Tootfinder

EventSSEG: Event-driven Self-Supervised Segmentation with Probabilistic Attention
Lakshmi Annamalai, Chetan Singh Thakur
https://arxiv.org/abs/2508.14856 https://

EventSSEG: Event-driven Self-Supervised Segmentation with Probabilistic Attention
Road segmentation is pivotal for autonomous vehicles, yet achieving low latency and low compute solutions using frame based cameras remains a challenge. Event cameras offer a promising alternative. To leverage their low power sensing, we introduce EventSSEG, a method for road segmentation that uses event only computing and a probabilistic attention mechanism. Event only computing poses a challenge in transferring pretrained weights from the conventional camera domain, requiring abundant labeled…

Magnetic reconnection sustains the mass budget of the solar wind
Yajie Chen, Hardi Peter, Damien Przybylski, Haruhisa Iijima, Lakshmi Pradeep Chitta
https://arxiv.org/abs/2509.11692

Magnetic reconnection sustains the mass budget of the solar wind
The solar wind originates from regions of open magnetic fields on the Sun, but the relevant processes remain unsolved. We present a self-consistent numerical model of the source region of the wind, in which jets similar to those observed on the Sun naturally emerge due to magnetic reconnection between closed and open magnetic fields. In this process material is transferred from closed to open field lines and fed into the solar wind.We quantify the mass flux through the magnetic field connected …

Game-Theoretic Resilience Framework for Cyber-Physical Microgrids using Multi-Agent Reinforcement Learning
S Krishna Niketh, Sagar Babu Mitikiri, V Vignesh, Vedantham Lakshmi Srinivas, Mayukha Pal
https://arxiv.org/abs/2509.08310

Game-Theoretic Resilience Framework for Cyber-Physical Microgrids using Multi-Agent Reinforcement Learning
The increasing reliance on cyber physical infrastructure in modern power systems has amplified the risk of targeted cyber attacks, necessitating robust and adaptive resilience strategies. This paper presents a mathematically rigorous game theoretic framework to evaluate and enhance microgrid resilience using a combination of quantitative resilience metrics Load Served Ratio LSR, Critical Load Resilience CLR, Topological Survivability Score TSS, and DER Resilience Score DRS. These are integrated…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
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- Efficient Fault Localization in a Cloud Stack Using End-to-End Application Service Topology
Dhanya R Mathews, Mudit Verma, Pooja Aggarwal, J. Lakshmi

Efficient Fault Localization in a Cloud Stack Using End-to-End Application Service Topology
Dhanya R Mathews, Mudit Verma, Pooja Aggarwal, J. Lakshmi
https://arxiv.org/abs/2509.05511

Efficient Fault Localization in a Cloud Stack Using End-to-End Application Service Topology
Cloud application services are distributed in nature and have components across the stack working together to deliver the experience to end users. The wide adoption of microservice architecture exacerbates failure management due to increased service components. To be effective, the strategies to enhance the application service resilience need to be autonomous and developed at the service's granularity, considering its end-to-end components. However, the massive amount of observability data gene…

Energy Efficient p-Circuits for Generative Neural Networks
Lakshmi A. Ghantasala, Ming-Che Li, Risi Jaiswal, Archisman Ghosh, Behtash Behin-Aein, Joseph Makin, Shreyas Sen, Supriyo Datta
https://arxiv.org/abs/2507.07763

Energy Efficient p-Circuits for Generative Neural Networks
A p-circuit comprising a network of $N$ p-bits can generate samples from a probability distribution with $2^N$ possibilities just like a network of q-bits, though there are interesting fundamental differences. A p-bit is essentially a binary stochastic neuron used in Boltzmann machines which involve the repeated application of a specific elementary operation, whereby each p-bit looks at the state of 'n' other p-bits, computes the probability for being $0$ or $1$, and chooses its next state acco…

Study of the elusive $5s-4f$ level crossing in highly charged osmium with optical transitions suitable for physics beyond the Standard Model searches
Nils-Holger Rehbehn, Lakshmi Priya Kozhiparambil Sajith, Michael K. Rosner, Charles Cheung, Sergey G. Porsev, Marianna S. Safronova, Steven Worm, Dmitry Budker, Thomas Pfeifer, Jos\'e R. Crespo L\'opez-Urrutia, Hendrik Bekker

Towards Next Generation Immersive Applications in 5G Environments
Rohail Asim, Ankit Bhardwaj, Lakshmi Suramanian, Yasir Zaki
https://arxiv.org/abs/2507.20050 https://

Towards Next Generation Immersive Applications in 5G Environments
The Multi-user Immersive Reality (MIR) landscape is evolving rapidly, with applications spanning virtual collaboration, entertainment, and training. However, wireless network limitations create a critical bottleneck, struggling to meet the high-bandwidth and ultra-low latency demands essential for next-generation MIR experiences. This paper presents Hera, a modular framework for next-generation immersive applications, comprising a high-level streaming and synchronization layer for AR/VR systems…

Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks
Saptarshi Bej, Muhammed Sahad E, Gouri Lakshmi, Harshit Kumar, Pritam Kar, Bikas C Das
https://arxiv.org/abs/2508.16216

Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks
We introduce Spike Agreement Dependent Plasticity (SADP), a biologically inspired synaptic learning rule for Spiking Neural Networks (SNNs) that relies on the agreement between pre- and post-synaptic spike trains rather than precise spike-pair timing. SADP generalizes classical Spike-Timing-Dependent Plasticity (STDP) by replacing pairwise temporal updates with population-level correlation metrics such as Cohen's kappa. The SADP update rule admits linear-time complexity and supports efficient h…

Active region upflows in various coronal structures and their coupling to the lower atmosphere
Yingjie Zhu, Louise Harra, Krzysztof Barczynski, Nils Janitzek, Joseph Plowman, Slimane Mzerguat, Fr\'ed\'eric Auch\`ere, William Thompson, Susanna Parenti, Lakshmi Pradeep Chitta, Hardi Peter, Terje Fredvik, Tim Grundy, Yi-Wei Ni, Peng-Fei Chen, Gherardo Valori

Active region upflows in various coronal structures and their coupling to the lower atmosphere
Plasma upflows with a Doppler shift exceeding -10 km/s at active region (AR) boundaries are considered potential sources of the nascent slow solar wind. We investigate the driving mechanisms of a pair of coronal upflow regions on the western and eastern peripheries of an AR, which have different magnetic topologies and surroundings. It is aimed to explore how these upflows couple to the lower atmosphere. Using observations of the Fe XII 19.51 nm line from Hinode, we identified two upflow region…

Probing Solar Polar Regions
Yuanyong Deng, Hui Tian, Jie Jiang, Shuhong Yang, Hao Li, Robert Cameron, Laurent Gizon, Louise Harra, Robert F. Wimmer-Schweingruber, Fr\'ed\'eric Auch\`ere, Xianyong Bai, Luis Bellot Rubio, Linjie Chen, Pengfei Chen, Lakshmi Pradeep Chitta, Jackie Davies, Fabio Favata, Li Feng, Xueshang Feng, Weiqun Gan, Don Hassler, Jiansen He, Junfeng Hou, Zhenyong Hou, Chunlan Jin, Wenya Li, Jiaben Lin, Dibyendu Nandy, Vaibhav Pant, Marco Romoli, Taro Sakao, Kri…

Probing Solar Polar Regions
The magnetic fields and dynamical processes in the solar polar regions play a crucial role in the solar magnetic cycle and in supplying mass and energy to the fast solar wind, ultimately being vital in controlling solar activities and driving space weather. Despite numerous efforts to explore these regions, to date no imaging observations of the Sun's poles have been achieved from vantage points out of the ecliptic plane, leaving their behavior and evolution poorly understood. This observation …