Tootfinder

Machine-Learned Potentials for Solvation Modeling
Roopshree Banchode, Surajit Das, Shampa Raghunathan, Raghunathan Ramakrishnan
https://arxiv.org/abs/2505.22402

Machine-Learned Potentials for Solvation Modeling
Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles remains difficult due to the complex interplay of interactions and unfavorable computational scaling of first-principles treatment with system size. Machine-learned potentials (MLPs) have recently emerged as efficient surrogates for quantum chemistry methods, offering first-principles accuracy at greatly reduced computa…

Tensor Networks for Liquids in Heterogeneous Systems
Zachary A. Johnson, Luciano G. Silvestri, Pierson Guthrey, Michael S. Murillo
https://arxiv.org/abs/2507.19352 https://

Tensor Networks for Liquids in Heterogeneous Systems
Many-body correlations in strongly coupled liquids and plasmas are critical for many applications in nanofluids, biology, and fusion-related plasma physics, but their description in fully heterogeneous environments remains challenging due to the high-dimensional equations involved. Recently, tensor network decompositions have emerged as powerful tools for tackling such equations by reducing memory usage and computational complexity. In this paper, we solve for equilibrium density and density-de…

Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
Gilberto Cunha, Alexandra Ram\^oa, Andr\'e Sequeira, Michael de Oliveira, Lu\'is Barbosa
https://arxiv.org/abs/2507.18606 …

Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks. Recent advances demonstrate that inference on sparse Bayesian networks can be accelerated using quantum rejection sampling combined with amplitude amplification, leading to a computational speedup in estimating acceptance probabilities.\\ Building on this resul…

Cloud Native System for LLM Inference Serving
Minxian Xu, Junhan Liao, Jingfeng Wu, Yiyuan He, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.18007 https://

Cloud Native System for LLM Inference Serving
Large Language Models (LLMs) are revolutionizing numerous industries, but their substantial computational demands create challenges for efficient deployment, particularly in cloud environments. Traditional approaches to inference serving often struggle with resource inefficiencies, leading to high operational costs, latency issues, and limited scalability. This article explores how Cloud Native technologies, such as containerization, microservices, and dynamic scheduling, can fundamentally impr…

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Wei Fan, JinYi Yoon, Xiaochang Li, Huajie Shao, Bo Ji
https://arxiv.org/abs/2507.17228

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Split Learning (SL) is an emerging privacy-preserving machine learning technique that enables resource constrained edge devices to participate in model training by partitioning a model into client-side and server-side sub-models. While SL reduces computational overhead on edge devices, it encounters significant challenges in heterogeneous environments where devices vary in computing resources, communication capabilities, environmental conditions, and privacy requirements. Although recent studie…

How Do Code Smells Affect Skill Growth in Scratch Novice Programmers?
Ricardo Hidalgo Arag\'on, Jes\'us M. Gonz\'alez-Barahona, Gregorio Robles
https://arxiv.org/abs/2507.17314

How Do Code Smells Affect Skill Growth in Scratch Novice Programmers?
Context. Code smells, which are recurring anomalies in design or style, have been extensively researched in professional code. However, their significance in block-based projects created by novices is still largely unknown. Block-based environments such as Scratch offer a unique, data-rich setting to examine how emergent design problems intersect with the cultivation of computational-thinking (CT) skills. Objective. This research explores the connection between CT proficiency and design-level c…

Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments
Amirreza Payandeh, Anuj Pokhrel, Daeun Song, Marcos Zampieri, Xuesu Xiao
https://arxiv.org/abs/2506.14233

Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments
Large Vision-Language Models (VLMs) have demonstrated potential in enhancing mobile robot navigation in human-centric environments by understanding contextual cues, human intentions, and social dynamics while exhibiting reasoning capabilities. However, their computational complexity and limited sensitivity to continuous numerical data impede real-time performance and precise motion control. To this end, we propose Narrate2Nav, a novel real-time vision-action model that leverages a novel self-su…

From Logic to Language: A Trust Index for Problem Solving with LLMs
Tehseen Rug, Felix B\"ohmer, Tessa Pfattheicher
https://arxiv.org/abs/2507.16028 h…

From Logic to Language: A Trust Index for Problem Solving with LLMs
Classical computation, grounded in formal, logical systems, has been the engine of technological progress for decades, excelling at problems that can be described with unambiguous rules. This paradigm, however, leaves a vast ocean of human problems -- those characterized by ambiguity, dynamic environments, and subjective context -- largely untouched. The advent of Large Language Models (LLMs) represents a fundamental shift, enabling computational systems to engage with this previously inaccessi…

Quantum Annealing Hyperparameter Analysis for Optimal Sensor Placement in Production Environments
Nico Kraus, Marvin Erdmann, Alexander Kuzmany, Daniel Porawski, Jonas Stein
https://arxiv.org/abs/2507.16584

Quantum Annealing Hyperparameter Analysis for Optimal Sensor Placement in Production Environments
To increase efficiency in automotive manufacturing, newly produced vehicles can move autonomously from the production line to the distribution area. This requires an optimal placement of sensors to ensure full coverage while minimizing the number of sensors used. The underlying optimization problem poses a computational challenge due to its large-scale nature. Currently, classical solvers rely on heuristics, often yielding non-optimal solutions for large instances, resulting in suboptimal senso…

3D Gaussian Splatting for Fine-Detailed Surface Reconstruction in Large-Scale Scene
Shihan Chen, Zhaojin Li, Zeyu Chen, Qingsong Yan, Gaoyang Shen, Ran Duan
https://arxiv.org/abs/2506.17636

3D Gaussian Splatting for Fine-Detailed Surface Reconstruction in Large-Scale Scene
Recent developments in 3D Gaussian Splatting have made significant advances in surface reconstruction. However, scaling these methods to large-scale scenes remains challenging due to high computational demands and the complex dynamic appearances typical of outdoor environments. These challenges hinder the application in aerial surveying and autonomous driving. This paper proposes a novel solution to reconstruct large-scale surfaces with fine details, supervised by full-sized images. Firstly, we…

Full-spectrum modeling of mobile gamma-ray spectrometry systems in scattering media
David Breitenmoser, Alberto Stabilini, Sabine Mayer
https://arxiv.org/abs/2506.17820

Full-spectrum modeling of mobile gamma-ray spectrometry systems in scattering media
Mobile gamma-ray spectrometry (MGRS) systems are essential for localizing, identifying, and quantifying gamma-ray sources in complex environments. Full-spectrum template matching offers the highest accuracy and sensitivity for these tasks but is limited by the computational cost of generating the required spectral templates. Here, we present a generalized full-spectrum modeling framework for MGRS systems in scattering media, enabling near real-time template generation through dynamic, anisotrop…

On Splitting Lightweight Semantic Image Segmentation for Wireless Communications
Ebrahim Abu-Helalah, Jordi Serra, Jordi Perez-Romero
https://arxiv.org/abs/2507.14199

On Splitting Lightweight Semantic Image Segmentation for Wireless Communications
Semantic communication represents a promising technique towards reducing communication costs, especially when dealing with image segmentation, but it still lacks a balance between computational efficiency and bandwidth requirements while maintaining high image segmentation accuracy, particularly in resource-limited environments and changing channel conditions. On the other hand, the more complex and larger semantic image segmentation models become, the more stressed the devices are when process…

NeuralPDR: Neural Differential Equations as surrogate models for Photodissociation Regions
Gijs Vermari\"en, Thomas G. Bisbas, Serena Viti, Yue Zhao, Xuefei Tang, Rahul Ravichandran
https://arxiv.org/abs/2506.14270

NeuralPDR: Neural Differential Equations as surrogate models for Photodissociation Regions
Computational astrochemical models are essential for helping us interpret and understand the observations of different astrophysical environments. In the age of high-resolution telescopes such as JWST and ALMA, the substructure of many objects can be resolved, raising the need for astrochemical modeling at these smaller scales, meaning that the simulations of these objects need to include both the physics and chemistry to accurately model the observations. The computational cost of the simulati…

Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
Joshua Murphy, Conor Rosato, Andrew Millard, Lee Devlin, Paul Horridge, Simon Maskell
https://arxiv.org/abs/2507.07461

Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
When performing Bayesian inference using Sequential Monte Carlo (SMC) methods, two considerations arise: the accuracy of the posterior approximation and computational efficiency. To address computational demands, Sequential Monte Carlo Squared (SMC$^2$) is well-suited for high-performance computing (HPC) environments. The design of the proposal distribution within SMC$^2$ can improve accuracy and exploration of the posterior as poor proposals may lead to high variance in importance weights and …

Detecting In-Person Conversations in Noisy Real-World Environments with Smartwatch Audio and Motion Sensing
Alice Zhang, Callihan Bertley, Dawei Liang, Edison Thomaz
https://arxiv.org/abs/2507.12002

Detecting In-Person Conversations in Noisy Real-World Environments with Smartwatch Audio and Motion Sensing
Social interactions play a crucial role in shaping human behavior, relationships, and societies. It encompasses various forms of communication, such as verbal conversation, non-verbal gestures, facial expressions, and body language. In this work, we develop a novel computational approach to detect a foundational aspect of human social interactions, in-person verbal conversations, by leveraging audio and inertial data captured with a commodity smartwatch in acoustically-challenging scenarios. To…

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones
Minze Li, Wei Zhao, Ran Chen, Mingqiang Wei
https://arxiv.org/abs/2507.13647 …

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones
Real-time trajectory planning for unmanned aerial vehicles (UAVs) in dynamic environments remains a key challenge due to high computational demands and the need for fast, adaptive responses. Traditional Particle Swarm Optimization (PSO) methods, while effective for offline planning, often struggle with premature convergence and latency in real-time scenarios. To overcome these limitations, we propose PE-PSO, an enhanced PSO-based online trajectory planner. The method introduces a persistent exp…

This https://arxiv.org/abs/2410.16316 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Computational Harmonic Detection Algorithm to Detect Data Leakage through EM Emanation
Unintended electromagnetic emissions, called EM emanations, can be exploited to recover sensitive information, posing security risks. Metal shielding, used by defense organizations to prevent data leakage, is costly and impractical for widespread use. This issue is particularly significant for IoT devices due to their sheer volume and varied deployment environments. Therefore, there is a research need for an automated detection method to monitor facilities and address data leakage promptly. To …

Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
Srinivasan Kidambi, Pravin Nair
https://arxiv.org/abs/2506.08520 https://…

Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
Multi-head self-attention (MHSA) has become a core component in modern computer vision models. However, its quadratic complexity with respect to input length poses a significant computational bottleneck in real-time and resource constrained environments. We propose PnP-Nystra, a Nyström based linear approximation of self-attention, developed as a plug-and-play (PnP) module that can be integrated into the pre-trained image and video restoration models without retraining. As a drop-in replacemen…

How attention simplifies mental representations for planning
Jason da Silva Castanheira, Nicholas Shea, Stephen M. Fleming
https://arxiv.org/abs/2506.09520

How attention simplifies mental representations for planning
Human planning is efficient -- it frugally deploys limited cognitive resources to accomplish difficult tasks -- and flexible -- adapting to novel problems and environments. Computational approaches suggest that people construct simplified mental representations of their environment, balancing the complexity of a task representation with its utility. These models imply a nested optimisation in which planning shapes perception, and perception shapes planning -- but the perceptual and attentional …

Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA
Jindong Li, Tenglong Li, Ruiqi Chen, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng
https://arxiv.org/abs/2507.03308

Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA
Deploying large language models (LLMs) on embedded devices remains a significant research challenge due to the high computational and memory demands of LLMs and the limited hardware resources available in such environments. While embedded FPGAs have demonstrated performance and energy efficiency in traditional deep neural networks, their potential for LLM inference remains largely unexplored. Recent efforts to deploy LLMs on FPGAs have primarily relied on large, expensive cloud-grade hardware a…

MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments
Sai Krishna Ghanta, Ramviyas Parasuraman
https://arxiv.org/abs/2506.23514

MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments
Relative localization is a crucial capability for multi-robot systems operating in GPS-denied environments. Existing approaches for multi-robot relative localization often depend on costly or short-range sensors like cameras and LiDARs. Consequently, these approaches face challenges such as high computational overhead (e.g., map merging) and difficulties in disjoint environments. To address this limitation, this paper introduces MGPRL, a novel distributed framework for multi-robot relative loca…

A Hybrid DEC-SIE Framework for Potential-Based Electromagnetic Analysis of Heterogeneous Media
Amgad Abdrabou, Luis J. Gomez, Weng Cho Chew
https://arxiv.org/abs/2507.02099

A Hybrid DEC-SIE Framework for Potential-Based Electromagnetic Analysis of Heterogeneous Media
Analyzing electromagnetic fields in complex, multi-material environments presents substantial computational challenges. To address these, we propose a hybrid numerical method that couples discrete exterior calculus (DEC) with surface integral equations (SIE) in the potential-based formulation of Maxwell's equations. The method employs the magnetic vector and electric scalar potentials ($\mathbf{A}$-$Φ$) under the Lorenz gauge, offering natural compatibility with multi-physics couplings and inh…

An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
Styliani-Christina Fragkouli, Somya Iqbal, Lisa Crossman, Barbara Gravel, Nagat Masued, Mark Onders, Devesh Haseja, Alex Stikkelman, Alfonso Valencia, Tom Lenaerts, Fotis Psomopoulos, Pilib \'O Broin, N\'uria Queralt-Rosinach, Davide Cirillo
htt…

An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
Synthetic data has emerged as a powerful resource in life sciences, offering solutions for data scarcity, privacy protection and accessibility constraints. By creating artificial datasets that mirror the characteristics of real data, allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of synthetic data in Life Sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the…

Searching Efficient Deep Architectures for Radar Target Detection using Monte-Carlo Tree Search
No\'e Lallouet, Tristan Cazenave, Cyrille Enderli, St\'ephanie Gourdin
https://arxiv.org/abs/2506.21772

Searching Efficient Deep Architectures for Radar Target Detection using Monte-Carlo Tree Search
Recent research works establish deep neural networks as high performing tools for radar target detection, especially on challenging environments (presence of clutter or interferences, multi-target scenarii...). However, the usually large computational complexity of these networks is one of the factors preventing them from being widely implemented in embedded radar systems. We propose to investigate novel neural architecture search (NAS) methods, based on Monte-Carlo Tree Search (MCTS), for find…

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
Alyssa Pinnock, Shakya Jayakody, Kawsher A Roxy, Md Rubel Ahmed
https://arxiv.org/abs/2506.09061

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
This paper introduces EdgeProfiler, a fast profiling framework designed for evaluating lightweight Large Language Models (LLMs) on edge systems. While LLMs offer remarkable capabilities in natural language understanding and generation, their high computational, memory, and power requirements often confine them to cloud environments. EdgeProfiler addresses these challenges by providing a systematic methodology for assessing LLM performance in resource-constrained edge settings. The framework pro…

This https://arxiv.org/abs/2412.13664 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A Skeleton-Based Topological Planner for Exploration in Complex Unknown Environments
The capability of autonomous exploration in complex, unknown environments is important in many robotic applications. While recent research on autonomous exploration have achieved much progress, there are still limitations, e.g., existing methods relying on greedy heuristics or optimal path planning are often hindered by repetitive paths and high computational demands. To address such limitations, we propose a novel exploration framework that utilizes the global topology information of observed …

A Navigation Framework Utilizing Vision-Language Models
Yicheng Duan, Kaiyu tang
https://arxiv.org/abs/2506.10172 https://arxiv.org/p…

A Navigation Framework Utilizing Vision-Language Models
Vision-and-Language Navigation (VLN) presents a complex challenge in embodied AI, requiring agents to interpret natural language instructions and navigate through visually rich, unfamiliar environments. Recent advances in large vision-language models (LVLMs), such as CLIP and Flamingo, have significantly improved multimodal understanding but introduced new challenges related to computational cost and real-time deployment. In this project, we propose a modular, plug-and-play navigation framework…

A three-dimensional energy flux acoustic propagation model
Mark Langhirt, Charles Holland, Ying-Tsong Lin
https://arxiv.org/abs/2506.03325 https://

A three-dimensional energy flux acoustic propagation model
This paper extends energy flux methods to handle three-dimensional ocean acoustic environments, the implemented solution captures horizontally refracted incoherent acoustic intensity, and its required computational effort is predominantly independent of range and frequency. Energy flux models are principally derived as incoherent solutions for acoustic propagation in bounded waveguides. The angular distribution of incoherent acoustic intensity may be derived from Wentzel-Kramers-Brillouin modes…

HERCULES: Hardware accElerator foR stoChastic schedULing in hEterogeneous Systems
Vairavan Palaniappan, Adam H. Ross, Amit Ranjan Trivedi, Debjit Pal
https://arxiv.org/abs/2507.01113

HERCULES: Hardware accElerator foR stoChastic schedULing in hEterogeneous Systems
Efficient workload scheduling is a critical challenge in modern heterogeneous computing environments, particularly in high-performance computing (HPC) systems. Traditional software-based schedulers struggle to efficiently balance workload distribution due to high scheduling overhead, lack of adaptability to dynamic workloads, and suboptimal resource utilization. These pitfalls are compounded in heterogeneous systems, where differing computational elements can have vastly different performance p…

Sparse Imagination for Efficient Visual World Model Planning
Junha Chun, Youngjoon Jeong, Taesup Kim
https://arxiv.org/abs/2506.01392 https://

Sparse Imagination for Efficient Visual World Model Planning
World model based planning has significantly improved decision-making in complex environments by enabling agents to simulate future states and make informed choices. However, ensuring the prediction accuracy of world models often demands substantial computational resources, posing a major challenge for real-time applications. This computational burden is particularly restrictive in robotics, where resources are severely constrained. To address this limitation, we propose a Sparse Imagination fo…

This https://arxiv.org/abs/2505.22402 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Machine-Learned Potentials for Solvation Modeling
Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles remains difficult due to the complex interplay of interactions and unfavorable computational scaling of first-principles treatment with system size. Machine-learned potentials (MLPs) have recently emerged as efficient surrogates for quantum chemistry methods, offering first-principles accuracy at greatly reduced computa…

3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting
Haitao Lu, Haijier Chen, Haoze Liu, Shoujian Zhang, Bo Xu, Ziao Liu
https://arxiv.org/abs/2507.05661

3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting
In autonomous robotic systems, precise localization is a prerequisite for safe navigation. However, in complex urban environments, GNSS positioning often suffers from signal occlusion and multipath effects, leading to unreliable absolute positioning. Traditional mapping approaches are constrained by storage requirements and computational inefficiency, limiting their applicability to resource-constrained robotic platforms. To address these challenges, we propose 3DGS-LSR: a large-scale relocaliz…

Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Wule Mao, Zhouheng Li, Yunhao Luo, Yilun Du, Lei Xie
https://arxiv.org/abs/2507.04384

Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Safe trajectory planning remains a significant challenge in complex environments, where traditional methods often trade off computational efficiency for safety. Comprehensive obstacle modeling improves safety but is computationally expensive, while approximate methods are more efficient but may compromise safety. To address this issue, this paper introduces a rapid and safe trajectory planning framework based on state-based diffusion models. Leveraging only low-dimensional vehicle states, the d…

Bio-Inspired Hybrid Map: Spatial Implicit Local Frames and Topological Map for Mobile Cobot Navigation
Tuan Dang, Manfred Huber
https://arxiv.org/abs/2507.04649

Bio-Inspired Hybrid Map: Spatial Implicit Local Frames and Topological Map for Mobile Cobot Navigation
Navigation is a fundamental capacity for mobile robots, enabling them to operate autonomously in complex and dynamic environments. Conventional approaches use probabilistic models to localize robots and build maps simultaneously using sensor observations. Recent approaches employ human-inspired learning, such as imitation and reinforcement learning, to navigate robots more effectively. However, these methods suffer from high computational costs, global map inconsistency, and poor generalization…

Reinforcement Learning with Data Bootstrapping for Dynamic Subgoal Pursuit in Humanoid Robot Navigation
Chengyang Peng, Zhihao Zhang, Shiting Gong, Sankalp Agrawal, Keith A. Redmill, Ayonga Hereid
https://arxiv.org/abs/2506.02206

Reinforcement Learning with Data Bootstrapping for Dynamic Subgoal Pursuit in Humanoid Robot Navigation
Safe and real-time navigation is fundamental for humanoid robot applications. However, existing bipedal robot navigation frameworks often struggle to balance computational efficiency with the precision required for stable locomotion. We propose a novel hierarchical framework that continuously generates dynamic subgoals to guide the robot through cluttered environments. Our method comprises a high-level reinforcement learning (RL) planner for subgoal selection in a robot-centric coordinate syste…

RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles
David Hunt, Shaocheng Luo, Spencer Hallyburton, Shafii Nillongo, Yi Li, Tingjun Chen, Miroslav Pajic
https://arxiv.org/abs/2507.00937

RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles
Low-cost indoor mobile robots have gained popularity with the increasing adoption of automation in homes and commercial spaces. However, existing lidar and camera-based solutions have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-base…