs cavity with InGaAs quantum dots as active medium. The cavities show distinct emission features with a characteristic photonic defect size-dependent mode separation and \emph{Q}-factors up to 17000 for 19 upper mirror pairs in excellent agreement with numeric simulations. Comprehensive investigations further reveal lasing operation with a systematic increase (decrease) of the $\beta$-factor (threshold pump power) with the number of mirror pairs in the upper dielectric DBR. Notably, due to the quasi-planar device geometry, the microlasers show high temperature stability, evidenced by the absence of temperature-induced red-shift of emission energy and linewidth broadening typically observed for nano- and microlasers at high excitation powers.

Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications., Comment: International Symposium on Federated Learning Technologies and Applications (FLTA), 2023

Efficient face detection is critical to provide natural human-robot interactions. However, computer vision tends to involve a large computational load due to the amount of data (i.e. pixels) that needs to be processed in a short amount of time. This is undesirable on robotics platforms where multiple processes need to run in parallel and where the processing power is limited by portability constraints. Existing solutions often involve reducing image quality which can negatively impact processing. The literature also reports methods to generate regions of interest in images from pixel data. Although it is a promising idea, these methods often involve heavy vision algorithms. In this paper, we evaluate how audio can be used to generate regions of interest in optical images to reduce the number of pixels to process with computer vision. Thereby, we propose a unique attention mechanism to localize a speech source and evaluate its impact on an existing face detection algorithm. Our results show that the attention mechanism reduces the computational load and offers an interesting trade-off between speed and accuracy. The proposed pipeline is flexible and can be easily adapted to other applications such as robot surveillance, video conferences or smart glasses.

We initiate the study of a novel problem in mechanism design without money, which we term Truthful Interval Covering (TIC). An instance of TIC consists of a set of agents each associated with an individual interval on a line, and the objective is to decide where to place a covering interval to minimize the total social or egalitarian cost of the agents, which is determined by the intersection of this interval with their individual ones. This fundamental problem can model situations of provisioning a public good, such as the use of power generators to prevent or mitigate load shedding in developing countries. In the strategic version of the problem, the agents wish to minimize their individual costs, and might misreport the position and/or length of their intervals to achieve that. Our goal is to design truthful mechanisms to prevent such strategic misreports and achieve good approximations to the best possible social or egalitarian cost. We consider the fundamental setting of known intervals with equal lengths and provide tight bounds on the approximation ratios achieved by truthful deterministic mechanisms. For the social cost, we also design a randomized truthful mechanism that outperforms all possible deterministic ones. Finally, we highlight a plethora of natural extensions of our model for future work, as well as some natural limitations of those settings.

We study elections where voters are faced with the challenge of expressing preferences over an extreme number of issues under consideration. This is largely motivated by emerging blockchain governance systems, which include voters with different weights and a massive number of community generated proposals. In such scenarios, it is natural to expect that voters will have incomplete preferences, as they may only be able to evaluate or be confident about a very small proportion of the alternatives. As a result, the election outcome may be significantly affected, leading to suboptimal decisions. Our central inquiry revolves around whether delegation of ballots to proxies possessing greater expertise or a more comprehensive understanding of the voters' preferences can lead to outcomes with higher legitimacy and enhanced voters' satisfaction in elections where voters submit incomplete preferences. To explore its aspects, we introduce the following model: potential proxies advertise their ballots over multiple issues, and each voter either delegates to a seemingly attractive proxy or casts a ballot directly. We identify necessary and sufficient conditions that could lead to a socially better outcome by leveraging the participation of proxies. We accompany our theoretical findings with experiments on instances derived from real datasets. Overall, our results enhance the understanding of the power of delegation towards improving election outcomes.

It was established in [8] that Lipschitz inf-compact functions are uniquely determined by their local slope and critical values. Compactness played a paramount role in this result, ensuring in particular the existence of critical points. We hereby emancipate from this restriction and establish a determination result for merely bounded from below functions, by adding an assumption controlling the asymptotic behavior. This assumption is trivially fulfilled if $f$ is inf-compact. In addition, our result is not only valid for the (De Giorgi) local slope, but also for the main paradigms of average descent operators as well as for the global slope, case in which the asymptotic assumption becomes superfluous. Therefore, the present work extends simultaneously the metric determination results of [8] and [18].

\ We combine density functional theory and molecular dynamics simulations to investigate the impact of Sr concentration and atomic ordering on the structural and ferroelectric properties of (Ba, Sr)TiO$_3$. On one hand, the macroscopic structural properties are rather insensitive to atomic ordering. On the other hand, the Curie temperature and polarization differ by $9$\% and $17$\% for different symmetries of the Sr distribution, respectively. Local ordering of Sr induces preferential polarization directions and influences the relative stability of the three ferroelectric phases., Comment: 10 pages, 7 figures