Showing 1–50 of 180 results for author: Narayanan, V

FishRoPE: Projective Rotary Position Embeddings for Omnidirectional Visual Perception

Authors: Rahul Ahuja, Mudit Jain, Bala Murali Manoghar Sai Sudhakar, Venkatraman Narayanan, Pratik Likhar, Varun Ravi Kumar, Senthil Yogamani

Abstract: Vision foundation models (VFMs) and Bird's Eye View (BEV) representation have advanced visual perception substantially, yet their internal spatial representations assume the rectilinear geometry of pinhole cameras. Fisheye cameras, widely deployed on production autonomous vehicles for their surround-view coverage, exhibit severe radial distortion that renders these representations geometrically in… ▽ More Vision foundation models (VFMs) and Bird's Eye View (BEV) representation have advanced visual perception substantially, yet their internal spatial representations assume the rectilinear geometry of pinhole cameras. Fisheye cameras, widely deployed on production autonomous vehicles for their surround-view coverage, exhibit severe radial distortion that renders these representations geometrically inconsistent. At the same time, the scarcity of large-scale fisheye annotations makes retraining foundation models from scratch impractical. We present \ours, a lightweight framework that adapts frozen VFMs to fisheye geometry through two components: a frozen DINOv2 backbone with Low-Rank Adaptation (LoRA) that transfers rich self-supervised features to fisheye without task-specific pretraining, and Fisheye Rotary Position Embedding (FishRoPE), which reparameterizes the attention mechanism in the spherical coordinates of the fisheye projection so that both self-attention and cross-attention operate on angular separation rather than pixel distance. FishRoPE is architecture-agnostic, introduces negligible computational overhead, and naturally reduces to the standard formulation under pinhole geometry. We evaluate \ours on WoodScape 2D detection (54.3 mAP) and SynWoodScapes BEV segmentation (65.1 mIoU), where it achieves state-of-the-art results on both benchmarks. △ Less

Submitted 11 April, 2026; originally announced April 2026.

Simulating the swimming motion of a flagellated bacterium in a microstructured bio-fluid

Authors: Arjun Sharma, Sabarish V. Narayanan, Sarah Hormozi, Donald L. Koch

Abstract: We develop a numerical framework to simulate the locomotion of a flagellated bacterium with a spheroidal head (such as Escherichia coli) in biological fluids like mucus, which are entangled polymer solutions exhibiting elasto-viscoplastic (EVP) rheology and porous microstructure. To account for the scale disparity between the large bacterial head and the slender flagellar bundle, whose thickness i… ▽ More We develop a numerical framework to simulate the locomotion of a flagellated bacterium with a spheroidal head (such as Escherichia coli) in biological fluids like mucus, which are entangled polymer solutions exhibiting elasto-viscoplastic (EVP) rheology and porous microstructure. To account for the scale disparity between the large bacterial head and the slender flagellar bundle, whose thickness is comparable to the pore size, we employ a two-fluid model in which the bundle directly drives the solvent and exchanges momentum with the polymer phase via drag proportional to their relative velocity. The numerical implementation combines a finite-difference discretization of the two-fluid equations with a slender-body theory (SBT) to model flagellar forcing. A key observation is that the coupled mass and momentum equations for these inertialess flows, together with SBT, are linear in the pressure and velocity fields and in the force distribution along the flagellar bundle. By treating the polymer stress as a body force, we decompose the flow field and hydrodynamic moments into three additive contributions: kinematic (motion), flagellar forcing, and polymer stress. This decomposition allows several components of the flow to be precomputed and enables the determination of swimming velocity via a resistivity formulation driven by polymer-induced forces, which greatly improves computational efficiency during transient calculations of the polymer stress and the resulting flow. We validate the method and use it to analyze how polymer microstructure and its interactions with the bacterial head and tail affect motility in complex biofluids. △ Less

Submitted 31 March, 2026; v1 submitted 28 March, 2026; originally announced March 2026.

Reliable and High Performance IGZO and In2O3 Transistors via Channel Capping

Authors: C. W. Cheng, J. Smith, K. Mashooq, P. Solomon, R. Watters, T. Philicelli, D. Piatek, C. Lavoie, M. Hopstaken, L. Gignac, B. Khan, M. BrightSky, G. Gionta, P. Hashemi, V. Narayanan, M. M. Frank

Abstract: A device and process strategy for achieving reliable indium gallium zinc oxide and indium oxide transistors compatible with a 400oC BEOL thermal budget and without performance degradation is demonstrated by fully exploiting intrinsic oxide material properties. An indium oxide transistor with a novel amorphous In2O3 mixed with SiO2 capping layer exhibits a positive threshold voltage, high extrinsic… ▽ More A device and process strategy for achieving reliable indium gallium zinc oxide and indium oxide transistors compatible with a 400oC BEOL thermal budget and without performance degradation is demonstrated by fully exploiting intrinsic oxide material properties. An indium oxide transistor with a novel amorphous In2O3 mixed with SiO2 capping layer exhibits a positive threshold voltage, high extrinsic saturation mobility 33.1 cm2/V.s ,and only a 5mV Vt shift after positive-bias stress at 3 MV/cm for 1000s at room temperature, superior to conventional SiO2 encapsulation. △ Less

Submitted 24 March, 2026; originally announced March 2026.

MambaFusion: Adaptive State-Space Fusion for Multimodal 3D Object Detection

Authors: Venkatraman Narayanan, Bala Sai, Rahul Ahuja, Pratik Likhar, Varun Ravi Kumar, Senthil Yogamani

Abstract: Reliable 3D object detection is fundamental to autonomous driving, and multimodal fusion algorithms using cameras and LiDAR remain a persistent challenge. Cameras provide dense visual cues but ill posed depth; LiDAR provides a precise 3D structure but sparse coverage. Existing BEV-based fusion frameworks have made good progress, but they have difficulties including inefficient context modeling, sp… ▽ More Reliable 3D object detection is fundamental to autonomous driving, and multimodal fusion algorithms using cameras and LiDAR remain a persistent challenge. Cameras provide dense visual cues but ill posed depth; LiDAR provides a precise 3D structure but sparse coverage. Existing BEV-based fusion frameworks have made good progress, but they have difficulties including inefficient context modeling, spatially invariant fusion, and reasoning under uncertainty. We introduce MambaFusion, a unified multi-modal detection framework that achieves efficient, adaptive, and physically grounded 3D perception. MambaFusion interleaves selective state-space models (SSMs) with windowed transformers to propagate the global context in linear time while preserving local geometric fidelity. A multi-modal token alignment (MTA) module and reliability-aware fusion gates dynamically re-weight camera-LiDAR features based on spatial confidence and calibration consistency. Finally, a structure-conditioned diffusion head integrates graph-based reasoning with uncertainty-aware denoising, enforcing physical plausibility, and calibrated confidence. MambaFusion establishes new state-of-the-art performance on nuScenes benchmarks while operating with linear-time complexity. The framework demonstrates that coupling SSM-based efficiency with reliability-driven fusion yields robust, temporally stable, and interpretable 3D perception for real-world autonomous driving systems. △ Less

Submitted 11 February, 2026; v1 submitted 8 February, 2026; originally announced February 2026.

Mitigating Task-Order Sensitivity and Forgetting via Hierarchical Second-Order Consolidation

Authors: Protik Nag, Krishnan Raghavan, Vignesh Narayanan

Abstract: We introduce $\textbf{Hierarchical Taylor Series-based Continual Learning (HTCL)}$, a framework that couples fast local adaptation with conservative, second-order global consolidation to address the high variance introduced by random task ordering. To address task-order effects, HTCL identifies the best intra-group task sequence and integrates the resulting local updates through a Hessian-regulari… ▽ More We introduce $\textbf{Hierarchical Taylor Series-based Continual Learning (HTCL)}$, a framework that couples fast local adaptation with conservative, second-order global consolidation to address the high variance introduced by random task ordering. To address task-order effects, HTCL identifies the best intra-group task sequence and integrates the resulting local updates through a Hessian-regularized Taylor expansion, yielding a consolidation step with theoretical guarantees. The approach naturally extends to an $L$-level hierarchy, enabling multiscale knowledge integration in a manner not supported by conventional single-level CL systems. Across a wide range of datasets and replay and regularization baselines, HTCL acts as a model-agnostic consolidation layer that consistently enhances performance, yielding mean accuracy gains of $7\%$ to $25\%$ while reducing the standard deviation of final accuracy by up to $68\%$ across random task permutations. △ Less

Submitted 30 January, 2026; originally announced February 2026.

Comments: 21 pages, 8 figures

Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priors

Authors: Md Abir Hossen, Mohammad Ali Javidian, Vignesh Narayanan, Jason M. O'Kane, Pooyan Jamshidi

Abstract: Multi-fidelity Bayesian optimization (MFBO) accelerates the search for the global optimum of black-box functions by integrating inexpensive, low-fidelity approximations. The central task of an MFBO policy is to balance the cost-efficiency of low-fidelity proxies against their reduced accuracy to ensure effective progression toward the high-fidelity optimum. Existing MFBO methods primarily capture… ▽ More Multi-fidelity Bayesian optimization (MFBO) accelerates the search for the global optimum of black-box functions by integrating inexpensive, low-fidelity approximations. The central task of an MFBO policy is to balance the cost-efficiency of low-fidelity proxies against their reduced accuracy to ensure effective progression toward the high-fidelity optimum. Existing MFBO methods primarily capture associational dependencies between inputs, fidelities, and objectives, rather than causal mechanisms, and can perform poorly when lower-fidelity proxies are poorly aligned with the target fidelity. We propose RESCUE (REducing Sampling cost with Causal Understanding and Estimation), a multi-objective MFBO method that incorporates causal calculus to systematically address this challenge. RESCUE learns a structural causal model capturing causal relationships between inputs, fidelities, and objectives, and uses it to construct a probabilistic multi-fidelity (MF) surrogate that encodes intervention effects. Exploiting the causal structure, we introduce a causal hypervolume knowledge-gradient acquisition strategy to select input-fidelity pairs that balance expected multi-objective improvement and cost. We show that RESCUE improves sample efficiency over state-of-the-art MF optimization methods on synthetic and real-world problems in robotics, machine learning (AutoML), and healthcare. △ Less

Submitted 31 January, 2026; originally announced February 2026.

Convective scalar transport from spherical drops in complex shearing flows

Authors: Sabarish V. Narayanan, Ganesh Subramanian

Abstract: We calculate the scalar transport rate, as characterized by the Nusselt number\,($Nu$), from a neutrally buoyant spherical drop in an ambient linear flow, in the absence of inertia and in the strong convection limit. This corresponds to the regime $Re \ll 1, Pe \gg 1$, where $Re$ and $Pe$ are the Reynolds and Péclet numbers, and denote the ratios of the diffusive and convective time scales associa… ▽ More We calculate the scalar transport rate, as characterized by the Nusselt number\,($Nu$), from a neutrally buoyant spherical drop in an ambient linear flow, in the absence of inertia and in the strong convection limit. This corresponds to the regime $Re \ll 1, Pe \gg 1$, where $Re$ and $Pe$ are the Reynolds and Péclet numbers, and denote the ratios of the diffusive and convective time scales associated with momentum and scalar transport. The focus is on the exterior problem, with the drop-phase transport resistance assumed negligible, and the scalar field being a constant on the drop surface. While $Nu \propto Pe^{\frac{1}{2}}$ for $Pe \gg 1$, owing to the transport occurring across a thin $O(a Pe^{-\frac{1}{2}})$ boundary layer\,($a$ being the drop radius), the proportionality factor in this relation depends sensitively on ambient flow geometry via the surface-streamline topology. Unlike a rigid sphere, a variety of surface-streamline topologies can drive transport across the boundary layer at widely differing rates. In contrast to earlier studies which almost exclusively focus on axisymmetric ambient flows, we calculate $Nu$ for a pair of non-axisymmetric linear flow families: (i) 3D extensional flows with aligned vorticity and (ii) Axisymmetric extensional flows with inclined vorticity, using a non-orthogonal surface-streamline aligned coordinate system. Taken together, the families span the entire gamut of surface-streamline topologies in the space of incompressible linear flows. Independent numerical simulations of the interior problem reveal the emergence of an $O(aPe^{-\frac{1}{2}})$ boundary layer beneath the drop surface, driven by chaotic streamlines, pointing to the possibility of $Nu \propto Pe^{\frac{1}{2}}$ for the conjugate problem, for sufficiently large $Pe$. △ Less

Submitted 23 January, 2026; originally announced January 2026.

RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection

Authors: Shreyas Shende, Varsha Narayanan, Vishal Fenn, Yiran Huang, Dincer Goksuluk, Gaurav Choudhary, Melih Agraz, Mengjia Xu

Abstract: Early detection of cancer plays a key role in improving survival rates, but identifying reliable biomarkers from RNA-seq data is still a major challenge. The data are high-dimensional, and conventional statistical methods often fail to capture the complex relationships between genes. In this study, we introduce RGE-GCN (Recursive Gene Elimination with Graph Convolutional Networks), a framework tha… ▽ More Early detection of cancer plays a key role in improving survival rates, but identifying reliable biomarkers from RNA-seq data is still a major challenge. The data are high-dimensional, and conventional statistical methods often fail to capture the complex relationships between genes. In this study, we introduce RGE-GCN (Recursive Gene Elimination with Graph Convolutional Networks), a framework that combines feature selection and classification in a single pipeline. Our approach builds a graph from gene expression profiles, uses a Graph Convolutional Network to classify cancer versus normal samples, and applies Integrated Gradients to highlight the most informative genes. By recursively removing less relevant genes, the model converges to a compact set of biomarkers that are both interpretable and predictive. We evaluated RGE-GCN on synthetic data as well as real-world RNA-seq cohorts of lung, kidney, and cervical cancers. Across all datasets, the method consistently achieved higher accuracy and F1-scores than standard tools such as DESeq2, edgeR, and limma-voom. Importantly, the selected genes aligned with well-known cancer pathways including PI3K-AKT, MAPK, SUMOylation, and immune regulation. These results suggest that RGE-GCN shows promise as a generalizable approach for RNA-seq based early cancer detection and biomarker discovery (https://rce-gcn.streamlit.app/ ). △ Less

Submitted 7 December, 2025; v1 submitted 3 December, 2025; originally announced December 2025.

Comments: 12 pages, 2 figures

Parts-Mamba: Augmenting Joint Context with Part-Level Scanning for Occluded Human Skeleton

Authors: Tianyi Shen, Huijuan Xu, Nilesh Ahuja, Omesh Tickoo, Philip Shin, Vijaykrishnan Narayanan

Abstract: Skeleton action recognition involves recognizing human action from human skeletons. The use of graph convolutional networks (GCNs) has driven major advances in this recognition task. In real-world scenarios, the captured skeletons are not always perfect or complete because of occlusions of parts of the human body or poor communication quality, leading to missing parts in skeletons or videos with m… ▽ More Skeleton action recognition involves recognizing human action from human skeletons. The use of graph convolutional networks (GCNs) has driven major advances in this recognition task. In real-world scenarios, the captured skeletons are not always perfect or complete because of occlusions of parts of the human body or poor communication quality, leading to missing parts in skeletons or videos with missing frames. In the presence of such non-idealities, existing GCN models perform poorly due to missing local context. To address this limitation, we propose Parts-Mamba, a hybrid GCN-Mamba model designed to enhance the ability to capture and maintain contextual information from distant joints. The proposed Parts-Mamba model effectively captures part-specific information through its parts-specific scanning feature and preserves non-neighboring joint context via a parts-body fusion module. Our proposed model is evaluated on the NTU RGB+D 60 and NTU RGB+D 120 datasets under different occlusion settings, achieving up to 12.9% improvement in accuracy. △ Less

Submitted 20 November, 2025; originally announced November 2025.

MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition

Authors: Shu Zhao, Nilesh Ahuja, Tan Yu, Tianyi Shen, Vijaykrishnan Narayanan

Abstract: Pre-trained vision language models have shown remarkable performance on visual recognition tasks, but they typically assume the availability of complete multimodal inputs during both training and inference. In real-world scenarios, however, modalities may be missing due to privacy constraints, collection difficulties, or resource limitations. While previous approaches have addressed this challenge… ▽ More Pre-trained vision language models have shown remarkable performance on visual recognition tasks, but they typically assume the availability of complete multimodal inputs during both training and inference. In real-world scenarios, however, modalities may be missing due to privacy constraints, collection difficulties, or resource limitations. While previous approaches have addressed this challenge using prompt learning techniques, they fail to capture the cross-modal relationships necessary for effective multimodal visual recognition and suffer from inevitable computational overhead. In this paper, we introduce MoRA, a parameter-efficient fine-tuning method that explicitly models cross-modal interactions while maintaining modality-specific adaptations. MoRA introduces modality-common parameters between text and vision encoders, enabling bidirectional knowledge transfer. Additionally, combined with the modality-specific parameters, MoRA allows the backbone model to maintain inter-modality interaction and enable intra-modality flexibility. Extensive experiments on standard benchmarks demonstrate that MoRA achieves an average performance improvement in missing-modality scenarios by 5.24% and uses only 25.90% of the inference time compared to the SOTA method while requiring only 0.11% of trainable parameters compared to full fine-tuning. △ Less

Submitted 8 November, 2025; originally announced November 2025.

Rethinking Pipe Flow Stability: Insights from a Meshless Global Analysis

Authors: Akash Unnikrishnan, Vinod Narayanan

Abstract: Despite extensive experimental evidence of turbulence in Hagen Poiseuille flow, linear stability analysis has not yet confirmed its instability. One challenge is the singularity introduced by the term 1/r in the center of the pipe, which complicates traditional stability approaches. In this study, we explore a global stability analysis using a meshless framework. Although this approach did not rec… ▽ More Despite extensive experimental evidence of turbulence in Hagen Poiseuille flow, linear stability analysis has not yet confirmed its instability. One challenge is the singularity introduced by the term 1/r in the center of the pipe, which complicates traditional stability approaches. In this study, we explore a global stability analysis using a meshless framework. Although this approach did not recover the expected unstable modes, it revealed a new set of modes with distinct characteristics from those observed in local stability analysis. We analyze these modes and their impact on transient energy growth, demonstrating the effectiveness of the global approach in capturing localized instabilities without requiring multiple simulations. △ Less

Submitted 28 October, 2025; originally announced October 2025.

Comments: 6 pages, 10 figures

Windsock is Dancing: Adaptive Multimodal Retrieval-Augmented Generation

Authors: Shu Zhao, Tianyi Shen, Nilesh Ahuja, Omesh Tickoo, Vijaykrishnan Narayanan

Abstract: Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a promising method to generate factual and up-to-date responses of Multimodal Large Language Models (MLLMs) by incorporating non-parametric knowledge from external knowledge bases. However, existing MRAG approaches suffer from static retrieval strategies, inflexible modality selection, and suboptimal utilization of retrieved informati… ▽ More Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a promising method to generate factual and up-to-date responses of Multimodal Large Language Models (MLLMs) by incorporating non-parametric knowledge from external knowledge bases. However, existing MRAG approaches suffer from static retrieval strategies, inflexible modality selection, and suboptimal utilization of retrieved information, leading to three critical challenges: determining when to retrieve, what modality to incorporate, and how to utilize retrieved information effectively. To address these challenges, we introduce Windsock, a query-dependent module making decisions on retrieval necessity and modality selection, effectively reducing computational overhead and improving response quality. Additionally, we propose Dynamic Noise-Resistance (DANCE) Instruction Tuning, an adaptive training strategy that enhances MLLMs' ability to utilize retrieved information while maintaining robustness against noise. Moreover, we adopt a self-assessment approach leveraging knowledge within MLLMs to convert question-answering datasets to MRAG training datasets. Extensive experiments demonstrate that our proposed method significantly improves the generation quality by 17.07% while reducing 8.95% retrieval times. △ Less

Submitted 26 October, 2025; originally announced October 2025.

Comments: Accepted at NeurIPS 2025 UniReps Workshop

Towards Information-Optimized Multi-Agent Path Finding: A Hybrid Framework with Reduced Inter-Agent Information Sharing

Authors: Bharath Muppasani, Ritirupa Dey, Biplav Srivastava, Vignesh Narayanan

Abstract: Multi-agent pathfinding (MAPF) remains a critical problem in robotics and autonomous systems, where agents must navigate shared spaces efficiently while avoiding conflicts. Traditional centralized algorithms with global information provide high-quality solutions but scale poorly in large-scale scenarios due to the combinatorial explosion of conflicts. Conversely, distributed approaches that have l… ▽ More Multi-agent pathfinding (MAPF) remains a critical problem in robotics and autonomous systems, where agents must navigate shared spaces efficiently while avoiding conflicts. Traditional centralized algorithms with global information provide high-quality solutions but scale poorly in large-scale scenarios due to the combinatorial explosion of conflicts. Conversely, distributed approaches that have local information, particularly learning-based methods, offer better scalability by operating with relaxed information availability, yet often at the cost of solution quality. In realistic deployments, information is a constrained resource: broadcasting full agent states and goals can raise privacy concerns, strain limited bandwidth, and require extra sensing and communication hardware, increasing cost and energy use. We focus on the core question of how MAPF can be solved with minimal inter-agent information sharing while preserving solution feasibility. To this end, we present an information-centric formulation of the MAPF problem and introduce a hybrid framework, IO-MAPF, that integrates decentralized path planning with a lightweight centralized coordinator. In this framework, agents use reinforcement learning (RL) to plan independently, while the central coordinator provides minimal, targeted signals, such as static conflict-cell indicators or short conflict trajectories, that are dynamically shared to support efficient conflict resolution. We introduce an Information Units (IU) metric to quantify information use and show that our alert-driven design achieves 2x to 23x reduction in information sharing, compared to the state-of-the-art algorithms, while maintaining high success rates, demonstrating that reliable MAPF is achievable under strongly information-restricted, privacy-preserving conditions. We demonstrate the effectiveness of our algorithm using simulation and hardware experiments. △ Less

Submitted 22 February, 2026; v1 submitted 10 October, 2025; originally announced October 2025.

NaviSense: A Multimodal Assistive Mobile application for Object Retrieval by Persons with Visual Impairment

Authors: Ajay Narayanan Sridhar, Fuli Qiao, Nelson Daniel Troncoso Aldas, Yanpei Shi, Mehrdad Mahdavi, Laurent Itti, Vijaykrishnan Narayanan

Abstract: People with visual impairments often face significant challenges in locating and retrieving objects in their surroundings. Existing assistive technologies present a trade-off: systems that offer precise guidance typically require pre-scanning or support only fixed object categories, while those with open-world object recognition lack spatial feedback for reaching the object. To address this gap, w… ▽ More People with visual impairments often face significant challenges in locating and retrieving objects in their surroundings. Existing assistive technologies present a trade-off: systems that offer precise guidance typically require pre-scanning or support only fixed object categories, while those with open-world object recognition lack spatial feedback for reaching the object. To address this gap, we introduce 'NaviSense', a mobile assistive system that combines conversational AI, vision-language models, augmented reality (AR), and LiDAR to support open-world object detection with real-time audio-haptic guidance. Users specify objects via natural language and receive continuous spatial feedback to navigate toward the target without needing prior setup. Designed with insights from a formative study and evaluated with 12 blind and low-vision participants, NaviSense significantly reduced object retrieval time and was preferred over existing tools, demonstrating the value of integrating open-world perception with precise, accessible guidance. △ Less

Submitted 23 September, 2025; originally announced September 2025.

Losing the Plot: How VLM responses degrade on imperfect charts

Authors: Philip Wootaek Shin, Jack Sampson, Vijaykrishnan Narayanan, Andres Marquez, Mahantesh Halappanavar

Abstract: Vision language models (VLMs) show strong results on chart understanding, yet existing benchmarks assume clean figures and fact based queries. Real world charts often contain distortions and demand reasoning beyond simple matching. We evaluate ChatGPT 4o, Claude Sonnet 4, and Gemini 2.5 Pro, finding sharp performance drops under corruption or occlusion, with hallucinations such as value fabricatio… ▽ More Vision language models (VLMs) show strong results on chart understanding, yet existing benchmarks assume clean figures and fact based queries. Real world charts often contain distortions and demand reasoning beyond simple matching. We evaluate ChatGPT 4o, Claude Sonnet 4, and Gemini 2.5 Pro, finding sharp performance drops under corruption or occlusion, with hallucinations such as value fabrication, trend misinterpretation, and entity confusion becoming more frequent. Models remain overconfident in degraded settings, generating plausible but unsupported explanations. To address this gap, we introduce CHART NOISe(Chart Hallucinations, Answers, and Reasoning Testing on Noisy and Occluded Input Selections), a dataset combining chart corruptions, occlusions, and exam style multiple choice questions inspired by Korea's CSAT English section. A key innovation is prompt reverse inconsistency, where models contradict themselves when asked to confirm versus deny the same statement. Our contributions are threefold: (1) benchmarking state of the art VLMs, exposing systematic vulnerabilities in chart reasoning; (2) releasing CHART NOISe, the first dataset unifying corruption, occlusion, and reverse inconsistency; and (3) proposing baseline mitigation strategies such as quality filtering and occlusion detection. Together, these efforts establish a rigorous testbed for advancing robustness and reliability in chart understanding. △ Less

Submitted 22 September, 2025; originally announced September 2025.

Single-Cell Universal Logic-in-Memory Using 2T-nC FeRAM: An Area and Energy-Efficient Approach for Bulk Bitwise Computation

Authors: Rudra Biswas, Jiahui Duan, Shan Deng, Xuezhong Niu, Yixin Qin, Prapti Panigrahi, Varun Parekh, Rajiv Joshi, Kai Ni, Vijaykrishnan Narayanan

Abstract: This work presents a novel approach to configure 2T-nC ferroelectric RAM (FeRAM) for performing single cell logic-in-memory operations, highlighting its advantages in energy-efficient computation over conventional DRAM-based approaches. Unlike conventional 1T-1C dynamic RAM (DRAM), which incurs refresh overhead, 2T-nC FeRAM offers a promising alternative as a non-volatile memory solution with low… ▽ More This work presents a novel approach to configure 2T-nC ferroelectric RAM (FeRAM) for performing single cell logic-in-memory operations, highlighting its advantages in energy-efficient computation over conventional DRAM-based approaches. Unlike conventional 1T-1C dynamic RAM (DRAM), which incurs refresh overhead, 2T-nC FeRAM offers a promising alternative as a non-volatile memory solution with low energy consumption. Our key findings include the potential of quasi-nondestructive readout (QNRO) sensing in 2T-nC FeRAM for logic-in-memory (LiM) applications, demonstrating its inherent capability to perform inverting logic without requiring external modifications, a feature absent in traditional 1T-1C DRAM. We successfully implement the MINORITY function within a single cell of 2T-nC FeRAM, enabling universal NAND and NOR logic, validated through SPICE simulations and experimental data. Additionally, the research investigates the feasibility of 3D integration with 2T-nC FeRAM, showing substantial improvements in storage and computational density, facilitating bulk-bitwise computation. Our evaluation of eight real-world, data-intensive applications reveals that 2T-nC FeRAM achieves 2x higher performance and 2.5x lower energy consumption compared to DRAM. Furthermore, the thermal stability of stacked 2T-nC FeRAM is validated, confirming its reliable operation when integrated on a compute die. These findings emphasize the advantages of 2T-nC FeRAM for LiM, offering superior performance and energy efficiency over conventional DRAM. △ Less

Submitted 22 September, 2025; originally announced September 2025.

Comments: 6 Pages, 7 Figures, To be presented at System on Chip Conference 2025

Innovative Oxide Transistor Satisfying Performance and Reliability Simultaneously by Understanding of Physics and Materials Properties

Authors: C. W. Cheng, J. Smith, P. Solomon, R. Watters, D. Piatek, C. Lavoie, M. Hopstaken, L. Gignac, D. Bishop, B. Khan, M. BrightSky, G. Gionta, P. Hashemi, V. Narayanan, M. M. Frank

Abstract: Guided by a comprehensive analysis of accumulation mode transistor physics and oxide semiconductor materials properties, we demonstrate an innovative oxide semiconductor transistor structure and process flow that break the constraint between performance and reliability observed in conventional InGaZnO4 (IGZO) transistors. The newly proposed 10 nm innovative IGZO transistor features high on-current… ▽ More Guided by a comprehensive analysis of accumulation mode transistor physics and oxide semiconductor materials properties, we demonstrate an innovative oxide semiconductor transistor structure and process flow that break the constraint between performance and reliability observed in conventional InGaZnO4 (IGZO) transistors. The newly proposed 10 nm innovative IGZO transistor features high on-current, high extrinsic mobility (20 cm2V-1s-1), near-zero hysteresis, and only 15 mV Vt shift after positive-bias-stress (PBS) of 3 MV/cm stress for 1000s at room temperature. △ Less

Submitted 9 September, 2025; originally announced September 2025.

Comments: Paper for Conference

Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery

Authors: Philip Wootaek Shin, Vishal Gaur, Rahul Ramachandran, Manil Maskey, Jack Sampson, Vijaykrishnan Narayanan, Sujit Roy

Abstract: High-resolution satellite imagery is essential for geospatial analysis, yet differences in spatial resolution across satellite sensors present challenges for data fusion and downstream applications. Super-resolution techniques can help bridge this gap, but existing methods rely on artificially downscaled images rather than real sensor data and are not well suited for heterogeneous satellite sensor… ▽ More High-resolution satellite imagery is essential for geospatial analysis, yet differences in spatial resolution across satellite sensors present challenges for data fusion and downstream applications. Super-resolution techniques can help bridge this gap, but existing methods rely on artificially downscaled images rather than real sensor data and are not well suited for heterogeneous satellite sensors with differing spectral, temporal characteristics. In this work, we develop a preliminary framework to align and upscale Harmonized Landsat Sentinel 30m(HLS 30) imagery using Harmonized Landsat Sentinel 10m(HLS10) as a reference from the HLS dataset. Our approach aims to bridge the resolution gap between these sensors and improve the quality of super-resolved Landsat imagery. Quantitative and qualitative evaluations demonstrate the effectiveness of our method, showing its potential for enhancing satellite-based sensing applications. This study provides insights into the feasibility of heterogeneous satellite image super-resolution and highlights key considerations for future advancements in the field. △ Less

Submitted 1 August, 2025; v1 submitted 30 July, 2025; originally announced July 2025.

Evolution of Entanglement Witness of Dicke State under Noise and Error Mitigation

Authors: Tomis Prajapati, Harsh Mehta, Shreya Banerjee, Prasanta K. Panigrahi, V. Narayanan

Abstract: The experimental verification of multipartite entangled states is essential for advancing quantum information processing. Entanglement witnesses (EWs) provide a widely used and experimentally accessible approach for detecting genuinely multipartite entangled states. In this work, we theoretically derive the entanglement witness for the four-qubit Dicke state and experimentally evaluate it on two d… ▽ More The experimental verification of multipartite entangled states is essential for advancing quantum information processing. Entanglement witnesses (EWs) provide a widely used and experimentally accessible approach for detecting genuinely multipartite entangled states. In this work, we theoretically derive the entanglement witness for the four-qubit Dicke state and experimentally evaluate it on two distinct IBM 127-qubit Quantum Processing Units (QPUs), namely ibm\_sherbrook and ibm\_brisbane. A negative expectation value of the witness operator serves as a sufficient condition for confirming genuine multipartite entanglement. We report the maximum (negative) values of the witness achieved on these QPUs as $-0.178 \pm 0.009$ and $-0.169 \pm 0.002$, corresponding to two different state preparation protocols. Additionally, we theoretically investigate the effect of various noise channels on the genuine entanglement of a four-qubit Dicke state using the Qiskit Aer simulator. We show the behavior of the EW constructed under the assumption of Markovian and non-Markovian amplitude damping and depolarizing noises, bit-phase flip noise, and readout errors. We also investigate the effect of varying thermal relaxation time on the EW, depicting a bound on the $T_1$ time required for successful generation of a Dicke State on a superconducting QPU. △ Less

Submitted 8 July, 2025; originally announced July 2025.

Journal ref: International Journal of Theoretical Physics, 64, 237 (2025)

Assessing the Performance of Analog Training for Transfer Learning

Authors: Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch, Takashi Ando, Tayfun Gokmen, Vijay Narayanan

Abstract: Analog in-memory computing is a next-generation computing paradigm that promises fast, parallel, and energy-efficient deep learning training and transfer learning (TL). However, achieving this promise has remained elusive due to a lack of suitable training algorithms. Analog memory devices exhibit asymmetric and non-linear switching behavior in addition to device-to-device variation, meaning that… ▽ More Analog in-memory computing is a next-generation computing paradigm that promises fast, parallel, and energy-efficient deep learning training and transfer learning (TL). However, achieving this promise has remained elusive due to a lack of suitable training algorithms. Analog memory devices exhibit asymmetric and non-linear switching behavior in addition to device-to-device variation, meaning that most, if not all, of the current off-the-shelf training algorithms cannot achieve good training outcomes. Also, recently introduced algorithms have enjoyed limited attention, as they require bi-directionally switching devices of unrealistically high symmetry and precision and are highly sensitive. A new algorithm chopped TTv2 (c-TTv2), has been introduced, which leverages the chopped technique to address many of the challenges mentioned above. In this paper, we assess the performance of the c-TTv2 algorithm for analog TL using a Swin-ViT model on a subset of the CIFAR100 dataset. We also investigate the robustness of our algorithm to changes in some device specifications, including weight transfer noise, symmetry point skew, and symmetry point variability △ Less

Submitted 16 May, 2025; originally announced May 2025.

A Novel Online Pseudospectral Method for Approximation of Nonlinear Systems Dynamics

Authors: Arian Yousefian, Avimanyu Sahoo, Vignesh Narayanan

Abstract: This note presents an online pseudospectral method for system identification using Chebyshev polynomial basis under aperiodic sampling. The system dynamics are approximated piecewise by introducing a sliding time window. The number of sampling instants (Chebyshev nodes) within each sliding window is selected dynamically based on a proposed node-selection criterion that guarantees desired approxima… ▽ More This note presents an online pseudospectral method for system identification using Chebyshev polynomial basis under aperiodic sampling. The system dynamics are approximated piecewise by introducing a sliding time window. The number of sampling instants (Chebyshev nodes) within each sliding window is selected dynamically based on a proposed node-selection criterion that guarantees desired approximation accuracy. The system states are measured at these aperiodic instants and used to estimate the coefficients of the basis polynomials using least squares. An adaptive state estimator is also proposed to reconstruct the continuous states using the approximated dynamics. The boundedness of the parameter and state estimation errors is proven analytically and validated numerically. △ Less

Submitted 10 November, 2025; v1 submitted 12 May, 2025; originally announced May 2025.

Comments: 8 pages, 5 figures, Substantially extends our earlier paper accepted for ACC 2025 (Denver, 8 Jul 2025). Submitted to IEEE Transactions on Automatic Control; under review

Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks

Authors: Erik L. Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan

Abstract: Recent advances in artificial intelligence have highlighted the remarkable capabilities of neural network (NN)-powered systems on classical computers. However, these systems face significant computational challenges that limit scalability and efficiency. Quantum computers hold the potential to overcome these limitations and increase processing power beyond classical systems. Despite this, integrat… ▽ More Recent advances in artificial intelligence have highlighted the remarkable capabilities of neural network (NN)-powered systems on classical computers. However, these systems face significant computational challenges that limit scalability and efficiency. Quantum computers hold the potential to overcome these limitations and increase processing power beyond classical systems. Despite this, integrating quantum computing with NNs remains largely unrealized due to challenges posed by noise, decoherence, and high error rates in current quantum hardware. Here, we propose a novel quantum echo-state network (QESN) design and implementation algorithm that can operate within the presence of noise on current IBM hardware. We apply classical control-theoretic response analysis to characterize the QESN, emphasizing its rich nonlinear dynamics and memory, as well as its ability to be fine-tuned with sparsity and re-uploading blocks. We validate our approach through a comprehensive demonstration of QESNs functioning as quantum observers, applied in both high-fidelity simulations and hardware experiments utilizing data from a prototypical chaotic Lorenz system. Our results show that the QESN can predict long time-series with persistent memory, running over 100 times longer than the median T1 and T2 of the IBM Marrakesh QPU, achieving state-of-the-art time-series performance on superconducting hardware. △ Less

Submitted 11 July, 2025; v1 submitted 10 May, 2025; originally announced May 2025.

Comments: 14 pages, 12 figures

Sigma-Delta Neural Network Conversion on Loihi 2

Authors: Matthew Brehove, Sadia Anjum Tumpa, Espoir Kyubwa, Naresh Menon, Vijaykrishnan Narayanan

Abstract: Neuromorphic computing aims to improve the efficiency of artificial neural networks by taking inspiration from biological neurons and leveraging temporal sparsity, spatial sparsity, and compute near/in memory. Although these approaches have shown efficiency gains, training these spiking neural networks (SNN) remains difficult. The original attempts at converting trained conventional analog neural… ▽ More Neuromorphic computing aims to improve the efficiency of artificial neural networks by taking inspiration from biological neurons and leveraging temporal sparsity, spatial sparsity, and compute near/in memory. Although these approaches have shown efficiency gains, training these spiking neural networks (SNN) remains difficult. The original attempts at converting trained conventional analog neural networks (ANN) to SNNs used the rate of binary spikes to represent neuron activations. This required many simulation time steps per inference, which degraded efficiency. Intel's Loihi 2 is a neuromorphic platform that supports graded spikes which can be used to represent changes in neuron activation. In this work, we use Loihi 2's graded spikes to develop a method for converting ANN networks to spiking networks, which take advantage of temporal and spatial sparsity. We evaluated the performance of this network on Loihi 2 and compared it to NVIDIA's Jetson Xavier edge AI platform. △ Less

Submitted 9 May, 2025; originally announced May 2025.

On exactness of SDP relaxation for the maximum cut problem

Authors: Avinash Bhardwaj, Hritiz Gogoi, Vishnu Narayanan, Abhishek Pathapati

Abstract: Semidefinite programming (SDP) provides a powerful relaxation for the maximum cut problem. For a graph with rational weights, the decision problem of whether the SDP relaxation for the maximum cut problem is exact is known to be $NP$-hard; however its complexity was unresolved for unweighted graphs. In this work, we extend the $NP$-hardness result to unweighted graphs. We characterize a few classe… ▽ More Semidefinite programming (SDP) provides a powerful relaxation for the maximum cut problem. For a graph with rational weights, the decision problem of whether the SDP relaxation for the maximum cut problem is exact is known to be $NP$-hard; however its complexity was unresolved for unweighted graphs. In this work, we extend the $NP$-hardness result to unweighted graphs. We characterize a few classes of graphs for which the SDP relaxation is exact. For each of these graph classes, we establish conditions for uniqueness of the SDP optimum. We complement these findings by identifying two graph operations that preserve the solution rank, and in turn exactness. These results reveal how the SDP relaxation for the maximum cut problem can remain exact in arbitrarily large graphs, owing to the presence of a small structural core that governs exactness. We further address two open problems posed by Mirka and Williamson (2024), by demonstrating that uniqueness of the maximum cut partition in exact relaxation does not imply uniqueness of the SDP optimum, and that exact relaxation with multiple optimal partitions may admit optimal SDP solutions lying outside the convex hull of rank-1 reference solutions. △ Less

Submitted 6 February, 2026; v1 submitted 8 May, 2025; originally announced May 2025.

MSC Class: 90C27; 90C22; 90C35

STAMP-2.5D: Structural and Thermal Aware Methodology for Placement in 2.5D Integration

Authors: Varun Darshana Parekh, Zachary Wyatt Hazenstab, Srivatsa Rangachar Srinivasa, Krishnendu Chakrabarty, Kai Ni, Vijaykrishnan Narayanan

Abstract: Chiplet-based architectures and advanced packaging has emerged as transformative approaches in semiconductor design. While conventional physical design for 2.5D heterogeneous systems typically prioritizes wirelength reduction through tight chiplet packing, this strategy creates thermal bottlenecks and intensifies coefficient of thermal expansion (CTE) mismatches, compromising long-term reliability… ▽ More Chiplet-based architectures and advanced packaging has emerged as transformative approaches in semiconductor design. While conventional physical design for 2.5D heterogeneous systems typically prioritizes wirelength reduction through tight chiplet packing, this strategy creates thermal bottlenecks and intensifies coefficient of thermal expansion (CTE) mismatches, compromising long-term reliability. Addressing these challenges requires holistic consideration of thermal performance, mechanical stress, and interconnect efficiency. We introduce STAMP-2.5D, the first automated floorplanning methodology that simultaneously optimizes these critical factors. Our approach employs finite element analysis to simulate temperature distributions and stress profiles across chiplet configurations while minimizing interconnect wirelength. Experimental results demonstrate that our thermal structural aware automated floorplanning approach reduces overall stress by 11% while maintaining excellent thermal performance with a negligible 0.5% temperature increase and simultaneously reducing total wirelength by 11% compared to temperature-only optimization. Additionally, we conduct an exploratory study on the effects of temperature gradients on structural integrity, providing crucial insights for reliability-conscious chiplet design. STAMP-2.5D establishes a robust platform for navigating critical trade-offs in advanced semiconductor packaging. △ Less

Submitted 22 September, 2025; v1 submitted 29 April, 2025; originally announced April 2025.

Comments: 8 Page, 10 Figures, To be presented at ICCD 2025

A Bio-inspired Asymmetric Double-Gate Ferroelectric FET for Emulating Astrocyte and Dendrite Dynamics in Neuromorphic Systems

Authors: Zhouhang Jiang, A N M Nafiul Islam, Zhuangyu Han, Zijian Zhao, Franz Müller, Jiahui Duan, Halid Mulaosmanovic, Stefan Dünkel, Sven Beyer, Sourav Dutta, Vijaykrishnan Narayanan, Thomas Kämpfe, Suma George Cardwell, Frances Chance, Abhronil Sengupta, Kai Ni

Abstract: Neuromorphic systems seek to replicate the functionalities of biological neural networks to attain significant improvements in performance and efficiency of AI computing platforms. However, these systems have generally remained limited to emulation of simple neurons and synapses; and ignored higher order functionalities enabled by other components of the brain like astrocytes and dendrites. In thi… ▽ More Neuromorphic systems seek to replicate the functionalities of biological neural networks to attain significant improvements in performance and efficiency of AI computing platforms. However, these systems have generally remained limited to emulation of simple neurons and synapses; and ignored higher order functionalities enabled by other components of the brain like astrocytes and dendrites. In this work, drawing inspiration from biology, we introduce a compact Double-Gate Ferroelectric Field Effect Transistor (DG-FeFET) cell that can emulate the dynamics of both astrocytes and dendrites within neuromorphic architectures. We demonstrate that with a ferroelectric top gate for synaptic weight programming as in conventional synapses and a non-ferroelectric back gate, the DG-FeFET realizes a synapse with a dynamic gain modulation mechanism. This can be leveraged as an analog for a compact astrocyte-tripartite synapse, as well as enabling dendrite-like gain modulation operations. By employing a fully-depleted silicon-on-insulator (FDSOI) FeFET as our double-gate device, we validate the linear control of the synaptic weight via the back gate terminal (i.e., the gate underneath the buried oxide (BOX) layer) through comprehensive theoretical and experimental studies. We showcase the promise such a tripartite synaptic device holds for numerous important neuromorphic applications, including autonomous self-repair of faulty neuromorphic hardware mediated by astrocytic functionality. Coordinate transformations based on dragonfly prey-interception circuitry models are also demonstrated based on dendritic function emulation by the device. This work paves the way forward for developing truly "brain-like" neuromorphic hardware that go beyond the current dogma focusing only on neurons and synapses. △ Less

Submitted 19 April, 2025; originally announced April 2025.

Comments: 37 pages, 6 figure, 2 tables

A Full Spectrum of 3D Ferroelectric Memory Architectures Shaped by Polarization Sensing

Authors: Jiahui Duan, Asif Khan, Xiao Gong, Vijaykrishnan Narayanan, Kai Ni

Abstract: Ferroelectric memories have attracted significant interest due to their non-volatile storage, energy efficiency, and fast operation, making them prime candidates for future memory technologies. As commercial Dynamic Random Access Memory (DRAM) and NAND flash memory are transiting or have moved toward three-dimensional (3D) integration, 3D ferroelectric memory architectures are also emerging, provi… ▽ More Ferroelectric memories have attracted significant interest due to their non-volatile storage, energy efficiency, and fast operation, making them prime candidates for future memory technologies. As commercial Dynamic Random Access Memory (DRAM) and NAND flash memory are transiting or have moved toward three-dimensional (3D) integration, 3D ferroelectric memory architectures are also emerging, provided they can achieve a competitive position within the modern memory hierarchy. Given the excellent scalability of ferroelectric HfO2, various dense 3D integrated ferroelectric memory architectures are feasible, each offering unique strengths and facing distinct challenges. In this work, we present a comprehensive classification of 3D ferroelectric memory architectures based on polarization sensing methods, highlighting their critical role in shaping memory cell design and operational efficiency. Through a systematic evaluation of these architectures, we develop a unified framework to assess their advantages and trade-offs. This classification not only enhances the understanding of current 3D ferroelectric memory technologies but also lays the foundation for designing next-generation architectures optimized for advanced computing and high-performance applications. △ Less

Submitted 13 April, 2025; originally announced April 2025.

Comments: 65 pages, 5 figures

An In-Situ Spatial-Temporal Sequence Detector for Neuromorphic Vision Sensor Empowered by High Density Vertical NAND Storage

Authors: Zijian Zhao, Varun Darshana Parekh, Po-Kai Hsu, Yixin Qin, Yiming Song, A N M Nafiul Islam, Ningyuan Cao, Siddharth Joshi, Thomas Kämpfe, Moonyoung Jung, Kwangyou Seo, Kwangsoo Kim, Wanki Kim, Daewon Ha, Sourav Dutta, Abhronil Sengupta, Xiao Gong, Shimeng Yu, Vijaykrishnan Narayanan, Kai Ni

Abstract: Neuromorphic vision sensors require efficient real-time pattern recognition, yet conventional architectures struggle with energy and latency constraints. Here, we present a novel in-situ spatiotemporal sequence detector that leverages vertical NAND storage to achieve massively parallel pattern detection. By encoding each cell with two single-transistor-based multi-level cell (MLC) memory elements,… ▽ More Neuromorphic vision sensors require efficient real-time pattern recognition, yet conventional architectures struggle with energy and latency constraints. Here, we present a novel in-situ spatiotemporal sequence detector that leverages vertical NAND storage to achieve massively parallel pattern detection. By encoding each cell with two single-transistor-based multi-level cell (MLC) memory elements, such as ferroelectric field-effect transistors (FeFETs), and mapping a pixel's temporal sequence onto consecutive word lines (WLs), we enable direct temporal pattern detection within NAND strings. Each NAND string serves as a dedicated reference for a single pixel, while different blocks store patterns for distinct pixels, allowing large-scale spatial-temporal pattern recognition via simple direct bit-line (BL) sensing, a well-established operation in vertical NAND storage. We experimentally validate our approach at both the cell and array levels, demonstrating that vertical NAND-based detector achieves more than six orders of magnitude improvement in energy efficiency and more than three orders of magnitude reduction in latency compared to conventional CPU-based methods. These findings establish vertical NAND storage as a scalable and energy-efficient solution for next-generation neuromorphic vision processing. △ Less

Submitted 30 March, 2025; originally announced March 2025.

Comments: 26 pages, 7 figures

Byzantine Distributed Function Computation

Authors: Hari Krishnan P. Anilkumar, Neha Sangwan, Varun Narayanan, Vinod M. Prabhakaran

Abstract: We study the distributed function computation problem with $k$ users of which at most $s$ may be controlled by an adversary and characterize the set of functions of the sources the decoder can reconstruct robustly in the following sense -- if the users behave honestly, the function is recovered with high probability (w.h.p.); if they behave adversarially, w.h.p, either one of the adversarial users… ▽ More We study the distributed function computation problem with $k$ users of which at most $s$ may be controlled by an adversary and characterize the set of functions of the sources the decoder can reconstruct robustly in the following sense -- if the users behave honestly, the function is recovered with high probability (w.h.p.); if they behave adversarially, w.h.p, either one of the adversarial users will be identified or the function is recovered with vanishingly small distortion. △ Less

Submitted 10 March, 2025; v1 submitted 3 March, 2025; originally announced March 2025.

Knots and non-orientable surfaces in 3-manifolds

Authors: Alessia Cattabriga, Paolo Cavicchioli, Rama Mishra, Visakh Narayanan

Abstract: In this article, we propose a new approach for describing and understanding knots and links in a 3-manifold through the use of an embedded non-orientable surface. Specifically, we define a plat-like representation based on this non-orientable surface. The method applies to manifolds of the form $M=\mathcal H\cup_{\varphi} \mathcal C(U)$ where $\mathcal H$ is a handlebody, $\mathcal C(U)$ is the ma… ▽ More In this article, we propose a new approach for describing and understanding knots and links in a 3-manifold through the use of an embedded non-orientable surface. Specifically, we define a plat-like representation based on this non-orientable surface. The method applies to manifolds of the form $M=\mathcal H\cup_{\varphi} \mathcal C(U)$ where $\mathcal H$ is a handlebody, $\mathcal C(U)$ is the mapping cylinder of the orientating two sheeted covering of a non-orientable closed surface $U$ and $\varphi:\partial \mathcal H\to \partial \mathcal C(U)$ is an attaching homeomorphism. We show that, by fixing such a splitting any link in the manifold can be represented as a plat-like closure of an element of the surface braid group of $\partial \mathcal H$. Manifolds of this type were extensively studied by J.H. Rubinstein \cite{rubinstein1978one}, where it is shown that any 3-manifold $M$, with a non-vanishing $H_2(M,\frac{\mathbb{Z}}{2\mathbb{Z}})$ will admit such a splitting. Thus the method is quite general. We provide explicit examples of such embeddings in lens spaces $L(2k,q)$ and the trivial circle bundles over orientable closed surfaces, $Σ\times S^1$ △ Less

Submitted 3 March, 2025; v1 submitted 10 February, 2025; originally announced February 2025.

MSC Class: 57K10; 20F36; 57K30

Disharmony: Forensics using Reverse Lighting Harmonization

Authors: Philip Wootaek Shin, Jack Sampson, Vijaykrishnan Narayanan, Andres Marquez, Mahantesh Halappanavar

Abstract: Content generation and manipulation approaches based on deep learning methods have seen significant advancements, leading to an increased need for techniques to detect whether an image has been generated or edited. Another area of research focuses on the insertion and harmonization of objects within images. In this study, we explore the potential of using harmonization data in conjunction with a s… ▽ More Content generation and manipulation approaches based on deep learning methods have seen significant advancements, leading to an increased need for techniques to detect whether an image has been generated or edited. Another area of research focuses on the insertion and harmonization of objects within images. In this study, we explore the potential of using harmonization data in conjunction with a segmentation model to enhance the detection of edited image regions. These edits can be either manually crafted or generated using deep learning methods. Our findings demonstrate that this approach can effectively identify such edits. Existing forensic models often overlook the detection of harmonized objects in relation to the background, but our proposed Disharmony Network addresses this gap. By utilizing an aggregated dataset of harmonization techniques, our model outperforms existing forensic networks in identifying harmonized objects integrated into their backgrounds, and shows potential for detecting various forms of edits, including virtual try-on tasks. △ Less

Submitted 17 January, 2025; originally announced January 2025.

KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing

Authors: Shu Zhao, Tan Yu, Xiaoshuai Hao, Wenchao Ma, Vijaykrishnan Narayanan

Abstract: Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. However, existing deep hashing methods predominantly rely on abundant training data, leaving the more challenging scenario of low-resource adaptation for deep hashing relatively underexplored. This setting involves adapting pre-trained models to downstream tasks with only… ▽ More Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. However, existing deep hashing methods predominantly rely on abundant training data, leaving the more challenging scenario of low-resource adaptation for deep hashing relatively underexplored. This setting involves adapting pre-trained models to downstream tasks with only an extremely small number of training samples available. Our preliminary benchmarks reveal that current methods suffer significant performance degradation due to the distribution shift caused by limited training samples. To address these challenges, we introduce Class-Calibration LoRA (CLoRA), a novel plug-and-play approach that dynamically constructs low-rank adaptation matrices by leveraging class-level textual knowledge embeddings. CLoRA effectively incorporates prior class knowledge as anchors, enabling parameter-efficient fine-tuning while maintaining the original data distribution. Furthermore, we propose Knowledge-Guided Discrete Optimization (KIDDO), a framework to utilize class knowledge to compensate for the scarcity of visual information and enhance the discriminability of hash codes. Extensive experiments demonstrate that our proposed method, Knowledge- Anchored Low-Resource Adaptation Hashing (KALAHash), significantly boosts retrieval performance and achieves a 4x data efficiency in low-resource scenarios. △ Less

Submitted 26 December, 2024; originally announced December 2024.

Comments: Accepted at AAAI 2025

SoK: Understanding the Attack Surface in Device Driver Isolation Frameworks

Authors: Yongzhe Huang, Kaiming Huang, Matthew Ennis, Vikram Narayanan, Anton Burtsev, Trent Jaeger, Gang Tan

Abstract: Device driver isolation is a promising approach for protecting the kernel from faulty or malicious drivers, but the actual security provided by such frameworks is often not well understood. Recent research has identified Compartment Interface Vulnerabilities (CIVs) in userspace compartmentalized applications, yet their impact on driver isolation frameworks remains poorly understood. This paper pro… ▽ More Device driver isolation is a promising approach for protecting the kernel from faulty or malicious drivers, but the actual security provided by such frameworks is often not well understood. Recent research has identified Compartment Interface Vulnerabilities (CIVs) in userspace compartmentalized applications, yet their impact on driver isolation frameworks remains poorly understood. This paper provides a comprehensive survey of the design and security guarantees of existing driver isolation frameworks and systemizes existing CIV classifications, evaluating them under driver isolation. The analysis shows that different classes of CIVs are prevalent across the studied drivers under a baseline threat model, with large drivers having more than 100 instances of different CIVs and an average of 33 instances across the studied drivers. Enforcing extra security properties, such as CFI, can reduce the number of CIVs to around 28 instances on average. This study provides insights for understanding existing driver isolation security and the prevalence of CIVs in the driver isolation context, and extracts useful insights that can provide security guidance for future driver isolation systems. △ Less

Submitted 21 December, 2024; originally announced December 2024.

Predicting Chaotic Systems with Quantum Echo-state Networks

Authors: Erik Connerty, Ethan Evans, Gerasimos Angelatos, Vignesh Narayanan

Abstract: Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon… ▽ More Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively large reservoirs by leveraging the unique capabilities of quantum computers, potentially allowing for more efficient and higher performing time-series prediction algorithms. The proposed QESN can be implemented on any digital quantum computer implementing a universal gate set, and does not require any sort of stopping or re-initialization of the circuit, allowing continuous evolution of the quantum state over long time horizons. We conducted simulated QC experiments on the chaotic Lorenz system, both with noisy and noiseless models, to demonstrate the circuit's performance and its potential for execution on noisy intermediate-scale quantum (NISQ) computers. △ Less

Submitted 10 December, 2024; originally announced December 2024.

Comments: 5 Pages, 4 figures, submitted to AAAI QC+AI workshop

Experimental demonstration of the Bell-type inequalities for four qubit Dicke state using IBM Quantum Processing Units

Authors: Tomis Prajapati, Harsh Mehta, Shreya Banerjee, Prasanta K. Panigrahi, V. Narayanan

Abstract: Violation of the Bell-type inequalities is necessary to confirm the existence of nonlocality in nonclassical (entangled) states. We have designed a customized operator which is made of the sum of the Pauli matrices ($σ_x$, $σ_y$, and $σ_z$). We theoretically and experimentally investigate the violation of Bell-type inequalities using two- and four-qubit Dicke states on IBM Quantum Processing Units… ▽ More Violation of the Bell-type inequalities is necessary to confirm the existence of nonlocality in nonclassical (entangled) states. We have designed a customized operator which is made of the sum of the Pauli matrices ($σ_x$, $σ_y$, and $σ_z$). We theoretically and experimentally investigate the violation of Bell-type inequalities using two- and four-qubit Dicke states on IBM Quantum Processing Units (QPUs). We compare two different state preparation methods for the four-qubit Dicke state -- gate-based and statevector-based -- and evaluate their performance on two IBM QPUs, \texttt{ibm\_kyiv} and \texttt{ibm\_sherbrook}. For the two-qubit case, we demonstrate clear violations of the CHSH inequality, with the highest observed Bell parameter reaching $2.821 \pm 0.0019$ using M3 error mitigation, which is within $0.7σ$ of the theoretical maximum $2\sqrt{2}$. In the four-qubit case, we employ a Bell-type inequality tailored for Dicke states and achieve a maximum violation of $2.607 \pm 0.029$ without the need for additional mitigation when using the statevector-based method. Our results reveal that advanced error mitigation techniques significantly enhance the observed violations in the gate-based method, while the statevector-based approach inherently yields more robust states with lower noise. This study highlights the critical role of state preparation and mitigation techniques in probing fundamental quantum correlations on near-term quantum hardware. △ Less

Submitted 5 May, 2025; v1 submitted 26 October, 2024; originally announced October 2024.

Journal ref: Advanced Quantum Technologies, 2025

Towards Effective Planning Strategies for Dynamic Opinion Networks

Authors: Bharath Muppasani, Protik Nag, Vignesh Narayanan, Biplav Srivastava, Michael N. Huhns

Abstract: In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate or official information through the nodes) to mitigate the influence of misinformation. However, as the n… ▽ More In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate or official information through the nodes) to mitigate the influence of misinformation. However, as the network size increases, the problem becomes computationally intractable. To address this, we first introduce a ranking algorithm to identify key nodes for disseminating accurate information, which facilitates the training of neural network classifiers that provide generalized solutions for the search and planning problems. Second, we mitigate the complexity of label generation, which becomes challenging as the network grows, by developing a reinforcement learning-based centralized dynamic planning framework. We analyze these NN-based planners for opinion networks governed by two dynamic propagation models. Each model incorporates both binary and continuous opinion and trust representations. Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks. Further, we observe that the reward strategies focusing on key metrics, such as the number of susceptible nodes and infection rates, outperform those prioritizing faster blocking strategies. Additionally, our findings reveal that graph convolutional network-based planners facilitate scalable centralized plans that achieve lower infection rates (higher control) across various network configurations, including Watts-Strogatz topology, varying action budgets, varying initial infected nodes, and varying degrees of infected nodes. △ Less

Submitted 2 November, 2024; v1 submitted 17 October, 2024; originally announced October 2024.

Comments: Accepted at NeurIPS 2024

Bifurcation in narrow gap spherical Couette flow

Authors: Ananthu J. P., Manjul Sharma, Sameen A., Vinod Narayanan

Abstract: Incompressible Navier-Stokes equations in the spherical coordinates are solved using a pseudo-spectral method to simulate the problem of spherical Couette flow. The flow is investigated for a narrow gap ratio with only the inner sphere rotating. We find that the flow is sensitive to the initial conditions and have used various initial conditions to obtain di!erent branches of the bifurcation curve… ▽ More Incompressible Navier-Stokes equations in the spherical coordinates are solved using a pseudo-spectral method to simulate the problem of spherical Couette flow. The flow is investigated for a narrow gap ratio with only the inner sphere rotating. We find that the flow is sensitive to the initial conditions and have used various initial conditions to obtain di!erent branches of the bifurcation curve of the flow. We have identified three di!erent branches dominated respectively by axisymmetric flow, traveling wave instability, and equatorial instability. The axisymmetric branch shows unsteadiness at large Reynolds numbers. The traveling wave instability branch shows spiral instability and is prominent near poles. The traveling wave instability branch further exhibits a reversal in the propagation direction of the spiral instability as the Reynolds number is increased. This branch also exhibits a multi-mode equatorial instability at larger Reynolds numbers. The equatorial instability branch exhibits twin jet streams on either side of the equator, which becomes unstable at larger Reynolds numbers. The flow topology on the three branches are also investigated in their phase space and the found to exhibit a chaotic behavior at large Reynolds numbers on the traveling wave instability branch. △ Less

Submitted 9 October, 2024; originally announced October 2024.

Comments: 22 pages

PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferences

Authors: Pingyi Huo, Anusha Devulapally, Hasan Al Maruf, Minseo Park, Krishnakumar Nair, Meena Arunachalam, Gulsum Gudukbay Akbulut, Mahmut Taylan Kandemir, Vijaykrishnan Narayanan

Abstract: Deep Learning Recommendation Models (DLRMs) have become increasingly popular and prevalent in today's datacenters, consuming most of the AI inference cycles. The performance of DLRMs is heavily influenced by available bandwidth due to their large vector sizes in embedding tables and concurrent accesses. To achieve substantial improvements over existing solutions, novel approaches towards DLRM opti… ▽ More Deep Learning Recommendation Models (DLRMs) have become increasingly popular and prevalent in today's datacenters, consuming most of the AI inference cycles. The performance of DLRMs is heavily influenced by available bandwidth due to their large vector sizes in embedding tables and concurrent accesses. To achieve substantial improvements over existing solutions, novel approaches towards DLRM optimization are needed, especially, in the context of emerging interconnect technologies like CXL. This study delves into exploring CXL-enabled systems, implementing a process-in-fabric-switch (PIFS) solution to accelerate DLRMs while optimizing their memory and bandwidth scalability. We present an in-depth characterization of industry-scale DLRM workloads running on CXL-ready systems, identifying the predominant bottlenecks in existing CXL systems. We, therefore, propose PIFS-Rec, a PIFS-based scheme that implements near-data processing through downstream ports of the fabric switch. PIFS-Rec achieves a latency that is 3.89x lower than Pond, an industry-standard CXL-based system, and also outperforms BEACON, a state-of-the-art scheme, by 2.03x. △ Less

Submitted 25 September, 2024; originally announced September 2024.

Synergistic and Efficient Edge-Host Communication for Energy Harvesting Wireless Sensor Networks

Authors: Cyan Subhra Mishra, Jack Sampson, Mahmut Taylan Kandmeir, Vijaykrishnan Narayanan, Chita R Das

Abstract: There is an increasing demand for intelligent processing on ultra-low-power internet of things (IoT) device. Recent works have shown substantial efficiency boosts by executing inferences directly on the IoT device (node) rather than transmitting data. However, the computation and power demands of Deep Neural Network (DNN)-based inference pose significant challenges in an energy-harvesting wireless… ▽ More There is an increasing demand for intelligent processing on ultra-low-power internet of things (IoT) device. Recent works have shown substantial efficiency boosts by executing inferences directly on the IoT device (node) rather than transmitting data. However, the computation and power demands of Deep Neural Network (DNN)-based inference pose significant challenges in an energy-harvesting wireless sensor network (EH-WSN). Moreover, these tasks often require responses from multiple physically distributed EH sensor nodes, which impose crucial system optimization challenges in addition to per-node constraints. To address these challenges, we propose Seeker, a hardware-software co-design approach for increasing on-sensor computation, reducing communication volume, and maximizing inference completion, without violating the quality of service, in EH-WSNs coordinated by a mobile device. Seeker uses a store-and-execute approach to complete a subset of inferences on the EH sensor node, reducing communication with the mobile host. Further, for those inferences unfinished because of the harvested energy constraints, it leverages task-aware coreset construction to efficiently communicate compact features to the host device. We evaluate Seeker for human activity recognition, as well as predictive maintenance and show ~8.9x reduction in communication data volume with 86.8% accuracy, surpassing the 81.2% accuracy of the state-of-the-art. △ Less

Submitted 26 August, 2024; originally announced August 2024.

Comments: arXiv admin note: substantial text overlap with arXiv:2204.13106

Line Segment Tracking: Improving the Phase 2 CMS High Level Trigger Tracking with a Novel, Hardware-Agnostic Pattern Recognition Algorithm

Authors: Emmanouil Vourliotis, Philip Chang, Peter Elmer, Yanxi Gu, Jonathan Guiang, Vyacheslav Krutelyov, Balaji Venkat Sathia Narayanan, Gavin Niendorf, Michael Reid, Mayra Silva, Andres Rios Tascon, Matevž Tadel, Peter Wittich, Avraham Yagil

Abstract: Charged particle reconstruction is one the most computationally heavy components of the full event reconstruction of Large Hadron Collider (LHC) experiments. Looking to the future, projections for the High Luminosity LHC (HL-LHC) indicate a superlinear growth for required computing resources for single-threaded CPU algorithms that surpass the computing resources that are expected to be available.… ▽ More Charged particle reconstruction is one the most computationally heavy components of the full event reconstruction of Large Hadron Collider (LHC) experiments. Looking to the future, projections for the High Luminosity LHC (HL-LHC) indicate a superlinear growth for required computing resources for single-threaded CPU algorithms that surpass the computing resources that are expected to be available. The combination of these facts creates the need for efficient and computationally performant pattern recognition algorithms that will be able to run in parallel and possibly on other hardware, such as GPUs, given that these become more and more available in LHC experiments and high-performance computing centres. Line Segment Tracking (LST) is a novel such algorithm which has been developed to be fully parallelizable and hardware agnostic. The latter is achieved through the usage of the Alpaka library. The LST algorithm has been tested with the CMS central software as an external package and has been used in the context of the CMS HL-LHC High Level Trigger (HLT). When employing LST for pattern recognition in the HLT tracking, the physics and timing performances are shown to improve with respect to the ones utilizing the current pattern recognition algorithms. The latest results on the usage of the LST algorithm within the CMS HL-LHC HLT are presented, along with prospects for further improvements of the algorithm and its CMS central software integration. △ Less

Submitted 25 July, 2024; originally announced July 2024.

Report number: CMS-CR-2024-141

Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models

Authors: Philip Wootaek Shin, Jihyun Janice Ahn, Wenpeng Yin, Jack Sampson, Vijaykrishnan Narayanan

Abstract: It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipulation of societal biases in leading text-to-image models: Stable Diffusion, DALL-E 3, and Adobe Firefly. Through a comprehensive analysis combining base prompts with modifiers and t… ▽ More It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipulation of societal biases in leading text-to-image models: Stable Diffusion, DALL-E 3, and Adobe Firefly. Through a comprehensive analysis combining base prompts with modifiers and their sequencing, we uncover the nuanced ways these AI technologies encode biases across gender, race, geography, and region/culture. Our findings reveal the challenges and potential of prompt engineering in controlling biases, highlighting the critical need for ethical AI development promoting diversity and inclusivity. This work advances AI ethics by not only revealing the nuanced dynamics of bias in text-to-image generation models but also by offering a novel framework for future research in controlling bias. Our contributions-panning comparative analyses, the strategic use of prompt modifiers, the exploration of prompt sequencing effects, and the introduction of a bias sensitivity taxonomy-lay the groundwork for the development of common metrics and standard analyses for evaluating whether and how future AI models exhibit and respond to requests to adjust for inherent biases. △ Less

Submitted 8 June, 2024; originally announced June 2024.

A viscous drop in a planar linear flow -- the role of deformation on streamline topology

Authors: Sabarish V. Narayanan, Ganesh Subramanian

Abstract: Planar linear flows are a one-parameter family, with the parameter $\hatα\in [-1,1]$ being a measure of the relative magnitudes of extension and vorticity; $\hatα = -1$, $0$ and $1$ correspond to solid-body rotation, simple shear flow and planar extension, respectively. For a neutrally buoyant spherical drop in a hyperbolic planar linear flow with $\hatα\in(0,1]$, the near-field streamlines are cl… ▽ More Planar linear flows are a one-parameter family, with the parameter $\hatα\in [-1,1]$ being a measure of the relative magnitudes of extension and vorticity; $\hatα = -1$, $0$ and $1$ correspond to solid-body rotation, simple shear flow and planar extension, respectively. For a neutrally buoyant spherical drop in a hyperbolic planar linear flow with $\hatα\in(0,1]$, the near-field streamlines are closed for $0 \leq \hatα < 1$ and for $λ> λ_c = 2 \hatα / (1 - \hatα)$, $λ$ being the drop-to-medium viscosity ratio; all streamlines are closed for an ambient elliptic linear flow with $\hatα\in[-1,0)$. We use both analytical and numerical tools to show that drop deformation, as characterized by a non-zero capillary number ($Ca$), destroys the aforementioned closed-streamline topology. While inertia has previously been shown to transform closed Stokesian streamlines into open spiraling ones that run from upstream to downstream infinity, the streamline topology around a deformed drop, for small but finite $Ca$, is more complicated. Only a subset of the original closed streamlines transforms to open spiraling ones, while the remaining ones densely wind around a configuration of nested invariant tori. Our results contradict previous efforts pointing to the persistence of the closed streamline topology exterior to a deformed drop and have important implications for transport and mixing. △ Less

Submitted 4 June, 2024; originally announced June 2024.

On generators of $k$-PSD closures of the positive semidefinite cone

Authors: Avinash Bhardwaj, Vishnu Narayanan, Abhishek Pathapati

Abstract: Positive semidefinite (PSD) cone is the cone of positive semidefinite matrices, and is the object of interest in semidefinite programming (SDP). A computational efficient approximation of the PSD cone is the $k$-PSD closure, $1 \leq k < n$, cone of $n\times n$ real symmetric matrices such that all of their $k\times k$ principal submatrices are positive semidefinite. For $k=1$, one obtains a polyhe… ▽ More Positive semidefinite (PSD) cone is the cone of positive semidefinite matrices, and is the object of interest in semidefinite programming (SDP). A computational efficient approximation of the PSD cone is the $k$-PSD closure, $1 \leq k < n$, cone of $n\times n$ real symmetric matrices such that all of their $k\times k$ principal submatrices are positive semidefinite. For $k=1$, one obtains a polyhedral approximation, while $k=2$ yields a second order conic (SOC) approximation of the PSD cone. These approximations of the PSD cone have been used extensively in real-world applications such as AC Optimal Power Flow (ACOPF) to address computational inefficiencies where SDP relaxations are utilized for convexification the non-convexities. However a theoretical discussion about the geometry of these conic approximations of the PSD cone is rather sparse. In this short communication, we attempt to provide a characterization of some family of generators of the aforementioned conic approximations. △ Less

Submitted 2 May, 2024; originally announced May 2024.

MSC Class: 90C22; 90C25; 52A27

Suvach -- Generated Hindi QA benchmark

Authors: Vaishak Narayanan, Prabin Raj KP, Saifudheen Nouphal

Abstract: Current evaluation benchmarks for question answering (QA) in Indic languages often rely on machine translation of existing English datasets. This approach suffers from bias and inaccuracies inherent in machine translation, leading to datasets that may not reflect the true capabilities of EQA models for Indic languages. This paper proposes a new benchmark specifically designed for evaluating Hindi… ▽ More Current evaluation benchmarks for question answering (QA) in Indic languages often rely on machine translation of existing English datasets. This approach suffers from bias and inaccuracies inherent in machine translation, leading to datasets that may not reflect the true capabilities of EQA models for Indic languages. This paper proposes a new benchmark specifically designed for evaluating Hindi EQA models and discusses the methodology to do the same for any task. This method leverages large language models (LLMs) to generate a high-quality dataset in an extractive setting, ensuring its relevance for the target language. We believe this new resource will foster advancements in Hindi NLP research by providing a more accurate and reliable evaluation tool. △ Less

Submitted 30 April, 2024; originally announced April 2024.

An open-source solver for finding global solutions to constrained derivative-free optimization problems

Authors: Gannavarapu Chandramouli, Vishnu Narayanan

Abstract: In this work, we propose a heuristic based open source solver for finding global solution to constrained derivative-free optimization (DFO) problems. Our solver named Global optimization using Surrogates for Derivative-free Optimization (GSDO) relies on surrogate approximation to the original problem. In the proposed algorithm, an initial feasible point is first generated. This point is subsequent… ▽ More In this work, we propose a heuristic based open source solver for finding global solution to constrained derivative-free optimization (DFO) problems. Our solver named Global optimization using Surrogates for Derivative-free Optimization (GSDO) relies on surrogate approximation to the original problem. In the proposed algorithm, an initial feasible point is first generated. This point is subsequently used to generate well spaced feasible points for formulating better radial basis function based surrogate approximations to original objective and constraint functions. Finally, these surrogates are used to solve the derivative-free global optimization problems. The proposed solver is capable of handling quantifiable and nonquantifiable as well as relaxable and unrelaxable constraints. We compared the performance of proposed solver with state of the art solvers like Nonlinear Optimization by Mesh Adaptive Direct Search (NOMAD), differential evolution (DE) and Simplicial Homology Global Optimization (SHGO) on standard test problems. The numerical results clearly demonstrate that the performance of our method is competitive with respect to other solvers. △ Less

Submitted 28 April, 2024; originally announced April 2024.

Comments: 17 pages, 5 figures, 8 tables

MSC Class: 90C26

High-order meshless global stability analysis of Taylor-Couette flows in complex domains

Authors: Akash Unnikrishnan, Vinod Narayanan, Surya Pratap Vanka

Abstract: Recently, meshless methods have become popular in numerically solving partial differential equations and have been employed to solve equations governing fluid flows, heat transfer, and species transport. In the present study, a numerical solver is developed employing the meshless framework to efficiently compute the hydrodynamic stability of fluid flows in complex geometries. The developed method… ▽ More Recently, meshless methods have become popular in numerically solving partial differential equations and have been employed to solve equations governing fluid flows, heat transfer, and species transport. In the present study, a numerical solver is developed employing the meshless framework to efficiently compute the hydrodynamic stability of fluid flows in complex geometries. The developed method is tested on two cases of Taylor-Couette flows. The concentric case represents the parallel flow assumption incorporated in the Orr-Sommerfeld model and the eccentric Taylor-Couette flow incorporates a non-parallel base flow with separation bubbles. The method was validated against earlier works by Marcus [1], Oikawa et al. [2], Leclercq et al. [3], and Mittal et al. [4]. The results for the two cases and the effectiveness of the method are discussed in detail. The method is then applied to Taylor-Couette flow in an elliptical enclosure and the stability of the flow is investigated. △ Less

Submitted 17 April, 2024; originally announced April 2024.

DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning

Authors: Senthil Yogamani, David Unger, Venkatraman Narayanan, Varun Ravi Kumar

Abstract: Semantic segmentation is an effective way to perform scene understanding. Recently, segmentation in 3D Bird's Eye View (BEV) space has become popular as its directly used by drive policy. However, there is limited work on BEV segmentation for surround-view fisheye cameras, commonly used in commercial vehicles. As this task has no real-world public dataset and existing synthetic datasets do not han… ▽ More Semantic segmentation is an effective way to perform scene understanding. Recently, segmentation in 3D Bird's Eye View (BEV) space has become popular as its directly used by drive policy. However, there is limited work on BEV segmentation for surround-view fisheye cameras, commonly used in commercial vehicles. As this task has no real-world public dataset and existing synthetic datasets do not handle amodal regions due to occlusion, we create a synthetic dataset using the Cognata simulator comprising diverse road types, weather, and lighting conditions. We generalize the BEV segmentation to work with any camera model; this is useful for mixing diverse cameras. We implement a baseline by applying cylindrical rectification on the fisheye images and using a standard LSS-based BEV segmentation model. We demonstrate that we can achieve better performance without undistortion, which has the adverse effects of increased runtime due to pre-processing, reduced field-of-view, and resampling artifacts. Further, we introduce a distortion-aware learnable BEV pooling strategy that is more effective for the fisheye cameras. We extend the model with an occlusion reasoning module, which is critical for estimating in BEV space. Qualitative performance of DaF-BEVSeg is showcased in the video at https://streamable.com/ge4v51. △ Less

Submitted 9 April, 2024; originally announced April 2024.

A swimming bacterium in a two-fluid model of a polymer solution

Authors: Sabarish V. Narayanan, Donald L. Koch, Sarah Hormozi

Abstract: We analyse the motion of a flagellated bacterium in a two-fluid medium using slender body theory. The two-fluid model is useful for describing a body moving through a complex fluid with a microstructure whose length scale is comparable to the characteristic scale of the body. This is true for bacterial motion in biological fluids (entangled polymer solutions), where the entanglement results in a p… ▽ More We analyse the motion of a flagellated bacterium in a two-fluid medium using slender body theory. The two-fluid model is useful for describing a body moving through a complex fluid with a microstructure whose length scale is comparable to the characteristic scale of the body. This is true for bacterial motion in biological fluids (entangled polymer solutions), where the entanglement results in a porous microstructure with typical pore diameters comparable to or larger than the flagellar bundle diameter but smaller than the diameter of the bacterial head. Thus the polymer and solvent satisfy different boundary conditions on the flagellar bundle and move with different velocities close to it. This gives rise to a screening length $L_B$ within which the fluids exchange momentum and the relative velocity between the two fluids decays. In this work, both the solvent and polymer of the two-fluid medium are modeled as Newtonian fluids with different viscosities $μ_s$ and $μ_p$ (viscosity ratio $λ= μ_p/μ_s$), thereby capturing the effects solely introduced by the microstructure of the complex fluid. From our calculations, we observe an increased drag anisotropy for a rigid, slender flagellar bundle moving through this two-fluid medium, resulting in an enhanced swimming velocity of the organism. The results are sensitive to the interaction between the bundle and the polymer and we discuss two physical scenarios corresponding to two types of interaction. Our model provides an explanation for the experimentally observed enhancement of swimming velocity of bacteria in entangled polymer solutions and motivates further experimental investigations. △ Less

Submitted 4 April, 2024; originally announced April 2024.

Journal ref: J. Fluid Mech. 1001 (2024) A8

Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC

Authors: Jonathan Guiang, Slava Krutelyov, Manos Vourliotis, Yanxi Gu, Avi Yagil, Balaji Venkat Sathia Narayanan, Matevz Tadel, Philip Chang, Mayra Silva, Gavin Niendorf, Peter Wittich, Tres Reid, Peter Elmer

Abstract: In this work, we present a study on ways that tracking algorithms can be improved with machine learning (ML). We base this study on the line segment tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized in order to efficiently run on modern processors. LST has been developed specifically for the CMS Experiment at the LHC, towards the High Luminosity LHC (HL-LHC… ▽ More In this work, we present a study on ways that tracking algorithms can be improved with machine learning (ML). We base this study on the line segment tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized in order to efficiently run on modern processors. LST has been developed specifically for the CMS Experiment at the LHC, towards the High Luminosity LHC (HL-LHC) upgrade. Moreover, we have already shown excellent efficiency and performance results as we iteratively improve LST, leveraging a full simulation of the CMS detector. At the same time, promising deep-learning-based tracking algorithms, such as Graph Neural Networks (GNNs), are being pioneered on the simplified TrackML dataset. These results suggest that parts of LST could be improved or replaced by ML. Thus, a thorough, step-by-step investigation of exactly how and where ML can be utilized, while still meeting realistic HL-LHC performance and efficiency constraints, is implemented as follows. First, a lightweight neural network is used to replace and improve upon explicitly defined track quality selections. This neural network is shown to be highly efficient and robust to displaced tracks while having little-to-no impact on the runtime of LST. These results clearly establish that ML can be used to improve LST without penalty. Next, exploratory studies of GNN track-building algorithms are described. In particular, low-level track objects from LST are considered as nodes in a graph, where edges represent higher-level objects or even entire track candidates. Then, an edge-classifier GNN is trained, and the efficiency of the resultant edge scores is compared with that of the existing LST track quality selections. These GNN studies provide insights into the practicality and performance of using more ambitious and complex ML algorithms for HL-LHC tracking at the CMS Experiment. △ Less

Submitted 19 March, 2024; originally announced March 2024.

Comments: Connecting the Dots Workshop (CTD 2023)

Paving the Way for Pass Disturb Free Vertical NAND Storage via A Dedicated and String-Compatible Pass Gate

Authors: Zijian Zhao, Sola Woo, Khandker Akif Aabrar, Sharadindu Gopal Kirtania, Zhouhang Jiang, Shan Deng, Yi Xiao, Halid Mulaosmanovic, Stefan Duenkel, Dominik Kleimaier, Steven Soss, Sven Beyer, Rajiv Joshi, Scott Meninger, Mohamed Mohamed, Kijoon Kim, Jongho Woo, Suhwan Lim, Kwangsoo Kim, Wanki Kim, Daewon Ha, Vijaykrishnan Narayanan, Suman Datta, Shimeng Yu, Kai Ni

Abstract: In this work, we propose a dual-port cell design to address the pass disturb in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. We demonstrate that: i) the pass disturb-free feature originates from weakening of the depolarization field by the pass bias at the high-${V}_{TH}$ (HVT) state and the screening of the applied field by channel at the low-… ▽ More In this work, we propose a dual-port cell design to address the pass disturb in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. We demonstrate that: i) the pass disturb-free feature originates from weakening of the depolarization field by the pass bias at the high-${V}_{TH}$ (HVT) state and the screening of the applied field by channel at the low-${V}_{TH}$ (LVT) state; ii) combined simulations and experimental demonstrations of dual-port design verify the disturb-free operation in a NAND string, overcoming a key challenge in single-port designs; iii) the proposed design can be incorporated in a highly scaled vertical NAND FeFET string and the pass gate can be incorporated into the existing 3D NAND with the negligible overhead of the pass gate interconnection through a global bottom pass gate contact in the substrate. △ Less

Submitted 7 March, 2024; originally announced March 2024.

Comments: 29 pages, 7 figures