Abstract

Federated Learning (FL) enables multiple users to collaboratively train a global model in a distributed manner without revealing their personal data. However, FL remains vulnerable to model poisoning attacks, where malicious actors inject crafted updates to compromise the global model’s accuracy. We propose a novel defense mechanism, Kernel-based Trust Segmentation (KeTS), to counter model poisoning attacks. Unlike existing approaches, KeTS analyzes the evolution of each client’s updates and effectively segments malicious clients using Kernel Density Estimation (KDE), even in the presence of benign outliers. We thoroughly evaluate KeTS’s performance against the six most effective model poisoning attacks (i.e., Trim-Attack, Krum-Attack, Min-Max attack, Min-Sum attack, and their variants) on four different datasets (i.e., MNIST, Fashion-MNIST, CIFAR-10, and KDD-CUP-1999) and compare its performance with three classical robust schemes (i.e., Krum, TrimMean, and Median) and a state-of-the-art defense (i.e., FLTrust). Our results show that KeTS outperforms the existing defenses in every attack setting; beating the best-performing defense by an overall average of >24% (on MNIST), >14% (on Fashion-MNIST), >9% (on CIFAR-10), > 11% (on KDD-CUP-1999). A series of further experiments (varying poisoning approaches, attacker population, etc.) reveal the consistent and superior performance of KeTS under diverse conditions. KeTS is a practical solution as it satisfies all three defense objectives (i.e., fidelity, robustness, and efficiency) without imposing additional overhead on the clients. Finally, we also discuss a simple, yet effective extension to KeTS to handle consistent-untargeted (e.g., sign-flipping) attacks as well as targeted attacks (e.g., label-flipping.

Abstract

Masonry arch bridges, which essentially form a significant part of our historic infrastructure, are constantly challenged by independent and a combination of hazard scenarios. Even though studies have quantified the behaviour of arch bridges subjected to independent structural demands, very few studies have considered the performance of these structures considering multiple structural demands. In this context, an analytical procedure is developed for masonry arches considering the interaction of support settlement and horizontal seismic forces. The adopted analysis procedure based on kinematic analysis utilizes an iterative algorithm to quantify the collapse of the arch. Linear and nonlinear kinematic frameworks are formulated to evaluate the bridge performance under the compound structural demands, which is validated against a numerical model. The proposed method provided key insights into the interaction between the collapse mechanisms initiated by support translation and seismic demand and their effect on the arch capacity. Keywords: limit analysis, seismic collapse factor, settlement, failure mechanism, masonry arch, multi-hazard

Abstract

This paper presents the submission of IIITHBUT to the IWSLT 2025 shared task on speech translation for the low-resource Bhojpuri-Hindi language pair. We explored the impact of hyperparameter optimisation and data augmentation techniques on the performance of the SeamlessM4T model fine-tuned for this specific task. We systematically investigated a range of hyperparameters including learning rate schedules, number of update steps, warm-up steps, label smoothing, and batch sizes; and report their effect on translation quality. To address data scarcity, we applied speed perturbation and SpecAugment and studied their effect on translation quality. We also examined the use of cross-lingual signal through joint training with Marathi and Bhojpuri speech data. Our experiments reveal that careful selection of hyperparameters and the application of simple yet effective augmentation techniques significantly improve performance in low-resource settings. We also analysed the translation hypotheses to understand various kinds of errors that impacted the translation quality in terms of BLEU

Abstract

Exploring promiscuous catalytic activity of enzymes to stereoselective transformations uncovers ecofriendly asymmetric catalysts and provides scope to execute new-to-nature chemistry. Biocatalytic promiscuous diastereoselective Henry reaction (DHR) is restricted to a few hydroxynitrile lyases (HNLs), often engineered, with narrow synthetic scope and anti-selectivity. Here, we report a single native enzyme exhibiting complementary diastereoselectivity in an asymmetric Henry reaction. Baliospermum montanum HNL (BmHNL)-catalyzed DHR enabled the production of thirty-two anti-(1S,2R)-β-nitroalcohols with up to >99% conversion, >99% ee, and >99% de using longer nitroalkanes. It afforded syn diastereomers, i.e., (1S,2S)-β-nitroalcohols as thermodynamically controlled products by manipulating the reaction conditions. Reuse of BmHNL in DHR for 20 cycles has empowered the synthesis of (1S,2R)-2-nitro-1-phenylpropan-1-ol (NPP), where it retained 82% of its initial activity, maintained >99% ee on each cycle, and provided total turnover number >3.7 × 104, which is 140-fold higher compared to the only existing biocatalytic DHR. Our study demonstrates a gram-scale synthesis of (1S,2R)-NPP and a preparative-scale synthesis of (1S,2S)-NPP, a precursor to d-norpseudoephedrine used for the treatment of obesity. The origin of the inverse diastereoselectivity was probed using isotope labeling studies. Molecular modeling of the reaction using density functional theory methods reveals the underlying mechanism of the diastereoselectivity and the competing kinetic vs thermodynamic nature of the reaction. Despite a promiscuous catalytic activity, the outstanding catalytic efficiency, broad synthetic scope, and complementary diastereoselectivity of the native enzyme of BmHNL on DHR are remarkable, which illustrates it as a specialized biocatalyst for greener technology and stereocontrolled production of diverse β-nitroalcohols.

Abstract

AuCu nanoclusters have widespread application in reactions like activation of CO2, selective oxidation, and cross-coupling reactions. In this study, we investigate the stepwise doping of copper atoms in a pure 13-atom gold cluster, denoted as AumCun (m+n = 13). The genetic algorithm based on the artificial bee colony algorithm has been utilized to model various isomers of each composition. The potential energy landscape of these clusters was analyzed by means of the density functional theory method with pure Perdew−Burke−Ernzerhof (PBE) functional. We identify the minimum energy isomer for each cluster composition to evaluate molecular properties like HOMO–LUMO gap, binding energy/atom, second order difference in energy, vertical ionization energy, and vertical electron affinity. Notably, the introduction of copper atoms in these clusters enhances their stability and reactivity. Distinct odd–even oscillations due to close shell electronic configurations are absent, as all cluster compositions have an overall open shell configuration. To assess the catalytic activity of the clusters, we study the adsorption energies of small molecules like O 2 and C 2 H 4 on all available sites on the cluster. This study thereby comprehensively explores the range of copper-doped 13-atom gold cluster compositions and their implications on their structure–property relationships vital for catalysis and nanomaterial applications.

Abstract

Evaluating tables qualitatively and quantitatively poses a significant challenge, as standard metrics often overlook subtle structural and content-level discrepancies. To address this, we propose a rubric-based evaluation framework that integrates multi-level structural descriptors with fine-grained contextual signals, enabling more precise and consistent table comparison. Building on this, we introduce TabXEval, an eXhaustive and eXplainable two-phase evaluation framework. TabXEval first aligns reference and predicted tables structurally via TabAlign, then performs semantic and syntactic comparison using TabCompare, offering interpretable and granular feedback. We evaluate TabXEval on TabXBench, a diverse, multi-domain benchmark featuring realistic table perturbations and human annotations. A sensitivity-specificity analysis further demonstrates the robustness and explainability of TabXEval across varied table tasks. Code and data are available at https://coral-lab-asu.github.io/tabxeval/.

Abstract

Cataract surgery is the most common surgical procedure globally, with a disproportionately higher burden in developing countries. While automated surgical video analysis has been explored in general surgery, its application to ophthalmic procedures remains limited. Existing research primarily focuses on Phaco cataract surgery, an expensive technique not accessible in regions where cataract treatment is most needed. In contrast, Manual Small-Incision Cataract Surgery (MSICS) is the preferred low-cost, faster alternative in high-volume settings and for complex cases. However, no dataset exists for MSICS. To address this gap, we introduce Cataract-MSICS, the first comprehensive dataset containing 53 surgical videos annotated for 18 surgical phases and 3,527 frames with 13 surgical tools at the pixel level. We also present ToolSeg, a novel framework that enhances tool segmentation with a phase-conditional decoder and a semi-supervised setup leveraging pseudo-labels from foundation models. Our approach significantly improves segmentation performance, achieving a 38.10% increase in mean Dice scores, with notable gains for less prevalent and smaller tools.

Abstract

Maritime communication, critical for global oceanic trade, faces challenges and opportunities with advancements in Information and Communication Technology (ICT). Traditional methods are susceptible to interception due to open channels, limited authentication, jamming, and other security risks. Securing vessel movements and locations in Internet of Vessels (IoV) is essential to prevent unauthorized data interception and tampering during transmission. We propose a ship authentication method using location-based secure keys to ensure the confidentiality of a vessel’s whereabouts. The proposed scheme’s robustness is validated through formal and informal security analyses, and formal verification using the Scyther automated verification tool, demonstrating its effectiveness against potential attacks in maritime networks. Comparative studies indicate that utilizing location-based keys maintains anonymity and untraceability without imposing significant computational or communication burdens. Experimental findings from comprehensive big data analytics and simulations using NS3 validate the scheme’s feasibility and performance.

Abstract

Many real-world systems can be modeled as dynamic graphs, where nodes and edges evolve over time, requiring specialized models to capture their evolving dynamics in risk-sensitive applications effectively. Graph neural networks (GNNs) for temporal graphs are one such category of specialized models. For the first time, our approach integrates a reject option strategy within the framework of GNNs for continuous-time dynamic graphs (CTDGs). This allows the model to strategically abstain from making predictions when the uncertainty is high and confidence is low, thus minimizing the risk of critical misclassification and enhancing the results and reliability. We propose a coverage-based abstention prediction model to implement the reject option that maximizes prediction within a specified coverage. It improves the prediction score for link prediction and node classification tasks. Temporal GNNs deal with extremely skewed datasets for the next state prediction or node classification task. In the case of class imbalance, our method can be further tuned to provide a higher weight to the minority class. Exhaustive experiments are presented on four datasets for dynamic link prediction and two datasets for dynamic node classification tasks. This demonstrates the effectiveness of our approach in improving the reliability and area under the curve (AUC)/average precision (AP) scores for predictions in dynamic graph scenarios. The results highlight our model's ability to efficiently handle the trade-offs between prediction confidence and coverage, making it a dependable solution for applications requiring high precision in dynamic and uncertain environments.

Abstract

Link prediction can help rectify inaccuracies in various graph algorithms, stemming from unaccounted-for or overlooked links within networks. However, many existing works use a baseline approach, which incurs unnecessary computational costs due to its high time complexity. Further, many studies focus on smaller graphs, which can lead to misleading conclusions. This submission introduces our parallel approach, called LHub, which predict links using neighborhood-based similarity measures on large graphs. LHub is a heuristic approach that disregards large hubs, based on the idea that high-degree nodes contribute little similarity among their neighbors. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, LHub is on average 1019x faster than not disregarding hubs, especially on web graphs and social networks, while maintaining similar prediction accuracy. Notably, LHub achieves a link prediction rate of 38.1M edges/s and improves performance at a rate of 1.6x for every doubling of threads.