Tootfinder

Task-Based Programming for Adaptive Mesh Refinement in Compressible Flow Simulations
Anjiang Wei, Hang Song, Mert Hidayetoglu, Elliott Slaughter, Sanjiva K. Lele, Alex Aiken
https://arxiv.org/abs/2508.05020

Task-Based Programming for Adaptive Mesh Refinement in Compressible Flow Simulations
High-order solvers for compressible flows are vital in scientific applications. Adaptive mesh refinement (AMR) is a key technique for reducing computational cost by concentrating resolution in regions of interest. In this work, we develop an AMR-based numerical solver using Regent, a high-level programming language for the Legion programming model. We address several challenges associated with implementing AMR in Regent. These include dynamic data structures for patch refinement/coarsening, mes…

A Kolmogorov-Arnold Network for Interpretable Cyberattack Detection in AGC Systems
Jehad Jilan, Niranjana Naveen Nambiar, Ahmad Mohammad Saber, Alok Paranjape, Amr Youssef, Deepa Kundur
https://arxiv.org/abs/2509.05259

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning
Shubham Mishra, The Anh Han, Bruno Silvester Lopes, Shatha Ghareeb, Zia Ush Shamszaman
https://arxiv.org/abs/2509.03551

Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning
Antimicrobial resistance (AMR) poses a significant public health and economic challenge, increasing treatment costs and reducing antibiotic effectiveness. This study employs machine learning to analyze genomic and epidemiological data from the public databases for molecular typing and microbial genome diversity (PubMLST), incorporating data from UK government-supported AMR surveillance by the Food Standards Agency and Food Standards Scotland. We identify AMR patterns in Campylobacter jejuni and…

A Refutation of Elmasry's $\tilde{O}(m \sqrt{n})$-Time Algorithm for Single-Source Shortest Paths
Sunny Atalig, Marek Chrobak
https://arxiv.org/abs/2508.04872 https://

A Refutation of Elmasry's $\tilde{O}(m \sqrt{n})$-Time Algorithm for Single-Source Shortest Paths
In this note we examine the recent paper "Breaking the Bellman-Ford Shortest-Path Bound" by Amr Elmasry, where he presents an algorithm for the single-source shortest path problem and claims that its running time complexity is $\tilde{O}(m\sqrt{n})$, where $n$ is the number of vertices and $m$ is the number of edges. We show that his analysis is incorrect, by providing an example of a weighted graph on which the running time of his algorithm is $Ω(mn)$.

ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue
Jeongwoo Kang, Maria Boritchev, Maximin Coavoux
https://arxiv.org/abs/2508.12819 https://arxiv…

ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue
We present our work to build a French semantic corpus by annotating French dialogue in Abstract Meaning Representation (AMR). Specifically, we annotate the DinG corpus, consisting of transcripts of spontaneous French dialogues recorded during the board game Catan. As AMR has insufficient coverage of the dynamics of spontaneous speech, we extend the framework to better represent spontaneous speech and sentence structures specific to French. Additionally, to support consistent annotation, we prov…

Emerging Paradigms for Securing Federated Learning Systems
Amr Akmal Abouelmagd, Amr Hilal
https://arxiv.org/abs/2509.21147 https://arxiv.org/pdf/2509.2114…

Emerging Paradigms for Securing Federated Learning Systems
Federated Learning (FL) facilitates collaborative model training while keeping raw data decentralized, making it a conduit for leveraging the power of IoT devices while maintaining privacy of the locally collected data. However, existing privacy- preserving techniques present notable hurdles. Methods such as Multi-Party Computation (MPC), Homomorphic Encryption (HE), and Differential Privacy (DP) often incur high compu- tational costs and suffer from limited scalability. This survey examines em…

Access Paths for Efficient Ordering with Large Language Models
Fuheng Zhao, Jiayue Chen, Yiming Pan, Tahseen Rabbani, Divyakant Agrawal, Amr El Abbadi
https://arxiv.org/abs/2509.00303

Access Paths for Efficient Ordering with Large Language Models
We present the LLM ORDER BY operator as a logical abstraction and study its physical implementations within a unified evaluation framework. Our experiments show that no single approach is universally optimal, with effectiveness depending on query characteristics and data. We introduce three new designs: an agreement-based batch-size policy, a majority voting mechanism for pairwise sorting, and a two-way external merge sort adapted for LLMs. With extensive experiments, our agreement-based proced…

Learning Conformal Explainers for Image Classifiers
Amr Alkhatib, Stephanie Lowry
https://arxiv.org/abs/2509.21209 https://arxiv.org/pdf/2509.21209

Learning Conformal Explainers for Image Classifiers
Feature attribution methods are widely used for explaining image-based predictions, as they provide feature-level insights that can be intuitively visualized. However, such explanations often vary in their robustness and may fail to faithfully reflect the reasoning of the underlying black-box model. To address these limitations, we propose a novel conformal prediction-based approach that enables users to directly control the fidelity of the generated explanations. The method identifies a subset…

Online Data-Driven Reachability Analysis using Zonotopic Recursive Least Squares
Alireza Naderi Akhormeh, Amr Hegazy, Amr Alanwar
https://arxiv.org/abs/2509.17058 https://

Online Data-Driven Reachability Analysis using Zonotopic Recursive Least Squares
Reachability analysis is a key formal verification technique for ensuring the safety of modern cyber physical systems subject to uncertainties in measurements, system models (parameters), and inputs. Classical model-based approaches rely on accurate prior knowledge of system dynamics, which may not always be available or reliable. To address this, we present a data-driven reachability analysis framework that computes over-approximations of reachable sets directly from online state measurements.…

LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection
Shubham Gupta, Shraban Kumar Chatterjee, Suman Kundu
https://arxiv.org/abs/2508.18819

LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection
The proliferation of misinformation in the digital age has led to significant societal challenges. Existing approaches often struggle with capturing long-range dependencies, complex semantic relations, and the social dynamics influencing news dissemination. Furthermore, these methods require extensive labelled datasets, making their deployment resource-intensive. In this study, we propose a novel self-supervised misinformation detection framework that integrates both complex semantic relations …

this is my favorite review ever

Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE
Akash Poptani, Alireza Khadem, Scott Mahlke, Jonah Miller, Joshua Dolence, Reetuparna Das
https://arxiv.org/abs/2509.19701

Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE
Hero-class HPC simulations rely on Adaptive Mesh Refinement (AMR) to reduce compute and memory demands while maintaining accuracy. This work analyzes the performance of Parthenon, a block-structured AMR benchmark, on CPU-GPU systems. We show that smaller mesh blocks and deeper AMR levels degrade GPU performance due to increased communication, serial overheads, and inefficient GPU utilization. Through detailed profiling, we identify inefficiencies, low occupancy, and memory access bottlenecks. W…

Optimization of sequential therapies to maximize extinction of resistant bacteria through collateral sensitivity
Javier Molina-Hern\'andez, Jos\'e A. Cuesta, Beatriz Pascual-Escudero, Sa\'ul Ares, Pablo Catal\'an
https://arxiv.org/abs/2510.01808

Optimization of sequential therapies to maximize extinction of resistant bacteria through collateral sensitivity
Antimicrobial resistance (AMR) threatens global health. A promising and underexplored strategy to tackle this problem are sequential therapies exploiting collateral sensitivity (CS), whereby resistance to one drug increases sensitivity to another. Here, we develop a four-genotype stochastic birth-death model with two bacteriostatic antibiotics to identify switching periods that maximize bacterial extinction under subinhibitory concentrations. We show that extinction probability depends nonlinea…

A Verified Compiler for Quantum Simulation
Liyi Li, Fenfen An, Federico Zahariev, Zhi Xiang Chong, Amr Sabry, Mark Gordon
https://arxiv.org/abs/2509.18583 https://

Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning
Bojun Zhang
https://arxiv.org/abs/2508.13474 https://arxiv.org/pdf/2508.…

Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning
Automatic Modulation Recognition (AMR) detects modulation schemes of received signals for further processing of signals without any priori information, which is critically important for civil spectrum regulation, information countermea sures, and communication security. Due to the powerful feature extraction and classification capabilities of Deep Learning (DL), DL-based AMR algorithms have achieved excellent performance gains compared with traditional modulation detection algorithm…

Minimal Neuron Circuits: Bursters
Amr Nabil, T. Nandha Kumar, Haider Abbas F. Almurib
https://arxiv.org/abs/2509.17731 https://arxiv.org/pdf/2509.17731

Minimal Neuron Circuits: Bursters
This work introduces a novel methodology for designing biologically plausible bursting neuron circuits using a minimal number of components. We hypothesize that to design circuits capable of bursting, the neuron circuit design must mimic a neuron model that inherently exhibits bursting dynamics. Consequently, classical models such as the Hodgkin-Huxley, $I_{Na,p}+I_{K}$, and FitzHugh-Nagumo models are not suitable choices. Instead, we propose a methodology for designing neuron circuits that emu…

GroundGazer: Camera-based indoor localization of mobile robots with millimeter accuracy at low cost
Sven Hinderer, Jakob H\"usken, Bohan Sun, Bin Yang
https://arxiv.org/abs/2509.17346

GroundGazer: Camera-based indoor localization of mobile robots with millimeter accuracy at low cost
Highly accurate indoor localization systems with mm positioning accuracy are currently very expensive. They include range finders (such as LiDAR), tachymeters, and motion capture systems relying on multiple high-end cameras. In this work, we introduce a high-accuracy, planar indoor localization system named GroundGazer (GG) for autonomous mobile robots (AMRs). GG estimates the AMR's position with mm and its heading with sub-degree accuracy. The system requires only a monocular (fisheye) camera,…

Room temperature giant magnetoresistance detection of spin hall nano-oscillator dynamics in synthetic antiferromagnetic Spin-Valve
Chunhao Li, Xiaotian Zhao, Wenlong Cai, Long Liu, Wei Liu, Zhidong Zhang
https://arxiv.org/abs/2508.18770

Room temperature giant magnetoresistance detection of spin hall nano-oscillator dynamics in synthetic antiferromagnetic Spin-Valve
Conventional spin Hall nano-oscillators (SHNOs) face fundamental power limitations due to the low anisotropic magnetoresistance (AMR < 0.3%) of ferromagnetic layers. To address this, we developed a synthetic antiferromagnetic spin-valve (SAF-SV) heterostructure [Ta/NiFe/Ru/NiFe/Cu/NiFe/Hf/Pt] that enables efficient giant magnetoresistance (GMR)-based detection of SHNO dynamics at room temperature. The NiFe/Ru/NiFe SAF reference layer, operating in the spin-flop state, couples with the NiFe free…

Modeling of silver transport in cubic SiC: Integrating molecular dynamics, bounds averaging, and uncertainty quantification
Mohamed AbdulHameed, Khadija Mahbuba, Mahmoud Yaseen, Amr Ibrahim, Daniel Moneghan, Benjamin Beeler
https://arxiv.org/abs/2508.14325

Modeling of silver transport in cubic SiC: Integrating molecular dynamics, bounds averaging, and uncertainty quantification
Silver released from TRISO fuel particles can migrate through the SiC layer and deposit on reactor components, posing radiation hazards and operational challenges. Despite numerous proposed mechanisms, the precise pathway of silver transport through intact 3C-SiC remains unresolved. We present a physics-informed model for estimating the effective diffusivity of silver in polycrystalline 3C-SiC. Molecular dynamics (MD) simulations yield diffusivities for {Σ3} and {Σ9} grain boundaries (GBs), w…

Application of a Pressured-Based OpenFOAM Solver for Rotating Detonation Engines
Keunjae Kwak, Hyoungwoo Kim, Je Ir Ryu, Donh-Hyuk Shin
https://arxiv.org/abs/2508.16105 https://…

Application of a Pressured-Based OpenFOAM Solver for Rotating Detonation Engines
This study aims to develop a simulation framework for rotating detonation engines (RDEs) using multicomponentFluid solver in OpenFOAM v12 and to demonstrate reducing the computational costs by adaptive mesh refinement (AMR) and dynamic load balancing (DLB). RDEs have been extensively studied for improvements in efficiency for power generation and aircraft propulsion systems. A well-established framework, showing both high accuracy and cost efficiency, is required to facilitate further research …

Approaching the Source of Symbol Grounding with Confluent Reductions of Abstract Meaning Representation Directed Graphs
Nicolas Goulet, Alexandre Blondin Mass\'e, Moussa Abdendi
https://arxiv.org/abs/2508.11068

Approaching the Source of Symbol Grounding with Confluent Reductions of Abstract Meaning Representation Directed Graphs
Abstract meaning representation (AMR) is a semantic formalism used to represent the meaning of sentences as directed acyclic graphs. In this paper, we describe how real digital dictionaries can be embedded into AMR directed graphs (digraphs), using state-of-the-art pre-trained large language models. Then, we reduce those graphs in a confluent manner, i.e. with transformations that preserve their circuit space. Finally, the properties of these reduces digraphs are analyzed and discussed in relat…

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Secure Set-based State Estimation for Safety-Critical Applications under Adversarial Attacks on S...
M. Umar B. Niazi, Michelle S. Chong, Amr Alanwar, Karl H. Johansson

Synthetic Protein-Ligand Complex Generation for Deep Molecular Docking
Sofiene Khiari, Matthew R. Masters, Amr H. Mahmoud, Markus A. Lill
https://arxiv.org/abs/2509.12915 https:…

Synthetic Protein-Ligand Complex Generation for Deep Molecular Docking
The scarcity of experimental protein-ligand complexes poses a significant challenge for training robust deep learning models for molecular docking. Given the prohibitive cost and time constraints associated with experimental structure determination, scalable generation of realistic protein-ligand complexes is needed to expand available datasets for model development. In this study, we introduce a novel workflow for the procedural generation and validation of synthetic protein-ligand complexes, …

A Lightweight Incentive-Based Privacy-Preserving Smart Metering Protocol for Value-Added Services
Farid Zaredar, Morteza Amini
https://arxiv.org/abs/2508.14703 https://

A Lightweight Incentive-Based Privacy-Preserving Smart Metering Protocol for Value-Added Services
The emergence of smart grids and advanced metering infrastructure (AMI) has revolutionized energy management. Unlike traditional power grids, smart grids benefit from two-way communication through AMI, which surpasses earlier automated meter reading (AMR). AMI enables diverse demand- and supply-side utilities such as accurate billing, outage detection, real-time grid control, load forecasting, and value-added services. Smart meters play a key role by delivering consumption values at predefined …

Replaced article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control
Yan Zhang, Ahmad Mohammad Saber, Amr Youssef, Deepa Kundur

A Comprehensive Pipeline for Aortic Segmentation and Shape Analysis
Nairouz Shehata, Amr Elsawy, Mohamed Nagy, Muhammad ElMahdy, Mariam Ali, Soha Romeih, Heba Aguib, Magdi Yacoub, Ben Glocker
https://arxiv.org/abs/2509.09718

A Comprehensive Pipeline for Aortic Segmentation and Shape Analysis
Aortic shape analysis plays a key role in cardiovascular diagnostics, treatment planning, and understanding disease progression. We present a robust, fully automated pipeline for aortic shape analysis from cardiac MRI, combining deep learning and statistical techniques across segmentation, 3D surface reconstruction, and mesh registration. We benchmark leading segmentation models including nnUNet, TotalSegmentator, and MedSAM2 highlighting the effectiveness of domain specific training and transf…

The Beauty of Anisotropic Mesh Refinement: Omnitrees for Efficient Dyadic Discretizations
Theresa Pollinger, Masado Ishii, Jens Domke
https://arxiv.org/abs/2508.06316 https://…

The Beauty of Anisotropic Mesh Refinement: Omnitrees for Efficient Dyadic Discretizations
Structured adaptive mesh refinement (AMR), commonly implemented via quadtrees and octrees, underpins a wide range of applications including databases, computer graphics, physics simulations, and machine learning. However, octrees enforce isotropic refinement in regions of interest, which can be especially inefficient for problems that are intrinsically anisotropic--much resolution is spent where little information is gained. This paper presents omnitrees as an anisotropic generalization of octr…

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Input-Output Extension of Underactuated Nonlinear Systems
Mirko Mizzoni, Amr Afifi, Antonio Franchi

The Alignment Waltz: Jointly Training Agents to Collaborate for Safety
Jingyu Zhang, Haozhu Wang, Eric Michael Smith, Sid Wang, Amr Sharaf, Mahesh Pasupuleti, Benjamin Van Durme, Daniel Khashabi, Jason Weston, Hongyuan Zhan
https://arxiv.org/abs/2510.08240

The Alignment Waltz: Jointly Training Agents to Collaborate for Safety
Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing challenges: vulnerability to adversarial attacks that elicit unsafe content, and a tendency for overrefusal on benign but sensitive prompts. Current approaches often navigate this dance with safeguard models that completely reject any content that contains unsafe portions. This approach cuts the music entirely-it may exacerbate overrefusals and fail…