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In this study, we investigate a pair of detectors operating in Minkowski space-time and analyze the characteristics of various quantum resources within this framework. Specifically, we focus on examining the properties of Bell nonlocality, quantum coherence, the nonlocal advantage of quantum coherence (NAQC), and measured uncertainty in relation to the energy ratio and the distance between the detectors. Additionally, we examine how the initial states influence these quantum properties. Notably, our findings reveal that both a larger energy ratio and a greater separation between the detectors degrade the system's quantumness. Moreover, we explore the evolution of entropic uncertainty and demonstrate its inverse correlation with both Bell nonlocality and coherence, highlighting the intricate interplay between these quantum resources. These insights provide a deeper understanding of quantumness in a relativistic framework and may contribute to the ongoing discussion on the black hole information paradox., Comment: 11 pages, 9 figures. Comments are welcome. Under review in PRD more...

Approval voting is widely used for making multi-winner voting decisions. The canonical rule (also called Approval Voting) used in the setting aims to maximize social welfare by selecting candidates with the highest number of approvals. We revisit approval-based multi-winner voting in scenarios where the information regarding the voters' preferences is uncertain. We present several algorithmic results for problems related to social welfare maximization under uncertainty, including computing an outcome that is social welfare maximizing with the highest probability, computing the social welfare probability distribution of a given outcome, computing the probability that a given outcome is social welfare maximizing, and understanding how robust an outcome is with respect to social welfare maximizing. more...

Motivated by the success of Sinkhorn's algorithm for entropic optimal transport, we study convergence properties of iterative proportional fitting procedures (IPFP) used to solve more general information projection problems. We establish exponential convergence guarantees for the IPFP whenever the set of probability measures which is projected onto is defined through constraints arising from linear function spaces. This unifies and extends recent results from multi-marginal, adapted and martingale optimal transport. The proofs are based on strong convexity arguments for the dual problem, and the key contribution is to illuminate the role of the geometric interplay between the subspaces defining the constraints. In this regard, we show that the larger the angle (in the sense of Friedrichs) between the linear function spaces, the better the rate of contraction of the IPFP. more...

This paper studies cooperative games where coalitions are formed online and the value generated by the grand coalition must be irrevocably distributed among the players at each timestep. We investigate the fundamental issue of strategic pariticipation incentives and address these concerns by formalizing natural participation incentive axioms. Our analysis reveals that existing value-sharing mechanisms fail to meet these criteria. Consequently, we propose several new mechanisms that not only fulfill these desirable participation incentive axioms but also satisfy the early arrival incentive for general valuation functions. Additionally, we refine our mechanisms under superadditive valuations to ensure individual rationality while preserving the previously established axioms. more...

We propose a new model for aggregating preferences over a set of indivisible items based on a quantile value. In this model, each agent is endowed with a specific quantile, and the value of a given bundle is defined by the corresponding quantile of the individual values of the items within it. Our model captures the diverse ways in which agents may perceive a bundle, even when they agree on the values of individual items. It enables richer behavioral modeling that cannot be easily captured by additive valuation functions. We study the problem of maximizing utilitarian and egalitarian welfare within the quantile-based valuation setting. For each of the welfare functions, we analyze the complexity of the objectives. Interestingly, our results show that the complexity of both objectives varies significantly depending on whether the allocation is required to be balanced. We provide near-optimal approximation algorithms for utilitarian welfare, and for egalitarian welfare, we present exact algorithms whenever possible. more...

Super-resolution Structured Illumination Microscopy (SR-SIM) enables fluorescence microscopy beyond the diffraction limit at high frame rates. Compared to other super-resolution microscopy techniques, the low photon fluence used in SR-SIM makes it readily compatible with live-cell imaging. Here, we combine SR-SIM with electro-optic fluorescence lifetime imaging (EOFLIM), adding the capability of monitoring physicochemical parameters with 156 nm spatial resolution at high frame rate for live-cell imaging. We demonstrate that our new SIMFLIM technique enables super-resolved multiplexed imaging of spectrally overlapping fluorophores, environmental sensing, and live-cell imaging. more...

Prior studies have shown that distinguishing text generated by large language models (LLMs) from human-written one is highly challenging, and often no better than random guessing. To verify the generalizability of this finding across languages and domains, we perform an extensive case study to identify the upper bound of human detection accuracy. Across 16 datasets covering 9 languages and 9 domains, 19 annotators achieved an average detection accuracy of 87.6%, thus challenging previous conclusions. We find that major gaps between human and machine text lie in concreteness, cultural nuances, and diversity. Prompting by explicitly explaining the distinctions in the prompts can partially bridge the gaps in over 50% of the cases. However, we also find that humans do not always prefer human-written text, particularly when they cannot clearly identify its source. more...

Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that the covariance is sample dependent and is often unknown. Consequently, recent methods learn the covariance through unsupervised frameworks, which unfortunately yield a trade-off between computational complexity and accuracy. While this trade-off could be alleviated through supervision, obtaining labels for the covariance is non-trivial. Here, we study self-supervised covariance estimation in deep heteroscedastic regression. We address two questions: (1) How should we supervise the covariance assuming ground truth is available? (2) How can we obtain pseudo labels in the absence of the ground-truth? We address (1) by analysing two popular measures: the KL Divergence and the 2-Wasserstein distance. Subsequently, we derive an upper bound on the 2-Wasserstein distance between normal distributions with non-commutative covariances that is stable to optimize. We address (2) through a simple neighborhood based heuristic algorithm which results in surprisingly effective pseudo labels for the covariance. Our experiments over a wide range of synthetic and real datasets demonstrate that the proposed 2-Wasserstein bound coupled with pseudo label annotations results in a computationally cheaper yet accurate deep heteroscedastic regression., Comment: ICLR 2025 more...

We consider a two-sided matching problem in which the agents on one side have dichotomous preferences and the other side representing institutions has strict preferences (priorities). It captures several important applications in matching market design in which the agents are only interested in getting matched to an acceptable institution. These include centralized daycare assignment and healthcare rationing. We present a compelling new mechanism that satisfies many prominent and desirable properties including individual rationality, maximum size, fairness, Pareto-efficiency on both sides, strategyproofness on both sides, non-bossiness and having polynomial time running time. As a result, we answer an open problem whether there exists a mechanism that is agent-strategyproof, maximum, fair and non-bossy. more...

This paper presents and analyzes a fast, robust, efficient, and optimally accurate fully discrete splitting algorithm for the Uncertainty Quantification (UQ) of parameterized Stochastic Navier-Stokes Equations (SNSEs) flow problems those occur in the convection-dominated regimes. The time-stepping algorithm is an implicit backward-Euler linearized method, grad-div and Ensemble Eddy Viscosity (EEV) regularized, and split using discrete Hodge decomposition. Additionally, the scheme's sub-problems are all designed to have different Right-Hand-Side (RHS) vectors but the same system matrix for all realizations at each time-step. The stability of the algorithm is rigorously proven, and it has been shown that appropriately large grad-div stabilization parameters vanish the splitting error. The proposed UQ algorithm is then combined with the Stochastic Collocation Methods (SCMs). Several numerical experiments are given to verify this superior scheme's predicted convergence rates and performance on benchmark problems for high expected Reynolds numbers ($Re$)., Comment: 24 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:2410.15510 more...

We explore solutions for fairly allocating indivisible items among agents assigned weights representing their entitlements. Our fairness goal is weighted-envy-freeness (WEF), where each agent deems their allocated portion relative to their entitlement at least as favorable as any others relative to their own. Often, achieving WEF necessitates monetary transfers, which can be modeled as third-party subsidies. The goal is to attain WEF with bounded subsidies. Previous work relied on characterizations of unweighted envy-freeness (EF), that fail in the weighted setting. This makes our new setting challenging. We present polynomial-time algorithms that compute WEF allocations with a guaranteed upper bound on total subsidy for monotone valuations and various subclasses thereof. We also present an efficient algorithm to compute a fair allocation of items and money, when the budget is not enough to make the allocation WEF. This algorithm is new even for the unweighted setting., Comment: 60 pages, 5 tables. arXiv admin note: substantial text overlap with arXiv:2411.12696 more...

Large vision-language models have revolutionized cross-modal object retrieval, but text-based person search (TBPS) remains a challenging task due to limited data and fine-grained nature of the task. Existing methods primarily focus on aligning image-text pairs into a common representation space, often disregarding the fact that real world positive image-text pairs share a varied degree of similarity in between them. This leads models to prioritize easy pairs, and in some recent approaches, challenging samples are discarded as noise during training. In this work, we introduce a boosting technique that dynamically identifies and emphasizes these challenging samples during training. Our approach is motivated from classical boosting technique and dynamically updates the weights of the weak positives, wherein, the rank-1 match does not share the identity of the query. The weight allows these misranked pairs to contribute more towards the loss and the network has to pay more attention towards such samples. Our method achieves improved performance across four pedestrian datasets, demonstrating the effectiveness of our proposed module. more...

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures more...

Very recently in [Das et al., Expo. Math., 2025], statistically characterized subgroups have been investigated for some non-arithmetic sequences, addressing certain cardinality-related questions. Building on this work, we investigate further and demonstrate that, for a particular class of non-arithmetic sequences, the statistically characterized subgroup coincides with the corresponding characterized subgroup. As a corollary, we identify a class of sequences for which statistically characterized subgroups are countably infinite. This result provides a negative solution to Problem 2.16 posed in [Das et al., Expo. Math., 2025] and Question 6.3 from [Dikranjan et al., Fund. Math., 2020]. Additionally, our findings resolve several open problems discussed in [Dikranjan et al., submitted] more...

Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces a fully integrated neuromorphic framework that combines superconducting memristor(SM) based spiking neurons and synapse topologies to achieve a low power neuromorphic network with non volatile synaptic strength.This neurosynaptic framework is validated by implementing the cart pole control task, a dynamic decision making problem requiring real time computation.Through detailed simulations, we demonstrate the network's ability to execute this task with an average fitness of 5965 timesteps across 1000 randomized test episodes, with 40 percent achieving the target fitness of 15,000 timesteps (0.02s per timestep).The system achieves 23 distinct spiking rates across neurons, ensuring efficient information encoding.Our findings establish the potential of SM based cryogenic neuromorphic systems to address the energy and scalability limitations of traditional computing, paving the way for biologically inspired, ultra low power computational frameworks. more...

With the broader use of language models (LMs) comes the need to estimate their ability to respond reliably to prompts (e.g., are generated responses likely to be correct?). Uncertainty quantification tools (notions of confidence and entropy, i.a.) can be used to that end (e.g., to reject a response when the model is `uncertain'). For example, Kuhn et al. (semantic entropy; 2022b) regard semantic variation amongst sampled responses as evidence that the model `struggles' with the prompt and that the LM is likely to err. We argue that semantic variability need not imply error--this being especially intuitive in open-ended settings, where prompts elicit multiple adequate but semantically distinct responses. Hence, we propose to annotate sampled responses for their adequacy to the prompt (e.g., using a classifier) and estimate the Probability the model assigns to Adequate Responses (PROBAR), which we then regard as an indicator of the model's reliability at the instance level. We evaluate PROBAR as a measure of confidence in selective prediction with OPT models (in two QA datasets and in next-word prediction, for English) and find PROBAR to outperform semantic entropy across prompts with varying degrees of ambiguity/open-endedness., Comment: 26 pages more...

Radiation damage tests in hydrogenated amorphous silicon (a-Si:H) flexible flux and dose-measuring devices have been performed with a 3 MeV proton beam, to evaluate combined displacement and total ionizing dose damage. The tested devices had two different configurations and thicknesses. The first device was a 2 um thick n-i-p diode having a 5 mm x 5 mm area. The second device was a 5 um thick charge selective contact detector having the same area. Both the devices were deposited on a flexible polyimide substrate and were irradiated up to the fluence of 1016 neq/cm2. The response to different proton fluxes has been measured before irradiation and after irradiation at 1016 neq/cm2 for charge-selective contacts and n-i-p devices. The effect of annealing for partial performance recovery at 100{\deg}C for 12 hours was also studied and a final characterization on annealed devices was performed. This test is the first combined displacement and total ionizing dose test on flexible a-Si:H devices., Comment: This paper was presented at the IEEE-RTSD2024 in Tampa (USA) with presentation number: R08-04 more...

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation., Comment: https://link.springer.com/article/10.1007/s12010-024-05110-2 more...

Quantum measurements often exhibit non-classical features, such as contextuality, which generalizes Bell's non-locality and serves as a resource in various quantum computation models. Existing frameworks have rigorously captured these phenomena, and recently, simplicial distributions have been introduced to deepen this understanding. The geometrical structure of simplicial distributions can be seen as a resource for applications in quantum information theory. In this work, we use topological foundations to study this geometrical structure, leveraging the fact that, in this simplicial framework, measurements and outcomes are represented as spaces. This allows us to depict contextuality as a topological phenomenon. We show that applying the cone construction to the measurement space makes the corresponding non-signaling polytope equal to the join of $m$ copies of the original polytope, where $m$ is the number of possible outcomes per measurement. Then we glue two copies of cone measurement spaces to obtain a suspension measurement space. The decomposition done for simplicial distributions on a cone measurement space provides deeper insights into the geometry of simplicial distributions on a suspension measurement space and aids in characterizing the contextuality there. Additionally, we apply these results to derive a new type of Bell inequalities (inequalities that determine the set of local joint probabilities/non-contextual simplicial distributions) and to offer a mathematical explanation for certain contextual vertices from the literature. more...

This paper explores the behaviour of commuting Jordan derivations over prime rings with non-trivial idempotents and demonstrates that they become zero maps. Further, it establishes this result for commuting Jordan higher derivations over prime rings under specific conditions. In fact, it introduces commuting generalized Jordan derivations over rings and establishes that the zero map is the only commuting generalized Jordan derivation over prime rings under certain assumptions., Comment: 10 more...

In approval-based budget division, a budget needs to be distributed to some candidates based on the voters' approval ballots over these candidates. In the pursuit of simple, well-behaved, and approximately fair rules for this setting, we introduce the class of sequential payment rules, where each voter controls a part of the budget and repeatedly spends his share on his approved candidates to determine the final distribution. We show that all sequential payment rules satisfy a demanding population consistency notion and we identify two particularly appealing rules within this class called the maximum payment rule (MP) and the $\frac{1}{3}$-multiplicative sequential payment rule ($\frac{1}{3}$-MP). More specifically, we prove that (i) MP is, apart from one other rule, the only monotonic sequential payment rule and gives a $2$-approximation to a fairness notion called average fair share, and (ii) $\frac{1}{3}$-MP gives a $\frac{3}{2}$-approximation to average fair share, which is optimal among sequential payment rules. more...

Proof-of-Work (PoW) systems face critical challenges, including excessive energy consumption and the centralization of mining power among entities with expensive hardware. Static mining pools exacerbate these issues by reducing competition and undermining the decentralized nature of blockchain networks, leading to economic inequality and inefficiencies in resource allocation. Their reliance on centralized pool managers further introduces vulnerabilities by creating a system that fails to ensure secure and fair reward distribution. This paper introduces a novel Collaborative Proof-of-Work (CPoW) mining approach designed to enhance efficiency and fairness in the Ethereum network. We propose a dynamic mining pool formation protocol that enables miners to collaborate based on their computational capabilities, ensuring fair and secure reward distribution by incorporating mechanisms to accurately verify and allocate rewards. By addressing the centralization and energy inefficiencies of traditional mining, this research contributes to a more sustainable blockchain ecosystem., Comment: accepted at the 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC 2025) more...

Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address this, we introduce MARVEL-40M+, an extensive dataset with 40 million text annotations for over 8.9 million 3D assets aggregated from seven major 3D datasets. Our contribution is a novel multi-stage annotation pipeline that integrates open-source pretrained multi-view VLMs and LLMs to automatically produce multi-level descriptions, ranging from detailed (150-200 words) to concise semantic tags (10-20 words). This structure supports both fine-grained 3D reconstruction and rapid prototyping. Furthermore, we incorporate human metadata from source datasets into our annotation pipeline to add domain-specific information in our annotation and reduce VLM hallucinations. Additionally, we develop MARVEL-FX3D, a two-stage text-to-3D pipeline. We fine-tune Stable Diffusion with our annotations and use a pretrained image-to-3D network to generate 3D textured meshes within 15s. Extensive evaluations show that MARVEL-40M+ significantly outperforms existing datasets in annotation quality and linguistic diversity, achieving win rates of 72.41% by GPT-4 and 73.40% by human evaluators. more...

The proliferation of AI techniques for image generation, coupled with their increasing accessibility, has raised significant concerns about the potential misuse of these images to spread misinformation. Recent AI-generated image detection (AGID) methods include CNNDetection, NPR, DM Image Detection, Fake Image Detection, DIRE, LASTED, GAN Image Detection, AIDE, SSP, DRCT, RINE, OCC-CLIP, De-Fake, and Deep Fake Detection. However, we argue that the current state-of-the-art AGID techniques are inadequate for effectively detecting contemporary AI-generated images and advocate for a comprehensive reevaluation of these methods. We introduce the Visual Counter Turing Test (VCT^2), a benchmark comprising ~130K images generated by contemporary text-to-image models (Stable Diffusion 2.1, Stable Diffusion XL, Stable Diffusion 3, DALL-E 3, and Midjourney 6). VCT^2 includes two sets of prompts sourced from tweets by the New York Times Twitter account and captions from the MS COCO dataset. We also evaluate the performance of the aforementioned AGID techniques on the VCT$^2$ benchmark, highlighting their ineffectiveness in detecting AI-generated images. As image-generative AI models continue to evolve, the need for a quantifiable framework to evaluate these models becomes increasingly critical. To meet this need, we propose the Visual AI Index (V_AI), which assesses generated images from various visual perspectives, including texture complexity and object coherence, setting a new standard for evaluating image-generative AI models. To foster research in this domain, we make our https://huggingface.co/datasets/anonymous1233/COCO_AI and https://huggingface.co/datasets/anonymous1233/twitter_AI datasets publicly available., Comment: 13 pages, 9 figures more...

A probabilistic approach to the stable matching problem has been identified as an important research area with several important open problems. When considering random matchings, ex-post stability is a fundamental stability concept. A prominent open problem is characterizing ex-post stability and establishing its computational complexity. We investigate the computational complexity of testing ex-post stability. Our central result is that when either side has ties in the preferences/priorities, testing ex-post stability is NP-complete. The result even holds if both sides have dichotomous preferences. On the positive side, we give an algorithm using an integer programming approach, that can determine a decomposition with a maximum probability of being weakly stable. We also consider stronger versions of ex-post stability (in particular robust ex-post stability and ex-post strong stability) and prove that they can be tested in polynomial time. more...

We use helicity-resolved ultrafast transient absorption spectroscopy to study spin-valley polarization dynamics in a vertically stacked MoSe$_{2}$/WSe$_{2}$ heterostructure. The experimental findings reveal details of interlayer charge transfer on ultrafast timescales, showing that the spin-valley polarized state of photoexcited carriers is conserved during the charge transfer and formation of interlayer excitons. Our results confirm that phonon scattering mediates the interlayer charge transfer process, while a high phonon population at elevated temperatures causes a significant decrease in spin-valley selective charge transfer. Moreover, the experimental findings demonstrate the possibility that interlayer excitons and their spin-valley polarization can be probed in the optical response of intralayer excitons. These findings pave the way for ultrafast detection, control, and manipulation of spin-valley polarized excitons in transition metal dichalcogenide-based 2D heterostructures. more...

This paper proposes a Graph Neural Network-guided algorithm for solving word equations, based on the well-known Nielsen transformation for splitting equations. The algorithm iteratively rewrites the first terms of each side of an equation, giving rise to a tree-like search space. The choice of path at each split point of the tree significantly impacts solving time, motivating the use of Graph Neural Networks (GNNs) for efficient split decision-making. Split decisions are encoded as multi-classification tasks, and five graph representations of word equations are introduced to encode their structural information for GNNs. The algorithm is implemented as a solver named DragonLi. Experiments are conducted on artificial and real-world benchmarks. The algorithm performs particularly well on satisfiable problems. For single word \mbox{equations}, DragonLi can solve significantly more problems than well-established string solvers. For the conjunction of multiple word equations, DragonLi is competitive with state-of-the-art string solvers. more...

This paper introduces a new application named ArPA for Arabic kids who have trouble with pronunciation. Our application comprises two key components: the diagnostic module and the therapeutic module. The diagnostic process involves capturing the child's speech signal, preprocessing, and analyzing it using different machine learning classifiers like K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Trees as well as deep neural network classifiers like ResNet18. The therapeutic module offers eye-catching gamified interfaces in which each correctly spoken letter earns a higher avatar level, providing positive reinforcement for the child's pronunciation improvement. Two datasets were used for experimental evaluation: one from a childcare centre and the other including Arabic alphabet pronunciation recordings. Our work uses a novel technique for speech recognition using Melspectrogram and MFCC images. The results show that the ResNet18 classifier on speech-to-image converted data effectively identifies mispronunciations in Arabic speech with an accuracy of 99.015\% with Mel-Spectrogram images outperforming ResNet18 with MFCC images. more...

Virtual reality (VR) sensors capture large amounts of user data, including body motion and eye tracking, that contain personally identifying information. While privacy-enhancing techniques can obfuscate this data, incomplete privacy protections risk privacy leakage, which may allow adversaries to leverage unprotected data to identify users without consent. This work examines the extent to which unprotected body motion data can undermine privacy protections for eye tracking data, and vice versa, to enable user identification in VR. These findings highlight a privacy consideration at the intersection of eye tracking and VR, and emphasize the need for privacy protections that address these technologies comprehensively., Comment: 12 pages, 5 figures more...

The obtention of quantum-grade rare-earth doped oxide thin films that can be integrated with optical cavities and microwave resonators is of great interest for the development of scalable quantum devices. Among the different growth methods, Chemical Vapour Deposition (CVD) offers high flexibility and has demonstrated the ability to produce oxide films hosting rare-earth ions with narrow linewidths. However, growing epitaxial films directly on silicon is challenging by CVD due to a native amorphous oxide layer formation at the interface. In this manuscript, we investigate the CVD growth of erbium-doped yttrium oxide (Er:Y2O3) thin films on different substrates, including silicon, sapphire, quartz or yttria stabilized zirconia (YSZ). Alternatively, growth was also attempted on an epitaxial Y2O3 template layer on Si (111) prepared by molecular beam epitaxy (MBE) in order to circumvent the issue of the amorphous interlayer. We found that the substrate impacts the film morphology and the crystalline orientations, with different textures observed for the CVD film on the MBE-oxide/Si template (111) and epitaxial growth on YSZ (001). In terms of optical properties, Er3+ ions exhibit visible and IR emission features that are comparable for all samples, indicating a high-quality local crystalline environment regardless of the substrate. Our approach opens interesting prospects to integrate such films into scalable devices for optical quantum technologies., Comment: 15 pages, 8 figures, 2 tables more...

The emergence of 6G technology represents a significant advancement in wireless communications, providing unprecedented speed, extremely low latency, and pioneering applications. In light of this development, an important question arises: Can the Open Radio Access Network (O-RAN), with its emphasis on openness, flexibility, RAN slicing, RAN Intelligent Controller (RIC), and cost-effectiveness, fulfill the complex requirements of 6G? This paper delves into the potential synergy between O-RAN and 6G, illustrating how O-RAN can facilitate customization, reduce expenses, and stimulate innovation in next-generation networks. We also tackle the challenges associated with 6G, such as the need for exceptional performance, integration with non-terrestrial networks, and heightened security. By examining the interaction between O-RAN and 6G, we underscore their joint role in shaping the future of wireless communication. Lastly, we demonstrate the potential of O-RAN through a unique, learning-based spectrum-sharing solution that aligns with the objectives of 6G for efficient spectrum usage. more...

A covariant relativistic approach based on the Vlasov equation is applied to the study of infinite asymmetric nuclear matter. We use several Walecka-type hadronic models and obtain the dispersion relations for the longitudinal modes. The isovector and isoscalar collective modes are determined for a wide range of densities as a function of isospin asymmetry and momentum transfer within a set of eleven relativistic mean field models with different nuclear matter properties. Special attention is given to beta-equilibrium matter. It is shown that the possible propagation of isoscalar and isovector-like modes depends directly on the density dependence of the symmetric nuclear matter equation of state and of the symmetry energy, with a stiff equation of state favouring the propagation of isoscalar like collective modes at high densities, and a stiff symmetry energy defining the behavior of the isovector like modes which propagate for densities below two times saturation density. The coupling of the nuclear modes to the electron plasmon is also discussed., Comment: 18 pages, 11 figures, 3 tables. Accepted for publication in Physical Review C more...

The purpose of this study was to explore Moroccan pre-service elementary teachers' attitudes toward integrated science, technology, engineering, and mathematics (STEM) education and the use of mobile devices in integrated STEM education. The research sample was selected using convenience sampling. Data were collected from 226 pre-service teachers in the Bachelor of Education Elementary Specialty (BEES) using a 28-item questionnaire. The validity of the items was tested by factor analysis using the extraction method of principal component analysis with varimax rotation. Reliability tests for the different constructs were conducted by calculating Cronbach's alpha. Frequency, mean, standard deviation and Mann-Whitney tests were used to analyze the data. The results revealed that pre-service elementary teachers have generally neutral attitudes toward integrated STEM education, and they also showed that pre-service teachers' attitudes toward integrated STEM education do not depend on gender or grade level. However, these attitudes are dependent on pre-university studies. Pre-service teachers with a scientific background have significantly more positive attitudes toward integrated STEM education than their counterparts with a literary background. Furthermore, the results of this study also revealed that pre-service teachers have positive attitudes toward the use of mobile devices in integrated STEM education, and these attitudes are not dependent on gender, grade level, or pre-university studies. more...

We present a real-time gaze-based interaction simulation methodology using an offline dataset to evaluate the eye-tracking signal quality. This study employs three fundamental eye-movement classification algorithms to identify physiological fixations from the eye-tracking data. We introduce the Rank-1 fixation selection approach to identify the most stable fixation period nearest to a target, referred to as the trigger-event. Our evaluation explores how varying constraints impact the definition of trigger-events and evaluates the eye-tracking signal quality of defined trigger-events. Results show that while the dispersion threshold-based algorithm identifies trigger-events more accurately, the Kalman filter-based classification algorithm performs better in eye-tracking signal quality, as demonstrated through a user-centric quality assessment using user- and error-percentile tiers. Despite median user-level performance showing minor differences across algorithms, significant variability in signal quality across participants highlights the importance of algorithm selection to ensure system reliability., Comment: 20 pages including supplementary materials more...

This work takes a pedagogical lens to explore the implications of generative AI (GenAI) models and tools, such as ChatGPT and GitHub Copilot, in a semester-long 2nd-year undergraduate Software Engineering Team Project. Qualitative findings from survey (39 students) and interviews (eight students) provide insights into the students' views on the impact of GenAI use on their coding experience, learning, and self-efficacy. Our results address a particular gap in understanding the role and implications of GenAI on teamwork, team-efficacy, and team dynamics. The analysis of the learning aspects is distinguished by the application of learning and pedagogy informed lenses to discuss the data. We propose a preliminary design space for GenAI-based programming learning tools highlighting the importance of considering the roles that GenAI can play during the learning process, the varying support-ability patterns that can be applied to each role, and the importance of supporting transparency in GenAI for team members and students in addition to educators. more...

To unlock access to stronger winds, the offshore wind industry is advancing with significantly larger and taller wind turbines. This massive upscaling motivates a departure from univariate wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE--a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate, nonstationary, and state-dependent kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from future offshore wind energy sites in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods. more...

We present a new method for the verification of quantum circuits based on a novel symbolic representation of sets of quantum states using level-synchronized tree automata (LSTAs). LSTAs extend classical tree automata by labeling each transition with a set of choices, which are then used to synchronize subtrees of an accepted tree. Compared to the traditional tree automata, LSTAs have an incomparable expressive power while maintaining important properties, such as closure under union and intersection, and decidable language emptiness and inclusion. We have developed an efficient and fully automated symbolic verification algorithm for quantum circuits based on LSTAs. The complexity of supported gate operations is at most quadratic, dramatically improving the exponential worst-case complexity of an earlier tree automata-based approach. Furthermore, we show that LSTAs are a promising model for parameterized verification, i.e., verifying the correctness of families of circuits with the same structure for any number of qubits involved, which principally lies beyond the capabilities of previous automated approaches. We implemented this method as a C++ tool and compared it with three symbolic quantum circuit verifiers and two simulators on several benchmark examples. The results show that our approach can solve problems with sizes orders of magnitude larger than the state of the art. more...