Tootfinder

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Likhitha Annapurna Kavuri, Akshay Mhatre, Akarsh K Nair, Deepti Gupta
https://arxiv.org/abs/2506.16458

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Federated Learning (FL) protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL framework for identifying and reducing the impact of such attackers. Phase 1 involves collecting model updates from participating clients and applying a dimensionality reduction approach to identify outlier…

Physisorption on Nanomechanical Resonators: The Overlooked Influence of Trace Moisture
Hemant Kumar Verma, Suman Kumar Mandal, Darkasha Khan, Faizan Tariq Beigh, Manoj Kandpal, Jaspreet Singh, Sushobhan Avasthi, Srinivasan Raghavan, Akshay Naik
https://arxiv.org/abs/2506.17553

Physisorption on Nanomechanical Resonators: The Overlooked Influence of Trace Moisture
Short gas pulses introduced in a vacuum chamber have long been utilized to showcase the ultra-low mass resolutions achievable with nanomechanical resonators. The resonance frequency shifts are used as evidence of gas adsorption. However, there is very little clarity as to what exactly is adsorbing on to the resonators. We demonstrate that the physisorption of gases on cantilevers is predominantly the effect of moisture content that is present even in ultra-high purity gases. The experimental wo…

An unusual type-I X-ray burst from the neutron star X-ray binary IGR J17591-2342: a double-photospheric-radius-expansion burst?
Sudip Bhattacharyya, Akshay Singh, Andrea Sanna
https://arxiv.org/abs/2506.16632

An unusual type-I X-ray burst from the neutron star X-ray binary IGR J17591-2342: a double-photospheric-radius-expansion burst?
Type-I X-ray bursts observed from neutron stars originate from intermittent unstable thermonuclear burning of accreted matter on these stars. Such bursts, particularly those reaching the Eddington luminosity and having a temporary photospheric radius-expansion due to radiation pressure, provide a testbed to study nuclear fusion processes in intense radiation, gravity, and magnetic fields. Here, we investigate time-resolved spectroscopic properties of a type-I burst from the accretion-powered mi…

CGVQM D: Computer Graphics Video Quality Metric and Dataset
Akshay Jindal, Nabil Sadaka, Manu Mathew Thomas, Anton Sochenov, Anton Kaplanyan
https://arxiv.org/abs/2506.11546

CGVQM+D: Computer Graphics Video Quality Metric and Dataset
While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full-reference…

Chance and Mass Interpretations of Probabilities in Markov Decision Processes (Extended Version)
Yun Chen Tsai, Kittiphon Phalakarn, S. Akshay, Ichiro Hasuo
https://arxiv.org/abs/2506.10377

Chance and Mass Interpretations of Probabilities in Markov Decision Processes (Extended Version)
Markov decision processes (MDPs) are a popular model for decision-making in the presence of uncertainty. The conventional view of MDPs in verification treats them as state transformers with probabilities defined over sequences of states and with schedulers making random choices. An alternative view, especially well-suited for modeling dynamical systems, defines MDPs as distribution transformers with schedulers distributing probability masses. Our main contribution is a unified semantical framew…

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu, Zhihan Zhang, Kate Lin, Yuwei Zhang, Akshay Paruchuri, Hong Yu, Mehran Kazemi, Kumar Ayush, A. Ali Heydari, Maxwell A. Xu, Girish Narayanswamy, Yun Liu, Ming-Zher Poh, Yuzhe Yang, Mark Malhotra, Shwetak Patel, Hamid Palangi, Xuhai Xu, Daniel McDuff, Tim Althoff, Xin Liu
https://

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Language models (LMs) are increasingly being deployed to perform autonomous data analyses. However, their data awareness -- the ability to recognize, reason over, and appropriately handle data artifacts such as missing values, outliers, and logical inconsistencies -- remains underexplored. These artifacts are especially common in real-world tabular data and, if mishandled, can significantly compromise the validity of analytical conclusions. To address this gap, we present RADAR, a benchmark for…

Adapting Online Customer Reviews for Blind Users: A Case Study of Restaurant Reviews
Mohan Sunkara, Akshay Kolgar Nayak, Sandeep Kalari, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok
https://arxiv.org/abs/2506.04865

Adapting Online Customer Reviews for Blind Users: A Case Study of Restaurant Reviews
Online reviews have become an integral aspect of consumer decision-making on e-commerce websites, especially in the restaurant industry. Unlike sighted users who can visually skim through the reviews, perusing reviews remains challenging for blind users, who rely on screen reader assistive technology that supports predominantly one-dimensional narration of content via keyboard shortcuts. In an interview study, we uncovered numerous pain points of blind screen reader users with online restaurant…

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction
Akshay Subramaniam, Dale Durran, David Pruitt, Nathaniel Cresswell-Clay, William Yik
https://arxiv.org/abs/2506.08285

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction
Forecasting weather accurately and efficiently is a critical capability in our ability to adapt to climate change. Data driven approaches to this problem have enjoyed much success recently providing forecasts with accuracy comparable to physics based numerical prediction models but at significantly reduced computational expense. However, these models typically do not incorporate any physics priors. In this work, we demonstrate improved skill of data driven weather prediction approaches by incor…

Portal for High-Precision Atomic Data and Computation
Amani Kiruga, Charles Cheung, Dmytro Filin, Parinaz Barakhshan, Akshay Bhosale, Vipul Badhan, Bindiya Arora, Rudolf Eigenmann, Marianna S. Safronova
https://arxiv.org/abs/2506.08170

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, David J. Love, Christopher G. Brinton
https://arxiv.org/abs/2506.00374

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generativ…

Should we care about the spatial heterogeneity in coral reefs under unidirectional turbulent flows?
Akshay Patil, Clara Garc\'ia-S\'anchez
https://arxiv.org/abs/2506.03021

Should we care about the spatial heterogeneity in coral reefs under unidirectional turbulent flows?
In this work, we systematically investigate the similarities and differences observed between a hydraulically rough wall comprised of an array of cylinders, massive corals, and branching corals arranged in a staggered manner, along with a stochastically generated coral bed using a scale-resolving computational framework. Our data suggests that for all the flow parameters of interest, there is a substantial difference observed between the stochastic coral bed and the regularly arranged coral bed…

Anisotropic In-plane Thermal Conductivity of Freestanding Few-layer ReS2
Manavendra Pratap Singh, Akshay Naik
https://arxiv.org/abs/2506.09500 https://

Anisotropic In-plane Thermal Conductivity of Freestanding Few-layer ReS2
Rhenium disulfide (ReS2) is a unique TMDC with a strong in-plane anisotropy and weak interlayer coupling. The pronounced anisotropy in the thermal conductivity observed in bulk ReS2 flakes (exceeding 60 nm) makes them valuable for applications that require directional heat management or isolation. Whether this anisotropy is maintained below 10 nm has not yet been studied. Here, we measured the thermal conductivity of freestanding, exfoliated, few-layer ReS2 samples (thickness < 10 nm) on SiO2/S…

Correlating Superconducting Qubit Performance Losses to Sidewall Near-Field Scattering via Terahertz Nanophotonics
Richard H. J. Kim, Samuel J. Haeuser, Joong-Mok Park, Randall K. Chan, Jin-Su Oh, Thomas Koschny, Lin Zhou, Matthew J. Kramer, Akshay A. Murthy, Mustafa Bal, Francesco Crisa, Sabrina Garattoni, Shaojiang Zhu, Andrei Lunin, David Olaya, Peter Hopkins, Alex Romanenko, Anna Grassellino, Jigang Wang

Correlating Superconducting Qubit Performance Losses to Sidewall Near-Field Scattering via Terahertz Nanophotonics
Elucidating dielectric losses, structural heterogeneity, and interface imperfections is critical for improving coherence in superconducting qubits. However, most diagnostics rely on destructive electron microscopy or low-throughput millikelvin quantum measurements. Here, we demonstrate noninvasive terahertz (THz) nano-imaging/-spectroscopy of encapsulated niobium transmon qubits, revealing sidewall near-field scattering that correlates with qubit coherence. We further employ a THz hyperspectral…