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In the Gaza Strip, 80% of the population receives humanitarian assistance. The level of need is overwhelming, and the political and socio-economic context has crippled the traditional social protection system. Efforts to build a stronger social protection system are under way, and cash interventions are on the rise., This report explores the humanitarian cash assistance landscape in the Gaza Strip and how it interacts with social protection. It sets out a vision for a social protection architecture that supports coherence, protection, accountability and the building of resilient systems, and achieves complementarity between actors and programmes. The report also provides recommendations on how the implementation of programmes can be improved. more...

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks. more...

The increasing use of Linux on commercial off-the-shelf (COTS) system-on-chip (SoC) in spaceborne computing inherits COTS susceptibility to radiation-induced failures like soft errors. Modern SoCs exacerbate this issue as aggressive transistor scaling reduces critical charge thresholds to induce soft errors and increases radiation effects within densely packed transistors, degrading overall reliability. Linux's monolithic architecture amplifies these risks, as tightly coupled kernel subsystems propagate errors to critical components (e.g., memory management), while limited error-correcting code (ECC) offers minimal mitigation. Furthermore, the lack of public soft error data from irradiation tests on COTS SoCs running Linux hinders reliability improvements. This study evaluates proton irradiation effects (20-50 MeV) on Linux across three COTS SoC architectures: Raspberry Pi Zero 2 W (40 nm CMOS, Cortex-A53), NXP i MX 8M Plus (14 nm FinFET, Cortex-A53), and OrangeCrab (40 nm FPGA, RISC-V). Irradiation results show the 14 nm FinFET NXP SoC achieved 2-3x longer Linux uptime without ECC memory versus both 40 nm CMOS counterparts, partially due to FinFET's reduced charge collection. Additionally, this work presents the first cross-architecture analysis of soft error-prone Linux kernel components in modern SoCs to develop targeted mitigations. The findings establish foundational data on Linux's soft error sensitivity in COTS SoCs, guiding mission readiness for space applications. more...

Current Visual Simultaneous Localization and Mapping (VSLAM) systems often struggle to create maps that are both semantically rich and easily interpretable. While incorporating semantic scene knowledge aids in building richer maps with contextual associations among mapped objects, representing them in structured formats like scene graphs has not been widely addressed, encountering complex map comprehension and limited scalability. This paper introduces visual S-Graphs (vS-Graphs), a novel real-time VSLAM framework that integrates vision-based scene understanding with map reconstruction and comprehensible graph-based representation. The framework infers structural elements (i.e., rooms and corridors) from detected building components (i.e., walls and ground surfaces) and incorporates them into optimizable 3D scene graphs. This solution enhances the reconstructed map's semantic richness, comprehensibility, and localization accuracy. Extensive experiments on standard benchmarks and real-world datasets demonstrate that vS-Graphs outperforms state-of-the-art VSLAM methods, reducing trajectory error by an average of 3.38% and up to 9.58% on real-world data. Furthermore, the proposed framework achieves environment-driven semantic entity detection accuracy comparable to precise LiDAR-based frameworks using only visual features. A web page containing more media and evaluation outcomes is available on https://snt-arg.github.io/vsgraphs-results/., Comment: 13 pages, 8 figures, 2 tables more...

In 3D object mapping, category-level priors enable efficient object reconstruction and canonical pose estimation, requiring only a single prior per semantic category (e.g., chair, book, laptop). Recently, DeepSDF has predominantly been used as a category-level shape prior, but it struggles to reconstruct sharp geometry and is computationally expensive. In contrast, NeRFs capture fine details but have yet to be effectively integrated with category-level priors in a real-time multi-object mapping framework. To bridge this gap, we introduce PRENOM, a Prior-based Efficient Neural Object Mapper that integrates category-level priors with object-level NeRFs to enhance reconstruction efficiency while enabling canonical object pose estimation. PRENOM gets to know objects on a first-name basis by meta-learning on synthetic reconstruction tasks generated from open-source shape datasets. To account for object category variations, it employs a multi-objective genetic algorithm to optimize the NeRF architecture for each category, balancing reconstruction quality and training time. Additionally, prior-based probabilistic ray sampling directs sampling toward expected object regions, accelerating convergence and improving reconstruction quality under constrained resources. Experimental results on a low-end GPU highlight the ability of PRENOM to achieve high-quality reconstructions while maintaining computational feasibility. Specifically, comparisons with prior-free NeRF-based approaches on a synthetic dataset show a 21% lower Chamfer distance, demonstrating better reconstruction quality. Furthermore, evaluations against other approaches using shape priors on a noisy real-world dataset indicate a 13% improvement averaged across all reconstruction metrics, and comparable pose and size estimation accuracy, while being trained for 5x less time. more...

Understanding the nature of the changes exhibited by evolving neuronal dynamics from high-dimensional activity data is essential for advancing neuroscience, particularly in the study of neuronal network development and the pathophysiology of neurological disorders. This work examines how advanced dimensionality reduction techniques can efficiently summarize and enhance our understanding of the development of neuronal networks over time and in response to stimulation. We develop a framework based on the Minimum-Distortion Embedding (MDE) methods and demonstrate how MDE outperforms better known benchmarks based on Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) by effectively preserving both global structures and local relationships within complex neuronal datasets. Our \emph{in silico} experiments reveal MDE's capability to capture the evolving connectivity patterns of simulated neuronal networks, illustrating a clear trajectory tracking the simulated network development. Complementary \emph{in vitro} experiments further validate MDE's advantages, highlighting its ability to identify behavioral differences and connectivity changes in neuronal cultures over a 35-day observation period. Additionally, we explore the effects of stimulation on neuronal activity, providing valuable insights into the plasticity and learning mechanisms of neuronal networks. Our findings underscore the importance of metric selection in dimensionality reduction, showing that correlation metrics yield more meaningful embeddings compared to Euclidean distance. The implications of this research extend to various areas, including the potential development of therapeutic intervention strategies for neurological disorders, and the identification of distinct phases of neuronal activity for advancing cortical-based computing devices. more...

In the present work, we examine how the recent quantum-computing algorithm known as the state-average orbital-optimized variational quantum eigensolver (SA-OO-VQE), viewed within the context of quantum chemistry as a type of multiconfiguration self-consistent field (MCSCF) electronic-structure approach, exhibits a propensity to produce an ab initio quasi-diabatic representation ``for free'' if considered as a least-transformed block-diagonalization procedure, as alluded to in our previous work [S. Yalouz et al., J. Chem. Theory Comput. 18 (2022) 776] and thoroughly assessed herein. To this end, we introduce intrinsic and residual descriptors of diabaticity and re-explore the definition and linear-algebra properties - as well as their consequences on the vibronic nonadiabatic couplings - of an optimal diabatic representation within this context, and how much one may deviate from it. Such considerations are illustrated numerically on the prototypical case of formaldimine, which presents a well-known conical intersection between its ground and first-excited singlet electronic states. more...

This paper pioneers the field of multi-user holographic unmanned aerial vehicle (UAV) communications, laying a solid foundation for future innovations in next-generation aerial wireless networks. The study focuses on the challenging problem of jointly optimizing hybrid holographic beamforming and 3D UAV positioning in scenarios where the UAV is equipped with a reconfigurable holographic surface (RHS) instead of conventional phased array antennas. Using the unique capabilities of RHSs, the system dynamically adjusts both the position of the UAV and its hybrid beamforming properties to maximize the sum rate of the network. To address this complex optimization problem, we propose an iterative algorithm combining zero-forcing digital beamforming and a gradient ascent approach for the holographic patterns and the 3D position optimization, while ensuring practical feasibility constraints. The algorithm is designed to effectively balance the trade-offs between power, beamforming, and UAV trajectory constraints, enabling adaptive and efficient communications, while assuring a monotonic increase in the sum-rate performance. Our numerical investigations demonstrate that the significant performance improvements with the proposed approach over the benchmark methods, showcasing enhanced sum rate and system adaptability under varying conditions. more...

Reconfigurable holographic surfaces (RHS) have emerged as a transformative material technology, enabling dynamic control of electromagnetic waves to generate versatile holographic beam patterns. This paper addresses the problem of joint hybrid holographic beamforming and user scheduling under per-user minimum quality-of-service (QoS) constraints, a critical challenge in resource-constrained networks. However, such a problem results in mixed-integer non-convex optimization, making it difficult to identify feasible solutions efficiently. To overcome this challenge, we propose a novel iterative optimization framework that jointly solves the problem to maximize the RHS-assisted network sum-rate, efficiently managing holographic beamforming patterns, dynamically scheduling users, and ensuring the minimum QoS requirements for each scheduled user. The proposed framework relies on zero-forcing digital beamforming, gradient-ascent-based holographic beamformer optimization, and a greedy user selection principle. Our extensive simulation results validate the effectiveness of the proposed scheme, demonstrating their superior performance compared to the benchmark algorithms in terms of sum-rate performance, while meeting the minimum per-user QoS constraints more...

Acute Coronary Syndromes (ACS), including ST-segment elevation myocardial infarctions (STEMI) and non-ST-segment elevation myocardial infarctions (NSTEMI), remain a leading cause of mortality worldwide. Traditional cardiovascular risk scores rely primarily on clinical data, often overlooking environmental influences like air pollution that significantly impact heart health. Moreover, integrating complex time-series environmental data with clinical records is challenging. We introduce TabulaTime, a multimodal deep learning framework that enhances ACS risk prediction by combining clinical risk factors with air pollution data. TabulaTime features three key innovations: First, it integrates time-series air pollution data with clinical tabular data to improve prediction accuracy. Second, its PatchRWKV module automatically extracts complex temporal patterns, overcoming limitations of traditional feature engineering while maintaining linear computational complexity. Third, attention mechanisms enhance interpretability by revealing interactions between clinical and environmental factors. Experimental results show that TabulaTime improves prediction accuracy by over 20% compared to conventional models such as CatBoost, Random Forest, and LightGBM, with air pollution data alone contributing over a 10% improvement. Feature importance analysis identifies critical predictors including previous angina, systolic blood pressure, PM10, and NO2. Overall, TabulaTime bridges clinical and environmental insights, supporting personalized prevention strategies and informing public health policies to mitigate ACS risk. more...

We study fundamental limits of first-order stochastic optimization in a range of nonconvex settings, including L-smooth functions satisfying Quasar-Convexity (QC), Quadratic Growth (QG), and Restricted Secant Inequalities (RSI). While the convergence properties of standard algorithms are well-understood in deterministic regimes, significantly fewer results address the stochastic case, where only unbiased and noisy gradients are available. We establish new lower bounds on the number of noisy gradient queries to minimize these classes of functions, also showing that they are tight (up to a logarithmic factor) in all the relevant quantities characterizing each class. Our approach reformulates the optimization task as a function identification problem, leveraging divergence composition arguments to construct a challenging subclass that leads to sharp lower bounds. Furthermore, we present a specialized algorithm in the one-dimensional setting that achieves faster rates, suggesting that certain dimensional thresholds are intrinsic to the complexity of non-convex stochastic optimization. more...

Query-driven recommendation with unknown items poses a challenge for users to understand why certain items are appropriate for their needs. Query-driven Contrastive Summarization (QCS) is a methodology designed to address this issue by leveraging language-based item descriptions to clarify contrasts between them. However, existing state-of-the-art contrastive summarization methods such as STRUM-LLM fall short of this goal. To overcome these limitations, we introduce Q-STRUM Debate, a novel extension of STRUM-LLM that employs debate-style prompting to generate focused and contrastive summarizations of item aspects relevant to a query. Leveraging modern large language models (LLMs) as powerful tools for generating debates, Q-STRUM Debate provides enhanced contrastive summaries. Experiments across three datasets demonstrate that Q-STRUM Debate yields significant performance improvements over existing methods on key contrastive summarization criteria, thus introducing a novel and performant debate prompting methodology for QCS. more...

In the age of misinformation, hallucination -- the tendency of Large Language Models (LLMs) to generate non-factual or unfaithful responses -- represents the main risk for their global utility. Despite LLMs becoming increasingly multilingual, the vast majority of research on detecting and quantifying LLM hallucination are (a) English-centric and (b) focus on machine translation (MT) and summarization, tasks that are less common ``in the wild'' than open information seeking. In contrast, we aim to quantify the extent of LLM hallucination across languages in knowledge-intensive long-form question answering. To this end, we train a multilingual hallucination detection model and conduct a large-scale study across 30 languages and 6 open-source LLM families. We start from an English hallucination detection dataset and rely on MT to generate (noisy) training data in other languages. We also manually annotate gold data for five high-resource languages; we then demonstrate, for these languages, that the estimates of hallucination rates are similar between silver (LLM-generated) and gold test sets, validating the use of silver data for estimating hallucination rates for other languages. For the final rates estimation, we build a knowledge-intensive QA dataset for 30 languages with LLM-generated prompts and Wikipedia articles as references. We find that, while LLMs generate longer responses with more hallucinated tokens for higher-resource languages, there is no correlation between length-normalized hallucination rates of languages and their digital representation. Further, we find that smaller LLMs exhibit larger hallucination rates than larger models. more...

Recent research has demonstrated the potential of deep neural networks (DNNs) to accurately predict next-day wildfire spread, based upon the current extent of a fire and geospatial rasters of influential environmental covariates e.g., vegetation, topography, climate, and weather. In this work, we investigate a recent transformer-based model, termed the SwinUnet, for next-day wildfire prediction. We benchmark Swin-based models against several current state-of-the-art models on WildfireSpreadTS (WFTS), a large public benchmark dataset of historical wildfire events. We consider two next-day fire prediction scenarios: when the model is given input of (i) a single previous day of data, or (ii) five previous days of data. We find that, with the proper modifications, SwinUnet achieves state-of-the-art accuracy on next-day prediction for both the single-day and multi-day scenarios. SwinUnet's success depends heavily upon utilizing pre-trained weights from ImageNet. Consistent with prior work, we also found that models with multi-day-input always outperformed models with single-day input. 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...

We present in this paper four new variants of the Tower of Hanoi problem, the optimal solution of each of these variants is related to one of the four known numbers Fibonacci, Lucas, Pell, and Jacobsthal. We give an optimal solution to each of these variants, and we present their associated graphs., Comment: 24 pages, 23 figures more...

Self-supervised learning (SSL) has demonstrated significant potential in pre-training robust models with limited labeled data, making it particularly valuable for remote sensing (RS) tasks. A common assumption is that pre-training on domain-aligned data provides maximal benefits on downstream tasks, particularly when compared to ImageNet-pretraining (INP). In this work, we investigate this assumption by collecting GeoNet, a large and diverse dataset of global optical Sentinel-2 imagery, and pre-training SwAV and MAE on both GeoNet and ImageNet. Evaluating these models on six downstream tasks in the few-shot setting reveals that SSL pre-training on RS data offers modest performance improvements over INP, and that it remains competitive in multiple scenarios. This indicates that the presumed benefits of SSL pre-training on RS data may be overstated, and the additional costs of data curation and pre-training could be unjustified. more...

This study utilises the R programming language for statistical data analysis to understand Tourism dynamics in Poland. It focuses on methods for data visualisation, multivariate statistics, and hypothesis testing. To investigate the expenditure behavior of tourist, spending patterns, correlations, and associations among variables were analysed in the dataset. The results revealed a significant relationship between accommodation type and the purpose of trip, showing that the purpose of a trip impacts the selection of accommodation. A strong correlation was observed between organizer expenditure and private expenditure, indicating that individual spending are more when the spending on organizing the trip are higher. However, no significant difference was observed in total expenditure across different accommodation types and purpose of the trip revealing that travelers tend to spend similar amounts regardless of their reason for travel or choice of accommodation. Although significant relationships were observed among certain variables, ANOVA could not be applied because the dataset was not able to hold on the normality assumption. In future, the dataset can be explored further to find more meaningful insights. The developed code is available on GitHub: https://github.com/SaadAhmedJamal/DataAnalysis RProgEnv., Comment: 5 pages more...

Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models depend on single-step statistical inference to capture the environment dynamics, and, hence, they are unable to perform long-term imagination tasks due to the accumulation of prediction errors. Inspired by the dual-process theory of human cognition, we propose a novel dual-mind world model (DMWM) framework that integrates logical reasoning to enable imagination with logical consistency. DMWM is composed of two components: an RSSM-based System 1 (RSSM-S1) component that handles state transitions in an intuitive manner and a logic-integrated neural network-based System 2 (LINN-S2) component that guides the imagination process through hierarchical deep logical reasoning. The inter-system feedback mechanism is designed to ensure that the imagination process follows the logical rules of the real environment. The proposed framework is evaluated on benchmark tasks that require long-term planning from the DMControl suite. Extensive experimental results demonstrate that the proposed framework yields significant improvements in terms of logical coherence, trial efficiency, data efficiency and long-term imagination over the state-of-the-art world models. more...

Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from users by offering \textit{free} services and then monetize it for advertising and marketing purposes. While major tech companies only sell aggregate behaviors for marketing purposes; data aggregators and data brokers offer access to individual location data. Some data brokers and aggregators have certain rules in place to preserve privacy; and the FTC has also started to vigorously regulate consumer privacy for location data. In this paper, we present an in-depth exploration of U.S. privacy perceptions with respect to specific location features derivable from data made available by location data brokers and aggregators. These results can provide policy implications that could assist organizations like the FTC in defining clear access rules. Using a factorial vignette survey, we collected responses from 1,405 participants to evaluate their level of comfort with sharing different types of location features, including individual trajectory data and visits to points of interest, available for purchase from data brokers worldwide. Our results show that trajectory-related features are associated with higher privacy concerns, that some data broker based obfuscation practices increase levels of comfort, and that race, ethnicity and education have an effect on data sharing privacy perceptions. We also model the privacy perceptions of people as a predictive task with F1 score \textbf{0.6}. more...

Fingerprint recognition remains one of the most reliable biometric technologies due to its high accuracy and uniqueness. Traditional systems rely on contact-based scanners, which are prone to issues such as image degradation from surface contamination and inconsistent user interaction. To address these limitations, contactless fingerprint recognition has emerged as a promising alternative, providing non-intrusive and hygienic authentication. This study evaluates the impact of image enhancement tech-niques on the performance of pre-trained deep learning models using transfer learning for touchless fingerprint recognition. The IIT-Bombay Touchless and Touch-Based Fingerprint Database, containing data from 200 subjects, was employed to test the per-formance of deep learning architectures such as VGG-16, VGG-19, Inception-V3, and ResNet-50. Experimental results reveal that transfer learning methods with fingerprint image enhance-ment (indirect method) significantly outperform those without enhancement (direct method). Specifically, VGG-16 achieved an accuracy of 98% in training and 93% in testing when using the enhanced images, demonstrating superior performance compared to the direct method. This paper provides a detailed comparison of the effectiveness of image enhancement in improving the accuracy of transfer learning models for touchless fingerprint recognition, offering key insights for developing more efficient biometric systems., Comment: 6 pages more...

Addressing global challenges such as greenhouse gas emissions and resource inequity demands advanced AI-driven coordination among autonomous agents. We propose CH-MARL (Constrained Hierarchical Multiagent Reinforcement Learning), a novel framework that integrates hierarchical decision-making with dynamic constraint enforcement and fairness-aware reward shaping. CH-MARL employs a real-time constraint-enforcement layer to ensure adherence to global emission caps, while incorporating fairness metrics that promote equitable resource distribution among agents. Experiments conducted in a simulated maritime logistics environment demonstrate considerable reductions in emissions, along with improvements in fairness and operational efficiency. Beyond this domain-specific success, CH-MARL provides a scalable, generalizable solution to multi-agent coordination challenges in constrained, dynamic settings, thus advancing the state of the art in reinforcement learning. more...

This paper investigates unmanned aerial vehicle (UAV) localization using time difference of arrival (TDOA) measurements under mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. A 3D TDOA Cram\'er-Rao lower bound (CRLB) model is developed accounting for varying altitudes and signal bandwidths. The model is compared to five real-world UAV flight experiments conducted at different altitudes (40 m, 70 m, 100 m) and bandwidths (1.25 MHz, 2.5 MHz, 5 MHz) using Keysight N6841A radio frequency (RF) sensors of the NSF AERPAW platform. Results show that altitude, bandwidth, and NLOS obstructions significantly impact localization accuracy. Higher bandwidths enhance signal time resolution, while increased altitudes mitigate multipath and NLOS biases, both contributing to improved performance. However, hovering close to RF sensors degrades accuracy due to antenna pattern misalignment and geometric dilution of precision. These findings emphasize the inadequacy of traditional LOS-based models in NLOS environments and highlight the importance of adaptive approaches for accurate localization in challenging scenarios., Comment: This paper has been submitted for review to the IEEE ICC 2025 Workshop on Positioning and Sensing over Wireless Networks more...

Localization of radio frequency (RF) sources has critical applications, including search and rescue, jammer detection, and monitoring of hostile activities. Unmanned aerial vehicles (UAVs) offer significant advantages for RF source localization (RFSL) over terrestrial methods, leveraging autonomous 3D navigation and improved signal capture at higher altitudes. Recent advancements in deep learning (DL) have further enhanced localization accuracy, particularly for outdoor scenarios. DL models often face challenges in real-world performance, as they are typically trained on simulated datasets that fail to replicate real-world conditions fully. To address this, we first propose the Enhanced Two-Ray propagation model, reducing the simulation-to-reality gap by improving the accuracy of propagation environment modeling. For RFSL, we propose the 3D Cluster-Based RealAdaptRNet, a DL-based method leveraging 3D clustering-based feature extraction for robust localization. Experimental results demonstrate that the proposed Enhanced Two-Ray model provides superior accuracy in simulating real-world propagation scenarios compared to conventional free-space and two-ray models. Notably, the 3D Cluster-Based RealAdaptRNet, trained entirely on simulated datasets, achieves exceptional performance when validated in real-world environments using the AERPAW physical testbed, with an average localization error of 18.2 m. The proposed approach is computationally efficient, utilizing 33.5 times fewer parameters, and demonstrates strong generalization capabilities across diverse trajectories, making it highly suitable for real-world applications., Comment: This paper has been submitted to IEEE ICC 2025 more...

Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be fast and resource-efficient by assigning more tasks to faster workers. The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. In this paper, we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times. Experimental results further demonstrate that ATA is resource-efficient, significantly reducing costs compared to the greedy approach, which can be arbitrarily expensive depending on the number of workers. more...

Event log analysis is an important task that security professionals undertake. Event logs record key information on activities that occur on computing devices, and due to the substantial number of events generated, they consume a large amount of time and resources to analyse. This demanding and repetitive task is also prone to errors. To address these concerns, researchers have developed automated techniques to improve the event log analysis process. Large Language Models (LLMs) have recently demonstrated the ability to successfully perform a wide range of tasks that individuals would usually partake in, to high standards, and at a pace and degree of complexity that outperform humans. Due to this, researchers are rapidly investigating the use of LLMs for event log analysis. This includes fine-tuning, Retrieval-Augmented Generation (RAG) and in-context learning, which affect performance. These works demonstrate good progress, yet there is a need to understand the developing body of knowledge, identify commonalities between works, and identify key challenges and potential solutions to further developments in this domain. This paper aims to survey LLM-based event log analysis techniques, providing readers with an in-depth overview of the domain, gaps identified in previous research, and concluding with potential avenues to explore in future. more...

Inspired by the quantization of classical quantities and Rankin Selberg convolution, we study the anticommutator operation $\{\cdot, \cdot\}$, where $\{A,B\} = AB + BA$, applied to real symmetric random matrix ensembles including Gaussian orthogonal ensemble (GOE), the palindromic Toeplitz ensemble (PTE), the $k$-checkerboard ensemble, and the block $k$-circulant ensemble ($k$-BCE). Using combinatorial and topological techniques related to non-crossing and free matching properties of GOE and PTE, we obtain closed-form formulae for the moments of the limiting spectral distributions of $\{$GOE, GOE$\}$, $\{$PTE, PTE$\}$, $\{$GOE, PTE$\}$ and establish the corresponding limiting spectral distributions with generating functions and convolution. On the other hand, $\{$GOE, $k$-checkerboard$\}$ and $\{$$k$-checkerboard, $j$-checkerboard$\}$ exhibit entirely different spectral behavior than the other anticommutator ensembles: while the spectrum of $\{$GOE, $k$-checkerboard$\}$ consists of 1 bulk regime of size $\Theta(N)$ and 1 blip regime of size $\Theta(N^{3/2})$, the spectrum of $\{$$k$-checkerboard, $j$-checkerboard$\}$ consists of 1 bulk regime of size $\Theta(N)$, 2 intermediary blip regimes of size $\Theta(N^{3/2})$, and 1 largest blip regime of size $\Theta(N^2)$. In both cases, with the appropriate weight function, we are able to isolate the largest regime for other regime(s) and analyze its moments and convergence results via combinatorics. We end with numerical computation of lower even moments of $\{$GOE, $k$-BCE$\}$ and $\{$$k$-BCE, $k$-BCE$\}$ based on genus expansion and discussion on the challenge with analyzing the intermediary blip regimes of $\{$$k$-checkerboard, $j$-checkerboard$\}$., Comment: 48 pages, 12 figures more...

We present a physics-informed deep learning framework to address common limitations in Confocal Laser Scanning Microscopy (CLSM), such as diffraction limited resolution, noise, and undersampling due to low laser power conditions. The optical system's point spread function (PSF) and common CLSM image degradation mechanisms namely photon shot noise, dark current noise, motion blur, speckle noise, and undersampling were modeled and were directly included into model architecture. The model reconstructs high fidelity images from heavily noisy inputs by using convolutional and transposed convolutional layers. Following the advances in compressed sensing, our approach significantly reduces data acquisition requirements without compromising image resolution. The proposed method was extensively evaluated on simulated CLSM images of diverse structures, including lipid droplets, neuronal networks, and fibrillar systems. Comparisons with traditional deconvolution algorithms such as Richardson-Lucy (RL), non-negative least squares (NNLS), and other methods like Total Variation (TV) regularization, Wiener filtering, and Wavelet denoising demonstrate the superiority of the network in restoring fine structural details with high fidelity. Assessment metrics like Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR), underlines that the AdaptivePhysicsAutoencoder achieved robust image enhancement across diverse CLSM conditions, helping faster acquisition, reduced photodamage, and reliable performance in low light and sparse sampling scenarios holding promise for applications in live cell imaging, dynamic biological studies, and high throughput material characterization. more...

There are currently no psychometrically valid tools to measure the perceived danger of robots. To fill this gap, we provided a definition of perceived danger and developed and validated a 12-item bifactor scale through four studies. An exploratory factor analysis revealed four subdimensions of perceived danger: affective states, physical vulnerability, ominousness, and cognitive readiness. A confirmatory factor analysis confirmed the bifactor model. We then compared the perceived danger scale to the Godspeed perceived safety scale and found that the perceived danger scale is a better predictor of empirical data. We also validated the scale in an in-person setting and found that the perceived danger scale is sensitive to robot speed manipulations, consistent with previous empirical findings. Results across experiments suggest that the perceived danger scale is reliable, valid, and an adequate predictor of both perceived safety and perceived danger in human-robot interaction contexts., Comment: 9 pages, 2 figures, to be published in the Proceedings of the 2025 ACM/IEEE International Conference on Human-Robot Interaction (HRI) more...

Recently, the first author as well as the second author with Ono, Pujahari, and Saikia determined the limiting distribution of values of certain finite field ${_2F_1}$ and ${_3F_2}$ hypergeometric functions. These hypergeometric values are related to Frobenius traces of elliptic curves and their limiting distribution is determined using connections to the theory of modular forms and harmonic Maass forms. Here we determine the limiting distribution of values of some ${_4F_3}$ hypergeometric functions which are sums of traces of Frobenius for a pair of elliptic curves. To obtain this result, we generalize Michel's work on Sato-Tate laws for families of elliptic curves to the setting of pairs of families, and we show that a generic pair admits an independent Sato-Tate distribution as the finite field grows. To this end, we use various results from the theory of \'etale cohomology, Deligne's work on the Weil conjectures, and the work of Katz on monodromy groups. In the cases previously studied using modular methods, we elucidate the connection between the modular forms that appear and the machinery of \'etale cohomology. more...

Recent research has revealed that the reported results of an emerging body of DL-based techniques for detecting software vulnerabilities are not reproducible, either across different datasets or on unseen samples. This paper aims to provide the foundation for properly evaluating the research in this domain. We do so by analyzing prior work and existing vulnerability datasets for the syntactic and semantic features of code that contribute to vulnerability, as well as features that falsely correlate with vulnerability. We provide a novel, uniform representation to capture both sets of features, and use this representation to detect the presence of both vulnerability and spurious features in code. To this end, we design two types of code perturbations: feature preserving perturbations (FPP) ensure that the vulnerability feature remains in a given code sample, while feature eliminating perturbations (FEP) eliminate the feature from the code sample. These perturbations aim to measure the influence of spurious and vulnerability features on the predictions of a given vulnerability detection solution. To evaluate how the two classes of perturbations influence predictions, we conducted a large-scale empirical study on five state-of-the-art DL-based vulnerability detectors. Our study shows that, for vulnerability features, only ~2% of FPPs yield the undesirable effect of a prediction changing among the five detectors on average. However, on average, ~84% of FEPs yield the undesirable effect of retaining the vulnerability predictions. For spurious features, we observed that FPPs yielded a drop in recall up to 29% for graph-based detectors. We present the reasons underlying these results and suggest strategies for improving DNN-based vulnerability detectors. We provide our perturbation-based evaluation framework as a public resource to enable independent future evaluation of vulnerability detectors. more...

This paper addresses a scheduling problem in the context of a cyber-physical system where a sensor and a controller communicate over an unreliable channel. The sensor observes the state of a source at each time, and according to a scheduling policy determines whether to transmit a compressed sampled state, transmit the uncompressed sampled state, or remain idle. Upon receiving the transmitted information, the controller executes a control action aimed at stabilizing the system, such that the effectiveness of stabilization depends on the quality of the received sensory information. Our primary objective is to derive an optimal scheduling policy that optimizes system performance subject to resource constraints, when the performance is measured by a dual-aspect metric penalizing both the frequency of transitioning to unstable states and the continuous duration of remaining in those states. We formulate this problem as a Markov decision process, and derive an optimal multi-threshold scheduling policy. more...

The task of converting natural language questions (NLQs) into executable SQL queries, known as text-to-SQL, has gained significant interest in recent years, as it enables non-technical users to interact with relational databases. Many benchmarks, such as SPIDER and WikiSQL, have contributed to the development of new models and the evaluation of their performance. In addition, other datasets, like SEDE and BIRD, have introduced more challenges and complexities to better map real-world scenarios. However, these datasets primarily focus on high-resource languages such as English and Chinese. In this work, we introduce Dialect2SQL, the first large-scale, cross-domain text-to-SQL dataset in an Arabic dialect. It consists of 9,428 NLQ-SQL pairs across 69 databases in various domains. Along with SQL-related challenges such as long schemas, dirty values, and complex queries, our dataset also incorporates the complexities of the Moroccan dialect, which is known for its diverse source languages, numerous borrowed words, and unique expressions. This demonstrates that our dataset will be a valuable contribution to both the text-to-SQL community and the development of resources for low-resource languages. more...

We present an Arabic crossword puzzle generator from a given text that utilizes advanced language models such as GPT-4-Turbo, GPT-3.5-Turbo and Llama3-8B-Instruct, specifically developed for educational purposes, this innovative generator leverages a meticulously compiled dataset named Arabic-Clue-Instruct with over 50,000 entries encompassing text, answers, clues, and categories. This dataset is intricately designed to aid in the generation of pertinent clues linked to specific texts and keywords within defined categories. This project addresses the scarcity of advanced educational tools tailored for the Arabic language, promoting enhanced language learning and cognitive development. By providing a culturally and linguistically relevant tool, our objective is to make learning more engaging and effective through gamification and interactivity. Integrating state-of-the-art artificial intelligence with contemporary learning methodologies, this tool can generate crossword puzzles from any given educational text, thereby facilitating an interactive and enjoyable learning experience. This tool not only advances educational paradigms but also sets a new standard in interactive and cognitive learning technologies. The model and dataset are publicly available., Comment: This paper has been accepted for presentation at LoResLM @ COLING 2025 more...

We present the GenAI Content Detection Task~1 -- a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025. The task consists of two subtasks: Monolingual (English) and Multilingual. The shared task attracted many participants: 36 teams made official submissions to the Monolingual subtask during the test phase and 26 teams -- to the Multilingual. We provide a comprehensive overview of the data, a summary of the results -- including system rankings and performance scores -- detailed descriptions of the participating systems, and an in-depth analysis of submissions. https://github.com/mbzuai-nlp/COLING-2025-Workshop-on-MGT-Detection-Task1, Comment: 18 pages more...

In this work, we investigate the efficacy of various adapter architectures on supervised binary classification tasks from the SuperGLUE benchmark as well as a supervised multi-class news category classification task from Kaggle. Specifically, we compare classification performance and time complexity of three transformer models, namely DistilBERT, ELECTRA, and BART, using conventional fine-tuning as well as nine state-of-the-art (SoTA) adapter architectures. Our analysis reveals performance differences across adapter architectures, highlighting their ability to achieve comparable or better performance relative to fine-tuning at a fraction of the training time. Similar results are observed on the new classification task, further supporting our findings and demonstrating adapters as efficient and flexible alternatives to fine-tuning. This study provides valuable insights and guidelines for selecting and implementing adapters in diverse natural language processing (NLP) applications. more...

Indoor localization in challenging non-line-of-sight (NLOS) environments often leads to mediocre accuracy with traditional approaches. Deep learning (DL) has been applied to tackle these challenges; however, many DL approaches overlook computational complexity, especially for floating-point operations (FLOPs), making them unsuitable for resource-limited devices. Transformer-based models have achieved remarkable success in natural language processing (NLP) and computer vision (CV) tasks, motivating their use in wireless applications. However, their use in indoor localization remains nascent, and directly applying Transformers for indoor localization can be both computationally intensive and exhibit limitations in accuracy. To address these challenges, in this work, we introduce a novel tokenization approach, referred to as Sensor Snapshot Tokenization (SST), which preserves variable-specific representations of power delay profile (PDP) and enhances attention mechanisms by effectively capturing multi-variate correlation. Complementing this, we propose a lightweight Swish-Gated Linear Unit-based Transformer (L-SwiGLU Transformer) model, designed to reduce computational complexity without compromising localization accuracy. Together, these contributions mitigate the computational burden and dependency on large datasets, making Transformer models more efficient and suitable for resource-constrained scenarios. The proposed tokenization method enables the Vanilla Transformer to achieve a 90th percentile positioning error of 0.388 m in a highly NLOS indoor factory, surpassing conventional tokenization methods. The L-SwiGLU ViT further reduces the error to 0.355 m, achieving an 8.51% improvement. Additionally, the proposed model outperforms a 14.1 times larger model with a 46.13% improvement, underscoring its computational efficiency., Comment: The paper has been submitted to IEEE Transactions on Machine Learning in Communications and Networking more...

Interacting with a software system via a chatbot can be challenging, especially when the chatbot needs to generate API calls, in the right order and with the right parameters, to communicate with the system. API calling in chatbot systems poses significant challenges, particularly in complex, multi-step tasks requiring accurate API selection and execution. We contribute to this domain in three ways: first, by introducing a novel dataset designed to assess models on API function selection, parameter generation, and nested API calls; second, by benchmarking state-of-the-art language models across varying levels of complexity to evaluate their performance in API function generation and parameter accuracy; and third, by proposing an enhanced API routing method that combines general-purpose large language models for API selection with fine-tuned models for parameter generation and some prompt engineering approach. These approaches lead to substantial improvements in handling complex API tasks, offering practical advancements for real-world API-driven chatbot systems. more...

Mapping input signals to a high-dimensional space is a critical concept in various computing paradigms. We propose using commercially available telecom devices developed for high-speed optical data transmission for computing through nonlinear mapping of optical signals into a high-dimensional space where linear processing can be applied. We manipulate the output feature dimension by applying temporal up-sampling (at the speed of commercially available telecom devices) of input signals and a well-established wave-division-multiplexing (WDM). This gives remarkable flexibility in the {\it dynamical phase masking} of the input signal. We demonstrate the general concept employing readily available photonic devices, including a semiconductor optical amplifier and nonlinear optical loop mirror (equivalent nonlinear Mach-Zender interferometer). We apply linear regression on the high-dimensional output states to solve forecasting and classification problems. We investigate how nonlinear mapping provided by these devices can be characterized in terms of the increased controlled separability and predictability of the output state. more...

The emergence of optical intelligent reflecting surface (OIRS) technologies marks a milestone in optical wireless communication (OWC) systems, enabling enhanced control over light propagation in indoor environments. This capability allows for the customization of channel conditions to achieve specific performance goals. This paper presents an enhancement in downlink cell-free OWC networks through the integration of OIRS. The key focus is on fine-tuning crucial parameters, including transmit power, receiver orientations, OIRS elements allocation, and strategic placement. In particular, a multi-objective optimization problem (MOOP) aimed at simultaneously improving the network's spectral efficiency (SE) and energy efficiency (EE) while adhering to the network's quality of service (QoS) constraints is formulated. The problem is solved by employing the $\epsilon$-constraint method to convert the MOOP into a single-objective optimization problem and solving it through successive convex approximation. Simulation results show the significant impact of OIRS on SE and EE, confirming its effectiveness in improving OWC network performance. more...

With increasing concerns over privacy in healthcare, especially for sensitive medical data, this research introduces a federated learning framework that combines local differential privacy and secure aggregation using Secure Multi-Party Computation for medical image classification. Further, we propose DPResNet, a modified ResNet architecture optimized for differential privacy. Leveraging the BloodMNIST benchmark dataset, we simulate a realistic data-sharing environment across different hospitals, addressing the distinct privacy challenges posed by federated healthcare data. Experimental results indicate that our privacy-preserving federated model achieves accuracy levels close to non-private models, surpassing traditional approaches while maintaining strict data confidentiality. By enhancing the privacy, efficiency, and reliability of healthcare data management, our approach offers substantial benefits to patients, healthcare providers, and the broader healthcare ecosystem., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024) - MusIML Workshop more...

We introduce a new predator-prey model by replacing the growth and predation constant by a square matrix, and the population density as a population vector. The classical Lotka-Volterra model describes a population that either modulates or converges. Stability analysis of such models have been extensively studied by the works of Merdan (https://doi.org/10.1016/j.chaos.2007.06.062). The new model adds complexity by introducing an age group structure where the population of each age group evolves as prescribed by the Leslie matrix. The added complexity changes the behavior of the model such that the population either displays roughly an exponential growth or decay. We first provide an exact equation that describes a time evolution and use analytic techniques to obtain an approximate growth factor. We also discuss the variants of the Leslie model, i.e., the complex value predator-prey model and the competitive model. We then prove the Last Species Standing theorem that determines the dominant population in the large time limit. The recursive structure of the model denies the application of simple regression. We discuss a machine learning scheme that allows an admissible fit for the population evolution of Paramecium Aurelia and Paramecium Caudatum. Another potential avenue to simplify the computation is to use the machinery of quantum operators. We demonstrate the potential of this approach by computing the Hamiltonian of a simple Leslie system. more...

This systematic literature review investigates perceptions, concerns, and expectations of young digital citizens regarding privacy in artificial intelligence (AI) systems, focusing on social media platforms, educational technology, gaming systems, and recommendation algorithms. Using a rigorous methodology, the review started with 2,000 papers, narrowed down to 552 after initial screening, and finally refined to 108 for detailed analysis. Data extraction focused on privacy concerns, data-sharing practices, the balance between privacy and utility, trust factors in AI, transparency expectations, and strategies to enhance user control over personal data. Findings reveal significant privacy concerns among young users, including a perceived lack of control over personal information, potential misuse of data by AI, and fears of data breaches and unauthorized access. These issues are worsened by unclear data collection practices and insufficient transparency in AI applications. The intention to share data is closely associated with perceived benefits and data protection assurances. The study also highlights the role of parental mediation and the need for comprehensive education on data privacy. Balancing privacy and utility in AI applications is crucial, as young digital citizens value personalized services but remain wary of privacy risks. Trust in AI is significantly influenced by transparency, reliability, predictable behavior, and clear communication about data usage. Strategies to improve user control over personal data include access to and correction of data, clear consent mechanisms, and robust data protection assurances. The review identifies research gaps and suggests future directions, such as longitudinal studies, multicultural comparisons, and the development of ethical AI frameworks., Comment: To appear in the 2024 IEEE Annual Information Technology, Electronics and Mobile Communication Conference proceedings more...

The Zee-Babu model is an economical framework for neutrino mass generation as two-loop quantum corrections. In this work, we present a UV completion of this model by embedding it into an $SU(5)$ unified framework. Interestingly, we find that loop-induced contributions to neutrino masses arising from colored scalars are just as important as those from color-neutral ones. These new states, which are required from gauge coupling unification and neutrino oscillation data to have masses below $\mathcal{O}(10^3)$ TeV, may be accessible to future collider experiments. Additionally, the model can be probed in proton decay searches. Our Markov chain Monte Carlo analysis of model parameters shows a high likelihood of observable $p \rightarrow e^+ \pi^0$ decay signal in the first decade of Hyper-Kamiokande operation. The model predicts a vector-like down-type quark at the TeV scale, utilized for realistic fermion mass generation and gauge coupling unification. The model is UV-complete in the sense that it is a unified theory which is realistic and asymptotically free that can be extrapolated to the Planck scale., Comment: 24 pages, 5 figures; version accepted in JHEP (to appear) more...

Invariant integration of vectors and tensors over manifolds was introduced around fifty years ago by V.N. Folomeshkin, though the concept has not attracted much attention among researchers. Although it is a sophisticated concept, the operation of the invariant integration of vectors is actually required to correctly solve some problems in mechanics. Two such problems are discussed in the present exposition, in the context of a two-dimensional Euclidean space covered by a polar coordinate system. The notion of invariant integration becomes necessary when the space is described without any reference to a Cartesian coordinate system., Comment: 9 pages, 2 figures more...

Social media platforms have become the hubs for various user interactions covering a wide range of needs, including technical support and services related to brands, products, or user accounts. Unfortunately, there has been a recent surge in scammers impersonating official services and providing fake technical support to users through these platforms. In this study, we focus on scammers engaging in such fake technical support to target users who are having problems recovering their accounts. More specifically, we focus on users encountering access problems with social media profiles (e.g., on platforms such as Facebook, Instagram, Gmail, and X) and cryptocurrency wallets. The main contribution of our work is the development of an automated system that interacts with scammers via a chatbot that mimics different personas. By initiating decoy interactions (e.g., through deceptive tweets), we have enticed scammers to interact with our system so that we can analyze their modus operandi. Our results show that scammers employ many social media profiles asking users to contact them via a few communication channels. Using a large language model (LLM), our chatbot had conversations with 450 scammers and provided valuable insights into their tactics and, most importantly, their payment profiles. This automated approach highlights how scammers use a variety of strategies, including role-playing, to trick victims into disclosing personal or financial information. With this study, we lay the foundation for using automated chat-based interactions with scammers to detect and study fraudulent activities at scale in an automated way. more...

We investigate two-neutrino double beta decay ($2\nu\beta\beta$) in chiral effective field theory. We find contributions from weak magnetism and double-weak pion-exchange at next-to-leading-order in the chiral power counting. We discuss the impact of the chiral corrections on the electron spectra and find that they should be included in analyses of $2\nu\beta\beta$ decay that aim to uncover new physics signatures in the electron spectrum. We illustrate this point by revisiting the effect of sterile neutrinos and non-standard charged interactions. We also find that the pion-exchange contributions involve nuclear matrix elements that are related to those appearing in neutrinoless double beta decay ($0\nu \beta \beta$). We investigate whether the $0\nu \beta \beta$ nuclear matrix elements can be obtained from detailed measurements of the energy spectrum of the outgoing electrons in $2\nu\beta\beta$ transitions., Comment: 25 pages, 10 figures more...

As language models become integral to critical workflows, assessing their behavior remains a fundamental challenge -- human evaluation is costly and noisy, while automated metrics provide only coarse, difficult-to-interpret signals. We introduce natural language unit tests, a paradigm that decomposes response quality into explicit, testable criteria, along with a unified scoring model, LMUnit, which combines multi-objective training across preferences, direct ratings, and natural language rationales. Through controlled human studies, we show this paradigm significantly improves inter-annotator agreement and enables more effective LLM development workflows. LMUnit achieves state-of-the-art performance on evaluation benchmarks (FLASK, BigGenBench) and competitive results on RewardBench. These results validate both our proposed paradigm and scoring model, suggesting a promising path forward for language model evaluation and development. more...

This paper presents the principal challenges and opportunities associated with computational biomechanics research. The underlying cognitive control involved in the process of human motion is inherently complex, dynamic, multidimensional, and highly non-linear. The dynamics produced by the internal and external forces and the body's ability to react to them is biomechanics. Complex and non-rigid bodies, needs a lot of computing power and systems to execute however, in the absence of adequate resources, one may rely on new technology, machine learning tools and model order reduction approaches. It is also believed that machine learning approaches can enable us to embrace this complexity, if we could use three arms of ML i.e. predictive modeling, classification, and dimensionality reduction. Biomechanics, since it deals with motion and mobility come with a huge set of data over time. Using computational (Computer Solvers), Numerical approaches (MOR) and technological advances (Wearable sensors), can let us develop computationally inexpensive frameworks for biomechanics focused studies dealing with a huge amount of data. A lot of misunderstanding arises because of extensive data, standardization of the tools to process this, database for the material property definitions, validation and verification of biomechanical models and analytical tools to model various phenomena using computational and modelling techniques. Study of biomechanics through computational simulations can improve the prevention and treatment of diseases, predict the injury to reduce the risk and hence can strengthen pivotal sectors like sports and lifestyle. This is why we choose to present all those challenges and problems associated with biomechanical simulation with complex geometries fail so as to help improve, analysis, performance and design for better lifestyle., Comment: 9 pages, 5 figures, 1 table, and conference paper more...

The Alternate Wetting and Drying (AWD) method is a rice-growing water management technique promoted as a sustainable alternative to Continuous Flooding (CF). Climate change has placed the agricultural sector in a challenging position, particularly as global water resources become increasingly scarce, affecting rice production on irrigated lowlands. Rice, a staple food for over half of the world's population, demands significantly more water than other major crops. In Bangladesh, Boro rice, in particular, requires considerable water inputs during its cultivation. Traditionally, farmers manually measure water levels, a process that is both time-consuming and prone to errors. While ultrasonic sensors offer improvements in water height measurement, they still face limitations, such as susceptibility to weather conditions and environmental factors. To address these issues, we propose a novel approach that automates water height measurement using computer vision, specifically through a convolutional neural network (CNN). Our attention-based architecture achieved an $R^2$ score of 0.9885 and a Mean Squared Error (MSE) of 0.2766, providing a more accurate and efficient solution for managing AWD systems., Comment: 15 pages, 6 figures more...