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This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://challenges.videoprocessing.ai/challenges/video-saliency-prediction.html., Comment: ECCVW 2024

A study of interference caused by incumbent radios to UWB devices is presented. Through an extensive set of measured spectral activities in the low-band IEEE802.15.4 UWB operating channels, we explore the outage probabilities of a UWB system when it is equipped with an intelligent detector for combating interferers and compare them against those of a naive system that has no interference avoidance capability. Our results reveal that a UWB system with an effective interference avoidance capability may lead to a few orders of magnitude improvement in its outage probabilities. The measured outage probabilities are confirmed through measuring the frame error rate of a simulated filter-bank UWB transceiver system when impacted by the interferences obtained through our experiments., Comment: 7 pages, 9 figures, submitted to the IEEE International Conference on Communications

Recently, filter-bank multicarrier spread spectrum (FBMC-SS) has been proposed as a candidate waveform for ultra-wideband (UWB) communications. It has been noted that FBMC-SS is a perfect match to this application, leading to a trivial method of matching to the required spectral mask at different regions of the world. FBMC-SS also allows easy rejection of high-power interfering signals that may appear over different parts of the UWB spectral band. In this paper, we concentrate on the use of staggered multitone spread spectrum (SMT-SS) for UWB communications. SMT makes use of offset quadrature amplitude modulation (OQAM) to transmit data symbols over narrowband, overlapping subcarrier bands. This form of FBMC-SS is well-suited to UWB communications because it has good spectral efficiency and a flat power spectral density (PSD), resulting in good utilization of the UWB spectral mask. Additionally, we explore new methods for multi-coding that result in higher bit rates than previous FBMC-SS systems. Moreover, we study methods for equalizing the UWB multipath channel and cancelling narrowband interference. Excellent performance of the proposed methods are substantiated by presenting simulation results., Comment: 6 pages, 6 figures, submitted to IEEE International Communications Conference

Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on modeling the entire dynamics of interconnected systems, the potential for identifying and modeling individual subsystems while operating as part of a larger system has been overlooked. This study addresses this gap by introducing a novel method for using pHNNs to identify such subsystems based solely on input-output measurements. By utilizing the inherent compositional property of the port-Hamiltonian systems, we developed an algorithm that learns the dynamics of individual subsystems, without requiring direct access to their internal states. On top of that, by choosing an output error (OE) model structure, we have been able to handle measurement noise effectively. The effectiveness of the proposed approach is demonstrated through tests on interconnected systems, including multi-physics scenarios, demonstrating its potential for identifying subsystem dynamics and facilitating their integration into new interconnected models., Comment: Preprint submitted to ECC 2025

While significant progress has been made in designing algorithms that minimize regret in online decision-making, real-world scenarios often introduce additional complexities, perhaps the most challenging of which is missing outcomes. Overlooking this aspect or simply assuming random missingness invariably leads to biased estimates of the rewards and may result in linear regret. Despite the practical relevance of this challenge, no rigorous methodology currently exists for systematically handling missingness, especially when the missingness mechanism is not random. In this paper, we address this gap in the context of multi-armed bandits (MAB) with missing outcomes by analyzing the impact of different missingness mechanisms on achievable regret bounds. We introduce algorithms that account for missingness under both missing at random (MAR) and missing not at random (MNAR) models. Through both analytical and simulation studies, we demonstrate the drastic improvements in decision-making by accounting for missingness in these settings., Comment: 38 pages, 5 figures, multi-armed bandits, missing data

The emergence of distinct local mark behaviours is becoming increasingly common in the applications of spatial marked point processes. This dynamic highlights the limitations of existing global mark correlation functions in accurately identifying the true patterns of mark associations/variations among points as distinct mark behaviours might dominate one another, giving rise to an incomplete understanding of mark associations. In this paper, we introduce a family of local indicators of mark association (LIMA) functions for spatial marked point processes. These functions are defined on general state spaces and can include marks that are either real-valued or function-valued. Unlike global mark correlation functions, which are often distorted by the existence of distinct mark behaviours, LIMA functions reliably identify all types of mark associations and variations among points. Additionally, they accurately determine the interpoint distances where individual points show significant mark associations. Through simulation studies, featuring various scenarios, and four real applications in forestry, criminology, and urban mobility, we study spatial marked point processes in $\R^2$ and on linear networks with either real-valued or function-valued marks, demonstrating that LIMA functions significantly outperform the existing global mark correlation functions., Comment: Submitted for publication

Evaluating datasets in data marketplaces, where the buyer aim to purchase valuable data, is a critical challenge. In this paper, we introduce an innovative task-agnostic data valuation method called PriArTa which is an approach for computing the distance between the distribution of the buyer's existing dataset and the seller's dataset, allowing the buyer to determine how effectively the new data can enhance its dataset. PriArTa is communication-efficient, enabling the buyer to evaluate datasets without needing access to the entire dataset from each seller. Instead, the buyer requests that sellers perform specific preprocessing on their data and then send back the results. Using this information and a scoring metric, the buyer can evaluate the dataset. The preprocessing is designed to allow the buyer to compute the score while preserving the privacy of each seller's dataset, mitigating the risk of information leakage before the purchase. A key feature of PriArTa is its robustness to common data transformations, ensuring consistent value assessment and reducing the risk of purchasing redundant data. The effectiveness of PriArTa is demonstrated through experiments on real-world image datasets, showing its ability to perform privacy-preserving, augmentation-robust data valuation in data marketplaces.

The correct specification of reward models is a well-known challenge in reinforcement learning. Hand-crafted reward functions often lead to inefficient or suboptimal policies and may not be aligned with user values. Reinforcement learning from human feedback is a successful technique that can mitigate such issues, however, the collection of human feedback can be laborious. Recent works have solicited feedback from pre-trained large language models rather than humans to reduce or eliminate human effort, however, these approaches yield poor performance in the presence of hallucination and other errors. This paper studies the advantages and limitations of reinforcement learning from large language model feedback and proposes a simple yet effective method for soliciting and applying feedback as a potential-based shaping function. We theoretically show that inconsistent rankings, which approximate ranking errors, lead to uninformative rewards with our approach. Our method empirically improves convergence speed and policy returns over commonly used baselines even with significant ranking errors, and eliminates the need for complex post-processing of reward functions., Comment: 13 pages, 8 figures, The 2024 Conference on Empirical Methods in Natural Language Processing

In this study, we present the pulse profile of the unique and the second brightest gamma-ray burst GRB 230307A, and analyze its temporal behavior using a joint GECAM--Fermi/GBM time-resolved spectral analysis. The utilization of GECAM data is advantageous as it successfully captured significant data during the pile-up period of the Fermi/GBM. We investigate the evolution of its flux, photon fluence, photon flux, peak energy, and the corresponding hardness-intensity and hardness-flux correlations. The findings within the first 27 seconds exhibit consistent patterns reported previously, providing valuable insights for comparing observations with predictions from the synchrotron radiation model invoking an expanding shell. Beyond the initial 27 seconds, we observe a notable transition in the emitted radiation, attributed to high latitude emission (HLE), influenced by the geometric properties of the shells and the relativistic Doppler effects. By modeling the data within the framework of the large-radius internal shock model, we discuss the required parameters as well as the limitations of the model. We conclude that a more complicated synchrotron emission model is needed to fully describe the observational data of GRB 230307A., Comment: Accepted for publication in The Astrophysical Journal (ApJ)

This thesis focuses on developing integrated circuit (IC) solutions for medical devices and automotive radars, and is divided into two main parts. Part One presents the design and evaluation of a miniaturized multi chip module (MCM) solution intended to deliver welldefined, charge balanced current stimuli directly to the inner ear. This section emphasizes the design of the supply chip, which includes a DC DC converter. It involves a comprehensive study aimed at optimizing and enhancing the efficiency of the design. Part Two investigates the fundamental principles of designing millimeter wave (mmWave) voltagecontrolled oscillators (VCOs). This section introduces a VCO with stateoftheart performance, showcasing advancements in mmWave technology. Overall, this thesis contributes to both the medical device field and automotive radar technology through innovative IC solutions., Comment: PhD thesis

We study the evolution of a newly formed magnetized neutron-star (NS) as a power source of gamma-ray bursts (GRBs) in the light of both gravitational wave (GW) and electromagnetic (EM) radiations. The compressible and incompressible fluids are employed in order to model the secular evolution of Maclaurian spheroids. It is shown that the GW and EM light curves evolve as a function of eccentricity and rotational frequency with time. We find that the light curve characteristics crucially depend on NS parameters such as magnitude and structure of magnetic field, ellipticity and the equation of state (EOS) of the fluid. The presence of X-ray flares, whose origins are not yet well understood, can be captured in our model regarding some specific nuclear EOSs. Our model allowing us to explain flares that occur within the wide range of $ 10$ to $10^4$ seconds and the peak luminosity in the order of $10^{46}$ - $10^{51}$ $\rm \text{erg}/s$ by using a reasonable set of parameters such as magnetic field strength around $10^{14}-10^{16}$ Gauss, the quadrupole to dipole ratio of magnetic field up to 500. By applying our model to a sample of GRB X-ray flares observed by Swift/XRT, we try to constraint the crucial parameters of a deformed magnetar via MCMC fitting method. Our analysis shows that ongoing and upcoming joint multi-messenger detections can be used to understand the nature of GRB's central engine and its evolution at the early times of the burst formation.

Self-service machines are a form of pseudo-automation; rather than actually automate tasks, they offset them to unpaid customers. Typically implemented for customer convenience and to reduce labor costs, self-service is often criticized for worsening customer service and increasing loss and theft for retailers. Though millions of frontline service workers continue to interact with these technologies on a day-to-day basis, little is known about how these machines change the nature of frontline labor. Through interviews with current and former cashiers who work with self-checkout technologies, we investigate how technology that offsets labor from an employee to a customer can reconfigure frontline work. We find three changes to cashiering tasks as a result of self-checkout: (1) Working at self-checkout involved parallel demands from multiple customers, (2) self-checkout work was more problem-oriented (including monitoring and policing customers), and (3) traditional checkout began to become more demanding as easier transactions were filtered to self-checkout. As their interactions with customers became more focused on problem solving and rule enforcement, cashiers were often positioned as adversaries to customers at self-checkout. To cope with perceived adversarialism, cashiers engaged in a form of relational patchwork, using techniques like scapegoating the self-checkout machine and providing excessive customer service in order to maintain positive customer interactions in the face of potential conflict. Our findings highlight how even under pseudo-automation, workers must engage in relational work to manage and mend negative human-to-human interactions so that machines can be properly implemented in context., Comment: Forthcoming in the Proceedings of the 2025 Conference on Computer Supported Cooperative Work and Social Computing

In the present paper, we aim to classify monomial ideals whose all matching powers are Cohen-Macaulay. We especially focus our attention on edge ideals. The Cohen-Macaulayness of the last matching power of an edge ideal is characterized, providing an algebraic analogue of the famous Tutte theorem regarding graphs having a perfect matching. For chordal graphs, very well-covered graphs and Cameron-Walker graphs, we completely solve our problem.

Deep learning models have revolutionized various domains, with Multi-Layer Perceptrons (MLPs) being a cornerstone for tasks like data regression and image classification. However, a recent study has introduced Kolmogorov-Arnold Networks (KANs) as promising alternatives to MLPs, leveraging activation functions placed on edges rather than nodes. This structural shift aligns KANs closely with the Kolmogorov-Arnold representation theorem, potentially enhancing both model accuracy and interpretability. In this study, we explore the efficacy of KANs in the context of data representation via autoencoders, comparing their performance with traditional Convolutional Neural Networks (CNNs) on the MNIST, SVHN, and CIFAR-10 datasets. Our results demonstrate that KAN-based autoencoders achieve competitive performance in terms of reconstruction accuracy, thereby suggesting their viability as effective tools in data analysis tasks., Comment: 12 pages, 5 figures, 1 table

One of the growing trends in machine learning is the use of data generation techniques, since the performance of machine learning models is dependent on the quantity of the training dataset. However, in many medical applications, collecting large datasets is challenging due to resource constraints, which leads to overfitting and poor generalization. This paper introduces a novel method, Artificial Data Point Generation in Clustered Latent Space (AGCL), designed to enhance classification performance on small medical datasets through synthetic data generation. The AGCL framework involves feature extraction, K-means clustering, cluster evaluation based on a class separation metric, and the generation of synthetic data points from clusters with distinct class representations. This method was applied to Parkinson's disease screening, utilizing facial expression data, and evaluated across multiple machine learning classifiers. Experimental results demonstrate that AGCL significantly improves classification accuracy compared to baseline, GN and kNNMTD. AGCL achieved the highest overall test accuracy of 83.33% and cross-validation accuracy of 90.90% in majority voting over different emotions, confirming its effectiveness in augmenting small datasets., Comment: 8 pages, 2 figures

Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans or other agents not co-trained together, limiting its applicability in ad-hoc teamwork scenarios. In this work, we propose a novel computational pipeline that aligns the communication space between MARL agents with an embedding space of human natural language by grounding agent communications on synthetic data generated by embodied Large Language Models (LLMs) in interactive teamwork scenarios. Our results demonstrate that introducing language grounding not only maintains task performance but also accelerates the emergence of communication. Furthermore, the learned communication protocols exhibit zero-shot generalization capabilities in ad-hoc teamwork scenarios with unseen teammates and novel task states. This work presents a significant step toward enabling effective communication and collaboration between artificial agents and humans in real-world teamwork settings., Comment: Accepted to Neurips 2024, 16 pages, 3 figures

Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical terms. Examples where sparse optimization fails abound, such as the classic Ikeda or optical-cavity map in nonlinear dynamics and a large variety of ecosystems. Exploiting the recently articulated Kolmogorov-Arnold networks, we develop a general model-discovery framework for any dynamical systems including those that do not satisfy the sparsity condition. In particular, we demonstrate non-uniqueness in that a large number of approximate models of the system can be found which generate the same invariant set with the correct statistics such as the Lyapunov exponents and Kullback-Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out., Comment: 6 pages, 4 figures

Growing traffic over the high-frequency (HF) band poses significant challenges to establishing robust communication links. While existing spread-spectrum HF transceivers are, to some degree, robust against harsh HF channel conditions, their performance significantly degrades in the presence of strong co-channel interference. To improve performance in congested channel conditions, we propose a filter-bank based multicarrier spread-spectrum waveform with noncontiguous subcarrier bands. The use of noncontiguous subcarriers allows the system to at once leverage the robustness of a wideband system while retaining the frequency agility of a narrowband system. In this study, we explore differences between contiguous and noncontiguous systems by considering their respective peak-to-average power ratios (PAPRs) and matched-filter responses. Additionally, we develop a modified filter-bank receiver structure to facilitate both efficient signal processing and noncontiguous channel estimation. We conclude by presenting simulated and over-the-air results of the noncontiguous waveform, demonstrating both its robustness in harsh HF channels and its enhanced performance in congested spectral conditions.

Sum-frequency generation (SFG) allows for coherent upconversion of an electromagnetic signal and has applications in mid-infrared vibrational spectroscopy of molecules. Recent experimental and theoretical studies have shown that plasmonic nanocavities, with their deep sub-wavelength mode volumes, may allow to obtain vibrational SFG signals from a single molecule. In this article, we compute the degree of second order coherence ($g^{(2)}(0)$) of the upconverted mid-infrared field under realistic parameters and accounting for the anharmonic potential that characterizes vibrational modes of individual molecules. On the one hand, we delineate the regime in which the device should operate in order to preserve the second-order coherence of the mid-infrared source, as required in quantum applications. On the other hand, we show that an anharmonic molecular potential can lead to antibunching of the upconverted photons under coherent, Poisson-distributed mid-infrared and visible drives. Our results therefore open a path toward a new kind of bright and tunable source of indistinguishable single photons by leveraging ``vibrational blockade'' in a resonantly and parametrically driven molecule, without the need for strong light-matter coupling., Comment: Includes Appendix

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks., Comment: Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

This study explores heat and turbulent modulation in three-dimensional multiphase Rayleigh-B\'enard convection using direct numerical simulations. Two immiscible fluids with identical reference density undergo systematic variations in dispersed-phase volume fractions, $0.0 \leq \Upphi \leq 0.5$, and ratios of dynamic viscosity, $\lambda_{\mu}$, and thermal diffusivity, $\lambda_{\alpha}$, within the range $[0.1-10]$. The Rayleigh, Prandtl, Weber, and Froude numbers are held constant at $10^8$, $4$, $6000$, and $1$, respectively. Initially, when both fluids share the same properties, a 10\% Nusselt number increase is observed at the highest volume fractions. In this case, despite a reduction in turbulent kinetic energy, droplets enhance energy transfer to smaller scales, smaller than those of single-phase flow, promoting local mixing. By varying viscosity ratios, while maintaining a constant Rayleigh number based on the average mixture properties, the global heat transfer rises by approximately 25\% at $\Upphi=0.2$ and $\lambda_{\mu}=10$. This is attributed to increased small-scale mixing and turbulence in the less viscous carrier phase. In addition, a dispersed phase with higher thermal diffusivity results in a 50\% reduction in the Nusselt number compared to the single-phase counterpart, owing to faster heat conduction and reduced droplet presence near walls. The study also addresses droplet-size distributions, confirming two distinct ranges dominated by coalescence and breakup with different scaling laws., Comment: Accepted on the Journal of Fluid Mechanics

Short- to medium-block-length polar-like and polarization-adjusted convolutional (PAC) codes have demonstrated exceptional error-correction performance through sequential decoding. Successive cancellation list (SCL) decoding of polar-like and PAC codes can potentially match the performance of sequential decoding though a relatively large list size is often required. By benefiting from an optimal metric function, sequential decoding can find the correct path corresponding to the transmitted data by following almost one path on average at high Eb/N0 regimes. When considering a large number of paths in SCL decoding, a main bottleneck emerges that is the need for a rather expensive sorting operation at each level of decoding of data bits. In this paper, we propose a method to obtain the optimal metric function for each depth of the polarization tree through a process that we call polarization of the metric function. One of the major advantages of the proposed metric function is that it can be utilized in fast SC-based (FSC) and SCL-based (FSCL) decoders, i.e., decoders that opt to skip the so-called rate-1 and rate-0 nodes in the binary tree representation for significantly more efficient implementation. Furthermore, based on the average value of the polarized metric function of FSC-based decoders, we introduce a pruning technique that keeps only the paths whose metric values are close to the average value. As a result, our proposed technique significantly reduces the number of required sorting operations for FSCL-based decoding algorithms. For instance, for a high-rate PAC(128,99) code, SCL decoding with a list size of 32 achieves error-correction performance comparable to the Fano algorithm. Our method reduces the number of sorting operations of FSCL decoding to 33%, further decreasing latency.

In this work, we enhance the FNet, a 1-dimensional convolutional neural network (CNN) with a residual neural network (ResNet) architecture, to perform spectral classification of quasars, galaxies, stars, and broad absorption line (BAL)-quasars in the SDSS-IV catalog from DR17 of eBOSS. Leveraging its convolutional layers and the ResNet structure with different kernel sizes, FNet autonomously identifies various patterns within the entire sample of spectra. Since FNet does not require the intermediate step of identifying specific lines, a simple modification enabled our current network to classify all SDSS spectra. This modification involves changing the final output layer from a single value (redshift) to multiple values (probabilities of all classes), and accordingly adjusting the loss function from mean squared error (MSE) to cross-entropy. FNet achieves a completeness of 99.00\% $\pm$ 0.20 for galaxies, 98.50\% $\pm$ 0.30 for quasars, 99.00\% $\pm$ 0.18 for BAL-quasars, and 98.80\% $\pm$ 0.20 for stars. These results are comparable to those obtained using QuasarNET, a standard CNN employed in the SDSS routine, comprises convolutional layers without the ResNet structure with equal kernel sizes, and is utilized for redshift measurement and classification by identifying seven emission lines. QuasarNET, in order to overcome the problem of finding a CIV emission line with broad absorption which is slightly more challenging than that of detecting emission lines requires to add BAL CIV line to the list of lines that the network learns to identify. However, this procedure is not necessary in FNet as it learns the features through a self-learning procedure., Comment: Accepted for publication in the Monthly Notices of the Royal Astronomical Society (MNRAS)

The rise of decentralized applications (dApps) has made smart contracts imperative components of blockchain technology. As many smart contracts process financial transactions, their security is paramount. Moreover, the immutability of blockchains makes vulnerabilities in smart contracts particularly challenging because it requires deploying a new version of the contract at a different address, incurring substantial fees paid in Ether. This paper proposes Ethstractor, the first smart contract collection tool for gathering a dataset of versioned smart contracts. The collected dataset is then used to evaluate the reliability of code metrics as indicators of vulnerabilities in smart contracts. Our findings indicate that code metrics are ineffective in signalling the presence of vulnerabilities. Furthermore, we investigate whether vulnerabilities in newer versions of smart contracts are mitigated and identify that the number of vulnerabilities remains consistent over time. Finally, we examine the removal of self-admitted technical debt in contracts and uncover that most of the introduced debt has never been subsequently removed., Comment: Copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

In the literature on spatial point processes, there is an emerging challenge in studying marked point processes with points being labelled by functions. In this paper, we focus on point processes living on linear networks and, from distinct points of view, propose several marked summary characteristics that are of great use in studying the average association and dispersion of the function-valued marks. Through a simulation study, we evaluate the performance of our proposed marked summary characteristics, both when marks are independent and when some sort of spatial dependence is evident among them. Finally, we employ our proposed mark summary characteristics to study the spatial structure of urban cycling profiles in Vancouver, Canada., Comment: submitted for publication

Contemporary approaches to optical multiple micro-manipulation typically involve careful pre-engineering of the laser beam shape. In various biomedical and microfluidic scenarios, especially those necessitating unconventional specimen chambers, there is a demand for controlling the collection of micro-objects at fluid-fluid interfaces. This requirement arises in contexts such as the transport of materials across liquid interfaces for applications like living cell manipulation, drug delivery, soft functional material creation, and various industrial processes. For many of these cases, a regular array of trap sites as well as tightly confinement are not essential. For such applications at interfaces, we expand on the concept of speckle tweezers (ST), which incorporate randomly distributed light fields for quasi-2D optical manipulation. The proposed technique is demonstrated experimentally by applying ST to govern the movement of PS micro-particles at water-oil and water-air interfaces. The efficacy of the method is validated through the temporal characterization of micro-particle motions using digital video microscopy.

For an ideal $I$ in a Noetherian ring $R$, we introduce and study its conductor as a tool to explore the Rees algebra of $I$. The conductor of $I$ is an ideal $C(I)\subset R$ obtained from the defining ideals of the Rees algebra and the symmetric algebra of $I$ by a colon operation. Using this concept we investigate when adding an element to an ideal preserves the property of being of linear type. In this regard, a generalization of a result by Valla in terms of the conductor ideal is presented. When the conductor of a graded ideal in a polynomial ring is the graded maximal ideal, a criteria is given for when the Rees algebra and the symmetric algebra have the same Krull dimension. Finally, noting the fact that the conductor of a monomial ideal is a monomial ideal, the conductor of some families of monomial ideals, namely bounded Veronese ideals and edge ideals of graphs, are determined., Comment: 16 pages, 1 figure

We study the statistical fluctuations (such as the variance) of causal set quantities, with particular focus on the causal set action. To facilitate calculating such fluctuations, we develop tools to account for correlations between causal intervals with different cardinalities. We present a convenient decomposition of the fluctuations of the causal set action into contributions that depend on different kinds of correlations. This decomposition can be used in causal sets approximated by any spacetime manifold $\mathcal M$. Our work paves the way for investigating a number of interesting discreteness effects, such as certain aspects of the Everpresent $\Lambda$ cosmological model., Comment: 34 pages

Type I gamma-ray bursts (GRBs) are believed to originate from compact binary merger usually with duration less than 2 seconds for the main emission. However, recent observations of GRB 211211A and GRB 230307A indicate that some merger-origin GRBs could last much longer. Since they show strikingly similar properties (indicating a common mechanism) which are different from the classic "long"-short burst (e.g. GRB 060614), forming an interesting subclass of type I GRBs, we suggest to name them as type IL GRBs. By identifying the first peak of GRB 230307A as a quasi-thermal precursor, we find that the prompt emission of type IL GRB is composed of three episodes: (1) a precursor followed by a short quiescent (or weak emission) period, (2) a long-duration main emission, and (3) an extended emission. With this burst pattern, a good candidate, GRB 170228A, was found in the Fermi/GBM archive data, and subsequent temporal and spectral analyses indeed show that GRB 170228A falls in the same cluster with GRB 211211A and GRB 230307A in many diagnostic figures. Thus this burst pattern could be a good reference for rapidly identifying type IL GRB and conducting low-latency follow-up observation. We estimated the occurrence rate and discussed the physical origins and implications for the three emission episodes of type IL GRBs. Our analysis suggests the pre-merger precursor model, especially the super flare model, is more favored for type IL GRBs.

This paper deals with the application of probabilistic time integration methods to semi-explicit partial differential-algebraic equations of parabolic type and its semi-discrete counterparts, namely semi-explicit differential-algebraic equations of index 2. The proposed methods iteratively construct a probability distribution over the solution of deterministic problems, enhancing the information obtained from the numerical simulation. Within this paper, we examine the efficacy of the randomized versions of the implicit Euler method, the midpoint scheme, and exponential integrators of first and second order. By demonstrating the consistency and convergence properties of these solvers, we illustrate their utility in capturing the sensitivity of the solution to numerical errors. Our analysis establishes the theoretical validity of randomized time integration for constrained systems and offers insights into the calibration of probabilistic integrators for practical applications.

Optical hybrid entanglement can be created between two qubits, one encoded in a single photon and another one in coherent states with opposite phases. It opens the path to a variety of quantum technologies, such as heterogeneous quantum networks, merging continuous and discrete variable encoding, and enabling the transport and interconversion of information. However, reliable characterization of the nature of this entanglement is limited so far to full quantum state tomography. Here, we perform a thorough study of Clauser-Horne-Shimony-Holt (CHSH) Bell inequality tests, enabling practical verification of quantum correlations for optical hybrid entanglement. We show that a practical violation of this inequality is possible with simple photon number on/off measurements if detection efficiencies stay above 82%. Another approach, based on photon-number parity measurements, requires 94% efficiency but works well in the limit of higher photon populations. Both tests use no postselection of the measurement outcomes and they are free of the fair-sampling hypothesis. Our proposal paves the way to performing loophole-free tests using feasible experimental tasks such as coherent state interference and photon counting, and to verification of hybrid entanglement in real-world applications., Comment: 9 pages, 6 figures

The future 6G network is envisioned to be AI-native, and as such, ML models will be pervasive in support of optimizing performance, reducing energy consumption, and in coping with increasing complexity and heterogeneity. A key challenge is automating the process of finding optimal model architectures satisfying stringent requirements stemming from varying tasks, dynamicity and available resources in the infrastructure and deployment positions. In this paper, we describe and review the state-of-the-art in Neural Architecture Search and Transfer Learning and their applicability in networking. Further, we identify open research challenges and set directions with a specific focus on three main requirements with elements unique to the future network, namely combining NAS and TL, multi-objective search, and tabular data. Finally, we outline and discuss both near-term and long-term work ahead.

Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this approach, each worker node processes a combination of the data, rather than the raw data itself. The final result then is decoded from the collective outputs of the worker nodes. However, there is a significant gap between current coded computing approaches and the broader landscape of general distributed computing, particularly when it comes to machine learning workloads. To bridge this gap, we propose a novel foundation for coded computing, integrating the principles of learning theory, and developing a framework that seamlessly adapts with machine learning applications. In this framework, the objective is to find the encoder and decoder functions that minimize the loss function, defined as the mean squared error between the estimated and true values. Facilitating the search for the optimum decoding and functions, we show that the loss function can be upper-bounded by the summation of two terms: the generalization error of the decoding function and the training error of the encoding function. Focusing on the second-order Sobolev space, we then derive the optimal encoder and decoder. We show that in the proposed solution, the mean squared error of the estimation decays with the rate of $\mathcal{O}(S^3 N^{-3})$ and $\mathcal{O}(S^{\frac{8}{5}}N^{\frac{-3}{5}})$ in noiseless and noisy computation settings, respectively, where $N$ is the number of worker nodes with at most $S$ slow servers (stragglers). Finally, we evaluate the proposed scheme on inference tasks for various machine learning models and demonstrate that the proposed framework outperforms the state-of-the-art in terms of accuracy and rate of convergence., Comment: 35 pages, 7 figures

Recent advancements in video saliency prediction (VSP) have shown promising performance compared to the human visual system, whose emulation is the primary goal of VSP. However, current state-of-the-art models employ spatio-temporal transformers trained on limited amounts of data, hindering generalizability adaptation to downstream tasks. The benefits of vision foundation models present a potential solution to improve the VSP process. However, adapting image foundation models to the video domain presents significant challenges in modeling scene dynamics and capturing temporal information. To address these challenges, and as the first initiative to design a VSP model based on video foundation models, we introduce SalFoM, a novel encoder-decoder video transformer architecture. Our model employs UnMasked Teacher (UMT) as feature extractor and presents a heterogeneous decoder which features a locality-aware spatio-temporal transformer and integrates local and global spatio-temporal information from various perspectives to produce the final saliency map. Our qualitative and quantitative experiments on the challenging VSP benchmark datasets of DHF1K, Hollywood-2 and UCF-Sports demonstrate the superiority of our proposed model in comparison with the state-of-the-art methods., Comment: 15 pages, 4 figures

Popularity bias in music recommendation systems -- where artists and tracks with the highest listen counts are recommended more often -- can also propagate biases along demographic and cultural axes. In this work, we identify these biases in recommendations for artists from underrepresented cultural groups in prototype-based matrix factorization methods. Unlike traditional matrix factorization methods, prototype-based approaches are interpretable. This allows us to directly link the observed bias in recommendations for minority artists (the effect) to specific properties of the embedding space (the cause). We mitigate popularity bias in music recommendation through capturing both users' and songs' cultural nuances in the embedding space. To address these challenges while maintaining recommendation quality, we propose two novel enhancements to the embedding space: i) we propose an approach to filter-out the irrelevant prototypes used to represent each user and item to improve generalizability, and ii) we introduce regularization techniques to reinforce a more uniform distribution of prototypes within the embedding space. Our results demonstrate significant improvements in reducing popularity bias and enhancing demographic and cultural fairness in music recommendations while achieving competitive -- if not better -- overall performance.

LLM-based assistants, such as GitHub Copilot and ChatGPT, have the potential to generate code that fulfills a programming task described in a natural language description, referred to as a prompt. The widespread accessibility of these assistants enables users with diverse backgrounds to generate code and integrate it into software projects. However, studies show that code generated by LLMs is prone to bugs and may miss various corner cases in task specifications. Presenting such buggy code to users can impact their reliability and trust in LLM-based assistants. Moreover, significant efforts are required by the user to detect and repair any bug present in the code, especially if no test cases are available. In this study, we propose a self-refinement method aimed at improving the reliability of code generated by LLMs by minimizing the number of bugs before execution, without human intervention, and in the absence of test cases. Our approach is based on targeted Verification Questions (VQs) to identify potential bugs within the initial code. These VQs target various nodes within the Abstract Syntax Tree (AST) of the initial code, which have the potential to trigger specific types of bug patterns commonly found in LLM-generated code. Finally, our method attempts to repair these potential bugs by re-prompting the LLM with the targeted VQs and the initial code. Our evaluation, based on programming tasks in the CoderEval dataset, demonstrates that our proposed method outperforms state-of-the-art methods by decreasing the number of targeted errors in the code between 21% to 62% and improving the number of executable code instances to 13%., Comment: 10 pages, 2 figures

Studies conducted on financial market prediction lack a comprehensive feature set that can carry a broad range of contributing factors; therefore, leading to imprecise results. Furthermore, while cooperating with the most recent innovations in explainable AI, studies have not provided an illustrative summary of market-driving factors using this powerful tool. Therefore, in this study, we propose a novel feature matrix that holds a broad range of features including Twitter content and market historical data to perform a binary classification task of one step ahead prediction. The utilization of our proposed feature matrix not only leads to improved prediction accuracy when compared to existing feature representations, but also its combination with explainable AI allows us to introduce a fresh analysis approach regarding the importance of the market-driving factors included. Thanks to the Lime interpretation technique, our interpretation study shows that the volume of tweets is the most important factor included in our feature matrix that drives the market's movements.

The observation of a gamma-ray burst (GRB) associated with a supernova (SN) coincides remarkably with the energy output from a binary system comprising a very massive carbon-oxygen (CO) core and an associated binary neutron star (NS) by the Binary-Driven Hypernova (BdHN) model. The dragging effect in the late evolution of such systems leads to co-rotation, with binary periods on the order of minutes, resulting in a very fast rotating core and a binary NS companion at a distance of $\sim 10^5$ km. Such a fast-rotating CO core, stripped of its hydrogen and helium, undergoes gravitational collapse and, within a fraction of seconds, leads to a supernova (SN) and a newly born, fast-spinning neutron star ($\nu$NS), we name the emergence of the SN and the $\nu$NS as the SN-rise and $\nu$NS-rise. Typically, the SN energies range from $10^{51}$ to $10^{53}$ erg. We address this issue by examining 10 cases of Type-I BdHNe, the most energetic ones, in which SN accretion onto the companion NS leads to the formation of a black hole (BH). In all ten cases, the energetics of the SN events are estimated, ranging between $0.18$ and $12 \times 10^{52}$ erg. Additionally, in all 8 sources at redshift $z$ closer than $4.61$, a clear thermal blackbody component has been identified, with temperatures between $6.2$ and $39.99$ keV, as a possible signature of pair-driven SN. The triggering of the X-ray afterglow induced by the $\nu$NS-rise are identified in three cases at high redshift where early X-ray observations are achievable, benefits from the interplay of cosmological effects., Comment: The previous version was uploaded on arXiv before being submitted to any Journal, this new version matches the one actually submitted for publication