Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their tendency to memorize training data raises concerns regarding privacy, copyright compliance, and security, particularly in cases involving Personally Identifiable Information (PII). Effective machine unlearning techniques are essential to mitigate these risks, yet existing methods remain underdeveloped for LLMs due to their open-ended output space. In this work, we apply the Adaptive Representation Misdirection Unlearning (RMU) technique to unlearn sensitive information from LLMs. Through extensive experiments, we analyze the effects of unlearning across different decoder layers to determine the most effective regions for sensitive information removal. Our technique ranked 4th on the official leaderboard of both 1B parameter and 7B parameter models.

Abstract

Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. These findings provide insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, paving the way for potential targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.

Abstract

Free-form electronics are one of the major chal- lenges being tackled in the present era. Along with the substrate and components, the conductive materials used in these also have to endure the applied strain, hence, thin film based conductors are being studied extensively. In this work, we report bending behavior of conductive CNT-PDMS thin film channels. Transmission Line Method (TLM) based resistance calculations have been done to precisely analyze the CNT thin film sheet resistance and the contact resistance. This exercise allows us to look at the effectiveness of CNT channels as a conductive path and also helps us understand their behavior over concentration, path length and strain caused due to bending. We report sheet resistance values of 254 Ω/□ and 1.41 Ω/□ for thin films of two different CNT concentrations. The contact resistances for these films were 230Ω and 315 Ω respectively. We observed that bending induced strain caused the resistance values to increase from 3.95 kΩ for flat to 13.1 kΩ for bending radius of 1.2 cm.

Abstract

Recent literature in soft robotics has been focused on bio-mimetic technologies, i.e., trying to replicate naturally-occurring structures, such as artificial muscles. Flapping structures have been reported, wherein a soft wing is paired with a solid actuator or vice-versa, but a fully soft actuator and wing setup has only recently been developed. In this work, we present the fabrication and characterization of the electrical response of a fully soft DEA-based flapping wing actuator. The actuator was fabricated using a urethane acrylate polymer elastomer with carbon nanotube (CNT) based electrodes. The 40 mm × 20 mm rectangular actuator was powered through two electrodes. It was subjected to static DC voltages in the range of 1 kV to 2 kV and the current and energy drawn by the actuator were observed. We report the average settling current varying from 7 μA to 43 μA and energy draw varying from 220 mJ to 715 mJ. Further tests on a similarly fabricated actuator revealed the possibility of Time Dependent Dielectric breakdown (TDDB) encountered at 2kV after nearly 120 minutes.

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/.